sensing based vegetation: Topics by Science.gov

Sample records for sensing based vegetation

[Estimation of desert vegetation coverage based on multi-source remote sensing data].

PubMed

Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

2012-12-01

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.
Remote sensing of vegetation pattern and condition to monitor changes in Everglades biogeochemistry

USGS Publications Warehouse

Jones, John W.

2011-01-01

Ground-based studies of biogeochemistry and vegetation patterning yield process understanding, but the amount of information gained by ground-based studies can be greatly enhanced by efficient, synoptic, and temporally resolute monitoring afforded by remote sensing. The variety of presently available Everglades vegetation maps reflects both the wide range of application requirements and the need to balance cost and capability. More effort needs to be applied to documenting and understanding vegetation distribution and condition as indicators of biogeochemistry and contamination. Ground-based and remote sensing studies should be modified to maximize their synergy and utility for adaptive management.
Remote sensing of vegetation pattern and condition to monitor changes in everglades biogeochemistry

USGS Publications Warehouse

Jones, J.W.

2011-01-01

Ground-based studies of biogeochemistry and vegetation patterning yield process understanding, but the amount of information gained by ground-based studies can be greatly enhanced by efficient, synoptic, and temporally resolute monitoring afforded by remote sensing. The variety of presently available Everglades vegetation maps reflects both the wide range of application requirements and the need to balance cost and capability. More effort needs to be applied to documenting and understanding vegetation distribution and condition as indicators of biogeochemistry and contamination. Ground-based and remote sensing studies should be modified to maximize their synergy and utility for adaptive management. Copyright ?? 2011 Taylor & Francis Group, LLC.
[Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing].

PubMed

Wang, Li-Wen; Wei, Ya-Xing

2013-10-01

Nitrogen is the necessary element in life activity of vegetation, which takes important function in biosynthesis of protein, nucleic acid, chlorophyll, and enzyme etc, and plays a key role in vegetation photosynthesis. The technology about inversion of vegetation nitrogen concentration by hyperspectral remote sensing has been the research hotspot since the 70s of last century. With the development of hyperspectral remote sensing technology in recent years, the advantage of spectral bands subdivision in a certain spectral region provides the powerful technology measure for correlative spectral characteristic research on vegetation nitrogen. In the present paper, combined with the newest research production about monitoring vegetation nitrogen concentration by hyperspectral remote sensing published in main geography science literature in recent several years, the principle and correlated problem about monitoring vegetation nitrogen concentration by hyperspectral remote sensing were introduced. From four aspects including vegetation nitrogen spectral index, vegetation nitrogen content inversion based on chlorophyll index, regression model, and eliminating influence factors to inversion of vegetation nitrogen concentration, main technology methods about inversion of vegetation nitrogen concentration by hyperspectral remote sensing were detailedly introduced. Correlative research conclusions were summarized and analyzed, and research development trend was discussed.
[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.
Remote sensing of plant functional types.

PubMed

Ustin, Susan L; Gamon, John A

2010-06-01

Conceptually, plant functional types represent a classification scheme between species and broad vegetation types. Historically, these were based on physiological, structural and/or phenological properties, whereas recently, they have reflected plant responses to resources or environmental conditions. Often, an underlying assumption, based on an economic analogy, is that the functional role of vegetation can be identified by linked sets of morphological and physiological traits constrained by resources, based on the hypothesis of functional convergence. Using these concepts, ecologists have defined a variety of functional traits that are often context dependent, and the diversity of proposed traits demonstrates the lack of agreement on universal categories. Historically, remotely sensed data have been interpreted in ways that parallel these observations, often focused on the categorization of vegetation into discrete types, often dependent on the sampling scale. At the same time, current thinking in both ecology and remote sensing has moved towards viewing vegetation as a continuum rather than as discrete classes. The capabilities of new remote sensing instruments have led us to propose a new concept of optically distinguishable functional types ('optical types') as a unique way to address the scale dependence of this problem. This would ensure more direct relationships between ecological information and remote sensing observations.
A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.
A Physically-Based Drought Product Using Thermal Remote Sensing of Evapotranspiration

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonst...
Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.
A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.
Status of Vegetation Classification in Redwood Ecosystems

Treesearch

Thomas M. Mahony; John D. Stuart

2007-01-01

Vegetation classifications, based primarily on physiognomic variability and canopy dominants and derived principally from remotely sensed imagery, have been completed for the entire redwood range (Eyre 1980, Fox 1989). However, systematic, quantitative, floristic-based vegetation classifications in old-growth redwood forests have not been completed for large portions...
Remote sensing for grassland management in the arid Southwest

USGS Publications Warehouse

Marsett, R.C.; Qi, J.; Heilman, P.; Biedenbender, S.H.; Watson, M.C.; Amer, S.; Weltz, M.; Goodrich, D.; Marsett, R.

2006-01-01

We surveyed a group of rangeland managers in the Southwest about vegetation monitoring needs on grassland. Based on their responses, the objective of the RANGES (Rangeland Analysis Utilizing Geospatial Information Science) project was defined to be the accurate conversion of remotely sensed data (satellite imagery) to quantitative estimates of total (green and senescent) standing cover and biomass on grasslands and semidesert grasslands. Although remote sensing has been used to estimate green vegetation cover, in arid grasslands herbaceous vegetation is senescent much of the year and is not detected by current remote sensing techniques. We developed a ground truth protocol compatible with both range management requirements and Landsat's 30 m resolution imagery. The resulting ground-truth data were then used to develop image processing algorithms that quantified total herbaceous vegetation cover, height, and biomass. Cover was calculated based on a newly developed Soil Adjusted Total Vegetation Index (SATVI), and height and biomass were estimated based on reflectance in the near infrared (NIR) band. Comparison of the remotely sensed estimates with independent ground measurements produced r2 values of 0.80, 0.85, and 0.77 and Nash Sutcliffe values of 0.78, 0.70, and 0.77 for the cover, plant height, and biomass, respectively. The approach for estimating plant height and biomass did not work for sites where forbs comprised more than 30% of total vegetative cover. The ground reconnaissance protocol and image processing techniques together offer land managers accurate and timely methods for monitoring extensive grasslands. The time-consuming requirement to collect concurrent data in the field for each image implies a need to share the high fixed costs of processing an image across multiple users to reduce the costs for individual rangeland managers.
[Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data].

