Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

USGS Publications Warehouse

Funk, Chris; Budde, Michael E.

2009-01-01

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-??-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.

Changes in the NDVI of Boreal Forests over the period 1984 to 2003 measured using time series of Landsat TM/ETM+ surface reflectance and the GIMMS AVHRR NDVI record.

NASA Astrophysics Data System (ADS)

McMillan, A. M.; Rocha, A. V.; Goulden, M. L.

2006-12-01

There is a prevailing opinion that the boreal landscape is undergoing change as a result of warming temperatures leading to earlier springs, greater forest fire frequency and possibly CO2 fertilization. One widely- used line of evidence is the GIMMS AVHRR NDVI record. Several studies suggest increasing rates of photosynthesis in boreal forests from 1982 to 1991 (based on NDVI increases) while others suggest declining photosynthesis from 1996 to 2003. We suspect that a portion of these changes are due to the successional stage of the forests. We compiled a time-series of atmospherically-corrected Landsat TM/ETM+ images spanning the period 1984 to 2003 over the BOREAS Northern Study Area and compared spatial and temporal patterns of NDVI between the two records. The Landsat time series is higher resolution and, together with the Canadian Fire Service Large Fire Database, provides stand-age information. We then (1) analyzed the agreement between the Landsat and GIMMS AVHRR time series; (2) determined how the stage of forest succession affected NDVI; (3) assessed how the calculation method of annual averages of NDVI affects decadal-scale trends. The agreement between the Landsat and the AVHRR was reasonable although the depression of NDVI associated with the aerosols from the Pinatubo volcano was greater in the GIMMS time series. Pixels containing high proportions of stands burned within a decade of the observation period showed very high gains in NDVI while the more mature stands were constant. While NDVI appears to exhibit a large sensitivity to the presence of snow, the choice of a May to September averaging period for NDVI over a June to August averaging period did not affect the interannual patterns in NDVI at this location because the snow pack was seldom present in either of these periods. Knowledge of the spatial and temporal patterns of wild fire will prove useful in interpreting trends of remotely-sensed proxies of photosynthesis.

[Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].

PubMed

Du, Jia-Qiang; Shu, Jian-Min; Wang, Yue-Hui; Li, Ying-Chang; Zhang, Lin-Bo; Guo, Yang

2014-02-01

Consistent NDVI time series are basic and prerequisite in long-term monitoring of land surface properties. Advanced very high resolution radiometer (AVHRR) measurements provide the longest records of continuous global satellite measurements sensitive to live green vegetation, and moderate resolution imaging spectroradiometer (MODIS) is more recent typical with high spatial and temporal resolution. Understanding the relationship between the AVHRR-derived NDVI and MODIS NDVI is critical to continued long-term monitoring of ecological resources. NDVI time series acquired by the global inventory modeling and mapping studies (GIMMS) and Terra MODIS were compared over the same time periods from 2000 to 2006 at four scales of Qinghai-Tibet Plateau (whole region, sub-region, biome and pixel) to assess the level of agreement in terms of absolute values and dynamic change by independently assessing the performance of GIMMS and MODIS NDVI and using 495 Landsat samples of 20 km x20 km covering major land cover type. High correlations existed between the two datasets at the four scales, indicating their mostly equal capability of capturing seasonal and monthly phenological variations (mostly at 0. 001 significance level). Simi- larities of the two datasets differed significantly among different vegetation types. The relative low correlation coefficients and large difference of NDVI value between the two datasets were found among dense vegetation types including broadleaf forest and needleleaf forest, yet the correlations were strong and the deviations were small in more homogeneous vegetation types, such as meadow, steppe and crop. 82% of study area was characterized by strong consistency between GIMMS and MODIS NDVI at pixel scale. In the Landsat NDVI vs. GIMMS and MODIS NDVI comparison of absolute values, the MODIS NDVI performed slightly better than GIMMS NDVI, whereas in the comparison of temporal change values, the GIMMS data set performed best. Similar with comparison results of GIMMS and MODIS NDVI, the consistency across the three datasets was clearly different among various vegetation types. In dynamic changes, differences between Landsat and MODIS NDVI were smaller than Landsat NDVI vs. GIMMS NDVI for forest, but Landsat and GIMMS NDVI agreed better for grass and crop. The results suggested that spatial patterns and dynamic trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI. It might be feasible to successfully integrate historical GIMMS and more recent MODIS NDVI to provide continuity of NDVI products. The accuracy of merging AVHRR historical data recorded with more modern MODIS NDVI data strongly depends on vegetation type, season and phenological period, and spatial scale. The integration of the two datasets for needleleaf forest, broadleaf forest, and for all vegetation types in the phenological transition periods in spring and autumn should be treated with caution.

[Relationships between horqin meadow NDVI and meteorological factors].

PubMed

Qu, Cui-ping; Guan, De-xin; Wang, An-zhi; Jin, Chang-jie; Wu, Jia-bing; Wang, Ji-jun; Ni, Pan; Yuan, Feng-hui

2009-01-01

Based on the 2000-2006 MODIS 8-day composite NDVI and day-by-day meteorological data, the seasonal and inter-annual variations of Horqin meadow NDVI as well as the relationships between the NDVI and relevant meteorological factors were studied. The results showed that as for the seasonal variation, Horqin meadow NDVI was more related to water vapor pressure than to precipitation. Cumulated temperature and cumulated precipitation together affected the inter-annual turning-green period significantly, and the precipitation in growth season (June and July), compared with that in whole year, had more obvious effects on the annual maximal NDVI. The analysis of time lag effect indicated that water vapor pressure had a persistent (about 12 days) prominent effect on the NDVI. The time lag effect of mean air temperature was 11-15 days, and the cumulated dual effect of the temperature and precipitation was 36-52 days.

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

NASA Astrophysics Data System (ADS)

Bindhu, V. M.; Narasimhan, B.

2015-03-01

Normalized Difference Vegetation Index (NDVI), a key parameter in understanding the vegetation dynamics, has high spatial and temporal variability. However, continuous monitoring of NDVI is not feasible at fine spatial resolution (<60 m) owing to the long revisit time needed by the satellites to acquire the fine spatial resolution data. Further, the study attains significance in the case of humid tropical regions of the earth, where the prevailing atmospheric conditions restrict availability of fine resolution cloud free images at a high temporal frequency. As an alternative to the lack of high resolution images, the current study demonstrates a novel disaggregation method (DisNDVI) which integrates the spatial information from a single fine resolution image and temporal information in terms of crop phenology from time series of coarse resolution images to generate estimates of NDVI at fine spatial and temporal resolution. The phenological variation of the pixels captured at the coarser scale provides the basis for relating the temporal variability of the pixel with the NDVI available at fine resolution. The proposed methodology was tested over a 30 km × 25 km spatially heterogeneous study area located in the south of Tamil Nadu, India. The robustness of the algorithm was assessed by an independent comparison of the disaggregated NDVI and observed NDVI obtained from concurrent Landsat ETM+ imagery. The results showed good spatial agreement across the study area dominated with agriculture and forest pixels, with a root mean square error of 0.05. The validation done at the coarser scale showed that disaggregated NDVI spatially averaged to 240 m compared well with concurrent MODIS NDVI at 240 m (R2 > 0.8). The validation results demonstrate the effectiveness of DisNDVI in improving the spatial and temporal resolution of NDVI images for utility in fine scale hydrological applications such as crop growth monitoring and estimation of evapotranspiration.

Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

NASA Technical Reports Server (NTRS)

Potter, C. S.

1997-01-01

This study describes the use of satellite data to calibrate a new climate-vegetation greenness function for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our empirical understanding of intraannual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global l(sup o) gridded data sets suggest that three climate indexes: growing degree days, annual precipitation total, and an annual moisture index together can account to 70-80 percent of the variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same climate index values from the previous year explained no significant additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes was closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from l(sup o) grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes were not accurately predicted are mainly high latitude ecosystems and other remote locations where climate station data are sparse.

A Non-Stationary 1981-2012 AVHRR NDVI(sub 3g) Time Series

NASA Technical Reports Server (NTRS)

Pinzon, Jorge E.; Tucker, Compton J.

2014-01-01

The NDVI(sub 3g) time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of plus or minus 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.

Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion.

PubMed

Wu, Mingquan; Yang, Chenghai; Song, Xiaoyu; Hoffmann, Wesley Clint; Huang, Wenjiang; Niu, Zheng; Wang, Changyao; Li, Wang; Yu, Bo

2018-01-31

To better understand the progression of cotton root rot within the season, time series monitoring is required. In this study, an improved spatial and temporal data fusion approach (ISTDFA) was employed to combine 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Different Vegetation Index (NDVI) and 10-m Sentinetl-2 NDVI data to generate a synthetic Sentinel-2 NDVI time series for monitoring this disease. Then, the phenology of healthy cotton and infected cotton was modeled using a logistic model. Finally, several phenology parameters, including the onset day of greenness minimum (OGM), growing season length (GLS), onset of greenness increase (OGI), max NDVI value, and integral area of the phenology curve, were calculated. The results showed that ISTDFA could be used to combine time series MODIS and Sentinel-2 NDVI data with a correlation coefficient of 0.893. The logistic model could describe the phenology curves with R-squared values from 0.791 to 0.969. Moreover, the phenology curve of infected cotton showed a significant difference from that of healthy cotton. The max NDVI value, OGM, GSL and the integral area of the phenology curve for infected cotton were reduced by 0.045, 30 days, 22 days, and 18.54%, respectively, compared with those for healthy cotton.

Efficient Maize and Sunflower Multi-year Mapping with NDVI Time Series of HJ-1A/1B in Hetao Irrigation District of Inner Mongolia, China

NASA Astrophysics Data System (ADS)

Yu, B.; Shang, S.

2016-12-01

Food shortage is one of the major challenges that human beings are facing. It is urgent to improve the monitoring of the plantation and distribution of the main crops to solve the following economic and social issues. Recently, with the extensive use of remote sensing satellite data, it has provided favorable conditions for crop identification in large irrigation district with complex planting structure. Difference of different crop phenology is the main basis for crop identification, and the normalized difference vegetation index (NDVI) time-series could better delineate crop phenology cycle. Therefore, the key of crop identification is to obtain high quality NDVI time-series. MODIS and Landsat TM satellite images are the most frequently used, however, neither of them could guarantee high temporal and spatial resolutions at once. Accordingly, this paper makes use of NDVI time-series extracted from China Environment Satellites data, which has two-day-repeat temporal and 30m spatial resolutions. The NDVI time-series are fitted with an asymmetric logistic curve, the fitting effect is good and the correlation coefficient is greater than 0.9. The phonological parameters are derived from NDVI fitting curves, and crop identification is carried out by different relation ellipses between NDVI and its phonological parameters of different crops. This paper takes Hetao Irrigation District of Inner Mongolia as an example, to identify multi-year maize and sunflower in the district, and the identification result is good. Compared with the official statistics, the relative errors are both lower than 5%. The results show that the NDVI time-series dataset derived from HJ-1A/1B CCD could delineate the crop phenology cycle accurately and demonstrate its application in crop identification in irrigated district.

Recent Efforts to Improve the Near Real Time Forest Disturbance Monitoring Capabilities of the ForWarn System

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William; Gasser, Gerald

2013-01-01

This presentation discusses the development of anew method for computing NDVI temporal composites from near real time eMODIS data This research is being conducted to improve forest change products used in the ForWarn system for monitoring regional forest disturbances in the United States. ForWarn provides nation-wide NDVI-based forest disturbance detection products that are refreshed every 8 days. Current eMODIS and historical MOD13 24 day NDVI data are used to compute the disturbance detection products. The eMODIS 24 day NDVI data re-aggregated from 7 day NDVI products. The 24 day eMODIS NDVIs are generally cloud free, but do not necessarily use the freshest quality data. To shorten the disturbance detection time, a method has been developed that performs adaptive length/maximum value compositing of eMODIS NDVI, along with cloud and shadow "noise" mitigation. Tests indicate that this method can reduce detection rates by 8-16 days for known recent disturbance events, depending on the cloud frequencies and disturbance type. The noise mitigation in these tests, though imperfect, helped to improve quality of the resulting NDVI and forest change products.

Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

NASA Technical Reports Server (NTRS)

Potter, C. S.; Brooks, V.

1997-01-01

This paper describes the use of satellite data to calibrate a new climate-vegetation greenness relationship for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes If the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980's in order to refine our understanding of intra-annual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global 1o gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80 percent of the geographic variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from lo grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.

Changing Seasonality of Panarctic Tundra Vegetation in Relationship to Climatic Variables

NASA Technical Reports Server (NTRS)

Bhatt, Uma S.; Walker, Donald A.; Raynolds, Martha K.; Bieniek, Peter A.; Epstein, Howard E.; Comiso, Josefino C.; Pinzon, Jorge E.; Tucker, Compton J.; Steele, Michael; Ermold, Wendy;

Detecting long-duration cloud contamination in hyper-temporal NDVI imagery

NASA Astrophysics Data System (ADS)

Ali, Amjad; de Bie, C. A. J. M.; Skidmore, A. K.

2013-10-01

Cloud contamination impacts on the quality of hyper-temporal NDVI imagery and its subsequent interpretation. Short-duration cloud impacts are easily removed by using quality flags and an upper envelope filter, but long-duration cloud contamination of NDVI imagery remains. In this paper, an approach that goes beyond the use of quality flags and upper envelope filtering is tested to detect when and where long-duration clouds are responsible for unreliable NDVI readings, so that a user can flag those data as missing. The study is based on MODIS Terra and the combined Terra-Aqua 16-day NDVI product for the south of Ghana, where persistent cloud cover occurs throughout the year. The combined product could be assumed to have less cloud contamination, since it is based on two images per day. Short-duration cloud effects were removed from the two products through using the adaptive Savitzky-Golay filter. Then for each 'cleaned' product an unsupervised classified map was prepared using the ISODATA algorithm, and, by class, plots were prepared to depict changes over time of the means and the standard deviations in NDVI values. By comparing plots of similar classes, long-duration cloud contamination appeared to display a decline in mean NDVI below the lower limit 95% confidence interval with a coinciding increase in standard deviation above the upper limit 95% confidence interval. Regression analysis was carried out per NDVI class in two randomly selected groups in order to statistically test standard deviation values related to long-duration cloud contamination. A decline in seasonal NDVI values (growing season) were below the lower limit of 95% confidence interval as well as a concurrent increase in standard deviation values above the upper limit of the 95% confidence interval were noted in 34 NDVI classes. The regression analysis results showed that differences in NDVI class values between the Terra and the Terra-Aqua imagery were significantly correlated (p < 0.05) with the corresponding standard deviation values of the Terra imagery in case of all NDVI classes of two selected NDVI groups. The method successfully detects long-duration cloud contamination that results in unreliable NDVI values. The approach offers scientists interested in time series analysis a method of masking by area (class) the periods when pre-cleaned NDVI values remain affected by clouds. The approach requires no additional data for execution purposes but involves unsupervised classification of the imagery to carry out the evaluation of class-specific mean NDVI and standard deviation values over time.

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products

PubMed Central

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-01-01

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics. PMID:28587266

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products.

PubMed

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-06-06

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

[Application of regression tree in analyzing the effects of climate factors on NDVI in loess hilly area of Shaanxi Province].

PubMed

Liu, Yang; Lü, Yi-he; Zheng, Hai-feng; Chen, Li-ding

2010-05-01

Based on the 10-day SPOT VEGETATION NDVI data and the daily meteorological data from 1998 to 2007 in Yan' an City, the main meteorological variables affecting the annual and interannual variations of NDVI were determined by using regression tree. It was found that the effects of test meteorological variables on the variability of NDVI differed with seasons and time lags. Temperature and precipitation were the most important meteorological variables affecting the annual variation of NDVI, and the average highest temperature was the most important meteorological variable affecting the inter-annual variation of NDVI. Regression tree was very powerful in determining the key meteorological variables affecting NDVI variation, but could not build quantitative relations between NDVI and meteorological variables, which limited its further and wider application.

Using NDVI to assess vegetative land cover change in central Puget Sound.

PubMed

Morawitz, Dana F; Blewett, Tina M; Cohen, Alex; Alberti, Marina

2006-03-01

We used the Normalized Difference Vegetation Index (NDVI) in the rapidly growing Puget Sound region over three 5-year time blocks between 1986-1999 at three spatial scales in 42 Watershed Administrative Units (WAUs) to assess changes in the amounts and patterns of green vegetation. On average, approximately 20% of the area in each WAU experienced significant NDVI change over each 5-year time block. Cumulative NDVI change over 15 years (summing change over each 5-year time block) was an average of approximately 60% of each WAU, but was as high as 100% in some. At the regional scale, seasonal weather patterns and green-up from logging were the primary drivers of observed increases in NDVI values. At the WAU scale, anthropogenic factors were important drivers of both positive and negative NDVI change. For example, population density was highly correlated with negative NDVI change over 15 years (r = 0.66, P < 0.01), as was road density (r = 0.71, P < 0.01). At the smallest scale (within 3 case study WAUs) land use differences such as preserving versus harvesting forest lands drove vegetation change. We conclude that large areas within most watersheds are continually and heavily impacted by the high levels of human use and development over short time periods. Our results indicate that varying patterns and processes can be detected at multiple scales using changes in NDVIa values.

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

NASA Astrophysics Data System (ADS)

Nasanbat, E.; Sharav, S.; Sanjaa, T.; Lkhamjav, O.; Magsar, E.; Tuvdendorj, B.

2018-04-01

This study examines MODIS NDVI satellite imagery time series can be used to determine hotspot of land degradation area in whole Mongolia. The trend statistical analysis of Mann-Kendall was applied to a 16-year MODIS NDVI satellite imagery record, based on 16-day composited temporal data (from May to September) for growing seasons and from 2000 to 2016. We performed to frequency analysis that resulting NDVI residual trend pattern would enable successful determined of negative and positive changes in photo synthetically health vegetation. Our result showed that negative and positive values and generated a map of significant trends. Also, we examined long-term of meteorological parameters for the same period. The result showed positive and negative NDVI trends concurred with land cover types change representing an improve or a degrade in vegetation, respectively. Also, integrated the climate parameters which were precipitation and air temperature changes in the same time period seem to have had an affecting on huge NDVI trend area. The time series trend analysis approach applied successfully determined hotspot of an improvement and a degraded area due to land degradation and desertification.

Deriving phenological metrics from NDVI through an open source tool developed in QGIS

NASA Astrophysics Data System (ADS)

Duarte, Lia; Teodoro, A. C.; Gonçalves, Hernãni

2014-10-01

Vegetation indices have been commonly used over the past 30 years for studying vegetation characteristics using images collected by remote sensing satellites. One of the most commonly used is the Normalized Difference Vegetation Index (NDVI). The various stages that green vegetation undergoes during a complete growing season can be summarized through time-series analysis of NDVI data. The analysis of such time-series allow for extracting key phenological variables or metrics of a particular season. These characteristics may not necessarily correspond directly to conventional, ground-based phenological events, but do provide indications of ecosystem dynamics. A complete list of the phenological metrics that can be extracted from smoothed, time-series NDVI data is available in the USGS online resources (http://phenology.cr.usgs.gov/methods_deriving.php).This work aims to develop an open source application to automatically extract these phenological metrics from a set of satellite input data. The main advantage of QGIS for this specific application relies on the easiness and quickness in developing new plug-ins, using Python language, based on the experience of the research group in other related works. QGIS has its own application programming interface (API) with functionalities and programs to develop new features. The toolbar developed for this application was implemented using the plug-in NDVIToolbar.py. The user introduces the raster files as input and obtains a plot and a report with the metrics. The report includes the following eight metrics: SOST (Start Of Season - Time) corresponding to the day of the year identified as having a consistent upward trend in the NDVI time series; SOSN (Start Of Season - NDVI) corresponding to the NDVI value associated with SOST; EOST (End of Season - Time) which corresponds to the day of year identified at the end of a consistent downward trend in the NDVI time series; EOSN (End of Season - NDVI) corresponding to the NDVI value associated with EOST; MAXN (Maximum NDVI) which corresponds to the maximum NDVI value; MAXT (Time of Maximum) which is the day associated with MAXN; DUR (Duration) defined as the number of days between SOST and EOST; and AMP (Amplitude) which is the difference between MAXN and SOSN. This application provides all these metrics in a single step. Initially, the data points are interpolated using a moving average graphic with five and three points. The eight metrics previously described are then obtained from the spline using numpy functions. In the present work, the developed toolbar was applied to MODerate resolution Imaging Spectroradiometer (MODIS) data covering a particular region of Portugal, which can be generally applied to other satellite data and study area. The code is open and can be modified according to the user requirements. Other advantage in publishing the plug-ins and the application code is the possibility of other users to improve this application.

Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Lary, David J.; Vrieling, Anton; Stathakis, Demetris; Mussa, Hamse

2008-01-01

The long term Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at one degree is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.

Estimation of different data compositions for early-season crop type classification.

PubMed

Hao, Pengyu; Wu, Mingquan; Niu, Zheng; Wang, Li; Zhan, Yulin

2018-01-01

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer's accuracies (PAs) and user's accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

Estimation of different data compositions for early-season crop type classification

PubMed Central

Wu, Mingquan; Wang, Li; Zhan, Yulin

2018-01-01

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study. PMID:29868265

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2003-01-01

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989–2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI–SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.

The Impact of Soil Reflectance on the Quantification of the Green Vegetation Fraction from NDVI

NASA Technical Reports Server (NTRS)

Montandon, L. M.; Small, E. E.

2008-01-01

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing rnodeP between two NDVI endmembers: bare soil NDVI (NDVI(sub o)) and full vegetation NDVI (NDVI(sub infinity)). Usually it is assumed that NDVI(sub o), is close to zero (NDVI(sub o) approx.-0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI=0.2) and is highly variable (standard deviation=O. 1). We show that the underestimation of NDVI(sub o) yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI(sub o) and NDVI(sub infinity) derived from global scenes yields overestimations of Fg ((Delta) Fg*) that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2

Global discrimination of land cover types from metrics derived from AVHRR pathfinder data

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeFries, R.; Hansen, M.; Townshend, J.

1995-12-01

Global data sets of land cover are a significant requirement for global biogeochemical and climate models. Remotely sensed satellite data is an increasingly attractive source for deriving these data sets due to the resulting internal consistency, reproducibility, and coverage in locations where ground knowledge is sparse. Seasonal changes in the greenness of vegetation, described in remotely sensed data as changes in the normalized difference vegetation index (NDVI) throughout the year, have been the basis for discriminating between cover types in previous attempts to derive land cover from AVHRR data at global and continental scales. This study examines the use ofmore » metrics derived from the NDVI temporal profile, as well as metrics derived from observations in red, infrared, and thermal bands, to improve discrimination between 12 cover types on a global scale. According to separability measures calculated from Bhattacharya distances, average separabilities improved by using 12 of the 16 metrics tested (1.97) compared to separabilities using 12 monthly NDVI values alone (1.88). Overall, the most robust metrics for discriminating between cover types were: mean NDVI, maximum NDVI, NDVI amplitude, AVHRR Band 2 (near-infrared reflectance) and Band 1 (red reflectance) corresponding to the time of maximum NDVI, and maximum land surface temperature. Deciduous and evergreen vegetation can be distinguished by mean NDVI, maximum NDVI, NDVI amplitude, and maximum land surface temperature. Needleleaf and broadleaf vegetation can be distinguished by either mean NDVI and NDVI amplitude or maximum NDVI and NDVI amplitude.« less

Using ESAP Software for Predicting the Spatial Distributions of NDVI and Transpiration of Cotton

USDA-ARS?s Scientific Manuscript database

The normalized difference vegetation index (NDVI) has many applications in agricultural management, including monitoring real-time crop coefficients for estimating crop evapotranspiration (ET). However, frequent monitoring of NDVI as needed in such applications is generally not feasible from aerial ...

Predicting evapotranspiration from sparse and dense vegetation communities in a semiarid environment using NDVI from satellite and ground measurements

NASA Astrophysics Data System (ADS)

Baghzouz, Malika

One of the most critical issues associated with using satellite data-based products to study and estimate surface energy fluxes and other ecosystem processes, has been the lack of frequent acquisition at a spatial scale equivalent to or finer than the footprint of field measurements. In this study, we incorporated continuous field measurements based on using Normalized difference vegetation index (NDVI) time series analysis of individual shrub species and transect measurements within 625 m2 size plots equivalent to the Landsat-5 Thematic Mapper spatial resolution. The NDVI system was a dual channel SKR-1800 radiometer that simultaneously measured incident solar radiation and upward reflectance in two broadband red and near-infrared channels comparable to Landsat-5 TM band 3 and band 4, respectively. The two study sites identified as Spring Valley 1 site (SV1) and Snake Valley 1 site (SNK1) were chosen for having different species composition, soil texture and percent canopy cover. NDVI time-series of greasewood (Sarcobatus vermiculatus) from the SV1 site allowed for clear distinction between the main phenological stages of the entire growing season during the period from January to November, 2007. Comparison of greasewood NDVI values between the two sites revealed a significant temporal difference associated with early canopy development and early dry down of greasewood at the SNK1 site. NDVI time series values were also significantly different between sagebrush (Artemisia tridentata ) and rabbitbrush (Chrysothamnus viscidiflorus) at SV1 as well as between the two bare soil types at the two sites, indicating the ability of the ground-based NDVI to distinguish between different plant species as well as between different desert soils based on their moisture level and color. The difference in phenological characteristics of greasewood between the two sites and between sagebrush, rabbitbrush and greasewood within the same site were not captured by the spatially integrated Landsat NDVI acquired during repeated overpasses. Greasewood NDVI from the SNK1 site produced significant correlations with many of the measured plant parameters, most closely with chlorophyll index (r = 0.97), leaf area index (r = 0.98) and leaf xylem water potential (r = 0.93). Whereas greasewood NDVI from the SV1 site produced lower correlations ( r = 0.89, r = 0.73), or non significant correlations (r = 0.32) with the same parameters, respectively. Total percent cover was estimated at 17.5% for SV1 and at 63% for SNK1. Transect measurements provided detailed information with regard to the spectral properties of shrub species and soil types, differentiating the two sites, which was not possible to discern with the spatial resolution of Landsat. Correlation between transect NDVI data and Landsat NDVI produced an r of 0.79. While correlation between transect NDVI data and ground-based NDVI sensors produced an r of 0.73. The linear regression equation between daily ET measured by the eddy covariance method and Landsat NDVI yielded a strong relationship (r = 0.88) for data combined across the experimental period (May to September) and across the two sites. The ET prediction equation was improved (r2 = 0.86) by introducing net solar radiation (Rn) which was the meteorological variable that had the highest prediction of ET (r2 = 0.82). A high correlation was found between weighted ground-based sensor NDVI estimates and Landsat derived NDVI at the pixel scale (r = 0.97) for the two study sites combined over time. While results from this study in scaling ground-based NDVI measurements and estimating ET were very promising, further verification and improvement is needed to determine the performance level of this approach over larger heterogeneous areas and over extended time periods.

Climate and land use change in an Andean watershed: An NDVI analysis for the years 1985 to 2010

NASA Astrophysics Data System (ADS)

Mazzarino, M.; Finn, J.

2013-12-01

We perform a Landsat 5-TM derived Normalized Difference Vegetation Index (NDVI) analysis in a watershed (approximately 2700 km2) in southern Peru for the years 1985 through 2010. There in the Andes the livelihoods of the predominately Quechua speaking agro-pastoralists depend on access to natural resources. Vegetation within high-elevation wetlands, locally known as bofedales, is a critical resource that sustains herds of alpaca, sheep, and cattle especially during dry season months (June through August) and in drought. The watershed experiences high inter-annual variability in precipitation (attributed to the El Niño Southern Oscillation) and there are documented increases in air temperature and glacier retreat throughout the Andes. Using one dry-season scene per year for 20 of the 26 years from 1985 to 2010, we calculated NDVI for each pixel in the watershed and used these calculations to perform three objectives. First, we calculated mean NDVI for the Nuñoa watershed for each dry season scene. Using this annual watershed averaged NDVI as the response variable we performed a multiple linear regression with the covariates year, precipitation, and temperature in order to determine the relationship between the response and explanatory variables and if there is a trend in mean watershed dry-season NDVI from 1985 to 2010. Second, we delineated the wetlands (bofedales) based on a threshold value applied to the 26 year dry-season mean NDVI for each pixel in the watershed. Third, we performed a multiple linear regression for each pixel in the watershed (3,070,160) using cell specific annual dry-season NDVI as the response variable (n=20) and year, regional precipitation, and regional temperature indices as the predictor variables in order to review the spatial nature of NDVI changes in vegetation in the watershed throughout time (1985-2010), particularly with respect to bofedales. The results of these analyses indicate that there is reduced variability in dry season NDVI and a general increase in NDVI values in the watershed with time. Variability in mean dry season NDVI is highly correlated with wet season (DJFM) precipitation (R2 = 0.77, p-value < 0.05) and this relationship may explain much of the shift in NDVI values however; the increasing trend in NDVI in the watershed is not explained by a trend in precipitation. We were not able to determine a relationship between NDVI and temperature with our methods. And while an increase in dry-season NDVI is seen in the majority of the vegetated pixels (81%) throughout the watershed, approximately 30% of the wetland areas display a decrease in NDVI over the time period. Contemporary socio-political factors and resulting changes in land management and production systems in the region may be resulting in more intensive use of wetland areas, thereby causing the decreasing vegetation trends seen there. These factors, together with potential reductions in glacier melt from several small ice-capped mountains in the north of the study area may all be contributing to the spatial and temporal changes in NDVI seen in the watershed.

An approach for using AVHRR data to monitor U.S. great plains grasslands

USGS Publications Warehouse

Reed, B.C.; Loveland, Thomas R.; Tieszen, L.L.

1996-01-01

Environmental monitoring requires regular observations regarding the status of the landscape- The concept behind most monitoring efforts using satellite data involve deriving normalized difference vegetation index (NDVI) values or accumulating the NDVI over a specified time period. These efforts attempt to estimate the continuous growth of green biomass by using continuous additions of NDVI as a surrogate measure. To build upon this concept, this study proposes three refinements; 1) use an objective definition of the current growing season to adjust the time window during which the NDVI is accumulated, 2) accumulate only the NDVI values which are affected by green vegetation, and 3) base monitoring units upon land cover type. These refinements improve the sensitivity of detecting interannual vegetation variability, reduce the need for extensive and detailed knowledge of ground conditions and crop calendars, provide a framework in which several types of monitoring can take place over diverse land cover types, and provide an objective time frame during which monitoring takes place.

Impact of Sensor Degradation on the MODIS NDVI Time Series

NASA Technical Reports Server (NTRS)

Wang, Dongdong; Morton, Douglas Christopher; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2012-01-01

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470 nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004 yr-1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistentwith simulated results,with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

Impact of Sensor Degradation on the MODIS NDVI Time Series

NASA Technical Reports Server (NTRS)

Wang, Dongdong; Morton, Douglas; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2011-01-01

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation.

Climatic controls of vegetation vigor in four contrasting forest types of India--evaluation from National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer datasets (1990-2000).

PubMed

Prasad, V Krishna; Anuradha, E; Badarinath, K V S

2005-09-01

Ten-day advanced very high resolution radiometer images from 1990 to 2000 were used to examine spatial patterns in the normalized difference vegetation index (NDVI) and their relationships with climatic variables for four contrasting forest types in India. The NDVI signal has been extracted from homogeneous vegetation patches and has been found to be distinct for deciduous and evergreen forest types, although the mixed-deciduous signal was close to the deciduous ones. To examine the decadal response of the satellite-measured vegetation phenology to climate variability, seven different NDVI metrics were calculated using the 11-year NDVI data. Results suggested strong spatial variability in forest NDVI metrics. Among the forest types studied, wet evergreen forests of north-east India had highest mean NDVI (0.692) followed by evergreen forests of the Western Ghats (0.529), mixed deciduous forests (0.519) and finally dry deciduous forests (0.421). The sum of NDVI (SNDVI) and the time-integrated NDVI followed a similar pattern, although the values for mixed deciduous forests were closer to those for evergreen forests of the Western Ghats. Dry deciduous forests had higher values of inter-annual range (RNDVI) and low mean NDVI, also coinciding with a high SD and thus a high coefficient of variation (CV) in NDVI (CVNDVI). SNDVI has been found to be high for wet evergreen forests of north-east India, followed by evergreen forests of the Western Ghats, mixed deciduous forests and dry deciduous forests. Further, the maximum NDVI values of wet evergreen forests of north-east India (0.624) coincided with relatively high annual total precipitation (2,238.9 mm). The time lags had a strong influence in the correlation coefficients between annual total rainfall and NDVI. The correlation coefficients were found to be comparatively high (R2=0.635) for dry deciduous forests than for evergreen forests and mixed deciduous forests, when the precipitation data with a lag of 30 days was correlated against NDVI. Using multiple regression approach models were developed for individual forest types using 16 different climatic indices. A high proportion of the temporal variance (>90%) has been accounted for by three of the precipitation parameters (maximum precipitation, precipitation of the wettest quarter and driest quarter) and two of the temperature parameters (annual mean temperature and temperature of the coldest quarter) for mixed deciduous forests. Similarly, in the case of deciduous forests, four precipitation parameters and three temperature parameters explained nearly 83.6% of the variance. These results suggest differences in the relationship between NDVI and climatic variables based upon the time of growing season, time interval and climatic indices over which they were summed. These results have implications for forest cover mapping and monitoring in tropical regions of India.

Role of MODIS Vegetation Phenology Products in the ForWarn System for Monitoring of Forest Disturbances in the Conterminous United States

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Norman, S.; Gasser, J.; Smoot, J.; Kuper, P.

2012-12-01

This presentation discusses MODIS vegetation phenology products used in the ForWarn Early Warning System (EWS) tool for near real time regional forest disturbance detection and surveillance at regional to national scales. The ForWarn EWS is being developed by the USDA Forest Service NASA, ORNL, and USGS to aid federal and state forest health management activities. ForWarn employs multiple historical land surface phenology products that are derived from MODIS MOD13 Normalized Difference Vegetation Index (NDVI) data. The latter is temporally processed into phenology products with the Time Series Product Tool (TSPT) and the Phenological Parameter Estimation Tool (PPET) software produced at NASA Stennis Space Center. TSPT is used to effectively noise reduce, fuse, and void interpolate MODIS NDVI data. PPET employs TSPT-processed NDVI time series data as an input, outputting multiple vegetation phenology products at a 232 meter resolution for 2000 to 2011, including NDVI magnitude and day of year products for seven key points along the growing season (peak of growing season and the minima, 20%, and 80% of the peak NDVI for both the left and right side of growing season), cumulative NDVI integral products for the most active part of the growing season and sequentially across the growing season at 8 day intervals, and maximum value NDVI products composited at 24 day intervals in which each product date has 8 days of overlap between the previous and following product dates. MODIS NDVI phenology products are also used to compute nationwide near real time forest change products every 8 days. These include percent change in forest NDVI products that compare the current NDVI from USGS eMODIS products to historical MODIS MOD13 NDVI. For each date, three forest change products are produced using three different maximum value NDVI baselines (from the previous year, three previous years, and all previous years). All change products are output with a rainbow color table in which forests with the most severe NDVI decreases are assigned hot colors (yellow to red) and forests with prominent NDVI increases are assigned cold colors (blue tones). All mentioned products have been integrated as data layers into ForWarn's geospatial data viewer known as the U.S. Forest Change Assessment Viewer (FCAV). The latter is used to view and assess the context of the mentioned forest change products with respect to ancillary data layers, such as land cover, elevation, hydrologic features, climatic data, storm data, aerial disturbance surveys, fire data, and land ownership. The FCAV also includes a temporal NDVI profiler for viewing phenological change in multi-year NDVI associated with known or suspected regionally apparent forest disturbances (e.g., from fire and insects). ForWarn forest change products have been used to detect, track, and assess several biotic and abiotic regional forest disturbance events across the country, including ephemeral and longer lasting damage from storms, drought, and insects. Such change products are most effective for viewing severe disturbance patches of multiple pixels. MODIS vegetation phenology products contribute vital current information on forest conditions to the ForWarn system and this role is expected to grow as these products are refined and derivative products are added.

Monitoring Drought Effects on Mediteranean Conifer Forests Using SPOT-Vegetation NDVI and NDWI Timeseries

NASA Astrophysics Data System (ADS)

Karamihalaki, Maria; Stagakis, Stavros; Sykioti, Olga; Kyparissis, Aris; Parcharidis, Issaak

2016-08-01

The aim of this study focuses in the investigation of vegetation's responses to precipitation variations and water stress conditions in three Pinus sp. (pine) forests in Greece and in the assessment of NDWI and NDVI in terms of drought and water stress detection capacity for this type of ecosystems. For the purpose of this study, 11-year time series of NDVI and NDWI indices, issued from SPOT - Vegetation data, were constructed and correlated with ground measured precipitation data for the same time period, for all three study sites. Results show a strong relationship between the two indices. Furthermore, NDWI shows a stronger correlation with precipitation than NDVI, indicating a better capacity for investigating the vegetation water status. Generally, high seasonal precipitation variations seem to have a strong effect on both NDVI and NDWI levels, while a smoother precipitation distribution results to a weaker relationship with the two indices.

A modified integrated NDVI for improving estimates of terrestrial net primary production

NASA Technical Reports Server (NTRS)

Running, Steven W.

1990-01-01

Logic is presented for a time-integrated NDVI that is modified by an AVHRR derived surface evaporation resistance factor sigma, and truncated by temperatures that cause plant dormancy, to improve environmental sensitivity. With this approach, NDVI observed during subfreezing temperatures is not integrated. Water stress-related impairment in plant activity is incorporated by reducing the effective NDVI at each integration with sigma, which is derived from the slope of the surface temperature to NDVI ratio for climatically similar zones of the scene. A comparison of surface resistance before and after an extended drought period for a 1200 sq km region of coniferous forest in Montana is presented.

Lag and seasonality considerations in evaluating AVHRR NDVI response to precipitation

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2005-01-01

Assessment of the relationship between the normalized difference vegetation index (NDVI) and precipitation is important in understanding vegetation and climate interaction at a large scale. NDVI response to precipitation, however, is difficult to quantify due to the lag and seasonality effects, which will vary due to vegetation cover type, soils and climate. A time series analysis was performed on biweekly NDVI and precipitation around weather stations in the northern and central U.S. Great Plains. Regression models that incorporate lag and seasonality effects were used to quantify the relationship between NDVI and lagged precipitation in grasslands and croplands. It was found that the time lag was shorter in the early growing season, but longer in the mid- to late-growing season for most locations. The regression models with seasonal adjustment indicate that the relationship between NDVI and precipitation over the entire growing season was strong, with R2 values of 0.69 and 0.72 for grasslands and croplands, respectively. We conclude that vegetation greenness can be predicted using current and antecedent precipitation, if seasonal effects are taken into account.

Large and Small-Scale Cropland Classification on the Foothills of Mount Kenya Based on SPOT-5 Take-5 Data Time Series

NASA Astrophysics Data System (ADS)

Eckert, Sandra

2016-08-01

The SPOT-5 Take 5 campaign provided SPOT time series data of an unprecedented spatial and temporal resolution. We analysed 29 scenes acquired between May and September 2015 of a semi-arid region in the foothills of Mount Kenya, with two aims: first, to distinguish rainfed from irrigated cropland and cropland from natural vegetation covers, which show similar reflectance patterns; and second, to identify individual crop types. We tested several input data sets in different combinations: the spectral bands and the normalized difference vegetation index (NDVI) time series, principal components of NDVI time series, and selected NDVI time series statistics. For the classification we used random forests (RF). In the test differentiating rainfed cropland, irrigated cropland, and natural vegetation covers, the best classification accuracies were achieved using spectral bands. For the differentiation of crop types, we analysed the phenology of selected crop types based on NDVI time series. First results are promising.

Variability of African Farming Systems from Phenological Analysis of NDVI Time Series

NASA Technical Reports Server (NTRS)

Vrieling, Anton; deBeurs, K. M.; Brown, Molly E.

2011-01-01

Food security exists when people have access to sufficient, safe and nutritious food at all times to meet their dietary needs. The natural resource base is one of the many factors affecting food security. Its variability and decline creates problems for local food production. In this study we characterize for sub-Saharan Africa vegetation phenology and assess variability and trends of phenological indicators based on NDVI time series from 1982 to 2006. We focus on cumulated NDVI over the season (cumNDVI) which is a proxy for net primary productivity. Results are aggregated at the level of major farming systems, while determining also spatial variability within farming systems. High temporal variability of cumNDVI occurs in semiarid and subhumid regions. The results show a large area of positive cumNDVI trends between Senegal and South Sudan. These correspond to positive CRU rainfall trends found and relate to recovery after the 1980's droughts. We find significant negative cumNDVI trends near the south-coast of West Africa (Guinea coast) and in Tanzania. For each farming system, causes of change and variability are discussed based on available literature (Appendix A). Although food security comprises more than the local natural resource base, our results can perform an input for food security analysis by identifying zones of high variability or downward trends. Farming systems are found to be a useful level of analysis. Diversity and trends found within farming system boundaries underline that farming systems are dynamic.

Estimating maize production in Kenya using NDVI: Some statistical considerations

USGS Publications Warehouse

Lewis, J.E.; Rowland, James; Nadeau , A.

1998-01-01

A regression model approach using a normalized difference vegetation index (NDVI) has the potential for estimating crop production in East Africa. However, before production estimation can become a reality, the underlying model assumptions and statistical nature of the sample data (NDVI and crop production) must be examined rigorously. Annual maize production statistics from 1982-90 for 36 agricultural districts within Kenya were used as the dependent variable; median area NDVI (independent variable) values from each agricultural district and year were extracted from the annual maximum NDVI data set. The input data and the statistical association of NDVI with maize production for Kenya were tested systematically for the following items: (1) homogeneity of the data when pooling the sample, (2) gross data errors and influence points, (3) serial (time) correlation, (4) spatial autocorrelation and (5) stability of the regression coefficients. The results of using a simple regression model with NDVI as the only independent variable are encouraging (r 0.75, p 0.05) and illustrate that NDVI can be a responsive indicator of maize production, especially in areas of high NDVI spatial variability, which coincide with areas of production variability in Kenya.

Changing Seasonality of Tundra Vegetation and Associated Climatic Variables

NASA Astrophysics Data System (ADS)

Bhatt, U. S.; Walker, D. A.; Raynolds, M. K.; Bieniek, P.; Epstein, H. E.; Comiso, J. C.; Pinzon, J.; Tucker, C. J.; Steele, M.; Ermold, W. S.; Zhang, J.

2014-12-01

This study documents changes in the seasonality of tundra vegetation productivity and its associated climate variables using long-term data sets. An overall increase of Pan-Arctic tundra greenness potential corresponds to increased land surface temperatures and declining sea ice concentrations. While sea ice has continued to decline, summer land surface temperature and vegetation productivity increases have stalled during the last decade in parts of the Arctic. To understand the processes behind these features we investigate additional climate parameters. This study employs remotely sensed weekly 25-km sea ice concentration, weekly surface temperature, and bi-weekly NDVI from 1982 to 2013. Maximum NDVI (MaxNDVI, Maximum Normalized Difference Vegetation Index), Time Integrated NDVI (TI-NDVI), Summer Warmth Index (SWI, sum of degree months above freezing during May-August), ocean heat content (PIOMAS, model incorporating ocean data assimilation), and snow water equivalent (GlobSnow, assimilated snow data set) are explored. We analyzed the data for the full period (1982-2013) and for two sub-periods (1982-1998 and 1999-2013), which were chosen based on the declining Pan-Arctic SWI since 1998. MaxNDVI has increased from 1982-2013 over most of the Arctic but has declined from 1999 to 2013 over western Eurasia, northern Canada, and southwest Alaska. TI-NDVI has trends that are similar to those for MaxNDVI for the full period but displays widespread declines over the 1999-2013 period. Therefore, as the MaxNDVI has continued to increase overall for the Arctic, TI-NDVI has been declining since 1999. SWI has large relative increases over the 1982-2013 period in eastern Canada and Greenland and strong declines in western Eurasia and southern Canadian tundra. Weekly Pan-Arctic tundra land surface temperatures warmed throughout the summer during the 1982-1998 period but display midsummer declines from 1999-2013. Weekly snow water equivalent over Arctic tundra has declined over most seasons but shows slight increases in spring in North America and during fall over Eurasia. Later spring or earlier fall snow cover can both lead to reductions in TI-NDVI. The time-varying spatial patterns of NDVI trends can be largely explained using either snow cover or land surface temperature trends.

Improving Post-Hurricane Katrina Forest Management with MODIS Time Series Products

NASA Technical Reports Server (NTRS)

Lewis, Mark David; Spruce, Joseph; Evans, David; Anderson, Daniel

2012-01-01

Hurricane damage to forests can be severe, causing millions of dollars of timber damage and loss. To help mitigate loss, state agencies require information on location, intensity, and extent of damaged forests. NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data products offers a potential means for state agencies to monitor hurricane-induced forest damage and recovery across a broad region. In response, a project was conducted to produce and assess 250 meter forest disturbance and recovery maps for areas in southern Mississippi impacted by Hurricane Katrina. The products and capabilities from the project were compiled to aid work of the Mississippi Institute for Forest Inventory (MIFI). A series of NDVI change detection products were computed to assess hurricane induced damage and recovery. Hurricane-induced forest damage maps were derived by computing percent change between MODIS MOD13 16-day composited NDVI pre-hurricane "baseline" products (2003 and 2004) and post-hurricane NDVI products (2005). Recovery products were then computed in which post storm 2006, 2007, 2008 and 2009 NDVI data was each singularly compared to the historical baseline NDVI. All percent NDVI change considered the 16-day composite period of August 29 to September 13 for each year in the study. This provided percent change in the maximum NDVI for the 2 week period just after the hurricane event and for each subsequent anniversary through 2009, resulting in forest disturbance products for 2005 and recovery products for the following 4 years. These disturbance and recovery products were produced for the Mississippi Institute for Forest Inventory's (MIFI) Southeast Inventory District and also for the entire hurricane impact zone. MIFI forest inventory products were used as ground truth information for the project. Each NDVI percent change product was classified into 6 categories of forest disturbance intensity. Stand age and stand type raster data, also provided by MIFI, were used along with the forest disturbance/recovery products to create forest damage stratification products integrating 3 stand type classes, 6 stand age classes, and 6 forest disturbance intensity classes. This stratification product will be used to aid MIFI timber inventory planning and to prepare for damage assessments due to future hurricane events. Validation of MODIS percent NDVI change products was performed by comparing the MODIS percent NDVI change products to those from Landsat data for the same time and MIFI inventory district area.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors.

PubMed

Lange, Maximilian; Dechant, Benjamin; Rebmann, Corinna; Vohland, Michael; Cuntz, Matthias; Doktor, Daniel

2017-08-11

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors

PubMed Central

Lange, Maximilian; Rebmann, Corinna; Cuntz, Matthias; Doktor, Daniel

2017-01-01

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure. PMID:28800065

Long-term vegetation activity trends in the Iberian Peninsula and The Balearic Islands using high spatial resolution NOAA-AVHRR data (1981 - 2015).

NASA Astrophysics Data System (ADS)

Martin-Hernandez, Natalia; Vicente-Serrano, Sergio; Azorin-Molina, Cesar; Begueria-Portugues, Santiago; Reig-Gracia, Fergus; Zabalza-Martínez, Javier

2017-04-01

We have analysed trends in the Normalized Difference Vegetation Index (NDVI) in the Iberian Peninsula and The Balearic Islands over the period 1981 - 2015 using a new high resolution data set from the entire available NOAA - AVHRR images (IBERIAN NDVI dataset). After a complete processing including geocoding, calibration, cloud removal, topographic correction and temporal filtering, we obtained bi-weekly time series. To assess the accuracy of the new IBERIAN NDVI time-series, we have compared temporal variability and trends of NDVI series with those results reported by GIMMS 3g and MODIS (MOD13A3) NDVI datasets. In general, the IBERIAN NDVI showed high reliability with these two products but showing higher spatial resolution than the GIMMS dataset and covering two more decades than the MODIS dataset. Using the IBERIAN NDVI dataset, we analysed NDVI trends by means of the non-parametric Mann-Kendall test and Theil-Sen slope estimator. In average, vegetation trends in the study area show an increase over the last decades. However, there are local spatial differences: the main increase has been recorded in humid regions of the north of the Iberian Peninsula. The statistical techniques allow finding abrupt and gradual changes in different land cover types during the analysed period. These changes are related with human activity due to land transformations (from dry to irrigated land), land abandonment and forest recovery.

Vegetation Greenness and Its Drivers across Ice-free Greenland

NASA Astrophysics Data System (ADS)

Pedersen, S. H.; Liston, G. E.; Tamstorf, M. P.; Schmidt, N. M.

2017-12-01

The coastal and mountain areas surrounding the Greenland Ice Sheet cover one-fifth of Greenland. This ice-free area spans more than 20 degrees latitude and includes high-, low-, and sub-Arctic climate zones and the terrain varies from sea level to 3700 m elevation. Hence, this area contains a wide range of vegetation growing conditions associated with precipitation, temperature, and incoming solar radiation found across these latitudinal, elevational, and coast-inland gradients. In this study, we mapped the spatial distribution of vegetation at 300-m spatial resolution across ice-free Greenland using the annual maximum vegetation greenness (MaxNDVI) and the timing of MaxNDVI derived from daily Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from 2000-2015. Further, we investigated the drivers of the annual MaxNDVI and its timing across the diverse vegetation growing conditions in Greenland using modeled climatic variables, including snow quantity and timing, at the same temporal and spatial resolutions. The annual average MaxNDVI varied between 0.3 and 0.5 in North Greenland, and 0.6 and 0.9 in South Greenland. The timing of MaxNDVI differed more than two weeks between North and South Greenland. The potential growing season, e.g., the period with no snow on the ground, was as short as one month in North Greenland (mainly August), and four to five times longer in South Greenland (typically starting in mid-May). The snow-free date varied with elevation, from valley bottoms to the mountain tops, having the same range that existed from South to North Greenland. Our results show that MaxNDVI and its timing are significantly driven by the timing of snow-free ground and the amount of meltwater available from the snowpack during spring snowmelt.

Possible causes of Arctic Tundra Vegetation Productivity Declines

NASA Astrophysics Data System (ADS)

Bhatt, U. S.; Walker, D. A.; Raynolds, M. K.; Bieniek, P.; Epstein, H. E.; Comiso, J. C.; Pinzon, J. E.; Tucker, C. J.

2017-12-01

Three decades of remotely sensed Normalized Difference Vegetation Index (NDVI) data document an overall increase in Arctic tundra vegetation greenness but the trends display considerable spatial variability. Pan-Arctic tundra vegetation greening is associated with increases in summer warmth that are, in large-part, driven by summer sea-ice retreat along Arctic coasts. Trends covering the period 1982-2016 are overall positive for summer open water, Summer Warmth Index (SWI, the sum of the degree months above zero from May-August), MaxNDVI (peak NDVI) and time integrated NDVI (TI-NDVI, sum of biweekly NDVI above 0.05 from May-September). Upon closer examination, it is clear that not all regions have positive trends, for example, there is an area of cooling in western Eurasia, which is broadly co-located with maxNDVI and TI-NDVI declines. While sea ice decline has continued over the satellite record, summer landsurface temperatures and vegetation productivity measures have not simply increased. Regional differences between warming and greening trends suggest that it is likely that multiple processes influence vegetation productivity beyond secular greening with increased summer warmth. This paper will present Pan-Arctic and regional analyses of the NDVI data in the context of climate drivers. Other possible drivers of vegetation productivity decline will be discussed such as increased standing water, delayed spring snow-melt, and winter thaw events. The status and limitations of data sets and modeling needed to advance our understanding of tundra vegetation productivity will be summarized and will serve as a starting point for planning the next steps in this topic. Methodical multi-disciplinary synthesis research that jointly considers vegetation type, permafrost conditions, altitude, as well as climate factors such as temperature, heat and moisture transport, and timing of snowfall and spring snowmelt is needed to better understand recent tundra vegetation productivity declines.

Retrieving pace in vegetation growth using precipitation and soil moisture

NASA Astrophysics Data System (ADS)

Sohoulande Djebou, D. C.; Singh, V. P.

2013-12-01

The complexity of interactions between the biophysical components of the watershed increases the challenge of understanding water budget. Hence, the perspicacity of the continuum soil-vegetation-atmosphere's functionality still remains crucial for science. This study targeted the Texas Gulf watershed and evaluated the behavior of vegetation covers by coupling precipitation and soil moisture patterns. Growing season's Normalized Differential Vegetation Index NDVI for deciduous forest and grassland were used over a 23 year period as well as precipitation and soil moisture data. The role of time scales on vegetation dynamics analysis was appraised using both entropy rescaling and correlation analysis. This resulted in that soil moisture at 5 cm and 25cm are potentially more efficient to use for vegetation dynamics monitoring at finer time scale compared to precipitation. Albeit soil moisture at 5 cm and 25 cm series are highly correlated (R2>0.64), it appeared that 5 cm soil moisture series can better explain the variability of vegetation growth. A logarithmic transformation of soil moisture and precipitation data increased correlation with NDVI for the different time scales considered. Based on a monthly time scale we came out with a relationship between vegetation index and the couple soil moisture and precipitation [NDVI=a*Log(% soil moisture)+b*Log(Precipitation)+c] with R2>0.25 for each vegetation type. Further, we proposed to assess vegetation green-up using logistic regression model and transinformation entropy using the couple soil moisture and precipitation as independent variables and vegetation growth metrics (NDVI, NDVI ratio, NDVI slope) as the dependent variable. The study is still ongoing and the results will surely contribute to the knowledge in large scale vegetation monitoring. Keywords: Precipitation, soil moisture, vegetation growth, entropy Time scale, Logarithmic transformation and correlation between soil moisture and NDVI, precipitation and NDVI. The analysis is performed by combining both scenes 7 and 8 data. Schematic illustration of the two dimension transinformation entropy approach. T(P,SM;VI) stand for the transinformation contained in the couple soil moisture (SM)/precipitation (P) and explaining vegetation growth (VI).

Study of Maowusu Sandy Land Vegetation Coverage Change Based on Modis Ndvi

NASA Astrophysics Data System (ADS)

Ye, Q.; Liu, H.; Lin, Y.; Han, R.

2018-04-01

This paper selected 2006-2016 MODIS NDVI data with a spatial resolution of 500m and time resolution of 16d, got the 11 years' time series NDVI data of Maowusu sandy land through mosaicking, projection transformation, cutting process in batch. Analysed the spatial and temporal distribution and variation characteristics of vegetation cover in year, season and month time scales by maximum value composite, and unary linear regression analysis. Then, we combined the meteorological data of 33 sites around the sandy area, analysed the response characteristics of vegetation cover change to temperature and precipitation through Pearson correlation coefficient. Studies have shown that: (1) The NDVI value has a stable increase trend, which rate is 0.0075 / a. (2) The vegetation growth have significantly difference in four seasons, the NDVI value of summer > autumn > spring > winter. (3) The NDVI value change trend is conformed to the gauss normal distribution in a year, and it comes to be largest in August, its green season is in April, and yellow season is in the middle of November, the growth period is about 220 d. (4) The vegetation has a decreasing trend from the southeast to the northwest, most part is slightly improved, and Etuokeqianqi improved significantly. (5) The correlation indexes of annual NDVI with temperature and precipitation are -0.2178 and 0.6309, the vegetation growth is mainly affected by precipitation. In this study, a complete vegetation cover analysis and evaluation model for sandy land is established. It has important guiding significance for the sand ecological environment protection.

A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity

NASA Astrophysics Data System (ADS)

Rune Karlsen, Stein; Anderson, Helen B.; van der Wal, René; Bremset Hansen, Brage

2018-02-01

Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000-2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-Normalised Difference Vegetation Index (NDVI) data set (2000-2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R 2 = 0.51 and 0.44, respectively). The commonly used ‘maximum NDVI’ plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

USGS Publications Warehouse

Gallo, Kevin P.; Ji, Lei; Reed, Bradley C.; Eidenshink, Jeffery C.; Dwyer, John L.

2005-01-01

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.

Detection of contaminated pixels based on the short-term continuity of NDVI and correction using spatio-temporal continuity

NASA Astrophysics Data System (ADS)

Cho, A.-Ra; Suh, Myoung-Seok

2013-08-01

The present study developed and assessed a correction technique (CSaTC: Correction based on Spatial and Temporal Continuity) for the detection and correction of contaminated Normalized Difference Vegetation Index (NDVI) time series data. Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data from 1982 to 2006 with a 15-day period and an 8-km spatial resolution was used. CSaTC utilizes short-term continuity of vegetation to detect contaminated pixels, and then, corrects the detected pixels using the spatio-temporal continuity of vegetation. CSaTC was applied to the NDVI data over the East Asian region, which exhibits diverse seasonal and interannual variations in vegetation activities. The correction skill of CSaTC was compared to two previously applied methods, IDR (iterative Interpolation for Data Reconstruction) and Park et al. (2011) using GIMMS NDVI data. CSaTC reasonably resolved the overcorrection and spreading phenomenon caused by excessive correction of Park et al. (2011). The validation using the simulated NDVI time series data showed that CSaTC shows a systematically better correction skill in bias and RMSE irrespective of phenology types of vegetation and noise levels. In general, CSaTC showed a good recovery of the contaminated data appearing over the short-term period on a level similar to that obtained using the IDR technique. In addition, it captured the multi-peak of NDVI, and the germination and defoliating patterns more accurately than that by IDR, which overly compensates for seasons with a high temporal variation and where NDVI data exhibit multi-peaks.

Role of MODIS Vegetation Phenology Products in the ForWarn System for Monitoring of Forest Disturbances in the Conterminous United States

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William; Norman, Steve; Gasser, Jerry; Smoot, James; Kuper, Philip D,

2012-01-01

This presentation discusses MODIS vegetation phenology products used in the ForWarn Early Warning System (EWS) tool for near real time regional forest disturbance detection and surveillance at regional to national scales. The ForWarn EWS is being developed by the USDA Forest Service NASA, ORNL, and USGS to aid federal and state forest health management activities. ForWarn employs multiple historical land surface phenology products that are derived from MODIS MOD13 Normalized Difference Vegetation Index (NDVI) data. The latter is temporally processed into phenology products with the Time Series Product Tool (TSPT) and the Phenological Parameter Estimation Tool (PPET) software produced at NASA Stennis Space Center. TSPT is used to effectively noise reduce, fuse, and void interpolate MODIS NDVI data. PPET employs TSPT-processed NDVI time series data as an input, outputting multiple vegetation phenology products at a 232 meter resolution for 2000 to 2011, including NDVI magnitude and day of year products for seven key points along the growing season (peak of growing season and the minima, 20%, and 80% of the peak NDVI for both the left and right side of growing season), cumulative NDVI integral products for the most active part of the growing season and sequentially across the growing season at 8 day intervals, and maximum value NDVI products composited at 24 day intervals in which each product date has 8 days of overlap between the previous and following product dates. MODIS NDVI phenology products are also used to compute nationwide NRT forest change products refreshed every 8 days. These include percent change in forest NDVI products that compare the current NDVI from USGS eMODIS products to historical MODIS MOD13 NDVI. For each date, three forest change products are produced using three different maximum value NDVI baselines (from the previous year, three previous years, and all previous years). All change products are output with a rainbow color table in which forests with the most severe NDVI decreases are assigned hot colors (yellow to red) and forests with prominent NDVI increases are assigned cold colors (blue tones). All mentioned products have been integrated as data layers into ForWarn s geospatial data viewer known as the U.S. Forest Change Assessment Viewer (FCAV). The latter is used to view and assess the context of the mentioned forest change products with respect to ancillary data layers, such as land cover, elevation, hydrologic features, climatic data, storm data, aerial disturbance surveys, fire data, and land ownership. The FCAV also includes a temporal NDVI profiler for viewing phenological change in multi-year NDVI associated with known or suspected regionally apparent forest disturbances (e.g., from fire and insects). ForWarn forest change products have been used to detect, track, and assess several biotic and abiotic regional forest disturbance events across the country, including ephemeral and longer lasting damage from storms, drought, and insects. Such change products are most effective for viewing severe disturbances affecting multiple MODIS pixels. MODIS vegetation phenology products contribute vital current information on forest conditions to the ForWarn system and this role is expected to grow as these products are refined and derivative products are added.

Comparison of MODIS and AVHRR 16-day normalized difference vegetation index composite data

USGS Publications Warehouse

Gallo, Kevin P.; Ji, Lei; Reed, Bradley C.; Dwyer, John L.; Eidenshink, Jeffery C.

2004-01-01

Normalized difference vegetation index (NDVI) data derived from visible and near-infrared data acquired by the MODIS and AVHRR sensors were compared over the same time periods and a variety of land cover classes within the conterminous USA. The relationship between the AVHRR derived NDVI values and those of future sensors is critical to continued long term monitoring of land surface properties. The results indicate that the 16-day composite values are quite similar over the 23 intervals of 2001 that were analyzed, and a linear relationship exists between the NDVI values from the two sensors. The composite AVHRR NDVI data were associated with over 90% of the variation in the MODIS NDVI values. Copyright 2004 by the American Geophysical Union.

NDVI indicated characteristics of vegetation cover change in China's metropolises over the last three decades.

PubMed

Sun, Jinyu; Wang, Xuhui; Chen, Anping; Ma, Yuecun; Cui, Mengdi; Piao, Shilong

2011-08-01

How urban vegetation was influenced by three decades of intensive urbanization in China is of great interest but rarely studied. In this paper, we used satellite derived Normalized Difference Vegetation Index (NDVI) and socioeconomic data to evaluate effects of urbanization on vegetation cover in China's 117 metropolises over the last three decades. Our results suggest that current urbanization has caused deterioration of urban vegetation across most cities in China, particularly in East China. At the national scale, average urban area NDVI (NDVI(u)) significantly decreased during the last three decades (P < 0.01), and two distinct periods with different trends can be identified, 1982-1990 and 1990-2006. NDVI(u) did not show statistically significant trend before 1990 but decrease remarkably after 1990 (P < 0.01). Different regions also showed difference in the timing of NDVI(u) turning point. The year when NDVI(u) started to decline significantly for Central China and East China was 1987 and 1990, respectively, while NDVI(u) in West China remained relatively constant until 1998. NDVI(u) changes in the Yangtze River Delta and the Pearl River Delta, two regions which has been undergoing the most rapid urbanization in China, also show different characteristics. The Pearl River Delta experienced a rapid decline in NDVI(u) from the early 1980s to the mid-1990s; while in the Yangtze River Delta, NDVI(u) did not decline significantly until the early 1990s. Such different patterns of NDVI(u) changes are closely linked with policy-oriented difference in urbanization dynamics of these regions, which highlights the importance of implementing a sustainable urban development policy.

Intra- and inter-basin mercury comparisons: Importance of basin scale and time-weighted methylmercury estimates

USGS Publications Warehouse

Bradley, Paul M.; Journey, Celeste A.; Bringham, Mark E.; Burns, Douglas A.; Button, Daniel T.; Riva-Murray, Karen

2013-01-01

To assess inter-comparability of fluvial mercury (Hg) observations at substantially different scales, Hg concentrations, yields, and bivariate-relations were evaluated at nested-basin locations in the Edisto River, South Carolina and Hudson River, New York. Differences between scales were observed for filtered methylmercury (FMeHg) in the Edisto (attributed to wetland coverage differences) but not in the Hudson. Total mercury (THg) concentrations and bivariate-relationships did not vary substantially with scale in either basin. Combining results of this and a previously published multi-basin study, fish Hg correlated strongly with sampled water FMeHg concentration (p = 0.78; p = 0.003) and annual FMeHg basin yield (p = 0.66; p = 0.026). Improved correlation (p = 0.88; p < 0.0001) was achieved with time-weighted mean annual FMeHg concentrations estimated from basin-specific LOADEST models and daily streamflow. Results suggest reasonable scalability and inter-comparability for different basin sizes if wetland area or related MeHg-source-area metrics are considered.

Monitoring 2009 Forest Disturbance across the Conterminous United States, Based on Near-Real Time and Historical MODIS 250 Meter NDVI Products

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Gasser, G.; Smoot, J. C.; Kuper, P.

2009-12-01

This presentation discusses a study on the use of MODIS NDVI data for viewing regional patterns of forest disturbance across the conterminous United States. This capability is a part of a national forest threat early warning system (EWS) being developed by the USDA Forest Service’s Eastern and Western Environmental Threat Centers with help from NASA Stennis Space Center and the Oak Ridge National Laboratory. The viewing capability of the EWS was recently demonstrated for 2009, using near-real time (NRT) MODIS NDVI data from the USGS eMODIS Web site and historical NDVI data from standard MOD13 products. For this study, a historical maximum NDVI baseline for CONUS was computed from fused Aqua and Terra MOD13 data for June 10-July 27 of each year during 2000-2006. Comparable 2009 MODIS NDVI imagery was computed from fusion and re-compositing of eMODIS NRT Aqua and Terra 7-day products. For the historical data, time series data processing software was used to remove poor quality data and to mitigate data gaps mainly due to clouds. Although the NRT component was not as rigorously processed to mitigate noise, the processing still yielded largely cloud-free clean, coherent CONUS NDVI imagery initially with only 21-days of compositing. The principal end product of the study was a forest disturbance visualization product based on an NDVI RGB image that combines data from 2 dates (i.e. time frames). For this RGB, the historical maximum NDVI for the observed temporal window was assigned to the red color gun and the 2009 NRT product for the same time frame was assigned to the blue and green guns. The resulting image was masked with a USFS FIA 250-m type map to include only forested areas. The forest disturbance areas on the forest-masked 2-date NDVI RGB are shown in red tones with non-disturbed closed canopy forest generally shown in medium to bright gray tones. This product highlighted several broad-scaled forest canopy disturbances for the observed time in 2009, including damage from caterpillars, bark beetles, ice storms, hail and wind storms, and wildfire. The MODIS forest disturbance products compared well with reference data (e.g., Landsat, aerial sketch maps, and news accounts). These products have been useful in aiding development of the forest threat EWS. Information on location and extent of regional forest disturbance is important to Federal, State, and private sector forest managers. The 2-date RGB product for 2009 was also processed into a classification of forest disturbance for the Colorado Front Range. Validation of this classification is underway. Regional forest disturbance classifications in conjunction with available CONUS forest biomass products could be useful for assessing carbon impacts from biotic threats such as mountain pine beetle and from abiotic threats related to climate change. The latency of the NRT eMODIS products addresses an important need of the USFS EWS.

Annual Corn Yield Estimation through Multi-temporal MODIS Data

NASA Astrophysics Data System (ADS)

Shao, Y.; Zheng, B.; Campbell, J. B.

2013-12-01

This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.

Near Real-time Operational Use of eMODIS Expedited NDVI for Monitoring Applications and Famine Early Warning

NASA Astrophysics Data System (ADS)

Rowland, J.; Budde, M. E.

2010-12-01

The Famine Early Warning Systems Network (FEWS NET) has requirements for near real-time monitoring of vegetation conditions for food security applications. Accurate and timely assessments of crop conditions are an important element of food security decision making. FEWS NET scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center are utilizing a new Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset for operational monitoring of crop and pasture conditions in parts of the world where food availability is highly dependent on subsistence agriculture and animal husbandry. The expedited MODIS, or eMODIS, production system processes NDVI data using MODIS surface reflectance provided by the Land Atmosphere Near-real-time Capability for EOS (LANCE). Benefits of this production system include customized compositing schedules, near real-time data availability, and minimized re-sampling. FEWS NET has implemented a 10-day compositing scheme every five days to accommodate the need for timely information on vegetation conditions. The data are currently being processed at 250-meter spatial resolution for Central America, Hispaniola, and Africa. Data are further enhanced by the application of a temporal smoothing filter which helps remove contamination due to clouds and other atmospheric effects. The results of this near real-time monitoring capability have been the timely provision of NDVI and NDVI anomaly maps for each of the FEWS NET monitoring regions and the availability of a consistently processed dataset to aid crop assessment missions and to facilitate customized analyses of crop production, drought, and agro-pastoral conditions.

Large-scale vegetation responses to terrestrial moisture storage changes

NASA Astrophysics Data System (ADS)

Andrew, Robert L.; Guan, Huade; Batelaan, Okke

2017-09-01

The normalised difference vegetation index (NDVI) is a useful tool for studying vegetation activity and ecosystem performance at a large spatial scale. In this study we use the Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) estimates to examine temporal variability of the NDVI across Australia. We aim to demonstrate a new method that reveals the moisture dependence of vegetation cover at different temporal resolutions. Time series of monthly GRACE TWS anomalies are decomposed into different temporal frequencies using a discrete wavelet transform and analysed against time series of the NDVI anomalies in a stepwise regression. The results show that combinations of different frequencies of decomposed GRACE TWS data explain NDVI temporal variations better than raw GRACE TWS alone. Generally, the NDVI appears to be more sensitive to interannual changes in water storage than shorter changes, though grassland-dominated areas are sensitive to higher-frequencies of water-storage changes. Different types of vegetation, defined by areas of land use type, show distinct differences in how they respond to the changes in water storage, which is generally consistent with our physical understanding. This unique method provides useful insight into how the NDVI is affected by changes in water storage at different temporal scales across land use types.

Deriving crop calendar using NDVI time-series

NASA Astrophysics Data System (ADS)

Patel, J. H.; Oza, M. P.

2014-11-01

Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting - peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.

An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains

USGS Publications Warehouse

Yang, Limin; Wylie, Bruce K.; Tieszen, Larry L.; Reed, Bradley C.

1998-01-01

Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989–1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negative correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R2) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models . The photosynthetic pathway is an important determinant of grassland performance with northern mixed prairie (mixture of C3 and C4 grassland) TI NDVI affected by both thermal and moisture conditions during the growing season while southern plains grasslands (primarily C4grassland) were predominantly influenced by spring and summer precipitation. Grassland land cover classes associated with sandy soils also demonstrated a strong relationship between TI NDVI and growing season rainfall. Significant impact of interannual climate variability on the TI NDVI–climate relationship was also observed. The study suggests an integrated approach involving numerical models, satellite remote sensing, and field observations to monitor grassland ecosystem dynamics on a regional scale.

NDVI (Normalized Difference Vegetation Index) signatures of transient ecohydrological systems: The case of post-mining landscapes

NASA Astrophysics Data System (ADS)

Brück, Yasemine; Schulte Overberg, Philipp; Pohle, Ina; Hinz, Christoph

2017-04-01

Assessing ecohydrological systems that undergo state transitions due to environmental change is becoming increasingly important. One system that can be used to study severe disturbances are post-mining landscapes as they usually are associated with complete removal of vegetation and afterwards subsequent ecosystem restoration or spontaneous rehabilitation in line with natural succession. Within this context it is of interest, whether and how (fast) the land cover in these areas returns to conditions comparable to those in the undisturbed surrounding or those prior mining. Many aspects of mine site rehabilitation depend on climatic, geomorphic and ecological settings, which determine at which rate vegetation may be re-established. In order to identify general patterns of vegetation establishment, we propose to use NDVI (Normalized Difference Vegetation Index) time series for mine affected land to estimate rate of recovery across climate regions and ecoregions. In this study we analysed the MODIS Terra Satellite 8 day-composite NDVI for areas influenced by surface mining in different climates from 2001 to 2015. The locations have been chosen based on their extent and the data availability of mining and rehabilitation activities. We selected coal extraction as a case study as strip mining generates well-defined chronosequences of disturbance. The selected mining areas are located in equatorial, arid, warm temperate or snow climates with different precipitation and temperature conditions according to the Köppen-Geiger classification. We analysed the NDVI time series regarding significant characteristics of the re-vegetation phase. We applied hierarchical cluster analysis to capture the spatial heterogeneity between different pixels (ca. 250 * 250 m2 each) in and around each open cast mine. We disentangled seasonality, trend and residual components in the NDVI time series by Seasonal and Trend decomposition using LOESS. As expected the time of the removal of vegetation can be clearly identified from the NDVI time series and provides the starting point of disturbance. The cluster analysis allowed us to distinguish between the non-mining land, the mine and the restored land of different ages. Based on these clusters, the time series decomposition revealed the dominance of the trend of increasing NDVI in areas undergoing the restoration process as well as the prevailing seasonality of the oldest restored sites. The determined phase of a dominant trend component, lasting until the NDVI is in the range of the surrounding landscape or the pre-mining conditions, is in the scale of a decade. The impacts of different hydroclimatic regimes and different rehabilitation strategies on long term NDVI development are currently being investigated. Furthermore, coherence analysis will be applied to quantify short term influences of hydrometeorological variables on vegetation development.

Characterizing Climate Controls on Vegetation Seasonality in the North American Southwest

NASA Astrophysics Data System (ADS)

Fish, M. A.; Cook, B.; Smerdon, J. E.; Seager, R.; Williams, P.

2014-12-01

The North American Southwest, which extends from Colorado to southern Mexico and California to eastern Texas, encompasses a diversity of climates, elevations, and ecosystems. This region is expected to experience significant climatic change, and associated impacts, in the coming decades. To better understand the spatiotemporal variability of vegetation in the Southwest and the expected climatic controls on timing and spatial extend of vegetation growth, we compared GIMMS normalized difference vegetation index (NDVI, 1981-2011) against temperature and precipitation data. Spatial variations in vegetation seasonality and the timing of peak NDVI are linked to spatial variability in the precipitation regimes across the Southwest. Regions with spring NDVI peaks are dominated by winter precipitation, while late summer and fall peaks are in regions with significant summer precipitation driven by the North American Monsoon. Inter-annual variability in peak NDVI is positively correlated with precipitation and negatively correlated with temperature, with the largest correlation coefficients at one-month lags. The only significant long-term trends in NDVI are for northern Mexico, where agricultural productivity has been increasing over the last 30 years.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, B.C.; Tieszen, L.T.

2003-01-01

Time integrated normalized difference vegetation index (??NDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989-1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ??NDVI and the ??NDVI coefficient of variation (CV ??NDVI) used as a proxy for interranual climate variability is analysed. Results suggest that the differences in the long-term climate control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primary C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ??NDVI values.

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

USGS Publications Warehouse

Suzuki, Rikie; Masuda, Kooiti; Dye, Dennis G.

2007-01-01

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.

Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series

USGS Publications Warehouse

Li, Ji; Lewis, J.; Rowland, James; Tappan, G.; Tieszen, L.L.