PubMed

Gao, Lin; Li, Chang-chun; Wang, Bao-shan; Yang Gui-jun; Wang, Lei; Fu, Kui

2016-01-01

With the innovation of remote sensing technology, remote sensing data sources are more and more abundant. The main aim of this study was to analyze retrieval accuracy of soybean leaf area index (LAI) based on multi-source remote sensing data including ground hyperspectral, unmanned aerial vehicle (UAV) multispectral and the Gaofen-1 (GF-1) WFV data. Ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), and triangle vegetation index (TVI) were used to establish LAI retrieval models, respectively. The models with the highest calibration accuracy were used in the validation. The capability of these three kinds of remote sensing data for LAI retrieval was assessed according to the estimation accuracy of models. The experimental results showed that the models based on the ground hyperspectral and UAV multispectral data got better estimation accuracy (R² was more than 0.69 and RMSE was less than 0.4 at 0.01 significance level), compared with the model based on WFV data. The RVI logarithmic model based on ground hyperspectral data was little superior to the NDVI linear model based on UAV multispectral data (The difference in E(A), R² and RMSE were 0.3%, 0.04 and 0.006, respectively). The models based on WFV data got the lowest estimation accuracy with R2 less than 0.30 and RMSE more than 0.70. The effects of sensor spectral response characteristics, sensor geometric location and spatial resolution on the soybean LAI retrieval were discussed. The results demonstrated that ground hyperspectral data were advantageous but not prominent over traditional multispectral data in soybean LAI retrieval. WFV imagery with 16 m spatial resolution could not meet the requirements of crop growth monitoring at field scale. Under the condition of ensuring the high precision in retrieving soybean LAI and working efficiently, the approach to acquiring agricultural information by UAV remote sensing could yet be regarded as an optimal plan. Therefore, in the case of more and more available remote sensing information sources, agricultural UAV remote sensing could become an important information resource for guiding field-scale crop management and provide more scientific and accurate information for precision agriculture research.
Remote Sensing-Based, 5-m, Vegetation Distributions, Kougarok Study Site, Seward Peninsula, Alaska, ca. 2009 - 2016

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

Langford, Zachary; Kumar, Jitendra; Hoffman, Forrest

A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5~m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. This data was developed using the fusion of hyperspectral, multispectral, and terrain datasets. The current data is located in the Kougarok watershed but we plan to expand this over the Seward Peninsula.
Improved ground-based remote-sensing systems help monitor plant response to climate and other changes

USGS Publications Warehouse

Dye, Dennis G.; Bogle, Rian

2016-05-26

Scientists at the U.S. Geological Survey are improving and developing new ground-based remote-sensing instruments and techniques to study how Earth’s vegetation responds to changing climates. Do seasonal grasslands and forests “green up” early (or late) and grow more (or less) during unusually warm years? How do changes in temperature and precipitation affect these patterns? Innovations in ground-based remote-sensing instrumentation can help us understand, assess, and mitigate the effects of climate change on vegetation and related land resources.
Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

USGS Publications Warehouse

Chastain, R.A.; Struckhoff, M.A.; He, H.S.; Larsen, D.R.

2008-01-01

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.
Quantifying vegetation distribution and structure using high resolution drone-based structure-from-motion photogrammetry

NASA Astrophysics Data System (ADS)

Zhang, J.; Okin, G.

2017-12-01

Vegetation is one of the most important driving factors of different ecosystem processes in drylands. The structure of vegetation controls the spatial distribution of moisture and heat in the canopy and the surrounding area. Also, the structure of vegetation influences both airflow and boundary layer resistance above the land surface. Multispectral satellite remote sensing has been widely used to monitor vegetation coverage and its change; however, it can only capture 2D images, which do not contain the vertical information of vegetation. In situ observation uses different methods to measure the structure of vegetation, and their results are accurate; however, these methods are laborious and time-consuming, and susceptible to undersampling in spatial heterogeneity. Drylands are sparsely covered by short plants, which allows the drone fly at a relatively low height to obtain ultra-high resolution images. Structure-from-motion (SfM) is a photogrammetric method that was proved to produce 3D model based on 2D images. Drone-based remote sensing can obtain the multiangle images for one object, which can be used to constructed 3D models of vegetation in drylands. Using these images detected by the drone, the orthomosaics and digital surface model (DSM) can be built. In this study, the drone-based remote sensing was conducted in Jornada Basin, New Mexico, in the spring of 2016 and 2017, and three derived vegetation parameters (i.e., canopy size, bare soil gap size, and plant height) were compared with those obtained with field measurement. The correlation coefficient of canopy size, bare soil gap size, and plant height between drone images and field data are 0.91, 0.96, and 0.84, respectively. The two-year averaged root-mean-square error (RMSE) of canopy size, bare soil gap size, and plant height between drone images and field data are 0.61 m, 1.21 m, and 0.25 cm, respectively. The two-year averaged measure error (ME) of canopy size, bare soil gap size, and plant height between drone images and field data are 0.02 m, -0.03, and -0.1 m, respectively. These results indicate a good agreement between drone-based remote sensing and field measurement.
Vegetation Coverage and Impervious Surface Area Estimated Based on the Estarfm Model and Remote Sensing Monitoring

NASA Astrophysics Data System (ADS)

Hu, Rongming; Wang, Shu; Guo, Jiao; Guo, Liankun

2018-04-01

Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC) and impervious layer with high spatiotemporal resolution (30 m, 8 day) were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1) ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2) The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.
Estimating Vegetation Rainfall Interception Using Remote Sensing Observations at Very High Resolution

NASA Astrophysics Data System (ADS)

Cui, Y.; Zhao, P.; Hong, Y.; Fan, W.; Yan, B.; Xie, H.

2017-12-01

Abstract: As an important compont of evapotranspiration, vegetation rainfall interception is the proportion of gross rainfall that is intercepted, stored and subsequently evaporated from all parts of vegetation during or following rainfall. Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception loss and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Keywords: rainfall interception; remote sensing; RS-Gash analytical model; high resolution
Seasonality of a boreal forest: a remote sensing perspective

NASA Astrophysics Data System (ADS)

Rautiainen, Miina; Heiskanen, Janne; Lukes, Petr; Majasalmi, Titta; Mottus, Matti; Pisek, Jan

2016-04-01

Understanding the seasonal dynamics of boreal ecosystems through interpretation of satellite reflectance data is needed for efficient large-scale monitoring of northern vegetation dynamics and productivity trends. Satellite remote sensing enables continuous global monitoring of vegetation status and is not limited to single-date phenological metrics. Using remote sensing also enables gaining a wider perspective to the seasonality of vegetation dynamics. The seasonal reflectance cycles of boreal forests observed in optical satellite images are explained by changes in biochemical properties and geometrical structure of vegetation as well as seasonal variation in solar illumination. This poster provides a synthesis of a research project (2010-2015) dedicated to monitoring the seasonal cycle of boreal forests. It is based on satellite and field data collected from the Hyytiälä Forestry Field Station in Finland. The results highlight the role understory vegetation has in forming the forest reflectance measured by satellite instruments.

Physics teaching by infrared remote sensing of vegetation

NASA Astrophysics Data System (ADS)

Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

2018-05-01

Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remote sensing is an interesting context for physics education. In this paper we give a brief overview of satellite remote sensing of vegetation and how to obtain your own, individual infrared remote sensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remote sensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.
Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing.

PubMed

William, David J; Rybicki, Nancy B; Lombana, Alfonso V; O'Brien, Tim M; Gomez, Richard B

2003-01-01

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.
How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment

USDA-ARS?s Scientific Manuscript database

This study aims to assess the relationship between Leaf Area Index (LAI) and remotely sensed Vegetation Indices (VIs) for major crops, based on a globally explicit dataset of in situ LAI measurements over a significant set of locations. We used a total of 1394 LAI measurements from 29 sites spannin...
Compositing MODIS Terra and Aqua 250m daily surface reflectance data sets for vegetation monitoring

USDA-ARS?s Scientific Manuscript database

Remote sensing based vegetation Indices have been proven valuable in providing a spatially complete view of crop’s vegetation condition, which also manifests the impact of the disastrous events such as massive flood and drought. VegScape, a web GIS application for crop vegetation condition monitorin...
Multiscaling of vegetative indexes from remote sensing images obtained at different spatial resolutions

NASA Astrophysics Data System (ADS)

Alonso, Carmelo; Tarquis, Ana M.; Zuñiga, Ignacio; Benito, Rosa M.

2017-04-01

Vegetation indexes, such as Normalized Difference Vegetation Index (NDVI) and enhanced Vegetation index (EVI), can been used to estimate root zone soil moisture through high resolution remote sensing images. These indexes are based in red (R), near infrared (NIR) and blue (B) wavelengths data. In this work we have studied the scaling properties of both vegetation indexes analyzing the information contained in two satellite data: Landsat-7 and Ikonos. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends possible data archives from present time to over several decades back. For this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. To study the influence of the spatial resolution the vegetation indexes map estimated with Ikonos-2 coded in 8 bits, with a resolution of 4m, have been compared through a multifractal analysis with the ones obtained with Lansat-7 8 bits, of 30 m. resolution, on the same area of study. The scaling behaviour of NDVI and EVI presents several differences that will be discussed based on the multifractal parameters extracted from the analysis. REFERENCES Alonso, C., Tarquis, A. M., Benito, R. M. and Zuñiga, I. Correlation scaling properties between soil moisture and vegetation indices. Geophysical Research Abstracts, 11, EGU2009-13932, 2009. Alonso, C., Tarquis, A. M. and Benito, R. M. Comparison of fractal dimensions based on segmented NDVI fields obtained from different remote sensors. Geophysical Research Abstracts, 14, EGU2012-14342, 2012. Escribano Rodriguez, J., Alonso, C., Tarquis, A.M., Benito, R.M. and Hernandez Diaz-Ambrona, C. Comparison of NDVI fields obtained from different remote sensors. Geophysical Research Abstracts,15, EGU2013-14153, 2013. Lovejoy, S., Tarquis, A., Gaonac'h, H. and Schertzer, D. Single and multiscale remote sensing techniques, multifractals and MODIS derived vegetation and soil moisture, Vadose Zone J., 7, 533-546, 2008. Renosh, P. R., Schmitt, F. G., and Loisel, H.: Scaling analysis of ocean surface turbulent heterogeneities from satellite remote sensing: use of 2D structure functions. PLoS ONE, 10, e0126975, 2015. Tarquis, A.M., Platonov, A., Matulka, A., Grau, J., Sekula, E., Diez, M. and Redondo J. M. Application of multifractal analysis to the study of SAR features and oil spills on the ocean surface. Nonlin. Processes Geophys., 21, 439-450, 2014.
Object-based vegetation classification with high resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions such as (1) whether the sample characteristics affect the classification accuracy and how significant if it does; (2) how much variance of classification uncertainty can be explained by above factors. This research is carried out on a hilly peninsular area in Mediterranean climate, Point Reyes National Seashore (PRNS) in Northern California. The area mainly consists of a heterogeneous, semi-natural broadleaf and conifer woodland, shrub land, and annual grassland. A detailed list of vegetation alliances is used in this study. Research results from the first phase indicates that vegetation spatial variation as reflected by the average local variance (ALV) keeps a high level of magnitude between 1 m and 4 m resolution. (Abstract shortened by UMI.)
The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

NASA Technical Reports Server (NTRS)

Hogan, Christine A.

1996-01-01

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation composition was noted in the change detection image.
Remote sensing new model for monitoring the east Asian migratory locust infections based on its breeding circle

NASA Astrophysics Data System (ADS)

Han, Xiuzhen; Ma, Jianwen; Bao, Yuhai

2006-12-01

Currently the function of operational locust monitor system mainly focused on after-hazards monitoring and assessment, and to found the way effectively to perform early warning and prediction has more practical meaning. Through 2001, 2002 two years continuously field sample and statistics for locusts eggs hatching, nymph growth, adults 3 phases observation, sample statistics and calculation, spectral measurements as well as synchronically remote sensing data processing we raise the view point of Remote Sensing three stage monitor the locust hazards. Based on the point of view we designed remote sensing monitor in three stages: (1) during the egg hitching phase remote sensing can retrieve parameters of land surface temperature (LST) and soil moisture; (2) during nymph growth phase locust increases appetite greatly and remote sensing can calculate vegetation index, leaf area index, vegetation cover and analysis changes; (3) during adult phase the locust move and assembly towards ponds and water ditches as well as less than 75% vegetation cover areas and remote sensing combination with field data can monitor and predicts potential areas for adult locusts to assembly. In this way the priority of remote sensing technology is elaborated effectively and it also provides technique support for the locust monitor system. The idea and techniques used in the study can also be used as reference for other plant diseases and insect pests.
Rapid-response tools and datasets for post-fire remediation: Linking remote sensing and process-based hydrological models

Treesearch

M. E. Miller; William Elliot; M. Billmire; Pete Robichaud; K. A. Endsley

2016-01-01

Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil...
A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

PubMed

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

2008-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.
A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

2009-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716
Multi-scale functional mapping of tidal marsh vegetation for restoration monitoring

NASA Astrophysics Data System (ADS)

Tuxen Bettman, Karin

2007-12-01

Nearly half of the world's natural wetlands have been destroyed or degraded, and in recent years, there have been significant endeavors to restore wetland habitat throughout the world. Detailed mapping of restoring wetlands can offer valuable information about changes in vegetation and geomorphology, which can inform the restoration process and ultimately help to improve chances of restoration success. I studied six tidal marshes in the San Francisco Estuary, CA, US, between 2003 and 2004 in order to develop techniques for mapping tidal marshes at multiple scales by incorporating specific restoration objectives for improved longer term monitoring. I explored a "pixel-based" remote sensing image analysis method for mapping vegetation in restored and natural tidal marshes, describing the benefits and limitations of this type of approach (Chapter 2). I also performed a multi-scale analysis of vegetation pattern metrics for a recently restored tidal marsh in order to target the metrics that are consistent across scales and will be robust measures of marsh vegetation change (Chapter 3). Finally, I performed an "object-based" image analysis using the same remotely sensed imagery, which maps vegetation type and specific wetland functions at multiple scales (Chapter 4). The combined results of my work highlight important trends and management implications for monitoring wetland restoration using remote sensing, and will better enable restoration ecologists to use remote sensing for tidal marsh monitoring. Several findings important for tidal marsh restoration monitoring were made. Overall results showed that pixel-based methods are effective at quantifying landscape changes in composition and diversity in recently restored marshes, but are limited in their use for quantifying smaller, more fine-scale changes. While pattern metrics can highlight small but important changes in vegetation composition and configuration across years, scientists should exercise caution when using metrics in their studies or to validate restoration management decisions, and multi-scale analyses should be performed before metrics are used in restoration science for important management decisions. Lastly, restoration objectives, ecosystem function, and scale can each be integrated into monitoring techniques using remote sensing for improved restoration monitoring.
[Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review].