2004-01-01

Time series of rainfall data and normalized difference vegetation index (NDVI) were used to evaluate land cover performance in Senegal, Africa, for the period 1982–1997, including analysis of woodland/forest, agriculture, savanna, and steppe land cover types. A strong relationship exists between annual rainfall and season-integrated NDVI for all of Senegal (r=0.74 to 0.90). For agriculture, savanna, and steppe areas, high positive correlations portray ‘normal’ land cover performance in relation to the rainfall/NDVI association. Regions of low correlation might indicate areas impacted by human influence. However, in the woodland/forest area, a negative or low correlation (with high NDVI) may reflect ‘normal’ land cover performance, due in part to the saturation effect of the rainfall/NDVI association. The analysis identified three areas of poor performance, where degradation has occurred over many years. Use of the ‘Standard Error of the Estimate’ provided essential information for detecting spatial anomalies associated with land degradation.

Temporal variations of NDVI and correlations between NDVI and hydro-climatological variables at Lake Baiyangdian, China.

PubMed

Wang, Fei; Wang, Xuan; Zhao, Ying; Yang, Zhifeng

2014-09-01

In this paper, correlations between vegetation dynamics (represented by the normalized difference vegetation index (NDVI)) and hydro-climatological factors were systematically studied in Lake Baiyangdian during the period from April 1998 to July 2008. Six hydro-climatological variables including lake volume, water level, air temperature, precipitation, evaporation, and sunshine duration were used, as well as extracted NDVI series data representing vegetation dynamics. Mann-Kendall tests were used to detect trends in NDVI and hydro-climatological variation, and a Bayesian information criterion method was used to detect their abrupt changes. A redundancy analysis (RDA) was used to determine the major hydro-climatological factors contributing to NDVI variation at monthly, seasonal, and yearly scales. The results were as follows: (1) the trend analysis revealed that only sunshine duration significantly increased over the study period, with an inter-annual increase of 3.6 h/year (p < 0.01), whereas inter-annual NDVI trends were negligible; (2) the abrupt change detection showed that a major hydro-climatological change occurred in 2004, when abrupt changes occurred in lake volume, water level, and sunlight duration; and (3) the RDA showed that evaporation and temperature were highly correlated with monthly changes in NDVI. At larger time scales, however, water level and lake volume gradually became more important than evaporation and precipitation in terms of their influence on NDVI. These results suggest that water availability is the most important factor in vegetation restoration. In this paper, we recommend a practical strategy for lake ecosystem restoration that takes into account changes in NDVI.

Flexible Computing Architecture for Real Time Skin Detection

DTIC Science & Technology

2010-03-01

Figure 6. Spectra of Light and Dark Skin Compared with Spectra of Other Materials .............. 2-5 Figure 7(a.) Joint Distribution of (NDSI/ NDVI ...Vegetation Index ( NDVI ). In Eqn. 2, and are the estimated reflectances of the 660 and 750 nm wavelengths, respectively. As can be seen from Eqn...values, while the rules based detectors use a rectangular bound on either (NDSI, NDVI ) or (NDSI, NDGRI) pairs. The last detection algorithm is the

Utilizing NASA Earth Observations to Enhance Flood Impact Products and Mitigation in the Lower Mekong Water Basin

NASA Astrophysics Data System (ADS)

Doyle, C.; Gao, M.; Spruce, J.; Bolten, J. D.; Weber, S.

2014-12-01

This presentation discusses results of a project to develop a near real time flood monitoring capability for the Lower Mekong Water Basin (LMB), the largest river basin in Southeast Asia and home to more than sixty million people. The region has seen rapid population growth and socio-economic development, fueling unsustainable deforestation, agricultural expansion, and stream-flow regulation. The basin supports substantial rice farming and other agrarian activities, which heavily depend upon seasonal flooding. But, floods due to typhoons and other severe weather events can result in disasters that cost millions of dollars and cause hardships to millions of people. This study uses near real time and historical Aqua and Terra MODIS 250-m resolution Normalized Difference Vegetation Index (NDVI) products to map flood and drought impact within the LMB. In doing so, NDVI change products are derived by comparing from NDVI during the wet season to a baseline NDVI from the dry season. The method records flood events, which cause drastic decreases in NDVI compared to non-flooded conditions. NDVI change product computation was automated for updating a near real-time system, as part of the Committee on Earth Observing Satellites Disaster Risk Management Observation Strategy. The system is a web-based 'Flood Dashboard that will showcase MODIS flood monitoring products, along with other flood mapping and weather data products. This flood dashboard enables end-users to view and assess a variety of geospatial data to monitor floods and flood impacts in near real-time, as well provides a platform for further data aggregation for flood prediction modeling and post-event assessment.

On the use of satellite VEGETATION time series for monitoring post fire vegetation recovery

NASA Astrophysics Data System (ADS)

de Santis, F.; Didonna, I.

2009-04-01

Fire is one of the most critical factors of disturbance in worldwide ecosystems. The effects of fires on soil, plants, landscape and ecosystems depend on many factors, among them fire frequency, fire severity and plant resistance. The characterization of vegetation post-fire behaviour is a fundamental issue to model and evaluate the fire resilience, which the ability of vegetation to recover after fire. Recent changes in fire regime, due to abandonment of local land use practice and climate change, can induce significant variations in vegetation fire resilience. In the Mediterranean-type communities, post fire vegetation trends have been analysed in a wide range of habitats, although pre- and post-fire investigation has been widely performed at stand level. But, factors controlling regeneration at the landscape scale are less well known. In this study, a time series of normalized difference vegetation index (NDVI) data derived from SPOT-VEGETATION was used to examine the recovery characteristics of fire affected vegetation in some test areas of the Mediterranean ecosystems of Southern Italy. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red against the high reflectance of leaf mesophyll in the near infrared. SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) data from 1998 to 2005 were analyzed in order to evaluate the resilient effects in a some significant test sites of southern Italy. In particular, we considered: (i) one stable area site, one site affected by one fire during the investigated time window, (iii) one site affected by two consecutive fires during the investigated time window. In order to eliminate the phenological fluctuations, for each decadal composition of each pixel, we focused on the departure NDVId = [NDVI - ]/, where is the decadal mean and  is the decadal standard deviation. The decadal mean and the standard deviation were calculated for each decade, e.g. 1st decade of January, by averaging over all years in the record. We analyzed both: 1) Time variation of NDVI from 1998 to 2005 of pixels for the fire affected and fire unaffected areas. 2) Post-fire NDVI spatial patterns on each image date were compared to the pre-fire pattern to determine the extent to which the pre-fire pattern was re-established, and the rate of this recovery. Results show the ability of vegetation to recovery after a single fire. Nevertheless, such ability can be strongly reduced by successive fires. The recursive fire occurrence can significantly diminish the green biomass especially when disturbances occur at short intervals of time.

Relationships of Leaf Area Index and NDVI for 12 Brassica Cultivars in Northeastern Montana

NASA Astrophysics Data System (ADS)

Jabro, Jay; Allen, Brett; Long, Dan; Isbell, Terry; Gesch, Russ; Brown, Jack; Hatfield, Jerry; Archer, David; Oblath, Emily; Vigil, Merle; Kiniry, Jim; Hunter, Kimberly; Shonnard, David

2017-04-01

To our knowledge, there is limited information on the relationship of the normalized difference vegetation index (NDVI) and leaf area index (LAI) in spring Brassica oilseed crops. The 2014 results of NDVI and LAI of 12 spring varieties of oilseed crops were measured in a field study conducted in Sidney, Montana, USA under dryland conditions. These 12 varieties were grouped under six species (B. napus, B. rapa, B. juncea, B. carinata, Sinapis alba, and Camelina sativa). The NDVI and LAI were measured weekly throughout the growing season. The NDVI was continually measured at one sample per second across the whole plot using a Crop Circle ACS-470 active crop canopy sensor. The LAI was measured at two locations at 12 samples per plot using an AccuPar model LP-80 Ceptometer. Treatments were replicated four times in a randomized complete block design in plots of 3 m×9 m. Temporal dynamics of NDVI and LAI in various growth stages of 12 varieties were evaluated throughout the growing season. Significant relationships and models between NDVI and LAI were obtained when 12 varieties were grouped under six species.

OTD Observations of Continental US Ground and Cloud Flashes

NASA Technical Reports Server (NTRS)

Koshak, William

2007-01-01

Lightning optical flash parameters (e.g., radiance, area, duration, number of optical groups, and number of optical events) derived from almost five years of Optical Transient Detector (OTD) data are analyzed. Hundreds of thousands of OTD flashes occurring over the continental US are categorized according to flash type (ground or cloud flash) using US National Lightning Detection Network TM (NLDN) data. The statistics of the optical characteristics of the ground and cloud flashes are inter-compared on an overall basis, and as a function of ground flash polarity. A standard two-distribution hypothesis test is used to inter-compare the population means of a given lightning parameter for the two flash types. Given the differences in the statistics of the optical characteristics, it is suggested that statistical analyses (e.g., Bayesian Inference) of the space-based optical measurements might make it possible to successfully discriminate ground and cloud flashes a reasonable percentage of the time.

Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

NASA Astrophysics Data System (ADS)

Chemura, Abel; Mutanga, Onisimo; Dube, Timothy

2017-05-01

The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and while it increased for the mature and old age classes with time. We concluded that the method could be useful for the identification of anomalous patches using Landsat scale time series data to monitor large coffee plantations and provide an indication of areas requiring particular field attention.

Monitoring vegetation response to episodic disturbance events by using multitemporal vegetation indices

USGS Publications Warehouse

Steyer, Gregory D.; Couvillion, Brady R.; Barras, John A.

2013-01-01

Normalized Difference Vegetation Index (NDVI) derived from MODerate-resolution Imaging Spectroradiometer (MODIS) satellite imagery and land/water assessments from Landsat Thematic Mapper (TM) imagery were used to quantify the extent and severity of damage and subsequent recovery after Hurricanes Katrina and Rita of 2005 within the vegetation communities of Louisiana's coastal wetlands. Field data on species composition and total live cover were collected from 232 unique plots during multiple time periods to corroborate changes in NDVI values over time. Aprehurricane 5-year baseline time series clearly identified NDVI values by habitat type, suggesting the sensitivity of NDVI to assess and monitor phenological changes in coastal wetland habitats. Monthly data from March 2005 to November 2006 were compared to the baseline average to create a departure from average statistic. Departures suggest that over 33% (4,714 km2) of the prestorm, coastal wetlands experienced a substantial decline in the density and vigor of vegetation by October 2005 (poststorm), mostly in the east and west regions, where landfalls of Hurricanes Katrina and Rita occurred. The percentage of area of persistent vegetation damage due to long-lasting formation of new open water was 91.8% in the east and 81.0% and 29.0% in the central and west regions, respectively. Although below average NDVI values were observed in most marsh communities through November 2006, recovery of vegetation was evident. Results indicated that impacts and recovery from large episodic disturbance events that influence multiple habitat types can be accurately determined using NDVI, especially when integrated with assessments of physical landscape changes and field verifications.

Monitoring vegetation response to episodic disturbance events by using multi-temporal vegetation indices

USGS Publications Warehouse

Steyer, Gregory D.; Couvillion, Brady R.; Barras, John A.

2013-01-01

Normalized Difference Vegetation Index (NDVI) derived from MODerate-resolution Imaging Spectroradiometer (MODIS) satellite imagery and land/water assessments from Landsat Thematic Mapper (TM) imagery were used to quantify the extent and severity of damage and subsequent recovery after Hurricanes Katrina and Rita of 2005 within the vegetation communities of Louisiana's coastal wetlands. Field data on species composition and total live cover were collected from 232 unique plots during multiple time periods to corroborate changes in NDVI values over time. Aprehurricane 5-year baseline time series clearly identified NDVI values by habitat type, suggesting the sensitivity of NDVI to assess and monitor phenological changes in coastal wetland habitats. Monthly data from March 2005 to November 2006 were compared to the baseline average to create a departure from average statistic. Departures suggest that over 33% (4,714 km2) of the prestorm, coastal wetlands experienced a substantial decline in the density and vigor of vegetation by October 2005 (poststorm), mostly in the east and west regions, where landfalls of Hurricanes Katrina and Rita occurred. The percentage of area of persistent vegetation damage due to long-lasting formation of new open water was 91.8% in the east and 81.0% and 29.0% in the central and west regions, respectively. Although below average NDVI values were observed in most marsh communities through November 2006, recovery of vegetation was evident. Results indicated that impacts and recovery from large episodic disturbance events that influence multiple habitat types can be accurately determined using NDVI, especially when integrated with assessments of physical landscape changes and field verifications.

Midwest Agriculture: A comparison of AVHRR NDVI3g data and crop yields in Corn Belt region of the United States from 1982 to 2014

NASA Astrophysics Data System (ADS)

Glennie, E.; Anyamba, A.; Eastman, R.

2016-12-01

A time series of Advanced Very High Resolution Radiometer (AVHRR) derived normalized difference vegetation index (NDVI) images was compared to National Agricultural Statistics Service (NASS) corn yield data in the Corn Belt of the United States from 1982 to 2014. The relationship between NDVI and crop yields under El Nino, neutral, and La Nina conditions was used to assess 1) the reliability of using NDVI as an indicator of crop productivity, and 2) the response of the Corn Belt to El Nino/ Southern Oscillation (ENSO) teleconnection effects. First, bi-monthly NDVI data were combined into monthly data using the maximum value compositing technique to reduce cloud contamination and other effects. The most representative seasonal curve of NDVI values over the course of the study period was extracted to define the growing season in the region - May to October. Standardized NDVI anomalies were calculated and averaged to produce a growing season NDVI metrics to represent each Agricultural Statistics Division (ASD) for each year in the study period. The corn yields were detrended in order to remove effects of technological advancements on crop productivity (use of genetically modified seeds, fertilizer, herbicides). Correlation (R) values between the NDVI anomalies and detrended corn yields varied across the Corn Belt, with a maximum of 0.81 and mean of 0.49. While corn is the dominant crop in the region, some inconsistencies between corn yield and NDVI may be accounted for by an increase in soy yield for a given year due to crop rotation practices. The 10 El Nino events and 9 La Nina events that occurred between 1982 and 2014 are not reflected in a consistent manner in NDVI or corn yield data. However, composites of NDVI and crop yields for all El Nino events indicate there is a tendency for higher than normal NDVI and increased corn yields. Conversely, the composite crop yield image for La Nina events shows a slight decrease in productivity.

Study on the vegetation dynamic change using long time series of remote sensing data

NASA Astrophysics Data System (ADS)

Fan, Jinlong; Zhang, Xiaoyu

2010-10-01

The vegetation covering land surface is main component of biosphere which is one of five significant spheres on the earth. The vegetation plays a very important role on the natural environment conservation and improvement to keep human being's living environment evergreen while the vegetation supplies many natural resources to human living and development continuously. Under the background of global warming, vegetation is changing as climate changes. It is not doubt that human activities have great effects on the vegetation dynamic. In general, there are two aspects of the interaction between vegetation and climate, the climatic adaptation of vegetation and the vegetation feedback on climate. On the base of the research on the long term vegetation growth dynamics, it can be found out the vegetation adaptation to climate change. The dynamic change of vegetation is the direct indicator of the ecological environment changes. Therefore, study on the dynamic change of vegetation will be very interest and useful. In this paper, the vegetation change in special region of China will be described in detail. In addition to the methods of the long term in-situ observation of vegetation, remote sensing technologies can also be used to study the long time series vegetation dynamic. The widely used NDVI was often employed to monitor the status of vegetation growth. Actually, NDVI can indicate the vigor and the fractional cover of vegetation effectively. So the long time series of NDVI datasets are a very valuable data source supporting the study on the long term vegetation dynamics. Since 1980, a series of NOAA satellites have been launched successfully, which have already supplied more than 20 years NOAA/AVHRR satellites data. In this paper, we selected Ningxia Hui autonomic region of China as the case study area and used 20 years pathfinder AVHRR NDVI data to carry out the case study on the vegetation dynamics in order to further understand the phenomena of 20 years vegetation dynamics of the whole Ningxia region. Ningxia Hui autonomic region is one of provinces in west china. Ningxia is a small region with square area of about 66, 4000 km2. Ningxia has special land cover with irrigated crop land in north and natural grass land in central and south. In addition to NDVI data, we also collected land cover and land use data and administrative border vector data with the scale of 1:4,000,000 and other data. The results show that (1)vegetation dynamic of Ningxia presents the characters of one season per year with the length of the growth season from the first decade May to the middle decade October and the range of NDVI value 0.05-0.25; the season characters vary with the local area; the max value of NDVI in the central dry area is only 0.2 and the date of reaching the peak of time series NDVI in the irrigation area is the latest while that in the south mountain area is the earliest; the Helan mountain area presents the characters of forest and the range of NDVI is narrower than those in the irrigation area and the south mountain area and higher in winter than those in two area above; in recent 18 years, the length of growth season in whole Ningxia has prolonged one decade, mainly in spring one decade in advance.(2) from 1982 to 1999, the trend of the whole Ningxia mean NDVI is increasing and presents the stable or better of vegetation growth; compared to NDVI in 1980's, NDVI in 1990's has increased already and the anomaly of growth season mean NDVI is mainly negative in 1980's while mainly positive in 1990's; NDVI in the central dry area is the lowest while NDVI in the Helan mountain is the highest; the values of NDVI in the irrigation area, the Helan mountain area and the south mountain area are higher than that of the whole Ningxia; the increasing trend of vegetation dynamic in the irrigation area, the south mountain area and the central dry area is similar with the whole Ningxia while the trend in the Helan mountain area is increasing from 1982-1988 but decreasing after 1988.

Spatio-temporal Patterns of Vegetation and Its Relationship with Precipitation and Temperature in the Yarlung Zangbo River Basin, China

NASA Astrophysics Data System (ADS)

LIU, X.; Xu, Z.; Peng, D.

2017-12-01

Vegetation growth plays a significant role on runoff variation at high altitude, and precipitation and temperature are both key factors affecting vegetation conditions. As one of the greatest international rivers in China, the Yarlung Zangbo River in the southern Qinghai-Tibetan Plateau was selected, and the spatio-temporal patterns of vegetation were analyzed by using NDVI (Normalized Difference Vegetation Index) during 1998 2014. The relationship between NDVI and precipitation as well as temperature was also investigated in this study. Results showed that the value of NDVI increases with the decrease of elevation and the largest value appears in the broadleaf forest cover. Almost all annual NDVI variations exhibit an increasing tendency, particularly for the broadleaf forest cover. On the viewpoint of statistics, only 29% pixels of NDVI with increasing tendency are of significance for the other cover, while for cultivated vegetation cover, around 82% pixels of NDVI were detected with significant increasing tendency. In addition, vegetation growth showed lagging response to precipitation, and the lag time is around one month. Moreover, in the region with elevation over 5000 m, negative relationship between NDVI and precipitation for alpine vegetation was found. Approximately 75% of NDVI variations are dominated by precipitation and temperature. These findings may provide a reference to investigate runoff variations and strengthen ecological protection for similar high-altitude areas in the future.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Jianguang; Piao, Shilong; Chen, Anping

Over the last century the Northern Hemisphere has experienced rapid climate warming, but this warming has not been evenly distributed seasonally, as well as diurnally. The implications of such seasonal and diurnal heterogeneous warming on regional and global vegetation photosynthetic activity, however, are still poorly understood. Here, we investigated for different seasons how photosynthetic activity of vegetation correlates with changes in seasonal daytime and night-time temperature across the Northern Hemisphere (>30°N), using Normalized Difference Vegetation Index (NDVI) data from 1982 to 2011 obtained from the Advanced Very High Resolution Radiometer (AVHRR). Our analysis revealed some striking seasonal differences in themore » response of NDVI to changes in day- versus night-time temperatures. For instance, while higher daytime temperature (T max) is generally associated with higher NDVI values across the boreal zone, the area exhibiting a statistically significant positive correlation between T max and NDVI is much larger in spring (41% of area in boreal zone – total area 12.6 × 10 6 km 2) than in summer and autumn (14% and 9%, respectively). In contrast to the predominantly positive response of boreal ecosystems to changes in T max, increases in T max tended to negatively influence vegetation growth in temperate dry regions, particularly during summer. Changes in night-time temperature (T min) correlated negatively with autumnal NDVI in most of the Northern Hemisphere, but had a positive effect on spring and summer NDVI in most temperate regions (e.g., Central North America and Central Asia). Such divergent covariance between the photosynthetic activity of Northern Hemispheric vegetation and day- and night-time temperature changes among different seasons and climate zones suggests a changing dominance of ecophysiological processes across time and space. Lastly, understanding the seasonally different responses of vegetation photosynthetic activity to diurnal temperature changes, which have not been captured by current land surface models, is important for improving the performance of next generation regional and global coupled vegetation-climate models« less

Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products

USDA-ARS?s Scientific Manuscript database

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and comb...

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, Bradley C.; Tieszen, Larry L.

2003-01-01

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

MODIS-informed greenness responsesto daytime land surface temperaturefluctuations and wildfire disturbancesin the Alaskan Yukon River Basin

USGS Publications Warehouse

Tan, Zhengxi; Liu, Shu-Guang; Jenkerson, Calli B.; Oeding, Jennifer; Wylie, Bruce K.; Rover, Jennifer R.; Young, Claudia J.

2012-01-01

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components

Satellite remote sensing assessment of climate impact on forest vegetation dynamics

NASA Astrophysics Data System (ADS)

Zoran, M.

2009-04-01

Forest vegetation phenology constitutes an efficient bio-indicator of impacts of climate and anthropogenic changes and a key parameter for understanding and modelling vegetation-climate interactions. Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vegetation Index (NDVIs), which requires NDVI time-series with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). Spatio-temporal vegetation dynamics have been quantified as the total amount of vegetation (mean NDVI) and the seasonal difference (annual NDVI amplitude) by a time series analysis of NDVI satellite images with the Harmonic ANalysis of Time Series algorithm. A climate indicator (CI) was created from meteorological data (precipitation over net radiation). The relationships between the vegetation dynamics and the CI have been determined spatially and temporally. The driest test regions prove to be the most sensitive to climate impact. The spatial and temporal patterns of the mean NDVI are the same, while they are partially different for the seasonal difference. The aim of this paper was to quantify this impact over a forest ecosystem placed in the North-Eastern part of Bucharest town, Romania, with Normalized Difference Vegetation Index (NDVI) parameter extracted from IKONOS and LANDSAT TM and ETM satellite images and meteorological data over l995-2007 period. For investigated test area, considerable NDVI decline was observed between 1995 and 2007 due to the drought events during 2003 and 2007 years. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics. Specific aim of this paper was to assess, forecast, and mitigate the risks of climatic changes on forest systems and its biodiversity as well as on adjacent environment areas and to provide early warning strategies on the basis of spectral information derived from satellite data regarding atmospheric effects of forest biome degradation . The paper aims to describe observed trends and potential impacts based on scenarios from simulations with regional climate models and other downscaling procedures.

Monitoring of vegetation condition using the NDVI/ENSO anomalies in Central Asia and their relationships with ONI (very strong) phases

NASA Astrophysics Data System (ADS)

Aralova, Dildora; Toderich, Kristina; Jarihani, Ben; Gafurov, Dilshod; Gismatulina, Liliya

2016-10-01

An investigation of temporal dynamics of El Niño-Southern Oscillation (ENSO) and spatial patterns of dryness/wetness period over arid and semi-arid zones of Central Asia and their relationship with Normalized Difference Vegetation Index (NDVI) values (1982-2011) have explored in this article. For identifying periodical oscillations and their relationship with NDVI values have selected El Nino 3.4 index and thirty years of new generation bi-weekly NDVI 3g acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites time-series data. Based on identification ONI (Oceanic Nino Index) is a very strong El Nino (warm) anomalies observed during 1982-1983, 1997-1998 and very strong La Nino (cool) period events have observed 1988-1989 years. For correlation these two factors and seeking positive and negative trends it has extracted from NDVI time series data as "low productivity period" following years: 1982-1983, 1997 -1998; and as "high productivity period" following years: 1988 -1989. Linear regression observed warm events as moderate phase period selected between moderate El Nino (ME) and NDVI with following periods:1986-1987; 1987-1988; 1991-1992; 2002-2003; 2009-2010; and moderate La Niña (ML) periods and NDVI (1998-1999; 1999-2000; 2007-2008) which has investigated a spatial patterns of wetness conditions. The results indicated that an inverse relationship between very strong El Nino and NDVI, decreased vegetation response with larger positive ONI value; and direct relationship between very strong La Niña and NDVI, increased vegetation response with smaller negative ONI value. Results assumed that significant impact of these anomalies influenced on vegetation productivity. These results will be a beneficial for efficient rangeland/grassland management and to propose drought periods for assessment and reducing quantity of flocks' due to a lack of fodder biomass for surviving livestock flocks on upcoming years in rangelands. Also results demonstrate that a non-anthropogenic drivers of variability effected to land surface vegetation signals, understanding of which will be beneficial for efficient rangeland and agriculture management and establish ecosystem services in precipitation-driven drylands of Central Asia.

Comparative Performance Analysis of a Hyper-Temporal Ndvi Analysis Approach and a Landscape-Ecological Mapping Approach

NASA Astrophysics Data System (ADS)

Ali, A.; de Bie, C. A. J. M.; Scarrott, R. G.; Ha, N. T. T.; Skidmore, A. K.

2012-07-01

Both agricultural area expansion and intensification are necessary to cope with the growing demand for food, and the growing threat of food insecurity which is rapidly engulfing poor and under-privileged sections of the global population. Therefore, it is of paramount importance to have the ability to accurately estimate crop area and spatial distribution. Remote sensing has become a valuable tool for estimating and mapping cropland areas, useful in food security monitoring. This work contributes to addressing this broad issue, focusing on the comparative performance analysis of two mapping approaches (i) a hyper-temporal Normalized Difference Vegetation Index (NDVI) analysis approach and (ii) a Landscape-ecological approach. The hyper-temporal NDVI analysis approach utilized SPOT 10-day NDVI imagery from April 1998-December 2008, whilst the Landscape-ecological approach used multitemporal Landsat-7 ETM+ imagery acquired intermittently between 1992 and 2002. Pixels in the time-series NDVI dataset were clustered using an ISODATA clustering algorithm adapted to determine the optimal number of pixel clusters to successfully generalize hyper-temporal datasets. Clusters were then characterized with crop cycle information, and flooding information to produce an NDVI unit map of rice classes with flood regime and NDVI profile information. A Landscape-ecological map was generated using a combination of digitized homogenous map units in the Landsat-7 ETM+ imagery, a Land use map 2005 of the Mekong delta, and supplementary datasets on the regions terrain, geo-morphology and flooding depths. The output maps were validated using reported crop statistics, and regression analyses were used to ascertain the relationship between land use area estimated from maps, and those reported in district crop statistics. The regression analysis showed that the hyper-temporal NDVI analysis approach explained 74% and 76% of the variability in reported crop statistics in two rice crop and three rice crop land use systems respectively. In contrast, 64% and 63% of the variability was explained respectively by the Landscape-ecological map. Overall, the results indicate the hyper-temporal NDVI analysis approach is more accurate and more useful in exploring when, why and how agricultural land use manifests itself in space and time. Furthermore, the NDVI analysis approach was found to be easier to implement, was more cost effective, and involved less subjective user intervention than the landscape-ecological approach.

Growing Degree Vegetation Production Index (GDVPI): A Novel and Data-Driven Approach to Delimit Season Cycles

NASA Astrophysics Data System (ADS)

Graham, W. D.; Spruce, J.; Ross, K. W.; Gasser, J.; Grulke, N.

2014-12-01

Growing Degree Vegetation Production Index (GDVPI) is a parametric approach to delimiting vegetation seasonal growth and decline cycles using incremental growing degree days (GDD), and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 8-day composite cumulative integral data. We obtain a specific location's daily minimum and maximum temperatures from the nearest National Oceanic and Atmospheric Administration (NOAA) weather stations posted on the National Climate Data Center (NCDC) Climate Data Online (CDO) archive and compute GDD. The date range for this study is January 1, 2000 through December 31, 2012. We employ a novel process, a repeating logistic product (RLP), to compensate for short-term weather variability and data drops from the recording stations and fit a curve to the median daily GDD values, adjusting for asymmetry, amplitude, and phase shift that minimize the sum of squared errors when comparing the observed and predicted GDD. The resulting curve, here referred to as the surrogate GDD, is the time-temperature phasing parameter used to convert Cartesian NDVI values into polar coordinate pairs, multiplying the NDVI values as the radial by the cosine and sine of the surrogate GDD as the angular. Depending on the vegetation type and the original NDVI curve, the polar NDVI curve may be nearly circular, kidney-shaped, or pear-shaped in the case of conifers, deciduous, or agriculture, respectively. We examine the points of tangency about the polar coordinate NDVI curve, identifying values of 1, 0, -1, or infinity, as each of these represent natural inflection points. Lines connecting the origin to each tangent point illustrate and quantify the parametrically segmentation of the growing season based on the GDD and NDVI ostensible dependency. Furthermore, the area contained by each segment represents the apparent vegetation production. A particular benefit is that the inflection points are determined near real-time, as MODIS NDVI, 8-day composite data become available, affording an effective forecasting and hindcasting tool.

Detection of long duration cloud contamination in hyper-temporal NDVI imagery

NASA Astrophysics Data System (ADS)

Ali, A.; de Bie, C. A. J. M.; Skidmore, A. K.; Scarrott, R. G.

2012-04-01

NDVI time series imagery are commonly used as a reliable source for land use and land cover mapping and monitoring. However long duration cloud can significantly influence its precision in areas where persistent clouds prevails. Therefore quantifying errors related to cloud contamination are essential for accurate land cover mapping and monitoring. This study aims to detect long duration cloud contamination in hyper-temporal NDVI imagery based land cover mapping and monitoring. MODIS-Terra NDVI imagery (250 m; 16-day; Feb'03-Dec'09) were used after necessary pre-processing using quality flags and upper envelope filter (ASAVOGOL). Subsequently stacked MODIS-Terra NDVI image (161 layers) was classified for 10 to 100 clusters using ISODATA. After classifications, 97 clusters image was selected as best classified with the help of divergence statistics. To detect long duration cloud contamination, mean NDVI class profiles of 97 clusters image was analyzed for temporal artifacts. Results showed that long duration clouds affect the normal temporal progression of NDVI and caused anomalies. Out of total 97 clusters, 32 clusters were found with cloud contamination. Cloud contamination was found more prominent in areas where high rainfall occurs. This study can help to stop error propagation in regional land cover mapping and monitoring, caused by long duration cloud contamination.

Preliminary comparison of landscape pattern-normalized difference vegetation index (NDVI) relationships to central plains stream conditions

USGS Publications Warehouse

Griffith, J.A.; Martinko, E.A.; Whistler, J.L.; Price, K.P.

2002-01-01

We explored relationships of water quality parameters with landscape pattern metrics (LPMs), land use-land cover (LULC) proportions, and the advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) or NDVI-derived metrics. Stream sites (271) in Nebraska, Kansas, and Missouri were sampled for water quality parameters, the index of biotic integrity, and a habitat index in either 1994 or 1995. Although a combination of LPMs (interspersion and juxtaposition index, patch density, and percent forest) within Ozark Highlands watersheds explained >60% of the variation in levels of nitrite-nitrate nitrogen and conductivity, in most cases the LPMs were not significantly correlated with the stream data. Several problems using landscape pattern metrics were noted: small watersheds having only one or two patches, collinearity with LULC data, and counterintuitive or inconsistent results that resulted from basic differences in land use-land cover patterns among ecoregions or from other factors determining water quality. The amount of variation explained in water quality parameters using multiple regression models that combined LULC and LPMs was generally lower than that from NDVI or vegetation phenology metrics derived from time-series NDVI data. A comparison of LPMs and NDVI indicated that NDVI had greater promise for monitoring landscapes for stream conditions within the study area.

Monitoring rangeland dynamics in Senegal with advanced very high resolution radiometer data

USGS Publications Warehouse

Tappan, G. Gray; Tyler, Dean J.; Wehde, M. E.; Moore, Donald G.

1992-01-01

Time‐series Normalized Difference Vegetation Index (NDVI) data, computed from Advanced Very High Resolution Radiometer data, are being used by regional and national programs in the African Sahel to monitor seasonal rangeland conditions. The data are often used as indicators of grazing conditions and drought. However, distinguishing rangelands from other vegetation cover types on NDVI images is difficult. A second complication is that rangeland types and their associated productivity vary geographically by soil type. To effectively assess rangeland conditions, seasonal fluctuations (due to climatic cycles) must be isolated from long‐term production characteristics associated with vegetation type and soil differences. Rangeland NDVI dynamics, including qualitative assessments of rangeland production, and the timing and length of the growing season in Senegal were examined by using 7.4‐km global area coverage satellite data. Analyses were based on 10‐day NDVI composite image data from 1982 through 1989. The NDVI image data were stratified by rangeland and soil polygons derived from locally available resource maps. Time‐series NDVI statistics were calculated from the resource polygons that had been interpreted into high, medium, and low production rangelands. Analysts monitoring rangeland conditions can better identify seasonal anomalies such as drought by comparing production potential within homogeneous; resource polygons with the current NDVI data.

Preliminary comparison of landscape pattern-normalized difference vegetation index (NDVI) relationships to Central Plains stream conditions.

PubMed

Griffith, Jerry A; Martinko, Edward A; Whistler, Jerry L; Price, Kevin P

2002-01-01

We explored relationships of water quality parameters with landscape pattern metrics (LPMs), land use-land cover (LULC) proportions, and the advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) or NDVI-derived metrics. Stream sites (271) in Nebraska, Kansas, and Missouri were sampled for water quality parameters, the index of biotic integrity, and a habitat index in either 1994 or 1995. Although a combination of LPMs (interspersion and juxtaposition index, patch density, and percent forest) within Ozark Highlands watersheds explained >60% of the variation in levels of nitrite-nitrate nitrogen and conductivity, in most cases the LPMs were not significantly correlated with the stream data. Several problems using landscape pattern metrics were noted: small watersheds having only one or two patches, collinearity with LULC data, and counterintuitive or inconsistent results that resulted from basic differences in land use-land cover patterns among ecoregions or from other factors determining water quality. The amount of variation explained in water quality parameters using multiple regression models that combined LULC and LPMs was generally lower than that from NDVI or vegetation phenology metrics derived from time-series NDVI data. A comparison of LPMs and NDVI indicated that NDVI had greater promise for monitoring landscapes for stream conditions within the study area.

Monitoring Agricultural Cropping Patterns in the Great Lakes Basin Using MODIS-NDVI Time Series Data

EPA Science Inventory

This research examined changes in agricultural cropping patterns across the Great Lakes Basin (GLB) using the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. Specific research objectives were to characterize the distribut...

Application of Satellite Solar-Induced Chlorophyll Fluorescence to Understanding Large-Scale Variations in Vegetation Phenology and Function Over Northern High Latitude Forests

NASA Technical Reports Server (NTRS)

Jeong, Su-Jong; Schimel, David; Frankenberg, Christian; Drewry, Darren T.; Fisher, Joshua B.; Verma, Manish; Berry, Joseph A.; Lee, Jung-Eun; Joiner, Joanna

2016-01-01

This study evaluates the large-scale seasonal phenology and physiology of vegetation over northern high latitude forests (40 deg - 55 deg N) during spring and fall by using remote sensing of solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and observation-based estimate of gross primary productivity (GPP) from 2009 to 2011. Based on GPP phenology estimation in GPP, the growing season determined by SIF time-series is shorter in length than the growing season length determined solely using NDVI. This is mainly due to the extended period of high NDVI values, as compared to SIF, by about 46 days (+/-11 days), indicating a large-scale seasonal decoupling of physiological activity and changes in greenness in the fall. In addition to phenological timing, mean seasonal NDVI and SIF have different responses to temperature changes throughout the growing season. We observed that both NDVI and SIF linearly increased with temperature increases throughout the spring. However, in the fall, although NDVI linearly responded to temperature increases, SIF and GPP did not linearly increase with temperature increases, implying a seasonal hysteresis of SIF and GPP in response to temperature changes across boreal ecosystems throughout their growing season. Seasonal hysteresis of vegetation at large-scales is consistent with the known phenomena that light limits boreal forest ecosystem productivity in the fall. Our results suggest that continuing measurements from satellite remote sensing of both SIF and NDVI can help to understand the differences between, and information carried by, seasonal variations vegetation structure and greenness and physiology at large-scales across the critical boreal regions.

Assessment of time-series MODIS data for cropland mapping in the U.S. central Great Plains

NASA Astrophysics Data System (ADS)

Masialeti, Iwake

This study had three general objectives. First, to explore ways of creating and refining a reference data set when reference data set is unobtainable. Second, extend work previously done in Kansas by Wardlow et al. (2007) to Nebraska, several exploratory approaches were used to further investigate the potential of MODIS NDVI 250-m data in agricultural-related land cover research other parts of the Great Plains. The objective of this part of the research was to evaluate the applicability of time-series MODIS 250-m NDVI data for crop-type discrimination by spectrally characterizing and discriminating major crop types in Nebraska using the reference data set collected and refined under research performed for the first objective. Third, conduct an initial investigation into whether time-series NDVI response curves for crops over a growing season for one year could be used to classify crops for a different year. In this case, time-series NDVI response curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 data set could be used to classify crops for 2005. GIS operations, and reference data refinement using clustering and visual assessment of each crop's NDVI cluster profiles in Nebraska, demonstrated that it is possible to devise an alternative reference data set and refinement plan that redresses the unexpected loss of training and validation data. The analysis enabled the identification and removal of crop pattern outliers and sites atypical of crop phenology under consideration, and after editing, a total of 1,288 field sites remained, which were used as a reference data set for classification of Nebraska crop types. A pixel-level analysis of the time-series MODIS 250-m NDVI for 1,288 field sites representing each of the eight cover types under investigation across Nebraska found that each crop type had a distinctive MODIS 250-m NDVI profile corresponding to the crop calendar. A visual and statistical comparison of the average NDVI profiles showed that the crop types were separable at different times of the growing season based on their phenology-driven spectral-temporal differences. Winter wheat and alfalfa, winter wheat and summer crops, and alfalfa and summer crops were clearly separable. Specific summer crop types were not easily distinguishable from each other due to their similar crop calendars. Their greatest separability however occurred during the initial spring green up and/or senescence plant growth phases. In Kansas, an initial investigation revealed that there was near-complete agreement between the winter wheat crop profiles but that there were some minor differences in the crop profiles for alfalfa and summer crops between 2001 and 2005. However, the profiles of summer crops---corn, grain sorghum, and soybeans---displayed a shift to the right by at least 1 composite date, indicative of possible late crop planting and emergence. Alfalfa and summer crops, seem to suggest that time series NDVI response curves for crops over a growing period for one year of valid ground reference data may not be used to map crops for a different year without taking into account the climatic and/or environmental conditions of each year.

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

NASA Technical Reports Server (NTRS)

Ulsig, Laura; Nichol, Caroline J.; Huemmrich, Karl F.; Landis, David R.; Middleton, Elizabeth M.; Lyapustin, Alexei I.; Mammarella, Ivan; Levula, Janne; Porcar-Castell, Albert

2017-01-01

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R (sup 2) equals 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R (sup 2) is greater than 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Land Surface Phenology in Kazakhstan: Climatic Variability and Institutional Change

NASA Astrophysics Data System (ADS)

de Beurs, K. M.; Henebry, G. M.

2002-12-01

Kazakhstan is the second largest country to emerge from the collapse of the Soviet Union. At 2.7 million sq km, Kazakhstan is nearly four times the size of Texas and more than one-third the size of the conterminous US. Kazakhstan is mostly rangeland: nearly 70% of the land area is grazed by cattle, sheep, goats, and other livestock. Consequent to the abrupt institutional changes surrounding the disintegration of the Soviet Union in the early 1990s, the Kazakhstan region has reportedly undergone extensive land-cover change. However, observing and quantifying these changes is difficult because of (1) the loss of regional environmental monitoring networks at the beginning of the 1990s and (2) the lack of historical Landsat imagery over much of the region, due to gaps in ground station reception masks. Were the institutional changes sufficiently great to affect NDVI phenology at spatial resolutions and extents relevant to mesoscale meteorological models? To explore this question, we used the NDVI time series from the Pathfinder AVHRR Land (PAL) data set, which consists of 10 d maximum NDVI composites at a spatial resolution of 8 km. Daily minimum and maximum temperatures, and daily precipitation rates were extracted from the NCEP/NCAR CDAS/Reanalysis Project. We produced 10 d composites of growing degree-days (GDD) and precipitation amounts. Simple quadratic models were used to relate NDVI time series to GDD. Two agricultural areas were examined: the region of rain-fed spring wheat cultivation in the north (25600 sq km near Kostanai) and the region of irrigated cotton and rice in the south (576 sq km near Kyzylorda). Two periods were evaluated: during the Soviet era (1985-89) and after the independence of Kazakhstan (1995-99). Models for the irrigated area had a better fit than the models for the rain-fed area, but all models were strongly significant. In the north, the temperature regime and the mean precipitation amounts were comparable for 1985-89 and 1995-99. The models displayed similar timing and magnitude for NDVI. The southern irrigated area displayed different temporal developments and magnitudes of NDVI between 1985-89 and 1995-99; the second period displays higher peak NDVI. The temperature regime and the accumulation of GDD were similar in both periods. Although the imputed precipitation was significantly different, it is not likely to be responsible for the observed differences in NDVI, due to the low magnitude of precipitation relative to the crop water demands. Thus, we conclude that the climatic conditions between the two periods are not effectively different and, further, that the observed differences in the temporal development of NDVI result from changes in agricultural practices.

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

NASA Astrophysics Data System (ADS)

Johnson, David M.

2016-10-01

An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250 m spatial resolution while the other products were 1000 m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250 m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250 m vs. 1000 m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250 m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008-2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000 m resolution NDVI showed somewhat poorer performance than the 250 m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

On the characterization of vegetation recovery after fire disturbance using Fisher-Shannon analysis and SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Lanorte, Antonio; Lovallo, Michele; Telesca, Luciano

2015-04-01

Time series can fruitfully support fire monitoring and management from statistical analysis of fire occurrence (Tuia et al. 2008) to danger estimation (lasaponara 2005), damage evaluation (Lanorte et al 2014) and post fire recovery (Lanorte et al. 2014). In this paper, the time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher-Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher-Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers. Reference Lanorte A, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbanceInternational Journal of Applied Earth Observation and Geoinformation 26 441-446 Lanorte A, M Danese, R Lasaponara, B Murgante 2014 Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis International Journal of Applied Earth Observation and Geoinformation 20, 42-51 Tuia D, F Ratle, R Lasaponara, L Telesca, M Kanevski 2008 Scan statistics analysis of forest fire clusters Communications in Nonlinear Science and Numerical Simulation 13 (8), 1689-1694 Telesca L, R Lasaponara 2006 Pre and post fire behavioral trends revealed in satellite NDVI time series Geophysical Research Letters 33 (14) Lasaponara R 2005 Intercomparison of AVHRR based fire susceptibility indicators for the Mediterranean ecosystems of southern Italy International Journal of Remote Sensing 26 (5), 853-870

Seasonally different response of photosynthetic activity to daytime and night-time warming in the Northern Hemisphere

DOE PAGES

Tan, Jianguang; Piao, Shilong; Chen, Anping; ...

2014-08-27

Over the last century the Northern Hemisphere has experienced rapid climate warming, but this warming has not been evenly distributed seasonally, as well as diurnally. The implications of such seasonal and diurnal heterogeneous warming on regional and global vegetation photosynthetic activity, however, are still poorly understood. Here, we investigated for different seasons how photosynthetic activity of vegetation correlates with changes in seasonal daytime and night-time temperature across the Northern Hemisphere (>30°N), using Normalized Difference Vegetation Index (NDVI) data from 1982 to 2011 obtained from the Advanced Very High Resolution Radiometer (AVHRR). Our analysis revealed some striking seasonal differences in themore » response of NDVI to changes in day- versus night-time temperatures. For instance, while higher daytime temperature (T max) is generally associated with higher NDVI values across the boreal zone, the area exhibiting a statistically significant positive correlation between T max and NDVI is much larger in spring (41% of area in boreal zone – total area 12.6 × 10 6 km 2) than in summer and autumn (14% and 9%, respectively). In contrast to the predominantly positive response of boreal ecosystems to changes in T max, increases in T max tended to negatively influence vegetation growth in temperate dry regions, particularly during summer. Changes in night-time temperature (T min) correlated negatively with autumnal NDVI in most of the Northern Hemisphere, but had a positive effect on spring and summer NDVI in most temperate regions (e.g., Central North America and Central Asia). Such divergent covariance between the photosynthetic activity of Northern Hemispheric vegetation and day- and night-time temperature changes among different seasons and climate zones suggests a changing dominance of ecophysiological processes across time and space. Lastly, understanding the seasonally different responses of vegetation photosynthetic activity to diurnal temperature changes, which have not been captured by current land surface models, is important for improving the performance of next generation regional and global coupled vegetation-climate models« less

Midwest agriculture and ENSO: A comparison of AVHRR NDVI3g data and crop yields in the United States Corn Belt from 1982 to 2014

NASA Astrophysics Data System (ADS)

Glennie, Erin; Anyamba, Assaf

2018-06-01

A time series of Advanced Very High Resolution Radiometer (AVHRR) derived normalized difference vegetation index (NDVI) data were compared to National Agricultural Statistics Service (NASS) corn yield data in the United States Corn Belt from 1982 to 2014. The main objectives of the comparison were to assess 1) the consistency of regional Corn Belt responses to El Niño/Southern Oscillation (ENSO) teleconnection signals, and 2) the reliability of using NDVI as an indicator of crop yield. Regional NDVI values were used to model a seasonal curve and to define the growing season - May to October. Seasonal conditions in each county were represented by NDVI and land surface temperature (LST) composites, and corn yield was represented by average annual bushels produced per acre. Correlation analysis between the NDVI, LST, corn yield, and equatorial Pacific sea surface temperature anomalies revealed patterns in land surface dynamics and corn yield, as well as typical impacts of ENSO episodes. It was observed from the study that growing seasons coincident with La Niña events were consistently warmer, but El Niño events did not consistently impact NDVI, temperature, or corn yield data. Moreover, the El Niño and La Niña composite images suggest that impacts vary spatially across the Corn Belt. While corn is the dominant crop in the region, some inconsistencies between corn yield and NDVI may be attributed to soy crops and other background interference. The overall correlation between the total growing season NDVI anomaly and detrended corn yield was 0.61(p = 0.00013), though the strength of the relationship varies across the Corn Belt.

Inter-annual variability of NDVI in response to long-term warming and fertilization in wet sedge and tussock tundra.

PubMed

Boelman, Natalie T; Stieglitz, Marc; Griffin, Kevin L; Shaver, Gaius R

2005-05-01

This study explores the relationship between the normalized difference vegetation index (NDVI) and aboveground plant biomass for tussock tundra vegetation and compares it to a previously established NDVI-biomass relationship for wet sedge tundra vegetation. In addition, we explore inter-annual variation in NDVI in both these contrasting vegetation communities. All measurements were taken across long-term experimental treatments in wet sedge and tussock tundra communities at the Toolik Lake Long Term Ecological Research (LTER) site, in northern Alaska. Over 15 years (for wet sedge tundra) and 14 years (for tussock tundra), N and P were applied in factorial experiments (N, P and N+P), air temperature was increased using greenhouses with and without N+P fertilizer, and light intensity was reduced by 50% using shade cloth. during the peak growing seasons of 2001, 2002, and 2003, NDVI measurements were made in both the wet sedge and tussock tundra experimental treatment plots, creating a 3-year time series of inter-annual variation in NDVI. We found that: (1) across all tussock experimental tundra treatments, NDVI is correlated with aboveground plant biomass (r2 = 0.59); (2) NDVI-biomass relationships for tussock and wet sedge tundra communities are community specific, and; (3) NDVI values for tussock tundra communities are typically, but not always, greater than for wet sedge tundra communities across all experimental treatments. We suggest that differences between the response of wet sedge and tussock tundra communities in the same experimental treatments result from the contrasting degree of heterogeneity in species and functional types that characterize each of these Arctic tundra vegetation communities.

A User's Version View of a Robustified, Bayesian Weighted Least-Squares Recursive Algorithm for Interpolating AVHRR-NDVI Data: Applications to an Animated Visualization of the Phenology of a Semi-Arid Study Area

NASA Astrophysics Data System (ADS)

Hermance, J. F.; Jacob, R. W.; Bradley, B. A.; Mustard, J. F.

2005-12-01

In studying vegetation patterns remotely, the objective is to draw inferences on the development of specific or general land surface phenology (LSP) as a function of space and time by determining the behavior of a parameter (in our case NDVI), when the parameter estimate may be biased by noise, data dropouts and obfuscations from atmospheric and other effects. We describe the underpinning concepts of a procedure for a robust interpolation of NDVI data that does not have the limitations of other mathematical approaches which require orthonormal basis functions (e.g. Fourier analysis). In this approach, data need not be uniformly sampled in time, nor do we expect noise to be Gaussian-distributed. Our approach is intuitive and straightforward, and is applied here to the refined modeling of LSP using 7 years of weekly and biweekly AVHRR NDVI data for a 150 x 150 km study area in central Nevada. This site is a microcosm of a broad range of vegetation classes, from irrigated agriculture with annual NDVIvalues of up to 0.7 to playas and alkali salt flats with annual NDVI values of only 0.07. Our procedure involves a form of parameter estimation employing Bayesian statistics. In utilitarian terms, the latter procedure is a method of statistical analysis (in our case, robustified, weighted least-squares recursive curve-fitting) that incorporates a variety of prior knowledge when forming current estimates of a particular process or parameter. In addition to the standard Bayesian approach, we account for outliers due to data dropouts or obfuscations because of clouds and snow cover. An initial "starting model" for the average annual cycle and long term (7 year) trend is determined by jointly fitting a common set of complex annual harmonics and a low order polynomial to an entire multi-year time series in one step. This is not a formal Fourier series in the conventional sense, but rather a set of 4 cosine and 4 sine coefficients with fundamental periods of 12, 6, 3 and 1.5 months. Instabilities during large time gaps in the data are suppressed by introducing an expectation of minimum roughness on the fitted time series. Our next significant computational step involves a constrained least squares fit to the observed NDVI data. Residuals between the observed NDVI value and the predicted starting model are computed, and the inverse of these residuals provide the weights for a weighted least squares analysis whereby a set of annual eighth-order splines are fit to the 7 years of NDVI data. Although a series of independent 8-th order annual functionals over a period of 7 years is intrinsically unstable when there are significant data gaps, the splined versions for this specific application are quite stable due to explicit continuity conditions on the values and derivatives of the functionals across contiguous years, as well as a priori constraints on the predicted values vis-a-vis the assumed initial model. Our procedure allows us to robustly interpolate original unequally-spaced NDVI data with a new time series having the most-appropriate, user-defined time base. We apply this approach to the temporal behavior of vegetation in our 150 x 150 km study area. Such a small area, being so rich in vegetation diversity, is particularly useful to view in map form and by animated annual and multi-year time sequences, since the interrelation between phenology, topography and specific usage patterns becomes clear.

Browning of the landscape of interior Alaska based on 1986-2009 Landsat sensor NDVI

Treesearch

Rebecca A. Baird; David Verbyla; Teresa N. Hollingsworth

2012-01-01

We used a time series of 1986-2009 Landsat sensor data to compute the Normalized Difference Vegetation Index (NDVI) for 30 m pixels within the Bonanza Creek Experimental Forest of interior Alaska. Based on simple linear regression, we found significant (p

Potential of VIIRS Data for Regional Monitoring of Gypsy Moth Defoliation: Implications for Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Ryan, Robert E.; Smoot, James C.; Prados, Donald; McKellip, Rodney; Sader. Steven A.; Gasser, Jerry; May, George; Hargrove, William

2007-01-01

A NASA RPC (Rapid Prototyping Capability) experiment was conducted to assess the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) data for monitoring non-native gypsy moth (Lymantria dispar) defoliation of forests. This experiment compares defoliation detection products computed from simulated VIIRS and from MODIS (Moderate Resolution Imaging Spectroradiometer) time series products as potential inputs to a forest threat EWS (Early Warning System) being developed for the USFS (USDA Forest Service). Gypsy moth causes extensive defoliation of broadleaved forests in the United States and is specifically identified in the Healthy Forest Restoration Act (HFRA) of 2003. The HFRA mandates development of a national forest threat EWS. This system is being built by the USFS and NASA is aiding integration of needed satellite data products into this system, including MODIS products. This RPC experiment enabled the MODIS follow-on, VIIRS, to be evaluated as a data source for EWS forest monitoring products. The experiment included 1) assessment of MODIS-simulated VIIRS NDVI products, and 2) evaluation of gypsy moth defoliation mapping products from MODIS-simulated VIIRS and from MODIS NDVI time series data. This experiment employed MODIS data collected over the approximately 15 million acre mid-Appalachian Highlands during the annual peak defoliation time frame (approximately June 10 through July 27) during 2000-2006. NASA Stennis Application Research Toolbox software was used to produce MODIS-simulated VIIRS data and NASA Stennis Time Series Product Tool software was employed to process MODIS and MODIS-simulated VIIRS time series data scaled to planetary reflectance. MODIS-simulated VIIRS data was assessed through comparison to Hyperion-simulated VIIRS data using data collected during gypsy moth defoliation. Hyperion-simulated MODIS data showed a high correlation with actual MODIS data (NDVI R2 of 0.877 and RMSE of 0.023). MODIS-simulated VIIRS data for the same date showed moderately high correlation with Hyperion-simulated VIIRS data (NDVI R2 of 0.62 and RMSE of 0.035), even though the datasets were collected about a half an hour apart during changing weather conditions. MODIS products (MOD02, MOD09, and MOD13) and MOD02-simulated VIIRS time series data were used to generate defoliation mapping products based on image classification and image differencing change detection techniques. Accuracy of final defoliation mapping products was assessed by image interpreting over 170 randomly sampled locations found on Landsat and ASTER data in conjunction with defoliation map data from the USFS. The MOD02-simulated VIIRS 400-meter NDVI classification produced a similar overall accuracy (87.28 percent with 0.72 Kappa) to the MOD02 250-meter NDVI classification (86.71 percent with 0.71 Kappa). In addition, the VIIRS 400-meter NDVI, MOD02 250-meter NDVI, and MOD02 500-meter NDVI showed good user and producer accuracies for the defoliated forest class (70 percent) and acceptable Kappa values (0.66). MOD02 and MOD02-simulated VIIRS data both showed promise as data sources for regional monitoring of forest disturbance due to insect defoliation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rafique, Rashid; Zhao, Fang; de Jong, Rogier

The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model–based NPP estimates (from five models of the TRENDY project) for the period 1982-2012, to study the role of terrestrial ecosystems in carbon cycle under the prevailing climate conditions. We found that 80% and 67% of the global land area showed positive NPP and NDVI values, respectively, for this period. The global NPP was estimated to be about 63 Pg C y -1, with an increase of 0.214 Pgmore » C y -1 y -1. Similarly, the global mean NDVI was estimated to be 0.33, with an increasing trend of 0.00041 y-1. The spatial patterns of NPP and NDVI demonstrated substantial variability, especially at the regional level, for most part of the globe. However, on temporal scale, both global NPP and NDVI showed a corresponding pattern of increase (decrease) for the duration of this study except for few years (e.g. 1990 and 1995-98). Generally, the Northern Hemisphere showed stronger NDVI and NPP increasing trends over time compared to the Southern Hemisphere; however, NDVI showed larger trends in Temperate regions while NPP showed larger trends in Boreal regions. Among the five models, the maximum and minimum NPP were produced by JULES (72.4 Pg C y -1) and LPJ (53.72 Pg C y -1) models, respectively. At latitudinal level, the NDVI and NPP ranges were ~0.035 y -1 to ~-0.016 y -1 and ~0.10 Pg C y -1 y -1 to ~-0.047 Pg C y -1 y -1, respectively. Overall, the results of this study suggest that the modeled NPP generally correspond to the NDVI trends in the temporal dimension. Lastly, the significant variability in spatial patterns of NPP and NDVI trends points to a need for research to understand the causes of these discrepancies between molded and observed ecosystem dynamics, and the carbon cycle.« less

Analysis of vegetation dynamics and climatic variability impacts on greenness across Canada using remotely sensed data from 2000 to 2009

NASA Astrophysics Data System (ADS)

Fang, Xiuqin; Zhu, Qiuan; Chen, Huai; Ma, Zhihai; Wang, Weifeng; Song, Xinzhang; Zhao, Pengxiang; Peng, Changhui

2014-01-01

Using time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from 2000 to 2009, we assessed decadal vegetation dynamics across Canada and examined the relationship between NDVI and climatic variables (precipitation and temperature). The Palmer drought severity index and vapor pressure difference (VPD) were used to relate the vegetation changes to the climate, especially in cases of drought. Results indicated that MODIS NDVI measurements provided a dynamic picture of interannual variation in Canadian vegetation patterns. Greenness declined in 2000, 2002, and 2009 and increased in 2005, 2006, and 2008. Vegetation dynamics varied across regions during the period. Most forest land shows little change, while vegetation in the ecozone of Pacific Maritime, Prairies, and Taiga Shield shows more dynamics than in the others. Significant correlations were found between NDVI and the climatic variables. The variation of NDVI resulting from climatic variability was more highly correlated to temperature than to precipitation in most ecozones. Vegetation grows better with higher precipitation and temperature in almost all ecozones. However, vegetation grows worse under higher temperature in the Prairies ecozone. The annual changes in NDVI corresponded well with the change in VPD in most ecozones.

Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest.

PubMed

Buma, Brian

2012-06-01

Forest disturbances around the world have the potential to alter forest type and cover, with impacts on diversity, carbon storage, and landscape composition. These disturbances, especially fire, are common and often large, making ground investigation of forest recovery difficult. Remote sensing offers a means to monitor forest recovery in real time, over the entire landscape. Typically, recovery monitoring via remote sensing consists of measuring vegetation indices (e.g., NDVI) or index-derived metrics, with the assumption that recovery in NDVI (for example) is a meaningful measure of ecosystem recovery. This study tests that assumption using MODIS 16-day imagery from 2000 to 2010 in the area of the Colorado's Routt National Forest Hinman burn (2002) and seedling density counts taken in the same area. Results indicate that NDVI is rarely correlated with forest recovery, and is dominated by annual and perennial forb cover, although topography complicates analysis. Utility of NDVI as a means to delineate areas of recovery or non-recovery are in doubt, as bootstrapped analysis indicates distinguishing power only slightly better than random. NDVI in revegetation analyses should carefully consider the ecology and seasonal patterns of the system in question.

Time-varying trends of global vegetation activity

NASA Astrophysics Data System (ADS)

Pan, N.; Feng, X.; Fu, B.

2016-12-01

Vegetation plays an important role in regulating the energy change, water cycle and biochemical cycle in terrestrial ecosystems. Monitoring the dynamics of vegetation activity and understanding their driving factors have been an important issue in global change research. Normalized Difference Vegetation Index (NDVI), an indicator of vegetation activity, has been widely used in investigating vegetation changes at regional and global scales. Most studies utilized linear regression or piecewise linear regression approaches to obtain an averaged changing rate over a certain time span, with an implicit assumption that the trend didn't change over time during that period. However, no evidence shows that this assumption is right for the non-linear and non-stationary NDVI time series. In this study, we adopted the multidimensional ensemble empirical mode decomposition (MEEMD) method to extract the time-varying trends of NDVI from original signals without any a priori assumption of their functional form. Our results show that vegetation trends are spatially and temporally non-uniform during 1982-2013. Most vegetated area exhibited greening trends in the 1980s. Nevertheless, the area with greening trends decreased over time since the early 1990s, and the greening trends have stalled or even reversed in many places. Regions with browning trends were mainly located in southern low latitudes in the 1980s, whose area decreased before the middle 1990s and then increased at an accelerated rate. The greening-to-browning reversals were widespread across all continents except Oceania (43% of the vegetated areas), most of which happened after the middle 1990s. In contrast, the browning-to-greening reversals occurred in smaller area and earlier time. The area with monotonic greening and browning trends accounted for 33% and 5% of the vegetated area, respectively. By performing partial correlation analyses between NDVI and climatic elements (temperature, precipitation and cloud cover) and analyzing the MEEMD-extracted trends of these climatic elements, we discussed possible driving factors of the time-varying trends of NDVI in several specific regions where trend reversals occurred.

Assessment of Climate Impact Changes on Forest Vegetation Dynamics by Satellite Remote Sensing

NASA Astrophysics Data System (ADS)

Zoran, Maria

Climate variability represents the ensemble of net radiation, precipitation, wind and temper-ature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Forest vegetation phenology constitutes an efficient bio-indicator of climate and anthropogenic changes impacts and a key parameter for understanding and modelling vegetation-climate in-teractions. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vege-tation Index (NDVIs), which requires NDVI time-series with good time resolution, over homo-geneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). Spatio-temporal vegetation dynamics have been quantified as the total amount of vegetation (mean NDVI) and the seasonal difference (annual NDVI amplitude) by a time series analysis of NDVI satellite images with the Harmonic ANalysis of Time Series algorithm. A climate indicator (CI) was created from meteorological data (precipitation over net radiation). The relationships between the vegetation dynamics and the CI have been determined spatially and temporally. The driest test regions prove to be the most sensitive to climate impact. The spatial and temporal patterns of the mean NDVI are the same, while they are partially different for the seasonal difference. The aim of this paper was to quantify this impact over a forest ecosystem placed in the North-Eastern part of Bucharest town, Romania, with Normalized Difference Vegetation Index (NDVI) parameter extracted from IKONOS and LANDSAT TM and ETM satellite images and meteorological data over l995-2007 period. For investigated test area, considerable NDVI decline was observed between 1995 and 2008 due to the drought events during 2003 and 2007 years. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and to-pography are not correlated with NDVI dynamics. Specific aim of this paper was to assess, forecast, and mitigate the risks of climatic changes on forest systems and its biodiversity as well as on adjacent environment areas and to provide early warning strategies on the basis of spectral information derived from satellite data regarding atmospheric effects of forest biome degradation . The paper aims to describe observed trends and potential impacts based on scenarios from simulations with regional climate models and other downscaling procedures.

Global and regional variability and change in terrestrial ecosystems net primary production and NDVI: A model-data comparison

DOE PAGES

Rafique, Rashid; Zhao, Fang; de Jong, Rogier; ...

2016-02-25

The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model–based NPP estimates (from five models of the TRENDY project) for the period 1982-2012, to study the role of terrestrial ecosystems in carbon cycle under the prevailing climate conditions. We found that 80% and 67% of the global land area showed positive NPP and NDVI values, respectively, for this period. The global NPP was estimated to be about 63 Pg C y -1, with an increase of 0.214 Pgmore » C y -1 y -1. Similarly, the global mean NDVI was estimated to be 0.33, with an increasing trend of 0.00041 y-1. The spatial patterns of NPP and NDVI demonstrated substantial variability, especially at the regional level, for most part of the globe. However, on temporal scale, both global NPP and NDVI showed a corresponding pattern of increase (decrease) for the duration of this study except for few years (e.g. 1990 and 1995-98). Generally, the Northern Hemisphere showed stronger NDVI and NPP increasing trends over time compared to the Southern Hemisphere; however, NDVI showed larger trends in Temperate regions while NPP showed larger trends in Boreal regions. Among the five models, the maximum and minimum NPP were produced by JULES (72.4 Pg C y -1) and LPJ (53.72 Pg C y -1) models, respectively. At latitudinal level, the NDVI and NPP ranges were ~0.035 y -1 to ~-0.016 y -1 and ~0.10 Pg C y -1 y -1 to ~-0.047 Pg C y -1 y -1, respectively. Overall, the results of this study suggest that the modeled NPP generally correspond to the NDVI trends in the temporal dimension. Lastly, the significant variability in spatial patterns of NPP and NDVI trends points to a need for research to understand the causes of these discrepancies between molded and observed ecosystem dynamics, and the carbon cycle.« less

Normalized difference vegetation index as an estimator for abundance and quality of avian herbivore forage in arctic Alaska

USGS Publications Warehouse

Hogrefe, Kyle R.; Patil, Vijay; Ruthrauff, Daniel R.; Meixell, Brandt W.; Budde, Michael E.; Hupp, Jerry W.; Ward, David H.

2017-01-01

Tools that can monitor biomass and nutritional quality of forage plants are needed to understand how arctic herbivores may respond to the rapidly changing environment at high latitudes. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess changes in abundance and distribution of terrestrial vegetative communities. However, the efficacy of NDVI to measure seasonal changes in biomass and nutritional quality of forage plants in the Arctic remains largely un-evaluated at landscape and fine-scale levels. We modeled the relationships between NDVI and seasonal changes in aboveground biomass and nitrogen concentration in halophytic graminoids, a key food source for arctic-nesting geese. The model was calibrated based on data collected at one site and validated using data from another site. Effects of spatial scale on model accuracy were determined by comparing model predictions between NDVI derived from moderate resolution (250 × 250 m pixels) satellite data and high resolution (20 cm diameter area) handheld spectrometer data. NDVI derived from the handheld spectrometer was a superior estimator (R2 ≥ 0.67) of seasonal changes in aboveground biomass compared to satellite-derived NDVI (R2 ≤ 0.40). The addition of temperature and precipitation variables to the model for biomass improved fit, but provided minor gains in predictive power beyond that of the NDVI-only model. This model, however, was only a moderately accurate estimator of biomass in an ecologically-similar halophytic graminoid wetland located 100 km away, indicating the necessity for site-specific validation. In contrast to assessments of biomass, satellite-derived NDVI was a better estimator for the timing of peak percent of nitrogen than NDVI derived from the handheld spectrometer. We confirmed that the date when NDVI reached 50% of its seasonal maximum was a reasonable approximation of the period of peak spring vegetative green-up and peak percent nitrogen. This study demonstrates the importance of matching the scale of NDVI measurements to the vegetation properties of biomass and nitrogen phenology.

Assessing the Application of Cloud-Shadow Atmospheric Correction Algorithm on HICO

DTIC Science & Technology

2014-05-01

multiple times and intercompare the results to assess variability in the retrieved reflectance spectra. Retrieved chlorophyll values from this...intercomparison are similar and also agree well with the In situ chlorophyll measurements. 15. SUBJECT TERMS Atmospheric correction, cloud-shadow...reflectance spectra. Re- trieved chlorophyll values from this intercomparison are similar and also agree well with the in situ chlorophyll measurements

Mapping Cropland and Major Crop Types Across the Great Lakes Basin Using MODIS-NDVI Data

EPA Science Inventory

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250-m time-series data to develop a cropland mapping capability throughout the 480 000 km2 Great Lakes Basin (GLB). Cropland mapping was conducted usi...

Assessing the Accuracy of MODIS-NDVI Derived Land-Cover Across the Great Lakes Basin

EPA Science Inventory

This research describes the accuracy assessment process for a land-cover dataset developed for the Great Lakes Basin (GLB). This land-cover dataset was developed from the 2007 MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data. Tr...

A VARI-Based Relative Greenness from MODIS Data for Computing the Fire Potential Index

NASA Technical Reports Server (NTRS)

Schneider, P.; Roberts, D. A.; Kyriakidis, P. C.

2008-01-01

The Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate the VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R2 of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FP outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.

Continuous 1985-2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park, California

USGS Publications Warehouse

Soulard, Christopher E.; Albano, Christine M.; Villarreal, Miguel; Walker, Jessica

2016-01-01

To assess how montane meadow vegetation recovered after a wildfire that occurred in Yosemite National Park, CA in 1996, Google Earth Engine image processing was applied to leverage the entire Landsat Thematic Mapper archive from 1985 to 2012. Vegetation greenness (normalized difference vegetation index [NDVI]) was summarized every 16 days across the 28-year Landsat time series for 26 meadows. Disturbance event detection was hindered by the subtle influence of low-severity fire on meadow vegetation. A hard break (August 1996) was identified corresponding to the Ackerson Fire, and monthly composites were used to compare NDVI values and NDVI trends within burned and unburned meadows before, immediately after, and continuously for more than a decade following the fire date. Results indicate that NDVI values were significantly lower at 95% confidence level for burned meadows following the fire date, yet not significantly lower at 95% confidence level in the unburned meadows. Burned meadows continued to exhibit lower monthly NDVI in the dormant season through 2012. Over the entire monitoring period, the negative-trending, dormant season NDVI slopes in the burned meadows were also significantly lower than unburned meadows at 90% confidence level. Lower than average NDVI values and slopes in the dormant season compared to unburned meadows, coupled with photographic evidence, strongly suggest that evergreen vegetation was removed from the periphery of some meadows after the fire. These analyses provide insight into how satellite imagery can be used to monitor low-severity fire effects on meadow vegetation.

Satellite imagery in the fight against Malaria, the case for Genetic Programming

NASA Astrophysics Data System (ADS)

Ssentongo, J. S.; Hines, E. L.

The analysis of multi-temporal data is a critical issue in the field of remote sensing and presents a constant challenge The approach used here relies primarily on utilising a method commonly used in statistics and signal processing Empirical Orthogonal Function EOF analysis Normalized Difference Vegetation Index NDVI and Rainfall Estimate RFE satellite images pertaining to the Sub-Saharan Africa region were obtained The images are derived from the Advanced Very High Resolution Radiometer AVHRR on the United States National Oceanic and Atmospheric Administration NOAA polar orbiting satellites spanning from January 2000 to December 2002 The region of interest was narrowed down to the Limpopo Province Northern Province of South Africa EOF analyses of the space-time-intensity series of dekadal mean NDVI values was been performed They reveal that NDVI can be accurately approximated by its principal component time series and contains a near sinusoidal oscillation pattern Peak greenness essentially what NDVI measures seasons last approximately 8 weeks This oscillation period is very similar to that of Malaria cases reported in the same period but lags behind by 4 dekads about 40 days Singular Value Decomposition SVD of Coupled Fields is performed on the spacetime-intensity series of dekadal mean NDVI and RFE values Correlation analyses indicate that both Malaria and greenness appear to be dependant on rainfall the onset of their seasonal highs always following an arrival of rain There is a greater

Remote sensing of surface water quality in relation to catchment condition in Zimbabwe

NASA Astrophysics Data System (ADS)

Masocha, Mhosisi; Murwira, Amon; Magadza, Christopher H. D.; Hirji, Rafik; Dube, Timothy

2017-08-01

The degradation of river catchments is one of the most important contemporary environmental problems affecting water quality in tropical countries. In this study, we used remotely sensed Normalised Difference Vegetation Index (NDVI) to assess how catchment condition varies within and across river catchments in Zimbabwe. We then used non-linear regression to test whether catchment condition assessed using the NDVI is significantly (α = 0.05) related with levels of Total Suspended Solids (TSS) measured at different sampling points in thirty-two sub-catchments in Zimbabwe. The results showed a consistent negative curvilinear relationship between Landsat 8 derived NDVI and TSS measured across the catchments under study. In the drier catchments of the country, 98% of the variation in TSS is explained by NDVI, while in wetter catchments, 64% of the variation in TSS is explained by NDVI. Our results suggest that NDVI derived from free and readily available multispectral Landsat series data (Landsat 8) is a potential valuable tool for the rapid assessment of physical water quality in data poor catchments. Overall, the finding of this study underscores the usefulness of readily available satellite data for near-real time monitoring of the physical water quality at river catchment scale, especially in resource-constrained areas, such as the sub-Saharan Africa.

Understanding Long-term Greenness, Water Use, and Redevelopment in Denver, Colorado

NASA Astrophysics Data System (ADS)

Neel, A.; Hogue, T. S.; Read, L.

2016-12-01

In 2015 the U.S. Census Bureau's found Denver to have the fastest growth rate among large cities in America. With the population of Metro Denver expected to increase from 2.9 to 3.3 million it is critical to consider the impacts of expected redevelopment and increased housing density on the City's ecosystem and future water supply. While prior studies have shown outdoor water use to account for as much as 40-60% of single-family residential water use in western cities, currently no published research examines patterns in urban vegetation, greenness, temperature and water use for cities in the Rocky Mountain West. Normalized Differential Vegetation Index (NDVI) calculated from Landsat imagery was examined to assess how redevelopment in Denver's urban center impacts regional greenness patterns, land surface temperatures and water budgets. Over the last twenty-seven years Denver has shown an overall 4.4% decrease in greenness, with a more rapid decline starting in 2006. While NDVI and cumulative precipitation have a significant relationship over the study period, decreasing NDVI trends across all seasons suggests other factors, such as redevelopment, may be influencing the city's greenness. Comparing water use, NDVI, and precipitation reveals that not only do climate and redevelopment affect NDVI patterns, but mandated water restrictions may also be having a significant impact on NDVI values. NDVI and precipitation patterns are being assessed against regional surface temperatures over time. Surface temperatures, taken from Landsat data, reveal that Urban Heat Island effect may become more pronounced with decreasing NDVI values. As Denver continues to grow, managers can utilize results to better inform decisions about landscape patterns relative to outdoor water use, the effectiveness of restrictions on consumption, and future planning for green infrastructure.

Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II

PubMed Central

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations’ carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982–1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast. PMID:22205868

Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor: LTDR, GIMMS, FASIR, and PAL-II.

PubMed

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations' carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982-1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast.

Climate Variations and Alaska Tundra Vegetation Productivity Declines in Spring

NASA Astrophysics Data System (ADS)

Bhatt, U. S.; Walker, D. A.; Bieniek, P.; Raynolds, M. K.; Epstein, H. E.; Comiso, J. C.; Pinzon, J. E.; Tucker, C. J.

2015-12-01

While sea ice has continued to decline, vegetation productivity increases have declined particularly during spring in Alaska as well as many parts of the Arctic tundra. To understand the processes behind these features we investigate spring climate variations that includes temperature, circulation patterns, and snow cover to determine how these may be contributing to spring browning. This study employs remotely sensed weekly 25-km sea ice concentration, weekly surface temperature, and bi-weekly NDVI from 1982 to 2014. Maximum NDVI (MaxNDVI, Maximum Normalized Difference Vegetation Index), Time Integrated NDVI (TI-NDVI), Summer Warmth Index (SWI, sum of degree months above freezing during May-August), atmospheric reanalysis data, dynamically downscaled climate data, meteorological station data, and snow water equivalent (GlobSnow, assimilated snow data set). We analyzed the data for the full period (1982-2014) and for two sub-periods (1982-1998 and 1999-2014), which were chosen based on the declining Alaska SWI since 1998. MaxNDVI has increased from 1982-2014 over most of the Arctic but has declined from 1999 to 2014 southwest Alaska. TI-NDVI has trends that are similar to those for MaxNDVI for the full period but display widespread declines over the 1999-2014 period. Therefore, as the MaxNDVI has continued to increase overall for the Arctic, TI-NDVI has been declining since 1999 and these declines are particularly noteworthy during spring in Alaska. Spring declines in Alaska have been linked to increased spring snow cover that can delay greenup (Bieniek et al. 2015) but recent ground observations suggest that after an initial warming and greening, late season freezing temperature are damaging the plants. The late season freezing temperature hypothesis will be explored with meteorological climate/weather data sets for Alaska tundra regions. References P.A. Bieniek, US Bhatt, DA Walker, MK Raynolds, JC Comiso, HE Epstein, JE Pinzon, CJ Tucker, RL Thoman, H Tran, N Mölders, M Steele, J Zhang, and W Ermold, 2015: Climate drivers of changing seasonality of Alaska coastal tundra vegetation productivity, (conditionally accepted) Earth Interactions.