PubMed

Wu, Chang-Guang; Li, Sheng; Ren, Hua-Dong; Yao, Xiao-Hua; Huang, Zi-Jie

2012-06-01

Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.
Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

2014-03-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.
Assessment of Wildfire Risk in Southern California with Live Fuel Moisture Measurement and Remotely Sensed Vegetation Water Content Proxies

NASA Astrophysics Data System (ADS)

Jia, S.; Kim, S. H.; Nghiem, S. V.; Kafatos, M.

2017-12-01

Live fuel moisture (LFM) is the water content of live herbaceous plants expressed as a percentage of the oven-dry weight of plant. It is a critical parameter in fire ignition in Mediterranean climate and routinely measured in sites selected by fire agencies across the U.S. Vegetation growing cycle, meteorological metrics, soil type, and topography all contribute to the seasonal and inter-annual variation of LFM, and therefore, the risk of wildfire. The optical remote sensing-based vegetation indices (VIs) have been used to estimate the LFM. Comparing to the VIs, microwave remote sensing products have advantages like less saturation effect in greenness and representing the water content of the vegetation cover. In this study, we established three models to evaluate the predictability of LFM in Southern California using MODIS NDVI, vegetation temperature condition index (VTCI) from downscaled Soil Moisture Active Passive (SMAP) products, and vegetation optical depth (VOD) derived by Land Parameter Retrieval Model. Other ancillary variables, such as topographic factors (aspects and slope) and meteorological metrics (air temperature, precipitation, and relative humidity), are also considered in the models. The model results revealed an improvement of LFM estimation from SMAP products and VOD, despite the uncertainties introduced in the downscaling and parameter retrieval. The estimation of LFM using remote sensing data can provide an assessment of wildfire danger better than current methods using NDVI-based growing seasonal index. Future study will test the VOD estimation from SMAP data using the multi-temporal dual channel algorithm (MT-DCA) and extend the LFM modeling to a regional scale.
Research on the remote sensing methods of drought monitoring in Chongqing

NASA Astrophysics Data System (ADS)

Yang, Shiqi; Tang, Yunhui; Gao, Yanghua; Xu, Yongjin

2011-12-01

There are regional and periodic droughts in Chongqing, which impacted seriously on agricultural production and people's lives. This study attempted to monitor the drought in Chongqing with complex terrain using MODIS data. First, we analyzed and compared three remote sensing methods for drought monitoring (time series of vegetation index, temperature vegetation dryness index (TVDI), and vegetation supply water index (VSWI)) for the severe drought in 2006. Then we developed a remote sensing based drought monitoring model for Chongqing by combining soil moisture data and meteorological data. The results showed that the three remote sensing based drought monitoring models performed well in detecting the occurrence of drought in Chongqing on a certain extent. However, Time Series of Vegetation Index has stronger sensitivity in time pattern but weaker in spatial pattern; although TVDI and VSWI can reflect inverse the whole process of severe drought in 2006 summer from drought occurred - increased - relieved - increased again - complete remission in spatial domain, but TVDI requires the situation of extreme drought and extreme moist both exist in study area which it is more difficult in Chongqing; VSWI is simple and practicable, which the correlation coefficient between VSWI and soil moisture data reaches significant levels. In summary, VSWI is the best model for summer drought monitoring in Chongqing.
Species Composition at the Sub-Meter Level in Discontinuous Permafrost in Subarctic Sweden

NASA Astrophysics Data System (ADS)

Anderson, S. M.; Palace, M. W.; Layne, M.; Varner, R. K.; Crill, P. M.

2013-12-01

Northern latitudes are experiencing rapid warming. Wetlands underlain by permafrost are particularly vulnerable to warming which results in changes in vegetative cover. Specific species have been associated with greenhouse gas emissions therefore knowledge of species compositional shift allows for the systematic change and quantification of emissions and changes in such emissions. Species composition varies on the sub-meter scale based on topography and other microsite environmental parameters. This complexity and the need to scale vegetation to the landscape level proves vital in our estimation of carbon dioxide (CO2) and methane (CH4) emissions and dynamics. Stordalen Mire (68°21'N, 18°49'E) in Abisko and is located at the edge of discontinuous permafrost zone. This provides a unique opportunity to analyze multiple vegetation communities in a close proximity. To do this, we randomly selected 25 1x1 meter plots that were representative of five major cover types: Semi-wet, wet, hummock, tall graminoid, and tall shrub. We used a quadrat with 64 sub plots and measured areal percent cover for 24 species. We collected ground based remote sensing (RS) at each plot to determine species composition using an ADC-lite (near infrared, red, green) and GoPro (red, blue, green). We normalized each image based on a Teflon white chip placed in each image. Textural analysis was conducted on each image for entropy, angular second momentum, and lacunarity. A logistic regression was developed to examine vegetation cover types and remote sensing parameters. We used a multiple linear regression using forwards stepwise variable selection. We found statistical difference in species composition and diversity indices between vegetation cover types. In addition, we were able to build regression model to significantly estimate vegetation cover type as well as percent cover for specific key vegetative species. This ground-based remote sensing allows for quick quantification of vegetation cover and species and also provides the framework for scaling to satellite image data to estimate species composition and shift on the landscape level. To determine diversity within our plots we calculated species richness and Shannon Index. We found that there were statistically different species composition within each vegetation cover type and also determined which species were indicative for cover type. Our logistical regression was able to significantly classify vegetation cover types based on RS parameters. Our multiple regression analysis indicated Betunla nana (Dwarf Birch) (r2= .48, p=<0.0001) and Sphagnum (r2=0.59, p=<0.0001) were statistically significant with respect to RS parameters. We suggest that ground based remote sensing methods may provide a unique and efficient method to quantify vegetation across the landscape in northern latitude wetlands.
Separating vegetation and soil temperature using airborne multiangular remote sensing image data

NASA Astrophysics Data System (ADS)

Liu, Qiang; Yan, Chunyan; Xiao, Qing; Yan, Guangjian; Fang, Li

2012-07-01

Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.
Providing a Spatial Context for Crop Insurance in Ethiopia: Multiscale Comparisons of Vegetation Metrics in Tigray

NASA Astrophysics Data System (ADS)

Mann, B. F.; Small, C.

2014-12-01

Weather-based index insurance projects are rapidly expanding across the developing world. Many of these projects use satellite-based observations to detect extreme weather events, which inform and trigger payouts to smallholder farmers. While most index insurance programs use precipitation measurements to determine payouts, the use of remotely sensed observations of vegetation is currently being explored. In order to use vegetation indices as a basis for payouts, it is necessary to establish a consistent relationship between the vegetation index and the health and abundance of agriculture on the ground. The accuracy with which remotely sensed vegetation indices can detect changes in agriculture depends on both the spatial scale of the agriculture and the spatial resolution of the sensor. This study analyzes the relationship between meter and decameter scale vegetation fraction estimates derived from linear spectral mixture models with a more commonly used vegetation index (NDVI, EVI) at hectometer spatial scales. In addition, the analysis incorporates land cover/land use field observations collected in Tigray Ethiopia in July 2013. . It also tests the flexibility and utility of a standardized spectral mixture model in which land cover is represented as continuous fields of rock and soil substrate (S), vegetation (V) and dark surfaces (D; water, shadow). This analysis found strong linear relationships with vegetation metrics at 1.6-meter, 30-meter and 250-meter resolutions across spectrally diverse subsets of Tigray, Ethiopia and significantly correlated relationships using the Spearman's rho statistic. The observed linear scaling has positive implications for future use of moderate resolution vegetation indices in similar landscapes; especially index insurance projects that are scaling up across the developing world using remotely-sensed environmental information.
Remotely sensed vegetation indices for seasonal crop yields predictions in the Czech Republic

NASA Astrophysics Data System (ADS)

Hlavinka, Petr; Semerádová, Daniela; Balek, Jan; Bohovic, Roman; Žalud, Zdeněk; Trnka, Miroslav

2015-04-01

Remotely sensed vegetation indices by satellites are valuable tool for vegetation conditions assessment also in the case of field crops. This study is based on the use of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard Terra satellite. Data available from the year 2000 were analyzed and tested for seasonal yields predictions within selected districts of the Czech Republic (Central Europe). Namely the yields of spring barley, winter wheat and oilseed winter rape during the period from 2000 to 2014 were assessed. Observed yields from 14 districts (NUTS 4) were collected and thus 210 seasons were included. Selected districts differ considerably in their soil fertility and terrain configuration and represent transect across various agroclimatic conditions (from warm and dry to relative cool and wet regions). Two approaches were tested: 1) using of composite remotely sensed data (available in 16 day time step) provided by the USGS (https://lpdaac.usgs.gov/); 2) using daily remotely sensed data in combination with originally developed smoothing method. The yields were successfully predicted based on established regression models (remotely sensed data used as independent parameter). Besides others the impact of severe drought episodes within vegetation were identified and yield reductions at district level predicted (even before harvest). As a result the periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above normal yields of field crops could be predicted by proposed method within study region up to 30 days prior to the harvest. It could be concluded that remotely sensed vegetation conditions assessment should be important part of early warning systems focused on drought. Such information should be widely available for various users (decision makers, farmers, etc.) in order to improve planning, business strategies but also to target the drought relief in case of major drought event. This study was funded by project "Building up a multidisciplinary scientific team focused on drought" No. CZ.1.07/2.3.00/20.0248, project supported by Czech National Agency of Agricultural Research No. QJ1310123 "Crop modelling as a tool for increasing the production potential and food security of the Czech Republic under Climate Change".

Modeling of vegetation canopy reflectance: Status, issues and recommended future strategy

NASA Technical Reports Server (NTRS)

Goel, N. S. (Editor)

1982-01-01

Various technical issues related to mapping of vegetative type, condition and stage of maturity, utilizing remotely sensed spectral data are reviewed. The existing knowledge base of models, especially of radiative properties of the vegetation canopy and atmosphere, is reviewed to establish the state of the art for addressing the problem of vegetation mapping. Activities to advance the state of the art are recommended. They include working on canopy reflectance and atmospheric scattering models, and field measurements of canopy reflectance as well as of canopy components. Leaf area index (LAI) and solar radiation interception (SRI) are identified as the two most important vegetation variables requiring further investigation. It is recommended that activities related to sensing them or understanding their relationships with measurable variables, should be encouraged and supported.
The use of LANDSAT digital data to detect and monitor vegetation water deficiencies. [South Dakota

NASA Technical Reports Server (NTRS)

Thompson, D. R.; Wehmanen, O. A.

1977-01-01

A technique devised using a vector transformation of LANDSAT digital data to indicate when vegetation is undergoing moisture stress is described. A relation established between the remote sensing-based criterion (the Green Index Number) and a ground-based criterion (Crop Moisture Index) is discussed.
Ground-Based Passive Microwave Remote Sensing Observations of Soil Moisture at S and L Band with Insight into Measurement Accuracy

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Crosson, William L.; Jackson, Thomas J.; Manu, Andrew; Tsegaye, Teferi D.; Soman, V.; Arnold, James E. (Technical Monitor)

2001-01-01

Accurate estimates of spatially heterogeneous algorithm variables and parameters are required in determining the spatial distribution of soil moisture using radiometer data from aircraft and satellites. A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, Alabama from July 1-14, 1996 to study retrieval algorithms and their sensitivity to variable and parameter specification. With high temporal frequency observations at S and L band, we were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cubic centimeter/cubic centimeter with an indication of a shallower emitting depth at higher moisture values. Surface moisture behavior was less apparent on the vegetated plots than it was on the bare plot because there was less moisture gradient and because of difficulty in determining vegetation water content and estimating the vegetation b parameter. Discrepancies between remotely sensed and gravimetric, soil moisture estimates on the vegetated plots point to an incomplete understanding of the requirements needed to correct for the effects of vegetation attenuation. Quantifying the uncertainty in moisture estimates is vital if applications are to utilize remotely-sensed soil moisture data. Computations based only on the real part of the complex dielectric constant and/or an alternative dielectric mixing model contribute a relatively insignificant amount of uncertainty to estimates of soil moisture. Rather, the retrieval algorithm is much more sensitive to soil properties, surface roughness and biomass.
Remote sensing-based characterization, 2-m, Plant Functional Type Distributions, Barrow Environmental Observatory, 2010

DOE Data Explorer

Langford, Zachary; Kumar, Jitendra; Hoffman, Forrest

2014-01-01

Arctic ecosystems have been observed to be warming faster than the global average and are predicted to experience accelerated changes in climate due to global warming. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivity under changing climate. Mapping and monitoring of changes in vegetation is essential to understand the effect of climate change on the ecosystem functions. Vegetation exhibits unique spectral characteristics which can be harnessed to discriminate plant types and develop quantitative vegetation indices. We have combined high resolution multi-spectral remote sensing from the WorldView 2 satellite with LIDAR-derived digital elevation models to characterize the tundra landscape on the North Slope of Alaska. Classification of landscape using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season to collect vegetation harvests from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. Vegetation distributions developed are being used to provide Plant Functional Type (PFT) maps for use in the Community Land Model (CLM).
Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.
Coupling Analysis of Heat Island Effects, Vegetation Coverage and Urban Flood in Wuhan

NASA Astrophysics Data System (ADS)

Liu, Y.; Liu, Q.; Fan, W.; Wang, G.