Trend analysis of GIMMS and MODIS NDVI time series for establishing a land degradation neutrality national baseline

NASA Astrophysics Data System (ADS)

Gichenje, Helene; Godinho, Sergio

2017-04-01

Land degradation is a key global environment and development problem that is recognized as a priority by the international development community. The Sustainable Development Goals (SDGs) were adopted by the global community in 2015, and include a goal related to land degradation and the accompanying target to achieve a land degradation-neutral (LDN) world by 2030. The LDN concept encompasses two joint actions of reducing the rate of degradation and increasing the rate of restoration. Using Kenya as the study area, this study aims to develop and test a spatially explicit methodology for assessing and monitoring the operationalization of a land degradation neutrality scheme at the national level. Time series analysis is applied to Normalized Difference Vegetation Index (NDVI) satellite data records, based on the hypothesis that the resulting NDVI residual trend would enable successful detection of changes in vegetation photosynthetic capacity and thus serve as a proxy for land degradation and regeneration processes. Two NDVI data sets are used to identify the spatial and temporal distribution of degraded and regenerated areas: the long term coarse resolution (8km, 1982-2015) third generation Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data record; and the shorter-term finer resolution (250m, 2001-2015) Moderate Resolution Imaging Spectroradiometer (MODIS) derived NDVI data record. Climate data (rainfall, temperature and soil moisture) are used to separate areas of human-induced vegetation productivity decline from those driven by climate dynamics. Further, weekly vegetation health (VH) indexes (4km, 1982-2015) developed by National Oceanic and Atmospheric Administration (NOAA), are assessed as indicators for early detection and monitoring of land degradation by estimating vegetation stress (moisture, thermal and combined conditions).

Mapping Cropland and Crop-type Distribution Using Time Series MODIS Data

NASA Astrophysics Data System (ADS)

Lu, D.; Chen, Y.; Moran, E. F.; Batistella, M.; Luo, L.; Pokhrel, Y.; Deb, K.

2016-12-01

Mapping regional and global cropland distribution has attracted great attention in the past decade, but the separation of crop types is challenging due to the spectral confusion and cloud cover problems during the growing season in Brazil. The objective of this study is to develop a new approach to identify crop types (including soybean, cotton, maize) and planting patterns (soybean-maize, soybean-cotton, and single crop) in Mato Grosso, Goias and Tocantins States, Brazil. The time series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (MOD13Q1) in 2015/2016 were used in this research and field survey data were collected in May 2016. The major steps include: (1) reconstruct time series NDVI data contaminated by noise and clouds using the temporal interpolation algorithm; (2) identify the best periods and develop temporal indices and phenology parameters to distinguish cropland from other land cover types based on time series NDVI data; (3) develop a crop temporal difference index (CTDI) to extract crop types and patterns using time series NDVI data. This research shows that (1) the cropland occupied approximately 16.85% of total land in these three states; (2) soybean-maize and soybean-cotton were two major crop patterns which occupied 54.80% and 19.30% of total cropland area. This research indicates that the proposed approach is promising for accurately and rapidly mapping cropland and crop-type distribution in these three states of Brazil.

Global Agricultural Monitoring (GLAM) using MODAPS and LANCE Data Products

NASA Astrophysics Data System (ADS)

Anyamba, A.; Pak, E. E.; Majedi, A. H.; Small, J. L.; Tucker, C. J.; Reynolds, C. A.; Pinzon, J. E.; Smith, M. M.

2012-12-01

The Global Inventory Modeling and Mapping Studies / Global Agricultural Monitoring (GIMMS GLAM) system is a web-based geographic application that offers Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and user interface tools to data query and plot MODIS NDVI time series. The system processes near real-time and science quality Terra and Aqua MODIS 8-day composited datasets. These datasets are derived from the MOD09 and MYD09 surface reflectance products which are generated and provided by NASA/GSFC Land and Atmosphere Near Real-time Capability for EOS (LANCE) and NASA/GSFC MODIS Adaptive Processing System (MODAPS). The GIMMS GLAM system is developed and provided by the NASA/GSFC GIMMS group for the U.S. Department of Agriculture / Foreign Agricultural Service / International Production Assessment Division (USDA/FAS/IPAD) Global Agricultural Monitoring project (GLAM). The USDA/FAS/IPAD mission is to provide objective, timely, and regular assessment of the global agricultural production outlook and conditions affecting global food security. This system was developed to improve USDA/FAS/IPAD capabilities for making operational quantitative estimates for crop production and yield estimates based on satellite-derived data. The GIMMS GLAM system offers 1) web map imagery including Terra & Aqua MODIS 8-day composited NDVI, NDVI percent anomaly, and SWIR-NIR-Red band combinations, 2) web map overlays including administrative and 0.25 degree Land Information System (LIS) shape boundaries, and crop land cover masks, and 3) user interface tools to select features, data query, plot, and download MODIS NDVI time series.

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Ryan, Robert E.; Smoot, James; Kuper, Phillip; Prados, Donald; Russell, Jeffrey; Ross, Kenton; Gasser, Gerald; Sader, Steven; McKellip, Rodney

2007-01-01

This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classification and change detection via image differencing. The latter enabled an automated defoliation detection product computed using percent change in maximum NDVI for a peak defoliation period during 2001 compared to maximum NDVI across the entire 2000-2006 time frame. Final gypsy moth defoliation mapping products were assessed for accuracy using randomly sampled locations found on available geospatial reference data (Landsat and ASTER data in conjunction with defoliation map data from the USFS). Extensive gypsy moth defoliation patches were evident on screen displays of multitemporal color composites derived from MODIS data and from simulated VIIRS vegetation index data. Such defoliation was particularly evident for 2001, although widespread denuded forests were also seen for 2000 and 2003. These visualizations were validated using aforementioned reference data. Defoliation patches were visible on displays of MODIS-based NDVI and NDMI data. The viewing of apparent defoliation patches on all of these products necessitated adoption of a specialized temporal data processing method (e.g., maximum NDVI during the peak defoliation time frame). The frequency of cloud cover necessitated this approach. Multitemporal simulated VIIRS and MODIS Terra data both produced effective general classifications of defoliated forest versus other land cover. For 2001, the MOD02-simulated VIIRS 400-meter NDVI classification produced a similar yet slightly lower overall accuracy (87.28 percent with 0.72 Kappa) than the MOD02 250-meter NDVI classification (88.44 percent with 0.75 Kappa). The MOD13 250-meter NDVI classification had a lower overall accuracy (79.13 percent) and a much lower Kappa (0.46). The report discusses accuracy assessment results in much more detail, comparing overall classification and individual class accuracy statistics for simulated VIIRS 400-meter NDVI, MOD02 250-meter NDVI, MOD02-500 meter NDVI, MOD13 250-meter NDVI, and MOD02 500-meter NDMI classifications. Automated defoliation detection products from simulated VIIRS and MOD02 data for 2001 also yielded similar, relatively high overall classification accuracy (85.55 percent for the VIIRS 400-meter NDVI versus 87.28 percent for the MOD02 250-meter NDVI). In contrast, the USFS aerial sketch map of gypsy moth defoliation showed a lower overall classification accuracy at 73.64 percent. The overall classification Kappa values were also similar for the VIIRS (approximately 0.67 Kappa) versus the MOD02 (approximately 0.72 Kappa) automated defoliation detection product, which were much higher than the values exhibited by the USFS sketch map product (overall Kappa of approximately 0.47). The report provides additional details on the accuracy of automated gypsy moth defoliation detection products compared with USFS sketch maps. The results suggest that VIIRS data can be effectively simulated from MODIS data and that VIIRS data will produce gypsy moth defoliation mapping products that are similar to MODIS-based products. The results of the RPC experiment indicate that VIIRS and MODIS data products have good potential for integration into the forest threat EWS. The accuracy assessment was performed only for 2001 because of time constraints and a relative scarcity of cloud-free Landsat and ASTER data for the peak defoliation period of the other years in the 2000-2006 time series. Additional work should be performed to assess the accuracy of gypsy moth defoliation detection products for additional years.The study area (mid-Appalachian highlands) and application (gypsy moth forest defoliation) are not necessarily representative of all forested regions and of all forest threat disturbance agents. Additional work should be performed on other inland and coastal regions as well as for other major forest threats.

Analyses of GIMMS NDVI Time Series in Kogi State, Nigeria

NASA Astrophysics Data System (ADS)

Palka, Jessica; Wessollek, Christine; Karrasch, Pierre

2017-10-01

The value of remote sensing data is particularly evident where an areal monitoring is needed to provide information on the earth's surface development. The use of temporal high resolution time series data allows for detecting short-term changes. In Kogi State in Nigeria different vegetation types can be found. As the major population in this region is living in rural communities with crop farming the existing vegetation is slowly being altered. The expansion of agricultural land causes loss of natural vegetation, especially in the regions close to the rivers which are suitable for crop production. With regard to these facts, two questions can be dealt with covering different aspects of the development of vegetation in the Kogi state, the determination and evaluation of the general development of the vegetation in the study area (trend estimation) and analyses on a short-term behavior of vegetation conditions, which can provide information about seasonal effects in vegetation development. For this purpose, the GIMMS-NDVI data set, provided by the NOAA, provides information on the normalized difference vegetation index (NDVI) in a geometric resolution of approx. 8 km. The temporal resolution of 15 days allows the already described analyses. For the presented analysis data for the period 1981-2012 (31 years) were used. The implemented workflow mainly applies methods of time series analysis. The results show that in addition to the classical seasonal development, artefacts of different vegetation periods (several NDVI maxima) can be found in the data. The trend component of the time series shows a consistently positive development in the entire study area considering the full investigation period of 31 years. However, the results also show that this development has not been continuous and a simple linear modeling of the NDVI increase is only possible to a limited extent. For this reason, the trend modeling was extended by procedures for detecting structural breaks in the time series.

Grassland Npp Monitoring Based on Multi-Source Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Cai, Y. R.; Zheng, J. H.; Du, M. J.; Mu, C.; Peng, J.

2018-04-01

Vegetation is an important part of the terrestrial ecosystem. It plays an important role in the energy and material exchange of the ground-atmosphere system and is a key part of the global carbon cycle process.Climate change has an important influence on the carbon cycle of terrestrial ecosystems. Net Primary Productivity (Net Primary Productivity)is an important parameter for evaluating global terrestrial ecosystems. For the Xinjiang region, the study of grassland NPP has gradually become a hot issue in the ecological environment.Increasing the estimation accuracy of NPP is of great significance to the development of the ecosystem in Xinjiang. Based on the third-generation GIMMS AVHRR NDVI global vegetation dataset and the MODIS NDVI (MOD13A3) collected each month by the United States Atmospheric and Oceanic Administration (NOAA),combining the advantages of different remotely sensed datasets, this paper obtained the maximum synthesis fusion for New normalized vegetation index (NDVI) time series in 2006-2015.Analysis of Net Primary Productivity of Grassland Vegetation in Xinjiang Using Improved CASA Model The method described in this article proves the feasibility of applying data processing, and the accuracy of the NPP calculation using the fusion processed NDVI has been greatly improved. The results show that: (1) The NPP calculated from the new normalized vegetation index (NDVI) obtained from the fusion of GIMMS AVHRR NDVI and MODIS NDVI is significantly higher than the NPP calculated from these two raw data; (2) The grassland NPP in Xinjiang Interannual changes show an overall increase trend; interannual changes in NPP have a certain relationship with precipitation.

Estimating agricultural yield gap in Africa using MODIS NDVI dataset

NASA Astrophysics Data System (ADS)

Luan, Y.; Zhu, W.; Luo, X.; Liu, J.; Cui, X.

2013-12-01

Global agriculture has undergone a period of rapid intensification characterized as 'Green Revolution', except for Africa, which is the region most affected by unreliable food access and undernourishment. Increasing crop production will be one of the most challenges and most effectual way to mitigate food insecurity there, as Africa's agricultural yield is on a much lower level comparing to global average. In this study we characterize cropland vegetation phenology in Africa based on MODIS NDVI time series between 2000 and 2012. Cumulated NDVI is a proxy for net primary productivity and used as an indicator for evaluating the potential yield gap in Africa. It is achieved via translating the gap between optimum attainable productivity level in each classification of cropping systems and actual productivity level by the relationship of cumulated NDVI and cereal-equivalent production. The results show most of cropland area in Africa have decreasing trend in cumulated NDVI, distributing in the Nile Delta, Eastern Africa and central of semi-arid to arid savanna area, except significant positive cumulated NDVI trends are mainly found between Senegal and Benin. Using cumulated NDVI and statistics of cereal equivalent production, we find remarkable potential yield gap at the Horn of East Africa (especially in Somalia), Northern Africa (Morocco, Algeria and Tunisia). Meanwhile, countries locating at the savanna area near Sahel desert and South Africa also show significant potential, though they already have a relatively high level of productivity. Our results can help provide policy recommendation for local government or NGO to tackle food security problems by identifying zones with high potential of yield improvement.

Corn response to climate stress detected with satellite-based NDVI time series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

Corn response to climate stress detected with satellite-based NDVI time series

DOE PAGES

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

2016-03-23

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

[Analysis on the relationship between malaria epidemics and NOAA-AVHRR NDVI in Hainan province].

PubMed

Wen, Liang; Xu, De-zhong; Wang, Shan-qing; Li, Cai-xu; Zhang, Zhi-ying; Su, Yong-qiang

2005-04-01

To explore the relationship between malaria epidemics and NOAA-AVHRR NDVI. Data on malaria were collected in all 19 counties in Hainan province from Feb, 1995 to Jan, 1996. Values regarding normalized difference vegetation index (NDVI)-related indicators including mean and maximum values of NDVI, the area proportion of NDVI values of 145- and 145+, months with NDVI values of 135+, 140+, 145+, 150+ of these counties in this period were all extracted from NOAA-AVHRR images, using ERDAS8.5 software. The coefficients of correlation of malaria incidences and these NDVI-related indicator values were then calculated with SPSS 11.0. The incidence of malaria showed positive correlations to mean and maximum values of NDVI, the area proportion of NDVI values of 145+ and months with NDVI values of 135+, 140+, 145+, 150+ respectively, but having negative correlation to the area of NDVI values of 145-. The malaria epidemic regions were in accordance with those regions that the NDVI values of 145+ were continuing for 9 months or more. Malaria prevalence was associated with NOAA-AVHRR NDVI value which could be considered to be use for malaria surveillance in Hainan province.

Modeling the effect of photosynthetic vegetation properties on the NDVI--LAI relationship.

PubMed

Steltzer, Heidi; Welker, Jeffrey M

2006-11-01

Developing a relationship between the normalized difference vegetation index (NDVI) and the leaf area index (LAI) is essential to describe the pattern of spatial or temporal variation in LAI that controls carbon, water, and energy exchange in many ecosystem process models. Photosynthetic vegetation (PV) properties can affect the estimation of LAI, but no models integrate the effects of multiple species. We developed four alternative NDVI-LAI models, three of which integrate PV effects: no PV effects, leaf-level effects, canopy-level effects, and effects at both levels. The models were fit to data across the natural range of variation in NDVI for a widespread High Arctic ecosystem. The weight of evidence supported the canopy-level model (Akaike weight, wr = 0.98), which includes species-specific canopy coefficients that primarily scale fractional PV cover to LAI by accounting for the area of unexposed PV. Modeling the canopy-level effects improved prediction of LAI (R2 = 0.82) over the model with no PV effect (R2 = 0.71) across the natural range of variation in NDVI but did not affect the site-level estimate of LAI. Satellite-based methods to estimate species composition, a variable in the model, will need to be developed. We expect that including the effects of PV properties in NDVI-LAI models will improve prediction of LAI where species composition varies across space or changes over time.

Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery

NASA Astrophysics Data System (ADS)

Sun, Chao; Liu, Yongxue; Zhao, Saishuai; Zhou, Minxi; Yang, Yuhao; Li, Feixue

2016-03-01

Salt marshes are seen as the most dynamic and valuable ecosystems in coastal zones, and in these areas, it is crucial to obtain accurate remote sensing information on the spatial distributions of species over time. However, discriminating various types of salt marsh is rather difficult because of their strong spectral similarities. Previous salt marsh mapping studies have focused mainly on high spatial and spectral (i.e., hyperspectral) resolution images combined with auxiliary information; however, the results are often limited to small regions. With a high temporal and moderate spatial resolution, the Chinese HuanJing-1 (HJ-1) satellite optical imagery can be used not only to monitor phenological changes of salt marsh vegetation over short-time intervals, but also to obtain coverage of large areas. Here, we apply HJ-1 satellite imagery to the middle coast of Jiangsu in east China to monitor changes in saltmarsh vegetation cover. First, we constructed a monthly NDVI time-series to classify various types of salt marsh and then we tested the possibility of using compressed time-series continuously, to broaden the applicability of this particular approach. Our principal findings are as follows: (1) the overall accuracy of salt marsh mapping based on the monthly NDVI time-series was 90.3%, which was ∼16.0% higher than the single-phase classification strategy; (2) a compressed time-series, including NDVI from six key months (April, June-September, and November), demonstrated very little reduction (2.3%) in overall accuracy but led to obvious improvements in unstable regions; and (3) a simple rule for Spartina alterniflora identification was established using a scene solely from November, which may provide an effective way for regularly monitoring its distribution.

Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

PubMed Central

Chávez, Roberto O.; Clevers, Jan G. P. W.; Verbesselt, Jan; Naulin, Paulette I.; Herold, Martin

2014-01-01

Heliotropic leaf movement or leaf ‘solar tracking’ occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (ΔNDVImo-mi) and between winter and summer (ΔNDVIW-S). In this paper, we showed that the ΔNDVImo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ΔNDVIW-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ΔNDVImo-mi and ΔNDVIW-S. For an 11-year time series without rainfall events, Landsat ΔNDVIW-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ΔNDVImo-mi and ΔNDVIW-S have potential to detect early water stress of paraheliotropic vegetation. PMID:25188305

Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981-2012

NASA Astrophysics Data System (ADS)

Cao, Xiaoming; Feng, Yiming; Wang, Juanle

2017-06-01

This paper has developed a general Ts-NDVI triangle space with vegetation index time-series data from AVHRR and MODIS to monitor soil moisture in the Mongolian Plateau during 1981-2012, and studied the spatio-temporal variations of drought based on the temperature vegetation dryness index (TVDI). The results indicated that (1) the developed general Ts-NDVI space extracted from the AVHRR and MODIS remote sensing data would be an effective method to monitor regional drought, moreover, it would be more meaningful if the single time Ts-NDVI space showed an unstable condition; (2) the inverted TVDI was expected to reflect the water deficit in the study area. It was found to be in close negative agreement with precipitation and 10 cm soil moisture; (3) in the Mongolian Plateau, TVDI presented a zonal distribution with changes in land use/land cover types, vegetation cover and latitude. The soil moisture is low in bare land, construction land and grassland. During 1981-2012, drought was widely spread throughout the plateau, and aridification was obvious in the study period. Vegetation degradation, overgrazing, and climate warming could be considered as the main reasons.

Bridging Multiple Lines Of Evidence To Quantify Plant Phenology And Assess Links To Dryland Ecosystem Function

NASA Astrophysics Data System (ADS)

Browning, D. M.; Tweedie, C. E.; Vivoni, E. R.; Maynard, J. J.; Karl, J.

2015-12-01

The clear and pressing need to reliably identify and predict shifts in plant phenology at landscape scales requires a critical link between mechanistic understanding of climate drivers and broad scale forecasts of plant responses to climate change. A multi-scale phenology study co-located with two eddy covariance towers was initiated on the Jornada Basin LTER in New Mexico in 2010 to bridge phenology patterns at the plant level with those representing aggregated signals at the landscape level. The study integrates phenology observations collected in the field along with those collected via remotely using imagery from phenocams, unmanned aerial vehicles (UAVs), and satellite sensors along with estimates of carbon flux. We applied the Breaks for Additive Seasonal and Trend (BFAST) time series algorithm to MODIS 250-m NDVI greenness index values to partition the NDVI signal into components representing the long-term trend, seasonal periodicity, and residuals and identified significant shifts in the NDVI signal (i.e., "breaks"). Previous work verified breaks representing significant deviations from the BFAST seasonal and trend models using field-estimated plant biomass collected between 2000 and 2014. We subsequently examine estimates of fractional cover by functional group derived from UAV images acquired 2010 through 2015. At a mixed grassland site, the BFAST algorithm detected four breaks in the trend model denoting significant increases in NDVI in May 2004, July 2006, and March 2010 and a significant decrease in May 2012. The 2004 and 2006 breaks corresponded to herbaceous vegetation responses to rainfall following prolonged periods of drought. The 2012 decrease in NDVI corresponded to the marked reduction of herbaceous biomass following an exceptionally dry period in late 2010-2011. Seasonal breaks representing changes in the timing and magnitude of NDVI identified in July 2006 and September 2008 coincide with rapid increases in production of annual species in 2006 and perennial grasses in 2008. Combining extensive spatially-explicit UAV depictions of land surface characteristics and geographically constrained long-term plot data yielded compelling evidence to support ecologically meaningful interpretations of BFAST break points from MODIS NDVI in this water-limited grassland ecosystem.

Spatiotemporal changes of normalized difference vegetation index (NDVI) and response to climate extremes and ecological restoration in the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Zhao, Anzhou; Zhang, Anbing; Liu, Xianfeng; Cao, Sen

2018-04-01

Extreme drought, precipitation, and other extreme climatic events often have impacts on vegetation. Based on meteorological data from 52 stations in the Loess Plateau (LP) and a satellite-derived normalized difference vegetation index (NDVI) from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS3g) dataset, this study investigated the relationship between vegetation change and climatic extremes from 1982 to 2013. Our results showed that the vegetation coverage increased significantly, with a linear rate of 0.025/10a ( P < 0.001) from 1982 to 2013. As for the spatial distribution, NDVI revealed an increasing trend from the northwest to the southeast, with about 61.79% of the LP exhibiting a significant increasing trend ( P < 0.05). Some temperature extreme indices, including TMAXmean, TMINmean, TN90p, TNx, TX90p, and TXx, increased significantly at rates of 0.77 mm/10a, 0.52 °C/10a, 0.62 °C/10a, 0.80 °C/10a, 5.16 days/10a, and 0.65 °C/10a, respectively. On the other hand, other extreme temperature indices including TX10p and TN10p decreased significantly at rates of -2.77 days/10a and 4.57 days/10a ( P < 0.01), respectively. Correlation analysis showed that only TMINmean had a significant relationship with NDVI at the yearly time scale ( P < 0.05). At the monthly time scale, vegetation coverage and different vegetation types responded significantly positively to precipitation and temperature extremes (TMAXmean, TMINmean, TNx, TNn, TXn, and TXx) ( P < 0.01). All of the precipitation extremes and temperature extremes exhibited significant positive relationships with NDVI during the spring and autumn ( P < 0.01). However, the relationship between NDVI and RX1day, TMAXmean, TXn, and TXx was insignificant in summer. Vegetation exhibited a significant negative relationship with precipitation extremes in winter ( P < 0.05). In terms of human activity, our results indicate a strong correlation between the cumulative afforestation area and NDVI in Yan'an and Yulin during 1998-2013, r = 0.859 and 0.85, n = 16, P < 0.001.

Disaggregating and mapping crop statistics using hypertemporal remote sensing

NASA Astrophysics Data System (ADS)

Khan, M. R.; de Bie, C. A. J. M.; van Keulen, H.; Smaling, E. M. A.; Real, R.

2010-02-01

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998-2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered "small scale maps". These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.

Methods and Best Practice to Intercompare Dissolved Oxygen Sensors and Fluorometers/Turbidimeters for Oceanographic Applications.

PubMed

Pensieri, Sara; Bozzano, Roberto; Schiano, M Elisabetta; Ntoumas, Manolis; Potiris, Emmanouil; Frangoulis, Constantin; Podaras, Dimitrios; Petihakis, George

2016-05-17

In European seas, ocean monitoring strategies in terms of key parameters, space and time scale vary widely for a range of technical and economic reasons. Nonetheless, the growing interest in the ocean interior promotes the investigation of processes such as oxygen consumption, primary productivity and ocean acidity requiring that close attention is paid to the instruments in terms of measurement setup, configuration, calibration, maintenance procedures and quality assessment. To this aim, two separate hardware and software tools were developed in order to test and simultaneously intercompare several oxygen probes and fluorometers/turbidimeters, respectively in the same environmental conditions, with a configuration as close as possible to real in-situ deployment. The chamber designed to perform chlorophyll-a and turbidity tests allowed for the simultaneous acquisition of analogue and digital signals of several sensors at the same time, so it was sufficiently compact to be used in both laboratory and onboard vessels. Methodologies and best practice committed to the intercomparison of dissolved oxygen sensors and fluorometers/turbidimeters have been used, which aid in the promotion of interoperability to access key infrastructures, such as ocean observatories and calibration facilities. Results from laboratory tests as well as field tests in the Mediterranean Sea are presented.

Methods and Best Practice to Intercompare Dissolved Oxygen Sensors and Fluorometers/Turbidimeters for Oceanographic Applications

PubMed Central

Pensieri, Sara; Bozzano, Roberto; Schiano, M. Elisabetta; Ntoumas, Manolis; Potiris, Emmanouil; Frangoulis, Constantin; Podaras, Dimitrios; Petihakis, George

2016-01-01

In European seas, ocean monitoring strategies in terms of key parameters, space and time scale vary widely for a range of technical and economic reasons. Nonetheless, the growing interest in the ocean interior promotes the investigation of processes such as oxygen consumption, primary productivity and ocean acidity requiring that close attention is paid to the instruments in terms of measurement setup, configuration, calibration, maintenance procedures and quality assessment. To this aim, two separate hardware and software tools were developed in order to test and simultaneously intercompare several oxygen probes and fluorometers/turbidimeters, respectively in the same environmental conditions, with a configuration as close as possible to real in-situ deployment. The chamber designed to perform chlorophyll-a and turbidity tests allowed for the simultaneous acquisition of analogue and digital signals of several sensors at the same time, so it was sufficiently compact to be used in both laboratory and onboard vessels. Methodologies and best practice committed to the intercomparison of dissolved oxygen sensors and fluorometers/turbidimeters have been used, which aid in the promotion of interoperability to access key infrastructures, such as ocean observatories and calibration facilities. Results from laboratory tests as well as field tests in the Mediterranean Sea are presented. PMID:27196908

Comparison of Bioclimatic, NDVI and Elevation variables in assessing extent of Commiphora wightii (Arnt.) Bhand.

NASA Astrophysics Data System (ADS)

Kulloli, R. N.; Kumar, S.

2014-11-01

Commiphora wightii (Arnt.) Bhand., is an important medicinal plant of Indian Medicine System (IMS) since ancient time. It is used in different ailments of obesity, arthritis, rheumatism and high cholesterol. Due to overexploitation its natural populations declined to large extent. IUCN has put it under Data Deficient (DD) category due to lack of data on its extent of occurrence in nature. Hence, the study was carried out using MaxEnt distribution modelling algorithm to estimate its geographic distribution and to identify potential habitats for its reintroduction. For modelling employed 68 presence locality data, 19 bioclimatic variables, Normalize Difference Vegetation Index (NDVI) and elevation data. These were tested for multicollinearity and those variables having r-value less than 0.8 were selected for further analysis, which was carried out in two ways i) Bioclimatic variables and elevation; ii) NDVI and elevation. Area Under the Curve (AUC) in both analysis was above 0.9 for all variables, indicating very high accuracy of prediction. Variables governing distribution of C. wightii in the analysis using bioclimatic and elevation data set are precipitation seasonality (56.6 %), annual precipitation (16.4 %) and elevation (14.7 %). Extent of occurrence of C.wightii predicted by model closely matched in the districts of Jaisalmer and Barmer. In the second analysis elevation (48.3 %), NDVI of June (11.1 %) and August (11.2 %) contributed for NDVI and Elevation data set. NDVI of June corresponds to its leafing phase while NDVI of August to flowering phase. Area of its occurrence predicted for NDVI and elevation data set are Bikaner, Churu, Jhunjhunun some part of Jodhpur which are completely sandy, where C. wightii is totally absent. Extent of occurrence was also validated in ground survey. Potential areas for its reintroduction were identified as Jaisalmer and Barmer districts in Indian arid zone.

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

PubMed Central

Liu, Xiaojun; Ferguson, Richard B.; Zheng, Hengbiao; Cao, Qiang; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-01-01

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI=(1+e−15.2829×(RAGDDi−0.1944))−1−(1+e−11.6517×(RAGDDi−1.0267))−1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status. PMID:28338637

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).

PubMed

Liu, Xiaojun; Ferguson, Richard B; Zheng, Hengbiao; Cao, Qiang; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-03-24

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = ( 1 + e - 15.2829 × ( R A G D D i - 0.1944 ) ) - 1 - ( 1 + e - 11.6517 × ( R A G D D i - 1.0267 ) ) - 1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

Contribution of National near Real Time MODIS Forest Maximum Percentage NDVI Change Products to the U.S. ForWarn System

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William; Gasser, Gerald; Smoot, James; Kuper, Philip D.

2012-01-01

This presentation reviews the development, integration, and testing of Near Real Time (NRT) MODIS forest % maximum NDVI change products resident to the USDA Forest Service (USFS) ForWarn System. ForWarn is an Early Warning System (EWS) tool for detection and tracking of regionally evident forest change, which includes the U.S. Forest Change Assessment Viewer (FCAV) (a publically available on-line geospatial data viewer for visualizing and assessing the context of this apparent forest change). NASA Stennis Space Center (SSC) is working collaboratively with the USFS, ORNL, and USGS to contribute MODIS forest change products to ForWarn. These change products compare current NDVI derived from expedited eMODIS data, to historical NDVI products derived from MODIS MOD13 data. A new suite of forest change products are computed every 8 days and posted to the ForWarn system; this includes three different forest change products computed using three different historical baselines: 1) previous year; 2) previous three years; and 3) all previous years in the MODIS record going back to 2000. The change product inputs are maximum value NDVI that are composited across a 24 day interval and refreshed every 8 days so that resulting images for the conterminous U.S. are predominantly cloud-free yet still retain temporally relevant fresh information on changes in forest canopy greenness. These forest change products are computed at the native nominal resolution of the input reflectance bands at 231.66 meters, which equates to approx 5.4 hectares or 13.3 acres per pixel. The Time Series Product Tool, a MATLAB-based software package developed at NASA SSC, is used to temporally process, fuse, reduce noise, interpolate data voids, and re-aggregate the historical NDVI into 24 day composites, and then custom MATLAB scripts are used to temporally process the eMODIS NDVIs so that they are in synch with the historical NDVI products. Prior to posting, an in-house snow mask classification product is computed for the current compositing period and integrated into the change images to account for snow related NDVI drops. The supplemental snow classification product was needed because other available QA cloud/snow mask typically underestimates snow cover. MODIS true and false color composites were also computed from eMODIS reflectance data and the true color RGBs are also posted on ForWarn?s FCAV; this data is used for assessing apparent occasional quality issues on the change products due to residual unmasked cloud cover. New forest change products are posted with typical latencies of 1-2 days after the last input eMODIS data collection date for a given 24 day compositing period.

Livelihoods Poised Between Cold and Dry: Modeling Land Surface Phenologies and Phenometric Lapse Rates in Central Asian Highland Pastures

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Tomaszewska, M. A.; Krehbiel, C. P.; Kelgenbaeva, K.

2016-12-01

To explore the vulnerability of high-elevation communities in the Kyrgyz Republic and in Uzbekistan to changing climatic, sociodemographic, and socioeconomic conditions, we assembled image time series to characterize the condition of pastures near villages at high elevation (>2000 masl) and in remote pastures at higher elevations. Here we describe the application of the convex quadratic (CxQ) model of land surface phenology to highland pasturelands for selected oblasts in the Kyrgyz Republic and in eastern Uzbekistan. We used 16 years (2000-2015) of Landsat normalized difference vegetation index (NDVI) data with MODIS land surface temperature data processed into accumulated growing degree-days. The peak height of the NDVI and the thermal time to peak are two key phenological metrics derived analytically from the fitted parameter coefficients of the CxQ model for each pixel time series. Both exhibited sensitivity to elevation, which we describe in terms of phenometric lapse rates (PLRs). Interannual variation in PLRs was expressed differently for the peak NDVI and the thermal time to peak. Peak NDVI increased with elevation up to a point but also exhibited more spatial variation in dry years than in wetter years. Thermal time to peak exhibited strong, highly significant negative linear relationships to elevation with steeper slopes in drier years. Both types of PLRs were modulated by aspect. These relationships and the associated CxQ models by elevation and aspect can provide expectations against which to detect changes in pasture status as a result of management or weather.

The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR

USGS Publications Warehouse

Meyer, David; Verstraete, M.; Pinty, B.

1995-01-01

Since terrestrial surfaces are anisotropic, all spectral reflectance measurements obtained with a small instantaneous field of view instrument are specific to these angular conditions, and the value of the corresponding NDVI, computed from these bidirectional reflectances, is relative to the particular geometry of illumination and viewing at the time of the measurement. This paper documents the importance of these geometric effects through simulations of the AVHRR data acquisition process, and investigates the systematic biases that result from the combination of ecosystem-specific anisotropies with instrument-specific sampling capabilities. Typical errors in the value of NDVI are estimated, and strategies to reduce these effects are explored. -from Authors

Landsat Based Woody Vegetation Loss Detection in Queensland, Australia Using the Google Earth Engine

NASA Astrophysics Data System (ADS)

Johansen, K.; Phinn, S. R.; Taylor, M.

2014-12-01

Land clearing detection and woody Foliage Projective Cover (FPC) monitoring at the state and national level in Australia has mainly been undertaken by state governments and the Terrestrial Ecosystem Research Network (TERN) because of the considerable expense, expertise, sustained duration of activities and staffing levels needed. Only recently have services become available, providing low budget, generalized access to change detection tools suited to this task. The objective of this research was to examine if a globally available service, Google Earth Engine Beta, could be used to predict woody vegetation loss with accuracies approaching the methods used by TERN and the government of the state of Queensland, Australia. Two change detection approaches were investigated using Landsat Thematic Mapper time series and the Google Earth Engine Application Programming Interface: (1) CART and Random Forest classifiers; and (2) a normalized time series of Foliage Projective Cover (FPC) and NDVI combined with a spectral index. The CART and Random Forest classifiers produced high user's and producer's mapping accuracies of clearing (77-92% and 54-77%, respectively) when detecting change within epochs for which training data were available, but extrapolation to epochs without training data reduced the mapping accuracies. The use of FPC and NDVI time series provided a more robust approach for calculation of a clearing probability, as it did not rely on training data but instead on the difference of the normalized FPC / NDVI mean and standard deviation of a single year at the change point in relation to the remaining time series. However, the FPC and NDVI time series approach represented a trade-off between user's and producer's accuracies. Both change detection approaches explored in this research were sensitive to ephemeral greening and drying of the landscape. However, the developed normalized FPC and NDVI time series approach can be tuned to provide automated alerts for large woody vegetation clearing events by selecting suitable thresholds to identify very likely clearing. This research provides a comprehensive foundation to build further capacity to use globally accessible, free, online image datasets and processing tools to accurately detect woody vegetation clearing in an automated and rapid manner.

Trends in NDVI and tundra community composition in the Arctic of NE Alaska between 1984 and 2009

Treesearch

Robert R. Pattison; Janet C. Jorgenson; Martha K. Raynolds; Jeffery M. Welker

2015-01-01

As Arctic ecosystems experience increases in surface air temperatures, plot-level analyses of tundra vegetation composition suggest that there are important changes occurring in tundra communities that are typified by increases in shrubs and declines in non-vascular species. At the same time analyses of NDVI indicate that the Arctic tundra is greening. Few studies have...

Modeling and Performance Estimation for Airborne Minefield Detection System

DTIC Science & Technology

2008-05-01

Difference Vegetation Index ( NDVI ) NDVI is defined as: NDVI = (NIR – RED)/ (NIR + RED...to minimize the effect of variable irradiance levels. NDVI is always bounded between -1 and 1. A higher positive value of NDVI indicates the...lakes, and rivers) which has low reflectance in both NIR as well as visible bands, results in very low positive or slightly negative NDVI values

Rural cases of equine West Nile virus encephalomyelitis and the normalized difference vegetation index

USGS Publications Warehouse

Ward, M.P.; Ramsay, B.H.; Gallo, K.

2005-01-01

Data from an outbreak (August to October, 2002) of West Nile virus (WNV) encephalomyelitis in a population of horses located in northern Indiana was scanned for clusters in time and space. One significant (p = 0.04) cluster of case premises was detected, occurring between September 4 and 10 in the south-west part of the study area (85.70??N, 45.50??W). It included 10 case premises (3.67 case premises expected) within a radius of 2264 m. Image data were acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard a National Oceanic and Atmospheric Administration polar-orbiting satellite. The Normalized Difference Vegetation Index (NDVI) was calculated from visible and near-infrared data of daily observations, which were composited to produce a weekly-1km2 resolution raster image product. During the epidemic, a significant (p<0.01) decrease (0.025 per week) in estimated NDVI was observed at all case and control premise sites. The median estimated NDVI (0.659) for case premises within the cluster identified was significantly (p<0.01) greater than the median estimated NDVI for other case (0.571) and control (0.596) premises during the same period. The difference in median estimated NDVI for case premises within this cluster, compared to cases not included in this cluster, was greatest (5.3% and 5.1%, respectively) at 1 and 5 weeks preceding occurrence of the cluster. The NDVI may be useful for identifying foci of WNV transmission. ?? Mary Ann Liebert, Inc.

Measuring phenological variability from satellite imagery

USGS Publications Warehouse

Reed, Bradley C.; Brown, Jesslyn F.; Vanderzee, D.; Loveland, Thomas R.; Merchant, James W.; Ohlen, Donald O.

1994-01-01

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large- area land cover mapping and monitoring. The utility of re- motely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

[Responses of normalized difference vegetation index (NDVI) to precipitation changes on the grassland of Tibetan Plateau from 2000 to 2015.

PubMed

Wang, Zhi Peng; Zhang, Xian Zhou; He, Yong Tao; Li, Meng; Shi, Pei Li; Zu, Jia Xing; Niu, Ben

2018-01-01

Precipitation change is an important factor in the inter-annual variation of grassland growth on the Tibetan Plateau. The total amount, distribution pattern and concentration time are three basic characteristics of precipitation change. The temporal and spatial characteristics of precipitation change were analyzed based on climate data of 145 meteorological stations on the Tibetan Plateau and nearby areas from 2000 to 2015. The total precipitation amount was characterized by annual precipitation, distribution pattern of precipitation during the year was characterized by improved precipitation concentration index (PCI), and precipitation centroid (PC) was defined to indicate the change in precipitation concentrated time. To better illustrate the response of grassland to precipitation change, vegetation growth status was characterized by the maximum value of normalized difference vegetation index (NDVI max ). Results indicated that the annual precipitation and PCI had an apparent gradient across the whole plateau and the latest PC occurred in the southern plateau. NDVI max of alpine shrub grassland was significantly correlated with the change of PCI,increased with even distribution of precipitation during growth period, and limited by the total annual precipitation. Alpine meadow did not show significantly correlations with these three indices. The inter-annual variability of NDVI max of steppe was controlled by both PCI and PC. NDVI max of alpine desert grassland was mainly controlled by annual precipitation. In addition to annual total amount of precipitation, the distribution characteristics of precipitation should be further considered when the influence of precipitation change on different types of vegetation on the Qinghai Tibet Plateau was studied.

Potential for Expanding the Near Real Time ForWarn Regional Forest Monitoring System to Include Alaska

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Gasser, Gerald; Hargrove, William; Smoot, James; Kuper, Philip D.

2014-01-01

The on-line near real time (NRT) ForWarn system is currently deployed to monitor regional forest disturbances within the conterminous United States (CONUS), using daily MODIS Aqua and Terra NDVI data to derive monitoring products. The Healthy Forest Restoration Act of 2003 mandated such a system. Work on ForWarn began in 2006 with development and validation of retrospective MODIS NDVI-based forest monitoring products. Subsequently, NRT forest disturbance monitoring products were demonstrated, leading to the actual system deployment in 2010. ForWarn provides new CONUS forest disturbance monitoring products every 8 days, using USGS eMODIS data for current NDVI. ForWarn currently does not cover Alaska, which includes extensive forest lands at risk to multiple biotic and abiotic threats. This poster discusses a case study using Alaska eMODIS Terra data to derive ForWarn like forest change products during the 2010 growing season. The eMODIS system provides current MODIS Terra NDVI products for Alaska. Resulting forest change products were assessed with ground, aerial, and Landsat reference data. When cloud and snow free, these preliminary products appeared to capture regional forest disturbances from insect defoliation and fires; however, more work is needed to mitigate cloud and snow contamination, including integration of eMODIS Aqua data.

White Light Sagnac Interferometer for Snapshot Multispectral Imaging (Preprint)

DTIC Science & Technology

2009-01-01

normalized difference vegetation index ( NDVI ) provided in Fig. 12 (b). The NDVI is calculated by ( ) ( ) ( )( ) ( ) , ,2 , ,3 , , ,2 , ,3 I l n I l n NDVI ... NDVI . Conversely, if the leaf has little to no chlorophyll, order 3 will have nearly equal reflected energy compared to order 2, yielding a low NDVI ...This is observed in the NDVI image, where the upper left portion of the scene (quadrant 2) contains the unhealthy leaf, while the lower right region

Drought assessment using multi-sattelite remote sensing in Brazil

NASA Astrophysics Data System (ADS)

Rebello, V.; Getirana, A.; Rotunno Filho, O. C.; Lakshmi, V.

2016-12-01

In this study, we investigated long-term Terra-MODIS Normalized Difference Vegetation Index (NDVI) response to a recent drought period in Brazil's Southeast (SE) and Northeast (NE) regions between 2012 and 2015. An analysis of precipitation anomaly from 1979 to 2015 suggests a dry period over NE in 2012-2013 and SE in 2014-2015. Through EOF analysis it was possible to note that the first two modes account for 76% of variability and depict the vegetation seasonal cycle. Moreover, the time series of the respective modes show a deviation of NDVI in 2012 when both regions had negative precipitation anomaly. The other EOF modes show a negative trend from 2012 until 2015 mainly in northeastern Brazil in the semiarid region named Caatinga. In order to examine the influence of hydro-meterological variables on vegetation changes, the SVD technique was used to identify coupled patterns between NDVI and precipitation, soil moisture and evapotranspiration. SVD results showed that the highest correlations are achieved between NDVI and precipitation, 0.81 and 0.83, respectively in the first two modes. Although less correlated than precipitation, significant coupling between NDVI and evapotranspiration was found for the second and third modes, the correlation between their expanded coefficients was respectively 0.82 and 0.90.

Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates

NASA Technical Reports Server (NTRS)

Running, Steven W.; Nemani, Ramakrishna R.

1988-01-01

Weekly AVHRR Normalized Difference Vegetation Index (NDVI) values for 1983-1984 for seven sites of diverse climate in North America were correlated with results of an ecosystem simulation model of a hypothetical forest stand for the corresponding period at each site. The tendency of raw NDVI data to overpredict photosynthesis and transpiration on water limited sites was shown to be partially corrected by using an aridity index of annual radiation/annual precipitation. The results suggest that estimates of vegetation productivity using the global vegetation index are only accurate as annual integrations, unless unsubsampled local area coverage NDVI data can be tested against forest photosynthesis, transpiration and aboveground net primary production data measured at shorter time intervals.

Normalization of multidirectional red and NIR reflectances with the SAVI

NASA Technical Reports Server (NTRS)

Huete, A. R.; Hua, G.; Qi, J.; Chehbouni, A.; Van Leeuwen, W. J. D.

1992-01-01

Directional reflectance measurements were made over a semi-desert gramma grassland at various times of the growing season. View angle measurements from +40 to -40 degrees were made at various solar zenith angles and soil moisture conditions. The sensitivity of the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) to bidirectional measurements was assessed for purposes of improving remote temporal monitoring of vegetation dynamics. The SAVI view angle response was found to be symmetric about nadir while the NDVI response was strongly anisotropic. This enabled the view angle behavior of the SAVI to be normalized with a cosine function. In contrast to the NDVI, the SAVI was able to minimize soil moisture and shadow influences for all measurement conditions.

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

Monitoring Thermal Status of Ecosystems with MODIS Land-Surface Temperature and Vegetation Index Products

NASA Technical Reports Server (NTRS)

Wan, Zhengming

2002-01-01

The global land-surface temperature (LST) and normalized difference vegetation index (NDVI) products retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2001 were used in this study. The yearly peak values of NDVI data at 5km grids were used to define six NDVI peak zones from -0.2 to 1 in steps of 0.2, and the monthly NDVI values at each grid were sorted in decreasing order, resulting in 12 layers of NDVI images for each of the NDVI peak zones. The mean and standard deviation of daytime LSTs and day-night LST differences at the grids corresponding to the first layer of NDVI images characterize the thermal status of terrestrial ecosystems in the NDVI peak zones. For the ecosystems in the 0.8-1 NDVI peak zone, daytime LSTs distribute from 0-35 C and day-night LST differences distribute from -2 to 22 C. The daytime LSTs and day-night LST differences corresponding to the remaining layers of NDVI images show that the growth of vegetation is limited at low and high LSTs. LSTs and NDVI may be used to monitor photosynthetic activity and drought, as shown in their applications to a flood-irrigated grassland in California and an unirrigated grassland in Nevada.

Field test to intercompare carbon monoxide, nitric oxide and hydroxyl instrumentation at Wallops Island, Virginia

NASA Technical Reports Server (NTRS)

Gregory, Gerald L.; Beck, Sherwin M.; Bendura, Richard J.

1987-01-01

Documentation of the first of three instrument intercomparisons conducted as part of NASA Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation (GTE/CITE-1) is given. This ground-based intercomparison was conducted during July 1983 at NASA Wallops Flight Facility. Instruments intercompared included one laser system and three grab-sample approaches for CO; two chemiluminescent systems and one laser-induced fluorescent (LIF) technique for NO; and two different LIF systems and a radiochemical tracer technique for OH. The major objectives of this intercomparison was to intercompare ambient measurements of CO, NO, and OH at a common site by using techniques of fundamentally different detection principles and to identify any major biases among the techniques prior to intercomparison on an aircraft platform. Included in the report are comprehensive discussions of workshop requirements, philosophies, and operations as well as intercomparison analyses and results. In addition, the large body of nonintercomparison data incorporated into the workshop measurements is summarized. The report is an important source document for those interested in conducting similar large and complex intercomparison tests as well as those interested in using the data base for purposes other than instrument intercomparison.

[Dynamic changes in vegetation NDVI from 1982 to 2012 and its responses to climate change and human activities in Xinjiang, China].

PubMed

Du, Jia-qiang; Jiaerheng, Ahati; Zhao, Chenxi; Fang, Guang-ling; Yin, Jun-qi; Xiang, Bao; Yuan, Xin-jie; Fang, Shi-feng

2015-12-01

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. Therefore, it has significance to monitor the growth of vegetation. Based on AVHRR GIMMS NDVI and MODIS NDVI datasets, we analyzed the spatiotemporal patterns of change in NDVI and their linkage with climate change and human activity from 1982 to 2012 in the typical arid region, Xinjiang of northwestern China, at pixel and regional scales. At regional scale, although a statistically significant positive trend of growing season NDVI with a rate of 4.09 x 10⁻⁴· a⁻¹ was found during 1982-2012, there were two distinct periods with opposite trends in growing season NDVI before and after 1998, respectively. NDVI in growing season first significantly increased with a rate of 10 x 10⁻⁴· a⁻¹ from 1982 to 1998, and then decreased with a rate of -3 x 10⁻⁴· a⁻¹ from 1998 to 2012. The change in trend of NDVI from increase to decrease mainly occurred in summer, followed by autumn, and the reversal wasn't observed in spring. At pixel scale, the NDVI in farmland significantly increased; the NDVI changes in the growing season and all seasons showed polarization: Areas with significant change mostly increased in size as the NDVI record grown in length. The rate of increase in size of areas with significantly decreasing NDVI was larger than that with significantly increasing NDVI, which led to the NDVI increase obviously slowing down or stopping at regional scale. The vegetation growth in the study area was regulated by both climate change and human activity. Temperature was the most important driving factor in spring and autumn, whereas precipitation in summer. Extensive use of fertilizers and increased farmland irrigated area promoted the vegetation growth. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation might reduce spring NDVI in the part of farmlands, and the increase in stocking levels of livestock might lead to a decrease in NDVI in some grasslands.

Grazed grass was estimated via satellite images better then mowed grass

NASA Astrophysics Data System (ADS)

Koncz, Péter; Gubányi, András; Gecse, Bernadett; Tolnai, Márton; Pintér, Krisztina; Kertész, Péter; Fóti, Szilvia; Balogh, János; Nagy, Zoltán

2017-04-01

Precise livestock management requires objective alert system about the potential threats of overgrazing and intensive mowing. This kind of system could be based on the estimation of the amount of grazed and mowed biomass by remote sensing of vegetation indices. In our study we used the Normalized Difference Vegetation Index (NDVI) derived from Landsat 7 and 8 satellites to establish a regression between the vegetation index and the biomass (cut from ten, 40× 40 cm plots, during 52 measurement campaigns, 2011-2013) in a semi-arid grassland of Hungary, Bugac. Based on the regression time series of NDVI data were converted into biomass data in case of grazed and mowed areas (2011?2016). Biomass changes, inferred from NDVI data, were compared to the estimated grazed (based on daily dry matter uptake of cattle) and measured mowed (weighted) biomass. We found significant correlation between the NDVI and the total biomass (r2=0.6, p<0.05, n=52, RMSE=52.8 g m-2) and a stronger one between the NDVI and the green biomass (r2=0.75, p<0.05, n=52, RMSE=36.8 g m-2). We found that the amount of grazed biomass based on dry matter uptake was in close agreement with the biomass changes inferred from NDVI data (r2=0.42, p=0.11, n=7, RMSE=25.2 g m-2). However, there was no correlation between the biomass of the measured hay and the biomass inferred from NDVI data (r2=0.16, p=0.49, n=5, RMSE=67.4 g m-2). This was most probably due to the fact that mowing is a sudden, while grazing is a prolonged event, hence satellite data are less likely to be available before and after the mowing events (i.e. within days) compared to the grazing periods which usually lasts for months (only 12±2 satellite images were suitable per year). Therefore, NDVI changes are more accurately captured when grazing is observed than when mowing. We concluded that NDVI data from satellite images could be used to estimate the amount of grazed biomass, however to estimate the amount of mowed hay more frequent data coverage would be needed.

Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali

PubMed Central

Gaudart, Jean; Touré, Ousmane; Dessay, Nadine; Dicko, A lassane; Ranque, Stéphane; Forest, Loic; Demongeot, Jacques; Doumbo, Ogobara K

2009-01-01

Background The risk of Plasmodium falciparum infection is variable over space and time and this variability is related to environmental variability. Environmental factors affect the biological cycle of both vector and parasite. Despite this strong relationship, environmental effects have rarely been included in malaria transmission models. Remote sensing data on environment were incorporated into a temporal model of the transmission, to forecast the evolution of malaria epidemiology, in a locality of Sudanese savannah area. Methods A dynamic cohort was constituted in June 1996 and followed up until June 2001 in the locality of Bancoumana, Mali. The 15-day composite vegetation index (NDVI), issued from satellite imagery series (NOAA) from July 1981 to December 2006, was used as remote sensing data. The statistical relationship between NDVI and incidence of P. falciparum infection was assessed by ARIMA analysis. ROC analysis provided an NDVI value for the prediction of an increase in incidence of parasitaemia. Malaria transmission was modelled using an SIRS-type model, adapted to Bancoumana's data. Environmental factors influenced vector mortality and aggressiveness, as well as length of the gonotrophic cycle. NDVI observations from 1981 to 2001 were used for the simulation of the extrinsic variable of a hidden Markov chain model. Observations from 2002 to 2006 served as external validation. Results The seasonal pattern of P. falciparum incidence was significantly explained by NDVI, with a delay of 15 days (p = 0.001). An NDVI threshold of 0.361 (p = 0.007) provided a Diagnostic Odd Ratio (DOR) of 2.64 (CI95% [1.26;5.52]). The deterministic transmission model, with stochastic environmental factor, predicted an endemo-epidemic pattern of malaria infection. The incidences of parasitaemia were adequately modelled, using the observed NDVI as well as the NDVI simulations. Transmission pattern have been modelled and observed values were adequately predicted. The error parameters have shown the smallest values for a monthly model of environmental changes. Conclusion Remote-sensed data were coupled with field study data in order to drive a malaria transmission model. Several studies have shown that the NDVI presents significant correlations with climate variables, such as precipitations particularly in Sudanese savannah environments. Non-linear model combining environmental variables, predisposition factors and transmission pattern can be used for community level risk evaluation. PMID:19361335

Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali.

PubMed

Gaudart, Jean; Touré, Ousmane; Dessay, Nadine; Dicko, A Lassane; Ranque, Stéphane; Forest, Loic; Demongeot, Jacques; Doumbo, Ogobara K

2009-04-10

The risk of Plasmodium falciparum infection is variable over space and time and this variability is related to environmental variability. Environmental factors affect the biological cycle of both vector and parasite. Despite this strong relationship, environmental effects have rarely been included in malaria transmission models.Remote sensing data on environment were incorporated into a temporal model of the transmission, to forecast the evolution of malaria epidemiology, in a locality of Sudanese savannah area. A dynamic cohort was constituted in June 1996 and followed up until June 2001 in the locality of Bancoumana, Mali. The 15-day composite vegetation index (NDVI), issued from satellite imagery series (NOAA) from July 1981 to December 2006, was used as remote sensing data.The statistical relationship between NDVI and incidence of P. falciparum infection was assessed by ARIMA analysis. ROC analysis provided an NDVI value for the prediction of an increase in incidence of parasitaemia.Malaria transmission was modelled using an SIRS-type model, adapted to Bancoumana's data. Environmental factors influenced vector mortality and aggressiveness, as well as length of the gonotrophic cycle. NDVI observations from 1981 to 2001 were used for the simulation of the extrinsic variable of a hidden Markov chain model. Observations from 2002 to 2006 served as external validation. The seasonal pattern of P. falciparum incidence was significantly explained by NDVI, with a delay of 15 days (p = 0.001). An NDVI threshold of 0.361 (p = 0.007) provided a Diagnostic Odd Ratio (DOR) of 2.64 (CI95% [1.26;5.52]).The deterministic transmission model, with stochastic environmental factor, predicted an endemo-epidemic pattern of malaria infection. The incidences of parasitaemia were adequately modelled, using the observed NDVI as well as the NDVI simulations. Transmission pattern have been modelled and observed values were adequately predicted. The error parameters have shown the smallest values for a monthly model of environmental changes. Remote-sensed data were coupled with field study data in order to drive a malaria transmission model. Several studies have shown that the NDVI presents significant correlations with climate variables, such as precipitations particularly in Sudanese savannah environments. Non-linear model combining environmental variables, predisposition factors and transmission pattern can be used for community level risk evaluation.

Shifts in vegetation growth in response to multiple factors on the Mongolian Plateau from 1982 to 2011

NASA Astrophysics Data System (ADS)

Miao, Lijuan; Liu, Qiang; Fraser, Richard; He, Bin; Cui, Xuefeng

The Mongolian Plateau (MP) steppe is one of the largest steppe environments in the world. To monitor the terrestrial vegetation dynamics on the MP and to ascertain what the driving forces, this study examined the vegetation dynamics in Republic of Mongolia (M) and the Inner Mongolia Autonomous Region (IM) of China from the period 1982 to 2011, based on the satellite-derived GIMMS NDVI3g (Normalized Difference Vegetation Index) data across three biomes (desert, grassland and forest). The results are as followed: (1) Vegetation coverage in IM was generally greater than that in M. Before 2002, time series of NDVI over the MP increased at an average rate of 0.05% yr-1. Additionally, after 2002, the NDVI increased at a rate of 0.21% yr-1. From 1982 to 2011, the area of IM and M with positive anomalies in the NDVI increased at a separate rate of 1.82% yr-1 and 1.76% yr-1, respectively. (2) At the biome scale, the inter-annual forest NDVI variation in IM and desert NDVI for the entire MP had a significant increasing trend (0.06% yr-1 and 0.04% yr-1, respectively). (3) Climate forcing was a dominant controlling factor affecting the vegetation, and the anthropogenic behavior exhibited no significant value in the whole region. However, overgrazing was the most important reason for the regional degradation, particularly in IM. (4) In the future, the forest biome will go to recovery, whereas both the grassland and desert biomes are predicted to degrade continuously.

Monitoring phenology of photosynthesis in temperate evergreen and mixed deciduous forests using the normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) at leaf and canopy scales

NASA Astrophysics Data System (ADS)

Wong, C. Y.; Arain, M. A.; Ensminger, I.

2016-12-01

Evergreen conifers in boreal and temperate regions undergo strong seasonal changes in photoperiod and temperatures, which determines their phenology of high photosynthetic activity in the growing season and downregulation during the winter. Monitoring the timing of the transition between summer activity and winter downregulation in evergreens is difficult since this is a largely invisible process, unlike in deciduous trees that have a visible budding and a sequence of leaf unfolding in the spring and leaf abscission in the fall. The light-use efficiency (LUE) model estimates gross primary productivity (GPP) and may be parameterized using remotely sensed vegetation indices. Using spectral reflectance data, we derived the normalized difference vegetation index (NDVI), a measure of leaf "greenness", and the photochemical reflectance index (PRI), a proxy for chlorophyll:carotenoid ratios which is related to photosynthetic activity. To better understand the relationship between these vegetation indices and photosynthetic activity and to contrast this relationship between plant functional types, the phenology of NDVI, PRI and photosynthesis was monitored in an evergreen forest and a mixed deciduous forest at the leaf and canopy scale. Our data indicates that the LUE model can be parameterized by NDVI and PRI to track forest phenology. Differences in the sensitivity of PRI and NDVI will be discussed. These findings have implications to address the phenology of evergreen conifers by using PRI to complement NDVI in the LUE model, potentially improving model productivity estimates in northern hemisphere forests, that are dominated by conifers.

Landscape variability of vegetation change across the forest to tundra transition of central Canada

NASA Astrophysics Data System (ADS)

Bonney, Mitchell Thurston

Widespread vegetation productivity increases in tundra ecosystems and stagnation, or even productivity decreases, in boreal forest ecosystems have been detected from coarse-scale remote sensing observations over the last few decades. However, finer-scale Landsat studies have shown that these changes are heterogeneous and may be related to landscape and regional variability in climate, land cover, topography and moisture. In this study, a Landsat Normalized Difference Vegetation Index (NDVI) time-series (1984-2016) was examined for a study area spanning the entirety of the sub-Arctic boreal forest to Low Arctic tundra transition of central Canada (i.e., Yellowknife to the Arctic Ocean). NDVI trend analysis indicated that 27% of un-masked pixels in the study area exhibited a significant (p < 0.05) trend and virtually all (99.3%) of those pixels were greening. Greening pixels were most common in the northern tundra zone and the southern forest-tundra ecotone zone. NDVI trends were positive throughout the study area, but were smallest in the forest zone and largest in the northern tundra zone. These results were supported by ground validation, which found a strong relationship (R2 = 0.81) between bulk vegetation volume (BVV) and NDVI for non-tree functional groups in the North Slave region of Northwest Territories. Field observations indicate that alder (Alnus spp.) shrublands and open woodland sites with shrubby understories were most likely to exhibit greening in that area. Random Forest (RF) modelling of the relationship between NDVI trends and environmental variables found that the magnitude and direction of trends differed across the forest to tundra transition. Increased summer temperatures, shrubland and forest land cover, closer proximity to major drainage systems, longer distances from major lakes and lower elevations were generally more important and associated with larger positive NDVI trends. These findings indicate that the largest positive NDVI trends were primarily associated with the increased productivity of shrubby environments, especially at, and north of the forest-tundra ecotone in areas with more favorable growing conditions. Smaller and less significant NDVI trends in boreal forest environments south of the forest-tundra ecotone were likely associated with long-term recovery from fire disturbance rather than the variables analyzed here.

Using NOAA AVHRR data to assess flood damage in China.

PubMed

Wang, Quan; Watanabe, Masataka; Hayashi, Seiji; Murakami, Shogo

2003-03-01

The article used two NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) datasets to assess flood damage in the middle and lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetrate the clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, since water has a much lower NDVI value than other surface features. Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished from other types of ground cover by changes in the NDVI value before and after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 were selected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs, and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculated for cloud-free pixels. The difference in the NDVI values between these two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the area lost to the flood. The results show a total of 358,867 ha, with 207,556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321,000 and 197,000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensing data (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded region in the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.

Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers

NASA Astrophysics Data System (ADS)

Sulla-Menashe, Damien; Woodcock, Curtis E.; Friedl, Mark A.

2018-01-01

Recent studies have used satellite-derived normalized difference vegetation index (NDVI) time series to explore geographic patterns in boreal forest greening and browning. A number of these studies indicate that boreal forests are experiencing widespread browning, and have suggested that these patterns reflect decreases in forest productivity induced by climate change. Here we use NDVI time series from Landsat, which has much higher quality and spatial resolution than imagery used in most previous studies, to characterize biogeographic patterns in greening and browning across Canada’s boreal forest and to explore the drivers behind observed trends. Our results show that the majority of NDVI changes in Canada’s boreal forest reflect disturbance-recovery dynamics not climate change impacts, that greening and browning trends outside of disturbed forests are consistent with expected ecological responses to regional changes in climate, and that observed NDVI changes are geographically limited and relatively small in magnitude. By examining covariance between changes in NDVI and temperature and precipitation in locations not affected by disturbance, our results isolate and characterize the nature and magnitude of greening and browning directly associated with climate change. Consistent with biogeographic theory, greening and browning unrelated to disturbance tended to be located in ecotones near boundaries of the boreal forest bioclimatic envelope. We observed greening to be most prevalent in Eastern Canada, which is more humid, and browning to be most prevalent in Western Canada, where forests are more prone to moisture stress. We conclude that continued long-term climate change has the potential to significantly alter the character and function of Canada’s boreal forest, but recent changes have been modest and near-term impacts are likely to be focused in or near ecotones.

SACRA - global data sets of satellite-derived crop calendars for agricultural simulations: an estimation of a high-resolution crop calendar using satellite-sensed NDVI

NASA Astrophysics Data System (ADS)

Kotsuki, S.; Tanaka, K.

2015-01-01

To date, many studies have performed numerical estimations of food production and agricultural water demand to understand the present and future supply-demand relationship. A crop calendar (CC) is an essential input datum to estimate food production and agricultural water demand accurately with the numerical estimations. CC defines the date or month when farmers plant and harvest in cropland. This study aims to develop a new global data set of a satellite-derived crop calendar for agricultural simulations (SACRA) and reveal advantages and disadvantages of the satellite-derived CC compared to other global products. We estimate global CC at a spatial resolution of 5 min (≈10 km) using the satellite-sensed NDVI data, which corresponds well to vegetation growth and death on the land surface. We first demonstrate that SACRA shows similar spatial pattern in planting date compared to a census-based product. Moreover, SACRA reflects a variety of CC in the same administrative unit, since it uses high-resolution satellite data. However, a disadvantage is that the mixture of several crops in a grid is not considered in SACRA. We also address that the cultivation period of SACRA clearly corresponds to the time series of NDVI. Therefore, accuracy of SACRA depends on the accuracy of NDVI used for the CC estimation. Although SACRA shows different CC from a census-based product in some regions, multiple usages of the two products are useful to take into consideration the uncertainty of the CC. An advantage of SACRA compared to the census-based products is that SACRA provides not only planting/harvesting dates but also a peak date from the time series of NDVI data.

Analysis of smoke and cloud impact on seasonal and interannual variations in normalized difference vegetation index in Amazon

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Dye, D. G.

2004-12-01

Normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) is a unique measurement of long-term variations in global vegetation dynamics. The NDVI data have been used for the detection of the seasonal and interannual variations in vegetation. However, as reported in several studies, NDVI decreases with the increase in clouds and/or smoke aerosol contaminated in the pixels. This study assesses the smoke and clouds effect on long-term Global Inventory Modeling and Mapping Studies (GIMMS) and Pathfinder AVHRR Land (PAL) NDVI data in Amazon. This knowledge will help developing the correction method in the tropics in the future. To assess the smoke and cloud effects on GIMMS and PAL, we used another satellite-derived data sets; NDVI derived from SPOT/VEGETATION (VGT) data and Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS). Since April 1998, VGT has measured the earth surface globally including in Amazon. The advantage of the VGT is that it has blue channel where the smoke and cloud can be easily detected. By analyzing the VGT NDVI and comparing with the AVHRR-based NDVI, we inferred smoke and cloud effect on the AVHRR-based NDVI. From the results of the VGT analysis, we found the large NDVI seasonality in South and Southeastern Amazon. In these areas, the NDVI gradually increased from April to July and decreased from August to October. However the sufficient NDVI data were not existed from August to November when the smoke and cloud pixels were masked using blue reflectance. Thus it is said that the smoke and clouds mainly cause the large decreases in NDVI between August and November and NDVI has little vegetation signature in these months. Also we examined the interannual variations in NDVI and smoke aerosol. Then the decrease in NDVI is well consistent with the increase in the increase in AI. Our results suggest that the months between April and July are the most reliable season to monitor the vegetation.

Phenology-based Spartina alterniflora mapping in coastal wetland of the Yangtze Estuary using time series of GaoFen satellite no. 1 wide field of view imagery

NASA Astrophysics Data System (ADS)

Ai, Jinquan; Gao, Wei; Gao, Zhiqiang; Shi, Runhe; Zhang, Chao

2017-04-01

Spartina alterniflora is an aggressive invasive plant species that replaces native species, changes the structure and function of the ecosystem across coastal wetlands in China, and is thus a major conservation concern. Mapping the spread of its invasion is a necessary first step for the implementation of effective ecological management strategies. The performance of a phenology-based approach for S. alterniflora mapping is explored in the coastal wetland of the Yangtze Estuary using a time series of GaoFen satellite no. 1 wide field of view camera (GF-1 WFV) imagery. First, a time series of the normalized difference vegetation index (NDVI) was constructed to evaluate the phenology of S. alterniflora. Two phenological stages (the senescence stage from November to mid-December and the green-up stage from late April to May) were determined as important for S. alterniflora detection in the study area based on NDVI temporal profiles, spectral reflectance curves of S. alterniflora and its coexistent species, and field surveys. Three phenology feature sets representing three major phenology-based detection strategies were then compared to map S. alterniflora: (1) the single-date imagery acquired within the optimal phenological window, (2) the multitemporal imagery, including four images from the two important phenological windows, and (3) the monthly NDVI time series imagery. Support vector machines and maximum likelihood classifiers were applied on each phenology feature set at different training sample sizes. For all phenology feature sets, the overall results were produced consistently with high mapping accuracies under sufficient training samples sizes, although significantly improved classification accuracies (10%) were obtained when the monthly NDVI time series imagery was employed. The optimal single-date imagery had the lowest accuracies of all detection strategies. The multitemporal analysis demonstrated little reduction in the overall accuracy compared with the use of monthly NDVI time series imagery. These results show the importance of considering the phenological stage for image selection for mapping S. alterniflora using GF-1 WFV imagery. Furthermore, in light of the better tradeoff between the number of images and classification accuracy when using multitemporal GF-1 WFV imagery, we suggest using multitemporal imagery acquired at appropriate phenological windows for S. alterniflora mapping at regional scales.

A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States

USGS Publications Warehouse

Gu, Yingxin; Brown, Jesslyn F.; Verdin, J.P.; Wardlow, B.

2007-01-01

A five-year (2001–2005) history of moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data was analyzed for grassland drought assessment within the central United States, specifically for the Flint Hills of Kansas and Oklahoma. Initial results show strong relationships among NDVI, NDWI, and drought conditions. During the summer over the Tallgrass Prairie National Preserve, the average NDVI and NDWI were consistently lower (NDVI < 0.5 and NDWI < 0.3) under drought conditions than under non-drought conditions (NDVI>0.6 and NDWI>0.4). NDWI values exhibited a quicker response to drought conditions than NDVI. Analysis revealed that combining information from visible, near infrared, and short wave infrared channels improved sensitivity to drought severity. The proposed normalized difference drought index (NDDI) had a stronger response to summer drought conditions than a simple difference between NDVI and NDWI, and is therefore a more sensitive indicator of drought in grasslands than NDVI alone.

Development of an Optical Device to Investigatechlorophyll Content of Tomato Leaves

NASA Astrophysics Data System (ADS)

Cui, Di; Li, Minzan; Li, Xiuhua

Chlorophyll content is an important indication for evaluating crop growth status and predicting crop yield. The NDVI (Normalized Difference Vegetation Index) is commonly used as an indicator in practical crop healthy monitoring. Hence, a spectroscopy-based device for indirectly measuring crop growth conditions in terms of NDVI is developed. This device consists of four channels: two are designed to measure the intensity of the sunlight and the other two are used to measure the reflected light from the crop canopy at the same time. An electronic control unit was designed to control the sensing and data recording processes, as well as to calculate the NDVI based on the sensed data. The measurable two wavelengths are 610 nm and 1220 nm. A series validation tests, comparing the measurement result against spectroradiometer readings, are conducted to evaluate the performance of the device. Leaf samples are collected to measure chlorophyll contents in laboratory. The correlation coefficient between the NDVI readings from the developed device and the chlorophyll content data measured by the UV-VIS Spectrophotometer reaches 0.81, which shows that the device can be used in practical crop management.

Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data

PubMed Central

van Leeuwen, Willem J. D.

2008-01-01

This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs of wildfire affected sites and a nearby unburned reference site were chosen to measure the post-disturbance recovery in relation to climate variation. All site pairs were chosen in forested uplands in Arizona and were restricted to the area of the Rodeo-Chediski fire that occurred in 2002. Fuel reduction treatments were performed in 1999 and 2001. The inter-annual and seasonal vegetation dynamics before, during, and after wildfire events can be monitored using a time series of biweekly composited MODIS NDVI (Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index) data. Time series analysis methods included difference metrics, smoothing filters, and fitting functions that were applied to extract seasonal and inter-annual change and phenological metrics from the NDVI time series data from 2000 to 2007. Pre- and post-fire Landsat data were used to compute the Normalized Burn Ratio (NBR) and examine burn severity at the selected sites. The phenological metrics (pheno-metrics) included the timing and greenness (i.e. NDVI) for the start, peak and end of the growing season as well as proxy measures for the rate of green-up and senescence and the annual vegetation productivity. Pre-fire fuel reduction treatments resulted in lower fire severity, which reduced annual productivity much less than untreated areas within the Rodeo-Chediski fire perimeter. The seasonal metrics were shown to be useful for estimating the rate of post-fire disturbance recovery and the timing of phenological greenness phases. The use of satellite time series NDVI data and derived pheno-metrics show potential for tracking vegetation cover dynamics and successional changes in response to drought, wildfire disturbances, and forest restoration treatments in fire-suppressed forests. PMID:27879809

Predominant role of water in regulating the tree-growth response to diurnal asymmetric warmin

NASA Astrophysics Data System (ADS)

Chen, Z.; Xia, J.; Cui, E.

2017-12-01

Growth of the Northern Hemisphere trees is affected by diurnal asymmetric warming, which is generally considered to touch off carbon assimilation and increment of carbon storage. Asymmetric effects of diurnal warming on vegetation greenness were validated in previous researches, however, the effect of diurnal warming on wood tissue which stores most carbon of a whole plant is still unknown. Here, we combined ring-width index (RWI), remote sensing-based normalized difference vegetation index (NDVI) and climate datasets to detect the effects of daytime and night-time warming on vegetation growth, respectively. Our results indicate that daytime warming enhances NDVI but has neutral effect on tree woody growth over the Northern Hemisphere. Response of wood growth to daytime warming is linearly regulated by soil water availability. The underlying mechanism of different response of canopy and wood growth to daytime warming may attribute to the biomass change, that is, allocation to foliage tissues increased at the expense of wood tissue under warming and water-limited conditions. Night-time warming show neutral effects on NDVI and RWI over the Northern Hemisphere, and the neutral Tmin-NDVI correlations result from the non-linear mediation of soil water availability. Our results highlight the current greening trend under daytime warming does not mean higher carbon sink capacity, the warming-drying climate may impair the large carbon sink of global forests.

USAID Expands eMODIS Coverage for Famine Early Warning

NASA Astrophysics Data System (ADS)

Jenkerson, C.; Meyer, D. J.; Evenson, K.; Merritt, M.

2011-12-01

Food security in countries at risk is monitored by U.S. Agency for International Development (USAID) through its Famine Early Warning Systems Network (FEWS NET) using many methods including Moderate Resolution Imaging Spectroradiometer (MODIS) data processed by U.S. Geological Survey (USGS) into eMODIS Normalized Difference Vegetation Index (NDVI) products. Near-real time production is used comparatively with trends derived from the eMODIS archive to operationally monitor vegetation anomalies indicating threatened cropland and rangeland conditions. eMODIS production over Central America and the Caribbean (CAMCAR) began in 2009, and processes 10-day NDVI composites every 5 days from surface reflectance inputs produced using predicted spacecraft and climatology information at Land and Atmosphere Near real time Capability for Earth Observing Systems (EOS) (LANCE). These expedited eMODIS composites are backed by a parallel archive of precision-based NDVI calculated from surface reflectance data ordered through Level 1 and Atmosphere Archive and Distribution System (LAADS). Success in the CAMCAR region led to the recent expansion of eMODIS production to include Africa in 2010, and Central Asia in 2011. Near-real time 250-meter products are available for each region on the last day of an acquisition interval (generally before midnight) from an anonymous file transfer protocol (FTP) distribution site (ftp://emodisftp.cr.usgs.gov/eMODIS). The FTP site concurrently hosts the regional historical collections (2000 to present) which are also searchable using the USGS Earth Explorer (http://edcsns17.cr.usgs.gov/NewEarthExplorer). As eMODIS coverage continues to grow, these geographically gridded, georeferenced tagged image file format (GeoTIFF) NDVI composites increase their utility as effective tools for operational monitoring of near-real time vegetation data against historical trends.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Evaluating Climate Causation of Conflict in Darfur Using Multi-temporal, Multi-resolution Satellite Image Datasets With Novel Analyses

NASA Astrophysics Data System (ADS)

Brown, I.; Wennbom, M.