2018-04-01

In this paper, satellite image, remote sensing technique and geographic information system technique are main technical bases. Spectral and other factors comprehensive analysis and visual interpretation are main methods. We use GF-1 and Landsat8 remote sensing satellite image of Wuhan as data source, and from which we extract vegetation distribution, urban heat island relative intensity distribution map and urban flood submergence range. Based on the extracted information, through spatial analysis and regression analysis, we find correlations among heat island effect, vegetation coverage and urban flood. The results show that there is a high degree of overlap between of urban heat island and urban flood. The area of urban heat island has buildings with little vegetation cover, which may be one of the reasons for the local heavy rainstorms. Furthermore, the urban heat island has a negative correlation with vegetation coverage, and the heat island effect can be alleviated by the vegetation to a certain extent. So it is easy to understand that the new industrial zones and commercial areas which under constructions distribute in the city, these land surfaces becoming bare or have low vegetation coverage, can form new heat islands easily.
Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

NASA Astrophysics Data System (ADS)

Jaafar, H. H.; Ahmad, F. A.

2015-04-01

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.
Hyperspectral remote sensing of the responses of vegetation ecosystems to physical and biological changes of the environment

NASA Astrophysics Data System (ADS)

Krezhova, Dora; Krezhov, Kiril; Maneva, Svetla; Moskova, Irina; Petrov, Nikolay

2016-07-01

Hyperspectral remote sensing technique, based on reflectance measurements acquired in a high number of contiguous spectral bands in the visible and near infrared spectral ranges, was used to detect the influence of some environmental changes to vegetation ecosystems. Adverse physical and biological conditions give rise to morphological, physiological, and biochemical changes in the plants that affect the manner in which they interact with the light. All green vegetation species have unique spectral features, mainly because of the chlorophyll and carotenoid, and other pigments, and water content. Because spectral reflectance is a function of the illumination conditions, tissue optical properties and biochemical content of the plants it may be used to collect information on several important biophysical parameters such as color and the spectral signature of features, vegetation chlorophyll absorption characteristics, vegetation moisture content, etc. Remotely sensed data collected by means of a portable fiber-optics spectrometer in the spectral range 350-1100 nm were used to extract information on the influence of some environmental changes. Stress factors such as enhanced UV-radiation, salinity, viral infections, were applied to some young plants species (potato, tomato, plums). The test data were subjected to different digital image processing techniques. This included statistical (Student's t-criterion), first derivative and cluster analyses and some vegetation indices. Statistical analyses were carried out in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (680-720 nm) and near infrared (720-780 nm). The strong relationship, which was found between the results from the remote sensing technique and some biochemical and serological analyses (stress markers, DAS-ELISA), indicates the importance of hyperspectral reflectance data for conducting, easily and without damage, rapid assessments of plant biophysical variables. Emphasis is put on current capability and future potential of remote sensing for assessment of the plant health and on the optimum spectral regions and vegetation indices for sensing these biophysical variables.
The use of Landsat digital data to detect and monitor vegetation water deficiencies

NASA Technical Reports Server (NTRS)

Thompson, D. R.; Wehmanen, O. A.

1977-01-01

In the Large Area Crop Inventory Experiment a technique was devised using a vector transformation of Landsat digital data to indicate when vegetation is undergoing moisture stress. A relation was established between the remote-sensing-based criterion (the Green Index Number) and a ground-based criterion (Crop Moisture Index).
Integrated study of biomass index in La Herreria (Sierra de Guadarrama)

NASA Astrophysics Data System (ADS)

Hernandez Díaz-Ambrona, Carlos G.

2016-04-01

Drought severity has many implications for society, including its impacts on the water supply, water pollution, reservoir management and ecosystem. There have been many attempts to characterize its severity, resulting in the numerous drought indices that have been developed (Niemeyer 2008). The'biomass index', based on satellite image derived Normalized Difference Vegetation Index (NDVI) has been used in several countries for pasture and forage crops for some years (Rao, 2010; Escribano-Rodriguez et al., 2014). NDVI generally provides a broad overview of the vegetation condition and spatial vegetation distribution in a region. Vegetative drought is closely related with weather impacts. However, in NDVI, the weather component gets subdued by the strong ecological component. Another vegetation index is Vegetation Condition Index (VCI) that separates the short-term weather-related NDVI fluctuations from the long-term ecosystem changes (Kogan, 1990). Therefore, while NDVI shows seasonal vegetation dynamics, VCI rescales vegetation dynamics between 0 and 100 to reflect relative changes in the vegetation condition from extremely bad to optimal (Kogan et al., 2003). In this work a pasture area at La Herreria (Sierra de Guadarrama, Spain) has been delimited. Then, NDVI historical data are reconstructed based on remote sensing imaging MODIS, with 500x500m2 resolution. From the closest meteorological station (Santolaria-Canales, 2015) records of weekly precipitation, temperature and evapotranspiration from 2001 till 2012 were obtained. Standard Precipitation Index (SPI), Crop Moisture Index (CMI) (Palmer, 1968) and Evapotranspiration-Precipitation Ratio (EPR) are calculated in an attempt to relate them to several vegetation indexes: NDVI, VCI and NDVI Change Ratio to Median (RMNDVI). The results are discussed in the context of pasture index insurance. References Escribano Rodriguez, J.Agustín, Carlos Gregorio Hernández Díaz-Ambrona and Ana María Tarquis Alfonso. Selection of vegetation indices to estimate pasture production in Dehesas. PASTOS, 44(2), 6-18, 2014. Kogan, F. N., 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int. J. Remote Sensing, 11(8), pp. 1405-1419. Kogan, F. N., Gitelson, A., Edige, Z., Spivak, l., and Lebed, L., 2003. AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation. Photogrammetric Engineering & Remote Sensing, 69(8), pp. 899-906. 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. Palmer, W.C., 1968. Keeping track of crop moisture conditions, nationwide: The new Crop Moisture Index. Weatherwise 21, 156-161. Rao, K.N. 2010. Index based Crop Insurance. Agriculture and Agricultural Science Procedia 1, 193-203. Santolaria-Canales, Edmundo and the GuMNet Consortium Team (2015). GuMNet - Guadarrama Monitoring Network. Installation and set up of a high altitude monitoring network, north of Madrid. Spain. Geophysical Research Abstracts, 17, EGU2015-13989-2 Web: http://www.ucm.es/gumnet/
[Remote sensing detection of vegetation health status after ecological restoration in soil and water loss region].

PubMed

Hu, Xiu Juan; Xu, Han Qiu; Guo, Yan Bin; Zhang, Bo Bo

2017-01-01

This paper proposed a vegetation health index (VHI) to rapidly monitor and assess vegetation health status in soil and water loss region based on remote sensing techniques and WorldView-2 imagery. VHI was constructed by three factors, i.e., the normalized mountain vegetation index, the nitrogen reflectance index and the reflectance of the yellow band, through the principal component transformation, in order to avoid the deviation induced by subjective method of weighted summation. The Hetian Basin of Changting County in Fujian Province, China, was taken as a test area to assess the vegetation health status in soil and water loss region using VHI. The results showed that the VHI could detect vegetation health status with a total accuracy of 91%. The vegetation of Hetian Basin in good, moderate and poor health status accounted for 10.1%, 49.2% and 40.7%, respectively. The vegetation of the study area was still under an unhealthy status because the soil was poor and the growth of newly planted vegetation was not good in the soil and water loss region.
Intercomparison of Remotely Sensed Vegetation Indices, Ground Spectroscopy, and Foliar Chemistry Data from NEON

NASA Astrophysics Data System (ADS)

Hulslander, D.; Warren, J. N.; Weintraub, S. R.