2013-12-01

Climate change, population growth and changes in traditional lifestyles have led to instabilities in traditional demarcations between neighboring ethic and religious groups in the Sahel region. This has resulted in a number of conflicts as groups resort to arms to settle disputes. Such disputes often centre on or are justified by competition for resources. The conflict in Darfur has been controversially explained by resource scarcity resulting from climate change. Here we analyse established methods of using satellite imagery to assess vegetation health in Darfur. Multi-decadal time series of observations are available using low spatial resolution visible-near infrared imagery. Typically normalized difference vegetation index (NDVI) analyses are produced to describe changes in vegetation ';greenness' or ';health'. Such approaches have been widely used to evaluate the long term development of vegetation in relation to climate variations across a wide range of environments from the Arctic to the Sahel. These datasets typically measure peak NDVI observed over a given interval and may introduce bias. It is furthermore unclear how the spatial organization of sparse vegetation may affect low resolution NDVI products. We develop and assess alternative measures of vegetation including descriptors of the growing season, wetness and resource availability. Expanding the range of parameters used in the analysis reduces our dependence on peak NDVI. Furthermore, these descriptors provide a better characterization of the growing season than the single NDVI measure. Using multi-sensor data we combine high temporal/moderate spatial resolution data with low temporal/high spatial resolution data to improve the spatial representativity of the observations and to provide improved spatial analysis of vegetation patterns. The approach places the high resolution observations in the NDVI context space using a longer time series of lower resolution imagery. The vegetation descriptors derived are evaluated using independent high spatial resolution datasets that reveal the pattern and health of vegetation at metre scales. We also use climate variables to support the interpretation of these data. We conclude that the spatio-temporal patterns in Darfur vegetation and climate datasets suggest that labelling the conflict a climate-change conflict is inaccurate and premature.

Can Dynamic Global Vegetation Models Reproduce Satellite Observed Extreme Browning and Greening Events in Vegetation Productivity?

NASA Astrophysics Data System (ADS)

van Eck, C. M.; Morfopoulos, C.; Betts, R. A.; Chang, J.; Ciais, P.; Friedlingstein, P.; Regnier, P. A. G.

2016-12-01

The frequency and severity of extreme climate events such as droughts, extreme precipitation and heatwaves are expected to increase in our changing climate. These extreme climate events will have an effect on vegetation either by enhanced or reduced productivity. Subsequently, this can have a substantial impact on the terrestrial carbon sink and thus the global carbon cycle, especially as extreme climate events are expected to increase in frequency and severity. Connecting observational datasets with modelling studies provides new insights into these climate-vegetation interactions. This study aims to compare extremes in vegetation productivity as derived from observations with that of Dynamic Global Vegetation Models (DGVMs). In this case GIMMS-NDVI 3g is selected as the observational dataset and both JULES (Joint UK Land Environment Simulator) and ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) as the DGVMs. Both models are forced with PGFv2 Global Meteorological Forcing Dataset according to the ISI-MIP2 protocol for historical runs. Extremes in vegetation productivity are the focal point, which are identified as NDVI anomalies below the 10th percentile or above the 90th percentile during the growing season, referred to as browning or greening events respectively. The monthly NDVI dataset GIMMS-NDVI 3g is used to obtain the location in time and space of the vegetation extremes. The global GIMMS-NDVI 3g dataset has been subdivided into IPCC's SREX-regions for which the NDVI anomalies are calculated and the extreme thresholds are determined. With this information we can identify the location in time and space of the browning and greening events in remotely-sensed vegetation productivity. The same procedure is applied to the modelled Gross Primary Productivity (GPP) allowing a comparison between the spatial and temporal occurrence of the browning and greening events in the observational dataset and the models' output. The capacity of the models to catch observed extremes in vegetation productivity is assessed and compared. Factors contributing to observed and modelled vegetation browning/greening extremes are analysed. The results of this study provide a stepping stone to modelling future extremes in vegetation productivity.

Characterising the Land Surface Phenology of Mediterranean Pinus species using the MODIS NDVI time series

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, Victor; Aragones, David; Navarro-Cerrillo, Rafael M.; Caparros-Santiago, Jose A.

2017-04-01

Land surface phenology (LSP) can improve the monitoring of forest areas and their change processes. The aim of this work is to characterize the temporal dynamics in Mediterranean Pinus forests. The different experiments were based on 679 mono-specific plots for the 5 native species in the Iberian Peninsula: P. sylvestris, P. pinea, P. halepensis, P. nigra and P. pinaster, which were obtained from the Third National Forest Inventory of Spain. The whole MODIS NDVI time series (2000-2016) were used to characterize the seasonal behavior of the pine forest. The following phenological parameters were extracted for each cycle from the smoothed time series: the day of beginning, end, middle and the length in days of season also base value, maximum value, amplitude and integrated value. Multi-temporal metrics were calculated to synthesize the inter-annual variability of the phenological parameters. An atypical behavior was detected for the years 2004 and 2011 and 2000, 2009 and 2015 for all Pinus species, matching wet and dry cycles, respectively. The inter and intra-species analysis of NDVI and LSP showed two different patterns: an important decreasing during the summer for those species such as P. halepensis, P. pinea y P. pinaster; and a lower NDVI variation among the year for P. sylvestris and P. nigra in certain areas. P. sylvestris had a phenological behavior different to P. pinea, P. halepensis and P. pinaster. P. nigra showed and heterogeneous intra-specific behaviour that might be associated to the existence of subspecies with different phenology.

[Winter wheat area estimation with MODIS-NDVI time series based on parcel].

PubMed

Li, Le; Zhang, Jin-shui; Zhu, Wen-quan; Hu, Tan-gao; Hou, Dong

2011-05-01

Several attributes of MODIS (moderate resolution imaging spectrometer) data, especially the short temporal intervals and the global coverage, provide an extremely efficient way to map cropland and monitor its seasonal change. However, the reliability of their measurement results is challenged because of the limited spatial resolution. The parcel data has clear geo-location and obvious boundary information of cropland. Also, the spectral differences and the complexity of mixed pixels are weak in parcels. All of these make that area estimation based on parcels presents more advantage than on pixels. In the present study, winter wheat area estimation based on MODIS-NDVI time series has been performed with the support of cultivated land parcel in Tongzhou, Beijing. In order to extract the regional winter wheat acreage, multiple regression methods were used to simulate the stable regression relationship between MODIS-NDVI time series data and TM samples in parcels. Through this way, the consistency of the extraction results from MODIS and TM can stably reach up to 96% when the amount of samples accounts for 15% of the whole area. The results shows that the use of parcel data can effectively improve the error in recognition results in MODIS-NDVI based multi-series data caused by the low spatial resolution. Therefore, with combination of moderate and low resolution data, the winter wheat area estimation became available in large-scale region which lacks completed medium resolution images or has images covered with clouds. Meanwhile, it carried out the preliminary experiments for other crop area estimation.

Phenological Impacts of Hurricane Katrina (2005) and Gustav (2008) on Louisiana Coastal Marshes

NASA Astrophysics Data System (ADS)

Mo, Y.; Kearney, M.; Riter, A.

2015-12-01

Coastal marshes provide indispensable ecological functions, such as offering habitat for economic fish and wildlife, improving water quality, protecting inland areas from floods, and stabilizing the shoreline. Hurricanes—though helping to maintain the elevation of coastal wetlands by depositing large amounts of sediments—pose one of the largest threats for coastal marshes in terms of eroding shorelines, scouring marsh surfaces, and resuspending sediments. Coastal marshes phenologies can be important for understanding broad response of marshes to stressors, like hurricanes. We investigated the phenological impacts of Katrina and Gustav (Category 3 and 2 hurricanes at landfall in southeast Louisiana on 29 August, 2005, and 1 September, 2008, respectively) on freshwater, intermediate, brackish, and saline marshes in southeastern Louisiana. Landsat-derived Normalized Difference Vegetation Index data were processed using ENVI 4.8. Phenological patterns of the marshes were modeled using a nonlinear mixed model using SAS 9.4. We created and compared marsh phenologies of 1994 and 2014, the reference years, to those of 2005 and 2008, the hurricane years. Preliminary results show that in normal years: (1) the NDVI of four marsh types peaked in July; (2) freshwater marshes had the highest peak NDVI, followed by intermediate, brackish, and saline marshes; and (3) the growth durations of the marshes are around three to six months. In 2005, the major phenological change was shortening of growth duration, which was most obvious for intermediate and brackish marshes. The peak NDVI values of the four marsh types were not affected because the hurricane occurred at the end of August, one month after the peak NDVI time. By comparison, there was no obvious phenological impact on the marshes by Gustav (2008) with respect to peak NDVI, peak NDVI day, and growth duration.

Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Tagestad, J. D.; Downs, J. L.; Murray, C. J.

2015-07-01

Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986-2011) of Landsat data for 400 km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans - the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p < 0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world.

Vegetation Cover Analysis in Shaanxi Province of China Based on Grid Pixel Ternd Analysis and Stability Evaluation

NASA Astrophysics Data System (ADS)

Yue, H.; Liu, Y.

2018-04-01

As a key factor affecting the biogeochemical cycle of human existence, terrestrial vegetation is vulnerable to natural environment and human activities, with obvious temporal and spatial characteristics. The change of vegetation cover will affect the ecological balance and environmental quality to a great extent. Therefore, the research on the causes and influencing factors of vegetation cover has become the focus of attention of scholars at home and abroad. In the evolution of human activities and natural environment, the vegetation coverage in Shaanxi has changed accordingly. Using MODIS/NDVI 2000-2014 time series data, using the method of raster pixel trend analysis, stability evaluation, rescaled range analysis and correlation analysis, the climatic factors in Shaanxi province were studied in the near 15 years vegetation spatial and temporal variation and influence of vegetation NDVI changes. The results show that NDVI in Shaanxi province in the near 15 years increased by 0.081, the increase of NDVI in Northern Shaanxi was obvious, and negative growth was found in some areas of Guanzhong, southern Shaanxi NDVI overall still maintained at a high level; the trend of vegetation change in Shaanxi province has obvious spatial differences, most of the province is a slight tendency to improve vegetation, there are many obvious improvement areas in Northern Shaanxi Province. Guanzhong area vegetation area decreased, the small range of variation of vegetation in Shaanxi province; the most stable areas are mainly concentrated in the southern, southern Yanan, Yulin, Xi'an area of Weinan changed greatly; Shaanxi Province in recent 15 a, the temperature and precipitation have shown an increasing trend, and the vegetation NDVI is more closely related to the average annual rainfall, with increase of 0.48 °C/10 years and 69.5 mm per year.

Optimal placement of off-stream water sources for ephemeral stream recovery

USGS Publications Warehouse

Rigge, Matthew B.; Smart, Alexander; Wylie, Bruce

2013-01-01

Uneven and/or inefficient livestock distribution is often a product of an inadequate number and distribution of watering points. Placement of off-stream water practices (OSWP) in pastures is a key consideration in rangeland management plans and is critical to achieving riparian recovery by improving grazing evenness, while improving livestock performance. Effective OSWP placement also minimizes the impacts of livestock use radiating from OSWP, known as the “piosphere.” The objective of this study was to provide land managers with recommendations for the optimum placement of OSWP. Specifically, we aimed to provide minimum offset distances of OSWP to streams and assess the effective range of OSWP using Normalized Difference Vegetation Index (NDVI) values, an indicator of live standing crop. NDVI values were determined from a time-series of Satellite Pour l'Observation de la Terre (SPOT) 20-m images of western South Dakota mixed-grass prairie. The NDVI values in ephemeral stream channels (in-channel) and uplands were extracted from pre- and post-OSWP images taken in 1989 and 2010, respectively. NDVI values were normalized to a reference imagine and subsequently by ecological site to produce nNDVI. Our results demonstrate a significant (P 2 = 0.49, P = 0.05) and increased with average distance to OSWP in a pasture (R2 = 0.43, P = 0.07). Piospheric reduction in nNDVI was observed within 200 m of OSWP, occasionally overlapping in-channel areas. The findings of this study suggest placement of OSWP 200 to 1 250 m from streams to achieve optimal results. These results can be used to increase grazing efficiency by effectively placing OSWP and insure that piospheres do not overlap ecologically important in-channel areas.

Analysis of postfire vegetation dynamics of Mediterranean shrub species based on terrestrial and NDVI data.

PubMed

Hernández-Clemente, Rocío; Cerrillo, R M Navarro; Hernández-Bermejo, J E; Royo, S Escuin; Kasimis, N A

2009-05-01

The present study offers an analysis of regeneration patterns and diversity dynamics after a wildfire, which occurred in 1993 and affected about 7000 ha in southern Spain. The aim of the work was to analyze the rule in the succession of shrub species after fire, relating it to the changes registered in the Normalized Difference Vegetation Index (NDVI). Fractional vegetation cover was recorded from permanent plots in 2000 and 2005. NDVI data related to each time were obtained from Landsat images. Both data sets, from fieldwork and remote sensing, were analyzed through statistical and quantitative analyses and then correlated. Results have permitted the description of the change in plant cover and species composition on a global and plot scale. It can be affirmed that, from the seventh to the twelfth year after the fire, the floristic composition within the burned area remained unchanged at a global level. However, on a smaller scale (plot level), the major shrub species, Ulex parviflorus, Rosmarinus officinalis, and Cistus clusii, underwent significant changes. The regeneration dynamics established by these species conditioned plant species composition and, consequently, diversity indexes such as Shannon (H) and Simpson (D). The changes recorded in the NDVI values corresponding to the surveyed plots were highly correlated with those found in the regrowth of the main species. Areas dominated by U. parviflorus in a senile phase were related to a decrease in NDVI values and an increase in the number of species. This result describes the successional dynamics; the dryness of the main colonizer shrub species is allowing the regrowth and re-establishment of other species. Within the study area, NDVI shows sensitivity to postfire plant cover changes and indirectly expresses the diversity dynamics.

Historical extension of operational NDVI products for livestock insurance in Kenya

NASA Astrophysics Data System (ADS)

Vrieling, Anton; Meroni, Michele; Shee, Apurba; Mude, Andrew G.; Woodard, Joshua; de Bie, C. A. J. M. (Kees); Rembold, Felix

2014-05-01

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981-2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R2 statistics (leave-one-year-out) for the overlapping period 2002-2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index.

Analysis of vegetation condition and its relationship with meteorological variables in the Yarlung Zangbo River Basin of China

NASA Astrophysics Data System (ADS)

Han, Xianming; Zuo, Depeng; Xu, Zongxue; Cai, Siyang; Gao, Xiaoxi

2018-06-01

The Yarlung Zangbo River Basin is located in the southwest border of China, which is of great significance to the socioeconomic development and ecological environment of Southwest China. Normalized Difference Vegetation Index (NDVI) is an important index for investigating the change of vegetation cover, which is widely used as the representation value of vegetation cover. In this study, the NDVI is adopted to explore the vegetation condition in the Yarlung Zangbo River Basin during the recent 17 years, and the relationship between NDVI and meteorological variables has also been discussed. The results show that the annual maximum value of NDVI usually appears from July to September, in which August occupies a large proportion. The minimum value of NDVI appears from January to March, in which February takes up most of the percentage. The higher values of NDVI are generally located in the lower elevation area. When the altitude is higher than 3250 m, NDVI began to decline gradually, and the NDVI became gradual stabilization as the elevation is up to 6000 m. The correlation coefficient between NDVI and precipitation in the Yarlung Zangbo River Basin is greater than that with temperature. The Hurst index of the whole basin is 0.51, indicating that the NDVI of the Yarlung Zangbo River Basin shows a weak sustainability.

Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A.

PubMed Central

Sharma, Lakesh K.; Bu, Honggang; Denton, Anne; Franzen, David W.

2015-01-01

Active-optical sensor readings from an N non-limiting area standard established within a farm field are used to predict yield in the standard. Lower yield predictions from sensor readings obtained from other parts of the field outside of the N non-limiting standard area indicate a need for supplemental N. Active-optical sensor algorithms for predicting corn (Zea mays, L.) yield to direct in-season nitrogen (N) fertilization in corn utilize red NDVI (normalized differential vegetative index). Use of red edge NDVI might improve corn yield prediction at later growth stages when corn leaves cover the inter-row space resulting in “saturation” of red NDVI readings. The purpose of this study was to determine whether the use of red edge NDVI in two active-optical sensors (GreenSeeker™ and Holland Scientific Crop Circle™) improved corn yield prediction. Nitrogen rate experiments were established at 15 sites in North Dakota (ND). Sensor readings were conducted at V6 and V12 corn. Red NDVI and red edge NDVI were similar in the relationship of readings with yield at V6. At V12, the red edge NDVI was superior to the red NDVI in most comparisons, indicating that it would be most useful in developing late-season N application algorithms. PMID:26540057

Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A.

PubMed

Sharma, Lakesh K; Bu, Honggang; Denton, Anne; Franzen, David W

2015-11-02

Active-optical sensor readings from an N non-limiting area standard established within a farm field are used to predict yield in the standard. Lower yield predictions from sensor readings obtained from other parts of the field outside of the N non-limiting standard area indicate a need for supplemental N. Active-optical sensor algorithms for predicting corn (Zea mays, L.) yield to direct in-season nitrogen (N) fertilization in corn utilize red NDVI (normalized differential vegetative index). Use of red edge NDVI might improve corn yield prediction at later growth stages when corn leaves cover the inter-row space resulting in "saturation" of red NDVI readings. The purpose of this study was to determine whether the use of red edge NDVI in two active-optical sensors (GreenSeeker™ and Holland Scientific Crop Circle™) improved corn yield prediction. Nitrogen rate experiments were established at 15 sites in North Dakota (ND). Sensor readings were conducted at V6 and V12 corn. Red NDVI and red edge NDVI were similar in the relationship of readings with yield at V6. At V12, the red edge NDVI was superior to the red NDVI in most comparisons, indicating that it would be most useful in developing late-season N application algorithms.

A Five-Year Analysis of MODIS NDVI and NDWI for Rangeland Drought Assessment: Preliminary Results

NASA Astrophysics Data System (ADS)

Gu, Y.; Brown, J. F.; Verdin, J. P.; Wardlow, B.

2006-12-01

Drought is one of the most costly natural disasters in the United States. Traditionally, drought monitoring has been based on weather station observations, which lack the continuous spatial coverage needed to adequately characterize and monitor detailed spatial patterns of drought conditions. Satellite remote sensing observations can provide a synoptic view of the land and provide a spatial context for measuring drought. A common satellite-based index, the normalized difference vegetation index (NDVI) has a 30-year history of use for vegetation condition monitoring. NDVI is calculated from the visible red and near infrared channels and measures the changes in chlorophyll absorption and reflection in the spongy mesophyll of the vegetation canopy that are reflected in these respective bands. The normalized difference water index (NDWI) is another index, derived from the near-infrared and short wave infrared channels, and reflects changes in both the water content and spongy mesophyll in the vegetation canopy. As a result, the NDWI is influenced by both desiccation and wilting in the vegetation canopy and may be a more sensitive indicator than the NDVI for large- area drought monitoring. The objective of this study was to process and evaluate a 5-year history of 500-meter NDVI and NDWI data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and to investigate methods for measuring and monitoring drought in rangeland over the southern plains of the United States. This initial study included: (1) the development of a climatological database for MODIS NDVI and NDWI, (2) a study of the relationship between the NDVI, NDWI, and drought condition over rangeland, (3) the development of a method to provide threshold NDVI/NDWI values under drought conditions based on the 5-year NDVI/NDWI/drought condition analysis, and (4) the investigation of additional vegetation drought information provided by the NDWI versus the NDVI in a 5-year comparison of the two indices. The MODIS data were obtained from the Land Processes Distributed Active Archive System. Results show strong relationships among NDVI, NDWI, and drought analyzed over grasslands in the Flint Hills region of Kansas and Oklahoma. During the summer months, the average NDVI and NDWI values were consistently lower (NDVI<0.5 and NDWI<0.3) for the tallgrass prairie under drought conditions than under normal climate conditions (NDVI>0.6 and NDWI>0.4). The distinctions between drought conditions and normal climate conditions are based on the historic U.S. Drought Monitor maps and the historic Palmer index data. To take advantage of information contained in both indices, we calculated the difference between NDVI and NDWI (NDVI-NDWI). The difference between NDVI and NDWI slightly increases during the summer drought condition. Based on these analyses, the NDWI appears to be more sensitive than NDVI to drought conditions. The results of statistical analysis of the relationships among these indices will be presented in the poster.

Monitoring Springs in the Mojave Desert Using Landsat Time Series Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2018-01-01

The purpose of this study, based on Landsat satellite data was to characterize variations and trends over 30 consecutive years (1985-2016) in perennial vegetation green cover at over 400 confirmed Mojave Desert spring locations. These springs were surveyed between in 2015 and 2016 on lands managed in California by the U.S. Bureau of Land Management (BLM) and on several land trusts within the Barstow, Needles, and Ridgecrest BLM Field Offices. The normalized difference vegetation index (NDVI) from July Landsat images was computed at each spring location and a trend model was first fit to the multi-year NDVI time series using least squares linear regression.Â

Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets

NASA Astrophysics Data System (ADS)

Osunmadewa, Babatunde Adeniyi; Gebrehiwot, Worku Zewdie; Csaplovics, Elmar; Adeofun, Olabinjo Clement

2018-03-01

Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover types in four selected locations. A significant increase in overall greenness (amplitude 0) and a significant decrease in other greenness trend maps (amplitude 1 and phase 1) was observed over the study period. Moreover significant positive trends in overall annual rainfall (amplitude 0) was found which follows similar pattern with vegetation trend. Variation in the timing of peak of greenness (phase 1) was seen in the four selected locations, this indicate a change in phenological trend. Additionally, strong relationship was revealed by the result of the pixel-wise regression between NDVI and rainfall. Change in vegetation phenology in the study area is attributed to climatic variability than anthropogenic activities.

Monitoring Regional Forest Disturbances across the US with Near Real Time MODIS NDVI Products included in the ForWarn Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William W.; Gasser, Gerald; Norman, Steve

2013-01-01

U.S. forests occupy approx.1/3 of total land area (approx. 304 million ha). Since 2000, a growing number of regionally evident forest disturbances have occurred due to abiotic and biotic agents. Regional forest disturbances can threaten human life and property, bio-diversity and water supplies. Timely regional forest disturbance monitoring products are needed to aid forest health management work. Near Real Time (NRT) twice daily MODIS NDVI data provide a means to monitor U.S. regional forest disturbances every 8 days. Since 2010, these NRT forest change products have been produced and posted on the US Forest Service ForWarn Early Warning System for Forest Threats.

Spectral modelling of multicomponent landscapes in the Sahel

NASA Technical Reports Server (NTRS)

Hanan, N. P.; Prince, S. D.; Hiernaux, P. H. Y.

1991-01-01

Simple additive models are used to examine the infuence of differing soil types on the spatial average spectral reflectance and normalized difference vegetation index (NDVI). The spatial average NDVI is shown to be a function of the brightness (red plus near-infrared reflectances), the NDVI, and the fractional cover of the components. In landscapes where soil and vegetation can be considered the only components, the NDVI-brightness model can be inverted to obtain the NDVI of vegetation. The red and near-infrared component reflectances of soil and vegetation are determined on the basis of aerial photoradiometer data from Mali. The relationship between the vegetation component NDVI and plant cover is found to be better than between the NDVI of the entire landscape and plant cover. It is concluded that the usefulness of this modeling approach depends on the existence of clearly distinguishable landscape components.

Using remotely sensed indices of primary productivity to evaluate large mammal abundance and movement in the arid Southwestern United States

NASA Astrophysics Data System (ADS)

Stoner, D.; Edwards, T.; Sexton, J. O.; Nagol, J.; Sims, A.; Ironside, K.; Choate, D.; Longshore, K.; Anand, A.; Mattson, D.

2013-12-01

Southwestern ecoregions are marked by topographic and climatic variability, which facilitates the coexistence of large herbivores exploiting different dietary niches. Mountain ungulates buffer this variability through physiological and behavioral adaptations such as fat accumulation and seasonal migrations. However, projected climatic shifts imply changes in vegetation biomass and phenology, and therefore mammalian distributions. Here we evaluate how the distribution of primary productivity and phenological rhythms influence abundance and seasonal movements of three widely distributed ungulate species and their principal predator. We used spatio-temporal patterns in the Normalized Difference Vegetation Index (NDVI) derived from MODerate-resolution Imaging Spectrometer (MODIS) measurements at 250-m, daily resolution to explain spatial variability in the abundance of mule deer, elk, and bighorn sheep. Ungulate population response to NDVI was assessed using annual survey data collected by state wildlife agencies with jurisdiction on and around the Colorado Plateau ecoregion. We used NDVI-ungulate relationships to evaluate the spatial requirements and potential densities of cougars; a predator whose diet, density, and distribution is closely tied to these species. Cougar location data were combined from nine radio-telemetry studies conducted over a range of climatic conditions. Focal ungulates demonstrated differing responses to patterns in NDVI. Mule deer abundance corresponded to the timing of green-up (late spring), elk abundance correlated best with peak green biomass (July-Aug), and bighorn showed no relationship to NDVI. Seasonal movements also differed, with deer migrating between distinct summer and winter ranges; bighorn residing on annual ranges, and elk demonstrating a mixed pattern of residency and migration. Cougar movements did not correspond to phenology per se, but home range size and diet diversity varied inversely with NDVI. Projected shifts in the timing and amount of precipitation suggest three considerations for large mammal conservation in the Southwest. First, being tied to June NDVI, mule deer distribution is likely to track areas defined by relatively early growing seasons, whereas elk abundance is likely to increase in response to enhanced summer precipitation. Second, in mesic environments bighorn sheep are both poor competitors and susceptible to predation. To the extent that bighorn sheep are forced to share ranges with deer or elk, they may be adversely affected by changing climate. Lastly, shifts in ungulate abundance may lead cougars to switch prey in some localities, or contract from the drier portions of their current range as energetic costs rise beyond threshold values.

Performance of Vegetation Indices for Wheat Yield Forecasting for Punjab, Pakistan

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Becker-Reshef, I.; Adusei, B.; Barker, B.

2013-12-01

Forecasting wheat yield in major producer countries early in the growing season allows better planning for harvest deficits and surplus with implications for food security, world market transactions, sustaining adequate grain stocks, policy making and other matters. Remote sensing imagery is well suited for yield forecasting over large areas. The Normalized Difference Vegetation Index (NDVI) has been the most-used spectral index derived from remote sensing imagery for assessing crop condition of major crops and forecasting crop yield. Many authors have found that the highest correlation between NDVI and yield of wheat crops occurs at the height of the growing season when NDVI values and photosynthetic activity of the wheat plants are at their relative maximum. At the same time NDVI saturates in very dense and vigorous (healthy, green) canopies such as wheat fields during the seasonal peak and shows significantly reduced sensitivity to further increases in photosynthetic activity. In this study we compare the performance of different vegetation indices derived from space-borne red and near-infrared spectral reflectance measurements for wheat yield forecasting in the Punjab Province, Pakistan. Areas covered by wheat crop each year were determined using a time series of MODIS 8-day composites at 250 m resolution converted to temporal metrics and classified using a bagged decision tree approach, driven by classified multi-temporal Landsat scenes. Within the wheat areas we analyze and compare wheat yield forecasts derived from three different satellite-based vegetation indices at the peak of the growing season. We regressed in turn NDVI, Wide Dynamic Range Vegetation Index (WDRVI) and the Vegetation Condition Index (VCI) from the four years preceding the wheat growing season 2011/12 against reported yield values and applied the regression equations to forecast wheat yield for the 2011/12 season per district for each of 36 Punjab districts. Yield forecasts overall corresponded well with reported values. NDVI-based forecasts showed high correlations of r squared = 0.881 and RMSE 11%. The VCI performed similarly well with r squared = 0.886 and RMSE 11%. WDRVI performed better than either of the other indices with r squared = 0.909 and RMSE 10%, probably due to the increased sensitivity of the index at high values. Wheat yields in Pakistan show on average a slow but steady annual increase but overall are comparatively stable due to the fact that the majority of fields are irrigated. The next steps in this study will be to compare NDVI- with WDRVI-based yield forecasts in other environments dominated by rain-fed agriculture, such as Ukraine, Australia and the United States.

Automatic Target Recognition for Hyperspectral Imagery

DTIC Science & Technology

2012-03-01

representation, b) NDVI representation .... 13 Figure 6. Vegetation Reflectance Spectra, taken directly from (Eismann, 2011) ........... 15 Figure 7...46 Figure 22. Example NDVI Mean and Shade Spectrum Signatures ................................. 47 Figure 23. Example Average...locate vegetation within an image normalized-difference vegetation index ( NDVI ) is applied. NDVI was first introduced by Rouse et al. while monitoring

Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China

NASA Astrophysics Data System (ADS)

Yao, Junqiang; Chen, Yaning; Zhao, Yong; Mao, Weiyi; Xu, Xinbing; Liu, Yang; Yang, Qing

2018-02-01

Observed data showed the climatic transition from warm-dry to warm-wet in Xinjiang during the past 30 years and will probably affect vegetation dynamics. Here, we analyze the interannual change of vegetation index based on the satellite-derived normalized difference vegetation index (NDVI) with temperature and precipitation extreme over the Xinjiang, using the 8-km NDVI third-generation (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) from 1982 to 2010. Few previous studies analyzed the link between climate extremes and vegetation response. From the satellite-based results, annual NDVI significantly increased in the first two decades (1981-1998) and then decreased after 1998. We show that the NDVI decrease over the past decade may conjointly be triggered by the increases of temperature and precipitation extremes. The correlation analyses demonstrated that the trends of NDVI was close to the trend of extreme precipitation; that is, consecutive dry days (CDD) and torrential rainfall days (R24) positively correlated with NDVI during 1998-2010. For the temperature extreme, while the decreases of NDVI correlate positively with warmer mean minimum temperature ( Tnav), it correlates negatively with the number of warmest night days ( Rwn). The results suggest that the climatic extremes have possible negative effects on the ecosystem.

NDVI saturation adjustment: a new approach for improving cropland performance estimates in the Greater Platte River Basin, USA

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.; Howard, Daniel M.; Phuyal, Khem P.; Ji, Lei

2013-01-01

In this study, we developed a new approach that adjusted normalized difference vegetation index (NDVI) pixel values that were near saturation to better characterize the cropland performance (CP) in the Greater Platte River Basin (GPRB), USA. The relationship between NDVI and the ratio vegetation index (RVI) at high NDVI values was investigated, and an empirical equation for estimating saturation-adjusted NDVI (NDVIsat_adjust) based on RVI was developed. A 10-year (2000–2009) NDVIsat_adjust data set was developed using 250-m 7-day composite historical eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. The growing season averaged NDVI (GSN), which is a proxy for ecosystem performance, was estimated and long-term NDVI non-saturation- and saturation-adjusted cropland performance (CPnon_sat_adjust, CPsat_adjust) maps were produced over the GPRB. The final CP maps were validated using National Agricultural Statistics Service (NASS) crop yield data. The relationship between CPsat_adjust and the NASS average corn yield data (r = 0.78, 113 samples) is stronger than the relationship between CPnon_sat_adjust and the NASS average corn yield data (r = 0.67, 113 samples), indicating that the new CPsat_adjust map reduces the NDVI saturation effects and is in good agreement with the corn yield ground observations. Results demonstrate that the NDVI saturation adjustment approach improves the quality of the original GSN map and better depicts the actual vegetation conditions of the GPRB cropland systems.

[Variability of vegetation growth season in different latitudinal zones of North China: a monitoring by NOAA NDVI and MSAVI].

PubMed

Wang, Hong; Li, Xiaobing; Han, Ruibo; Ge, Yongqin

2006-12-01

In this study, North China was latitudinally divided into five zones, i.e., 32 degrees - 36 degrees N (Zone I), 36 degrees - 40 degrees N (Zone II), 40 degrees - 44 degrees N (Zone III), 44 degrees - 48 degrees N (Zone IV) and 48 degrees - 52 degrees N (Zone V), and the NOAA/ AVHRR NDVI and MSAVI time-series images from 1982 to 1999 were smoothed with Savitzky-Golay filter algorithm. Based on the EOF analysis, the principal components of NDVI and MSAVI for the vegetations in different latitudinal zones of North China were extracted, the annual beginning and ending dates and the length of growth season in 1982 - 1999 were estimated, and the related parameters were linearly fitted, aimed to analyze the variability of vegetation growth season. The results showed that the beginning date of the growth season in different zones tended to be advanced, while the ending date tended to be postponed with increasing latitude. The length of the growth season was also prolonged, with the prolonging time exceeded 10 days.

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

PubMed

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

Trends in the normalized difference vegetation index (NDVI) associated with urban development in arctic and subarctic Western Siberia

NASA Astrophysics Data System (ADS)

Outten, S.; Miles, V.; Ezau, I.

2015-12-01

Changes in normalized difference vegetation index (NDVI) in the high Arctic have been reliably documented, with widespread "greening" (increase in NDVI), specifically along the northern rim of Eurasia and Alaska. Whereas in West Siberia south of 65N, widespread "browning" (decrease in NDVI) has been noted, although the causes remain largely unclear. In this study we report results of statistical analysis of the spatial and temporal changes in NDVI around 28 major urban areas in the arctic and subarctic Western Siberia. Exploration and exploitation of oil and gas reserves has led to rapid industrialization and urban development in the region. This development has significant impact on the environment and particularly in the vegetation cover in and around the urbanized areas. The analysis is based on 15 years (2000-2014) of high-resolution (250 m) Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired for summer months (June through August) over the entire arctic and subarctic Western Siberian region. The analysis shows that the NDVI background trends are generally in agreement with the trends reported in previous coarse-resolution NDVI studies. Our study reveals greening over the arctic (tundra and tundra-forest) part of the region. Simultaneously, the southern (boreal taiga forest) part is browning, with the more densely vegetation areas or areas with highest NDVI, particularly along Ob River showing strong negative trend. The unexpected and interesting finding of the study is statistically robust indication of the accelerated increase of NDVI ("greening") in the older urban areas. Many Siberian cities become greener even against the decrease in the NDVI background. Moreover, interannual variations of urban NDVI are not coherent with the NDVI background variability. We also find that in tundra zones, NDVI values are higher in a 5-10 km buffer zone around the city edge than in rural areas (40 km distance from the city edge), and in taiga in a 5-10 km buffer zone NDVI value are lower compare with rural zone. We speculate that the observed "greening" or "browning" around urban areas could be caused by the vegetation cover response to the anthropogenic urban heat island (UHI) effect. The general regional trend is amplified in very close proximity to the urban areas, possibly due to UHI effect.

Limitations of Vegetation Indices For Detecting Pasture Degradation: A Case Study of Montane Pastoral Systems in Central Asia

NASA Astrophysics Data System (ADS)

Eddy, I. M. S.; Gergel, S. E.

2015-12-01

Grazing is the most extensive land use on Earth. Widespread consequences of overgrazing pastures include long-term decreases in plant biomass and limited recovery of vegetation. Remotely-sensed vegetation indices linked to biomass (e.g. NDVI) are routinely used to monitor pasture health over broad areas to track pasture degradation and recovery over time. Unfortunately, overgrazing can impact vegetation in various other ways not easily evaluated using satellite imagery, such as by altering species composition. Furthermore, the response of vegetation to grazing may be influenced by underlying terrain and topographic gradients. We examined multi-decadal trends in pasture condition in Kyrgyzstan, a country where pasture degradation is of serious concern. Using a chronosequence of Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery, we compared fifteen-year trends in NDVI with contemporary field-based measurements of pasture health in thirty 1-km 2 sites. Multivariate regression was used to discern the relationship between long-term NDVI trends and pasture health in pastures of differing terrain (areas of varying topographic wetness index and solar insolation). Preliminary results suggest that pasture degradation can be correlated with either positive or negative changes in NDVI depending upon the topographic position of the pasture. Furthermore, terrain characteristics explained a considerable portion of the observed variance in NDVI trends across the region. Improving our understanding of grazing impacts in montane systems is critical given their vulnerability to impending climate change.

Crop Surveillance Demonstration Using a Near-Daily MODIS Derived Vegetation Index Time Series

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Ryan, Robert E.; Blonski, Slawomir; Prados, Don

2005-01-01

Effective response to crop disease outbreaks requires rapid identification and diagnosis of an event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index (NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting diseases. NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent coarse-resolution data source for rapid, comprehensive surveillance of agricultural areas. A proof-of-concept wide area crop surveillance system using daily MODIS imagery was developed and tested on a set of San Joaquin cotton fields over a growing season. This area was chosen in part because excellent ground truth data were readily available. Preliminary results indicate that, at least in the southwestern part of the United States, near-daily NDVI products can be generated that show the natural variations in the crops as well as specific crop practices. Various filtering methods were evaluated and compared with standard MOD13 NDVI MODIS products. We observed that specific chemical applications that produce defoliation, which would have been missed using the standard 16-day product, were easily detectable with the filtered daily NDVI products.

Spatial distribution of volcanic ash deposits of 2011 Puyehue-Cordón Caulle eruption in Patagonia as measured by a perturbation in NDVI temporal dynamics

NASA Astrophysics Data System (ADS)

Easdale, M. H.; Bruzzone, O.

2018-03-01

Volcanic ash fallout is a recurrent environmental disturbance in forests, arid and semi-arid rangelands of Patagonia, South America. The ash deposits over large areas are responsible for several impacts on ecological processes, agricultural production and health of local communities. Public policy decision making needs monitoring information of the affected areas by ash fallout, in order to better orient social, economic and productive aids. The aim of this study was to analyze the spatial distribution of volcanic ash deposits from the eruption of Puyehue-Cordón Caulle in 2011, by identifying a sudden change in the Normalized Difference Vegetation Index (NDVI) temporal dynamics, defined as a perturbation located in the time series. We applied a sparse-wavelet transform using the Basis Pursuit algorithm to NDVI time series obtained from the Moderate Resolution Image Spectroradiometer (MODIS) sensor, to identify perturbations at a pixel level. The spatial distribution of the perturbation promoted by ash deposits in Patagonia was successfully identified and characterized by means of a perturbation in NDVI temporal dynamics. Results are encouraging for the future development of a new platform, in combination with data from forecasting models and tracking of ash cloud trajectories and dispersion, to inform stakeholders to mitigate impact of volcanic ash on agricultural production and to orient public intervention strategies after a volcanic eruption followed by ash fallout over a wide region.

Climate forcing and desert malaria: the effect of irrigation.

PubMed

Baeza, Andres; Bouma, Menno J; Dobson, Andy P; Dhiman, Ramesh; Srivastava, Harish C; Pascual, Mercedes

2011-07-14

Rainfall variability and associated remote sensing indices for vegetation are central to the development of early warning systems for epidemic malaria in arid regions. The considerable change in land-use practices resulting from increasing irrigation in recent decades raises important questions on concomitant change in malaria dynamics and its coupling to climate forcing. Here, the consequences of irrigation level for malaria epidemics are addressed with extensive time series data for confirmed Plasmodium falciparum monthly cases, spanning over two decades for five districts in north-west India. The work specifically focuses on the response of malaria epidemics to rainfall forcing and how this response is affected by increasing irrigation. Remote sensing data for the Normalized Difference Vegetation Index (NDVI) are used as an integrated measure of rainfall to examine correlation maps within the districts and at regional scales. The analyses specifically address whether irrigation has decreased the coupling between malaria incidence and climate variability, and whether this reflects (1) a breakdown of NDVI as a useful indicator of risk, (2) a weakening of rainfall forcing and a concomitant decrease in epidemic risk, or (3) an increase in the control of malaria transmission. The predictive power of NDVI is compared against that of rainfall, using simple linear models and wavelet analysis to study the association of NDVI and malaria variability in the time and in the frequency domain respectively. The results show that irrigation dampens the influence of climate forcing on the magnitude and frequency of malaria epidemics and, therefore, reduces their predictability. At low irrigation levels, this decoupling reflects a breakdown of local but not regional NDVI as an indicator of rainfall forcing. At higher levels of irrigation, the weakened role of climate variability may be compounded by increased levels of control; nevertheless this leads to no significant decrease in the actual risk of disease. This implies that irrigation can lead to more endemic conditions for malaria, creating the potential for unexpectedly large epidemics in response to excess rainfall if these climatic events coincide with a relaxation of control over time. The implications of our findings for control policies of epidemic malaria in arid regions are discussed.

Climate and anthropogenic impacts on forest vegetation derived from satellite data

NASA Astrophysics Data System (ADS)

Zoran, M.; Savastru, R.; Savastru, D.; Tautan, M.; Miclos, S.; Baschir, L.

2010-09-01

Vegetation and climate interact through a series of complex feedbacks, which are not very well understood. The patterns of forest vegetation are largely determined by temperature, precipitation, solar irradiance, soil conditions and CO2 concentration. Vegetation impacts climate directly through moisture, energy, and momentum exchanges with the atmosphere and indirectly through biogeochemical processes that alter atmospheric CO2 concentration. Changes in forest vegetation land cover/use alter the surface albedo and radiation fluxes, leading to a local temperature change and eventually a vegetation response. This albedo (energy) feedback is particularly important when forests mask snow cover. Forest vegetation-climate feedback regimes are designated based on the temporal correlations between the vegetation and the surface temperature and precipitation. The different feedback regimes are linked to the relative importance of vegetation and soil moisture in determining land-atmosphere interactions. Forest vegetation phenology constitutes an efficient bio-indicator of impacts of climate and anthropogenic changes and a key parameter for understanding and modeling vegetation-climate interactions. Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vegetation Index (NDVIs), which requires NDVI time-series with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). Spatio-temporal forest vegetation dynamics have been quantified as the total amount of vegetation (mean NDVI) and the seasonal difference (annual NDVI amplitude) by a time series analysis of NDVI satellite images over 1989 - 2009 period for a forest ecosystem placed in the North-Eastern part of Bucharest town, Romania, from IKONOS and LANDSAT TM and ETM satellite images and meteorological data. A climate indicator (CI) was created from meteorological data (precipitation over net radiation). The relationships between the vegetation dynamics and the CI have been determined spatially and temporally. The driest test regions prove to be the most sensitive to climate impact. The spatial and temporal patterns of the mean NDVI are the same, while they are partially different for the seasonal difference. For investigated test area, considerable NDVI decline was observed for drought events during 2003 and 2007 years. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics. Specific aim of this paper was to assess, forecast, and mitigate the risks of climatic changes on forest systems and its biodiversity as well as on adjacent environment areas and to provide early warning strategies on the basis of spectral information derived from satellite data regarding atmospheric effects of forest biome degradation .

Evaluating the Usefulness of High-Temporal Resolution Vegetation Indices to Identify Crop Types

NASA Astrophysics Data System (ADS)

Hilbert, K.; Lewis, D.; O'Hara, C. G.

2006-12-01

The National Aeronautical and Space Agency (NASA) and the United States Department of Agriculture (USDA) jointly sponsored research covering the 2004 to 2006 South American crop seasons that focused on developing methods for the USDA's Foreign Agricultural Service's (FAS) Production Estimates and Crop Assessment Division (PECAD) to identify crop types using MODIS-derived, hyper-temporal Normalized Difference Vegetation Index (NDVI) images. NDVI images were composited in 8 day intervals from daily NDVI images and aggregated to create a hyper-termporal NDVI layerstack. This NDVI layerstack was used as input to image classification algorithms. Research results indicated that creating high-temporal resolution Normalized Difference Vegetation Index (NDVI) composites from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data products provides useful input to crop type classifications as well as potential useful input for regional crop productivity modeling efforts. A current NASA-sponsored Rapid Prototyping Capability (RPC) experiment will assess the utility of simulated future Visible Infrared Imager / Radiometer Suite (VIIRS) imagery for conducting NDVI-derived land cover and specific crop type classifications. In the experiment, methods will be considered to refine current MODIS data streams, reduce the noise content of the MODIS, and utilize the MODIS data as an input to the VIIRS simulation process. The effort also is being conducted in concert with an ISS project that will further evaluate, verify and validate the usefulness of specific data products to provide remote sensing-derived input for the Sinclair Model a semi-mechanistic model for estimating crop yield. The study area encompasses a large portion of the Pampas region of Argentina--a major world producer of crops such as corn, soybeans, and wheat which makes it a competitor to the US. ITD partnered with researchers at the Center for Surveying Agricultural and Natural Resources (CREAN) of the National University of Cordoba, Argentina, and CREAN personnel collected and continue to collect field-level, GIS-based in situ information. Current efforts involve both developing and optimizing software tools for the necessary data processing. The software includes the Time Series Product Tool (TSPT), Leica's ERDAS Imagine, and Mississippi State University's Temporal Map Algebra computational tools.

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

NASA Technical Reports Server (NTRS)

Epperson, David L.; Davis, Jerry M.; Bloomfield, Peter; Karl, Thomas R.; Mcnab, Alan L.; Gallo, Kevin P.

1995-01-01

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data -- the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data -- to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km x 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1 deg x 1 deg latitude-longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40 C for monthly minimum temperatures, near 0.25 C for monthly mean temperatures, and near 0.10 C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near -1.1 C for rural stations to 2.4 C for stations from the largest urban areas. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Intra-seasonal NDVI change projections in semi-arid Africa

USGS Publications Warehouse

Funk, Christopher C.; Brown, Molly E.

2006-01-01

Early warning systems (EWS) tend to focus on the identification of slow onset disasters such famine and epidemic disease. Since hazardous environmental conditions often precede disastrous outcomes by many months, effective monitoring via satellite and in situ observations can successfully guide mitigation activities. Accurate short term forecasts of NDVI could increase lead times, making early warning earlier. This paper presents a simple empirical model for making 1 to 4 month NDVI projections. These statistical projections are based on parameterized satellite rainfall estimates (RFE) and relative humidity demand (RHD). A quasi-global, 1 month ahead, 1° study demonstrates reasonable accuracies in many semi-arid regions. In Africa, a 0.1° cross-validated skill assessment quantifies the technique's applicability at 1 to 4 month forecast intervals. These results suggest that useful projections can be made over many semi-arid, food insecure regions of Africa, with plausible extensions to drought prone areas of Asia, Australia and South America.

Response of spectral vegetation indices to soil moisture in grasslands and shrublands

USGS Publications Warehouse

Zhang, Li; Ji, Lei; Wylie, Bruce K.

2011-01-01

The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.

Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment.

PubMed

Guay, Kevin C; Beck, Pieter S A; Berner, Logan T; Goetz, Scott J; Baccini, Alessandro; Buermann, Wolfgang

2014-10-01

Satellite-derived indices of photosynthetic activity are the primary data source used to study changes in global vegetation productivity over recent decades. Creating coherent, long-term records of vegetation activity from legacy satellite data sets requires addressing many factors that introduce uncertainties into vegetation index time series. We compared long-term changes in vegetation productivity at high northern latitudes (>50°N), estimated as trends in growing season NDVI derived from the most widely used global NDVI data sets. The comparison included the AVHRR-based GIMMS-NDVI version G (GIMMSg ) series, and its recent successor version 3g (GIMMS3g ), as well as the shorter NDVI records generated from the more modern sensors, SeaWiFS, SPOT-VGT, and MODIS. The data sets from the latter two sensors were provided in a form that reduces the effects of surface reflectance associated with solar and view angles. Our analysis revealed large geographic areas, totaling 40% of the study area, where all data sets indicated similar changes in vegetation productivity over their common temporal record, as well as areas where data sets showed conflicting patterns. The newer, GIMMS3g data set showed statistically significant (α = 0.05) increases in vegetation productivity (greening) in over 15% of the study area, not seen in its predecessor (GIMMSg ), whereas the reverse was rare (<3%). The latter has implications for earlier reports on changes in vegetation activity based on GIMMSg , particularly in Eurasia where greening is especially pronounced in the GIMMS3g data. Our findings highlight both critical uncertainties and areas of confidence in the assessment of ecosystem-response to climate change using satellite-derived indices of photosynthetic activity. Broader efforts are required to evaluate NDVI time series against field measurements of vegetation growth, primary productivity, recruitment, mortality, and other biological processes in order to better understand ecosystem responses to environmental change over large areas. © 2014 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment

PubMed Central

Guay, Kevin C; Beck, Pieter S A; Berner, Logan T; Goetz, Scott J; Baccini, Alessandro; Buermann, Wolfgang

2014-01-01

Satellite-derived indices of photosynthetic activity are the primary data source used to study changes in global vegetation productivity over recent decades. Creating coherent, long-term records of vegetation activity from legacy satellite data sets requires addressing many factors that introduce uncertainties into vegetation index time series. We compared long-term changes in vegetation productivity at high northern latitudes (>50°N), estimated as trends in growing season NDVI derived from the most widely used global NDVI data sets. The comparison included the AVHRR-based GIMMS-NDVI version G (GIMMSg) series, and its recent successor version 3g (GIMMS3g), as well as the shorter NDVI records generated from the more modern sensors, SeaWiFS, SPOT-VGT, and MODIS. The data sets from the latter two sensors were provided in a form that reduces the effects of surface reflectance associated with solar and view angles. Our analysis revealed large geographic areas, totaling 40% of the study area, where all data sets indicated similar changes in vegetation productivity over their common temporal record, as well as areas where data sets showed conflicting patterns. The newer, GIMMS3g data set showed statistically significant (α = 0.05) increases in vegetation productivity (greening) in over 15% of the study area, not seen in its predecessor (GIMMSg), whereas the reverse was rare (<3%). The latter has implications for earlier reports on changes in vegetation activity based on GIMMSg, particularly in Eurasia where greening is especially pronounced in the GIMMS3g data. Our findings highlight both critical uncertainties and areas of confidence in the assessment of ecosystem-response to climate change using satellite-derived indices of photosynthetic activity. Broader efforts are required to evaluate NDVI time series against field measurements of vegetation growth, primary productivity, recruitment, mortality, and other biological processes in order to better understand ecosystem responses to environmental change over large areas. PMID:24890614

Herbivore Impact on Tundra Plant Community Dynamics Using Long-term Remote Sensing Observation

NASA Astrophysics Data System (ADS)

Yu, Q.; Engstrom, R.; Shiklomanov, N. I.

2014-12-01

Arctic tundra biome is now experiencing dramatic environmental changes accentuated by summer sea-ice decline, permafrost thaw, and shrub expansion. Multi-decadal time-series of the Normalized Difference Vegetation Index (NDVI, a spectral metric of vegetation productivity) shows an overall "greening" trend across the Arctic tundra biome. Regional trends in climate plausibly explain large-scale patterns of increasing plant productivity, as diminished summer sea-ice extent warms the adjacent land causing tundra vegetation to respond positively (increased photosynthetic aboveground biomass). However, at more local scales, there is a great deal of spatial variability in NDVI trends that likely reflects differences in hydrology and soil conditions, disturbance history, and use by wildlife and humans. Particularly, habitat use by large herbivores, such as reindeer and caribou, has large impacts on vegetation dynamics at local and regional scales, but the role of herbivores in modulating the response of vegetation to warming climate has received little attention. This study investigates regional tundra plant community dynamics within inhabits of different sizes of wild caribou/reindeer herds across the Arctic using GIMMS NDVI (Normalized Difference Vegetation Index) 3g data product. The Taimyr herd in Russia is one of the largest herds in the world with a population increase from 450,000 in 1975 to about 1 million animals in 2000. The population of the porcupine caribou herd has fluctuated in the past three decades between 100,000 and 180,000. Time-series of the maximum NDVI within the inhabit area of the Taimyr herd has increased about 2% per decade over the past three decades, while within the inhabit area of the Porcupine herd the maximum NDVI has increased about 5% per decade. Our results indicate that the impact of large herbivores can be detected from space and further analyses on seasonal dynamics of vegetation indices and herbivore behavior may provide more understanding of the plant-herbivore interactions within the context of a 'greening' Arctic.

Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China).

PubMed

Cui, Lifang; Wang, Lunche; Singh, Ramesh P; Lai, Zhongping; Jiang, Liangliang; Yao, Rui

2018-05-23

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years.

[Vegetation change of Yamzho Yumco Basin in southern Tibet based on SPOT-VGT NDVI].

PubMed

Yu, Shu-Mei; Liu, Jing-Shi; Yuan, Jin-Guo

2010-06-01

The area we studied is Lake Yamzho Yumco Basin (28 degrees 27'-29 degrees 12'N, 90 degrees 08'-91 degrees 45'E), the largest inland lake basin in southern Tibetan Plateau, China. Using the SPOT-VGT NDVI vegetation index from 1998 to 2007 in the basin, the temporal and spatial variation characteristics of NDVI and its correlation with the major climatic factors (air temperature, precipitation) were analyzed. The results show that the average NDVI of the lake basin ranges from 0.12 to 0.31 and its seasonal change is obvious; the NDVI begins to rise rapidly in May and reaches the maximum value in early September. The average NDVI of the basin shows the slow increasing trend during 1998 to 2007, and it indicates that the eco-environment of the basin is recovering. The high value of NDVI has close relationships with water supply, altitude and vegetation types, so NDVI is relatively high near water sources and is the highest in meadow grassland. The summer air temperature and precipitation are the important climate elements that influence the vegetation in the basin, and the linear correlation coefficients between NDVI and air temperature and precipitation are 0.7 and 0.71, respectively. In recent years, warm and humid trend of the local climate is prevailing to improve the ecological environment in Yamzho Yumco Basin.

[Kriging analysis of vegetation index depression in peak cluster karst area].

PubMed

Yang, Qi-Yong; Jiang, Zhong-Cheng; Ma, Zu-Lu; Cao, Jian-Hua; Luo, Wei-Qun; Li, Wen-Jun; Duan, Xiao-Fang

2012-04-01

In order to master the spatial variability of the normal different vegetation index (NDVI) of the peak cluster karst area, taking into account the problem of the mountain shadow "missing" information of remote sensing images existing in the karst area, NDVI of the non-shaded area were extracted in Guohua Ecological Experimental Area, in Pingguo County, Guangxi applying image processing software, ENVI. The spatial variability of NDVI was analyzed applying geostatistical method, and the NDVI of the mountain shadow areas was predicted and validated. The results indicated that the NDVI of the study area showed strong spatial variability and spatial autocorrelation resulting from the impact of intrinsic factors, and the range was 300 m. The spatial distribution maps of the NDVI interpolated by Kriging interpolation method showed that the mean of NDVI was 0.196, apparently strip and block. The higher NDVI values distributed in the area where the slope was greater than 25 degrees of the peak cluster area, while the lower values distributed in the area such as foot of the peak cluster and depression, where slope was less than 25 degrees. Kriging method validation results show that interpolation has a very high prediction accuracy and could predict the NDVI of the shadow area, which provides a new idea and method for monitoring and evaluation of the karst rocky desertification.

Improved Multispectral Skin Detection and its Application to Search Space Reduction for Dismount Detection Based on Histograms of Oriented Gradients

DTIC Science & Technology

2010-03-01

2-29 2.7.4 Normalized Difference Skin Index (NDSI) . . . . 2-30 2.7.5 Normalized Difference Vegetation Index ( NDVI ) 2-31 2.7.6...C-1 C.2 NDVI Method . . . . . . . . . . . . . . . . . . . . . . . C-4 Bibliography... NDVI ,NDSI) and (NDGRI,NDSI) values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6 4.3. Joint distributions of ( NDVI ,NDSI) and

The utility of estimating net primary productivity over Alaska using baseline AVHRR data

USGS Publications Warehouse

Markon, C.J.; Peterson, Kim M.

2002-01-01

Net primary productivity (NPP) is a fundamental ecological variable that provides information about the health and status of vegetation communities. The Normalized Difference Vegetation Index, or NDVI, derived from the Advanced Very High Resolution Radiometer (AVHRR) is increasingly being used to model or predict NPP, especially over large remote areas. In this article, seven seasonally based metrics calculated from a seven-year baseline NDVI dataset were used to model NPP over Alaska, USA. For each growing season, they included maximum, mean and summed NDVI, total days, product of total days and maximum NDVI, an integral estimate of NDVI and a summed product of NDVI and solar radiation. Field (plot) derived NPP estimates were assigned to 18 land cover classes from an Alaskan statewide land cover database. Linear relationships between NPP and each NDVI metric were analysed at four scales: plot, 1-km, 10-km and 20-km pixels. Results show moderate to poor relationship between any of the metrics and NPP estimates for all data sets and scales. Use of NDVI for estimating NPP may be possible, but caution is required due to data seasonality, the scaling process used and land surface heterogeneity.

NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands

USGS Publications Warehouse

Nouri, Hamideh; Anderson, Sharolyn; Sutton, Paul; Beecham, Simon; Nagler, Pamela L.; Jarchow, Christopher J.; Roberts, Dar A.

2017-01-01

This research addresses the question as to whether or not the Normalised Difference Vegetation Index (NDVI) is scale invariant (i.e. constant over spatial aggregation) for pure pixels of urban vegetation. It has been long recognized that there are issues related to the modifiable areal unit problem (MAUP) pertaining to indices such as NDVI and images at varying spatial resolutions. These issues are relevant to using NDVI values in spatial analyses. We compare two different methods of calculation of a mean NDVI: 1) using pixel values of NDVI within feature/object boundaries and 2) first calculating the mean red and mean near-infrared across all feature pixels and then calculating NDVI. We explore the nature and magnitude of these differences for images taken from two sensors, a 1.24 m resolution WorldView-3 and a 0.1 m resolution digital aerial image. We apply these methods over an urban park located in the Adelaide Parklands of South Australia. We demonstrate that the MAUP is not an issue for calculation of NDVI within a sensor for pure urban vegetation pixels. This may prove useful for future rule-based monitoring of the ecosystem functioning of green infrastructure.

Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data

USGS Publications Warehouse

Gu, Yingxin; Brown, Jesslyn F.; Miura, Tomoaki; van Leeuwen, Willem J.D.; Reed, Bradley C.

2010-01-01

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies.

Mapping and spatial-temporal modeling of Bromus tectorum invasion in central Utah

NASA Astrophysics Data System (ADS)

Jin, Zhenyu

Cheatgrass, or Downy Brome, is an exotic winter annual weed native to the Mediterranean region. Since its introduction to the U.S., it has become a significant weed and aggressive invader of sagebrush, pinion-juniper, and other shrub communities, where it can completely out-compete native grasses and shrubs. In this research, remotely sensed data combined with field collected data are used to investigate the distribution of the cheatgrass in Central Utah, to characterize the trend of the NDVI time-series of cheatgrass, and to construct a spatially explicit population-based model to simulate the spatial-temporal dynamics of the cheatgrass. This research proposes a method for mapping the canopy closure of invasive species using remotely sensed data acquired at different dates. Different invasive species have their own distinguished phenologies and the satellite images in different dates could be used to capture the phenology. The results of cheatgrass abundance prediction have a good fit with the field data for both linear regression and regression tree models, although the regression tree model has better performance than the linear regression model. To characterize the trend of NDVI time-series of cheatgrass, a novel smoothing algorithm named RMMEH is presented in this research to overcome some drawbacks of many other algorithms. By comparing the performance of RMMEH in smoothing a 16-day composite of the MODIS NDVI time-series with that of two other methods, which are the 4253EH, twice and the MVI, we have found that RMMEH not only keeps the original valid NDVI points, but also effectively removes the spurious spikes. The reconstructed NDVI time-series of different land covers are of higher quality and have smoother temporal trend. To simulate the spatial-temporal dynamics of cheatgrass, a spatially explicit population-based model is built applying remotely sensed data. The comparison between the model output and the ground truth of cheatgrass closure demonstrates that the model could successfully simulate the spatial-temporal dynamics of cheatgrass in a simple cheatgrass-dominant environment. The simulation of the functional response of different prescribed fire rates also shows that this model is helpful to answer management questions like, "What are the effects of prescribed fire to invasive species?" It demonstrates that a medium fire rate of 10% can successfully prevent cheatgrass invasion.

RGB-NDVI colour composites for visualizing forest change dynamics

NASA Technical Reports Server (NTRS)

Sader, S. A.; Winne, J. C.

1992-01-01

The study presents a simple and logical technique to display and quantify forest change using three dates of satellite imagery. The normalized difference vegetation index (NDVI) was computed for each date of imagery to define high and low vegetation biomass. Color composites were generated by combining each date of NDVI with either the red, green, or blue (RGB) image planes in an image display monitor. Harvest and regeneration areas were quantified by applying a modified parallelepiped classification creating an RGB-NDVI image with 27 classes that were grouped into nine major forest change categories. Aerial photographs and stand history maps are compared with the forest changes indicated by the RGB-NDVI image. The utility of the RGB-NDVI technique for supporting forest inventories and updating forest resource information systems are presented and discussed.

Standardized principal components for vegetation variability monitoring across space and time

NASA Astrophysics Data System (ADS)

Mathew, T. R.; Vohora, V. K.

2016-08-01

Vegetation at any given location changes through time and in space. In what quantity it changes, where and when can help us in identifying sources of ecosystem stress, which is very useful for understanding changes in biodiversity and its effect on climate change. Such changes known for a region are important in prioritizing management. The present study considers the dynamics of savanna vegetation in Kruger National Park (KNP) through the use of temporal satellite remote sensing images. Spatial variability of vegetation is a key characteristic of savanna landscapes and its importance to biodiversity has been demonstrated by field-based studies. The data used for the study were sourced from the U.S. Agency for International Development where AVHRR derived Normalized Difference Vegetation Index (NDVI) images available at spatial resolutions of 8 km and at dekadal scales. The study area was extracted from these images for the time-period 1984-2002. Maximum value composites were derived for individual months resulting in an image dataset of 216 NDVI images. Vegetation dynamics across spatio-temporal domains were analyzed using standardized principal components analysis (SPCA) on the NDVI time-series. Each individual image variability in the time-series is considered. The outcome of this study demonstrated promising results - the variability of vegetation change in the area across space and time, and also indicated changes in landscape on 6 individual principal components (PCs) showing differences not only in magnitude, but also in pattern, of different selected eco-zones with constantly changing and evolving ecosystem.

Active sensing: An innovative tool for evaluating grain yield and nitrogen use efficiency of multiple wheat genotypes

NASA Astrophysics Data System (ADS)

Naser, Mohammed Abdulridha

Precision agricultural practices have significantly contributed to the improvement of crop productivity and profitability. Remote sensing based indices, such as Normalized Difference Vegetative Index (NDVI) have been used to obtain crop information. It is used to monitor crop development and to provide rapid and nondestructive estimates of plant biomass, nitrogen (N) content and grain yield. Remote sensing tools are helping improve nitrogen use efficiency (NUE) through nitrogen management and could also be useful for high NUE genotype selection. The objectives of this study were: (i) to determine if active sensor based NDVI readings can differentiate wheat genotypes, (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes, (iii) to identify and quantify the main sources of variation in NUE across wheat genotypes, and (iv) to determine if normalized difference vegetation index (NDVI) could characterize variability in NUE across wheat genotypes. This study was conducted in north eastern Colorado for two years, 2010 and 2011. The NDVI readings were taken weekly during the winter wheat growing season from March to late June, in 2010 and 2011 and NUE were calculated as partial factor productivity and as partial nitrogen balance at the end of the season. For objectives i and ii, the correlation between NDVI and grain yield was determined using Pearson's product-moment correlation coefficient (r) and linear regression analysis was used to explain the relationship between NDVI and grain yield. The K-means clustering algorithm was used to classify mean NDVI and mean grain yield into three classes. For objectives iii and iv, the parameters related to NUE were also calculated to measure their relative importance in genotypic variation of NUE and power regression analysis between NDVI and NUE was used to characterize the relationship between NDVI and NUE. The results indicate more consistent association between grain yield and NDVI and between NDVI and NUE later in the season, after anthesis and during mid-grain filling stage under dryland and a poor association in wheat grown in irrigated conditions. The results suggest that below saturation of NDVI values (about 0.9), (i.e. prior to full canopy closure and after the beginning of senescence or most of the season under dryland conditions) NDVI could assess grain yield and NUE. The results also indicate that nitrogen uptake efficiency was the main source of variation of NUE among genotypes grown in site-years with lower yield. Overall, results from this study demonstrate that NDVI readings successfully classified wheat genotypes into grain yield classes across dryland and irrigated conditions and characterized variability in NUE across wheat genotypes.

Advances in Remote Sensing of Vegetation Merging NDVI, Soil Moisture, and Chlorophyll Fluorescence

NASA Astrophysics Data System (ADS)

Tucker, Compton

2016-04-01

I will describe an advance in remote sensing of vegetation in the time domain that combines simultaneous measurements of the normalized difference vegetation index, soil moisture, and chlorophyll fluorescence, all from different satellite sensors but acquired for the same areas at the same time step. The different sensor data are MODIS NDVI data from both Terra and Aqua platforms, soil moisture data from SMOS & SMP (aka SMAP but with only the passive radiometer), and chlorophyll fluorescence data from GOME-2. The complementary combination of these data provide important crop yield information for agricultural production estimates at critical phenological times in the growing season, provide a scientific basis to map land degradation, and enable quantitative determination of the end of the growing season in temperate zones.

Improving predictive capabilities of environmental change with GLOBE data

NASA Astrophysics Data System (ADS)

Robin, Jessica Hill

This dissertation addresses two applications of Normalized Difference Vegetation Index (NDVI) essential for predicting environmental changes. The first study focuses on whether NDVI can improve model simulations of evapotranspiration for temperate Northern (>35°) regions. The second study focuses on whether NDVI can detect phenological changes in start of season (SOS) for high Northern (>60°) environments. The overall objectives of this research were to (1) develop a methodology for utilizing GLOBE data in NDVI research; and (2) provide a critical analysis of NDVI as a long-term monitoring tool for environmental change. GLOBE is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. The first study utilized data collected by one GLOBE school in Greenville, Pennsylvania and the second utilized phenology observations made by GLOBE students in Alaska. Results from the first study showed NDVI could predict transpiration periods for environments like Greenville, Pennsylvania. In phenological terms, these environments have three distinct periods (QI, QII, and QIII). QI reflects onset of the growing season (mid March--mid May) when vegetation is greening up (NDVI < 0.60) and transpiration is less than 2mm/day. QII reflects end of the growing season (mid September--October) when vegetation is greening down and transpiration is decreasing. QIII reflects height of the growing season (mid May--mid September) when transpiration rates average between 2 and 5 mm per day and NDVI is at its maximum (>0.60). Results from the second study showed that a climate threshold of 153 +/- 22 growing degree days was a better predictor of SOS for Fairbanks than a NDVI threshold applied to temporal AVHRR and MODIS datasets. Accumulated growing degree days captured the interannual variability of SOS better than the NDVI threshold and most closely resembled actual SOS observations made by GLOBE students. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska. Both studies did show that GLOBE data provides an important source of input and validation information for NDVI research.

The Effect of Spatial and Spectral Resolution in Determining NDVI

NASA Astrophysics Data System (ADS)

Boelman, N. T.

2003-12-01

We explore the impact that varying spatial and spectral resolutions of several sensors (a field portable spectroradiometer, Landsat, MODIS and AVHRR) has in determining the average Normalized Difference Vegetation Index (NDVI) at Imnavait Creek, a small arctic tundra watershed located on the north slope of Alaska. We found that at the field-of-views (FOVs) of less than 20 m2 that were sampled, the average NDVI value for this watershed is 0.65, compared to 0.77 at FOVs equal to and greater than 20 m2. In addition, we found that at FOVs less than 20 m2, the average NDVI value calculated according to each of Landsat, MODIS and AVHRR band definitions (controlled by spectral resolution) was similar. However, at FOVs equal to and greater than 20 m2, the average NDVI value calculated according to AVHRR's broad-band definitions was significantly and consistently higher than that from both Landsat and MODIS's narrow-band NDVI values. We speculate that these differences in NDVI exist because high leaf-area-index vegetation communities associated with watertracks are commonly spaced between 10 and 20 m apart in arctic tundra landscapes and are often only included when spectral sampling is conducted at FOVs greater than tens of square meters. These results suggest that both spatial resolution alone and its interaction with spectral resolution have to be considered when interpreting commonly used global-scale NDVI datasets. This is because traditionally, the fundamental relationships established between NDVI and ecosystem parameters, such as CO2 fluxes, aboveground biomass and net primary productivity, have been established at scales less than 20 m2. Other ecosystems, such as landscapes with isolated tree islands in boreal forest-tundra ecotones, may exhibit similar scaling patterns that need to be considered when interpreting global-scale NDVI datasets.

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fanwei; Collatz, George James; Pinzon, Jorge E.; Ivanoff, Alvaro

2013-01-01

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

Aggregation and Association of NDVI, Boll Injury, and Stink Bugs in North Carolina Cotton.

PubMed

Reisig, Dominic D; Reay-Jones, F P F; Meijer, A D

2015-01-01

Sampling of herbivorous stink bugs in southeastern U.S. cotton remains problematic. Remote sensing was explored to improve sampling of these pests and associated boll injury. Two adjacent 14.5-ha cotton fields were grid sampled in 2011 and 2012 by collecting stink bug adults and bolls every week during the third, fourth, and fifth weeks of bloom. Satellite remote sensing data were collected during the third week of bloom during both years, and normalized difference vegetation index (NDVI) values were calculated. Stink bugs were spatially aggregated on the third week of bloom in 2011. Boll injury from stink bugs was spatially aggregated during the fourth week of bloom in 2012. The NDVI values were aggregated during both years. There was a positive association and correlation between stink bug numbers and NDVI values, as well as injured bolls and NDVI values, during the third week of bloom in 2011. During the third week of bloom in 2012, NDVI values were negatively correlated with stink bug numbers. During the fourth week of bloom in 2011, stink bug numbers and boll injury were both positively associated and correlated with NDVI values. During the fourth week of bloom in 2012, stink bugs were negatively correlated with NDVI values, and boll injury was negatively associated and correlated with NDVI values. This study suggests the potential of remote sensing as a tool to assist with sampling stink bugs in cotton, although more research is needed using NDVI and other plant measurements to predict stink bug injury. © The Author 2015. Published by Oxford University Press on behalf of the Entomological Society of America.

MODIS NDVI Response Following Fires in Siberia

NASA Technical Reports Server (NTRS)

Ranson, K. Jon; Sun, G.; Kovacs, K.; Kharuk, V. I.

2003-01-01

The Siberian boreal forest is considered a carbon sink but may become an important source of carbon dioxide if climatic warming predictions are correct. The forest is continually changing through various disturbance mechanisms such as insects, logging, mineral exploitation, and especially fires. Patterns of disturbance and forest recovery processes are important factors regulating carbon flux in this area. NASA's Terra MODIS provides useful information for assessing location of fires and post fire changes in forests. MODIS fire (MOD14), and NDVI (MOD13) products were used to examine fire occurrence and post fire variability in vegetation cover as indicated by NDVI. Results were interpreted for various post fire outcomes, such as decreased NDVI after fire, no change in NDVI after fire and positive NDVI change after fire. The fire frequency data were also evaluated in terms of proximity to population centers, and transportation networks.

Monitoring start of season in Alaska with GLOBE, AVHRR, and MODIS data

NASA Astrophysics Data System (ADS)

Robin, Jessica; Dubayah, Ralph; Sparrow, Elena; Levine, Elissa

2008-03-01

This work evaluates whether continuity between Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) is achievable for monitoring phenological changes in Alaska. This work also evaluates whether NDVI can detect changes in start of the growing season (SOS) in this region. Six quadratic regression models with NDVI as a function of accumulated growing degree days (AGDD) were developed from 2001 through 2004 AVHRR and MODIS NDVI data sets for urban, mixed, and forested land covers. Model parameters determined NDVI values for start of the observational period as well as peak and length of the growing season. NDVI values for start of the growing season were determined from the model equations and field observations of SOS made by GLOBE students and researchers at University of Alaska Fairbanks. AGDD was computed from daily air temperature. AVHRR and MODIS models were significantly different from one another with differences in the start of the observational season as well as start, peak, and length of the growing season. Furthermore, AGDD for SOS was significantly lower during the 1990s than the 1980s. NDVI values at SOS did not detect this change. There are limitations with using NDVI to monitor phenological changes in these regions because of snow, the large extent of conifers, and clouds, which restrict the composite period. In addition, differing processing and spectral characteristics restrict continuity between AVHRR and MODIS NDVI data sets.

[Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices.

PubMed

Zuo, Lu; Wang, Huan Jiong; Liu, Rong Gao; Liu, Yang; Shang, Rong

2018-02-01

Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes. Remote sensing spectrum has been widely used in the extraction of vegetation phenology information. However, there are many differences between phenology extracted by remote sensing and site observations, with their physical meaning remaining unclear. We selected one tile of MODIS data in northeastern China (2000-2014) to examine the SOS and EOS differences derived from the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR) based on both the red and near-infrared bands. The results showed that there were significant differences between NDVI-phenology and SR-phenology. SOS derived from NDVI averaged 18.9 days earlier than that from SR. EOS derived from NDVI averaged 19.0 days later than from SR. NDVI-phenology had a longer growing season. There were significant differences in the inter-annual variation of phenology from NDVI and SR. More than 20% of the pixel SOS and EOS derived from NDVI and SR showed the opposite temporal trend. These results caused by the seasonal curve characteristics and noise resistance differences of NDVI and SR. The observed data source of NDVI and SR were completely consistent, only the mathematical expressions were different, but phenology results were significantly different. Our results indicated that vegetation phenology monitoring by remote sensing is highly dependent on the mathematical expression of vegetation index. How to establish a reliable method for extracting vegetation phenology by remote sensing needs further research.

Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain

NASA Astrophysics Data System (ADS)

Liu, Zhengjia; Wu, Chaoyang; Liu, Yansui; Wang, Xiaoyue; Fang, Bin; Yuan, Wenping; Ge, Quansheng

2017-08-01

Satellite temporal resolution affects the fitting accuracy of vegetation growth curves. However, there are few studies that evaluate the impact of different satellite data (including temporal resolution and time series change) on spring green-up date (GUD) extraction. In this study, four GUD algorithms and two different temporal resolution satellite data (GIMMS3g during 1982-2013 and SPOT-VGT during 1999-2013) were used to investigate winter wheat GUD in the North China Plain. Four GUD algorithms included logistic-NDVI (normalized difference vegetation index), logistic-cumNDVI (cumulative NDVI), polynomial-NDVI and polynomial-cumNDVI algorithms. All algorithms and data were first regrouped into eight controlled cases. At site scale, we evaluated the performance of each case using correlation coefficient (r), bias and root mean square error (RMSE). We further compared spatial patterns and inter-annual trends of GUD inferred from different algorithms, and then analyzed the difference between GIMMS3g-based GUD and SPOT-VGT-based GUD. Our results showed that all satellite-based GUD were correlated with observations with r ranging from 0.32 to 0.57 (p < 0.01). SPOT-VGT-based GUD generally had better correlations with observed GUD than those of GIMMS3g. Spatially, SPOT-VGT-based GUD performed more reasonable spatial distributions. Inter-annual regional averaged satellite-based GUD presented overall advanced trends during 1982-2013 (0.3-2.0 days/decade) while delayed trends were observed during 1999-2013 (1.7-7.4 days/decade for GIMMS3g and 3.8-7.4 days/decade for SPOT-VGT). However, their significance levels were highly dependent on the data and algorithms used. Our findings suggest cautions on previous results of inter-annual variability of phenology from a single data/method.

Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters.

PubMed

Chakraborty, Abhishek; Seshasai, M V R; Dadhwal, V K

2014-07-01

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8 × 8 km spatial resolution for the period of 1982-2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann-Kendall test and further assessed at meteorological subdivision level using field significance test (α = 0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α = 0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35-40 % of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24-30 % of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.

Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data

USGS Publications Warehouse

Rigge, Matthew B.; Smart, Alexander; Wylie, Bruce K.; de Van Kamp, Kendall

2014-01-01

Various best management practices (BMPs) have been implemented on rangelands with the goals of controlling nonpoint source pollution, reducing the impact of livestock in ecologically important riparian areas, and improving grazing distribution. Providing off-stream water sources to livestock in pastures, cross-fencing, and rotational grazing are common rangeland BMPs that have demonstrated success in drawing livestock grazing pressure away from streams. We evaluated the effects of rangeland BMP implementation with six commercial-scale pastures in the northern mixed-grass prairie. Four pastures received a BMP suite consisting of off-stream water, cross-fencing, and deferred-rotation grazing, and two pastures did not receive BMPs. We hypothesized that the BMPs increased the quantity of riparian vegetation cover relative to the conditions in these pastures during the pre-BMP period and to the two pastures that did not receive BMPs. We used a series of 30-m Landsat normalized difference vegetation index (NDVI) images to track the spatial and temporal changes (1984–2010, n = 24) in vegetation cover, to which NDVI has been well correlated. Validation indicated that the remotely sensed signal from in-channel vegetation was representative of ground conditions. The BMP suite was associated with a 15% increase in the in-channel NDVI (0–30 m from stream centerline) and 18% increase in the riparian NDVI (30–180 m from stream center line). Conversely, the in-channel and riparian NDVI of non-BMP pastures declined 30% and 18% over the study period. The majority of change occurred within 2 yr of BMP implementation. The patterns of in-channel NDVI among pastures suggested that BMP implementation likely altered grazing distribution by decreasing the preferential use of riparian and in-channel areas. We demonstrated that satellite imagery time series are useful in retrospectively evaluating the efficacy of conservation practices, providing critical information to guide adaptive management and decision makers.

Using the satellite-derived normalized difference vegetation index (NDVI) to explain ranging patterns in a lek-breeding antelope: the importance of scale.

PubMed

Bro-Jørgensen, Jakob; Brown, Molly E; Pettorelli, Nathalie

2008-11-01

Lek-breeding species are characterized by a negative association between territorial resource availability and male mating success; however, the impact of resources on the overall distribution patterns of the two sexes in lek systems is not clear. The normalized difference vegetation index (NDVI) has recently emerged as a powerful proxy measure for primary productivity, allowing the links between the distributions of animals and resources to be explored. Using NDVI at four spatial resolutions, we here investigate how the distribution of the two sexes in a lek-breeding population of topi antelopes relates to resource abundance before and during the rut. We found that in the dry season preceding the rut, topi density correlated positively with NDVI at the large, but not the fine, scale. This suggests that before the rut, when resources were relatively scant, topi preferred pastures where green grass was widely abundant. The pattern was less pronounced in males, suggesting that the need for territorial attendance prevents males from tracking resources as freely as females do. During the rut, which occurs in the wet season, both male and female densities correlated negatively with NDVI at the fine scale. At this time, resources were generally plentiful and the results suggest that, rather than by resource maximization, distribution during the rut was determined by benefits of aggregating on relatively resource-poor leks for mating, and possibly antipredator, purposes. At the large scale, no correlation between density and NDVI was found during the rut in either sex, which can be explained by leks covering areas too small to be reflected at this resolution. The study illustrates that when investigating spatial organization, it is important: (1) to choose the appropriate analytic scale, and (2) to consider behavioural as well as strictly ecological factors.

Analysis of the Relationship Between Climate and NDVI Variability at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fan-Wei; Collatz, G. James; Pinzon, Jorge; Ivanoff, Alvaro

2011-01-01

interannual variability in modeled (CASA) C flux is in part caused by interannual variability in Normalized Difference Vegetation Index (NDVI) Fraction of Photosynthetically Active Radiation (FPAR). This study confirms a mechanism producing variability in modeled NPP: -- NDVI (FPAR) interannual variability is strongly driven by climate; -- The climate driven variability in NDVI (FPAR) can lead to much larger fluctuation in NPP vs. the NPP computed from FPAR climatology

Using MODIS NDVI phenoclasses and phenoclusters to characterize wildlife habitat: Mexican spotted owl as a case study

Treesearch

Serra J. Hoagland; Paul Beier; Danny Lee

2018-01-01

Most uses of remotely sensed satellite data to characterize wildlife habitat have used metrics such as mean NDVI (Normalized Difference Vegetation Index) in a year or season. These simple metrics do not take advantage of the temporal patterns in NDVI within and across years and the spatial arrangement of cells with various temporal NDVI signatures. Here we use 13 years...

Monitoring Global Food Security with New Remote Sensing Products and Tools

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Husak, G. J.; Magadzire, T.; Verdin, J. P.

2012-12-01

Global agriculture monitoring is a crucial aspect of monitoring food security in the developing world. The Famine Early Warning Systems Network (FEWS NET) has a long history of using remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and climate change. In recent years, it has become apparent that FEWS NET requires the ability to apply monitoring and modeling frameworks at a global scale to assess potential impacts of foreign production and markets on food security at regional, national, and local levels. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara (UCSB) Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of the increased mandate for remote monitoring. We present our monitoring products for measuring actual evapotranspiration (ETa), normalized difference vegetation index (NDVI) in a near-real-time mode, and satellite-based rainfall estimates and derivatives. USGS FEWS NET has implemented a Simplified Surface Energy Balance (SSEB) model to produce operational ETa anomalies for Africa and Central Asia. During the growing season, ETa anomalies express surplus or deficit crop water use, which is directly related to crop condition and biomass. We present current operational products and provide supporting validation of the SSEB model. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with an improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a relatively high spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. We provide an overview of these data and cite specific applications for crop monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production and driving crop water balance models. We present a series of derived rainfall products and provide an update on efforts to improve satellite-based estimates. We also present advancements in monitoring tools, namely, the Early Warning eXplorer (EWX) and interactive rainfall and NDVI time series viewers. The EWX is a data analysis and visualization tool that allows users to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The interactive time series viewers allow users to analyze rainfall and NDVI time series over multiple spatial domains. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

The Consequences of Precipitation Seasonality for Mediterranean-Ecosystem Vegetation of South Africa.

PubMed

Cramer, Michael D; Hoffman, M Timm

2015-01-01

Globally, mediterranean-climate ecosystem vegetation has converged on an evergreen, sclerophyllous and shrubby growth form. The particular aspects of mediterranean-climate regions that contribute to this convergence include summer droughts and relatively nutrient-poor soils. We hypothesised that winter-precipitation implies stressful summer droughts and leaches soils due to greater water availability (i.e. balance between precipitation and potential evapotranspiration; P-PET) during cold periods. We conducted a comparative analysis of normalised difference vegetation indices (NDVI) and edaphic and climate properties across the biomes of South Africa. NDVI was strongly correlated with both precipitation and P-PET (r2 = 0.8). There was no evidence, however, that winter-precipitation reduces NDVI in comparison to similar amounts of summer-precipitation. Base saturation (BS), a measure of soil leaching was, however, negatively related to P-PET (r2 = 0.64). This led to an interaction between P-PET and BS in determining NDVI, indicating the existence of a trade-off between water availability and soil nutrients that enables NDVI to increase with precipitation, despite negative consequences for soil nutrient availability. The mechanism of this trade-off is suggested to be that water increases nutrient accessibility. This implies that along with nutrient-depauperate geologies and long periods of time since glaciation, the winter-precipitation may have contributed to the highly leached status of the soils. Since many of the ecophysiological characteristics of mediterranean-ecosystem flora are associated with low nutrient availabilities (e.g. evergreen foliage, sclerophylly, cluster roots), we conclude that mediterranean-climates promote convergence of growth-forms in these regions through high leaching capacity.

Assessing onset and length of greening period in six vegetation types in Oaxaca, Mexico, using NDVI-precipitation relationships.

PubMed

Gómez-Mendoza, L; Galicia, L; Cuevas-Fernández, M L; Magaña, V; Gómez, G; Palacio-Prieto, J L

2008-07-01

Variations in the normalized vegetation index (NDVI) for the state of Oaxaca, in southern Mexico, were analyzed in terms of precipitation anomalies for the period 1997-2003. Using 10-day averages in NDVI data, obtained from AVHRR satellite information, the response of six types of vegetation to intra-annual and inter-annual fluctuations in precipitation were examined. The onset and temporal evolution of the greening period were studied in terms of precipitation variations through spectral analysis (coherence and phase). The results indicate that extremely dry periods, such as those observed in 1997 and 2001, resulted in low values of NDVI for much of Oaxaca, while good precipitation periods produced a rapid response (20-30 days of delay) from a stressed to a non-stressed condition in most vegetation types. One of these rapid changes occurred during the transition from dry to wet conditions during the summer of 1998. As in many parts of the tropics and subtropics, the NDVI reflects low frequency variations in precipitation on several spatial scales. Even after long dry periods (2001-2002), the various regional vegetation types are capable of recovering when a good rainy season takes place, indicating that vegetation types such as the evergreen forests in the high parts of Oaxaca respond better to rainfall characteristics (timing, amount) than to temperature changes, as is the case in most mid-latitudes. This finding may be relevant to prepare climate change scenarios for forests, where increases in surface temperature and precipitation anomalies are expected.

The Consequences of Precipitation Seasonality for Mediterranean-Ecosystem Vegetation of South Africa

PubMed Central

2015-01-01

Globally, mediterranean-climate ecosystem vegetation has converged on an evergreen, sclerophyllous and shrubby growth form. The particular aspects of mediterranean-climate regions that contribute to this convergence include summer droughts and relatively nutrient-poor soils. We hypothesised that winter-precipitation implies stressful summer droughts and leaches soils due to greater water availability (i.e. balance between precipitation and potential evapotranspiration; P–PET) during cold periods. We conducted a comparative analysis of normalised difference vegetation indices (NDVI) and edaphic and climate properties across the biomes of South Africa. NDVI was strongly correlated with both precipitation and P–PET (r2 = 0.8). There was no evidence, however, that winter-precipitation reduces NDVI in comparison to similar amounts of summer-precipitation. Base saturation (BS), a measure of soil leaching was, however, negatively related to P–PET (r2 = 0.64). This led to an interaction between P–PET and BS in determining NDVI, indicating the existence of a trade-off between water availability and soil nutrients that enables NDVI to increase with precipitation, despite negative consequences for soil nutrient availability. The mechanism of this trade-off is suggested to be that water increases nutrient accessibility. This implies that along with nutrient-depauperate geologies and long periods of time since glaciation, the winter-precipitation may have contributed to the highly leached status of the soils. Since many of the ecophysiological characteristics of mediterranean-ecosystem flora are associated with low nutrient availabilities (e.g. evergreen foliage, sclerophylly, cluster roots), we conclude that mediterranean-climates promote convergence of growth-forms in these regions through high leaching capacity. PMID:26650081

Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest.

PubMed

Yang, Hualei; Yang, Xi; Heskel, Mary; Sun, Shucun; Tang, Jianwu

2017-04-28

Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporal resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). We found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.

NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands.

PubMed

Nouri, Hamideh; Anderson, Sharolyn; Sutton, Paul; Beecham, Simon; Nagler, Pamela; Jarchow, Christopher J; Roberts, Dar A

2017-04-15

This research addresses the question as to whether or not the Normalised Difference Vegetation Index (NDVI) is scale invariant (i.e. constant over spatial aggregation) for pure pixels of urban vegetation. It has been long recognized that there are issues related to the modifiable areal unit problem (MAUP) pertaining to indices such as NDVI and images at varying spatial resolutions. These issues are relevant to using NDVI values in spatial analyses. We compare two different methods of calculation of a mean NDVI: 1) using pixel values of NDVI within feature/object boundaries and 2) first calculating the mean red and mean near-infrared across all feature pixels and then calculating NDVI. We explore the nature and magnitude of these differences for images taken from two sensors, a 1.24m resolution WorldView-3 and a 0.1m resolution digital aerial image. We apply these methods over an urban park located in the Adelaide Parklands of South Australia. We demonstrate that the MAUP is not an issue for calculation of NDVI within a sensor for pure urban vegetation pixels. This may prove useful for future rule-based monitoring of the ecosystem functioning of green infrastructure. Copyright © 2017 Elsevier B.V. All rights reserved.

Advances in the Use of In Situ Flux Tower Measures of Canopy Structure and Function for Evaluation of MODIS and VIIRS Vegetation Indices and Phenology

NASA Astrophysics Data System (ADS)

Huete, A. R.; Ma, X.; Devadas, R.; Miura, T.; Obata, K.; Restrepo-Coupe, N.; Kato, A.

2016-12-01

Prior studies have linked tower-based measures of ecosystem productivity (GEP) and satellite vegetation indices (VIs) over primarily phenologically driven ecosystems. In situ flux towers are advantageous for satellite product assessments due to their comparable footprints and finer temporal resolution measurements. However, weaker relationships have been reported in meteorological driven ecosystems in which satellite and flux tower measures of productivity are asynchronous. This suggests that satellite derived biophysical measures are not a measure of GEP, but rather a proxy for ecosystem structure (e.g. LAI) and function. Here we investigate the use of flux tower measures of photosynthetic infrastructure, including photosynthetic capacity and light use efficiency, as more appropriate measures for the evaluation of satellite VI products and phenology. We related MODIS and VIIRS VIs (NDVI, EVI, EVI2) and phenology over a series of flux tower sites located across a climatically-diverse north to south gradient in central Australia, encompassing wet and dry subtropcal savannas, semi-arid shrub/grass, and temperate broadleaf evergreen forests. These represented environmental conditions where phenology and meteorology are not always synchronized. Our objectives were to (1) advance the use of in situ tower data for more accurate evaluation of satellite products and assess challenges and limitations of in situ tower networks; (2) intercompare cross-sensor VI products and derived phenology with tower measures across biomes to assess consistencies; and (3) better understand satellite and tower relationships to improve upon interpretation of satellite VI and phenology data. Our results show, that in contrast to measures of GEP, our measures of photosynthetic infrastructure were much better related to satellite VIs; they removed strong hysteresis influences on VI- productivity relationships across phenologic green-up and brown-down phases; and thus were better related to satellite phenology profiles. We conclude that flux tower networks offer a valuable source of in situ and consistent data that can be used for satellite time series continuity and cross sensor studies. This can also bring forward the validation of vegetation indices beyond simple reflectance-based accuracy assessments.

A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

2011-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and show sample output from summer 2010, compare the MODIS GVF data to the AVHRR monthly climatology, and illustrate the sensitivity of the Noah LSM within LIS and/or the coupled LIS/WRF system to the new MODIS GVF dataset.

An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

NASA Astrophysics Data System (ADS)

Huang, Qing; Zhou, Qing-bo; Zhang, Li

2009-07-01

China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

History of Animals using Isotope Records (HAIR): A 6-year dietary history of one family of African elephants

PubMed Central

Cerling, Thure E.; Wittemyer, George; Ehleringer, James R.; Remien, Christopher H.; Douglas-Hamilton, Iain

2009-01-01

The dietary and movement history of individual animals can be studied using stable isotope records in animal tissues, providing insight into long-term ecological dynamics and a species niche. We provide a 6-year history of elephant diet by examining tail hair collected from 4 elephants in the same social family unit in northern Kenya. Sequential measurements of carbon, nitrogen, and hydrogen isotope rations in hair provide a weekly record of diet and water resources. Carbon isotope ratios were well correlated with satellite-based measurements of the normalized difference vegetation index (NDVI) of the region occupied by the elephants as recorded by the global positioning system (GPS) movement record; the absolute amount of C4 grass consumption is well correlated with the maximum value of NDVI during individual wet seasons. Changes in hydrogen isotope ratios coincided very closely in time with seasonal fluctuations in rainfall and NDVI whereas diet shifts to relatively high proportions of grass lagged seasonal increases in NDVI by ≈2 weeks. The peak probability of conception in the population occurred ≈3 weeks after peak grazing. Spatial and temporal patterns of resource use show that the only period of pure browsing by the focal elephants was located in an over-grazed, communally managed region outside the protected area. The ability to extract time-specific longitudinal records on animal diets, and therefore the ecological history of an organism and its environment, provides an avenue for understanding the impact of climate dynamics and land-use change on animal foraging behavior and habitat relations. PMID:19365077

History of Animals using Isotope Records (HAIR): a 6-year dietary history of one family of African elephants.

PubMed

Cerling, Thure E; Wittemyer, George; Ehleringer, James R; Remien, Christopher H; Douglas-Hamilton, Iain

2009-05-19

The dietary and movement history of individual animals can be studied using stable isotope records in animal tissues, providing insight into long-term ecological dynamics and a species niche. We provide a 6-year history of elephant diet by examining tail hair collected from 4 elephants in the same social family unit in northern Kenya. Sequential measurements of carbon, nitrogen, and hydrogen isotope rations in hair provide a weekly record of diet and water resources. Carbon isotope ratios were well correlated with satellite-based measurements of the normalized difference vegetation index (NDVI) of the region occupied by the elephants as recorded by the global positioning system (GPS) movement record; the absolute amount of C(4) grass consumption is well correlated with the maximum value of NDVI during individual wet seasons. Changes in hydrogen isotope ratios coincided very closely in time with seasonal fluctuations in rainfall and NDVI whereas diet shifts to relatively high proportions of grass lagged seasonal increases in NDVI by approximately 2 weeks. The peak probability of conception in the population occurred approximately 3 weeks after peak grazing. Spatial and temporal patterns of resource use show that the only period of pure browsing by the focal elephants was located in an over-grazed, communally managed region outside the protected area. The ability to extract time-specific longitudinal records on animal diets, and therefore the ecological history of an organism and its environment, provides an avenue for understanding the impact of climate dynamics and land-use change on animal foraging behavior and habitat relations.

Satellite-based drought monitoring in Kenya in an operational setting

NASA Astrophysics Data System (ADS)

Klisch, A.; Atzberger, C.; Luminari, L.

2015-04-01

The University of Natural Resources and Life Sciences (BOKU) in Vienna (Austria) in cooperation with the National Drought Management Authority (NDMA) in Nairobi (Kenya) has setup an operational processing chain for mapping drought occurrence and strength for the territory of Kenya using the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at 250 m ground resolution from 2000 onwards. The processing chain employs a modified Whittaker smoother providing consistent NDVI "Mondayimages" in near real-time (NRT) at a 7-daily updating interval. The approach constrains temporally extrapolated NDVI values based on reasonable temporal NDVI paths. Contrary to other competing approaches, the processing chain provides a modelled uncertainty range for each pixel and time step. The uncertainties are calculated by a hindcast analysis of the NRT products against an "optimum" filtering. To detect droughts, the vegetation condition index (VCI) is calculated at pixel level and is spatially aggregated to administrative units. Starting from weekly temporal resolution, the indicator is also aggregated for 1- and 3-monthly intervals considering available uncertainty information. Analysts at NDMA use the spatially/temporally aggregated VCI and basic image products for their monthly bulletins. Based on the provided bio-physical indicators as well as a number of socio-economic indicators, contingency funds are released by NDMA to sustain counties in drought conditions. The paper shows the successful application of the products within NDMA by providing a retrospective analysis applied to droughts in 2006, 2009 and 2011. Some comparisons with alternative products (e.g. FEWS NET, the Famine Early Warning Systems Network) highlight main differences.

Identification of rice field using Multi-Temporal NDVI and PCA method on Landsat 8 (Case Study: Demak, Central Java)

NASA Astrophysics Data System (ADS)

Sukmono, Abdi; Ardiansyah

2017-01-01

Paddy is one of the most important agricultural crop in Indonesia. Indonesia’s consumption of rice per capita in 2013 amounted to 78,82 kg/capita/year. In 2017, the Indonesian government has the mission of realizing Indonesia became self-sufficient in food. Therefore, the Indonesian government should be able to seek the stability of the fulfillment of basic needs for food, such as rice field mapping. The accurate mapping for rice field can use a quick and easy method such as Remote Sensing. In this study, multi-temporal Landsat 8 are used for identification of rice field based on Rice Planting Time. It was combined with other method for extract information from the imagery. The methods which was used Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA) and band combination. Image classification is processed by using nine classes, those are water, settlements, mangrove, gardens, fields, rice fields 1st, rice fields 2nd, rice fields 3rd and rice fields 4th. The results showed the rice fields area obtained from the PCA method was 50,009 ha, combination bands was 51,016 ha and NDVI method was 45,893 ha. The accuracy level was obtained PCA method (84.848%), band combination (81.818%), and NDVI method (75.758%).

Dynamics of a fringe mangrove forest detected by Landsat images in the Mekong delta, Vietnam

NASA Astrophysics Data System (ADS)

Fagherazzi, S.; Nardin, W.; Woodcock, C. E.; Locatelli, S.; Rulli, M. C.; Pasquarella, V. J.

2016-02-01

Mangrove forests dominate many tropical coastlines and are one of the most bio-diverse and productive environments on Earth. However, little is known of the large scale dynamics of mangrove canopies and how they colonize intertidal areas. Here we focus on a fringe mangrove forest located in the Mekong delta, Vietnam, a fast prograding shoreline where mangroves are encroaching tidal flats. The spatial and temporal evolution of the mangrove canopy is studied using a time series of Landsat images spanning two decades as well as Shuttle Radar Topography Mission (SRTM) elevation data. Our results show that fast mangrove expansion is followed by an increase in Normalized Difference Vegetation Index (NDVI) in the newly established canopy. We observe two different dynamics of the mangrove fringe: near the mouth of the rivers where the fringe boundary is linear the canopy expands uniformly on the tidal flats with a high colonization rate and high NDVI values. Far from the river mouths the fringe boundary is highly irregular and mangroves expansion in characterized by sparse vegetated patches displaying low NDVI values. We conclude that high NDVI values and a regular vegetation-water interface are indicative of stable mangrove canopies undergoing expansion, and therefore of resilient coastlines. In the Mekong delta these area are more likely located near a river mouth.

Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems.

PubMed

Dworak, Volker; Selbeck, Joern; Dammer, Karl-Heinz; Hoffmann, Matthias; Zarezadeh, Ali Akbar; Bobda, Christophe

2013-01-24

The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed.

Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems

PubMed Central

Dworak, Volker; Selbeck, Joern; Dammer, Karl-Heinz; Hoffmann, Matthias; Zarezadeh, Ali Akbar; Bobda, Christophe

2013-01-01

The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed. PMID:23348037

Verification and Validation of NASA-Supported Enhancements to PECAD's Decision Support Tools

NASA Technical Reports Server (NTRS)

McKellipo, Rodney; Ross, Kenton W.

2006-01-01

The NASA Applied Sciences Directorate (ASD), part of the Earth-Sun System Division of NASA's Science Mission Directorate, has partnered with the U.S. Department of Agriculture (USDA) to enhance decision support in the area of agricultural efficiency-an application of national importance. The ASD integrated the results of NASA Earth science research into USDA decision support tools employed by the USDA Foreign Agricultural Service (FAS) Production Estimates and Crop Assessment Division (PECAD), which supports national decision making by gathering, analyzing, and disseminating global crop intelligence. Verification and validation of the following enhancements are summarized: 1) Near-real-time Moderate Resolution Imaging Spectroradiometer (MODIS) products through PECAD's MODIS Image Gallery; 2) MODIS Normalized Difference Vegetation Index (NDVI) time series data through the USDA-FAS MODIS NDVI Database; and 3) Jason-1 and TOPEX/Poseidon lake level estimates through PECAD's Global Reservoir and Lake Monitor. Where possible, each enhanced product was characterized for accuracy, timeliness, and coverage, and the characterized performance was compared to PECAD operational requirements. The MODIS Image Gallery and the GRLM are more mature and have achieved a semi-operational status, whereas the USDA-FAS MODIS NDVI Database is still evolving and should be considered

Spatio-Temporal Dynamics of Vegetation and Their Relationships with Climate in Southeast Asia Based on Three Satellite NDVI Products

NASA Astrophysics Data System (ADS)

Zhang, Y.; Zeng, Z.; Piao, S.

2014-12-01

Tropical vegetation plays an essential role for global biogeochemical cycles. An abundant literature focused on the vegetation dynamics in Amazon. It is shown that the Amazonian rainforest is strongly controlled by radiation, even during dry season. However, only few researches deal with tropical rainforest in Southeast Asia; the vegetation dynamics in Southeast Asia remain poorly understood. In this study, we investigated the spatio-temporal dynamics of vegetation in Southeast Asia with three independent satellite derived Normalized Difference Vegetation Index (NDVI) products (GIMMS AVHRR NDVI3g, SPOT, and MODIS) as well as the recently developed Sun Induced chlorophyll Fluorescence (SIF). We furthermore examined how climate drivers (precipitation, temperature and radiation) exert influences on the vegetation dynamics. We find that the three NDVI datasets are generally consistent with each other. At seasonal scale, NDVI decreases from the beginning to the end of the dry season; at interannual scale, dry season NDVI is positively correlated to precipitation but negatively correlated to radiation, while wet season NDVI is positively correlated to radiation. Compared to evergreen forests, deciduous forests have a larger NDVI decrease rate and more extended area with positive relationships between NDVI and precipitation during the dry season. SIF is lower during dry season than during wet season. Our results indicate that most forests in Southeast Asia, unlike in the Amazonian basin, are water-limited in the dry season but radiation-limited in the wet season. These results imply that droughts may have a stronger impact on forests in Southeast Asia than in Amazon.

NDVI, C3 and C4 production, and distributions in Great Plains grassland land cover classes

USGS Publications Warehouse

Tieszen, L.L.; Reed, Bradley C.; Bliss, Norman B.; Wylie, Bruce K.; DeJong, Benjamin D.

1997-01-01

The distributions of C3 and C4 grasses were used to interpret the distribution, seasonal performance, and potential production of grasslands in the Great Plains of North America. Thirteen major grassland seasonal land cover classes were studied with data from three distinct sources. Normalized Difference Vegetation Index (NDVI) data derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor were collected for each pixel over a 5-yr period (1989–1993), analyzed for quantitative attributes and seasonal relationships, and then aggregated by land cover class. Data from the State Soil Geographic (STATSGO) database were used to identify dominant plant species contributing to the potential production in each map unit. These species were identified as C3 or C4, and contributions to production were aggregated to provide estimates of the percentage of C3 and C4 production for each intersection of the STATSGO map units and the seasonal land cover classes. Carbon isotope values were obtained at specific sites from the soil organic matter of the upper horizon of soil cores and were related to STATSGO estimates of potential production.The grassland classes were distributed with broad northwest-to-southeast orientations. Some classes had large variations in C3 and C4 composition with high proportions of C4species in the south and low proportions in the north. This diversity of photosynthetic types within land cover classes that cross regions of different temperature and precipitation results in similar seasonal patterns and magnitudes of NDVI. The easternmost class, 65, containing tallgrass prairie components, bluestem, Indiangrass, and switchgrass, possessed the highest maximum NDVI and time-integrated NDVI values each year. Grassland classes varied over 5 yr from a high integrated NDVI mean of 4.9 in class 65 in the east to a low of 1.2 in class 76 (sand sage, blue grama, wheatgrass, and buffalograss) in the southwest. Although environmental conditions varied widely during the 5 yr, the rankings of class performance were consistent across years for these NDVI metrics. Land cover classes were less consistent in time of onset, which was often earlier in areas in the north dominated by C3 grasses than in areas to the south dominated by C4grasses. At the level of seasonal land cover classes, no significant relationship was found between the proportions of C3 and C4 species and estimates of potential production derived from the STATSGO database or inferred from the seasonal patterns of NDVI. The isotopic data from specific sites and the potential production data from STATSGO suggest similar patterns of high proportional production by C4 species throughout the south and a decline in proportional production north of the central Great Plains. The land cover classes integrate ecosystem units that encompass a wide diversity of species and C3 and C4 proportions and provide a classification that consistently captures significant ecosystem parameters for the Great Plains.

Analysis Of The Land Surface Temperature And NDVI Using MODIS Data On The Arctic Tundra During The Last Decade

NASA Astrophysics Data System (ADS)

Mattar, C.; Duran-Alarcon, C.; Jimenez-Munoz, J. C.; Sobrino, J. A.

2013-12-01

The arctic tundra is one of the most sensible biome to climate conditions which has experienced important changes in the spatial distribution of temperature and vegetation in the last decades. In this paper we analyzed the spatio-temporal trend of the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) over the arctic tundra biome during the last decade (2001-2012) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) land products MOD11C3 (LST) and MOD13C2 (NDVI) were used. Anomalies for each variable were analyzed at monthly level, and the magnitude and statistical significance of the trends were computed using the non-parametric tests of Sen's Slope and Mann-Kendal respectively. The results obtained from MODIS LST data showed a significant increase (p-value < 0.05) on surface temperature over the arctic tundra in the last decade. In the case of the NDVI, the trend was positive (increase on NDVI) but statistically not significant (p-value < 0.05). All tundra regions defined in the Circumpolar Arctic Vegetation Map have presented positive and statistically significant trends in NDVI and LST. Values of trends obtained from MODIS data over all the tundra regions were +1.10 [°C/dec] in the case of LST and +0.005 [NDVI value/dec] in the case of NDVI.

Causes of spring vegetation greenness trends in the northern mid-high latitudes from 1982 to 2004

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mao, Jiafu; Shi, Xiaoying; Thornton, Peter E

2012-01-01

The Community Land Model version 4 (CLM4) is applied to explore the spatial temporal patterns of spring (April May) vegetation growth trends over the northern mid high latitudes (NMH) (>25 N) between 1982 and 2004. During the spring season through the 23 yr period, both the satellite-derived and simulated normalized difference vegetation index (NDVI) anomalies show a statistically significant correlation and an overall greening trend within the study area. Consistently with the observed NDVI temperature relation, the CLM4 NDVI shows a significant positive association with the spring temperature anomaly for the NMH, North America and Eurasia. Large study areas experiencemore » temperature discontinuity associated with contrasting NDVI trends. Before and after the turning point (TP) of the temperature trends, climatic variability plays a dominant role, while the other environmental factors exert minor effects on the NDVI tendencies. Simulated vegetation growth is broadly stimulated by the increasing atmospheric CO2. Trends show that nitrogen deposition increases NDVI mostly in southeastern China, and decreases NDVI mainly in western Russia after the temperature TP. Furthermore, land use-induced NDVI trends vary roughly with the respective changes in land management practices (crop areas and forest coverage). Our results highlight how non-climatic factors mitigate or exacerbate the impact of temperature on spring vegetation growth, particularly across regions with intensive human activity.« less

The effect of topography on arctic-alpine aboveground biomass and NDVI patterns

NASA Astrophysics Data System (ADS)

Riihimäki, Henri; Heiskanen, Janne; Luoto, Miska

2017-04-01

Topography is a key factor affecting numerous environmental phenomena, including Arctic and alpine aboveground biomass (AGB) distribution. Digital Elevation Model (DEM) is a source of topographic information which can be linked to local growing conditions. Here, we investigated the effect of DEM derived variables, namely elevation, topographic position, radiation and wetness on AGB and Normalized Difference Vegetation Index (NDVI) in a Fennoscandian forest-alpine tundra ecotone. Boosted regression trees were used to derive non-parametric response curves and relative influences of the explanatory variables. Elevation and potential incoming solar radiation were the most important explanatory variables for both AGB and NDVI. In the NDVI models, the response curves were smooth compared with AGB models. This might be caused by large contribution of field and shrub layer to NDVI, especially at the treeline. Furthermore, radiation and elevation had a significant interaction, showing that the highest NDVI and biomass values are found from low-elevation, high-radiation sites, typically on the south-southwest facing valley slopes. Topographic wetness had minor influence on AGB and NDVI. Topographic position had generally weak effects on AGB and NDVI, although protected topographic position seemed to be more favorable below the treeline. The explanatory power of the topographic variables, particularly elevation and radiation demonstrates that DEM-derived land surface parameters can be used for exploring biomass distribution resulting from landform control on local growing conditions.

Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest

DOE PAGES

Yang, Hualei; Yang, Xi; Heskel, Mary; ...

2017-04-28

Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporalmore » resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). Here we found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.« less

Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Hualei; Yang, Xi; Heskel, Mary

Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporalmore » resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). Here we found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.« less

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

NASA Astrophysics Data System (ADS)

Stanton, Carly; Starek, Michael J.; Elliott, Norman; Brewer, Michael; Maeda, Murilo M.; Chu, Tianxing

2017-04-01

A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.

NDVI to Detect Sugarcane Aphid Injury to Grain Sorghum.

PubMed

Elliott, N C; Backoulou, G F; Brewer, M J; Giles, K L

2015-06-01

Multispectral remote sensing has potential to provide quick and inexpensive information on sugarcane aphid, Melanaphis sacchari (Zehntner), pest status in sorghum fields. We describe a study conducted to determine if injury caused by sugarcane aphid to sorghum plants in fields of grain sorghum could be detected using multispectral remote sensing from a fixed wing aircraft. A study was conducted in commercial grain sorghum fields in the Texas Gulf Coast region in June 2014. Twenty-six commercial grain sorghum fields were selected and rated for the level of injury to sorghum plants in the field caused by sugarcane aphid. Plant growth stage ranged from 5.0 (watery ripe) to 7.0 (hard dough) among fields; and plant injury rating from sugarcane aphid ranged from 1.0 (little or no injury) to 4.0 (>40% of plants displaying injury) among fields. The normalized differenced vegetation index (NDVI) is calculated from light reflectance in the red and near-infrared wavelength bands in multispectral imagery and is a common index of plant stress. High NDVI indicates low levels of stress and low NDVI indicates high stress. NDVI ranged from -0.07 to 0.26 among fields. The correlation between NDVI and plant injury rating was negative and significant, as was the correlation between NDVI and plant growth stage. The negative correlation of NDVI with injury rating indicated that plant stress increased with increasing plant injury. Reduced NDVI with increasing plant growth probably resulted from reduced photosynthetic activity in more mature plants. The correlation between plant injury rating and plant growth stage was positive and significant indicating that plant injury from sugarcane aphid increased as plants matured. The partial correlation of NDVI with plant injury rating was negative and significant indicating that NDVI decreased with increasing plant injury after adjusting for its association with plant growth stage. We demonstrated that remotely sensed imagery acquired from grain sorghum fields using an airborne multi-spectral imaging system was sensitive to injury to sorghum plants caused by sugarcane aphid. Published by Oxford University Press on behalf of Entomological Society of America 2015. This work is written by US Government employees and is in the public domain in the US.

Spatiotemporal variations in vegetation cover on the Loess Plateau, China, between 1982 and 2013: possible causes and potential impacts.

PubMed

Kong, Dongxian; Miao, Chiyuan; Borthwick, Alistair G L; Lei, Xiaohui; Li, Hu

2018-05-01

Vegetation is a key component of the ecosystem and plays an important role in water retention and resistance to soil erosion. In this study, we used a multiyear normalized difference vegetation index (NDVI) dataset (1982-2013) and corresponding datasets for observed climatic variables to analyze changes in the NDVI at both temporal and spatial scales. The relationships between NDVI, climate change, and human activities were also investigated. The annual average NDVI showed an upward trend over the 32-year study period, especially in the center of the Loess Plateau. NDVI variations lagged behind monthly temperature changes by approximately 1 month. The contribution of human activities to variations in NDVI has become increasingly significant in recent years, with human activities responsible for 30.4% of the change in NDVI during the period 2001-2013. The increased vegetation coverage has reduced soil erosion on the Loess Plateau in recent years. It is suggested that natural restoration of vegetation is the most effective measure for control of erosion; engineering measures that promote this should feature in the future governance of the Loess Plateau.

Semi-arid vegetation response to antecedent climate and water balance windows

USGS Publications Warehouse

Thoma, David P.; Munson, Seth M.; Irvine, Kathryn M.; Witwicki, Dana L.; Bunting, Erin

2016-01-01

Questions Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation? Location Semi-arid grasslands in Capitol Reef National Park, Utah, USA. Methods We built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years. Results Water balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology. Conclusions We determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.

Spatial Variations in Salinity Stress Across a Coastal Landscape Using Vegetation Indices Derived from Hyperspectral Imagery

DTIC Science & Technology

2009-01-01

NDVI , WBI970, Chlorophyll fluorescence, Salinity, Hyperspectral reflectance JC_Naumann, DR_Young, JE_Anderson Virginia Commonwealth University 800...DF=F0m for M. cerifera (r2 = 0.79) and I. frutescens (r2 = 0.72). The normalized difference vegetation index ( NDVI ), the chlorophyll index (CI), and...frutescens, while there were no differences in NDVI during the 2 years. PRI was not significantly related to NDVI , suggesting that the indices are spatially

USACE National Coastal Mapping Program and the Next Generation of Data Products

DTIC Science & Technology

2010-06-01

Difference Vegetation Index ( NDVI ) equation. This equation uses a near infrared band (NIR) at 738 nm and a red band (RED) at 624 nm [6]. This equation is...shown in (1), NIR - RED / NIR + RED = NDVI value. (1) The pixels that have a NDVI value less than -0.05 are then classified into the...classify these pixels as the “No Lidar” class. Step 5 utilizes the NDVI equation, (1), to separate out the vegetation pixels from the non

Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images

NASA Astrophysics Data System (ADS)

Chiang, Y.; Chen, K.

2013-12-01

This study aims at quantifying vegetation fractional cover (VFC) by incorporating multi-resolution satellite images, including Formosat-2(RSI), SPOT(HRV/HRG), Landsat (MSS/TM) and Terra/Aqua(MODIS), to investigate long-term and seasonal vegetation dynamics in Taiwan. We used 40-year NDVI records for derivation of VFC, with field campaigns routinely conducted to calibrate the critical NDVI threshold. Given different sensor capabilities in terms of their spatial and spectral properties, translation and infusion of NDVIs was used to assure NDVI coherence and to determine the fraction of vegetation cover at different spatio-temporal scales. Based on the proposed method, a bimodal sequence of intra-annual VFC which corresponds to the dual-cropping agriculture pattern was observed. Compared to seasonal VFC variation (78~90%), decadal VFC reveals moderate oscillations (81~86%), which were strongly linked with landuse changes and several major disturbances. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

Trends in spring and autumn phenology over the Tibetan Plateau based on four NDVI datasets

NASA Astrophysics Data System (ADS)

Wang, X.; Xiao, J.; Li, X.; Cheng, G.; Ma, M.

2016-12-01

Vegetation phenology is a sensitive indicator of climate change, and has significant effects on ecosystem carbon uptake. As the Earth's "third pole", the Tibetan Plateau has witnessed rapid warming during the last several decades. The Tibetan Plateau is a unique region to study the trends in vegetation phenology in response to climate change because of the sensitivity of its ecosystems to climate and its low-level human disturbance. The trends in spring and autumn phenology over the plateau are highly controversial. In this study, we examine the trends in the start of growing season (SOS) and end of growing season (EOS) for alpine meadow and steppe using the GIMMS NDVI3g dataset (1982-2013), the GIMMS NDVI dataset (1982-2006), the MODIS NDVI dataset (2001-2013) and the SPOT Vegetation NDVI dataset (1999-2013). Both logistic and polynomial fitting models are used to estimate the SOS and EOS dates. The results are evaluated at four meadow/steppe phenology observation stations. The NDVI-derived SOS and EOS dates are systematically greater than the field-based SOS (emergence seedling date) and EOS (wilting date). There are large discrepancies in both spring and autumn phenology among the different NDVI datasets. For a given NDVI dataset, both SOS and EOS also exhibit significant differences between the two different approaches. Our results show that the trends in spring and autumn phenology over the Tibetan Plateau depend on both the NDVI dataset used and the method for retrieving the SOS and EOS dates. There is no consistent evidence that the "green-up" dates (SOS) has been advancing over the Tibetan Plateau during the last two decades.

Integrating proximal and satellite optical data for the analysis of ecosystem carbon uptake and plant phenology at the European larch Specnet site

NASA Astrophysics Data System (ADS)

Galvagno, Marta; Gamon, John; Cremonese, Edoardo; Garrity, Steven; Huemmrich, K. Fred; Filippa, Gianluca; Morra di Cella, Umberto; Rossini, Micol

2017-04-01

Automated canopy-level optical sampling in tandem with ecosystem-atmosphere flux observations is continuously carried on at a variety of ecosystems through the Specnet network (http://specnet.info/). Specifically, 9 sites within US and Europe were selected since 2015, to investigate the use of novel NDVI and PRI low-cost sensors for the analysis of ecosystem functioning and phenology. Different plant functional types, such as grasslands, deciduous, and evergreen forests belong to the network, here we present specific data from the larch (Larix decidua Mill.) forest Italian site. Three automated NDVI and three automated PRI spectral reflectance sensors (Decagon Devices Inc.) were installed in 2015 on the top of the 20-meters eddy covariance tower, pointing toward the west, north, and east orientations. An additional system, composed by one NDVI and PRI system was installed to monitor the understory component. The objective of this analysis is the comparison between these in-situ inexpensive sensors, independent NDVI and PRI sensors (Skye Instruments) previously installed on the 20-meters tower and satellite-derived NDVI. Both MODIS and Sentinel NDVI data were used for the comparison. Moreover, the newly derived chlorophyll/carotenoid index (CCI, Gamon et al. 2016), computed as the normalized difference between the NDVI red band and PRI 532 nm band, was tested to estimate the seasonal pattern of daily Gross Primary Productivity (GPP) of the larch forest. Results showed that the seasonality of NDVI was comparable among in-situ sensors and satellite data, though orientation-specific differences were observed. Both NDVI and CCI tracked daily GPP, but with different sensitivity to its seasonality. Future analysis will be directed toward a comparison between this site-based results with the other sites within the Specnet network.

Recent Change of Vegetation Growth Trend in China

NASA Technical Reports Server (NTRS)

Peng, Shushi; Chen, Anping; Xu, Liang; Cao, Chunxiang; Fang, Jingyun; Myneni, Ranga B.; Pinzon, Jorge E.; Tucker, COmpton J.; Piao, Shilong

2011-01-01

Using satellite-derived normalized difference vegetation index (NDVI) data, several previous studies have indicated that vegetation growth significantly increased in most areas of China during the period 1982-99. In this letter, we extended the study period to 2010. We found that at the national scale the growing season (April-October) NDVI significantly increased by 0.0007/yr from 1982 to 2010, but the increasing trend in NDVI over the last decade decreased in comparison to that of the 1982-99 period. The trends in NDVI show significant seasonal and spatial variances. The increasing trend in April and May (AM) NDVI (0.0013/yr is larger than those in June, July and August (JJA) (0.0003/yr) and September and October (SO) (0.0008/yr). This relatively small increasing trend of JJA NDVI during 1982-2010 compared with that during 1982-99 (0.0012/yr) (Piao et al 2003 J. Geophys. Res.-Atmos. 108 4401) implies a change in the JJA vegetation growth trend, which significantly turned from increasing (0.0039/yr) to slightly decreasing (0:0002/yr) in 1988. Regarding the spatial pattern of changes in NDVI, the growing season NDVI increased (over 0.0020/yr) from 1982 to 2010 in southern China, while its change was close to zero in northern China, as a result of a significant changing trend reversal that occurred in the 1990s and early 2000s. In northern China, the growing season NDVI significantly increased before the 1990s as a result of warming and enhanced precipitation, but decreased after the 1990s due to drought stress strengthened by warming and reduced precipitation. Our results also show that the responses of vegetation growth to climate change vary across different seasons and ecosystems.

Estimation of Actual Crop ET of Paddy Using the Energy Balance Model SMARET and Validation with Field Water Balance Measurements and a Crop Growth Model (ORYZA)

NASA Astrophysics Data System (ADS)

Nallasamy, N. D.; Muraleedharan, B. V.; Kathirvel, K.; Narasimhan, B.

2014-12-01

Sustainable management of water resources requires reliable estimates of actual evapotranspiration (ET) at fine spatial and temporal resolution. This is significant in the case of rice based irrigation systems, one of the major consumers of surface water resources and where ET forms a major component of water consumption. However huge tradeoff in the spatial and temporal resolution of satellite images coupled with lack of adequate number of cloud free images within a growing season act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. The scale at which ET is determined is decided by the spatial and temporal scale of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which form inputs to energy balance models. In this context, the current study employed disaggregation algorithms (NL-DisTrad and DisNDVI) to generate time series of LST and NDVI images at fine resolution. The disaggregation algorithms aimed at generating LST and NDVI at finer scale by integrating temporal information from concurrent coarse resolution data and spatial information from a single fine resolution image. The temporal frequency of the disaggregated images is further improved by employing composite images of NDVI and LST in the spatio-temporal disaggregation method. The study further employed half-hourly incoming surface insolation and outgoing long wave radiation obtained from the Indian geostationary satellite (Kalpana-1) to convert the instantaneous ET into daily ET and subsequently to the seasonal ET, thereby improving the accuracy of ET estimates. The estimates of ET were validated with field based water balance measurements carried out in Gadana, a subbasin predominated by rice paddy fields, located in Tamil Nadu, India.

Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater.

PubMed

Kobryn, H T; Lantzke, R; Bell, R; Admiraal, R

2015-03-01

The installation of deep drains is an engineering approach to remediate land salinised by the influence of shallow groundwater. It is a costly treatment and its economic viability is, in part, dependent on the lateral extent to which the drain increases biological productivity by lowering water tables and soil salinity (referred to as the drains' zone of benefit). Such zones may be determined by assessing the biological productivity response of adjacent vegetation over time. We tested a multi-temporal satellite remote sensing method to analyse temporal and spatial changes in vegetation condition surrounding deep drainage sites at five locations in the Western Australian wheatbelt affected by dryland salinity-Morawa, Pithara, Beacon, Narembeen and Dumbleyung. Vegetation condition as a surrogate for biological productivity was assessed by Normalised Difference Vegetation Index (NDVI) during the peak growing season. Analysis was at the site scale within a 1000 m buffer zone from the drains. There was clear evidence of NDVI increasing with elevation, slope and distance from the drain. After accounting for elevation, slope and distance from the drain, there was a significant increase in NDVI across the five locations after installation of deep drains. Changes in NDVI after drainage were broadly consistent with measured changes at each site in groundwater levels after installation of the deep drains. However, this study assessed the lateral extent of benefit for biological productivity and gave a measure of the area of benefit along the entire length of the drain. The method demonstrated the utility of spring NDVI images for rapid and relatively simple assessment of the change in site condition after implementation of drainage, but approaches for further improvement of the procedure were identified. Copyright © 2014 Elsevier Ltd. All rights reserved.

Monitoring Start of Season in Alaska

NASA Astrophysics Data System (ADS)

Robin, J.; Dubayah, R.; Sparrow, E.; Levine, E.

2006-12-01

In biomes that have distinct winter seasons, start of spring phenological events, specifically timing of budburst and green-up of leaves, coincides with transpiration. Seasons leave annual signatures that reflect the dynamic nature of the hydrologic cycle and link the different spheres of the Earth system. This paper evaluates whether continuity between AVHRR and MODIS normalized difference vegetation index (NDVI) is achievable for monitoring land surface phenology, specifically start of season (SOS), in Alaska. Additionally, two thresholds, one based on NDVI and the other on accumulated growing degree-days (GDD), are compared to determine which most accurately predicts SOS for Fairbanks. Ratio of maximum greenness at SOS was computed from biweekly AVHRR and MODIS composites for 2001 through 2004 for Anchorage and Fairbanks regions. SOS dates were determined from annual green-up observations made by GLOBE students. Results showed that different processing as well as spectral characteristics of each sensor restrict continuity between the two datasets. MODIS values were consistently higher and had less inter-annual variability during the height of the growing season than corresponding AVHRR values. Furthermore, a threshold of 131-175 accumulated GDD was a better predictor of SOS for Fairbanks than a NDVI threshold applied to AVHRR and MODIS datasets. The NDVI threshold was developed from biweekly AVHRR composites from 1982 through 2004 and corresponding annual green-up observations at University of Alaska-Fairbanks (UAF). The GDD threshold was developed from 20+ years of historic daily mean air temperature data and the same green-up observations. SOS dates computed with the GDD threshold most closely resembled actual green-up dates observed by GLOBE students and UAF researchers. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska.

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

USGS Publications Warehouse

Olexa, Edward M.; Lawrence, Rick L

2014-01-01

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Development of relative thermal stress index (RTSI) for Monitoring and Management of Dry Deciduous Ecosystem

NASA Astrophysics Data System (ADS)

Gupta, R. K.; Vijayan, D.

Gir wildlife sanctuary located between 20 r 57 to 21 r 20 N and 70 r 28 to 71 r 13 E is the last home of Asiatic lions Its biodiversity comprises of 450 recorded flowering plant species 32 species of mammals 26 species of reptiles about 300 species of birds and more than 2000 species of insects As per 1995 census it has 304 lions and 268 leopards The movement of wildlife to thermally comfortable zones to reduce stress conditions forces the changes in management plan with reference to change in localized water demand This necessitates the use of space based thermal data available from AVHRR MODIS etc to monitor temperature of Gir-ecosystem for meso-scale level operational utility As the time scale of the variability of NDVI parameter is much higher than that for lower boundary temperature LBT the dense patch in riverine forest having highest NDVI value would not experience change in its vigour with the change in the season NDVI value of such patch would be near invariant over the year and temperature of this pixel could serve as reference temperature for developing the concept of relative thermal stress index RTSI which is defined as RTSI T p -T r T max -T r wherein T r T max and T p refer to LBT over the maximum NDVI reference point maximum LBT observed in the Gir ecosystem and the temperature of the pixel in the image respectively RTSI images were computed from AVHRR images for post-monsoon leaf-shedded and summer seasons Scatter plot between RTSI and NDVI for summer seasons

Research for annual travel-route changes of reindeer living around the Arctic Circle using satellite remote sensing

NASA Astrophysics Data System (ADS)

Suzuki, G.; Sakka, T.; Tashiro, T.; Kawamata, H.; Tatsuzawa, S.; Naruse, N.; Takahashi, Y.

2017-12-01

For a long time, nomads living in the Arctic Circle around Siberia have been making a living by hunting reindeer traditionally. Wild reindeer have a recurrent migration every year, however, the travel-route of reindeer has been changing recently, so the nomads cannot expect the route in their traditional experience. To support them, one of authors (Tatsuzawa) investigated the route by installing GPS transmitter to some reindeer. The reason of the changing route, however, remain unclear. Previous works indicated that the reason of changing the route must be a global warming, forest fires, thunders, and floods, but they only discuss only on the basis of measurements in specific area. The purpose of this study is to research why the arctic reindeer alter the travel route annually through 1) the annual change of vegetation (NDVI: normalized difference vegetation index) in reindeer ground, and through 2) the annual change of soil water content (mNDWI: modified normalized difference water index) which can be reflected precipitation near Lena river. First, we analyzed NDVI using MODIS images that can be observed over a wide area, filmed in July and August; the reindeer started to travel. We have compared the seasonal changes of the NDVI images with the trace obtained by GPS data from 2010 to 2012. Although NDVI images in July showed similar numerical values in every year, the satellite images taken at August 29 is annually different; NDVI values become lower (0.5 or less) when the reindeer travel to the north area in winter. This suggests that reindeer move to secure enough food in the end of summer. In contrast, mNDWI becomes high when the reindeer travel to the north area. The annual changes of the route may be related to the amount of rainfall.

Integrating Statistical and Expert Knowledge to Develop Phenoregions for the Continental United States

NASA Astrophysics Data System (ADS)

Betancourt, J. L.; Biondi, F.; Bradford, J. B.; Foster, J. R.; Betancourt, J. L.; Foster, J. R.; Biondi, F.; Bradford, J. B.; Henebry, G. M.; Post, E.; Koenig, W.; Hoffman, F. M.; de Beurs, K.; Hoffman, F. M.; Kumar, J.; Hargrove, W. W.; Norman, S. P.; Brooks, B. G.

2016-12-01

Vegetated ecosystems exhibit unique phenological behavior over the course of a year, suggesting that remotely sensed land surface phenology may be useful for characterizing land cover and ecoregions. However, phenology is also strongly influenced by temperature and water stress; insect, fire, and weather disturbances; and climate change over seasonal, interannual, decadal and longer time scales. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a useful proxy for land surface phenology. We used NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) every eight days at 250 m resolution for the period 2000-2015 to develop phenological signatures of emergent ecological regimes called phenoregions. We employed a "Big Data" classification approach on a supercomputer, specifically applying an unsupervised data mining technique, to this large collection of NDVI measurements to develop annual maps of phenoregions. This technique produces a prescribed number of prototypical phenological states to which every location belongs in any year. To reduce the impact of short-term disturbances, we derived a single map of the mode of annual phenological states for the CONUS, assigning each map cell to the state with the largest integrated NDVI in cases where multiple states tie for the highest frequency of occurrence. Since the data mining technique is unsupervised, individual phenoregions are not associated with an ecologically understandable label. To add automated supervision to the process, we applied the method of Mapcurves, developed by Hargrove and Hoffman, to associate individual phenoregions with labeled polygons in expert-derived maps of biomes, land cover, and ecoregions. We will present the phenoregions methodology and resulting maps for the CONUS, describe the "label-stealing" technique for ascribing biome characteristics to phenoregions, and introduce a new polar plotting scheme for processing NDVI data by localized seasonality.

Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data.

PubMed

Rogers, Brendan M; Solvik, Kylen; Hogg, Edward H; Ju, Junchang; Masek, Jeffrey G; Michaelian, Michael; Berner, Logan T; Goetz, Scott J

2018-06-01

Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree-level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot-level productivity, in turn, has been related to satellite-based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI-based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse-scale imagery in the heterogeneous aspen parkland was limited. Longer-term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems. © 2018 John Wiley & Sons Ltd.

The Response of African Land Surface Phenology to Large Scale Climate Oscillations

NASA Technical Reports Server (NTRS)

Brown, Molly E.; de Beurs, Kirsten; Vrieling, Anton

2010-01-01

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.

Temporal variation and scaling of parameters for a monthly hydrologic model

NASA Astrophysics Data System (ADS)

Deng, Chao; Liu, Pan; Wang, Dingbao; Wang, Weiguang

2018-03-01

The temporal variation of model parameters is affected by the catchment conditions and has a significant impact on hydrological simulation. This study aims to evaluate the seasonality and downscaling of model parameter across time scales based on monthly and mean annual water balance models with a common model framework. Two parameters of the monthly model, i.e., k and m, are assumed to be time-variant at different months. Based on the hydrological data set from 121 MOPEX catchments in the United States, we firstly analyzed the correlation between parameters (k and m) and catchment properties (NDVI and frequency of rainfall events, α). The results show that parameter k is positively correlated with NDVI or α, while the correlation is opposite for parameter m, indicating that precipitation and vegetation affect monthly water balance by controlling temporal variation of parameters k and m. The multiple linear regression is then used to fit the relationship between ε and the means and coefficient of variations of parameters k and m. Based on the empirical equation and the correlations between the time-variant parameters and NDVI, the mean annual parameter ε is downscaled to monthly k and m. The results show that it has lower NSEs than these from model with time-variant k and m being calibrated through SCE-UA, while for several study catchments, it has higher NSEs than that of the model with constant parameters. The proposed method is feasible and provides a useful tool for temporal scaling of model parameter.

Evaluating ESA CCI soil moisture in East Africa.

PubMed

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P

2016-06-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Response of NDVI, biomass, and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra.

PubMed

Boelman, Natalie T; Stieglitz, Marc; Rueth, Heather M; Sommerkorn, Martin; Griffin, Kevin L; Shaver, Gaius R; Gamon, John A

2003-05-01

This study explores the relationship between the normalized difference vegetation index (NDVI), aboveground plant biomass, and ecosystem C fluxes including gross ecosystem production (GEP), ecosystem respiration (ER) and net ecosystem production. We measured NDVI across long-term experimental treatments in wet sedge tundra at the Toolik Lake LTER site, in northern Alaska. Over 13 years, N and P were applied in factorial experiments (N, P and N + P), air temperature was increased using greenhouses with and without N + P fertilizer, and light intensity (photosynthetically active photon flux density) was reduced by 50% using shade cloth. Within each treatment plot, NDVI, aboveground biomass and whole-system CO(2) flux measurements were made at the same sampling points during the peak-growing season of 2001. We found that across all treatments, NDVI is correlated with aboveground biomass ( r(2)=0.84), GEP ( r(2)=0.75) and ER ( r(2)=0.71), providing a basis for linking remotely sensed NDVI to aboveground biomass and ecosystem carbon flux.

[Variation trends of the vegetations in distribution region of Amur tiger based on MODIS NDVI].

PubMed

Wang, Hua-Ru; Wang, Tian-Ming; Ge, Han-Ping

2012-10-01

By using the averaged 250 m MODIS NDVI data in growth seasons of 2000-2010 and the approach of ordinary linear regression, this paper analyzed the variation trends of the vegetations in the distribution region of Amur tiger (Panthera tigris altaica), the Far East region of Russia and the eastern part of Northeast China, as well as the relationships between these variation trends and the anthropogenic activities. In 2000 - 2010, the areas with significantly decreased NDVI were sparsely distributed and accounted for 9.6% of the total, while the areas with significantly increased NDVI were mainly concentrated in the central part of northern Russia Far East Region and only accounted for 0.5% of the total. The percentage of the areas with significantly decreased NDVI in the distribution region of Amur tiger was slightly higher than that in the whole study region. The areas with significantly decreased NDVI were mainly distributed in the places of low elevation, gentle slope, and close to roads/railroads. The number of the pixels with significantly decreased NDVI increased with the increase of the nearest distance to residential locations first, and then decreased gradually. The significant decrease of the NDVI was closely related to the anthropogenic activities, and thus, to adopt effective measures to reduce human disturbances could control the vegetation degradation, and further, provide sustainable basis for the protection of Amur tiger and the conservation of the biodiversity in the studied region.

Studies on MODIS NDVI and its relation with the south west monsoon, western ghats, India

NASA Astrophysics Data System (ADS)

Lakshmi Kumar, Tv; Barbosa, Humberto; Uma, R.; Rao, Koteswara

2012-07-01

Eleven years (2000 to 2010) of Normalized Difference Vegetation Index (NDVI) data, derived from Moderate Imaging Spectroradiometer (MODIS) Terra with 250m resolution are used in the present study to discuss the changes in the trends of vegetal cover. The interannual variability of NDVI over western ghats (number of test sites are 17) showed increasing trend and the pronounced changes are resulted due to the monsoon variability in terms of its distribution (wide spread/fairly wide spread/scattered/isolated) and activity (vigorous/normal/weak) and are studied in detail. The NDVI progression is observed from June with a minimum value of 0.179 and yielded to maximum at 0.565 during September/October, on average. The study then relates the NDVI with the no of light, moderate and heavy rainfall events via statistical techniques such as correlation and regression to understand the connection in between the ground vegetation and the south west monsoon. The results of the study inferred i) NDVI, Antecedent Precipitation Index (API) are in good agreement throughout the monsoon which is evidenced by correlation as well as by Morlett Wavelet Analysis, ii) NDVI maintained good correlation with no of Light Rainy and Moderate Rainy alternatively but not with no of Heavy Rainy days, iii) Relation of NDVI with Isolated, Scattered distributions and active monsoons is substantial and iv) Phenological stages captured the Rate of Green Up during the crop season over western ghats.

Temporal Variation of NDVI and the Drivers of Climate Variables in the Arctic Tundra Transition Zone

NASA Astrophysics Data System (ADS)

Lee, J.; Ryu, Y.; Lee, Y. K.

2016-12-01

The Arctic is a sensitive region to temperature, which is drastically increasing with climate change. Vegetation in transition zones of the sub-arctic tundra biome are most sensitive to the warming climate, as temperature in the Arctic ecosystem is one of important limiting factors of vegetation growth and decomposition. Previous research in the transition zone show that there is a difference of sensible heat flux (21 Wm-2), Leaf Area Index increase from 0.58 - 2.76 and canopy height from 0.1 - 6.1m across dwarf and tall shrubs to forest, however, we lack understanding of NDVI trend of this zone. To better understand the vegetation in transition zones of the arctic ecosystem, we analyze the long-term trend of NDVI (AVHRR 3g GIMMs data), temperature and precipitation (Climate Research Unit data) trend from 1982 - 2010 in Council, Alaska that is a region where arctic tundra is transitioning to boreal forest. We also analyze how the climatic factors, temperature or precipitation, affect NDVI. Annual precipitation had the highest interannual variability compared to temperature and NDVI. There was an overall decreasing trend of annual maximum NDVI (y = -0.0019x+4.7). During 1982 to 2003, NDVI and temperature had a similar pattern, but when temperature suddenly jumped to 13.2°C in 2004, NDVI and precipitation declined. This study highlights that temperature increase does not always lead to greening, but after a certain threshold they may cause damage to sub-arctic tundra vegetation.

Effects of distribution density and cell dimension of 3D vegetation model on canopy NDVI simulation base on DART

NASA Astrophysics Data System (ADS)

Tao, Zhu; Shi, Runhe; Zeng, Yuyan; Gao, Wei

2017-09-01

The 3D model is an important part of simulated remote sensing for earth observation. Regarding the small-scale spatial extent of DART software, both the details of the model itself and the number of models of the distribution have an important impact on the scene canopy Normalized Difference Vegetation Index (NDVI).Taking the phragmitesaustralis in the Yangtze Estuary as an example, this paper studied the effect of the P.australias model on the canopy NDVI, based on the previous studies of the model precision, mainly from the cell dimension of the DART software and the density distribution of the P.australias model in the scene, As well as the choice of the density of the P.australiass model under the cost of computer running time in the actual simulation. The DART Cell dimensions and the density of the scene model were set by using the optimal precision model from the existing research results. The simulation results of NDVI with different model densities under different cell dimensions were analyzed by error analysis. By studying the relationship between relative error, absolute error and time costs, we have mastered the density selection method of P.australias model in the simulation of small-scale spatial scale scene. Experiments showed that the number of P.australias in the simulated scene need not be the same as those in the real environment due to the difference between the 3D model and the real scenarios. The best simulation results could be obtained by keeping the density ratio of about 40 trees per square meter, simultaneously, of the visual effects.

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

USGS Publications Warehouse

Wilson, Natalie R.; Norman, Laura

2018-01-01

Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products

PubMed Central

Zheng, Yang; Wu, Bingfang; Zhang, Miao; Zeng, Hongwei

2016-01-01

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5’s spatial resolution and at MODIS’s temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R2 of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R2 of the SOS ranging from 0.68 to 0.86 and with an R2 of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture. PMID:27973404

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.

PubMed

Zheng, Yang; Wu, Bingfang; Zhang, Miao; Zeng, Hongwei

2016-12-10

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5's spatial resolution and at MODIS's temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R ² of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R ² of the SOS ranging from 0.68 to 0.86 and with an R ² of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.

Evidence of compounded disturbance effects on vegetation recovery following high-severity wildfire and spruce beetle outbreak

USGS Publications Warehouse

Carlson, Amanda R.; Sibold, Jason S.; Assal, Timothy J.; Negrón, José F.

2017-01-01

Spruce beetle (Dendroctonus rufipennis) outbreaks are rapidly spreading throughout subalpine forests of the Rocky Mountains, raising concerns that altered fuel structures may increase the ecological severity of wildfires. Although many recent studies have found no conclusive link between beetle outbreaks and increased fire size or canopy mortality, few studies have addressed whether these combined disturbances produce compounded effects on short-term vegetation recovery. We tested for an effect of spruce beetle outbreak severity on vegetation recovery in the West Fork Complex fire in southwestern Colorado, USA, where much of the burn area had been affected by severe spruce beetle outbreaks in the decade prior to the fire. Vegetation recovery was assessed using the Landsat-derived Normalized Difference Vegetation Index (NDVI) two years after the fire, which occurred in 2013. Beetle outbreak severity, defined as the basal area of beetle-killed trees within Landsat pixels, was estimated using vegetation index differences (dVIs) derived from pre-outbreak and post-outbreak Landsat images. Of the seven dVIs tested, the change in Normalized Difference Moisture Index (dNDMI) was most strongly correlated with field measurements of beetle-killed basal area (R2 = 0.66). dNDMI was included as an explanatory variable in sequential autoregressive (SAR) models of NDVI2015. Models also included pre-disturbance NDVI, topography, and weather conditions at the time of burning as covariates. SAR results showed a significant correlation between NDVI2015 and dNDMI, with more severe spruce beetle outbreaks corresponding to reduced post-fire vegetation cover. The correlation was stronger for models which were limited to locations in the red stage of outbreak (outbreak ≤ 5 years old at the time of fire) than for models of gray-stage locations (outbreak > 5 years old at the time of fire). These results indicate that vegetation recovery processes may be negatively impacted by severe spruce beetle outbreaks occurring within a decade of stand-replacing wildfire.

Evidence of compounded disturbance effects on vegetation recovery following high-severity wildfire and spruce beetle outbreak

PubMed Central

Carlson, Amanda R.; Sibold, Jason S.; Assal, Timothy J.; Negrón, Jose F.

2017-01-01

Spruce beetle (Dendroctonus rufipennis) outbreaks are rapidly spreading throughout subalpine forests of the Rocky Mountains, raising concerns that altered fuel structures may increase the ecological severity of wildfires. Although many recent studies have found no conclusive link between beetle outbreaks and increased fire size or canopy mortality, few studies have addressed whether these combined disturbances produce compounded effects on short-term vegetation recovery. We tested for an effect of spruce beetle outbreak severity on vegetation recovery in the West Fork Complex fire in southwestern Colorado, USA, where much of the burn area had been affected by severe spruce beetle outbreaks in the decade prior to the fire. Vegetation recovery was assessed using the Landsat-derived Normalized Difference Vegetation Index (NDVI) two years after the fire, which occurred in 2013. Beetle outbreak severity, defined as the basal area of beetle-killed trees within Landsat pixels, was estimated using vegetation index differences (dVIs) derived from pre-outbreak and post-outbreak Landsat images. Of the seven dVIs tested, the change in Normalized Difference Moisture Index (dNDMI) was most strongly correlated with field measurements of beetle-killed basal area (R2 = 0.66). dNDMI was included as an explanatory variable in sequential autoregressive (SAR) models of NDVI2015. Models also included pre-disturbance NDVI, topography, and weather conditions at the time of burning as covariates. SAR results showed a significant correlation between NDVI2015 and dNDMI, with more severe spruce beetle outbreaks corresponding to reduced post-fire vegetation cover. The correlation was stronger for models which were limited to locations in the red stage of outbreak (outbreak ≤ 5 years old at the time of fire) than for models of gray-stage locations (outbreak > 5 years old at the time of fire). These results indicate that vegetation recovery processes may be negatively impacted by severe spruce beetle outbreaks occurring within a decade of stand-replacing wildfire. PMID:28777802

Understanding the time-lag effect of terrestrial ecosystem response to drought: a regional case study of the 2000s Millennium Drought in Australia

NASA Astrophysics Data System (ADS)

Zhao, M.; A, G.; Velicogna, I.; Kimball, J. S.

2016-12-01

Drought is one of the major drivers of the reduction in terrestrial ecosystem productivity. Ecosystem productivity may not primarily be driven by present moisture conditions. Instead, earlier drought conditions may have the largest impact on vegetation growth. We investigate this time-lag effect in Australia by comparing MODIS NDVI data with multiple drought metrics that are sensitive to water deficits at different soil depths. These metrics include 1) soil moisture (SM) from microwave satellite-retrievals that is sensitive to top-centimeter SM variations; 2) the Palmer drought severity index (PDSI) which is sensitive to atmosphere moisture demand and shallow-depth ( 1 meter) SM changes; 3) the newly developed GRACE drought severity index (GRACE-DSI) that is sensitive to changes in overall terrestrial water storage component of the hydrologic cycle and complements satellite SM observations and the PDSI by providing information about deep groundwater storage changes. We quantify the temporal lags between NDVI and these drought metrics during 2002-2014. We find that the NDVI closely evolves with the GRACE-DSI but lags 1-3 months behind the PDSI and satellite-retrievals of SM in western Australia. This pattern however is reverse in eastern Australia. These contrasting NDVI response patterns indicate that vegetation in western Australia is more sensitive to water storage in relatively deeper soil depths than vegetation in the east. This suggests that, in western Australia, vegetation might experience a protracted recovery period after extreme drought since, usually, moisture recharge in deeper soil depths takes a relatively longer period. We conclude that the time-lag effect in Australia is associated with the relative depth of SM to which vegetation is most sensitive. We suggest that characterizing the relative vegetation moisture sensitive depth at the global scale is important for understanding the nature and pace of terrestrial ecosystem recovery from extreme drought events under the background of global climate change.

Low noise buffer amplifiers and buffered phase comparators for precise time and frequency measurement and distribution

NASA Technical Reports Server (NTRS)

Eichinger, R. A.; Dachel, P.; Miller, W. H.; Ingold, J. S.

1982-01-01

Extremely low noise, high performance, wideband buffer amplifiers and buffered phase comparators were developed. These buffer amplifiers are designed to distribute reference frequencies from 30 KHz to 45 MHz from a hydrogen maser without degrading the hydrogen maser's performance. The buffered phase comparators are designed to intercompare the phase of state of the art hydrogen masers without adding any significant measurement system noise. These devices have a 27 femtosecond phase stability floor and are stable to better than one picosecond for long periods of time. Their temperature coefficient is less than one picosecond per degree C, and they have shown virtually no voltage coefficients.

The Circumpolar Arctic Vegetation Map: A tool for analysis of change in permafrost regions

NASA Astrophysics Data System (ADS)

Walker, D. A.; Raynolds, M. K.; Maier, H. A.

2003-12-01

Arctic vegetation occurs beyond the northern limit of trees, in areas that have an Arctic climate and Arctic flora. Here we present an overview of the recently published Circumpolar Arctic Vegetation Map (CAVM), an area analysis of the vegetation map, and a discussion of its potential for analysis of change in the Arctic. Six countries have Arctic tundra vegetation, Canada, Greenland, Iceland, Russia, Norway (Svalbard), and the US (Total Arctic area = 7.1 million km2). Some treeless areas, such as most of Iceland and the Aluetian Islands are excluded from the map because they lack an Arctic climate. The CAVM divides the Arctic into five bioclimate subzones, A thru E (Subzone A is the coldest and Subzone E is the warmest), based on a combination of summer temperature and vegetation. Fifteen vegetation types are mapped based on the dominant plant growth forms. More detailed, plant-community-level, information is contained in the database used to construct the map. The reverse side of the vegetation map has a false-color infrared image constructed from Advanced Very-High Resolution (AVHRR) satellite-derived raster data, and maps of bioclimate subzones, elevation, landscape types, lake cover, substrate chemistry, floristic provinces, the maximum normalized difference vegetation index (NDVI), and aboveground phytomass. The vegetation map was analyzed by vegetation type and biomass for each county, bioclimate subzone, and floristic province. Biomass distribution was analyzed by means of a correlation between aboveground phytomass and the normalized difference vegetation index (NDVI), a remote-sensing index of surface greenness. Biomass on zonal surfaces roughly doubles within each successively warmer subzone, from about 50 g m-2 in Subzone A to 800 g m-2- in Subzone E. But the pattern of vegetation increase is highly variable, and depends on a number of other factors. The most important appears to be the glacial history of the landscape. Areas that were glaciated during the late-Pleistocene, such as Canada, Svalbard, and Greenland, do not show such strong increases in NDVI with temperature as do areas that were not glaciated. Abundant lakes and rocky surfaces limit the greenness of these recently glaciated surfaces. The highest NDVI and phytomass are found in non-glaciated regions of Alaska and Russia. Soil acidity also affects NDVI patterns. In Subzone D, where the NDVI/ soil acidity relationship has been studied most closely, NDVI is lower on nonacidic surfaces. This has been attributed to fewer shrubs and higher proportion of graminoids (more standing dead sedge leaves) in nonacidic areas. This trend is probably caused by generally drier soils, with less production, on limestone-derived soils. The trend is less clear in Subzone E because of fewer nonacidic surfaces, and the abundance of glacial lakes with low NDVI on the acidic shield areas of Canada. Time series analysis of trends in NDVI in Subzones C, D, and E in Alaska have shown a 17% increase in the NDVI over the 21-year record. The increases have been greatest in moist nonacidic tundra. Future analyses of the circumpolar database will be directed at examining which geographic regions and vegetation types have shown the strongest increases, and how these are correlated with temperature changes.

Monitoring of land degradation from overgrazing using space-borne radar and optical imagery: a case study in Randi Forest, Cyprus

NASA Astrophysics Data System (ADS)

Papoutsa, C.; Kouhartsiouk, D.; Themistocleous, K.; Christoforou, M.; Hadjimitsis, D. G.

2016-10-01

This paper examines how radar and optical imagery combined can be employed for the study of land degradation. A case study was conducted in the Randi Forest, Cyprus, a known overgrazed area for the past 70 years. Satellite optical imagery was used for the calculation of the Normalised Difference Vegetation Index (NDVI) for the time period between December 2015 to July 2016 and C-Band Synthetic Aperture Radar imagery was used to derive correlative changes in backscatter intensity (σ0). The results are indicative of the overgrazing in the area with the temporal and spatial variations of grazing defined. Both the NDVI and the σ0 values demonstrate sudden shifts in vegetation cover following the start of the grazing period with the greatest shifts being evident in close proximity to the location of farms. NDVI and backscatter coefficient correlation was measured at 0.7 and 0.8 for the months of February and April respectively. Shifts in NDVI value by 0.1 correspond to a shift in σ0 by 4 db. VH cross-polarization showed greater sensitivity to changes in vegetation than VV. The paper also examines the capability of C-Band Synthetic Aperture Radar to measure changes in plant structure and vegetation fraction as the result of grazing. Depending on grazing intensity, backscatter coefficient varies according to vegetation density.