2017-12-01

Hyperspectral imaging systems can be used to produce spectral reflectance curves giving rich information about composition, relative abundances of materials, mixes and combinations. Indices based on just a few spectral bands have been used for over 40 years to study vegetation health, mineral abundance, and more. These indices are much simpler to visualize and use than a full hyperspectral data set which may contain over 400 bands. Yet historically, it has been difficult to directly relate remotely sensed spectral indices to quantitative biophysical properties significant to forest ecology such as canopy nitrogen, lignin, and chlorophyll. This linkage is a critical piece in enabling the detection of high value ecological information, usually only available from labor-intensive canopy foliar chemistry sampling, to the geographic and temporal coverage available via remote sensing. Previous studies have shown some promising results linking ground-based data and remotely sensed indices, but are consistently limited in time, geographic extent, and land cover type. Moreover, previous studies are often focused on tuning linkage algorithms for the purpose of achieving good results for only one study site or one type of vegetation, precluding development of more generalized algorithms. The National Ecological Observatory Network (NEON) is a unique system of 47 terrestrial sites covering all of the major eco-climatic domains of the US, including AK, HI, and Puerto Rico. These sites are regularly monitored and sampled using uniform instrumentation and protocols, including both foliar chemistry sampling and remote sensing flights for high resolution hyperspectral, LiDAR, and digital camera data acquisition. In this study we compare the results of foliar chemistry analysis to the remote sensing vegetation indices and investigate possible sources for variance and difference through the use of the larger hyperspectral dataset as well as ground based spectrometer measurements of samples subsequently analyzed for foliar chemistry.
Analysis of Differences in Phenology Extracted from the Enhanced Vegetation Index and the Leaf Area Index

PubMed Central

Wang, Cong; Li, Jing; Wu, Shanlong; Xia, Chuanfu

2017-01-01

Remote-sensing phenology detection can compensate for deficiencies in field observations and has the advantage of capturing the continuous expression of phenology on a large scale. However, there is some variability in the results of remote-sensing phenology detection derived from different vegetation parameters in satellite time-series data. Since the enhanced vegetation index (EVI) and the leaf area index (LAI) are the most widely used vegetation parameters for remote-sensing phenology extraction, this paper aims to assess the differences in phenological information extracted from EVI and LAI time series and to explore whether either index performs well for all vegetation types on a large scale. To this end, a GLASS (Global Land Surface Satellite Product)-LAI-based phenology product (GLP) was generated using the same algorithm as the MODIS (Moderate Resolution Imaging Spectroradiometer)-EVI phenology product (MLCD) over China from 2001 to 2012. The two phenology products were compared in China for different vegetation types and evaluated using ground observations. The results show that the ratio of missing data is 8.3% for the GLP, which is less than the 22.8% for the MLCD. The differences between the GLP and the MLCD become stronger as the latitude decreases, which also vary among different vegetation types. The start of the growing season (SOS) of the GLP is earlier than that of the MLCD in most vegetation types, and the end of the growing season (EOS) of the GLP is generally later than that of the MLCD. Based on ground observations, it can be suggested that the GLP performs better than the MLCD in evergreen needleleaved forests and croplands, while the MLCD performs better than the GLP in shrublands and grasslands. PMID:28867773
A national-scale remote sensing-based methodology for quantifying tidal marsh biomass to support "Blue Carbon" accounting

NASA Astrophysics Data System (ADS)

Byrd, K. B.; Ballanti, L.; Nguyen, D.; Simard, M.; Thomas, N.; Windham-Myers, L.; Castaneda, E.; Kroeger, K. D.; Gonneea, M. E.; O'Keefe Suttles, J.; Megonigal, P.; Troxler, T.; Schile, L. M.; Davis, M.; Woo, I.

2016-12-01

According to 2013 IPCC Wetlands Supplement guidelines, tidal marsh Tier 2 or Tier 3 accounting must include aboveground biomass carbon stock changes. To support this need, we are using free satellite and aerial imagery to develop a national scale, consistent remote sensing-based methodology for quantifying tidal marsh aboveground biomass. We are determining the extent to which additional satellite data will increase the accuracy of this "blue carbon" accounting. Working in 6 U.S. estuaries (Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA), we built a tidal marsh biomass dataset (n=2404). Landsat reflectance data were matched spatially and temporally with field plots using Google Earth Engine. We quantified percent cover of green vegetation, non-vegetation, and open water in Landsat pixels using segmentation of 1m National Agriculture Imagery Program aerial imagery. Sentinel-1A C-band backscatter data were used in Chesapeake, Mississippi Delta and Puget Sound. We tested multiple Landsat vegetation indices and Sentinel backscatter metrics in 30m scale biomass linear regression models by region. Scaling biomass by fraction green vegetation significantly improved biomass estimation (e.g. Cape Cod: R2 = 0.06 vs. R2 = 0.60, n=28). The best vegetation indices differed by region, though indices based on the shortwave infrared-1 and red bands were most predictive in the Everglades and the Mississippi Delta, while the soil adjusted vegetation index was most predictive in Puget Sound and Chesapeake. Backscatter metrics significantly improved model predictions over vegetation indices alone; consistently across regions, the most significant metric was the range in backscatter values within the green vegetation segment of the Landsat pixel (e.g. Mississippi Delta: R2 = 0.47 vs. R2 = 0.59, n=15). Results support using remote sensing of biomass stock change to estimate greenhouse gas emission factors in tidal wetlands.
Design and Performance of a Multiwavelength Airborne Polarimetric Lidar for Vegetation Remote Sensing

NASA Astrophysics Data System (ADS)

Tan, Songxin; Narayanan, Ram M.

2004-04-01

The University of Nebraska has developed a multiwavelength airborne polarimetric lidar (MAPL) system to support its Airborne Remote Sensing Program for vegetation remote sensing. The MAPL design and instrumentation are described in detail. Characteristics of the MAPL system include lidar waveform capture and polarimetric measurement capabilities, which provide enhanced opportunities for vegetation remote sensing compared with current sensors. Field tests were conducted to calibrate the range measurement. Polarimetric calibration of the system is also discussed. Backscattered polarimetric returns, as well as the cross-polarization ratios, were obtained from a small forested area to validate the system's ability for vegetation canopy detection. The system has been packaged to fly abroad a Piper Saratoga aircraft for airborne vegetation remote sensing applications.
Plot-scale evidence of tundra vegetation change and links to recent summer warming