The 2010 Russian Drought Impact on Satellite Measurements of Solar-Induced Chlorophyll Fluorescence: Insights from Modeling and Comparisons with the Normalized Differential Vegetation Index (NDVI)

NASA Technical Reports Server (NTRS)

Yoshida, Y.; Joiner, J.; Tucker, C.; Berry, J.; Lee, J. -E.; Walker, G.; Reichle, R.; Koster, R.; Lyapustin, A.; Wang, Y.

2015-01-01

We examine satellite-based measurements of chlorophyll solar-induced fluorescence (SIF) over the region impacted by the Russian drought and heat wave of 2010. Like the popular Normalized Difference Vegetation Index (NDVI) that has been used for decades to measure photosynthetic capacity, SIF measurements are sensitive to the fraction of absorbed photosynthetically-active radiation (fPAR). However, in addition, SIF is sensitive to the fluorescence yield that is related to the photosynthetic yield. Both SIF and NDVI from satellite data show drought-related declines early in the growing season in 2010 as compared to other years between 2007 and 2013 for areas dominated by crops and grasslands. This suggests an early manifestation of the dry conditions on fPAR. We also simulated SIF using a global land surface model driven by observation-based meteorological fields. The model provides a reasonable simulation of the drought and heat impacts on SIF in terms of the timing and spatial extents of anomalies, but there are some differences between modeled and observed SIF. The model may potentially be improved through data assimilation or parameter estimation using satellite observations of SIF (as well as NDVI). The model simulations also offer the opportunity to examine separately the different components of the SIF signal and relationships with Gross Primary Productivity (GPP).

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

NASA Technical Reports Server (NTRS)

Mueller, Thomas; Tucker, Compton J.; Dressler, Gunnar; Pinzon, Jorge E.; Leimgruber, Peter; Dubayah, Ralph O.; Hurtt, George C.; Boehning-Gaese, Katrin; Fagan, William F.

2014-01-01

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

Estimating sugarcane yield potential using an in-season determination of normalized difference vegetative index.

PubMed

Lofton, Josh; Tubana, Brenda S; Kanke, Yumiko; Teboh, Jasper; Viator, Howard; Dalen, Marilyn

2012-01-01

Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be estimated using an in-season estimation of normalized difference vegetative index (NDVI). Sensor readings were taken using the GreenSeeker® handheld sensor from 2008 to 2011 in St. Gabriel and Jeanerette, LA, USA. In-season estimates of yield (INSEY) values were calculated by dividing NDVI by thermal variables. Optimum timing for estimating sugarcane yield was between 601-750 GDD. In-season estimated yield values improved the yield potential (YP) model compared to using NDVI. Generally, INSEY value showed a positive exponential relationship with yield (r(2) values 0.48 and 0.42 for cane tonnage and sugar yield, respectively). When models were separated based on canopy structure there was an increase the strength of the relationship for the erectophile varieties (r(2) 0.53 and 0.47 for cane tonnage and sugar yield, respectively); however, the model for planophile varieties weakened slightly. Results of this study indicate using an INSEY value for predicting sugarcane yield shows potential of being a valuable management tool for sugarcane producers in Louisiana.

Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

PubMed Central

Bajocco, Sofia; Dragoz, Eleni; Gitas, Ioannis; Smiraglia, Daniela; Salvati, Luca; Ricotta, Carlo

2015-01-01

Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies. PMID:25822505

The Vegetation Trends and Drivers in Beijing-Tianjing Region from 1982 TO 2013 Based on Time Series Gimms NDVI3g

NASA Astrophysics Data System (ADS)

Liu, S.; Tian, H.; Wang, X.; Li, H.; He, Y.

2018-04-01

Vegetation plays a leading role in ecosystems. Plant communities are the main components of ecosystems. Green plants in ecosystems are the primary producers, and they provide the living organic matter for the survival of other organisms. The dynamics of most landscapes are driven by both natural processes and human activities. In this study, the growing season GIMMS NDVI3g and climatic data were used to analyse the vegetation trends and drivers in Beijing-Tianjin-Hebei region from 1982 to 2013. Result shows that, the vegetation in Beijing-Tianjin-Hebei region shows overall restoration and partial degradation trend. The significant restoration region accounts for 61.5 % of Beijing-Tianjin-Hebei region, while the significant degradation region accounts for 2.1 %. The dominant climatic factor for time series NDVI were analyzed using the multi-linear regression model. Vegetation growth in 17.9 % of Beijing-Tianjin-Hebei region is dominated by temperature, 35.5 % is dominated by precipitation, and 11.68 % is dominated by solar radiance. Human activities play important role for vegetation restoration in Beijing-Tianjin-Hebei Region, where the large scale forest restoration programs are the main human activities, such as the three-north shelterbelt construction project, Beijing-Tianjin-Hebei sandstorm source control project and grain for green projects.

NDVI indicated long-term interannual changes in vegetation activities and their responses to climatic and anthropogenic factors in the Three Gorges Reservoir Region, China.

PubMed

Wen, Zhaofei; Wu, Shengjun; Chen, Jilong; Lü, Mingquan

2017-01-01

Natural and social environmental changes in the China's Three Gorges Reservoir Region (TGRR) have received worldwide attention. Identifying interannual changes in vegetation activities in the TGRR is an important task for assessing the impact these changes have on the local ecosystem. We used long-term (1982-2011) satellite-derived Normalized Difference Vegetation Index (NDVI) datasets and climatic and anthropogenic factors to analyze the spatiotemporal patterns of vegetation activities in the TGRR, as well as their links to changes in temperature (TEM), precipitation (PRE), downward radiation (RAD), and anthropogenic activities. At the whole TGRR regional scale, a statistically significant overall uptrend in NDVI variations was observed in 1982-2011. More specifically, there were two distinct periods with different trends split by a breakpoint in 1991: NDVI first sharply increased prior to 1991, and then showed a relatively weak rate of increase after 1991. At the pixel scale, most parts of the TGRR experienced increasing NDVI before the 1990s but different trend change types after the 1990s: trends were positive in forests in the northeastern parts, but negative in farmland in southwest parts of the TGRR. The TEM warming trend was the main climate-related driver of uptrending NDVI variations pre-1990s, and decreasing PRE was the main climate factor (42%) influencing the mid-western farmland areas' NDVI variations post-1990s. We also found that anthropogenic factors such as population density, man-made ecological restoration, and urbanization have notable impacts on the TGRR's NDVI variations. For example, large overall trend slopes in NDVI were more likely to appear in TGRR regions with large fractions of ecological restoration within the last two decades. The findings of this study may help to build a better understanding of the mechanics of NDVI variations in the periods before and during TGDP construction for ongoing ecosystem monitoring and assessment in the post-TGDP period. Copyright © 2016 Elsevier B.V. All rights reserved.

[Correlation analysis on normalized difference vegetation index (NDVI) of different vegetations and climatic factors in Southwest China].

PubMed

Zhang, Yuan-Dong; Zhang, Xiao-He; Liu, Shi-Rong

2011-02-01

Based on the 1982-2006 NDVI remote sensing data and meteorological data of Southwest China, and by using GIS technology, this paper interpolated and extracted the mean annual temperature, annual precipitation, and drought index in the region, and analyzed the correlations of the annual variation of NDVI in different vegetation types (marsh, shrub, bush, grassland, meadow, coniferous forest, broad-leaved forest, alpine vegetation, and cultural vegetation) with corresponding climatic factors. In 1982-2006, the NDVI, mean annual temperature, and annual precipitation had an overall increasing trend, and the drought index decreased. Particularly, the upward trend of mean annual temperature was statistically significant. Among the nine vegetation types, the NDVI of bush and mash decreased, and the downward trend was significant for bush. The NDVI of the other seven vegetation types increased, and the upward trend was significant for coniferous forest, meadow, and alpine vegetation, and extremely significant for shrub. The mean annual temperature in the areas with all the nine vegetation types increased significantly, while the annual precipitation had no significant change. The drought index in the areas with marsh, bush, and cultural vegetation presented an increasing trend, that in the areas with meadow and alpine vegetation decreased significantly, and this index in the areas with other four vegetation types had an unobvious decreasing trend. The NDVI of shrub and coniferous forest had a significantly positive correlation with mean annual temperature, and that of shrub and meadow had significantly negative correlation with drought index. Under the conditions of the other two climatic factors unchanged, the NDVI of coniferous forest, broad-leaved forest, and alpine vegetation showed the strongest correlation with mean annual temperature, that of grass showed the strongest correlation with annual precipitation, and the NDVI of mash, shrub, grass, meadow, and cultural vegetation showed the strongest correlation with drought index. There existed definite correlations among the climatic factors. If the correlations among the climatic factors were ignored, the significant level of the correlations between NDVI and climatic factors would be somewhat reduced.

High-latitude tree growth and satellite vegetation indices: Correlations and trends in Russia and Canada (1982-2008)

NASA Astrophysics Data System (ADS)

Berner, Logan T.; Beck, Pieter S. A.; Bunn, Andrew G.; Lloyd, Andrea H.; Goetz, Scott J.

2011-03-01

Vegetation in northern high latitudes affects regional and global climate through energy partitioning and carbon storage. Spaceborne observations of vegetation, largely based on the normalized difference vegetation index (NDVI), suggest decreased productivity during recent decades in many regions of the Eurasian and North American boreal forests. To improve interpretation of NDVI trends over forest regions, we examined the relationship between NDVI from the advanced very high resolution radiometers and tree ring width measurements, a proxy of tree productivity. We collected tree core samples from spruce, pine, and larch at 22 sites in northeast Russia and northwest Canada. Annual growth rings were measured and used to generate site-level ring width index (RWI) chronologies. Correlation analysis was used to assess the association between RWI and summer NDVI from 1982 to 2008, while linear regression was used to examine trends in both measurements. The correlation between NDVI and RWI was highly variable across sites, though consistently positive (r = 0.43, SD = 0.19, n = 27). We observed significant temporal autocorrelation in both NDVI and RWI measurements at sites with evergreen conifers (spruce and pine), though weak autocorrelation at sites with deciduous conifers (larch). No sites exhibited a positive trend in both NDVI and RWI, although five sites showed negative trends in both measurements. While there are technological and physiological limitations to this approach, these findings demonstrate a positive association between NDVI and tree ring measurements, as well as the importance of considering lagged effects when modeling vegetation productivity using satellite data.

Spatial-temporal dynamics of NDVI and Chl-a concentration from 1998 to 2009 in the East coastal zone of China: integrating terrestrial and oceanic components.

PubMed

Hou, Xiyong; Li, Mingjie; Gao, Meng; Yu, Liangju; Bi, Xiaoli

2013-01-01

Annual normalized difference vegetation index (NDVI) and chlorophyll-a (Chl-a) concentration are the most important large-scale indicators of terrestrial and oceanic ecosystem net primary productivity. In this paper, the Sea-viewing Wide Field-of-view Sensor level 3 standard mapped image annual products from 1998 to 2009 are used to study the spatial-temporal characters of terrestrial NDVI and oceanic Chl-a concentration on two sides of the coastline of China by using the methods of mean value (M), coefficient of variation (CV), the slope of unary linear regression model (Slope), and the Hurst index (H). In detail, we researched and analyzed the spatial-temporal dynamics, the longitudinal zonality and latitudinal zonality, the direction, intensity, and persistency of historical changes. The results showed that: (1) spatial patterns of M and CV between NDVI and Chl-a concentration from 1998 to 2009 were very different. The dynamic variation of terrestrial NDVI was much mild, while the variation of oceanic Chl-a concentration was relatively much larger; (2) distinct longitudinal zonality was found for Chl-a concentration and NDVI due to their hypersensitivity to the distance to shoreline, and strong latitudinal zonality existed for Chl-a concentration while terrestrial NDVI had a very weak latitudinal zonality; (3) overall, the NDVI showed a slight decreasing trend while the Chl-a concentration showed a significant increasing trend in the past 12 years, and both of them exhibit strong self-similarity and long-range dependence which indicates opposite future trends between land and ocean.

Integrating Remote Sensing Data with Directional Two- Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management.

PubMed

Lin, Yun-Bin; Lin, Yu-Pin; Deng, Dong-Po; Chen, Kuan-Wei

2008-02-19

In Taiwan, earthquakes have long been recognized as a major cause oflandslides that are wide spread by floods brought by typhoons followed. Distinguishingbetween landslide spatial patterns in different disturbance regimes is fundamental fordisaster monitoring, management, and land-cover restoration. To circumscribe landslides,this study adopts the normalized difference vegetation index (NDVI), which can bedetermined by simply applying mathematical operations of near-infrared and visible-redspectral data immediately after remotely sensed data is acquired. In real-time disastermonitoring, the NDVI is more effective than using land-cover classifications generatedfrom remotely sensed data as land-cover classification tasks are extremely time consuming.Directional two-dimensional (2D) wavelet analysis has an advantage over traditionalspectrum analysis in that it determines localized variations along a specific direction whenidentifying dominant modes of change, and where those modes are located in multi-temporal remotely sensed images. Open geospatial techniques comprise a series ofsolutions developed based on Open Geospatial Consortium specifications that can beapplied to encode data for interoperability and develop an open geospatial service for sharing data. This study presents a novel approach and framework that uses directional 2Dwavelet analysis of real-time NDVI images to effectively identify landslide patterns andshare resulting patterns via open geospatial techniques. As a case study, this study analyzedNDVI images derived from SPOT HRV images before and after the ChiChi earthquake(7.3 on the Richter scale) that hit the Chenyulan basin in Taiwan, as well as images aftertwo large typhoons (Xangsane and Toraji) to delineate the spatial patterns of landslidescaused by major disturbances. Disturbed spatial patterns of landslides that followed theseevents were successfully delineated using 2D wavelet analysis, and results of patternrecognitions of landslides were distributed simultaneously to other agents using geographymarkup language. Real-time information allows successive platforms (agents) to work withlocal geospatial data for disaster management. Furthermore, the proposed is suitable fordetecting landslides in various regions on continental, regional, and local scales usingremotely sensed data in various resolutions derived from SPOT HRV, IKONOS, andQuickBird multispectral images.

Canadian Boreal Forest Greening and Browning Trends: An Analysis of Biogeographic Patterns and the Relative Roles of Disturbance versus Climate Drivers

NASA Astrophysics Data System (ADS)

Sulla-menashe, D. J.; Woodcock, C. E.; Friedl, M. A.

2017-12-01

Recent studies have used satellite-derived normalized difference vegetation index (NDVI) time series derived from the Advanced Very High Resolution Radiometer (AVHRR) to explore geographic patterns in boreal forest greening and browning. A number of these studies indicate that boreal forests are experiencing widespread browning, and have suggested that these patterns reflect decreases in forest productivity induced by climate change. A key limitation of these studies, however, is their reliance on AVHRR data, which provides imagery with very coarse spatial resolution and lower radiometric quality relative to other available remote sensing time series. Here we use NDVI time series from Landsat, which has much higher radiometric quality and spatial resolution than AVHRR, to characterize spatial patterns in greening and browning across Canada's boreal forest and to explore the drivers behind the observed trends. Our results show that the majority of NDVI changes in Canada's boreal forest reflect disturbance-recovery dynamics not climate change impacts, that greening and browning trends outside of disturbed forests are consistent with expected ecological responses to regional changes in climate, and that observed NDVI changes are geographically limited and relatively small in magnitude. Consistent with biogeographic theory, greening and browning unrelated to disturbance tended to be located in ecotones near boundaries of the boreal forest bioclimatic envelope. We observe greening to be most prevalent in Eastern Canada, which is more humid, and browning to be most prevalent in Western Canada, where there is more moisture stress. We conclude that continued long-term climate change has the potential to significantly alter the character and function of Canada's boreal forest, but recent changes have been modest and near-term impacts are likely to be focused in or near ecotones. As part of a NASA funded project supporting the Arctic-Boreal Vulnerability Experiment (ABoVE), we have extended the scope of this study from a set of 46 sites to the entire ABoVE domain covering Alaska and Northwestern Canada (over 6 million square kilometers). Using the full Landsat record, we will also be investigating climate change impacts to the timing of leaf phenology and disturbance frequency in these rapidly warming regions.

Investigations of possible contributions NDVI's have to misclassification in AVHRR data analysis

Treesearch

David L. Evans; Raymond L. Czaplewski

1996-01-01

Numerous subcontinental-scale projects have placed significant emphasis on the use of Normalized Difference Vegetation Indices (NDVI's) derived from Advanced Very High Resolution Radiometer (AVHRR) satellite data for vegetation type recognition. In multi-season AVHRR data, overlap of NDVI ranges for vegetation classes may degrade overall classification performance...

Towards the Mitigation of Correlation Effects in the Analysis of Hyperspectral Imagery with Extensions to Robust Parameter Design

DTIC Science & Technology

2012-08-01

Difference Vegetation Index ( NDVI ) ..................................... 15  2.3  Methodology...Atmospheric Compensation ........................................................................ 31  3.2.3.1  Normalized Difference Vegetation Index ( NDVI ...anomaly detection algorithms are contrasted and implemented, and explains the use of the Normalized Difference Vegetation Index ( NDVI ) in post

[Spatiotemporal variation of vegetation in northern Shaanxi of Northwest China based on SPOT-VGT NDVI].

PubMed

Yang, Yan-Zheng; Zhao, Peng-Xiang; Hao, Hong-Ke; Chang, Ming

2012-07-01

By using 1998-2010 SPOT-VGT NDVI images, this paper analyzed the spatiotemporal variation of vegetation in northern Shaanxi. In 1998-2010, the NDVI in northern Shaanxi had an obvious seasonal variation. The average monthly NDVI was the minimum (0.14) in January and the maximum (0.46) in August, with a mean value of 0.28. The average annual NDVI presented an overall increasing trend, indicating that the vegetation in this area was in restoring. Spatially, the restoration of vegetation in this area was concentrated in central south part, and the degradation mainly occurred in the north of the Great Wall. Air temperature and precipitation were the important climate factors affecting the variation of vegetation, with the linear correlation coefficients to NDVI being 0.72 and 0.58, respectively. The regions with better restored vegetation were mainly on the slopes of 15 degrees-25 degrees, indicating that the Program of Conversion of Cropland to Forestland and Grassland had a favorable effect in the vegetation restoration in northern Shaanxi.

[Regional scale remote sensing-based yield estimation of winter wheat by using MODIS-NDVI data: a case study of Jining City in Shandong Province].

PubMed

Ren, Jianqiang; Chen, Zhongxin; Tang, Huajun

2006-12-01

Taking Jining City of Shandong Province, one of the most important winter wheat production regions in Huanghuaihai Plain as an example, the winter wheat yield was estimated by using the 250 m MODIS-NDVI data smoothed by Savitzky-Golay filter. The NDVI values between 0. 20 and 0. 80 were selected, and the sum of NDVI value for each county was calculated to build its relation with winter wheat yield. By using stepwise regression method, the linear regression model between NDVI and winter wheat yield was established, with the precision validated by the ground survey data. The results showed that the relative error of predicted yield was between -3.6% and 3.9%, suggesting that the method was relatively accurate and feasible.

Developing satellite-derived estimates of surface moisture status

NASA Technical Reports Server (NTRS)

Nemani, Ramakhrishna; Pierce, Lars; Running, Steve; Goward, Samuel

1993-01-01

An evaluation is made of the remotely sensed surface temperature (Ts)/normalized difference vegetation index (NDVI) relationship in studies of the influence of biome type on the slope of Ts/NDVI, and of the automation of the process of defining the relationship so that the surface moisture status can be compared with Ts/NDVI at continental scales. The analysis is conducted using the NOAA AVHRR over a 300 x 300 km area in western Montana, as well as biweekly composite AVHRR data. A strong negative relationship is established between NDVI and Ts over all biome types.

Analysis of regional-scale vegetation dynamics of Mexico using stratified AVHRR NDVI data. [Normalized Difference Vegetaion Index

NASA Technical Reports Server (NTRS)

Turcotte, Kevin M.; Kramber, William J.; Venugopal, Gopalan; Lulla, Kamlesh

1989-01-01

Previous studies have shown that a good relationship exists between AVHRR Normalized Difference Vegetation Index (NDVI) measurements, and both regional-scale patterns of vegetation seasonality and productivity. Most of these studies used known samples of vegetation types. An alternative approach, and the objective was to examine the above relationships by analyzing one year of AVHRR NDVI data that was stratified using a small-scale vegetation map of Mexico. The results show that there is a good relationship between AVHRR NDVI measurements and regional-scale vegetation dynamics of Mexico.

The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau.

PubMed

Sun, Jian; Qin, Xiaojing; Yang, Jun

2016-01-01

The spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) of three vegetation types (alpine steppe, alpine meadow, and alpine desert steppe) across the Tibetan Plateau was analyzed from 1982 to 2013. In addition, the annual mean temperature (MAT) and annual mean precipitation (MAP) trends were quantified to define the spatiotemporal climate patterns. Meanwhile, the relationships between climate factors and NDVI were analyzed in order to understand the impact of climate change on vegetation dynamics. The results indicate that the maximum of NDVI increased by 0.3 and 0.2 % per 10 years in the entire regions of alpine steppe and alpine meadow, respectively. However, no significant change in the NDVI of the alpine desert steppe has been observed since 1982. A negative relationship between NDVI and MAT was found in all these alpine grassland types, while MAP positively impacted the vegetation dynamics of all grasslands. Also, the effects of temperature and precipitation on different vegetation types differed, and the correlation coefficient for MAP and NDVI in alpine meadow is larger than that for other vegetation types. We also explored the percentages of precipitation and temperature influence on NDVI variation, using redundancy analysis at the observation point scale. The results show that precipitation is a primary limiting factor for alpine vegetation dynamic, rather than temperature. Most importantly, the results can serve as a tool for grassland ecosystem management.

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation.

PubMed

Lin, Yu-Pin; Chu, Hone-Jay; Huang, Yu-Long; Tang, Chia-Hsi; Rouhani, Shahrokh

2011-06-01

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.

Application of Vegetation Indices to Estimate Acorn Production at Iberian Peninsula

NASA Astrophysics Data System (ADS)

Escribano, Juan A.; Díaz-Ambrona, Carlos G. H.; Recuero, Laura; Huesca, Margarita; Cicuendez, Victor; Palacios, Alicia; Tarquis, Ana M.

2014-05-01

The Iberian pig valued natural resources of the pasture when fattened in mountain. The variability of acorn production is not contained in any line of Spanish agricultural insurance. However, the production of arable pasture is covered by line insurance number 133 for loss of pasture compensation. This scenario is only contemplated for breeding cows and brave bulls, sheep, goats and horses, although pigs are not included. This insurance is established by monitoring ten-day composites Normalized Difference Vegetation Index (NDVI) measured by satellite over treeless pastures, using MODIS TERRA satellite. The aim of this work is to check if we can use a satellite vegetation index to estimate the production of acorns. In order to do so, two Spanish grassland locations have been analyzed: regions of Olivenza (Jerez-Oliva) and Merida (Badajoz). The acorns production was evaluated through 2002-2005 gauging conducted by the Grupo Habitat de la Orden (Badajoz). Medium resolution (500x500 m2) MODIS images were used during the same time period to estimate the ten-day composites NDVI at these locations. Finally, meteorological data was obtained from SIAR and MAGRAMA network stations, calculating the ten-day averaged temperature and ten day accumulated precipitation. Considering two accumulated factors, NDVI and temperature, three phenological stages were well defined being the second one which pointed differences among campaigns. Then, accumulated precipitation versus accumulated NDVI was plot for this second phenological stage obtaining maximum differences at 300 mm of cumulative rainfall. Analyzing acorn production with accumulated NDVI in that moment a production function was obtained with a correlation coefficient of 0.71. These results will be discussed in detail. References J.A. Escribano, C.G.H. Diaz-Ambrona, L. Recuero, M. Huesca, V. Cicuendez, A. Palacios-Orueta y A.M. Tarquis. Aplicacion de Indices de Vegetacion para evaluar la falta de produccion de pastos y montaneras en dehesas. I Congreso Iberico de la Dehesa y el Montado. 6-7 Noviembre, 2013, Badajoz. J.A. Escribano Rodriguez, A.M. Tarquis, C.G. Hernandez Diaz-Ambrona. Pasture Drought Insurance Based on NDVI and SAVI. Geophysical Research Abstracts, 14, EGU2012-13945, 2012. EGU General Assembly 2012. Juan Escribano Rodriguez, Carmelo Alonso, Ana Maria Tarquis, Rosa Maria Benito, Carlos Hernandez Diaz-Ambrona. Comparison of NDVI ?elds obtained from different remote sensors. Geophysical Research Abstracts, 15, EGU2013-14153, 2013. EGU General Assembly 2013 Acknowledgements. This work was partially supported by ENESA under project P10 0220C-823.

Phenomapping of rangelands in South Africa using time series of RapidEye data

NASA Astrophysics Data System (ADS)

Parplies, André; Dubovyk, Olena; Tewes, Andreas; Mund, Jan-Peter; Schellberg, Jürgen

2016-12-01

Phenomapping is an approach which allows the derivation of spatial patterns of vegetation phenology and rangeland productivity based on time series of vegetation indices. In our study, we propose a new spatial mapping approach which combines phenometrics derived from high resolution (HR) satellite time series with spatial logistic regression modeling to discriminate land management systems in rangelands. From the RapidEye time series for selected rangelands in South Africa, we calculated bi-weekly noise reduced Normalized Difference Vegetation Index (NDVI) images. For the growing season of 2011⿿2012, we further derived principal phenology metrics such as start, end and length of growing season and related phenological variables such as amplitude, left derivative and small integral of the NDVI curve. We then mapped these phenometrics across two different tenure systems, communal and commercial, at the very detailed spatial resolution of 5 m. The result of a binary logistic regression (BLR) has shown that the amplitude and the left derivative of the NDVI curve were statistically significant. These indicators are useful to discriminate commercial from communal rangeland systems. We conclude that phenomapping combined with spatial modeling is a powerful tool that allows efficient aggregation of phenology and productivity metrics for spatially explicit analysis of the relationships of crop phenology with site conditions and management. This approach has particular potential for disaggregated and patchy environments such as in farming systems in semi-arid South Africa, where phenology varies considerably among and within years. Further, we see a strong perspective for phenomapping to support spatially explicit modelling of vegetation.

Terrain Classification Using Multi-Wavelength Lidar Data

DTIC Science & Technology

2015-09-01

Figure 9. Pseudo- NDVI of three layers within the vertical structure of the forest. (Top) First return from the LiDAR instrument, including the ground...in NDVI throughout the vertical canopy. ........................................................17 Figure 10. Optech Titan operating wavelengths...and Ranging LMS LiDAR Mapping Suite ML Maximum Likelihood NIR Near Infrared N-D VIS n-Dimensional Visualizer NDVI Normalized Difference

Evaluating ESA CCI Soil Moisture in East Africa

NASA Technical Reports Server (NTRS)

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.

2016-01-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASAs Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

NASA Astrophysics Data System (ADS)

Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

2017-12-01

Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

Improvement of Alternative Crop Phenology Detection Algorithms using MODIS NDVI Time Series Data in US Corn Belt Region

NASA Astrophysics Data System (ADS)

Lee, J.; Kang, S.; Seo, B.; Lee, K.

2017-12-01

Predicting crop phenology is important for understanding of crop development and growth processes and improving the accuracy of crop model. Remote sensing offers a feasible tool for monitoring spatio-temporal patterns of crop phenology in region and continental scales. Various methods have been developed to determine the timing of crop phenological stages using spectral vegetation indices (i.e. NDVI and EVI) derived from satellite data. In our study, it was compared four alternative detection methods to identify crop phenological stages (i.e. the emergence and harvesting date) using high quality NDVI time series data derived from MODIS. Also we investigated factors associated with crop development rate. Temperature and photoperiod are the two main factors which would influence the crop's growth pattern expressed in the VI data. Only the effect of temperature on crop development rate was considered. The temperature response function in the Wang-Engel (WE) model was used, which simulates crop development using nonlinear models with response functions that range from zero to one. It has attempted at the state level over 14 years (2003-2016) in Iowa and Illinois state of USA, where the estimated phenology date by using four methods for both corn and soybean. Weekly crop progress reports produced by the USDA NASS were used to validate phenology detection algorithms effected by temperature. All methods showed substantial uncertainty but the threshold method showed relatively better agreement with the State-level data for soybean phenology.

Response of Vegetation Dynamics to Projected Climate Change based on NDVI Simulations using Stepwise Cluster Analysis in the Three-River Headwaters Region of China

NASA Astrophysics Data System (ADS)

Zheng, Y.; Lv, E.; Huang, Y.

2016-12-01

Located in the hinterland of the Qinghai-Tibetan Plateau, the Three-River Headwaters region (THR) features unique eco-environmental conditions and fragile ecosystems, and is very vulnerable to climate change. To investigate the effects of climate change on the ecosystem, the Normalized Difference Vegetation Index (NDVI) was employed as an indicator to reflect the vegetation dynamics in response to climate change. This study proposed a model based on Stepwise-cluster analysis to predict the temporal and spatial distributions of NDVI values for five future years according to Global Circulation Models (GCMs) climate projections under the RCP4.5 scenario. The obtained spatial results showed very good agreements between simulations and remote sensing observations of the NDVI value for both training and validation, and the developed model demonstrated its capability of predicting the monthly changes of NDVI through representing the relationships between it and various climatic factors, including remote sensed precipitation and temperature with no, 1 and 2-month lag period. The monthly average precipitation with one-month lag period was further found to be the most important climatic factor that drives the changes of NDVI in the THR. Compared with the values of NDVI in 2000 - 2013, the predicting results indicate the values of NDVI for the THR in growing season (May to October) will decrease by 15.74% in the next 100 years, suggesting that the THR is going to experience an environmental degradation. The results also show that precipitation is the primary driving factor relative to temperature, especially the one-month-lag precipitation. Findings from this study would help policy makers draw up effective water resource and eco-environmental management strategies for adapting to climate change in the THR.

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change.

PubMed

Schut, Antonius G T; Ivits, Eva; Conijn, Jacob G; Ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982-2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17-36% of all productive areas depending on the NDVI metric used. For only 1-2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity.

Spectral entropy as a mean to quantify water stress history for natural vegetation and irrigated agriculture in a water-stressed tropical environment

NASA Astrophysics Data System (ADS)

Kim, Y.; Johnson, M. S.

2017-12-01

Spectral entropy (Hs) is an index which can be used to measure the structural complexity of time series data. When a time series is made up of one periodic function, the Hs value becomes smaller, while Hs becomes larger when a time series is composed of several periodic functions. We hypothesized that this characteristic of the Hs could be used to quantify the water stress history of vegetation. For the ideal condition for which sufficient water is supplied to an agricultural crop or natural vegetation, there should be a single distinct phenological cycle represented in a vegetation index time series (e.g., NDVI and EVI). However, time series data for a vegetation area that repeatedly experiences water stress may include several fluctuations that can be observed in addition to the predominant phenological cycle. This is because the process of experiencing water stress and recovering from it generates small fluctuations in phenological characteristics. Consequently, the value of Hs increases when vegetation experiences several water shortages. Therefore, the Hs could be used as an indicator for water stress history. To test this hypothesis, we analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data for a natural area in comparison to a nearby sugarcane area in seasonally-dry western Costa Rica. In this presentation we will illustrate the use of spectral entropy to evaluate the vegetative responses of natural vegetation (dry tropical forest) and sugarcane under three different irrigation techniques (center pivot irrigation, drip irrigation and flood irrigation). Through this comparative analysis, the utility of Hs as an indicator will be tested. Furthermore, crop response to the different irrigation methods will be discussed in terms of Hs, NDVI and yield.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

NASA Astrophysics Data System (ADS)

Chen, Yaoliang; Lu, Dengsheng; Moran, Emilio; Batistella, Mateus; Dutra, Luciano Vieira; Sanches, Ieda Del'Arco; da Silva, Ramon Felipe Bicudo; Huang, Jingfeng; Luiz, Alfredo José Barreto; de Oliveira, Maria Antonia Falcão

2018-07-01

The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

NDVI derived from IR-enabled digital cameras: applicability across different plant functional types

NASA Astrophysics Data System (ADS)

Filippa, Gianluca; Cremonese, Edoardo; Galvagno, Marta; Migliavacca, Mirco; Sonnentag, Oliver; Hufkens, Koen; Ryu, Youngryel; Humphreys, Elyn; Morra di Cella, Umberto; Richardson, Andrew D.

2017-04-01

Phenological time-series based on the deployment of radiometric measurements are now being constructed at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of e.g. radiometers or digital cameras. In situ measurements are strongly required to provide high-frequency validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from NIR-enabled digital cameras (NDVIC) at 17 sites encompassing 6 plant functional types and totalizing 74 year-sites of data from the PHENOCAM network. The seasonality of NDVIC was comparable to both NDVI measured by ground light emitting diode (LED) sensors and by MODIS, whereas site-specific scaling factors are required to compare absolute values of NDVIC to standard NDVI measurements. We also compared green chromatic coordinate (GCC) extracted from RGB-only images to NDVIC and found that the two are characterized by slight different dynamics, dependent on the plant functional type. During senescence, NDVIC lags behind GCC in deciduous broad-leaf forests and grasslands, suggesting that GCC is more sensitive to leaf decoloration and NDVIC to the biomass reduction resulting from leaf abscission and green to dry biomass ratio of the canopy. In evergreen forests, NDVIC peaks later than GCC in spring, likely tracking the processes of shoot elongation and new needle formation. Our findings suggest therefore that NDVIC and GCC can complement each other in describing ecosystem phenology.

Study of atmospheric and bidirectional effects on surface reflectance and vegetation index time series: Application to NOAA AVHRR and preparation for future space missions

NASA Technical Reports Server (NTRS)

Frouin, Robert

1993-01-01

The objectives of the investigation, namely 'to characterize the atmospheric and directional effects on surface reflectance and vegetation index using the First International Satellite Cloud Climatology Project (ISLCSP) Field Experiment (FIFE) data set, develop new algorithms to obtain better Advanced Very High Resolution Radiometer (AVHRR) indices, and define possible improvements for future satellite missions', were addressed in three separate, yet complementary studies. First, it was shown, from theoretical calculations, that visible and near infrared reflectances combined linearly at optimum (one or two) viewing angles relate linearly to the fraction of photosynthetically available radiation absorbed by plants, f(sub par), can be used independently of the type of foliage and substrate, eliminate the effects of sub-pixel spatial heterogeneity, and improve the accuracy of the f(sub par) estimates when compared to the Normalized Difference Vegetation Index, NDVI. Second, it was demonstrated that NDVI, even though it is not a linear combination of radiances or reflectances, can be spatially integrated without significant loss of information from scales of 300 to 1000 m. Third, AVHRR visible and near-infrared reflectances over the FIFE site, separating temporal and bidirectional components and determining the model parameters through an original iterative scheme was successfully modeled. It appears that NDVI generated from the top-of-atmosphere reflectances normalized by the bidirectional effects (as determined in the scheme) is a better vegetation index than maximum NDVI. Details about the three studies are presented.

Assessing the Influence of Precipitation Variability on the Vegetation Dynamics of the Mediterranean Rangelands using NDVI and Machine Learning

NASA Astrophysics Data System (ADS)

Daliakopoulos, Ioannis; Tsanis, Ioannis

2017-04-01

Mitigating the vulnerability of Mediterranean rangelands against degradation is limited by our ability to understand and accurately characterize those impacts in space and time. The Normalized Difference Vegetation Index (NDVI) is a radiometric measure of the photosynthetically active radiation absorbed by green vegetation canopy chlorophyll and is therefore a good surrogate measure of vegetation dynamics. On the other hand, meteorological indices such as the drought assessing Standardised Precipitation Index (SPI) are can be easily estimated from historical and projected datasets at the global scale. This work investigates the potential of driving Random Forest (RF) models with meteorological indices to approximate NDVI-based vegetation dynamics. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The updated E-OBS-v13.1 dataset of the ENSEMBLES EU FP6 program provides observed monthly meteorological input to estimate SPI over the Mediterranean rangelands. RF models are trained to depict vegetation dynamics using the latest version (3g.v1) of the third generation GIMMS NDVI generated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensors. Analysis is conducted for the period 1981-2015 at a gridded spatial resolution of 25 km. Preliminary results demonstrate the potential of machine learning algorithms to effectively mimic the underlying physical relationship of drought and Earth Observation vegetation indices to provide estimates based on precipitation variability.

Abstracting GIS Layers from Hyperspectral Imagery

DTIC Science & Technology

2009-03-01

Difference Vegetative Index ( NDVI ) 2-20 2.2.10 Separating Trees from Grass . . . . . . . . . . . 2-22 2.3 Spatial Analysis...2-18 2.10. Example of the Normalized Difference Vegetation Index ( NDVI ) applied to a hyperspectral image. . . . . . . . . . . . . . . . . . 2-20...3.5. Example of applying NDVI to a SOM. . . . . . . . . . . . . . . 3-8 3.6. Visualization of the NIR scatter tree ID algorithm. . . . . . . . 3-9 ix

Seasonal decoupling between vegetation greenness and function over northern high latitude forests

NASA Astrophysics Data System (ADS)

Jeong, S. J.; Schimel, D.; Frankenberg, C.; Drewry, D.; Fisher, J. B.; Verma, M.; Berry, J. A.; Lee, J. E.; Joiner, J.; Guanter, L.

2014-12-01

It is still unclear how seasonal variations in vegetation greenness relate to vegetation function (i.e., photosynthesis). Currently, normalized difference vegetation index (NDVI) is a widely used proxy for the period of terrestrial carbon uptake. However, new complementary measures are now available. In this study, we compare the seasonal cycle of NDVI with remote sensing of solar-induced chlorophyll fluorescence (SIF) and data-driven gross primary productivity (GPP) over the Northern Hemisphere high latitude forests (40°-55°N). Comparison of the seasonal cycle between these three datasets shows that the NDVI-based phenology has a longer estimated growing season than the growing season estimated using SIF/GPP. The differences are largely explained by a slower decrease in NDVI in the fall relative to SIF/GPP. In the transition seasons, NDVI is linearly related to temperature, while SIF/GPP show nonlinear relationships with respect to temperature. These results imply that autumn greening related to warming found in recent studies may not result in enhanced photosynthesis. Our method of combining remote sensing of NDVI and SIF can help improve our understanding of the large-scale vegetation structural and functional changes.

Variation of biomass and carbon pool with NDVI and altitude in sub-tropical forests of northwestern Himalaya.

PubMed

Bhardwaj, D R; Banday, Muneesa; Pala, Nazir A; Rajput, Bhalendra Singh

2016-11-01

In the present study, forests at three altitudes, viz., A 1 (600-900 m), A 2 (900-1200 m) and A 3 (1200-1500 m) above mean sea level having normalised differential vegetation index (NDVI) values of N 1 (0.0-0.1), N 2 (0.1-0.2), N 3 (0.2-0.3), N 4 (0.3-0.4) and N 5 (0.4-0.5) were selected for studying their relationship with the biomass and carbon pool in the state of Himachal Pradesh, India. The study reported maximum stem density of (928 trees ha -1 ) at the A 2 altitude and minimum in the A 3 and A 1 with 600 trees ha -1 each. The stem densities in relation to NDVIs were observed in the order N 5 > N 3 > N 4 > N 1 > N 2 and did not show any definite trend with increasing altitude. Highest stem volume (295.7 m 3  ha -1 ) was observed in N 1 NDVI and minimum (194.1 m 3  ha -1 ) in N 3 index. The trend observed for stem biomass at different altitudes was A 3 > A 1 > A 2 and for NDVIs, it was N 5 > N 1 > N 4 > N 2 > N 3 . Maximum aboveground biomass (265.83 t ha -1 ) was recorded in the 0.0-0.1 NDVI and minimum (169.05 t ha -1 ) in 0.2-0.3 NDVI index. Significantly, maximum total soil carbon density (90.82 t C ha -1 ) was observed in 0.4-0.5 NDVI followed by 0.3-0.4 NDVI (77.12 t C ha -1 ). The relationship between soil carbon and other studied parameters was derived through different functions simultaneously. Cubic function showed highest r 2 in most cases, followed by power, inverse and exponential function. The relationship with NDVI showed highest r 2 (0.62) through cubic functions. In relationship between ecosystem carbon with other parameters of different altitudinal gradient and NDVI, only one positively significant relation was formed with total density (0.579) through cubic function. The present study thus reveals that soil carbon density was directly related to altitude and NDVIs, but the vegetation carbon density did not bear any significant relation with altitude and NDVI.

Noise Rating Criteria for Elevated Rapid Transit Structures

DOT National Transportation Integrated Search

1980-05-01

The purpose of this report is to recommend criteria for rating the noise radiated from elevated rapid transit structures during train passages, so that different types of structures can be inter-compared with respect to their noise impact on the imme...

Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing.

PubMed

Nihei, Naoko; Hashida, Yoshihiko; Kobayashi, Mutsuo; Ishii, Akira

2002-10-01

We applied remote sensing using satellite images capable of obtaining data over a broad range, transcending national borders, as a method of rapidly, precisely, and safely increasing our understanding of the potential distribution of malaria. Our target region was the so-called Mekong malaria region on the Indochina Peninsula. As a malaria index, we used existing distribution maps of total reported malaria cases, malaria mortality, vivax malaria and falciparum malaria incidences, and so forth for 1997 and 1998. We produced monthly distribution maps of a normalized difference vegetation index (NDVI) with values of 0.2+, 0.3+, 0.35+, and 0.4+ using the geographical information system/remote sensing software based on the East Asia monthly NDVI maps of 1997. These maps were overlaid with various malaria index distribution maps, and cross-tabulations were carried out. The resulting maps with NDVI values of 0.3+ and 0.4+ matched the falciparum malaria distribution well, and we realized, in particular, that falciparum malaria is prevalent in regions in which NDVI values of 0.4+ continue for 6 months or more, while cases are fewer in regions with NDVI values of 0.4+ that continue for 5 months or less. It will be necessary in the future to examine the relationship between NDVI values and the habitats of the various vector mosquitoes using high-resolution satellite images and to implement detailed forecasts for malaria endemic areas by means of NDVI.

On the use of a snow aridity index to predict remotely sensed forest productivity in the presence of bark beetle disturbance

NASA Astrophysics Data System (ADS)

Knowles, John F.; Lestak, Leanne R.; Molotch, Noah P.

2017-06-01

We used multiple sources of remotely sensed and ground based information to evaluate the spatiotemporal variability of snowpack accumulation, potential evapotranspiration (PET), and Normalized Difference Vegetation Index (NDVI) throughout the Southern Rocky Mountain ecoregion, USA. Relationships between these variables were used to establish baseline values of expected forest productivity given water and energy inputs. Although both the snow water equivalent (SWE) and a snow aridity index (SAI), which used SWE to normalize PET, were significant predictors of the long-term (1989-2012) NDVI, SAI explained 11% more NDVI variability than SWE. Deviations from these relationships were subsequently explored in the context of widespread forest mortality due to bark beetles. Over the entire study area, NDVI was lower per unit SAI in beetle-disturbed compared to undisturbed areas during snow-related drought; however, both SAI and NDVI were spatially heterogeneous within this domain. As a result, we selected three focus areas inside the larger study area within which to isolate the relative impacts of SAI and disturbance on NDVI using multivariate linear regression. These models explained 66%-85% of the NDVI and further suggested that both SAI and disturbance effects were significant, although the disturbance effect was generally greater. These results establish the utility of SAI as a measure of moisture limitation in snow-dominated systems and demonstrate a reduction in forest productivity due to bark beetle disturbance that is particularly evident during drought conditions resultant from low snow accumulation during the winter.

Remote sensing data with the conditional latin hypercube sampling and geostatistical approach to delineate landscape changes induced by large chronological physical disturbances.

PubMed

Lin, Yu-Pin; Chu, Hone-Jay; Wang, Cheng-Long; Yu, Hsiao-Hsuan; Wang, Yung-Chieh

2009-01-01

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional Latin hypercube sampling approach was applied to select samples from multiple NDVI images. Kriging and sequential Gaussian simulation with sufficient samples were then used to generate maps of NDVI images. The variography of NDVI image results demonstrate that spatial patterns of disturbed landscapes were successfully delineated by variogram analysis in study areas. The high-magnitude Chi-Chi earthquake created spatial landscape variations in the study area. After the earthquake, the cumulative impacts of typhoons on landscape patterns depended on the magnitudes and paths of typhoons, but were not always evident in the spatiotemporal variability of landscapes in the study area. The statistics and spatial structures of multiple NDVI images were captured by 3,000 samples from 62,500 grids in the NDVI images. Kriging and sequential Gaussian simulation with the 3,000 samples effectively reproduced spatial patterns of NDVI images. However, the proposed approach, which integrates the conditional Latin hypercube sampling approach, variogram, kriging and sequential Gaussian simulation in remotely sensed images, efficiently monitors, samples and maps the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial variability and heterogeneity.

Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia

NASA Astrophysics Data System (ADS)

Zaitunah, A.; Samsuri; Ahmad, A. G.; Safitri, R. A.

2018-03-01

Watershed is an ecosystem area confined by topography and has function as a catcher, storage, and supplier of water, sediments, pollutants and nutrients in the river system and exit through a single outlet. Various activities around watershed areas of Besitang have changed the land cover and vegetation index (NDVI) that exist in the region. In order to detect changes in land cover and NDVI quickly and accurately, we used remote sensing technology and geographic information systems (GIS). The study aimed to assess changes in land cover and vegetation density (NDVI) between 2005 and 2015, as well as obtaining the density of vegetation (NDVI) on each of the land cover of 2005 and 2015. The research showed the extensive of forest area of 949.65 Ha and a decline of mangrove forest area covering an area of 2,884.06 Ha. The highest vegetation density reduced 39,714.58 Ha, and rather dense increased 24,410.72 Ha between 2005 and 2015. The land cover that have the highest NDVI value range with very dense vegetation density class is the primary dry forest (0.804 to 0.876), followed by secondary dry forest (0.737 to 0.804) for 2015. In 2015 the land cover has NDVI value range the primary dry forest (0.513 to 0.57), then secondary dry forest (0.456 to 0.513) with dense vegetation density class

Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach.

PubMed

Sun, J; Li, Y P; Gao, P P; Suo, C; Xia, B C

2018-06-13

In this study, a stepwise cluster modeling approach (SCMA) is developed for analyzing urban ecosystem variation via Normalized Difference Vegetation Index (NDVI). NDVI is an indicator of vegetation growth and coverage and useful in reflecting urban ecosystem. SCMA is established on a cluster tree that can characterize the complex relationship between independent and dependent variables. SCMA is applied to the City of Dongguan for simulating the urban NDVI and identifying associated drivers of human activity, topography and meteorology without specific functions. Results show that SCMA performances better than conventional statistical methods, illustrating the ability of SCMA in capturing the complex and nonlinear features of urban ecosystem. Results disclose that human activities play negative effects on NDVI due to the destruction of green space for pursuing more space for buildings. NDVI reduces gradually from the south part to the north part of Dongguan due to increased gross domestic product and population density, indicating that the ecosystem in Dongguan is better in the south part. NDVI in the northeast part (dominated by agriculture) is sensitive to the growth of economy and population. More attention should be paid to this part for sustainable development, such as increasing afforestation, planting grass and constructing parks. Precipitation has a positive effect on NDVI due to the promotion of soil moisture that is beneficial to plants' growth. Awareness of these complexities is helpful for sustainable development of urban ecosystem. Copyright © 2018 Elsevier Inc. All rights reserved.

LINKING IN SITU TIME SERIES FOREST CANOPY LAI AND PHENOLOGY METRICS WITH MODIS AND LANDSAT NDVI AND LAI PRODUCTS

EPA Science Inventory

The subject of this presentation is forest vegetation dynamics as observed by the TERRA spacecraft's Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper, and complimentary in situ time series measurements of forest canopy metrics related to Leaf Area...

Monitoring land use and degradation using satellite and airborne data

NASA Technical Reports Server (NTRS)

Ray, Terrill W.; Farr, Thomas G.; Blom, Ronald G.; Crippen, Robert E.

1993-01-01

In July 1990 AVIRIS and AIRSAR data were collected over the Manix Basin Area of the Mojave Desert to study land degradation in an arid area where centerpivot irrigation had been in use. The Manix Basin is located NE of Barstow, California, along Interstate-15 at 34 deg 57 min N 116 deg 35 min W. This region was covered by a series of lakes during the Late Pleistocence and Early Holocene. Beginning in the 1960's, areas were cleared of the native creosote bush-dominated plant community to be used for agricultural purposes. Starting in 1972 fields have been abandoned due to the increased cost of electricity needed to pump the irrigation water, with some fields abandoned as recently as 1988 and 1992. These circumstances provide a time series of abandoned fields which provide the possibility of studying the processes which act on agricultural fields in arid regions when they are abandoned. Ray et al. reported that polarimetric SAR (AIRSAR) could detect that the concentric circular planting furrows plowed on these fields persists for a few years after abandonment and then disappear over time and that wind ripples which form on these fields over time due to wind erosion can be detected with polarimetric radar. Ray et al. used Landsat Thematic Mapper (TM) bandpasses to generate NDVI images of the Manix Basin which showed that the fields abandoned for only a few years had higher NDVI's than the undisturbed desert while the fields abandoned for a longer time had NDVI levels lower than that of the undisturbed desert. The purpose of this study is to use a fusion of a time series of satellite data with airborne data to provide a context for the airborne data. The satellite data time series will additionally help to validate the observation and analysis of time-dependent processes observed in the single AVIRIS image of fields abandoned for different periods of time.

Quantifying Temperature Effects on Snow, Plant and Streamflow Dynamics in Headwater Catchments

NASA Astrophysics Data System (ADS)

Wainwright, H. M.; Sarah, T.; Siirila-Woodburn, E. R.; Newcomer, M. E.; Williams, K. H.; Hubbard, S. S.; Enquist, B. J.; Steltzer, H.; Carroll, R. W. H.

2017-12-01

Quantifying Temperature Effects on Snow, Plant and Streamflow Dynamics in Headwater Catchments Snow-dominated headwater catchments are critical for water resource throughout the world; particularly in Western US. Under climate change, temperature increases are expected to be amplified in mountainous regions. We use a data-driven approach to better understand the coupling among inter-annual variability in temperature, snow and plant community dynamics and stream discharge. We apply data mining methods (e.g., principal component analysis, random forest) to historical spatiotemporal datasets, including the SNOTEL data, Landsat-based normalized difference vegetation index (NDVI) and airborne LiDAR-based snow distribution. Although both snow distribution and NDVI are extremely heterogeneous spatially, the inter-annual variability and temporal responses are spatially consistent, providing an opportunity to quantify the effect of temperature in the catchment-scale. We demonstrate our approach in the East River Watershed of the Upper Colorado River Basin, including Rocky Mountain Biological Laboratory, where the changes in plant communities and their dynamics have been extensively documented. Results indicate that temperature - particularly spring temperature - has a significant control not only on the timing of snowmelt, plant NDVI and peak flow but also on the magnitude of peak NDVI, peak flow and annual discharge. Monthly temperature in spring explains the variability of snowmelt by the equivalent standard deviation of 3.4-4.4 days, and total discharge by 10-11%. In addition, the high correlation among June temperature, peak NDVI and annual discharge suggests a primary role of spring evapotranspiration on plant community phenology, productivity, and streamflow volume. On the other hand, summer monsoon precipitation does not contribute significantly to annual discharge, further emphasizing the importance of snowmelt. This approach is mostly based on a set of datasets typically available throughout the US, providing a powerful approach to link remote sensing techniques with long-term monitoring of temperature, snowfall, plant, and streamflow dynamics.

A new approach to monitoring spatial distribution and dynamics of wetlands and associated flows of Australian Great Artesian Basin springs using QuickBird satellite imagery

NASA Astrophysics Data System (ADS)

White, Davina C.; Lewis, Megan M.

2011-09-01

SummaryThis study develops an expedient digital mapping technique using Very High Resolution satellite imagery to monitor the temporal response of permanent wetland vegetation to changes in spring flow rates from the Australian Great Artesian Basin at Dalhousie Springs Complex, South Australia. Three epochs of QuickBird satellite multispectral imagery acquired between 2006 and 2010 were analysed using the Normalised Difference Vegetation Index (NDVI). A regression of 2009 NDVI values against vegetation cover from field botanical survey plots provided a relationship of increasing NDVI with increased vegetation cover ( R2 = 0.86; p < 0.001). On the basis of this relationship a vegetation threshold was determined (NDVI ⩾ 0.35), which discriminated perennial and ephemeral wetland vegetation from surrounding dryland vegetation in the imagery. The extent of wetlands for the entire Dalhousie Springs Complex mapped from the imagery increased from 607 ha in December 2006 to 913 ha in May 2009 and 1285 ha in May 2010. Comparison of the three NDVI images showed considerable localised change in wetland vegetation greenness, distribution and extent in response to fires, alien vegetation removal, rainfall and fluctuations in spring flow. A strong direct relationship ( R2 = 0.99; p < 0.001) was exhibited between spring flow rate and the area of associated wetland vegetation for eight individual springs. This relationship strongly infers that wetland area is an indicator of spring flow and can be used for monitoring purposes. This method has the potential to determine the sensitivity of spring wetland vegetation extent and distribution to associated changes in spring flow rates due to land management and aquifer extractions. Furthermore, this approach is timely and provides reliable and repeatable monitoring, particularly needed given the projected increased demand for groundwater extractions from the GAB for mining operations.

Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia

NASA Astrophysics Data System (ADS)

Gopp, N. V.; Nechaeva, T. V.; Savenkov, O. A.; Smirnova, N. V.; Smirnov, V. V.

2017-11-01

The informativeness of NDVI for predictive mapping of the physical and chemical properties of plow horizons of soils on different slope positions within the first (280-310 m a.s.l.) and second (240-280 m a.s.l.) altitudinal steps has been examined. This index is uninformative for mapping soil properties in small hollows, whose factual width is less than the Landsat image resolution (30 m). In regression models, NDVI index explains 52% of variance in the content of humus; 35 and 24% of variance in the contents of total and nitrate nitrogen; 19 and 29% of variance in the contents of total and available phosphorus; 25 and 50% of variance in the contents of exchangeable calcium and manganese; and 30 and 29% of variance in the contents of fine silt and soil water, respectively. On the basis of the models obtained, prognostic maps of the soil properties have been developed. Spatial distribution patterns of NDVI calculated from Landsat 8 images (30-m resolution) serve as the cartographic base and the main indicator of the soil properties. The NDVI values and the contents of humus, physical clay (<0.01 mm) and fine silt particles, total and nitrate nitrogen, total phosphorus, and exchangeable calcium and manganese in the soils of the first altitudinal step are higher than those in the soils of the second altitudinal step. An opposite tendency has been found for the available phosphorus content: in the soils of the second altitudinal step and the hollow, its content is higher than that in the soils of the first altitudinal step by 1.8 and 2.4 times, respectively. Differences in the pH of soil water suspensions, easily available phosphorus, and clay in the soils of the compared topographic positions (first and second altitudinal steps and the hollow) are statistically unreliable.

PDO and ENSO Sea Surface Temperature Anomalies Control Grassland Plant Production across the United States Great Plains

NASA Astrophysics Data System (ADS)

Parton, W. J.; Del Grosso, S. J.; Smith, W. K.; Chen, M.

2017-12-01

The El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are multi-annual to multi-decadal climate patterns defined by ocean temperature anomalies that can strongly modulate climate variability. Here we evaluated the impacts of PDO and ENSO sea surface temperature (SST) anomalies on observed grassland above ground plant production (ANPP; 1940 to 2015), spring (April to July) cumulative actual evapotranspiration (iAET; 1900 to 2015) , and satellite-derived growing season (April to October) cumulative normalized difference vegetation index (iNDVI 1982 to 2015) across the United States Great Plains. The results showed that grassland ANPP is well correlated to iAET (r2=0.69) and iNDVI (r2=0.50 to 0.70) for the Cheyenne Wyoming and Northeastern Colorado long-term ANPP sites. At the site scale, during the negative phase of the PDO, we find ANPP is much lower (25%) and that variability of iAET, iNDVI, and ANPP are much higher (2 to 3 times) compared to the warm phase PDO. Further, we find there is a high frequency of below normal iAET when PDO and ENSO SST's are both negative, while there is a high frequency of above normal iAET when PDO and ENSO values are positive. At the regional scale, iAET, iNDVI, and modeled ANPP data sets show that plant production and iAET values are high in the southern Great Plains and low in the northern Great Plains when spring PDO and ENSO are both in the positive phase, while the opposite pattern is observed when both PDO and ENSO are both in the negative phase. Variability of iAET, iNDVI, and modeled ANPP are much higher in the central Great Plains during the negative phase PDO. We demonstrate clearly that the PDO and ENSO SST anomalies have large impacts on mean and variability of grassland plant production across the Great Plains.

Phenology Analysis of Forest Vegetation to Environmental Variables during - and Post-Monsoon Seasons in Western Himalayan Region of India

NASA Astrophysics Data System (ADS)

Khare, S.; Latifi, H.; Ghosh, K.

2016-06-01

To assess the phenological changes in Moist Deciduous Forest (MDF) of western Himalayan region of India, we carried out NDVI time series analysis from 2013 to 2015 using Landsat 8 OLI data. We used the vegetation index differencing method to calculate the change in NDVI (NDVIchange) during pre and post monsoon seasons and these changes were used to assess the phenological behaviour of MDF by taking the effect of a set of environmental variables into account. To understand the effect of environmental variables on change in phenology, we designed a linear regression analysis with sample-based NDVIchange values as the response variable and elevation aspect, and Land Surface Temperature (LST) as explanatory variables. The Landsat-8 derived phenology transition stages were validated by calculating the phenology variation from Nov 2008 to April 2009 using Landsat-7 which has the same spatial resolution as Landsat-8. The Landsat-7 derived NDVI trajectories were plotted in accordance with MODIS derived phenology stages (from Nov 2008 to April 2009) of MDF. Results indicate that the Landsat -8 derived NDVI trajectories describing the phenology variation of MDF during spring, monsoon autumn and winter seasons agreed closely with Landsat-7 and MODIS derived phenology transition from Nov 2008 to April 2009. Furthermore, statistical analysis showed statistically significant correlations (p < 0.05) amongst the environmental variables and the NDVIchange between full greenness and maximum frequency stage of Onset of Greenness (OG) activity.. The major change in NDVI was observed in medium (600 to 650 m) and maximum (650 to 750 m) elevation areas. The change in LST showed also to be highly influential. The results of this study can be used for large scale monitoring of difficult-to-reach mountainous forests, with additional implications in biodiversity assessment. By means of a sufficient amount of available cloud-free imagery, detailed phenological trends across mountainous forests could be explained.

Soil erosion and sediment yield and their relationships with vegetation cover in upper stream of the Yellow River.

PubMed

Ouyang, Wei; Hao, Fanghua; Skidmore, Andrew K; Toxopeus, A G

2010-12-15

Soil erosion is a significant concern when considering regional environmental protection, especially in the Yellow River Basin in China. This study evaluated the temporal-spatial interaction of land cover status with soil erosion characteristics in the Longliu Catchment of China, using the Soil and Water Assessment Tool (SWAT) model. SWAT is a physical hydrological model which uses the RUSLE equation as a sediment algorithm. Considering the spatial and temporal scale of the relationship between soil erosion and sediment yield, simulations were undertaken at monthly and annual temporal scales and basin and sub-basin spatial scales. The corresponding temporal and spatial Normalized Difference Vegetation Index (NDVI) information was summarized from MODIS data, which can integrate regional land cover and climatic features. The SWAT simulation revealed that the annual soil erosion and sediment yield showed similar spatial distribution patterns, but the monthly variation fluctuated significantly. The monthly basin soil erosion varied from almost no erosion load to 3.92 t/ha and the maximum monthly sediment yield was 47,540 tones. The inter-annual simulation focused on the spatial difference and relationship with the corresponding vegetation NDVI value for every sub-basin. It is concluded that, for this continental monsoon climate basin, the higher NDVI vegetation zones prevented sediment transport, but at the same time they also contributed considerable soil erosion. The monthly basin soil erosion and sediment yield both correlated with NDVI, and the determination coefficients of their exponential correlation model were 0.446 and 0.426, respectively. The relationships between soil erosion and sediment yield with vegetation NDVI indicated that the vegetation status has a significant impact on sediment formation and transport. The findings can be used to develop soil erosion conservation programs for the study area. Copyright © 2010 Elsevier B.V. All rights reserved.

Use of timesat to estimate phenological parameters in Northwestern Patagonia

NASA Astrophysics Data System (ADS)

Oddi, Facundo; Minotti, Priscilla; Ghermandi, Luciana; Lasaponara, Rosa

2015-04-01

Under a global change context, ecosystems are receiving high pressure and the ecology science play a key role for monitoring and assessment of natural resources. To achieve an effective resources management to develop an ecosystem functioning knowledge based on spatio-temporal perspective is useful. Satellite imagery periodically capture the spectral response of the earth and remote sensing have been widely utilized as classification and change detection tool making possible evaluate the intra and inter-annual plant dynamics. Vegetation spectral indices (e.g., NDVI) are particularly suitable to study spatio-temporal processes related to plant phenology and remote sensing specific software, such as TIMESAT, has been developed to carry out time series analysis of spectral indexes. We used TIMESAT software applied to series of 25 years of NDVI bi-monthly composites (240 images covering the period 1982-2006) from the NOAA-AVHRR sensor (8 x 8 km) to assessment plant pheonology over 900000 ha of shrubby-grasslands in the Northwestern of Patagonia, Argentina. The study area corresponds to a Mediterranean environment and is part of a gradient defined by a sharp drop west-east in the precipitation regime (600 mm to 280 mm). We fitted the temporal series of NDVI data to double logistic functions by least-squares methods evaluating three seasonality parameters: a) start of growing season, b) growing season length, c) NDVI seasonal integral. According to fitted models by TIMESAT, start average of growing season was the second half of September (± 10 days) with beginnings latest in the east (dryer areas). The average growing season length was 180 days (± 15 days) without a clear spatial trend. The NDVI seasonal integral showed a clear trend of decrease in west-east direction following the precipitation gradient. The temporal and spatial information allows revealing important patterns of ecological interest, which can be of great importance to environmental monitoring. In this work we also show as utilizing TIMESAT to characterize the plant phenology at regional scale.

Combining SAR with LANDSAT for Change Detection of Riparian Buffer Zone in a Semi-arid River Basin

NASA Astrophysics Data System (ADS)

Chang, N.

2006-12-01

A combination of RADARSAT-1 and Landsat 5 TM satellite images linking the soil moisture variation with Normalized Difference Vegetation Index (NDVI) measurements were used to accomplish remotely sensed change detection of riparian buffer zone in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the study significant due to the interception capability of non-point source impact within the riparian buffer zone and the maintenance of ecosystem integrity region wide. First of all, an estimation of soil moisture using RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery was conducted. With its all-weather capability, the RADARSAT-1 is a promising tool for measuring the surface soil moisture over seasons. The time constraint is almost negligible since the RADARSAT-1 is able to capture surface soil moisture over a large area in a matter of seconds, if the area is within its swath. RADARSAT-1 images presented at here were captured in two acquisitions, including April and September 2004. With the aid of five corner reflectors deployed by Alaska Satellite Facility (ASF), essential radiometric and geometric calibrations were performed to improve the accuracy of the SAR imagery. The horizontal errors were reduced from initially 560 meter down to less than 5 meter at the best try. Then two Landsat 5 TM satellite images were summarized based on its NDVI. The combination of and NDVI and SAR data obviously show that soil moisture and vegetation biomass wholly varies in space and time in the CCRW leading to identify the riparian buffer zone evolution over seasons. It is found that the seasonal soil moisture variation is highly tied with the NDVI values and the change detection of buffer zone is technically feasible. It will contribute to develop more effective management strategies for non-point source pollution control, bird habitat monitoring, and grazing and live stock handlings in the future. Future research focuses on comparison of soil moisture variability within RADARSAT-1 footprints and NDVI variations against interferometric SAR for studying riparian ecosystem functioning on a seasonal basis.

Monitoring 2009 Forest Disturbance Across the Conterminous United States, Based on Near-Real Time and Historical MODIS 250 Meter NDVI Products

NASA Technical Reports Server (NTRS)

Spruce, J.; Hargrove, W. W.; Gasser, G.; Smoot, J. C.; Kuper, P.

2009-01-01

This case study shows the promise of computing current season forest disturbance detection products at regional to CONUS scales. Use of the eMODIS expedited product enabled a NRT CONUS forest disturbance detection product, a requirement for an eventual, operational forest threat EWS. The 2009 classification product from this study can be used to quantify the areal extent of forest disturbance across CONUS, although a quantitative accuracy assessment still needs to be completed. However, the results would not include disturbances that occurred after July 27, such as the Station Fire. While not shown here, the project also produced maximum NDVI products for the June 10-July 27 period of each year of the 2000-2009 time frame. These products could be applied to compute forest change products on an annual basis. GIS could then be used to assess disturbance persistence. Such follow-on work could lead to attribution of year in which a disturbance occurred. These products (e.g., Figures 6 and 7) may also be useful for assessing forest change associated with climate change, such as carbon losses from bark beetle-induced forest mortality in the Western United States. Other MODIS phenological products are being assessed for aiding forest monitoring needs of the EWS, including cumulative NDVI products (Figure 10).

Vegetation Response to Rainfall and Soil Moisture Variability in Botswana

DTIC Science & Technology

1991-01-01

Effects of Varying Soil Type on the NDVI /Rainfall and NDVI /Soil Moisture...examine the effects of different soil types on the vegetation growth/rainfall relationship. The goals are to determine whether differences in the water-use...34first step" in removing the soil effect (Huete et al., 1985). Indeed, no large-scale soil corrections have been attempted as yet on NDVI data.

Evapotranspiration, Water Table Fluctuations, and Riparian Restoration: Report Documentary 2007-2008 Work

DTIC Science & Technology

2010-09-01

is delineated in upper third in 2006 image. .............................................................................. 23 Figure 19. NDVI ...values are compared for SPOT imagery from 29 May 2006 and 24 August 2006. Fire areas with reduced NDVI from Malpais fire are clearly seen on both sides...results comparing vegetation type and normalized difference vegetation index ( NDVI ), and (4) presents initial results from the groundwater flow field

ESTCP Pilot Project Wide Area Assessment for Munitions Response

DTIC Science & Technology

2008-07-01

Data A broadband normalized difference vegetation index ( NDVI ) was computed from the high- resolution spectral data to provide a detection of canopy...chlorophyll content. The NDVI strongly correlates with the green yucca, cactus, juniper, and other SAR-responsive vegetation species on the site...Vegetation Index. NDVI is broadband normalized difference vegetation index computed from high resolution spectral data using (RED-NIR) / (RED +NIR) to

Mapping Collective Identity: Territories and Boundaries of Human Terrain

DTIC Science & Technology

2011-06-10

Line MAP-HT Mapping the Human Terrain NDVI Normalized Difference Vegetation Index NGA National Geospatial-Intelligence Agency xi OBIA Object-Based...The Normalized Difference Vegetation Index ( NDVI ) uses the red band to represent the low reflectance from vegetation and the expanded near infrared...spectrum to provide greater delineation of agricultural areas. This layer highlights different fields, crops, and their boundaries. NDVI layers are

Estimating tree species diversity in the savannah using NDVI and woody canopy cover

NASA Astrophysics Data System (ADS)

Madonsela, Sabelo; Cho, Moses Azong; Ramoelo, Abel; Mutanga, Onisimo; Naidoo, Laven

2018-04-01

Remote sensing applications in biodiversity research often rely on the establishment of relationships between spectral information from the image and tree species diversity measured in the field. Most studies have used normalized difference vegetation index (NDVI) to estimate tree species diversity on the basis that it is sensitive to primary productivity which defines spatial variation in plant diversity. The NDVI signal is influenced by photosynthetically active vegetation which, in the savannah, includes woody canopy foliage and grasses. The question is whether the relationship between NDVI and tree species diversity in the savanna depends on the woody cover percentage. This study explored the relationship between woody canopy cover (WCC) and tree species diversity in the savannah woodland of southern Africa and also investigated whether there is a significant interaction between seasonal NDVI and WCC in the factorial model when estimating tree species diversity. To fulfil our aim, we followed stratified random sampling approach and surveyed tree species in 68 plots of 90 m × 90 m across the study area. Within each plot, all trees with diameter at breast height of >10 cm were sampled and Shannon index - a common measure of species diversity which considers both species richness and abundance - was used to quantify tree species diversity. We then extracted WCC in each plot from existing fractional woody cover product produced from Synthetic Aperture Radar (SAR) data. Factorial regression model was used to determine the interaction effect between NDVI and WCC when estimating tree species diversity. Results from regression analysis showed that (i) WCC has a highly significant relationship with tree species diversity (r2 = 0.21; p < 0.01), (ii) the interaction between the NDVI and WCC is not significant, however, the factorial model significantly reduced the error of prediction (RMSE = 0.47, p < 0.05) compared to NDVI (RMSE = 0.49) or WCC (RMSE = 0.49) model during the senescence period. The result justifies our assertion that combining NDVI with WCC will be optimal for biodiversity estimation during the senescence period.

No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wang, Xufeng; Xiao, Jingfeng; Li, Xin; Cheng, Guodong; Ma, Mingguo; Che, Tao; Dai, Liyun; Wang, Shaoying; Wu, Jinkui

2017-12-01

Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth's "third pole," is a unique region for studying the long-term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low-level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982-2014), the GIMMS NDVI data set (1982-2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001-2014), the Satellite Pour l'Observation de la Terre Vegetation (SPOT-VEG) NDVI data set (1999-2013), and the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) NDVI data set (1998-2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology ("green-up" dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground-based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology.

Performance of vegetation indices from Landsat time series in deforestation monitoring

NASA Astrophysics Data System (ADS)

Schultz, Michael; Clevers, Jan G. P. W.; Carter, Sarah; Verbesselt, Jan; Avitabile, Valerio; Quang, Hien Vu; Herold, Martin

2016-10-01

The performance of Landsat time series (LTS) of eight vegetation indices (VIs) was assessed for monitoring deforestation across the tropics. Three sites were selected based on differing remote sensing observation frequencies, deforestation drivers and environmental factors. The LTS of each VI was analysed using the Breaks For Additive Season and Trend (BFAST) Monitor method to identify deforestation. A robust reference database was used to evaluate the performance regarding spatial accuracy, sensitivity to observation frequency and combined use of multiple VIs. The canopy cover sensitive Normalized Difference Fraction Index (NDFI) was the most accurate. Among those tested, wetness related VIs (Normalized Difference Moisture Index (NDMI) and the Tasselled Cap wetness (TCw)) were spatially more accurate than greenness related VIs (Normalized Difference Vegetation Index (NDVI) and Tasselled Cap greenness (TCg)). When VIs were fused on feature level, spatial accuracy was improved and overestimation of change reduced. NDVI and NDFI produced the most robust results when observation frequency varies.

Intra-annual NDVI validation of the Landsat 5 TM radiometric calibration

USGS Publications Warehouse

Chander, G.; Groeneveld, D.P.

2009-01-01

Multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone of the extensive archive of moderate‐resolution Earth imagery. Even after more than 24 years of service, the L5 TM is still operational. Given the longevity of the satellite, the detectors have aged and the sensor's radiometric characteristics have changed since launch. The calibration procedures and parameters in the National Land Archive Production System (NLAPS) have also changed with time. Revised radiometric calibrations in 2003 and 2007 have improved the radiometric accuracy of recently processed data. This letter uses the Normalized Difference Vegetation Index (NDVI) as a metric to evaluate the radiometric calibration. The calibration change has improved absolute calibration accuracy, consistency over time, and consistency with Landsat 7 (L7) Enhanced Thematic radiometry and will provide the basis for continued long‐term studies of the Earth's land surfaces.

The Landsat Phenology Study (LaPS): Preliminary CONUS Results for 2008

NASA Astrophysics Data System (ADS)

Henebry, Geoffrey M.; Roy, David P.; Ju, Junchang; Kovalskyy, Valeriy

2010-05-01

Most studies of land surface phenology (LSP) have used time series derived from moderate spatial resolution satellite sensor data (e.g., AVHRR, MODIS, VEGETATION) because these data are freely available and because they provide an acceptable trade-off between higher, near daily, temporal frequency of observation needed to reduce cloud contamination against lower (500m-5km) spatial resolution. The recent opening of the USGS Landsat archive to web-enabled access presents the opportunity to explore how well Landsat time series can portray LSPs at high spatial resolution. The NASA Web-enabled Landsat data (WELD) project (http://landsat.usgs.gov/WELD.php) has produced 30m composited mosaics for all the conterminous US (CONUS) from Landsat 7 ETM+ data. The composited mosaics are generated on monthly, seasonal, and annual basis and include spectral reflectance, normalized difference vegetation index (NDVI), and the acquisition date of each composited pixel. The WELD compositing approach is designed to select valid land surface observations with minimal cloud, snow, and atmospheric contamination. We extracted 30m pixel time series from the twelve monthly WELD composited mosaics for 2008 at 320 locations across the CONUS where we have ground phenological observations that are heterogeneous with respect to the types of plants observed, the phenophases recorded (predominantly spring green-up) and the ground sampling protocols used. The ground data came from several sources, including the cloned lilac/honeysuckle network, the Phenocam network, five LTER sites (H.J. Andrews, Harvard Forest, Jornada, Konza Prairie, and Sevilleta), and a private woodlot in Maine. Temporal profiles of the 30m WELD Landsat NDVI, the green NDVI (GNDVI), the normalized difference infrared index (NDII) derived from the composited reflectances, are compared to the ground observations. Results show that (i) inclusion of the Landsat acquisition date for each pixel improves the characterization of the LSP, (ii) the use of the NDII and GNDVI in conjunction with the NDVI improves identification of the onset both of canopy development and senescence, and (iii) the WELD compositing approach and the resulting mosaics provide a rich new data source for phenological investigations.

Impervious surfaces mapping using high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Shirmeen, Tahmina

In recent years, impervious surfaces have emerged not only as an indicator of the degree of urbanization, but also as an indicator of environmental quality. As impervious surface area increases, storm water runoff increases in velocity, quantity, temperature and pollution load. Any of these attributes can contribute to the degradation of natural hydrology and water quality. Various image processing techniques have been used to identify the impervious surfaces, however, most of the existing impervious surface mapping tools used moderate resolution imagery. In this project, the potential of standard image processing techniques to generate impervious surface data for change detection analysis using high-resolution satellite imagery was evaluated. The city of Oxford, MS was selected as the study site for this project. Standard image processing techniques, including Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA), a combination of NDVI and PCA, and image classification algorithms, were used to generate impervious surfaces from multispectral IKONOS and QuickBird imagery acquired in both leaf-on and leaf-off conditions. Accuracy assessments were performed, using truth data generated by manual classification, with Kappa statistics and Zonal statistics to select the most appropriate image processing techniques for impervious surface mapping. The performance of selected image processing techniques was enhanced by incorporating Soil Brightness Index (SBI) and Greenness Index (GI) derived from Tasseled Cap Transformed (TCT) IKONOS and QuickBird imagery. A time series of impervious surfaces for the time frame between 2001 and 2007 was made using the refined image processing techniques to analyze the changes in IS in Oxford. It was found that NDVI and the combined NDVI--PCA methods are the most suitable image processing techniques for mapping impervious surfaces in leaf-off and leaf-on conditions respectively, using high resolution multispectral imagery. It was also found that IS data generated by these techniques can be refined by removing the conflicting dry soil patches using SBI and GI obtained from TCT of the same imagery used for IS data generation. The change detection analysis of the IS time series shows that Oxford experienced the major changes in IS from the year 2001 to 2004 and 2006 to 2007.

Comparison of animated jet stream visualizations

NASA Astrophysics Data System (ADS)

Nocke, Thomas; Hoffmann, Peter

2016-04-01

The visualization of 3D atmospheric phenomena in space and time is still a challenging problem. In particular, multiple solutions of animated jet stream visualizations have been produced in recent years, which were designed to visually analyze and communicate the jet and related impacts on weather circulation patterns and extreme weather events. This PICO integrates popular and new jet animation solutions and inter-compares them. The applied techniques (e.g. stream lines or line integral convolution) and parametrizations (color mapping, line lengths) are discussed with respect to visualization quality criteria and their suitability for certain visualization tasks (e.g. jet patterns and jet anomaly analysis, communicating its relevance for climate change).

Comparison of NDVI fields obtained from different remote sensors

NASA Astrophysics Data System (ADS)

Escribano Rodriguez, Juan; Alonso, Carmelo; Tarquis, Ana Maria; Benito, Rosa Maria; Hernandez Díaz-Ambrona, Carlos

2013-04-01

Satellite image data have become an important source of information for monitoring vegetation and mapping land cover at several scales. Beside this, the distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Various vegetation indices have been developed for qualitative and quantitative assessment of vegetation using remote spectral measurements. In particular, sensors with spectral bands in the red (RED) and near-infrared (NIR) lend themselves well to vegetation monitoring and based on them [(NIR - RED) / (NIR + RED)] Normalized Difference Vegetation Index (NDVI) has been widespread used. Given that the characteristics of spectral bands in RED and NIR vary distinctly from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The spatial resolution also varies significantly between sensors, as well as within a given scene in the case of wide-angle and oblique sensors. As a result, NDVI values will vary according to combinations of the heterogeneity and scale of terrestrial surfaces and pixel footprint sizes. Therefore, the question arises as to the impact of differences in spectral and spatial resolutions on vegetation indices like the NDVI and their interpretation as a drought index. During 2012 three locations (at Salamanca, Granada and Córdoba) were selected and a periodic pasture monitoring and botanic composition were achieved. Daily precipitation, temperature and monthly soil water content were measurement as well as fresh and dry pasture weight. At the same time, remote sensing images were capture by DEIMOS-1 and MODIS of the chosen places. DEIMOS-1 is based on the concept Microsat-100 from Surrey. It is conceived for obtaining Earth images with a good enough resolution to study the terrestrial vegetation cover (20x20 m), although with a great range of visual field (600 km) in order to obtain those images with high temporal resolution and at a reduced cost. By contranst, MODIS images present a much lower spatial resolution (500x500 m). The aim of this study is to establish a comparison between two different sensors in their NDVI values at different spatial resolutions. Acknowledgements. This work was partially supported by ENESA under project P10 0220C-823. Funding provided by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no. MTM2009-14621 and i-MATH No. CSD2006-00032 is greatly appreciated.

The relationship between satellite-derived indices and species diversity across African savanna ecosystems

NASA Astrophysics Data System (ADS)

Mapfumo, Ratidzo B.; Murwira, Amon; Masocha, Mhosisi; Andriani, R.

2016-10-01

The ability to use remotely sensed diversity is important for the management of ecosystems at large spatial extents. However, to achieve this, there is still need to develop robust methods and approaches that enable large-scale mapping of species diversity. In this study, we tested the relationship between species diversity measured in situ with the Normalized Difference Vegetation Index (NDVI) and the Coefficient of Variation in the NDVI (CVNDVI) derived from high and medium spatial resolution satellite data at dry, wet and coastal savanna woodlands. We further tested the effect of logging on NDVI along the transects and between transects as disturbance may be a mechanism driving the patterns observed. Overall, the results of this study suggest that high tree species diversity is associated with low and high NDVI and at intermediate levels is associated with low tree species diversity and NDVI. High tree species diversity is associated with high CVNDVI and vice versa and at intermediate levels is associated with high tree species diversity and CVNDVI.

Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.

[Effects of climate and grazing on the vegetation cover change in Xilinguole League of Inner Mongolia, North China].

PubMed

Wang, Hai-Mei; Li, Zheng-Hai; Wang, Zhen

2013-01-01

Based on the monthly temperature and precipitation data of 15 meteorological stations and the statistical data of livestock density in Xilinguole League in 1981-2007, and by using ArcGIS, this paper analyzed the spatial distribution of the climate aridity and livestock density in the League, and in combining with the ten-day data of the normalized difference vegetation index (NDVI) in 1981-2007, the driving factors of the vegetation cover change in the League were discussed. In the study period, there was a satisfactory linear regression relationship between the climate aridity and the vegetation coverage. The NDVI and the livestock density had a favorable binomial regression relationship. With the increase of NDVI, the livestock density increased first and decreased then. The vegetation coverage had a complex linear relationship with livestock density and climate aridity. The NDVI had a positive correlation with climate aridity, but a negative correlation with livestock density. Compared with livestock density, climate aridity had far greater effects on the NDVI.

A NDVI assisted remote sensing image adaptive scale segmentation method

NASA Astrophysics Data System (ADS)

Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

2018-03-01

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

Satellite-Based Evidence for Shrub and Graminoid Tundra Expansion in Northern Quebec from 1986-2010

NASA Technical Reports Server (NTRS)

McManus, K. M.; Morton, D. C.; Masek, J. G.; Wang, D.; Sexton, J. O.; Nagol, J.; Ropars, P.; Boudreau, S.

2012-01-01

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air-photo studies have documented recent changes in high-latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24-year (1986-2010) Landsat time series in a latitudinal transect across the boreal forest-tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last forty years. Using a per-pixel (30 m) trend analysis, 30% of the observable (cloud-free) land area experienced a significant (p < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak-summer conditions. The average NDVI trend (0.007/yr) corresponds to a leaf-area index (LAI) increase of 0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer (MODIS). Across the entire transect, the area-averaged LAI increase was 0.2 during 1986-2010. A higher area-averaged LAI change (0.3) within the shrub-tundra portion of the transect represents a 20-60% relative increase in LAI during the last two decades. Our Landsat-based analysis subdivides the overall high-latitude greening trend into changes in peak-summer greenness by cover type. Different responses within and among shrub, graminoid, and tree-dominated cover types in this study indicate important fine-scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low-biomass vegetation types over multi-decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.

Studying the Post-Fire Response of Vegetation in California Protected Areas with NDVI-based Pheno-Metrics

NASA Astrophysics Data System (ADS)

Jia, S.; Gillespie, T. W.

2016-12-01

Post-fire response from vegetation is determined by the intensity and timing of fires as well as the nature of local biomes. Though the field-based studies focusing on selected study sites helped to understand the mechanisms of post-fire response, there is a need to extend the analysis to a broader spatial extent with the assistance of remotely sensed imagery of fires and vegetation. Pheno-metrics, a series of variables on the growing cycle extracted from basic satellite measurements of vegetation coverage, translate the basic remote sensing measurements such as NDVI to the language of phenology and fire ecology in a quantitative form. In this study, we analyzed the rate of biomass removal after ignition and the speed of post-fire recovery in California protected areas from 2000 to 2014 with USGS MTBS fire data and USGS eMODIS pheno-metrics. NDVI drop caused by fire showed the aboveground biomass of evergreen forest was removed much slower than shrubland because of higher moisture level and greater density of fuel. In addition, the above two major land cover types experienced a greatly weakened immediate post-fire growing season, featuring a later start and peak of season, a shorter length of season, and a lower start and peak of NDVI. Such weakening was highly correlated with burn severity, and also influenced by the season of fire and the land cover type, according to our modeling between the anomalies of pheno-metrics and the difference of normalized burn ratio (dNBR). The influence generally decayed over time, but can remain high within the first 5 years after fire, mostly because of the introduction of exotic species when the native species were missing. Local-specific variables are necessary to better address the variance within the same fire and improve the outcomes of models. This study can help ecologists in validating the theories of post-fire vegetation response mechanisms and assist local fire managers in post-fire vegetation recovery.

Beyond precipitation: physiographic gradients dictate the relative importance of environmental drivers on Savanna vegetation.

PubMed

Campo-Bescós, Miguel A; Muñoz-Carpena, Rafael; Kaplan, David A; Southworth, Jane; Zhu, Likai; Waylen, Peter R

2013-01-01

Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients. We applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique to ten years of monthly remote sensing data (MODIS-derived normalized difference vegetation index, NDVI) and a suite of environmental covariates: precipitation, mean and maximum temperature, soil moisture, relative humidity, fire and potential evapotranspiration. Monthly NDVI was described by cyclic seasonal variation with distinct spatiotemporal patterns in different physiographic regions. Results support existing work emphasizing the importance of precipitation, soil moisture and fire on NDVI, but also reveal overlooked effects of temperature and evapotranspiration, particularly in regions with higher mean annual precipitation. Critically, spatial distributions of the weights of environmental covariates point to a transition in the importance of precipitation and soil moisture (strongest in grass-dominated regions with precipitation<750 mm) to fire, potential evapotranspiration, and temperature (strongest in tree-dominated regions with precipitation>950 mm). We quantified the combined spatiotemporal effects of an available suite of environmental drivers on NDVI across a large and diverse savanna region. The analysis supports known drivers of savanna vegetation but also uncovers important roles of temperature and evapotranspiration. Results highlight the utility of applying the DFA approach to remote sensing products for regional analyses of landscape change in the context of global environmental change. With the dramatic increase in global change research, this methodology augurs well for further development and application of spatially explicit time series modeling to studies at the intersection of ecology and remote sensing.

Temporal and spatial analysis of vegetation coverage changes in Ordos area based on time series GIMMS-NDVI data

NASA Astrophysics Data System (ADS)

Han, Ruimei; Zou, Youfeng; Ma, Chao; Liu, Pei

2014-11-01

Ordos area is the desert-wind erosion desertification steppe transition zone and the complex ecological zone. As the research area, Ordos City has the similar natural geographic environment to ShenDong coalfield. To research its ecological patterns and natural evolution law, it has instructive to reveal temporal and spatial changes of ecological environment with artificial disturbance in western mining. In this paper, a time series of AVHRR-NDVI(Normalized Difference Vegetation Index) data was used to monitor the change of vegetation temporal and spatial dynamics from 1981 to 2006 in Ordos City and ShenDong coalfield, where were as the research area. The MVC (Maximum Value Composites) method, average operation, linear regression, and gradation for NDVI change trend were used to obtained some results, as follows: ¬vegetation coverage had obvious characteristics with periodic change in research area for 26 years, and vegetation growth peak appeared on August, while the lowest appeared on January. The extreme values in Ordos City were 0.2351 and 0.1176, while they were 0.2657 and 0.1272 in ShenDong coalfield. The NDVI value fluctuation was a modest rise trend overall in research area. The extreme values were 0.3071 and 0.1861 in Ordos City, while they were 0.3454 and 0.1904 in ShenDong coalfield. In spatial distribution, slight improvement area and slight degradation area were accounting for 42.49% and 8.37% in Ordos City, while slight improvement area moderate improvement area were accounting for 70.59% and 29.41% in ShenDong coalfield. Above of results indicated there was less vegetation coverage in research area, which reflected the characteristics of fragile natural geographical environment. In addition, vegetation coverage was with a modest rise on the whole, which reflected the natural environment change.

Modelling the Phenological Relationships of Questing Immature Ixodes Ricinus (Ixodidae) Using Temperature and NDVI Data.

PubMed

Alonso-Carné, J; García-Martín, A; Estrada-Peña, A

2016-02-01

All active stages of the tick Ixodes ricinus were collected monthly at two sites in northern Spain between the years 2000 and 2007. We used percentile accumulation of the active stage in the environment to evaluate simple and coherent correlations between accumulation of the active stages of larvae and nymphs and medium-resolution MODIS satellite-derived information on the climate, including monthly and accumulated temperature and the Normalized Difference Vegetation Index (NDVI). This framework is not intended to predict the actual abundance of ticks in the field as a measure of the hazard to humans, but to provide a basic structure for addressing the phenology of the tick in its geographic range. We demonstrated that the accumulation of larval ticks in the active stage is a sigmoid function of the accumulated temperature from the beginning of the calendar year. We also demonstrated that the accumulated temperature necessary to recruit nymphs from the questing larval stage is a function of the changes in accumulated larvae and nymphs and the accumulated temperature and NDVI recorded by the Aqua sensor. The low p-values obtained in the regressions confirmed that such recruitment can be calculated using time intervals to estimate, for example, the beginning of the questing period or the time of the year when a population peak can be expected. The comparison among predicted and actual accumulated temperatures between larvae and nymph recruitment had an averaged error of ±20 days in one complete year. The use of accumulated temperature and NDVI proposed in this study opens up the re-evaluation of reports on the phenology of the tick in Europe. This framework is intended to evaluate the same correlations along the tick's range and predict its phenological patterns in areas of pathogen transmission risk for humans. © 2015 Blackwell Verlag GmbH.

Beyond Precipitation: Physiographic Gradients Dictate the Relative Importance of Environmental Drivers on Savanna Vegetation

PubMed Central

Campo-Bescós, Miguel A.; Muñoz-Carpena, Rafael; Kaplan, David A.; Southworth, Jane; Zhu, Likai; Waylen, Peter R.

2013-01-01

Background Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients. Methodology/Principal Findings We applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique to ten years of monthly remote sensing data (MODIS-derived normalized difference vegetation index, NDVI) and a suite of environmental covariates: precipitation, mean and maximum temperature, soil moisture, relative humidity, fire and potential evapotranspiration. Monthly NDVI was described by cyclic seasonal variation with distinct spatiotemporal patterns in different physiographic regions. Results support existing work emphasizing the importance of precipitation, soil moisture and fire on NDVI, but also reveal overlooked effects of temperature and evapotranspiration, particularly in regions with higher mean annual precipitation. Critically, spatial distributions of the weights of environmental covariates point to a transition in the importance of precipitation and soil moisture (strongest in grass-dominated regions with precipitation<750 mm) to fire, potential evapotranspiration, and temperature (strongest in tree-dominated regions with precipitation>950 mm). Conclusions/Significance We quantified the combined spatiotemporal effects of an available suite of environmental drivers on NDVI across a large and diverse savanna region. The analysis supports known drivers of savanna vegetation but also uncovers important roles of temperature and evapotranspiration. Results highlight the utility of applying the DFA approach to remote sensing products for regional analyses of landscape change in the context of global environmental change. With the dramatic increase in global change research, this methodology augurs well for further development and application of spatially explicit time series modeling to studies at the intersection of ecology and remote sensing. PMID:24023616

Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

NASA Astrophysics Data System (ADS)

Zhou, Xianfeng; Huang, Wenjiang; Kong, Weiping; Ye, Huichun; Luo, Juhua; Chen, Pengfei

2016-11-01

Timely and accurate assessment of canopy nitrogen content (CNC) provides valuable insight into rapid and real-time nitrogen status monitoring in crops. A semi-empirical approach based on spectral index was extensively used for nitrogen content estimation. However, in many cases, due to specific vegetation types or local conditions, the applicability and robustness of established spectral indices for nitrogen retrieval were limited. The objective of this study was to investigate the optimal spectral index for winter wheat (Triticum aestivum L.) CNC estimation using Pushbroom Hyperspectral Imager (PHI) airborne hyperspectral data. Data collected from two different field experiments that were conducted during the major growth stages of winter wheat in 2002 and 2003 were used. Our results showed that a significant linear relationship existed between nitrogen and chlorophyll content at the canopy level, and it was not affected by cultivars, growing conditions and nutritional status of winter wheat. Nevertheless, it varied with growth stages. Periods around heading stage mainly worsened the relationship and CNC estimation, and CNC assessment for growth stages before and after heading could improve CNC retrieval accuracy to some extent. CNC assessment with PHI airborne hyperspectra suggested that spectral indices based on red-edge band including narrowband and broadband CIred-edge, NDVI-like and ND705 showed convincing results in CNC retrieval. NDVI-like and ND705 were sensitive to detect CNC changes less than 5 g/m2, narrowband and broadband CIred-edge were sensitive to a wide range of CNC variations. Further evaluation of CNC retrieval using field measured hyperspectra indicated that NDVI-like was robust and exhibited the highest accuracy in CNC assessment, and spectral indices (CIred-edge and CIgreen) that established on narrow or broad bands showed no obvious difference in CNC assessment. Overall, our study suggested that NDVI-like was the optimal indicator for winter wheat CNC retrieval.

Development and Testing of a Laboratory Spray Table Methodology to Bioassay Simulated Levels of Aerial Spray Drift

DTIC Science & Technology

2009-05-01

was measured on Mylar cards through fluorometric analysis. Plant health measures height and normalized difference vegetation index NDVI were...plant health data were used to generate dose-response relationships. Dose-response curves relating change in plant height and change in measured NDVI ...Held Sensor Model 505, NTech Industries, Inc., Ukiah, California to measure the normalized difference vegetation index NDVI which is directly

Application of Hyperspectral Vegetation Indices to Detect Variations in High Leaf Area Index Temperate Shrub Thicket Canopies

DTIC Science & Technology

2011-01-01

sensing an attractive technique for estimating LAI. Many vegetation indices, such as Normalized Difference Vegetation Index ( NDVI ), tend to saturate at...little or no improvement over NDVI . Furthermore, indirect ground-sampling techniques often used to evaluate the potential of vegetation indices also...landscapes makes remote sensing an attractive technique for estimating LAI. Many vegetation indices, such as Normalized Difference Vegetation Index ( NDVI

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

DOE PAGES

Robinson, Nathaniel; Allred, Brady; Jones, Matthew; ...

2017-08-21

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodologicalmore » challenges. Here, we address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.« less

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robinson, Nathaniel; Allred, Brady; Jones, Matthew

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodologicalmore » challenges. Here, we address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.« less

Evaluation of NDVI to assess avian abundance and richness along the upper San Pedro River

USGS Publications Warehouse

McFarland, T.M.; van Riper, Charles; Johnson, G.E.

2012-01-01

Remote-sensing models have become increasingly popular for identifying, characterizing, monitoring, and predicting avian habitat but have largely focused on single bird species. The Normalized Difference Vegetation Index (NDVI) has been shown to positively correlate with avian abundance and richness and has been successfully applied to southwestern riparian systems which are uniquely composed of narrow bands of vegetation in an otherwise dry landscape. Desert riparian ecosystems are important breeding and stopover sites for many bird species but have been degraded due to altered hydrology and land management practices. Here we investigated the use of NDVI, coupled with vegetation, to model the avian community structure along the San Pedro River, Arizona. We also investigated how vegetation and physical features measured locally compared to those data that can be gathered through remote-sensing. We found that NDVI has statistically significant relationships with both avian abundance and species richness, although is better applied at the individual species level. However, the amount of variation explained by even our best models was quite low, suggesting that NDVI habitat models may not presently be an accurate tool for extensive modeling of avian communities. We suggest additional studies in other watersheds to increase our understanding of these bird/NDVI relationships.

Stability of Spatial Distributions of Stink Bugs, Boll Injury, and NDVI in Cotton.

PubMed

Reay-Jones, Francis P F; Greene, Jeremy K; Bauer, Philip J

2016-10-01

A 3-yr study was conducted to determine the degree of aggregation of stink bugs and boll injury in cotton, Gossypium hirsutum L., and their spatial association with a multispectral vegetation index (normalized difference vegetation index [NDVI]). Using the spatial analysis by distance indices analyses, stink bugs were less frequently aggregated (17% for adults and 4% for nymphs) than boll injury (36%). NDVI values were also significantly aggregated within fields in 19 of 48 analyses (40%), with the majority of significant indices occurring in July and August. Paired NDVI datasets from different sampling dates were frequently associated (86.5% for weekly intervals among datasets). Spatial distributions of both stink bugs and boll injury were less stable than for NDVI, with positive associations varying from 12.5 to 25% for adult stink bugs for weekly intervals, depending on species. Spatial distributions of boll injury from stink bug feeding were more stable than stink bugs, with 46% positive associations among paired datasets with weekly intervals. NDVI values were positively associated with boll injury from stink bug feeding in 11 out of 22 analyses, with no significant negative associations. This indicates that NDVI has potential as a component of site-specific management. Future work should continue to examine the value of remote sensing for insect management in cotton, with an aim to develop tools such as risk assessment maps that will help growers to reduce insecticide inputs. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change

PubMed Central

Schut, Antonius G. T.; Ivits, Eva; Conijn, Jacob G.; ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982–2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17–36% of all productive areas depending on the NDVI metric used. For only 1–2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity. PMID:26466347

Discriminating the Mediterranean Pinus spp. using the land surface phenology extracted from the whole MODIS NDVI time series and machine learning algorithms

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, Victor; Aragones, David; Caparros-Santiago, Jose A.; Navarro-Cerrillo, Rafael M.

2017-10-01

Land surface phenology (LSP) can improve the characterisation of forest areas and their change processes. The aim of this work was: i) to characterise the temporal dynamics in Mediterranean Pinus forests, and ii) to evaluate the potential of LSP for species discrimination. The different experiments were based on 679 mono-specific plots for the 5 native species on the Iberian Peninsula: P. sylvestris, P. pinea, P. halepensis, P. nigra and P. pinaster. The entire MODIS NDVI time series (2000-2016) of the MOD13Q1 product was used to characterise phenology. The following phenological parameters were extracted: the start, end and median days of the season, and the length of the season in days, as well as the base value, maximum value, amplitude and integrated value. Multi-temporal metrics were calculated to synthesise the inter-annual variability of the phenological parameters. The species were discriminated by the application of Random Forest (RF) classifiers from different subsets of variables: model 1) NDVI-smoothed time series, model 2) multi-temporal metrics of the phenological parameters, and model 3) multi-temporal metrics and the auxiliary physical variables (altitude, slope, aspect and distance to the coastline). Model 3 was the best, with an overall accuracy of 82%, a kappa coefficient of 0.77 and whose most important variables were: elevation, coast distance, and the end and start days of the growing season. The species that presented the largest errors was P. nigra, (kappa= 0.45), having locations with a similar behaviour to P. sylvestris or P. pinaster.

Shifts of Start and End of Season in Response to Air Temperature Variation Based on Gimms Dataset in Hyrcanian Forests

NASA Astrophysics Data System (ADS)

Kiapasha, K. H.; Darvishsefat, A. A.; Zargham, N.; Julien, Y.; Sobrino, J. A.; Nadi, M.

2017-09-01

Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes. The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region. The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012. In order to find response of vegetation activity to local temperature variations, we used air temperature provided from I.R. Iran Meteorological Organization (IRIMO). At the first step in order to remove the existing gap from the original time series, the iterative Interpolation for Data Reconstruction (IDR) model was applied to GIMMS and temperature dataset. Then we applied significant Mann Kendall test to determine significant trend for each pixel of GIMMS and temperature datasets over the Hyrcanian forests. The results demonstrated that start and end of season (SOS & EOS respectively) derived from GIMMS3g NDVI time series increased by -0.16 and +0.41 days per year respectively. The trends derived from temperature time series indicated increasing trend in the whole of this region. Results of this study showed that global warming and its effect on growth and photosynthetic activity can increased the vegetation activity in our study area. Otherwise extension of the growing season, including an earlier start of the growing season, later autumn and higher rate of production increased NDVI value during the study period.

Monitoring Regional Forest Disturbances across the US with Near Real Time MODIS NDVI Products included in the ForWarn Forest Threat Early Warning System

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Gasser, J.; Norman, S. P.

2013-12-01

Forest threats across the US have become increasingly evident in recent years. These include regionally extensive disturbances (e.g., from drought, bark beetle outbreaks, and wildfires) that can occur across multiyear durations and result in extensive forest mortality. In addition, forests can be subject to ephemeral, sometimes yearly defoliation from various insects and types of storm damage. After prolonged severe disturbance, signs of forest recovery can vary in terms of satellite-based Normalized Difference Vegetation Index (NDVI) values. The increased extent and threat of forest disturbances in part led to the enactment of the 2003 Healthy Forest Restoration Act, which mandated that a national forest threat Early Warning System (EWS) be deployed. In response, the US Forest Service collaborated with NASA, DOE Oak Ridge National Laboratory, and the USGS Eros Data Center to build the near real time ForWarn forest threat EWS for monitoring regionally evident forest disturbances, starting on-line operations in 2010. Given the diversity of disturbance types, severities, and durations, ForWarn employs multiple historical baselines used with current NDVI to derive a suite of six nationwide 'weekly' forest change products. ForWarn uses daily 232 meter MODIS Aqua and Terra satellite NDVI data, including MOD13 products for deriving historical baseline NDVIs and eMODIS products for compiling current NDVI. Separately pre-processing the current and historical NDVIs, the Time Series Product Tool and the Phenological Parameters Estimation Tool are used to temporally reduce noise, fuse, and aggregate MODIS NDVIs into 24 day composites refreshed every 8 days with 46 dates of forest change products per year. The 24 day compositing interval typically enables new disturbances to be detected, while minimizing the frequency of residual atmospheric contamination. ForWarn's three standard forest change products compare current NDVI to that from the previous year, previous 3 years, and all previous years since 2000. Other forest change products added in 2013 include one for quicker disturbance detection and two others that adjust for seasonal fluctuations in normal vegetation phenology. This product suite and ForWarn's geospatial data viewer allow end users to view and assess disturbance dynamics for many regionally evident biotic and abiotic forest disturbances throughout a given current year. ForWarn's change products are also being used for forest change trend analysis and for developing regional forest overstory mortality products. They are used to alert forest health specialists about new regional forest disturbances. Such alerts also typically consider available Landsat, aerial, and ground data as well as communications with forest health specialists and previous experience. ForWarn products have been used to detect and track many types of regional disturbances for multiple forest types, including defoliation from caterpillars and severe storms, as well as mortality from both biotic and abiotic agents (e.g., bark beetles, drought, fire, anthropogenic clearing). ForWarn provides forest change products that could be combined with other geospatial data on forest biomass to help assess forest disturbance carbon impacts within the conterminous US.

Characterization of Landsat-7 to Landsat-8 Reflective Wavelength and Normalized Difference Vegetation Index Continuity

NASA Technical Reports Server (NTRS)

Roy, D. P.; Kovalskyy, V.; Zhang, H. K.; Vermote, E. F.; Yan, L.; Kumar, S. S.; Egorov, A.

2016-01-01

At over 40 years, the Landsat satellites provide the longest temporal record of space-based land surface observations, and the successful 2013 launch of the Landsat-8 is continuing this legacy. Ideally, the Landsat data record should be consistent over the Landsat sensor series. The Landsat-8 Operational Land Imager (OLI) has improved calibration, signal to noise characteristics, higher 12-bit radiometric resolution, and spectrally narrower wavebands than the previous Landsat-7 Enhanced Thematic Mapper (ETM+). Reflective wavelength differences between the two Landsat sensors depend also on the surface reflectance and atmospheric state which are difficult to model comprehensively. The orbit and sensing geometries of the Landsat- 8 OLI and Landsat-7 ETM+ provide swath edge overlapping paths sensed only one day apart. The overlap regions are sensed in alternating backscatter and forward scattering orientations so Landsat bi-directional reflectance effects are evident but approximately balanced between the two sensors when large amounts of time series data are considered. Taking advantage of this configuration a total of 59 million 30m corresponding sensor observations extracted from 6,317 Landsat-7 ETM+ and Landsat-8 OLI images acquired over three winter and three summer months for all the conterminous United States (CONUS) are compared. Results considering different stages of cloud and saturation filtering, and filtering to reduce one day surface state differences, demonstrate the importance of appropriate per-pixel data screening. Top of atmosphere (TOA) and atmospherically corrected surface reflectance for the spectrally corresponding visible, near infrared and shortwave infrared bands, and derived normalized difference vegetation index (NDVI), are compared and their differences quantified. On average the OLI TOA reflectance is greater than the ETM+ TOA reflectance for all bands, with greatest differences in the near-infrared (NIR) and the shortwave infrared bands due to the quite different spectral response functions between the sensors. The atmospheric correction reduces the mean difference in the NIR and shortwave infrared but increases the mean difference in the visible bands. Regardless of whether TOA or surface reflectance are used to generate NDVI, on average, for vegetated soil and vegetation surfaces (0 = NDVI = 1), the OLI NDVI is greater than the ETM+ NDVI. Statistical functions to transform between the comparable sensor bands and sensor NDVI values are presented so that the user community may apply them in their own research to improve temporal continuity between the Landsat-7 ETM+ and Landsat-8 OLI sensor data. The transformation functions were developed using ordinary least squares (OLS) regression and were fit quite reliably (r2 values is greater than 0.7 for the reflectance data and greater than 0.9 for the NDVI data, p-values less than 0.0001).

Value of Available Global Soil Moisture Products for Agricultural Monitoring

NASA Astrophysics Data System (ADS)

Mladenova, Iliana; Bolten, John; Crow, Wade; de Jeu, Richard

2016-04-01

The first operationally derived and publicly distributed global soil moil moisture product was initiated with the launch of the Advanced Scanning Microwave Mission on the NASA's Earth Observing System Aqua satellite (AMSR-E). AMSR-E failed in late 2011, but its legacy is continued by AMSR2, launched in 2012 on the JAXA Global Change Observation Mission-Water (GCOM-W) mission. AMSR is a multi-frequency dual-polarization instrument, where the lowest two frequencies (C- and X-band) were used for soil moisture retrieval. Theoretical research and small-/field-scale airborne campaigns, however, have demonstrated that soil moisture would be best monitored using L-band-based observations. This consequently led to the development and launch of the first L-band-based mission-the ESA's Soil Moisture Ocean Salinity (SMOS) mission (2009). In early 2015 NASA launched the second L-band-based mission, the Soil Moisture Active Passive (SMAP). These satellite-based soil moisture products have been demonstrated to be invaluable sources of information for mapping water stress areas, crop monitoring and yield forecasting. Thus, a number of agricultural agencies routinely utilize and rely on global soil moisture products for improving their decision making activities, determining global crop production and crop prices, identifying food restricted areas, etc. The basic premise of applying soil moisture observations for vegetation monitoring is that the change in soil moisture conditions will precede the change in vegetation status, suggesting that soil moisture can be used as an early indicator of expected crop condition change. Here this relationship was evaluated across multiple microwave frequencies by examining the lag rank cross-correlation coefficient between the soil moisture observations and the Normalized Difference Vegetation Index (NDVI). A main goal of our analysis is to evaluate and inter-compare the value of the different soil moisture products derived using L-band (SMOS) versus C-/X-band (AMSR2) observations. The soil moisture products analyzed here were derived using the Land Parameter Retrieval Model.

NASA Giovanni: A Tool for Visualizing, Analyzing, and Inter-Comparing Soil Moisture Data

NASA Technical Reports Server (NTRS)

Teng, William; Rui, Hualan; Vollmer, Bruce; deJeu, Richard; Fang, Fan; Lei, Guang-Dih

2012-01-01

There are many existing satellite soil moisture algorithms and their derived data products, but there is no simple way for a user to inter-compare the products or analyze them together with other related data (e.g., precipitation). An environment that facilitates such inter-comparison and analysis would be useful for validation of satellite soil moisture retrievals against in situ data and for determining the relationships between different soil moisture products. The latter relationships are particularly important for applications users, for whom the continuity of soil moisture data, from whatever source, is critical. A recent example was provided by the sudden demise of EOS Aqua AMSR-E and the end of its soil moisture data production, as well as the end of other soil moisture products that had used the AMSR-E brightness temperature data. The purpose of the current effort is to create an environment, as part of the NASA Giovanni family of portals, that facilitates inter-comparisons of soil moisture algorithms and their derived data products.

Changes in Meadow Vegetation Cover in Kings Canyon National Park (California) Based on Three Decades of Landsat Image Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher

2015-01-01

Landsat (30 meter resolution) image analysis over the past 25 years in Kings Canyon National Park was used to track changes in the normalized difference vegetation index (NDVI). Results showed that NDVI values from the wet year of 2010 were significantly lower than NDVI values from the comparatively dry year of 2013 in the majority of meadow areas in the National Park.

Using Panchromatic Imagery in Place of Multispectral Imagery for Kelp Detection in Water

DTIC Science & Technology

2010-01-01

Normalized Difference Vegetation Index ( NDVI ). In broadband panchromatic imagery, the kelp appears brighter than the water because of the strong...response of vegetation in the NIR, and can be reliably detected by means of a simple threshold; overall brightness is generally proportional to the NDVI ...Index ( NDVI ). In broadband panchromatic imagery, the kelp appears brighter than the water because of the strong response of vegetation in the NIR, and

Monitoring of wildfires in boreal forests using large area AVHRR NDVI composite image data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kasischke, E.S.; French, N.H.F.; Harrell, P.

1993-06-01

Normalized difference vegetation index (NDVI) composite image data, produced from AVHRR data collected in 1990, were evaluated for locating and mapping the areal extent of wildfires in the boreal forests of Alaska during that year. A technique was developed to map forest fire boundaries by subtracting a late-summer AVHRR NDVI image from an early summer scene. The locations and boundaries of wildfires within the interior region of Alaska were obtained from the Alaska Fire Service, and compared to the AVHRR-derived fire-boundary map. It was found that AVHRR detected 89.5% of all fires with sizes greater than 2,000ha with no falsemore » alarms and that, for most cases, the general shape of the fire boundary detected by AVHRR matched those mapped by field observers. However, the total area contained within the fire boundaries mapped by AVHRR were only 61% of those mapped by the field observers. However, the AVHRR data used in this study did not span the entire time period during which fires occurred, and it is believed the areal estimates could be improved significantly if an expanded AVHRR data set were used.« less

MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland

NASA Astrophysics Data System (ADS)

Gong, Z.; Kawamura, K.; Ishikawa, N.; Goto, M.; Wulan, T.; Alateng, D.; Yin, T.; Ito, Y.

2015-11-01

The Inner Mongolia grassland, one of the most important grazing regions in China, has long been threatened by land degradation and desertification, mainly due to overgrazing. To understand vegetation responses over the last decade, this study evaluated trends in vegetation cover and phenology dynamics in the Inner Mongolia grassland by applying a normalized difference vegetation index (NDVI) time series obtained by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002-2014. The results showed that the cumulative annual NDVI increased to over 77.10 % in the permanent grassland region (2002-2014). The mean value of the total change showed that the start of season (SOS) date and the peak vegetation productivity date of the season (POS) had advanced by 5.79 and 2.43 days, respectively. The end of season (EOS) was delayed by 5.07 days. These changes lengthened the season by 10.86 days. Our results also confirmed that grassland changes are closely related to spring precipitation and increasing temperature at the early growing period because of global warming. Overall, productivity in the Inner Mongolia Autonomous Region tends to increase, but in some grassland areas with grazing, land degradation is ongoing.

MODIS NDVI and vegetation phenology dynamics in the Inner Mongolia grassland

NASA Astrophysics Data System (ADS)

Gong, Z.; Kawamura, K.; Ishikawa, N.; Goto, M.; Wulan, T.; Alateng, D.; Yin, T.; Ito, Y.

2015-08-01

The Inner Mongolia grassland, one of the most important grazing regions in China, has long been threatened by land degradation and desertification, mainly due to overgrazing. To understand vegetation responses over the last decade, this study evaluated trends in vegetation cover and phenology dynamics in the Inner Mongolia grassland by applying a normalized difference vegetation index (NDVI) time series obtained by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002-2014. The results showed that the cumulative annual NDVI increased to over 77.10 % in the permanent grassland region (2002-2014). The mean value of the total change showed that the start of season (SOS) date and the peak vegetation productivity date of the season (POS) had advanced by 5.79 and 2.43 days respectively. The end of season (EOS) was delayed by 5.07 days. These changes lengthened the season by 10.86 days. Our results also confirmed that grassland changes are closely related to spring precipitation (February-May) and increasing temperature during the growing period because of the global warming. Overall, productivity in the Inner Mongolia Autonomous Region tends to increase, but in some grassland areas with grazing, land degradation is ongoing.

Sensitivity analysis of the Commonly Used Drought Indices on the different land use Types - Case Study over Turkey

NASA Astrophysics Data System (ADS)

Ersoy, E. N.; Hüsami Afşar, M.; Bulut, B.; Onen, A.; Yilmaz, M. T.

2017-12-01

Droughts are climatic phenomenon that may impact large and small regions alike for long or short time periods and influence society in terms of industrial, agricultural, domestic and many more aspects. The characteristics of the droughts are commonly investigated using indices like Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI). On the other hand, these indices may not necessarily yield similar performance over different vegetation types. The aim is to analyze the sensitivity of drought indices (SPI, SPEI, PDSI) to vegetation types over different climatic regions in Turkey. Here the magnitude of the drought severity is measured using MODIS NDVI data, while the vegetation type (e.g., non-irrigated arable lands, vineyards, fruit trees and berry plantations, olive groves, pastures, land principally occupied by agriculture) information is obtained using CORINE land cover classification. This study has compared the drought characteristics and vegetation conditions on different land use types using remotely sensed datasets (e.g., CORINE land use data, MODIS NDVI), and commonly used drought indices between 2000 and 2016 using gauge based precipitation and temperature measurements.

Monitoring of wildfires in boreal forests using large area AVHRR NDVI composite image data

NASA Technical Reports Server (NTRS)

Kasischke, Eric S.; French, Nancy H. F.; Harrell, Peter; Christensen, Norman L., Jr.; Ustin, Susan L.; Barry, Donald

1993-01-01

Normalized difference vegetation index (NDVI) composite image data, produced from AVHRR data collected in 1990, were evaluated for locating and mapping the areal extent of wildfires in the boreal forests of Alaska during that year. A technique was developed to map forest fire boundaries by subtracting a late-summer AVHRR NDVI image from an early summer scene. The locations and boundaries of wildfires within the interior region of Alaska were obtained from the Alaska Fire Service, and compared to the AVHRR-derived fire-boundary map. It was found that AVHRR detected 89.5 percent of all fires with sizes greater than 2000 ha with no false alarms and that, for most cases, the general shape of the fire boundary detected by AVHRR matched those mapped by field observers. However, the total area contained within the fire boundaries mapped by AVHRR were only 61 percent of those mapped by the field observers. However, the AVHRR data used in this study did not span the entire time period during which fires occurred, and it is believed the areal estimates could be improved significantly if an expanded AVHRR data set were used.

Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra

PubMed Central

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-01-01

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from −12.67% to 36.30% for the red reflectance, −8.52% to −0.23% for the NIR reflectance, and −9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors. PMID:24287529

Comparability of red/near-infrared reflectance and NDVI based on the spectral response function between MODIS and 30 other satellite sensors using rice canopy spectra.

PubMed

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-11-26

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from -12.67% to 36.30% for the red reflectance, -8.52% to -0.23% for the NIR reflectance, and -9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.

Mapping of topsoil organic carbon in agro-ecosystems of a flat terrain area (Lombardy) by means of legacy soil data, climatic data and NDVI time series predictors with machine learning methods

NASA Astrophysics Data System (ADS)

Schillaci, Calogero; Saia, Sergio; Braun, Andreas; Acutis, Marco

2017-04-01

Topsoil organic carbon plays an important role in the agricultural yield, yield potential, and to deliver many ecosystem services, such as the potential to reduce greenhouse gas (GHG) emission from soil. In particular, SOC content sturdily affects soil properties, thus the precision of its estimation can support broad-scale agricultural and environmental management policy. Soils in temperate agro-ecosystem are generally highly productive and cropland occupies about 60% of their surface (Ramankutty et al 2008). In such contexts, lands is frequently subjected to SOC degrading operations, mostly ploughing, with drawbacks on soil fertility and erosion. In temperate agro-ecosystems, a strong role in SOC maintenance can be played by manure and residues inputs after husbandry and related activities and return of plant biomass to the soil (Acutis et al 2014). In this perspective, soil management can have a major role in SOC spatial distribution to maintain soil fertility and ecosystem services in a target area. Due to the considerable importance of SOC on both agronomical and ecological aspects of the agro-ecosystems, regional soil surveys over the years frequently take into account the measurement of SOC concentration and often stock. In the present study, we integrated a highly detailed legacy SOC dataset with climatic data and RS data to produce a reliable SOC maps from a farm to a district scale. In particular, the Normalized Difference Vegetation Index (NDVI)was used after the computation of its average value in a given pixel derived from several approximately cloud-free images. The input dataset was made of about 3000 Ap horizons implemented of SOC concentration, texture, bulk density and metadata. Climatic data (Worldclim), soil type (from the pedological map 1:250000 WRB), and a time series NDVI were applied. The NDVI data were derived from a set of Landsat 5 scenes (path 193, row 28,29) whereas the path 194, (row 28 and 29) contributes for less than one fourth of the study area. The use of machine learning approach for the generation of a SOC map of the flat terrain agricultural topsoil of Lombardy using the regional soil database relies on two assumptions: (1) the slow change in the content of the stabilised soil organic matter (SOM) fraction, which is almost everywhere the most represented SOM fraction; and (2) the intrinsic low erosion rates due to the low mean slope. In particular, NDVI, which is related land cover and to the amount of biomass returned the soil, can have drawbacks when applied in cultivated fields. These drawbacks mainly concern the variability on crop biomass within and across the year. Notwithstanding, this issue makes NDVI very suitable for differentiating contrasting land use (e.g. field crops vs. orchards) when computed from images captured in particular crop cycle moments (e.g. in summer). However, the same issue reduces NDVI suitability to estimate the amount of biomass within each land use or when aiming at highly detailed resolution. Different grade of cloud cover were admitted to construct the average NDVI. Boosted regression trees were used to reveal the effect of each spatial covariate in predicting the SOC content. Preliminary results highlighted that the integration of the soil pedological classification and the mean NDVI improved the pixel classification in SOC classes according to crop type and management. As expected, climatic gradient played an important role in SOC modelling but did not affect the spatial resolution of the final map. In conclusion, SOC estimate strongly depends on sample density and homogeneity of distribution and the environmental heterogeneity. The lack of the strong topographical traits in flat terrain areas represents a challenge for soil mapping. In such conditions, the computation of a reliable biomass-related RS trait such as the mean NDVI can increase the prediction ability of the models and reduce the mapping biases. References Acutis, M., Alfieri, L., Giussani, A., Provolo, G., Di Guardo, A., Colombini, S., Bertoncini, G.,Castelnuovo, M., Sali, G., Moschini, M., Sanna, M., Perego, A., Carozzi, M., Chiodini, M.E., Fumagalli, M., 2014. ValorE: An integrated and GIS-based decision support system for livestock manure management in the Lombardy region (northern Italy). Land use policy 41, 149-162. doi:10.1016/j.landusepol.2014.05.007 Ramankutty, N., A. T. Evan, C. Monfreda, and J. A. Foley (2008), Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000, Global Biogeochem. Cycles , 22, GB1003, doi:10.1029/2007GB002952.

Trends in normalized difference vegetation index (NDVI) associated with urban development in northern West Siberia

NASA Astrophysics Data System (ADS)

Esau, Igor; Miles, Victoria V.; Davy, Richard; Miles, Martin W.; Kurchatova, Anna

2016-08-01

Exploration and exploitation of oil and gas reserves of northern West Siberia has promoted rapid industrialization and urban development in the region. This development leaves significant footprints on the sensitive northern environment, which is already stressed by the global warming. This study reports the region-wide changes in the vegetation cover as well as the corresponding changes in and around 28 selected urbanized areas. The study utilizes the normalized difference vegetation index (NDVI) from high-resolution (250 m) MODIS data acquired for summer months (June through August) over 15 years (2000-2014). The results reveal the increase of NDVI (or "greening") over the northern (tundra and tundra-forest) part of the region. Simultaneously, the southern, forested part shows the widespread decrease of NDVI (or "browning"). These region-wide patterns are, however, highly fragmented. The statistically significant NDVI trends occupy only a small fraction of the region. Urbanization destroys the vegetation cover within the developed areas and at about 5-10 km distance around them. The studied urbanized areas have the NDVI values by 15 to 45 % lower than the corresponding areas at 20-40 km distance. The largest NDVI reduction is typical for the newly developed areas, whereas the older areas show recovery of the vegetation cover. The study reveals a robust indication of the accelerated greening near the older urban areas. Many Siberian cities become greener even against the wider browning trends at their background. Literature discussion suggests that the observed urban greening could be associated not only with special tending of the within-city green areas but also with the urban heat islands and succession of more productive shrub and tree species growing on warmer sandy soils.

Feasibility of inter-comparing airborne and spaceborne obsevations of radar backscattering coefficients

USDA-ARS?s Scientific Manuscript database

The Soil Moisture Active Passive (SMAP) mission will provide global soil moisture products that will facilitate new science and application areas. The SMAP mission, scheduled for launch in November 2014, will offer synthetic aperture radar (SAR) measurements of backscattering coefficients for the re...

Monitoring Coastal Marshes for Persistent Saltwater Intrusion

DTIC Science & Technology

2010-06-01

for the normalized difference indices (vegetation, soil, and water– NDVI , NDSI, and NDWI) for both MODIS and Landsat 5 and 7, referred to as the...Normalized Difference Index transformation [4]. The MODIS indices are 250 m ( NDVI ) and 500 m (NDWI and NDSI), and the Landsat indices are 30 m...indices are shown for two locations in Fig. 1 and Fig 2. Each figure shows the NDSI (soil), NDVI (vegetation), and NDWI (water) index as a function of

Analyzing millet price regimes and market performance in Niger with remote sensing data

NASA Astrophysics Data System (ADS)

Essam, Timothy Michael

This dissertation concerns the analysis of staple food prices and market performance in Niger using remotely sensed vegetation indices in the form of normalized differenced vegetation index (NDVI). By exploiting the link between weather-related vegetation production conditions, which serve as a proxy for spatially explicit millet yields and thus millet availability, this study analyzes the potential causal links between NDVI outcomes and millet market performance and presents an empirical approach for predicting changes in market performance based on NDVI outcomes. Overall, the thesis finds that inter-market price spreads and levels of market integration can be reasonably explained by deviations in vegetation index outcomes from the growing season. Negative (positive) NDVI shocks are associated with better (worse) than expected market performance as measured by converging inter-market price spreads. As the number of markets affected by negatively abnormal vegetation production conditions in the same month of the growing season increases, inter-market price dispersion declines. Positive NDVI shocks, however, do not mirror this pattern in terms of the magnitude of inter-market price divergence. Market integration is also found to be linked to vegetation index outcomes as below (above) average NDVI outcomes result in more integrated (segmented) markets. Climate change and food security policies and interventions should be guided by these findings and account for dynamic relationships among market structures and vegetation production outcomes.

Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa

PubMed Central

Sedda, Luigi; Tatem, Andrew J.; Morley, David W.; Atkinson, Peter M.; Wardrop, Nicola A.; Pezzulo, Carla; Sorichetta, Alessandro; Kuleszo, Joanna; Rogers, David J.

2015-01-01

Background Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI. Methods In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa. Results This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found. Conclusions These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality. PMID:25733559

Inversion of Farmland Soil Moisture in Large Region Based on Modified Vegetation Index

NASA Astrophysics Data System (ADS)

Wang, J. X.; Yu, B. S.; Zhang, G. Z.; Zhao, G. C.; He, S. D.; Luo, W. R.; Zhang, C. C.

2018-04-01

Soil moisture is an important parameter for agricultural production. Efficient and accurate monitoring of soil moisture is an important link to ensure the safety of agricultural production. Remote sensing technology has been widely used in agricultural moisture monitoring because of its timeliness, cyclicality, dynamic tracking of changes in things, easy access to data, and extensive monitoring. Vegetation index and surface temperature are important parameters for moisture monitoring. Based on NDVI, this paper introduces land surface temperature and average temperature for optimization. This article takes the soil moisture in winter wheat growing area in Henan Province as the research object, dividing Henan Province into three main regions producing winter wheat and dividing the growth period of winter wheat into the early, middle and late stages on the basis of phenological characteristics and regional characteristics. Introducing appropriate correction factor during the corresponding growth period of winter wheat, correcting the vegetation index in the corresponding area, this paper establishes regression models of soil moisture on NDVI and soil moisture on modified NDVI based on correlation analysis and compare models. It shows that modified NDVI is more suitable as a indicator of soil moisture because of the better correlation between soil moisture and modified NDVI and the higher prediction accuracy of the regression model of soil moisture on modified NDVI. The research in this paper has certain reference value for winter wheat farmland management and decision-making.

[Study on the relationship between Terra-MODIS image and the snail distribution in marshland of Jiangning county, Jiangsu province].

PubMed

Zhang, Bo; Zhang, Zhi-ying; Xu, De-zhong; Sun, Zhi-dong; Zhou, Xiao-nong; Gong, Zi-li; Liu, Shi-jun; Liu, Cheng; Xu, Bin; Zhou, Yun

2003-04-01

To analyze the relationship between the normalized difference vegetation index (NDVI) and the snail distribution in marshland of Jiangning county in Jiangsu province, and to explore the utility of Terra-MODIS image map in the small scale snail habitats surveillance. NDVI were extracted from MODIS image by vector chart of the snail distribution using ArcView 8.1 and ERDAS 8.5 software. The relationship between NDVI and the snail distribution were Investigated using Bivariate correlations and stepwise linear regression. The snail density on marshland was positively correlated with the mean NDVI in the first ten-day of May and the maximum NDVI (N(20max)) in the last ten-day of May. Incidence of pixel with the live snail on marshland was positively correlated with the mean NDVI (N(2mean)) in the first ten-day of May. An equation Y(1) = 0.009 47 x N(20max) (R(2) = 0.73), Y(2) = 0.018 6 x N(2mean) (R(2) = 0.906) was established. This study showed that the Terra-MODIS satellite images reflecting the status of the vegetation on marshland in Jiangning county could be applied to the study to supervise the snail habitat. The results suggested that MODIS images could be used to survey the small scale snail habitats on marshland.

Vegetation spatial variability and its effect on vegetation indices

NASA Technical Reports Server (NTRS)

Ormsby, J. P.; Choudhury, B. J.; Owe, M.

1987-01-01

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification program. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12.7 per cent in determining these areas. NDVI values less than 0.3 represented fractional vegetated areas of 5 per cent or less, while a value of 0.7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0.89 and 0.95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.

Using NDVI to measure precipitation in semi-arid landscapes

USGS Publications Warehouse

Birtwhistle, Amy N.; Laituri, Melinda; Bledsoe, Brian; Friedman, Jonathan M.

2016-01-01

Measuring precipitation in semi-arid landscapes is important for understanding the processes related to rainfall and run-off; however, measuring precipitation accurately can often be challenging especially within remote regions where precipitation instruments are scarce. Typically, rain-gauges are sparsely distributed and research comparing rain-gauge and RADAR precipitation estimates reveal that RADAR data are often misleading, especially for monsoon season convective storms. This study investigates an alternative way to map the spatial and temporal variation of precipitation inputs along ephemeral stream channels using Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper imagery. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG), a region covering 3,367 km2 of semiarid landscapes in southwestern Arizona, USA. The change in NDVI from a pre-to post-monsoon season image along ephemeral stream channels explained 73% of the variance in annual monsoonal precipitation totals from a nearby rain-gauge. In addition, large seasonal changes in NDVI along channels were useful in determining when and where flow events have occurred.

Impact of changes in GRACE derived terrestrial water storage on vegetation growth in Eurasia

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Kim, Y.

2015-12-01

We use GRACE-derived terrestrial water storage (TWS) and ERA-interim air temperature, as proxy for available water and temperature constraints on vegetation productivity, inferred from MODIS satellite normalized difference vegetation index (NDVI), in Northern Eurasia during 2002-2011. We investigate how changes in TWS affect the correlation between NDVI and temperature during the non-frozen season. We find that vegetation growth exhibits significant spatial and temporal variability associated with varying trend in TWS and temperature. The largest NDVI gains occur over boreal forests associated with warming and wetting. The largest NDVI losses occur over grasslands in the Southwestern Ob associated with regional drying and cooling, with dominant constraint from TWS. Over grasslands and temperate forests in the Southeast Ob and South Yenisei, wetting and cooling lead to a dominant temperature constraint due to the relaxation of TWS constraints. Overall, we find significant monthly correlation of NDVI with TWS and temperature over 35% and 50% of the domain, respectively. These results indicate that water availability (TWS) plays a major role in modulating Eurasia vegetation response to temperature changes.

Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive

USGS Publications Warehouse

Huntington, Justin; McGwire, Kenneth C.; Morton, Charles; Snyder, Keirith A.; Peterson, Sarah; Erickson, Tyler; Niswonger, Richard G.; Carroll, Rosemary W.H.; Smith, Guy; Allen, Richard

2016-01-01

Groundwater dependent ecosystems (GDEs) rely on near-surface groundwater. These systems are receiving more attention with rising air temperature, prolonged drought, and where groundwater pumping captures natural groundwater discharge for anthropogenic use. Phreatophyte shrublands, meadows, and riparian areas are GDEs that provide critical habitat for many sensitive species, especially in arid and semi-arid environments. While GDEs are vital for ecosystem services and function, their long-term (i.e. ~ 30 years) spatial and temporal variability is poorly understood with respect to local and regional scale climate, groundwater, and rangeland management. In this work, we compute time series of NDVI derived from sensors of the Landsat TM, ETM +, and OLI lineage for assessing GDEs in a variety of land and water management contexts. Changes in vegetation vigor based on climate, groundwater availability, and land management in arid landscapes are detectable with Landsat. However, the effective quantification of these ecosystem changes can be undermined if changes in spectral bandwidths between different Landsat sensors introduce biases in derived vegetation indices, and if climate, and land and water management histories are not well understood. The objective of this work is to 1) use the Landsat 8 under-fly dataset to quantify differences in spectral reflectance and NDVI between Landsat 7 ETM + and Landsat 8 OLI for a range of vegetation communities in arid and semiarid regions of the southwestern United States, and 2) demonstrate the value of 30-year historical vegetation index and climate datasets for assessing GDEs. Specific study areas were chosen to represent a range of GDEs and environmental conditions important for three scenarios: baseline monitoring of vegetation and climate, riparian restoration, and groundwater level changes. Google's Earth Engine cloud computing and environmental monitoring platform is used to rapidly access and analyze the Landsat archive along with downscaled North American Land Data Assimilation System gridded meteorological data, which are used for both atmospheric correction and correlation analysis. Results from the cross-sensor comparison indicate a benefit from the application of a consistent atmospheric correction method, and that NDVI derived from Landsat 7 and 8 are very similar within the study area. Results from continuous Landsat time series analysis clearly illustrate that there are strong correlations between changes in vegetation vigor, precipitation, evaporative demand, depth to groundwater, and riparian restoration. Trends in summer NDVI associated with riparian restoration and groundwater level changes were found to be statistically significant, and interannual summer NDVI was found to be moderately correlated to interannual water-year precipitation for baseline study sites. Results clearly highlight the complementary relationship between water-year PPT, NDVI, and evaporative demand, and are consistent with regional vegetation index and complementary relationship studies. This work is supporting land and water managers for evaluation of GDEs with respect to climate, groundwater, and resource management.

Variation in Phenometric Lapse Rates in Pasture Resources across Four Rayons in Kyrgyzstan

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Tomaszewska, M. A.; Kelgenbaeva, K.

2017-12-01

High elevation pasture resources form the foundation of agro-pastoralist livelihoods in Kyrgyzstan and elsewhere in montane Central Asia. We explore the temporal and the topographical variation in phenometric lapse rates (PLRs: the change in a phenometric as a function of elevation) across four rayons in two oblasts of the Kyrgyz Republic—Alay, At-Bashy, Chong Alay, and Naryn—with the aim of identifying and quantifying robust generic patterns in the PLRs. We evaluate two fundamental phenometrics derived from the downward convex quadratic model of land surface phenology that links the NDVI to accumulated growing degree-day (AGDD). The peak height (PH) is the maximum NDVI value obtained from the fitted model. The thermal time to peak (TTP) is the amount of AGDD required to reach the PH. We fitted sixteen years of Landsat NDVI data at 30 m spatial resolution to annual AGDD progressions derived from MODIS land surface temperature time series at 1 km spatial resolution, yielding maps for each phenometric. If the coefficient of determination was less than 0.5, then the model fit was deemed a failure. We classified the reliability of pasture resources into five classes based on the number of years of successful model fit: very persistent (14-16 y); persistent (11-13 y); marginal (7-10 y); occasional (4-6); and rare (1-3). We explore the interactive roles of elevation, slope, aspect, latitude, and rayon on the PLRs and pasture resource persistence to identify critical areas for resource management.

A framework for tracking post-wildfire trajectories and desired future conditions using NDVI time series

NASA Astrophysics Data System (ADS)

Norman, S. P.; Hargrove, W. W.; Lee, D. C.; Spruce, J.

2013-12-01

Wildfires could provide a cost-effective means to maintain or restore some aspects of fire-adapted landscapes. Yet with the added influence of climate change and invasives, wildfires may also facilitate or accelerate undesired type conversions. As megafires are becoming increasingly common across portions of the US West, managers require a framework for long-term monitoring that integrates the trajectories of fire-prone landscapes and objectives, not just conditions immediately after a burn. Systematic use of satellite data provides an efficient cross-jurisdictional solution to this problem. Since 2000, MODIS-technology has provided high frequency, 240m resolution observations of Earth. Using this data stream, the ForWarn system, developed through a partnership of the US Forest Service, NASA-Stennis and others, provides 46 estimates of the Normalized Difference Vegetation Index (NDVI) per year for the conterminous US. From this time series, a variety of secondary metrics have been derived including median annual NDVI, amplitude, and phenological spikiness. Each is both a fire and recovery sensitive measure that allows managers to systematically track conditions with respect to either the pre-fire baseline or desired future conditions more adaptively. In dry interior forests where wildfires could be used to thin stands, recovery to untreated conditions may not be desired given fuels objectives or climate change. In more mesic systems, fire effects may be monitored as staged succession. With both coarse filter monitoring and desired conditions in hand, managers can better recognize and prioritize problems in disturbance-prone landscapes.

Temporal Stability of the NDVI-LAI Relationship in a Napa Valley Vineyard

NASA Technical Reports Server (NTRS)

Johnson, L. F.

2003-01-01

Remotely sensed normalized difference vegetation index (NDVI) values, derived from high-resolution satellite images, were compared with ground measurements of vineyard leaf area index (LAI) periodically during the 2001 growing season. The two variables were strongly related at six ground calibration sites on each of four occasions (r squared = 0.91 to 0.98). Linear regression equations relating the two variables did not significantly differ by observation date, and a single equation accounted for 92 percent of the variance in the combined dataset. Temporal stability of the relationship opens the possibility of transforming NDVI maps to LAI in the absence of repeated ground calibration fieldwork. In order to take advantage of this circumstance, however, steps should be taken to assure temporal consistency in spectral data values comprising the NDVI.

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

Treesearch

Joseph P. Spruce; Steven Sader; Robert E. Ryan; James Smoot; Philip Kuper; al. et.

2011-01-01

This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth (Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation...

Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

USDA-ARS?s Scientific Manuscript database

Airborne imagery has been successfully used for mapping cotton root rot within cotton fields toward the end of the growing season. To better understand the progression of cotton root rot within the season, time series monitoring is required. In this study, an improved spatial and temporal data fusio...

Post-Katrina Land-Cover, Elevation, and Volume Change Assessment along the South Shore of Lake Pontchartrain, Louisiana, U.S.A.

DTIC Science & Technology

2011-01-01

was greater than 1 or less than 0. The second was a Normalized Difference Vegetation Index ( NDVI ) band ratio between a near-infrared band (738 nm) and...separation methods worked well, neither produced perfect results. Ultimately, the NDVI method was chosen because it could also be used to further...In addition, it is a broadly tested method often used to identify and measure vegetation (Tucker, 1979). The NDVI result was also used to separate

Satellite Vegetation Index Data as a Tool to Forecast Population Dynamics of Medically Important Mosquitoes at Military Installations in the Continental United States

DTIC Science & Technology

2008-07-01

for the two installations. We obtained monthly North American normalized differ- ence vegetation index ( NDVI ) satellite climate data sets for 1981-2005...from the Goddard Space Flight Center."*-" The NDVI measures the greenness of the earth, capturing in one index the combined effects of temperature...humidity, insola- tion, elevation, soils, land use. and precipitation on vegeta- tion. There is an almost-linear relationship between NDVI values and

Concepts for Sensor Data Fusion to Detect Vegetation Stress and Implications on Ecosystem Health Following Hurricane Katrina

DTIC Science & Technology

2008-09-01

Description NDVI Narrow-band Normalized Difference Vegetation Index (can check all possible two-band combinations, and determine best band combinations...were calculated for each site. The band indices were: • NDVI (Hyperion bands 45 & 33) (Figure 2) • NDWI (Hyperion bands 51 & 109) • PRI (Hyperion...between categories for these groups. NDVI and NDWI were very close to achiev- ing a significant result, and were still particularly good at separating two

Daytime Mud Detection for Unmanned Ground Vehicle Autonomous Navigation

DTIC Science & Technology

2008-12-01

disambiguate shadows from wet soil than shadows from dry soil. (a) Red band (b) NIR band (c) NDVI image (d) Brightness image wet soil Red...spectral bands to segment wet soil. Red and NIR bands (Figures 5a and 5b) can be used to generate a Normalized Difference Vegetation Index ( NDVI ...along the soil line image (Figure 5f) can be generated. The NDVI and normal distance to the soil line images can be used to segment soil from

Integrated NDVI images for Niger 1986-1987. [Normalized Difference Vegetation Index

NASA Technical Reports Server (NTRS)

Harrington, John A., Jr.; Wylie, Bruce K.; Tucker, Compton J.

1988-01-01

Two NOAA AVHRR images are presented which provide a comparison of the geographic distribution of an integration of the normalized difference vegetation index (NDVI) for the Sahel zone in Niger for the growing seasons of 1986 and 1987. The production of the images and the application of the images for resource management are discussed. Daily large area coverage with a spatial resolution of 1.1 km at nadir were transformed to the NDVI and geographically registered to produce the images.

Classification and Accuracy Assessment for Coarse Resolution Mapping within the Great Lakes Basin, USA

EPA Science Inventory

This study applied a phenology-based land-cover classification approach across the Laurentian Great Lakes Basin (GLB) using time-series data consisting of 23 Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) composite images (250 ...

Feasibility of inter-comparing airborne and spaceborne observations of radar backscattering coefficients

USDA-ARS?s Scientific Manuscript database

This paper investigates the feasibility of using an airborne synthetic aperture radar (SAR) to validate spaceborne SAR data. This is directed at soil moisture sensing and the recently launched Soil Moisture Active Passive (SMAP) satellite. The value of this approach is related to the fact that vicar...

Influence of the choice of gas-phase mechanism on predictions of key gaseous pollutants during the AQMEII phase-2 intercomparison

EPA Science Inventory

The formulations of tropospheric gas-phase chemistry (“mechanisms”)used in the regional-scale chemistry-transport models participating in theAir Quality Modelling Evaluation International Initiative (AQMEII) Phase2 are intercompared by the means of box model studies. Simulations ...

Turbulent pressure fluctuations measured during CHATS

Treesearch

Steven P. Oncley; William J. Massman; Edward G. Patton

2008-01-01

Fast-response pressure fluctuations were included in the Canopy Horizontal Array of Turbulence Study (CHATS) at several heights within and just above the canopy in a walnut orchard. Two independent systems were intercompared and then separated. We present an evaluation of turbulence statistics - including the pressure transport term in the turbulence kinetic energy...

The role of simulation chambers in the development of spectroscopic techniques: campaigns at EUPHORE

NASA Astrophysics Data System (ADS)

Ródenas, Milagros; Muñoz, Amalia; Euphore Team

2016-04-01

Simulation chambers represent a very useful tool for the study of chemical reactions and their products, but also to characterize instruments. The development of spectroscopic techniques throughout the last decades has benefited from tests and intercomparison exercises carried out in chambers. In fact, instruments can be exposed to various controlled atmospheric scenarios that account for different environmental conditions, eliminating the uncertainties associated to fluctuations of the air mass, which must be taken into account when extrapolating results to the real conditions. Hence, a given instrument can be characterized by assessing its precision, accuracy, detection limits, time response and potential interferences in the presence of other chemical compounds, aerosols, etc. This implies that the instrument can be calibrated and validated, which allows to enhance the features of the instrument. Moreover, chambers are also the scenario of intercomparison trials, permitting multiple instruments to sample from the same well-mixed air mass simultaneously. An overview of different campaigns to characterize and/or intercompare spectroscopic techniques that have taken place in simulation chambers will be given; in particular, those carried out at EUPHORE (two twin domes, 200 m3 each, Spain), where various intercomparison exercises have been deployed under the frame of European projects (e.g. TOXIC, FIONA, PSOA campaigns supported by EUROCHAMP-II). With the common aim of measuring given compounds (e.g. HONO, NO2, OH, glyoxal, m-glyoxal, etc), an important number of spectroscopic instruments and institutions have been involved in chamber experiments, having the chance to intercompare among them and also with other non-spectroscopic systems (e.g. monitors, cromatographs, etc) or model simulations.

SeaWiFs Technical Report Series. Volume 34: The Third SeaWiFS Intercalibration Round-Robin Experiment (SIRREX-3), 19-30 September 1994

NASA Technical Reports Server (NTRS)

Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor); Acker, James G. (Editor); Mueller, James L.; Johnson, B. Carol; Cromer, Christopher L.; McLean, James T.; Biggar, Stuart F.

1996-01-01

This report presents results of the third Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Intercalibration Round- Robin Experiment (SIRREX-3), which was held at the San Diego State University (SDSU) Center for Hydro-Optics and Remote Sensing (CHORS) on 19-30 September 1994. Spectral irradiances of FEL lamps belonging to each participant were intercompared by reference to the National Institute of Standards and Technology (NIST) scale of spectral irradiance using secondary standard lamps F268, F269, and F182, with a Type A uncertainty between 1.1-1.5%. This level of uncertainty was achieved despite difficulties with lamp F269. The average spectral irradiances of FEL lamps, compared in both SIRREX-2 and SIRREX-3, differed between the two experiments by 1.5%, which probably indicates that the values assigned to the secondary standard lamp at the time of SIRREX-2 were in error. With two exceptions, spectral radiance values of integrating sphere sources were measured during SIRREX-3 with uncertainties in temporal stability of less than 0.3% and absolute uncertainties of 1.5-2.0%. This is a significant improvement over similar intercomparisons in SIRREX- I and SIRREX-2. Plaque reflectances were intercompared with an uncertainty of about 1-2%, but the absolute uncertainty is undefined. Although this is an improvement over results of previous SIRREXS, the sources and magnitude of uncertainty associated with transfers of spectral radiance using plaques requires further evaluation in future experiments.

Characterizing post-drainage succession in Thermokarst Lake Basins on the Seward Peninsula, Alaska with TerraSAR-X Backscatter and Landsat-based NDVI data

USGS Publications Warehouse

Regmi, Prajna; Grosse, Guido; Jones, Miriam C.; Jones, Benjamin M.; Walter Anthony, Katey

2012-01-01

Drained thermokarst lake basins accumulate significant amounts of soil organic carbon in the form of peat, which is of interest to understanding carbon cycling and climate change feedbacks associated with thermokarst in the Arctic. Remote sensing is a tool useful for understanding temporal and spatial dynamics of drained basins. In this study, we tested the application of high-resolution X-band Synthetic Aperture Radar (SAR) data of the German TerraSAR-X satellite from the 2009 growing season (July–September) for characterizing drained thermokarst lake basins of various age in the ice-rich permafrost region of the northern Seward Peninsula, Alaska. To enhance interpretation of patterns identified in X-band SAR for these basins, we also analyzed the Normalized Difference Vegetation Index (NDVI) calculated from a Landsat-5 Thematic Mapper image acquired on July 2009 and compared both X-band SAR and NDVI data with observations of basin age. We found significant logarithmic relationships between (a) TerraSAR-X backscatter and basin age from 0 to 10,000 years, (b) Landat-5 TM NDVI and basin age from 0 to 10,000 years, and (c) TerraSAR-X backscatter and basin age from 50 to 10,000 years. NDVI was a better indicator of basin age over a period of 0–10,000 years. However, TerraSAR-X data performed much better for discriminating radiocarbon-dated basins (50–10,000 years old). No clear relationships were found for either backscatter or NDVI and basin age from 0 to 50 years. We attribute the decreasing trend of backscatter and NDVI with increasing basin age to post-drainage changes in the basin surface. Such changes include succession in vegetation, soils, hydrology, and renewed permafrost aggradation, ground ice accumulation and localized frost heave. Results of this study show the potential application of X-band SAR data in combination with NDVI data to map long-term succession dynamics of drained thermokarst lake basins.

Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest

NASA Astrophysics Data System (ADS)

Tian, Jinyan; Wang, Le; Li, Xiaojuan; Gong, Huili; Shi, Chen; Zhong, Ruofei; Liu, Xiaomeng

2017-09-01

Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2). In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R2 = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R2 = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation.

[Spatial and Temporal Variations in Spectrum-Derived Vegetation Growth Trend in Qinghai-Tibetan Plateau from 1982 to 2014].

PubMed

Wang, Zhi-wei; Wu, Xiao-dong; Yue, Guang-yang; Zhao, Lin; Wang, Qian; Nan, Zhuo-tong; Qin, Yu; Wu, Tong-hua; Shi, Jian-zong; Zou, De-fu

2016-02-01

Recently considerable researches have focused on monitoring vegetation changes because of its important role in regula- ting the terrestrial carbon cycle and the climate system. There were the largest areas with high-altitudes in the Qinghai-Tibet Plateau (QTP), which is often referred to as the third pole of the world. And vegetation in this region is significantly sensitive to the global warming. Meanwhile NDVI dataset was one of the most useful tools to monitor the vegetation activity with high spatial and temporal resolution, which is a normalized transform of the near-infrared radiation (NIR) to red reflectance ratio. Therefore, an extended GIMMS NDVI dataset from 1982-2006 to 1982-2014 was presented using a unary linear regression by MODIS dataset from 2000 to 2014 in QTP. Compared with previous researches, the accuracy of the extended NDVI dataset was improved again with consideration the residuals derived from scale transformation. So the model of extend NDVI dataset could be a new method to integrate different NDVI products. With the extended NDVI dataset, we found that in growing season there was a statistically significant increase (0.000 4 yr⁻¹, r² = 0.585 9, p < 0.001) in QTP from 1982 to 2014. During the study pe- riod, the trends of NDVI were significantly increased in spring (0.000 5 yr⁻¹, r² = 0.295 4, p = 0.001), summer (0.000 3 yr⁻¹, r² = 0.105 3, p = 0.065) and autumn respectively (0.000 6 yr⁻¹, r² = 0.436 7, p < 0.001). Due to the increased vegeta- tion activity in Qinghai-Tibet Plateau from 1982 to 2014, the magnitude of carbon sink was accumulated in this region also at this same period. Then the data of temperature and precipitation was used to explore the reason of vegetation changed. Although the trends of them are both increased, the correlation between NDVI and temperature is higher than precipitation in vegetation grow- ing season, spring, summer and autumn. Furthermore, there is significant spatial heterogeneity of the changing trends for ND- VI, temperature and precipitation at Qinghai-Tibet Plateau scale.

Investigating the Relationship Between Liquid Water and Leaf Area in Clonal Populus

NASA Technical Reports Server (NTRS)

Roberts, Dar; Brown, K.; Green, R.; Ustin, S.; Hinckley, T.

1998-01-01

Leaf Area Index (LAI) is one of the most commonly employed biophysical parameters used to characterize vegetation canopies and scale leaf physiological processes to larger scales. For example, LAI is a critical parameter used in regional scale estimates of evapotranspiration, photosynthesis, primary productivity, and carbon cycling (Running et al., 1989; Dorman and Sellers, 1989; Potter et al., 1993). LAI is typically estimated using ratio-based techniques, such as the Normalized Difference Vegetation Index (NDVI: e.g. Tucker 1979; Asrar et al., 1989; Sellers 1985, 1987). The physical basis behind this relationship depends on the high spectral contrast between scattered near-infrared (NIR) and absorbed red radiation in canopies. As the number of leaves present in a canopy increases over a unit area, NIR reflectance increases, while red reflectance decreases, resulting in an increase in the ratio. Through time series and image compositing, NDVI provides an additional temporal measure of how these parameters change, providing a means to monitor fluxes and productivity (Tucker et al., 1983). NDVI, while highly successful for agriculture and grassland ecosystems has been found to be less successful in evergreen chaparral and forested ecosystems (Badhwar et al., 1986; Gamon et al., 1993; Hall et al., 1995). Typically, the relationship between NDVI and LAI becomes progressively more asymptotic at LAI values above three (Sellers, 1985), although linear relationships have been observed in conifers at LAis as high as 13 (Spanner et al., 1990). In this paper, we explore an alternative approach for estimating LAI for remotely sensed data from AVIRIS based on estimates of canopy liquid water. Our primary objective is to test the hypothesis that the depth of the liquid water bands expressed in canopy reflectance spectra at 960, 1200, 1400 and 1900 nm increases with increasing LAI in canopies. This study builds from work by Roberts et al. (1997), in which liquid water was shown to increase following a gradient of increasing LAI ranging from grasslands to coniferous forests. In that study, it was observed that forests, which showed little variation in NDVI, showed significant variation in liquid water. In order to test this hypothesis, we analyzed field spectra measured over Populus resprouts of known LAI and monitored changes in liquid water in young Populus stands as they aged over a 4-year time span. The study was conducted in south-central Washington, in a clonal Populus fiber farm owned and operated by Boise-Cascade near the town of Wallula.

The impact of ecological variability on the reproductive endocrinology of wild female African elephants.

PubMed

Wittemyer, George; Ganswindt, André; Hodges, Keith

2007-03-01

Non-invasive endocrine methods enable investigation of the relationship between ecological variation and ovarian activity and how this impacts on demographic processes. The underlying physiological factors driving high variation in inter-calving intervals among multi-parous African elephants offer an interesting system for such an investigation. This study investigates the relationship between Normalized Differential Vegetation Index (NDVI), an ecosystem surrogate measure of primary productivity, and fecal progestin concentrations among wild female elephants. Matched fecal samples and behavioral data on reproductive activity were collected from 37 focal individuals during the two-year study. Linear mixed models were used to explore the relationship between fecal 5alpha-pregnane-3-ol-20-one concentrations and the independent variables of NDVI, calf sex, female age, gestation day, and time since last parturition. Among both non-pregnant and pregnant females, fecal 5alpha-pregnane-3-ol-20-one concentrations were significantly correlated with time-specific NDVI indicating a strong relationship between ecological conditions and endocrine activity regulating reproduction. In addition, the age of a female and time since her last parturition impacted hormone concentrations. These results indicate that the identification of an individual's reproductive status from a single hormone sample is possible, but difficult to achieve in practice since numerous independent factors, particularly season, impact fecal hormone concentrations. Regardless of season, however, fecal 5alpha-pregnane-3-ol-20-one concentrations below 1 microg/g were exclusively collected from non-pregnant females, which could be used as a threshold value to identify non-pregnant individuals. Collectively the information generated contributes to a better understanding of environmental regulation of reproductive endocrinology in wild elephant populations, information salient to the management and manipulation of population dynamics in this species.

Generation of the global cloud free data set of MODIS

NASA Astrophysics Data System (ADS)

Oguro, Y.; Tsuchiya, K.

To extract temporal change of the land cover from remotely sensed data from space the generation of the reliable cloud free data set is the first priority item With the objectives of generating accurate global basic data and to find the effects of spectral and spatial resolution differences and observation time an attempt is made to generate reliable global cloud free data set of Terra and Aqua MODIS utilizing personal computers Out of 36 bands seven bands with similar spectral features to those of Landsat TM i e Band 1 through 7 are selected These bands cover the most important spectra to derive landcover features The procedure of the data set generation is as follows 1 Download the global Terra and Aqua MODIS day time data MOD02 Level-1B Calibrated Geolocation Data Set of 250 meter Band 1 and 2 and 500 meter Band 3 through 7 resolution from NASA web site 2 Separate the data into several BSQ Band SeQuential image and several text geolocation information of pixels files 3 The geolocation information is given to the pixels of several kms interval Based on the information resampling of the data are made at 1 2 and 1 4 degrees intervals of latitude and longitude thus the resampled pixels are distributed in the latitude and longitudinal axis plane at 1 4 degrees high resolution and 1 2 degrees low resolution intervals 4 A global data for one day is composed 5 Compute NDVI for each pixel 6 Compare the value of NDVI of successive days and keep the larger NDVI At the same time keep the values of each band of the day of the larger

Estimating Crop Growth Stage by Combining Meteorological and Remote Sensing Based Techniques

NASA Astrophysics Data System (ADS)

Champagne, C.; Alavi-Shoushtari, N.; Davidson, A. M.; Chipanshi, A.; Zhang, Y.; Shang, J.

2016-12-01

Estimations of seeding, harvest and phenological growth stage of crops are important sources of information for monitoring crop progress and crop yield forecasting. Growth stage has been traditionally estimated at the regional level through surveys, which rely on field staff to collect the information. Automated techniques to estimate growth stage have included agrometeorological approaches that use temperature and day length information to estimate accumulated heat and photoperiod, with thresholds used to determine when these stages are most likely. These approaches however, are crop and hybrid dependent, and can give widely varying results depending on the method used, particularly if the seeding date is unknown. Methods to estimate growth stage from remote sensing have progressed greatly in the past decade, with time series information from the Normalized Difference Vegetation Index (NDVI) the most common approach. Time series NDVI provide information on growth stage through a variety of techniques, including fitting functions to a series of measured NDVI values or smoothing these values and using thresholds to detect changes in slope that are indicative of rapidly increasing or decreasing `greeness' in the vegetation cover. The key limitations of these techniques for agriculture are frequent cloud cover in optical data that lead to errors in estimating local features in the time series function, and the incongruity between changes in greenness and traditional agricultural growth stages. There is great potential to combine both meteorological approaches and remote sensing to overcome the limitations of each technique. This research will examine the accuracy of both meteorological and remote sensing approaches over several agricultural sites in Canada, and look at the potential to integrate these techniques to provide improved estimates of crop growth stage for common field crops.

Thirty Years of Change in Subalpine Forest Cover from Landsat Image Analysis in the Sierra Nevada Mountains of California

NASA Technical Reports Server (NTRS)

Potter, Christopher

2015-01-01

Landsat imagery was analyzed to understand changes in subalpine forest stands since the mid-1980s in the Sierra-Nevada region of California. At locations where long-term plot measurements have shown that stands are becoming denser in the number of small tree stems (compared to the early 1930s), the 30-year analysis of Landsat greenness index (NDVI) indicated that no consistent increases in canopy leaf cover have occurred at these same locations since the mid-1980s. Interannual variations in stand NDVI closely followed snow accumulation amounts recorded at nearby stations. In contrast, at eastern Sierra whitebark pine stand locations where it has been observed that widespread tree mortality has occurred, decreasing NDVI trends over the past 5-10 years were consistent with rapid loss of forest canopy cover. Landsat imagery was further analyzed to understand patterns of post-wildfire vegetation recovery, focusing on high burn severity (HBS) patches within burned areas dating from the late 1940s. Analysis of landscape metrics showed that the percentage of total HBS area comprised by the largest patch of recovered woody cover was relatively small in all fires that occurred since 1995, but increased rapidly with time since fire. Patch complexity of recovered woody cover decreased notably after more than 50 years of regrowth, but was not readily associated with time for fires that occurred since the mid 1990s. The aggregation level of patches with recovery of woody cover increased steadily with time since fire. The study approach using satellite remote sensing can be expanded to assess the consequences of stand-replacing wildfires in all forests of the region.

Use of Current 2010 Forest Disturbance Monitoring Products for the Conterminous United States in Aiding a National Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William; Gasser, J.; Smoot, J.; Kuper, P.

2010-01-01

This presentation discusses contributions of near real time (NRT) MODIS forest disturbance detection products for the conterminous United States to an emerging national forest threat early warning system (EWS). The latter is being developed by the USDA Forest Service s Eastern and Western Environmental Threat Centers with help from NASA Stennis Space Center and the Oak Ridge National Laboratory. Building off work done in 2009, this national and regional forest disturbance detection and viewing capability of the EWS employs NRT MODIS NDVI data from the USGS eMODIS group and historical NDVI data from standard MOD13 products. Disturbance detection products are being computed for 24 day composites that are refreshed every 8 days. Products for 2010 include 42 dates of the 24 day composites. For each compositing date, we computed % change in forest maximum NDVI products for 2010 with respect to each of three historical baselines of 2009, 2007-2009, and 2003-2009,. The three baselines enable one to view potential current, recent, and longer term forest disturbances. A rainbow color table was applied to each forest change product so that potential disturbances (NDVI drops) were identified in hot color tones and growth (NDVI gains) in cold color tones. Example products were provided to end-users responsible for forest health monitoring at the Federal and State levels. Large patches of potential forest disturbances were validated based on comparisons with available reference data, including Landsat and field survey data. Products were posted on two internet mapping systems for US Forest Service internal and collaborator use. MODIS forest disturbance detection products were computed and posted for use in as little as 1 day after the last input date of the compositing period. Such products were useful for aiding aerial disturbance detection surveys and for assessing disturbance persistence on both inter- and intra-annual scales. Multiple 2010 forest disturbance events were detected across the nation, including damage from ice storms, tornadoes, caterpillars, bark beetles, and wildfires. This effort enabled improved NRT forest disturbance monitoring capabilities for this nation-wide forest threat EWS.

Use of Current 2010 Forest Disturbance Monitoring Products for the Conterminous United States in Aiding a National Forest Threat Early Warning System

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Gasser, J.; Smoot, J.; Kuper, P.

2010-12-01

This presentation discusses contributions of near real time (NRT) MODIS forest disturbance detection products for the conterminous United States to an emerging national forest threat early warning system (EWS). The latter is being developed by the USDA Forest Service’s Eastern and Western Environmental Threat Centers with help from NASA Stennis Space Center and the Oak Ridge National Laboratory. Building off work done in 2009, this national and regional forest disturbance detection and viewing capability of the EWS employs NRT MODIS NDVI data from the USGS eMODIS group and historical NDVI data from standard MOD13 products. Disturbance detection products are being computed for 24 day composites that are refreshed every 8 days. Products for 2010 include 42 dates of the 24 day composites. For each compositing date, we computed % change in forest maximum NDVI products for 2010 with respect to each of three historical baselines of 2009, 2007-2009, and 2003-2009. The three baselines enable one to view potential current, recent, and longer term forest disturbances. A rainbow color table was applied to each forest change product so that potential disturbances (NDVI drops) were identified in hot color tones and growth (NDVI gains) in cold color tones. Example products were provided to end-users responsible for forest health monitoring at the Federal and State levels. Large patches of potential forest disturbances were validated based on comparisons with available reference data, including Landsat and field survey data. Products were posted on two internet mapping systems for US Forest Service internal and collaborator use. MODIS forest disturbance detection products were computed and posted for use in as little as 1 day after the last input date of the compositing period. Such products were useful for aiding aerial disturbance detection surveys and for assessing disturbance persistence on both inter- and intra-annual scales. Multiple 2010 forest disturbance events were detected across the nation, including damage from ice storms, tornados, caterpillars, bark beetles, and wildfires. This effort enabled improved NRT forest disturbance monitoring capabilities for this nation-wide forest threat EWS.

Detection of phonological transitions of spring maize in Northeast China during the last 20 years using daily NOAA/AVHRR NDVI temporal series data

NASA Astrophysics Data System (ADS)

Zhang, X.; Xue, Y.

2015-12-01

The normalized difference vegetation index (NDVI) provides a rough measure of vegetation amount and growing condition of crops when vegetation activity is low to moderate. Based on the Leaf Collar Method, two key phenological phases, i.e., third leaf collar (TLC) and the maturity, are selected for NDVI modeling. The records on crop phenology were available from 1992 to 2013 at 103 stations in the Northeast China. However, there are large amount of missing data. Therefore, a statistic model is desirable to fill the gaps then, analyze the characteristics of the TLC and the maturity stage with the full data set. The Savitzky-Golay filter was used for noise reduction and temporal NDVI smoothing. The slope analysis was used for detection of TLC and maturity date of spring maize in the spring and in the fall, respectively. When NDVI slope values reach the turning point in certain period, the corresponding date is selected as TLC or maturity. Through comparison between observation and estimation, we find that 5-day slope method is robust to detect the changes of maize phenology. This study shows that the average estimation is 2 days earlier than observation. We then use this method to generate the TLC and mature dates for all the stations. The analyses of this full data set shows that the average TLC of spring maize in Northeast China emerges on Jun.2. The average maturity of spring maize appears on Sep. 18. The shortest growing season of 104 days appears in Jilin Province, while the longest growing season appears in Heilongjiang province of 116 days. When the latitude decreases, the annual average temperature and precipitation amount increases. Accordingly, TLC becomes earlier from Heilongjiang, Jilin to Liaoning Province. There is a significantly negative correlation between TLC that is around June and temperature of April and May. One-month time lags of climate factor, therefore, should be added to detection of phonological transitions of spring maize.

Spatio-temporal variability of NDVI-precipitation over southernmost South America: possible linkages between climate signals and epidemics

NASA Astrophysics Data System (ADS)

Tourre, Y. M.; Jarlan, L.; Lacaux, J.-P.; Rotela, C. H.; Lafaye, M.

2008-10-01

Climate-environment variability affects the rates of incidence of vector-borne and zoonotic diseases and is possibly associated with epidemics outbreaks. Over southernmost South America the joint spatio-temporal evolution of climate-environment is analyzed for the 1982-2004 period. Detailed mapping of normalized difference vegetation index (NDVI) and rainfall variability are then compared to zones with preliminary epidemiological reports. A significant quasi-biennial signal (2.2- to 2.4-year periods, or QB) for joint NDVI-rainfall variability is revealed. From rotated EOFs, dominant NDVI patterns are partitioned according to their lead frequencies: (1) the 'QB group' (2.1-to 3-year periods) includes six modes over southern Brazil, Uruguay, northern-central Argentina (two modes), the southern Paraguay-northern Argentina border, and the Santa Cruz Province; (2) the QB1 (2.4- to 3-year periods) + quasi-quadrennial (QQ) mode over the Misiones Province; and (3) the QB2 (2.1- to 2.5-year periods) + QQ + inter-annual (IA) (3- to 7-year periods) two modes over south-eastern Argentina. Modes within the 'QB group' are positively correlated with global climate signals and SST. The Uruguayan mode is correlated with global ENSO (8-month lag) whilst the southern Entre-Rios/northern Buenos Aires provinces are correlated with central equatorial Pacific SSTs (3-month lag). The Santa Cruz (Patagonia) Province is most correlated with the Pacific South America (PSA) index and SST patterns (3-month lag) along the Antarctica circumpolar current. The spatial distribution of lead NDVI modes includes the Formosa, Misiones, Chaco and Buenos Aires provinces among others, known for being prone to vector-borne epidemics such as dengue fever, malaria, leishmaniasis (American cutaneous leishmaniasis or ACL), hantivirus, chagas and Argentine hemorrhagic fever (AHF). Some provinces also correspond to regions where lead NDVI PCs' modes are associated with high-frequency climate signals such as the quasi-biennial oscillation in northwest Argentina. The joint preliminary results (climate-environment-public health reports) presented here for the first time are meant: (1) to contribute to a better understanding of climate-environment-epidemics process-based and modeling studies and (2) to facilitate, in the long run, the implementation of local and regional health early warning systems (HEWS) over southernmost South America. The latter is becoming crucial with ever-increasing migration, urban sprawl (re-emergence of dengue fever epidemics since the late 1990s), all embedded in a climate change context.

Cereal Production Ratio and NDVI in Spain

NASA Astrophysics Data System (ADS)

Saa-Requejo, Antonio; Recuero, Laura; Palacios, Alicia; Díaz-Ambrona, Carlos G. H.; Tarquis, Ana M.

2014-05-01

Droughts are long-term phenomena affecting large regions causing significant damages both in human lives and economic losses. The use of remote sensing has proved to be very important in monitoring the growth of agricultural crops and trying to asses weather impact on crop loss. Several indices has been developed based in remote sensing data being one of them the normalized difference vegetation index (NDVI). In this study we have focus to know the correlation between NDVI data and the looses of rain fed cereal in the Spanish area where this crop is majority. For this propose data from drought damage in cereal come from the pool of agricultural insurance in Spain (AGROSEGURO) including 2007/2008 to 2011/2012 (five agricultural campaigns). This data is given as a ratio between drought party claims against the insured value of production aggregated at the agrarian region level. Medium resolution (500x500 m2) MODIS images were used during the same campaigns to estimate the eight-day composites NDVI at these locations. The NDVI values are accumulated following the normal cycle of the cereal taking in account the sowing date at different sites. At the same time, CORINE Land Cover (2006) was used to classify the pixels belonging to rain fed cereal use including a set of conditions such as pixels showing dry during summer, area in which there has been no change of use. Fallow presence is studied with particular attention as it imposes an inter annual variation between crop and bare soil and causes decreases in greenness in a pixel and mix both situations. This is more complex in the situation in which the avoid fallow and a continuous monoculture is performed. The results shown that around 40% of the area is subject to the regime of fallow while 60% have growing every year. In addition, another variation is detected if the year is humid (decrease of fallow) or dry (increase of fallow). The level of correlation between the drought damage ratios and cumulative NDVI for the cereal campaign obtained are classified according to their level of significance at 99, 95, 90 and 85%. Approximately half of the regions with high surface assurance have meaningful relationships. In the regions where no significant relationships are achieved several situations are discussed such as extreme situations in critical phenological periods that could have great influence on the final yields. Acknowledgements. This work was partially supported by ENESA under project P10 0220C-823.

NDVI statistical distribution of pasture areas at different times in the Community of Madrid (Spain)

NASA Astrophysics Data System (ADS)

Martín-Sotoca, Juan J.; Saa-Requejo, Antonio; Díaz-Ambrona, Carlos G. H.; Tarquis, Ana M.

2015-04-01

The severity of drought has many implications for society, including its impacts on the water supply, water pollution, reservoir management and ecosystem. However, its impacts on rain-fed agriculture are especially direct. Because of the importance of drought, there have been many attempts to characterize its severity, resulting in the numerous drought indices that have been developed (Niemeyer 2008). 'Biomass index' based on satellite image derived Normalized Difference Vegetation Index (NDVI) has been used in countries like United States of America, Canada and Spain for pasture and forage crops for some years (Rao, 2010). This type of agricultural insurance is named as 'index-based insurance' (IBI). IBI is perceived to be substantially less costly to operate and manage than multiple peril insurance. IBI contracts pay indemnities based not on the actual yield (or revenue) losses experienced by the insurance purchaser but rather based on realized NDVI values (historical data) that is correlated with farm-level losses (Xiaohui Deng et al., 2008). Definition of when drought event occurs is defined on NDVI threshold values mainly based in statistical parameters, average and standard deviation that characterize a normal distribution. In this work a pasture area at the north of Community of Madrid (Spain) has been delimited. Then, NDVI historical data was reconstructed based on remote sensing imaging MODIS, with 500x500m2 resolution. A statistical analysis of the NDVI histograms at consecutives 46 intervals of that area was applied to search for the best statistical distribution based on the maximum likelihood criteria. The results show that the normal distribution is not the optimal representation when IBI is available; the implications in the context of crop insurance are discussed (Martín-Sotoca, 2014). References Kolli N Rao. 2010. Index based Crop Insurance. Agriculture and Agricultural Science Procedia 1, 193-203. Martín-Sotoca, J.J. (2014) Estructura Espacial de la Sequía en Pastos y sus Aplicaciones en el Seguro Agrario. Master Thesis, UPM (In Spanish). Niemeyer, S., 2008: New drought indices. First Int. Conf. on Drought Management: Scientific and Technological Innovations, Zaragoza, Spain, Joint Research Centre of the European Commission. [Available online at http://www.iamz.ciheam.org/medroplan/zaragoza2008/Sequia2008/Session3/S.Niemeyer.pdf.] Xiaohui Deng, Barry J. Barnett, Gerrit Hoogenboom, Yingzhuo Yu and Axel Garcia y Garcia 2008. Alternative Crop Insurance Indexes. Journal of Agricultural and Applied Economics, 40(1), 223-237. Acknowledgements First author acknowledges the Research Grant obtained from CEIGRAM in 2014

Characterization and classification of South American land cover types using satellite data

NASA Technical Reports Server (NTRS)

Townshend, J. R. G.; Justice, C. O.; Kalb, V.

1987-01-01

Various methods are compared for carrying out land cover classifications of South America using multitemporal Advanced Very High Resolution Radiometer data. Fifty-two images of the normalized difference vegetation index (NDVI) from a 1-year period are used to generate multitemporal data sets. Three main approaches to land cover classification are considered, namely the use of the principal components transformed images, the use of a characteristic curves procedure based on NDVI values plotted against time, and finally application of the maximum likelihood rule to multitemporal data sets. Comparison of results from training sites indicates that the last approach yields the most accurate results. Despite the reliance on training site figures for performance assessment, the results are nevertheless extremely encouraging, with accuracies for several cover types exceeding 90 per cent.

Monitoring Regional Forest Disturbances across the US with near Real Time MODIS NDVI Products Resident to the ForWarn Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William W.; Gasser, Gerald

2013-01-01

Forest threats across the US have become increasingly evident in recent years. Sometimes these have resulted in regionally evident disturbance progressions (e.g., from drought, bark beetle outbreaks, and wildfires) that can occur across multiyear durations and have resulted in extensive forest overstory mortality. In addition to stand replacement disturbances, other forests are subject to ephemeral, sometimes yearly defoliation from various insects and varying types and intensities of ephemeral damage from storms. Sometimes, after prolonged severe disturbance, signs of recovery in terms of Normalized Difference Vegetation Index (NDVI) can occur. The growing prominence and threat of forest disturbances in part have led to the formation and implementation of the 2003 Healthy Forest Restoration Act which mandated that national forest threat early warning system be developed and deployed. In response, the US Forest Service collaborated with NASA, DOE Oakridge National Laboratory, and the USGS Eros Data Center to build and roll-out the near real time ForWarn early warning system for monitoring regionally evident forest disturbances. Given the diversity of disturbance types, severities, and durations, ForWarn employs multiple historical baselines that are used with current NDVI to derive a suite of six forest change products that are refreshed every 8 days. ForWarn employs daily quarter kilometer MODIS NDVI data from the Aqua and Terra satellites, including MOD13 data for deriving historical baseline NDVIs and eMODIS 7 NDVI for compiling current NDVI. In doing so, the Time Series Product Tool and the Phenological Parameters Estimation Tool are used to temporally de-noise, fuse, and aggregate current and historical MODIS NDVIs into 24 day composites refreshed every 8 days with 46 dates of products per year. The 24 day compositing interval enables disturbances to be detected, while minimizing the frequency of residual atmospheric contamination. Forest change products are computed versus the previous 1, previous 3, and all previous years in the MODIS record for a given 24 day interval. Other "weekly" forest change products include one computed using an adaptive length compositing method for quicker detection of disturbances, two others that adjust for seasonal fluctuations in normal vegetation phenology (e.g., early versus late springs). This overall approach enables forest disturbance dynamics from a variety of regionally evident biotic and abiotic forest disturbances to be viewed and assessed through the calendar year. The change products are also being utilized for forest change trend analysis and for developing regional forest overstory mortality products. ForWarn's forest change products are used to alert forest health specialists about new forest disturbances. Such alerts are also typically based on available Landsat, aerial, and ground data as well as communications with forest health specialists and previous experience. ForWarn products have been used to detect and track many types of regional disturbances to multiple forest types, including defoliation from caterpillars and severe storms, as well as mortality from both biotic and abiotic agents (e.g., bark beetles, drought, fire, anthropogenic clearing). ForWarn offers products that could be combined with other geospatial data on forest biomass to assess forest disturbance carbon impacts within the conterminous US.

Linking phenology and biomass productivity in South Dakota mixed-grass prairie

USGS Publications Warehouse

Rigge, Matthew; Smart, Alexander; Wylie, Bruce; Gilmanov, Tagir; Johnson, Patricia

2013-01-01

Assessing the health of rangeland ecosystems based solely on annual biomass production does not fully describe plant community condition; the phenology of production can provide inferences on species composition, successional stage, and grazing impacts. We evaluate the productivity and phenology of western South Dakota mixed-grass prairie using 2000 to 2008 Moderate Resolution Imaging Spectrometer (MODIS) normalized difference vegetation index (NDVI) satellite imagery at 250 m spatial resolution. Growing season NDVI images were integrated weekly to produce time-integrated NDVI (TIN), a proxy of total annual biomass production, and integrated seasonally to represent annual production by cool (C3) and warm (C4) season species. Additionally, a variety of phenological indicators including cool season percentage of TIN were derived from the seasonal profiles of NDVI. Cool season percentage and TIN were combined to generate vegetation classes, which served as proxies of plant community condition. TIN decreased with precipitation from east to west across the study area. Alternatively, cool season percentage increased from east to west, following patterns related to the reliability (interannual coefficient of variation [CV]) and quantity of mid-summer precipitation. Cool season TIN averaged 76.8% of total. Seasonal accumulation of TIN corresponded closely (R2 > 0.90) to that of gross photosynthesis data from a carbon flux tower. Field-collected biomass and community composition data were strongly related to the TIN and cool season percentage products. The patterns of vegetation classes were responsive to topographic, edaphic, and land management influences on plant communities. Accurate maps of biomass production, cool/warm season composition, and vegetation classes can improve the efficiency of land management by adjusting stocking rates and season of use to maximize rangeland productivity and achieve conservation objectives. Further, our results clarify the spatial and temporal dynamics of phenology and TIN in mixed-grass prairie.

A conceptual method for monitoring locust habitat

USGS Publications Warehouse

Howard, Stephen M.; Loveland, Thomas R.; Ohlen, Donald O.; Moore, Donald G.; Gallo, Kevin P.; Olsson, Jonathon

1987-01-01

A procedure to map and monitor vegetation conditions in near-real time was developed at the United States Geological Survey;s Earth Resources Observation Systems Data Center for use in locust control efforts. Meteorological satellite dat were acquired daily for 3 weeks in October and November 1986 over a 1.4-million-square-kilometer study area centered on Botswana in southern Africa. Advanced Very High Resolution Radiometer data were screened to remove cloud-contaminated data and registered to a 1-kilometer geographic base. Each day the normalized difference vegetation index (NDVI) was calculated to determine the presence and relative amounts of green vegetation in the area. Over a 10-day cycle, subsequent dates of NDVI data were composited to fill in data removed by the cloud-screening process. At any pixel location, the maximum NDVI value was retained. At the end of the 10-day cycle, a composite vegetation-greenness map was produced and another cycle started. Greenness-change maps were produced by comparing two 10-day composite greenness images. Automated map production procedures were used to merge the NDVI image data with cartographic data (boundaries, roads, tick marks) digitized from 1:1,000,000-scale operational navigation charts. The vegetation-greenness map shoes the current distribution of vegetation in the region and can be used to locate potential locust breeding area. The change map shows areas where increases and decreases in greenness have occurred between processing cycles. Significant areas of locust damage in remote regions are characterized by an unexpected decrease in greenness. These maps can be used by locust control teams to efficiently target areas for reconnaissance. In general, the procedures and products have utility for resource managers who are required to monitor vegetation resources over large geographic regions.

Spatial and Temporal Variation in Primary Productivity (NDVI) of Coastal Alaskan Tundra: Decreased Vegetation Growth Following Earlier Snowmelt

NASA Technical Reports Server (NTRS)

Gamon, John A.; Huemmrich, K. Fred; Stone, Robert S.; Tweedie, Craig E.

2015-01-01

In the Arctic, earlier snowmelt and longer growing seasons due to warming have been hypothesized to increase vegetation productivity. Using the Normalized Difference Vegetation Index (NDVI) from both field and satellite measurements as an indicator of vegetation phenology and productivity, we monitored spatial and temporal patterns of vegetation growth for a coastal wet sedge tundra site near Barrow, Alaska over three growing seasons (2000-2002). Contrary to expectation, earlier snowmelt did not lead to increased productivity. Instead, productivity was associated primarily with precipitation and soil moisture, and secondarily with growing degree days, which, during this period, led to reduced growth in years with earlier snowmelt. Additional moisture effects on productivity and species distribution, operating over a longer time scale, were evident in spatial NDVI patterns associated with microtopography. Lower, wetter regions dominated by graminoids were more productive than higher, drier locations having a higher percentage of lichens and mosses, despite the earlier snowmelt at the more elevated sites. These results call into question the oft-stated hypothesis that earlier arctic growing seasons will lead to greater vegetation productivity. Rather, they agree with an emerging body of evidence from recent field studies indicating that early-season, local environmental conditions, notably moisture and temperature, are primary factors determining arctic vegetation productivity. For this coastal arctic site, early growing season conditions are strongly influenced by microtopography, hydrology, and regional sea ice dynamics, and may not be easily predicted from snowmelt date or seasonal average air temperatures alone. Our comparison of field to satellite NDVI also highlights the value of in-situ monitoring of actual vegetation responses using field optical sampling to obtain detailed information on surface conditions not possible from satellite observations alone.

InfoSequia: the first operational remote sensing-based Drought Monitoring System of Spain

NASA Astrophysics Data System (ADS)

Contreras, Sergio; Hunink, Johannes E.

2016-04-01

We present a satellite-based Drought Monitoring System that provides weekly updates of maps and bulletins with vegetation drought indices over the Iberian Peninsula. The web portal InfoSequía (http://infosequia.es) aims to complement the current Spanish Drought Monitoring System which relies on a hydrological drought index computed at the basin level using data on river flows and water stored in reservoirs. Drought indices computed by InfoSequia are derived from satellite data provided by MODIS sensors (TERRA and AQUA satellites), and report the relative anomaly observed in the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and in an additive combination of both. Similar to the U.S. Drought Monitoring System by NOAA, the indices include the Vegetation Condition Index (VCI, relative NDVI anomaly), the Temperature Condition Index (TCI, relative LST anomaly) and the Vegetation Health Index (VHI, relative NDVI-LST anomaly). Relative anomalies are codified into four warning levels, and all of them are provided for short periods of time (8-day windows), or longer periods (e.g. 1 year) in order to capture the cumulative effects of droughts in the state variables. Additionally, InfoSequia quantifies the seasonal trajectories of the cumulative deviation of the observed NDVI in relation with the averaged seasonal trajectory observed over a reference period. Through the weekly bulletins, the Drought Monitoring System InfoSequia aims to provide practical information to stakeholders on the sensitivity and resilience of native ecosystems and rainfed agrosystems during drought periods. Also, the remote sensed indices can be used as drought impact indicator to evaluate the skill of seasonal agricultural drought forecasting systems. InfoSequia is partly funded by the Spanish Ministry of Economy and Competiveness through a Torres-Quevedo grant.

Mapping wildfire danger at regional scale with an index model integrating coarse spatial resolution remote sensing data

NASA Astrophysics Data System (ADS)

ChéRet, VéRonique; Denux, Jean Philippe

2007-06-01

Wildfires are a prevalent natural hazard in the south of France. Planners need a permanent fire danger assessment valid for several years over a territory as large and heterogeneous as Midi-Pyrénées region. To this end, we developed an expert knowledge-based index model adapted to the specific features of the study area. The fire danger depends on two complementary elements: spatial occurrence and fire intensity. Among the GIS layers identified as input variables for modeling, vegetation fire susceptibility is one of the most influent. However, the main difficulty at this scale is the scarcity or the lack of exhaustiveness of the data. In this respect, remote sensing imagery is capable of providing relevant information. We proposed to calculate an annual relative greenness index (annual RGRE) that reflects vegetation dryness in summer. We processed times series of Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION images over the last six available years (1998 to 2003). The first step was to verify that these images characterize vegetation types and highlight intraannual and interannual response variability. It is then possible to identify phenological stages corresponding to the maximum NDVI (and therefore to maximum photosynthetic activity) during the growing season, the minimum NDVI at the end of the growing season and the minimum NDVI during winter period. These phenology metrics ground the annual RGRE calculation. Values obtained for each observation year show significant correlation (r2 = 0.70) with the De Martonne aridity index calculated for the same period. A synthesis of yearly index was integrated in the model as a variable that expresses fire susceptibility.

Optimization of the time series NDVI-rainfall relationship using linear mixed-effects modeling for the anti-desertification area in the Beijing and Tianjin sandstorm source region

NASA Astrophysics Data System (ADS)

Wang, Jin; Sun, Tao; Fu, Anmin; Xu, Hao; Wang, Xinjie

2018-05-01

Degradation in drylands is a critically important global issue that threatens ecosystem and environmental in many ways. Researchers have tried to use remote sensing data and meteorological data to perform residual trend analysis and identify human-induced vegetation changes. However, complex interactions between vegetation and climate, soil units and topography have not yet been considered. Data used in the study included annual accumulated Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m normalized difference vegetation index (NDVI) from 2002 to 2013, accumulated rainfall from September to August, digital elevation model (DEM) and soil units. This paper presents linear mixed-effect (LME) modeling methods for the NDVI-rainfall relationship. We developed linear mixed-effects models that considered the random effects of sample points nested in soil units for nested two-level modeling and single-level modeling of soil units and sample points, respectively. Additionally, three functions, including the exponential function (exp), the power function (power), and the constant plus power function (CPP), were tested to remove heterogeneity, and an additional three correlation structures, including the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and the compound symmetry structure (CS), were used to address the spatiotemporal correlations. It was concluded that the nested two-level model considering both heteroscedasticity with (CPP) and spatiotemporal correlation with [ARMA(1,1)] showed the best performance (AMR = 0.1881, RMSE = 0.2576, adj- R 2 = 0.9593). Variations between soil units and sample points that may have an effect on the NDVI-rainfall relationship should be included in model structures, and linear mixed-effects modeling achieves this in an effective and accurate way.

Remote Sensing of Vineyard FPAR, with Implications for Irrigation Scheduling

NASA Technical Reports Server (NTRS)

Johnson, Lee F.; Scholasch, Thibaut

2004-01-01

Normalized difference vegetation index (NDVI) data, acquired at two-meter resolution by an airborne ADAR System 5500, were compared with fraction of photosynthetically active radiation (FPAR) absorbed by commercial vineyards in Napa Valley, California. An empirical line correction was used to transform image digital counts to surface reflectance. "Apparent" NDVI (generated from digital counts) and "corrected" NDVI (from reflectance) were both strongly related to FPAR of range 0.14-0.50 (both r(sup 2) = 0.97, P < 0.01). By suppressing noise, corrected NDVI should form a more spatially and temporally stable relationship with FPAR, reducing the need for repeated field support. Study results suggest the possibility of using optical remote sensing to monitor the transpiration crop coefficient, thus providing an enhanced spatial resolution component to crop water budget calculations and irrigation management.

[Change trend of vegetation cover in Beijing metropolitan region before and after the 2008 Olympics].

PubMed

Sun, Xiao-Peng; Wang, Tian-Ming; Wu, Jian-Guo; Ge, Jian-Ping

2012-11-01

The MODIS-NDVI data from 2000 to 2009 were used to analyze the temporal dynamics and spatial distribution of the vegetation cover in the Beijing metropolitan region before and after the 2008 Olympics. During the study period, the proportion of the significantly increased pixels of NDVI occupied 20.7% while that of the significantly decreased pixels only occupied 4.1% of the total, and the decreasing rate of the NDVI was slightly faster than the increasing rate. The significant changes of the NDVI were mainly concentrated in the low altitude and small slope areas with intensive human activities, and two bands were formed in the plain area, i. e., the vegetation increasing band within the 5th Ring Road and the vegetation decreasing band from the 5th Ring Road to the outside areas of the 6th Ring Road. In the areas with significant vegetation change, there was an obvious transition between the high and low NDVI sections but less change in the medium NDVI section, mainly due to the conversion of land cover type. In the Capital function core area and ecological conservation zones, vegetation change represented a positive trend; while in the urban function expansion area and urban development area, vegetation change had the dual characteristics of both positive and negative trends. A series of ecological engineering projects during the preparatory period of the 2008 Olympics was the main cause of the vegetation increase in the study area.

Pattern of NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming.

PubMed

Li, Haidong; Jiang, Jiang; Chen, Bin; Li, Yingkui; Xu, Yuyue; Shen, Weishou

2016-03-01

The eastern Himalayas, especially the Yarlung Zangbo Grand Canyon Nature Reserve (YNR), is a global hotspot of biodiversity because of a wide variety of climatic conditions and elevations ranging from 500 to > 7000 m above sea level (a.s.l.). The mountain ecosystems at different elevations are vulnerable to climate change; however, there has been little research into the patterns of vegetation greening and their response to global warming. The objective of this paper is to examine the pattern of vegetation greening in different altitudinal zones in the YNR and its relationship with vegetation types and climatic factors. Specifically, the inter-annual change of the normalized difference vegetation index (NDVI) and its variation along altitudinal gradient between 1999 and 2013 was investigated using SPOT-VGT NDVI data and ASTER global digital elevation model (GDEM) data. We found that annual NDVI increased by 17.58% in the YNR from 1999 to 2013, especially in regions dominated by broad-leaved and coniferous forests at lower elevations. The vegetation greening rate decreased significantly as elevation increased, with a threshold elevation of approximately 3000 m. Rising temperature played a dominant role in driving the increase in NDVI, while precipitation has no statistical relationship with changes in NDVI in this region. This study provides useful information to develop an integrated management and conservation plan for climate change adaptation and promote biodiversity conservation in the YNR.

NDVI as a predictor of canopy arthropod biomass in the Alaskan arctic tundra.

PubMed

Sweet, Shannan K; Asmus, Ashley; Rich, Matthew E; Wingfield, John; Gough, Laura; Boelman, Natalie T

2015-04-01

The physical and biological responses to rapid arctic warming are proving acute, and as such, there is a need to monitor, understand, and predict ecological responses over large spatial and temporal scales. The use of the normalized difference vegetation index (NDVI) acquired from airborne and satellite sensors addresses this need, as it is widely used as a tool for detecting and quantifying spatial and temporal dynamics of tundra vegetation cover, productivity, and phenology. Such extensive use of the NDVI to quantify vegetation characteristics suggests that it may be similarly applied to characterizing primary and secondary consumer communities. Here, we develop empirical models to predict canopy arthropod biomass with canopy-level measurements of the NDVI both across and within distinct tundra vegetation communities over four growing seasons in the Arctic Foothills region of the Brooks Range, Alaska, USA. When canopy arthropod biomass is predicted with the NDVI across all four growing seasons, our overall model that includes all four vegetation communities explains 63% of the variance in canopy arthropod biomass, whereas our models specific to each of the four vegetation communities explain 74% (moist tussock tundra), 82% (erect shrub tundra), 84% (riparian shrub tundra), and 87% (dwarf shrub tundra) of the observed variation in canopy arthropod biomass. Our field-based study suggests that measurements of the NDVI made from air- and spaceborne sensors may be able to quantify spatial and temporal variation in canopy arthropod biomass at landscape to regional scales.

Early Detection of Eruptive Dykes Revealed by Normalized Difference Vegetation Index (NDVI) on Nyiragongo and Etna Volcanoes: Implications for Dyke Wedge Emplacement, Monitoring, and Risk Assessment.

NASA Astrophysics Data System (ADS)

Komorowski, J.; Houlié, N.; Kasereka, C. M.; Ciraba, H.

2006-12-01

Flank-fissure eruptions involve lateral injection and propagation of magma in a volcanic edifice along pre- existing fractures in the direction of a volcanic rift zone (VRZ) where magma intrusion and lava flow production are concentrated over time. Gradual dyke wedge emplacement on volcano flanks and in VRZ's does not necessarily trigger large amplitude deformation signals susceptible to be recorded months or even years before the actual eruption. We show that active and potentially eruptive areas in a VRZ can be detected up to 2 years before the arrival to the surface of the final eruptive dyke and venting of lava flows by processing satellite images applying a Normalized Difference Vegetation Index (NDVI) algorithm. A positive NDVI anomaly is indicative of excessive photosynthetic plant activity. A posteriori analysis of satellite images reveal that a high- NDVI linear anomaly was apparent in vegetated areas of VRZ's on Etna from 2000 to 2002 and on Nyiragongo in June 2001, several months to years before eruptive fractures formed directly above the NDVI anomaly. We propose that the observed NDVI linear anomalies are the signature of the integrated physico-chemical effects (increased heat and CO2 flux, H2O condensation) caused by the structurally-controlled progressive injection and propagation, in a VRZ and a few months to years before the eruption, of a series of dykes (dyke wedge) that did not reach the surface. We focus of Nyiragongo volcano where historical flank-fissure eruptions from lava lake drainage in 1977 and 2002 show a link with tectonics of the Kivu rift (western branch of the East African Rift System). In 2002, dykes were injected in the southern VRZ bounded by Kivu rift normal faults and propagated over 14 km producing lava flows that caused widespread destruction in the city of Goma. Data from Nyiragongo suggest that as a dyke wedge is formed and repeatedly reactivated, final eruptive dykes can be injected easily and can propagate rapidly further along the VRZ impacting populated areas far from the magmatic conduit. This has important implications for understanding the current process of magma convection feeding the active Nyiragongo lava lake as well as potential small-volume lateral magma injections into the reactivated southern VRZ. The NDVI processing methodology has potentially important implications for monitoring networks on deeply vegetated restless volcanoes with limited or difficult access. More importantly, on volcanoes where eruptive style changes from crater-centered to eccentric flank activity or for which new inactive VRZ's could be reactivated towards populated areas, our methodology constitutes a new tool for early detection of potential flank eruptive vents. By improving the understanding of the link between edifice structure and eruptive activity of effusive volcanoes it can significantly improve integrated risk analysis and the effectiveness of early-detection warning systems for populations at risk.

Comparison of Sub-Pixel Classification Approaches for Crop-Specific Mapping

EPA Science Inventory

This paper examined two non-linear models, Multilayer Perceptron (MLP) regression and Regression Tree (RT), for estimating sub-pixel crop proportions using time-series MODIS-NDVI data. The sub-pixel proportions were estimated for three major crop types including corn, soybean, a...

Identifying high production, low production and degraded rangelands in Senegal with normalized difference vegetation index data

USGS Publications Warehouse

Tappan, G. Gray; Wood, Lynette; Moore, Donald G.

1993-01-01

Seasonal herbaceous vegetation production on Senegal's native rangelands exhibits high spatial and temporal variability. This variability can be monitored using normalized difference vegetation index (NDVI) data computed from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) image data. Although annual fluctuations in rainfall account for some of the variability, numerous long-term production patterns are evident in the AVHRR time-series data. Different n productivity reflect variations in the region's climate, topography, soils, and land use. Areas of overgrazing and intensive cultivation have caused long-term soil and vegetation degradation. Rangelands of high and low productivity, and degraded rangelands were identified using NDVI. Time-series image data from 1987 though 1992 were used to map relative rangeland productivity. The results were compared to detailed resource maps on soils, vegetation and land use. Much of the variation in rangeland productivity correlated well to the known distribution of resources. The study developed an approach that identified a number of areas of degraded soils and low vegetation production.

Monitoring LongBao Wetland Ecosystem in Tibetan Plateau using time-series SAR and Optical dataset

NASA Astrophysics Data System (ADS)

Brisco, B.; Wei, Q.; Xie, C.; Shao, Y.; Tian, B.; Li, K.

2017-12-01

As a highly productive and sensitive ecosystem, plateau wetlands provide indispensable habitats for the black-necked crane, an endangered species of crane. In this research, we focus on Longbao plateau wetland, the only habitat of black-necked crane in Tibetan Plateau, located in Yushu, Qinghai province, with an area of about 100 km2 and elevation about 4100 4200m. Monitoring Longbao wetland during the past 30 years using time series SAR and optical dataset and analysis its effect on black-necked crane have great significance for endangered species protection. Water and vegetation resources are two important indicators of wetland productivity. In this study, we aim at providing the open water area dynamics and the variation of vegetation during the past 30 years using SAR and optical imageries and analyzing their effect on black-necked cranes. The changes of the open water area and NDVI reflect the environment variety of Longbao wetland. And the relationship between these biological parameters and climates were analyzed, especially their influence on the black-necked cranes, which is the only kind of crane in the world that grows and breeds in the plateau. The method of level set segmentation with KummerU distribution was applied to open water bodies (wetlands) delimitation using time series SAR dataset, including Envisat-ASAR acquired from 2003 to 2010 and Radasat-2 from 2013 to 2014. Also the NDVI is calculated from Landsat images (acquired during 2003-2015) using google earth engine which is a cloud-based platform for planetary-scale environmental data analysis.The results indicate that the open water area fluctuates with seasons and reaches the maximum in summer. While in the spring and winter the wetland is usually covered by ice and snow. The highest values of NDVI occurred in years with a sufficient amount of precipitation. The abundant vegetation, water and suitable temperature of Longbao wetland in summer effectively promote the boost and growth of the chicks of black-necked cranes. The results of this study show that the open water area of Longbao wetland has expanded from 3.50km2 to 3.97km2 slowly from 2003 to 2014. The open water area has stronger correlation with precipitation than other climate factors. We will continue to analyze the change of NDVI and how they affect the black-necked crane.

Spring and autumn phenological variability across environmental gradients of Great Smoky Mountains National Park, USA

Treesearch

Steven P. Norman; William W. Hargrove; William M. Christie

2017-01-01

Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for...

Leipzig Ice Nucleation chamber Comparison (LINC): intercomparison of four online ice nucleation counters

NASA Astrophysics Data System (ADS)

Burkert-Kohn, Monika; Wex, Heike; Welti, André; Hartmann, Susan; Grawe, Sarah; Hellner, Lisa; Herenz, Paul; Atkinson, James D.; Stratmann, Frank; Kanji, Zamin A.

2017-09-01

Ice crystal formation in atmospheric clouds has a strong effect on precipitation, cloud lifetime, cloud radiative properties, and thus the global energy budget. Primary ice formation above 235 K is initiated by nucleation on seed aerosol particles called ice-nucleating particles (INPs). Instruments that measure the ice-nucleating potential of aerosol particles in the atmosphere need to be able to accurately quantify ambient INP concentrations. In the last decade several instruments have been developed to investigate the ice-nucleating properties of aerosol particles and to measure ambient INP concentrations. Therefore, there is a need for intercomparisons to ensure instrument differences are not interpreted as scientific findings.In this study, we intercompare the results from parallel measurements using four online ice nucleation chambers. Seven different aerosol types are tested including untreated and acid-treated mineral dusts (microcline, which is a K-feldspar, and kaolinite), as well as birch pollen washing waters. Experiments exploring heterogeneous ice nucleation above and below water saturation are performed to cover the whole range of atmospherically relevant thermodynamic conditions that can be investigated with the intercompared chambers. The Leipzig Aerosol Cloud Interaction Simulator (LACIS) and the Portable Immersion Mode Cooling chAmber coupled to the Portable Ice Nucleation Chamber (PIMCA-PINC) performed measurements in the immersion freezing mode. Additionally, two continuous-flow diffusion chambers (CFDCs) PINC and the Spectrometer for Ice Nuclei (SPIN) are used to perform measurements below and just above water saturation, nominally presenting deposition nucleation and condensation freezing.The results of LACIS and PIMCA-PINC agree well over the whole range of measured frozen fractions (FFs) and temperature. In general PINC and SPIN compare well and the observed differences are explained by the ice crystal growth and different residence times in the chamber. To study the mechanisms responsible for the ice nucleation in the four instruments, the FF (from LACIS and PIMCA-PINC) and the activated fraction, AF (from PINC and SPIN), are compared. Measured FFs are on the order of a factor of 3 higher than AFs, but are not consistent for all aerosol types and temperatures investigated. It is shown that measurements from CFDCs cannot be assumed to produce the same results as those instruments exclusively measuring immersion freezing. Instead, the need to apply a scaling factor to CFDCs operating above water saturation has to be considered to allow comparison with immersion freezing devices. Our results provide further awareness of factors such as the importance of dispersion methods and the quality of particle size selection for intercomparing online INP counters.

eMODIS Expedited: Overview of a Near Real Time MODIS Production System for Operational Vegetation Monitoring

NASA Astrophysics Data System (ADS)

Jenkerson, C.; Meyer, D. J.; Werpy, J.; Evenson, K.; Merritt, M.

2010-12-01

The expedited MODIS, or eMODIS production system derives near-real time Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance provided by the Land and Atmosphere Near-real time Capability for EOS (LANCE). There are currently three regions covered by this U.S. Geological Survey (USGS) capability, including the continental U.S., Africa, and the Central America/Caribbean regions. Each of the eMODIS production streams is configured to output its data in map projections, compositing intervals, spatial resolutions, and file formats specific to its region and user community. The challenges of processing 1,000-meter, 500-m, and especially 250-m products by midnight on the last day of a product interval have been met with increasingly effective software and system architecture. An anonymous file transfer protocol (FTP) distribution site (ftp://emodisftp.cr.usgs.gov/eMODIS) allows users direct access to eMODIS NDVI products for operational (near-real time) monitoring of vegetation conditions like drought, crop failure, insect infestation, and other threats, thus supporting subsequent early warning of famine and the targeting of vulnerable populations for insecure food situations.

Estimating grassland curing with remotely sensed data

NASA Astrophysics Data System (ADS)

Chaivaranont, Wasin; Evans, Jason P.; Liu, Yi Y.; Sharples, Jason J.

2018-06-01

Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, and therefore ground-observed measurements are rather limited. In this study, we explore the possibility of whether adding satellite-observed data responding to vegetation water content (vegetation optical depth, VOD) will improve DOC prediction when compared with the existing satellite-observed data responding to DOC prediction models based on vegetation greenness (normalised difference vegetation index, NDVI). First, statistically significant relationships are established between selected ground-observed DOC and satellite-observed vegetation datasets (NDVI and VOD) with an r2 up to 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.44 to 0.55. Results suggest that VOD-based DOC estimation can reasonably reproduce ground-based observations in space and time and is comparable to the existing NDVI-based DOC estimation models.

Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa.

PubMed

Yang, S; Li, X; Chen, C; Kyveryga, P; Yang, X B

2016-08-01

Moderate resolution imaging spectroradiometer (MODIS) satellite imagery from 2004 to 2013 were used to assess the field-specific risks of soybean sudden death syndrome (SDS) caused by Fusarium virguliforme in Iowa. Fields with a high frequency of significant decrease (>10%) of the normalized difference vegetation index (NDVI) observed in late July to middle August on historical imagery were hypothetically considered as high SDS risk. These high-risk fields had higher slopes and shorter distances to flowlines, e.g., creeks and drainages, particularly in the Des Moines lobe. Field data in 2014 showed a significantly higher SDS level in the high-risk fields than fields selected without considering NDVI information. On average, low-risk fields had 10 times lower F. virguliforme soil density, determined by quantitative polymerase chain reaction, compared with other surveyed fields. Ordinal logistic regression identified positive correlations between SDS and slope, June NDVI, and May maximum temperature, but high June maximum temperature hindered SDS. A modeled SDS risk map showed a clear trend of potential disease occurrences across Iowa. Landsat imagery was analyzed similarly, to discuss the ability to utilize higher spatial resolution data. The results demonstrated the great potential of both MODIS and Landsat imagery for SDS field-specific risk assessment.

Seasonal variability of aerosol optical depth over Indian subcontinent

USGS Publications Warehouse

Prasad, A.K.; Singh, R.P.; Singh, A.; Kafatos, M.

2005-01-01

Ganga basin extends 2000 km E-W and about 400 km N-S and is bounded by Himalayas in the north. This basin is unequivocally found to be affected by high aerosols optical depth (AOD) (>0.6) throughout the year. Himalayas restricts movement of aerosols toward north and as a result dynamic nature of aerosol is seen over the Ganga basin. High AOD in this region has detrimental effects on health of more than 460 million people living in this part of India besides adversely affecting clouds formation, monsoonal rainfall pattern and Normalized Difference Vegetation Index (NDVI). Severe drought events (year 2002) in Ganga basin and unexpected failure of monsoon several times, occurred in different parts of Indian subcontinent. Significant rise in AOD (18.7%) over the central part of basin (Kanpur region) have been found to cause substantial decrease in NDVI (8.1%) since 2000. A negative relationship is observed between AOD and NDVI, magnitude of which differs from region to region. Efforts have been made to determine general distribution of AOD and its dominant departure in recent years spatially using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The seasonal changes in aerosol optical depth over the Indo-Gangetic basin is found to very significant as a result of the increasing dust storm events in recent years. ?? 2005 IEEE.

Vegetation changes associated with a population irruption by Roosevelt elk

USGS Publications Warehouse

Starns, H D; Weckerly, Floyd W.; Ricca, Mark; Duarte, Adam

2015-01-01

Interactions between large herbivores and their food supply are central to the study of population dynamics. We assessed temporal and spatial patterns in meadow plant biomass over a 23-year period for meadow complexes that were spatially linked to three distinct populations of Roosevelt elk (Cervus elaphus roosevelti) in northwestern California. Our objectives were to determine whether the plant community exhibited a tolerant or resistant response when elk population growth became irruptive. Plant biomass for the three meadow complexes inhabited by the elk populations was measured using Normalized Difference Vegetation Index (NDVI), which was derived from Landsat 5 Thematic Mapper imagery. Elk populations exhibited different patterns of growth through the time series, whereby one population underwent a complete four-stage irruptive growth pattern while the other two did not. Temporal changes in NDVI for the meadow complex used by the irruptive population suggested a decline in forage biomass during the end of the dry season and a temporal decline in spatial variation of NDVI at the peak of plant biomass in May. Conversely, no such patterns were detected in the meadow complexes inhabited by the nonirruptive populations. Our findings suggest that the meadow complex used by the irruptive elk population may have undergone changes in plant community composition favoring plants that were resistant to elk grazing.

Bark Beetles Modify the Impact of Snow Drought on Remotely Sensed Forest Productivity in the Southern Rocky Mountains, USA

NASA Astrophysics Data System (ADS)

Knowles, J. F.; Lestak, L.; Molotch, N. P.

2016-12-01

We evaluated the long term (1989-2012) relationship between the satellite-observed Normalized Difference Vegetation Index (NDVI), snowpack accumulation, and atmospheric demand throughout the Southern Rocky Mountain Ecoregion, USA. Deviations from this relationship were further explored during pre- and post-disturbance conditions associated with bark beetles and drought. Over the entire study area, both the snow water equivalent (SWE) and a snow aridity index (SAI), which used the SWE to normalize potential evapotranspiration (PET), were significant predictors of the long-term AVHRR NDVI, but the SAI was a better predictor of NDVI relative to SWE regardless of disturbance. Since these relationships were weaker in disturbed areas, we also introduced a metric of tree mortality, and subsequent multiple linear regression of SAI and cumulative mortality best predicted the NDVI from a pair of heavily impacted focus areas within the larger study area. The post-disturbance NDVI was systematically reduced per unit SAI in these areas, and the difference between the observed and predicted (from pre-disturbance regressions) post-disturbance NDVI was significantly correlated with the cumulative forest mortality. At the Ecoregion scale, these disturbance effects were not clearly evident, and we attribute this to spatial variability of both SAI and NDVI throughout the large study area as evidenced by spatial analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived data. These results constrain the expected reduction in forest productivity due to disturbance and demonstrate that this reduction can be particularly evident during drought conditions resultant from low snow accumulation during the winter. Hence, terrestrial carbon uptake may decrease non-linearly post disturbance. This work has implications for predicting the ecohydrological response to climate change in the southern Rocky Mountains, as reductions in SWE and increases in PET are predicted for this area in the future, and therefore changes in the terrestrial carbon, water, and energy cycles should be expected.

Analysis of the spatial-temporal change of the vegetation index in the upper reach of Han River Basin in 2000-2016

NASA Astrophysics Data System (ADS)

Luan, Jinkai; Liu, Dengfeng; Zhang, Lianpeng; Huang, Qiang; Feng, Jiuliang; Lin, Mu; Li, Guobao

2018-06-01

Han River is the water source region of the middle route of South-to-North Water Diversion in China and the ecological projects were implemented since many years ago. In order to monitor the change of vegetation in Han River and evaluate the effect of ecological projects, it is needed to reveal the spatial-temporal change of the vegetation in the upper reach of Han River quantitatively. The study is based on MODIS/Terra NDVI remote sensing data, and analyzes the spatial-temporal changes of the NDVI in August from 2000 to 2016 at pixel scale in the upper reach of Han River Basin. The results show that, the area with increasing NDVI between 0 and 0.005 per year accounts for 62.07 % of the area of upper reach of Han River Basin, and the area with changing rate between -0.005 and 0 per year accounts for 26.65 % of the research area. The area with significant decreasing trend only accounts for 2.76 %, while area significant increasing trend accounts for 13.47 %, and the area with increasing NDVI is much larger than the area with reducing NDVI. The vegetation index of each county is evaluated and found that, the areal proportion with significant decreasing trend in Hantai is the biggest, reaching 35.57 %. The areal proportion with significant increasing trend in Zhenba County, Ziyang County, Xunyang County, Zhashui County, Shangzhou District, Shanyang County and Yun County is larger than the others, and the areal proportions are more than 20 %. The largest areal proportion with significant increasing trend is in Shangzhou District and it reaches 31.11 %. On the whole, the area ratio in all districts and counties with increasing NDVI is much larger than the area ratio with decreasing NDVI.

Monitoring pasture variability: optical OptRx(®) crop sensor versus Grassmaster II capacitance probe.

PubMed

Serrano, João M; Shahidian, Shakib; Marques da Silva, José Rafael

2016-02-01

Estimation of pasture productivity is an important step for the farmer in terms of planning animal stocking, organizing animal lots, and determining supplementary feeding needs throughout the year. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Two types of sensors were evaluated: an active optical sensor ("OptRx(®)," which measures the NDVI, "Normalized Difference Vegetation Index") and a capacitance probe ("GrassMaster II" which estimates plant mass). The results showed the potential of NDVI for monitoring the evolution of spatial and temporal patterns of vegetative growth of biodiverse pasture. Higher NDVI values were registered as pasture approached its greatest vegetative vigor, with a significant fall in the measured NDVI at the end of Spring, when the pasture began to dry due to the combination of higher temperatures and lower soil moisture content. This index was also effective for identifying different plant species (grasses/legumes) and variability in pasture yield. Furthermore, it was possible to develop calibration equations between the capacitance and the NDVI (R(2) = 0.757; p < 0.01), between capacitance and GM (R(2) = 0.799; p < 0.01), between capacitance and DM (R(2) =0.630; p < 0.01), between NDVI and GM (R(2) = 0.745; p < 0.01), and between capacitance and DM (R(2) = 0.524; p < 0.01). Finally, a direct relationship was obtained between NDVI and pasture moisture content (PMC, in %) and between capacitance and PMC (respectively, R(2) = 0.615; p < 0.01 and R(2) = 0.561; p < 0.01) in Alentejo dryland farming systems.

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2012-01-01

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Combining Landsat and MODIS Data to Assess Trends in Bioproductivity in the Context of Land Use/Land Cover Change in Semi-arid West Africa

NASA Astrophysics Data System (ADS)

Herrmann, S. M.; Tappan, G. G.

2015-12-01

Semi-arid West Africa is experiencing change at many levels (climatic, agricultural, socioeconomic), which leaves an imprint on the land surface that can be characterized by a range of long term satellite observations. This research addresses the questions of (1) what dominant trajectories of land use/land cover (LULC) change have occurred in the region and (2) whether particular LULC trajectories are associated with significant positive or negative trends in bioproductivity. Two types of satellite data were used in complementary fashion: (1) Landsat multispectral data were visually interpreted using the traditional dot grid method, whereby the interpreter identifies and attributes LULC at point locations spaced 2km apart. Interpreted LULC maps were produced for three points in time (1975, 2000, 2013), and LULC change statistics extracted from them. (2) The MODIS Normalized Difference Vegetation Index (NDVI) was used as a proxy for bioproductivity and temporal trends of annual mean, maximum and minimum NDVI extracted at the sampling dots of known LULC for the period 2000-2013. The trends were analyzed with respect to the most prominent LULC classes and transitions, in particular from agriculture to natural vegetation and vice versa, and stratified by regions of similar mean annual precipitation. The most important LULC change over the almost 40-year period is a progressive expansion of agricultural lands, which has been responsible for major incursions into the region's remaining savannas and woodlands. To a lesser extent, abandonment of agriculture has given rise to long term fallow and eventually reversion to steppe or savanna. Another important change observed is the expansion of open steppe at the expense of savanna in the Sahel region. In terms of bioproductivity, while no significant trends in NDVI predominate overall, there are more instances of positive than of negative significant trends across the region. Contrary to our initial expectations, preliminary results show little systematic association between LULC change and direction and magnitude of trends in NDVI over the same time period 2000-2013. Though drastically altering vegetation composition and biodiversity, the expansion of agriculture into savanna is not found to be associated with a widespread loss of bioproductivity.

Validation of Vegetation Index Time Series from Suomi NPP Visible Infrared Imaging Radiometer Suite Using Tower Radiation Flux Measurements

NASA Astrophysics Data System (ADS)

Miura, T.; Kato, A.; Wang, J.; Vargas, M.; Lindquist, M.

2015-12-01

Satellite vegetation index (VI) time series data serve as an important means to monitor and characterize seasonal changes of terrestrial vegetation and their interannual variability. It is, therefore, critical to ensure quality of such VI products and one method of validating VI product quality is cross-comparison with in situ flux tower measurements. In this study, we evaluated the quality of VI time series derived from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) spacecraft by cross-comparison with in situ radiation flux measurements at select flux tower sites over North America and Europe. VIIRS is a new polar-orbiting satellite sensor series, slated to replace National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer in the afternoon overpass and to continue the highly-calibrated data streams initiated with Moderate Resolution Imaging Spectrometer of National Aeronautics and Space Administration's Earth Observing System. The selected sites covered a wide range of biomes, including croplands, grasslands, evergreen needle forest, woody savanna, and open shrublands. The two VIIRS indices of the Top-of-Atmosphere (TOA) Normalized Difference Vegetation Index (NDVI) and the atmospherically-corrected, Top-of-Canopy (TOC) Enhanced Vegetation Index (EVI) (daily, 375 m spatial resolution) were compared against the TOC NDVI and a two-band version of EVI (EVI2) calculated from tower radiation flux measurements, respectively. VIIRS and Tower VI time series showed comparable seasonal profiles across biomes with statistically significant correlations (> 0.60; p-value < 0.01). "Start-of-season (SOS)" phenological metric values extracted from VIIRS and Tower VI time series were also highly compatible (R2 > 0.95), with mean differences of 2.3 days and 5.0 days for the NDVI and the EVI, respectively. These results indicate that VIIRS VI time series can capture seasonal evolution of vegetated land surface as good as in situ radiometric measurements. Future studies that address biophysical or physiological interpretations of Tower VI time series derived from radiation flux measurements are desirable.

Evaluation of Aromatic Oxidation Reactions in Seven Chemical Mechanisms with an Outdoor Chamber

EPA Science Inventory

Simulations using seven chemical mechanisms are intercompared against O₃, NO_x and hydrocarbon data from photooxidation experiments conducted at the University of North Carolina outdoor smog chamber. The mechanisms include CB4–2002, CB05, CB05-TU, a CB05 vari...

Spatial distribution of threshold wind speeds for dust outbreaks in northeast Asia

NASA Astrophysics Data System (ADS)

Kimura, Reiji; Shinoda, Masato

2010-01-01

Asian windblown dust events cause human and animal health effects and agricultural damage in dust source areas such as China and Mongolia and cause "yellow sand" events in Japan and Korea. It is desirable to develop an early warning system to help prevent such damage. We used our observations at a Mongolian station together with data from previous studies to model the spatial distribution of threshold wind speeds for dust events in northeast Asia (35°-45°N and 100°-115°E). Using a map of Normalized Difference Vegetation Index (NDVI), we estimated spatial distributions of vegetation cover, roughness length, threshold friction velocity, and threshold wind speed. We also recognized a relationship between NDVI in the dust season and maximum NDVI in the previous year. Thus, it may be possible to predict the threshold wind speed in the next dust season using the maximum NDVI in the previous year.

Mapping the change of Phragmites australis live biomass in the lower Mississippi River Delta marshes

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, Amina

2017-07-28

Multiyear remote sensing mapping of the normalized difference vegetation index (NDVI) was carried out as an indicator of live biomass composition of the Phragmites australis (hereafter Phragmites) marsh in the lower Mississippi River Delta (hereafter delta) from 2014 to 2017. Maps of NDVI change showed that the Phragmites condition was fairly stable between May 2014 and July 2015. From July 2015 to April 2016 NDVI change indicated Phragmites suffered a widespread decline in the live biomass proportion.  Between April and September 2016, most marsh remained unchanged from the earlier period or showed improvement; although there were pockets of continued decline scattered throughout the lower delta. From September 2016 to May 2017 a pronounced and widely exhibited decline in the condition of Phragmites marsh again occurred throughout the lower delta. This final NDVI change mapping supported field observations of Phragmites decline during the same period.

How Do A-train Sensors Intercompare in the Retrieval of Above-cloud Aerosol Optical Depth? A Case Study-based Assessment

NASA Technical Reports Server (NTRS)

Jethva, Hiren; Torres, Omar; Waquet, Fabien; Chand, Duli; Hu, Yongxiang

2014-01-01

We intercompare the above-cloud aerosol optical depth (ACAOD) of biomass burning plumes retrieved from A-train sensors, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Polarization and Directionality of Earth Reflectances (POLDER), and Ozone Monitoring Instrument (OMI). These sensors have shown independent capabilities to retrieve aerosol loading above marine boundary layer clouds-a kind of situation often found over the southeast Atlantic Ocean during dry burning season. A systematic comparison reveals that all passive sensors and CALIOP-based research methods derive comparable ACAOD with differences mostly within 0.2 over homogeneous cloud fields. The 532 nm ACAOD retrieved by CALIOP operational algorithm is underestimated. The retrieved 1064 nm AOD however shows closer agreement with passive sensors. Given the different types of measurements processed with different algorithms, the reported close agreement between them is encouraging. Due to unavailability of direct measurements above cloud, the validation of satellite-based ACAOD remains an open challenge. The intersatellite comparison however can be useful for the relative evaluation and consistency check

NASA Giovanni: A Tool for Visualizing, Analyzing, and Inter-comparing Soil Moisture Data

NASA Technical Reports Server (NTRS)

Teng, William; Rui, Hualan; Vollmer, Bruce; deJeu, Richard; Fang, Fan; Lei, Guang-Dih; Parinussa, Robert

2014-01-01

There are many existing satellite soil moisture algorithms and their derived data products, but there is no simple way for a user to inter-compare the products or analyze them together with other related data. An environment that facilitates such inter-comparison and analysis would be useful for validation of satellite soil moisture retrievals against in situ data and for determining the relationships between different soil moisture products. As part of the NASA Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) family of portals, which has provided users worldwide with a simple but powerful way to explore NASA data, a beta prototype Giovanni Inter-comparison of Soil Moisture Products portal has been developed. A number of soil moisture data products are currently included in the prototype portal. More will be added, based on user requirements and feedback and as resources become available. Two application examples for the portal are provided. The NASA Giovanni Soil Moisture portal is versatile and extensible, with many possible uses, for research and applications, as well as for the education community.

Temporally inter-comparable maps of terrestrial wilderness and the Last of the Wild

NASA Astrophysics Data System (ADS)

Allan, James R.; Venter, Oscar; Watson, James E. M.

2017-12-01

Wilderness areas, defined as areas free of industrial scale activities and other human pressures which result in significant biophysical disturbance, are important for biodiversity conservation and sustaining the key ecological processes underpinning planetary life-support systems. Despite their importance, wilderness areas are being rapidly eroded in extent and fragmented. Here we present the most up-to-date temporally inter-comparable maps of global terrestrial wilderness areas, which are essential for monitoring changes in their extent, and for proactively planning conservation interventions to ensure their preservation. Using maps of human pressure on the natural environment for 1993 and 2009, we identified wilderness as all 'pressure free' lands with a contiguous area >10,000 km2. These places are likely operating in a natural state and represent the most intact habitats globally. We then created a regionally representative map of wilderness following the well-established 'Last of the Wild' methodology; which identifies the 10% area with the lowest human pressure within each of Earth's 60 biogeographic realms, and identifies the ten largest contiguous areas, along with all contiguous areas >10,000 km2.

Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Potter, Christopher; Klooster, Steven; de Carvalho, Claudio Reis; Genovese, Vanessa Brooks; Torregrosa, Alicia; Dungan, Jennifer; Bobo, Matthew; Coughlan, Joseph

2001-05-01

Previous field measurements have implied that undisturbed Amazon forests may represent a substantial terrestrial sink for atmospheric carbon dioxide. We investigated this hypothesis using a regional ecosystem model for net primary production (NPP) and soil biogeochemical cycling. Seasonal and interannual controls on net ecosystem production (NEP) were studied with integration of high-resolution (8-km) multiyear satellite data to characterize Amazon land surface properties over time. Background analysis of temporal and spatial relationships between regional rainfall patterns and satellite observations (for vegetation land cover, fire counts, and smoke aerosol effects) reveals several notable patterns in the model driver data. Autocorrelation analysis for monthly vegetation "greenness" index (normalized difference vegetation index, NDVI) from the advanced very high resolution radiometer (AVHRR) and monthly rainfall indicates a significant lag time correlation of up to 12 months. At lag times approaching 36 months, autocorrelation function (ACF) values did not exceed the 95% confidence interval at locations west of about 47°W, which is near the transition zone of seasonal tropical forest and other (nonforest) vegetation types. Even at lag times of 12 months or less, the location near Manaus (approximately 60°W) represents the farthest western point in the Amazon region where seasonality of rainfall accounts significantly for monthly variations in forest phenology, as observed using NDVI. Comparisons of NDVI seasonal profiles in areas of the eastern Amazon widely affected by fires (as observed from satellite) suggest that our adjusted AVHRR-NDVI captures year-to-year variation in land cover greenness with minimal interference from small fires and smoke aerosols. Ecosystem model results using this newly generated combination of regional forcing data from satellite suggest that undisturbed Amazon forests can be strong net sinks for atmospheric carbon dioxide, particularly during wet (non El Niño) years. However, drought effects during El Niño years can reduce NPP in primary forests of the eastern Amazon by 10-20%, compared to long-term average estimates of regional productivity. Annual NEP for the region is predicted to range from -0.4 Pg C yr-1 (net CO2 source) to 0.5 Pg C yr-1 (net CO2 sink), with large interannual variability over the states of Pará, Maranhao, and Amazonas. As in the case of predicted NPP, it appears that periods of relatively high solar surface irradiance combined with several months of adequate rainfall are required to sustain the forest carbon sink for positive yearly NEP estimates.

Potential and limitations of using digital repeat photography to track structural and physiological phenology in Mediterranean tree-grass ecosystems

NASA Astrophysics Data System (ADS)

Luo, Yunpeng; EI-Madany, Tarek; Filippa, Gianluca; Carrara, Arnaud; Cremonese, Edoardo; Galvagno, Marta; Hammer, Tiana; Pérez-Priego, Oscar; Reichstein, Markus; Martín Isabel, Pilar; González Cascón, Rosario; Migliavacca, Mirco

2017-04-01

Tree-Grass ecosystems are global widely distributed (16-35% of the land surface). However, its phenology (especially in water-limited areas) has not yet been well characterized and modeled. By using commercial digital cameras, continuous and relatively vast phenology data becomes available, which provides a good opportunity to monitor and develop a robust method used to extract the important phenological events (phenophases). Here we aimed to assess the usability of digital repeat photography for three Tree-Grass Mediterranean ecosystems over two different growing seasons (Majadas del Tietar, Spain) to extract critical phenophases for grass and evergreen broadleaved trees (autumn regreening of grass- Start of growing season; resprouting of tree leaves; senescence of grass - End of growing season), assess their uncertainty, and to correlate them with physiological phenology (i.e. phenology of ecosystem scale fluxes such as Gross Primary Productivity, GPP). We extracted green chromatic coordinates (GCC) and camera based normalized difference vegetation index (Camera-NDVI) from an infrared enabled digital camera using the "Phenopix" R package. Then we developed a novel method to retrieve important phenophases from GCC and Camera-NDVI from various region of interests (ROIs) of the imagery (tree areas, grass, and both - ecosystem) as well as from GPP, which was derived from Eddy Covariance tower in the same experimental site. The results show that, at ecosystem level, phenophases derived from GCC and Camera-NDVI are strongly correlated (R2 = 0.979). Remarkably, we observed that at the end of growing season phenophases derived from GCC were systematically advanced (ca. 8 days) than phenophase from Camera-NDVI. By using the radiative transfer model Soil Canopy Observation Photochemistry and Energy (SCOPE) we demonstrated that this delay is related to the different sensitivity of GCC and NDVI to the fraction of green/dry grass in the canopy, resulting in a systematic higher NDVI during the dry-down of the canopy. Phenophases derived from GCC and Camera-NDVI are correlated with phenophase extracted from GPP across sites and years (R2 =0.966 and 0.976 respectively). For the start of growing season the determination coefficient was higher (R2 =0.89 and 0.98 for GCC vs GPP and Camera-NDVI vs GPP, respectively) than for the end of growing season (R2 =0.75 and 0.70, for GCC and Camera-NDVI, respectively). The statistics obtained using phenophases derived from grass or ecosystem ROI are similar. In contrast, GCC and Camera-NDVI derived from trees ROI are relatively constant and not related to the seasonality of GPP. However, the GCC of tree shows a characteristic peak that is synchronous to leaf flushing in spring assessed using regular Chlorophyll content measurements and automatic dendrometers. Concluding, we first developed a method to derive phenological events of Tree-Grass ecosystems using digital repeat photography, second we demonstrated that the phenology of GPP is strongly dominated by the phenology of grassland layer, third we discussed the uncertainty related to the use of GCC and Camera-NDVI in senescence, and finally we demonstrate the capability of GCC to track in evergreen broadleaved forest crucial phenological events. Our findings confirm digital repeat photography is a vital data source for characterizing phenology in Mediterranean Tree-Grass Ecosystem.

Trends in land surface phenology and atmospheric CO2 seasonality in the Northern Hemisphere terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Gonsamo, A.; Chen, J. M.

2017-12-01

Northern terrestrial ecosystems have shown global warming-induced advances in start, delays in end, and thus increased lengths of growing season and gross photosynthesis in recent decades. The tradeoffs between seasonal dynamics of two opposing fluxes, CO2 uptake through photosynthesis and release through respiration, determine the influence of the terrestrial ecosystems on the atmospheric CO2 concentration and 13C/12C isotope ratio seasonality. Atmospheric CO2 and 13C/12C seasonality is controlled by vegetation phenology, but is not identical because growth will typically commence some time before and terminate some time after the net carbon exchange changes sign in spring and autumn, respectively. Here, we use 34-year satellite normalized difference vegetation index (NDVI) observations to determine how changes in vegetation productivity and phenology affect both the atmospheric CO2 and 13C/12C seasonality. Differences and similarities in recent trends of CO2 and 13C/12C seasonality and vegetation phenology will be discussed. Furthermore, we use the NDVI observations, and atmospheric CO2 and 13C/12C data to show the trends and variability of the timing of peak season plant activity. Preliminary results show that the peak season plant activity of the Northern Hemisphere extra-tropical terrestrial ecosystems is shifting towards spring, largely in response to the warming-induced advance of the start of growing season. Besides, the spring-ward shift of the peak plant activity is contributing the most to the increasing peak season productivity. In other words, earlier start of growing season is highly linked to earlier arrival of peak of season and higher NDVI. Changes in the timing of peak season plant activity are expected to disrupt the synchrony of biotic interaction and exert strong biophysical feedbacks on climate by modifying the surface albedo and energy budget.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112