Treesearch

Sarah C. Elmendorf; Gregory H.R. Henry; Robert D. Hollister; Robert G. Bjork; Noemie Boulanger-Lapointe; Elisabeth J. Cooper; Johannes H.C. Cornelissen; Thomas A. Day; Ellen Dorrepaal; Tatiana G. Elumeeva; Mike Gill; William A. Gould; John Harte; David S. Hik; Annika Hofgaard; David R. Johnson; Jill F. Johnstone; Ingijorg Svala Jonsdottir; Janet C. Jorgenson; Kari Klanderud; Julia A. Klein; Saewan Koh; Gaku Kudo; Mark Lara; Esther Levesque; Borgthor Magnusson; Jeremy L. May; Joel A. Mercado; Anders Michelsen; Ulf Molau; Isla H. Myers-Smith; Steven F. Oberbauer; Vladimir G. Onipchenko; Christian Rixen; Niels Martin Schmidt; Gaius R. Shaver; Marko J. Spasojevic; Pora Ellen Porhallsdottir; Anne Tolvanen; Tiffany Troxler; Craig E. Tweedie; Sandra Villareal; Carl-Henrik Wahren; Xanthe Walker; Patrick J. Webber; Jeffrey M. Welker; Sonja Wipf

2012-01-01

Temperature is increasing at unprecedented rates across most of the tundra biome1. Remote-sensing data indicate that contemporary climate warming has already resulted in increased productivity over much of the Arctic2,3, but plot-based evidence for vegetation transformation is not widespread. We analysed change in tundra vegetation surveyed between 1980 and 2010 in 158...
Quantifying biological integrity of California sage scrub communities using plant life-form cover.

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

Hamada, Y.; Stow, D. A.; Franklin, J.

2010-01-01

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems supports a large number of rare, threatened, and endangered species, and is critically degraded and endangered. Monitoring ecological variables that provide information about community integrity is vital to conserving these biologically diverse communities. Fractional cover of true shrub, subshrub, herbaceous vegetation, and bare ground should fill information gaps between generalized vegetation type maps and detailed field-based plot measurements of species composition and provide an effective means for quantifying CSS community integrity. Remote sensing is the only tool available for estimating spatially comprehensive fractional cover over large extent, and fractionalmore » cover of plant life-form types is one of the measures of vegetation state that is most amenable to remote sensing. The use of remote sensing does not eliminate the need for either field surveying or vegetation type mapping; rather it will likely require a combination of approaches to reliably estimate life-form cover and to provide comprehensive information for communities. According to our review and synthesis, life-form fractional cover has strong potential for providing ecologically meaningful intermediate-scale information, which is unattainable from vegetation type maps and species-level field measurements. Thus, we strongly recommend incorporating fractional cover of true shrub, subshrub, herb, and bare ground in CSS community monitoring methods. Estimating life-form cover at a 25 m x 25 m spatial scale using remote sensing would be an appropriate approach for initial implementation. Investigation of remote sensing techniques and an appropriate spatial scale; collaboration of resource managers, biologists, and remote sensing specialists, and refinement of protocols are essential for integrating life-form fractional cover mapping into strategies for sustainable long-term CSS community management.« less
FLUXNET to MODIS: Connecting the dots to capture heterogenious biosphere metabolism

NASA Astrophysics Data System (ADS)

Woods, K. D.; Schwalm, C.; Huntzinger, D. N.; Massey, R.; Poulter, B.; Kolb, T.

2015-12-01

Eddy co-variance flux towers provide our most widely distributed network of direct observations for land-atmosphere carbon exchange. Carbon flux sensitivity analysis is a method that uses in situ networks to understand how ecosystems respond to changes in climatic variables. Flux towers concurrently observe key ecosystem metabolic processes (e..g. gross primary productivity) and micrometeorological variation, but only over small footprints. Remotely sensed vegetation indices from MODIS offer continuous observations of the vegetated land surface, but are less direct, as they are based on light use efficiency algorithms, and not on the ground observations. The marriage of these two data products offers an opportunity to validate remotely sensed indices with in situ observations and translate information derived from tower sites to globally gridded products. Here we provide correlations between Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and MODIS gross primary production with FLUXNET derived estimates of gross primary production, respiration and net ecosystem exchange. We demonstrate remotely sensed vegetation products which have been transformed to gridded estimates of terrestrial biosphere metabolism on a regional-to-global scale. We demonstrate anomalies in gross primary production, respiration, and net ecosystem exchange as predicted by both MODIS-carbon flux sensitivities and meteorological driver-carbon flux sensitivities. We apply these sensitivities to recent extreme climatic events and demonstrate both our ability to capture changes in biosphere metabolism, and differences in the calculation of carbon flux anomalies based on method. The quantification of co-variation in these two methods of observation is important as it informs both how remotely sensed vegetation indices are correlated with on the ground tower observations, and with what certainty we can expand these observations and relationships.
A Study on Spectral Signature Analysis of Wetland Vegetation Based on Ground Imaging Spectrum Data

NASA Astrophysics Data System (ADS)

Ling, Chengxing; Liu, Hua; Ju, Hongbo; Zhang, Huaiqing; You, Jia; Li, Weina

2017-10-01

The objective of this study was to verify the application of imaging spectrometer in wetland vegetation remote sensing monitoring, based on analysis of wetland vegetation spectral features. Spectral information of Carex vegetation spectral data under different water environment was collected bySOC710VP and ASD FieldSpec 3; Meanwhile, the chlorophyll contents of wheat leaves were tested in the lab. A total 9 typical vegetation indices were calculated by using two instruments’ data which were spectral values from 400nm to 1000 nm. Then features between the same vegetation indices and soil water contents for two applications were analyzed and compared. The results showed that there were same spectrum curve trends of Carex vegetation (soil moisture content of 51%, 32%, 14% and three regional comparative analysis)reflectance between SOC710VP and ASD FieldSpec 3, including the two reflectance peak of 550nm and 730 nm, two reflectance valley of 690 nm and 970nm, and continuous near infrared reflectance platform. However, The two also have a very clear distinction: (1) The reflection spectra of SOC710VP leaves of Carex Carex leaf spectra in the three soil moisture environment values are greater than ASD FieldSpec 3 collected value; (2) The SOC710VP reflectivity curve does not have the smooth curve of the original spectrum measured by the ASD FieldSpec 3, the amplitude of fluctuation is bigger, and it is more obvious in the near infrared band. It is concluded that SOC710VP spectral data are reliable, with the image features, spectral curve features reliable. It has great potential in the research of hyperspectral remote sensing technology in the development of wetland near earth, remote sensing monitoring of wetland resources.
Tasseled cap transformation for HJ multispectral remote sensing data

NASA Astrophysics Data System (ADS)

Han, Ling; Han, Xiaoyong

2015-12-01

The tasseled cap transformation of remote sensing data has been widely used in environment, agriculture, forest and ecology. Tasseled cap transformation coefficients matrix of HJ multi-spectrum data has been established through Givens rotation matrix to rotate principal component transform vector to whiteness, greenness and blueness direction of ground object basing on 24 scenes year-round HJ multispectral remote sensing data. The whiteness component enhances the brightness difference of ground object, and the greenness component preserves more detailed information of vegetation change while enhances the vegetation characteristic, and the blueness component significantly enhances factory with blue plastic house roof around the town and also can enhance brightness of water. Tasseled cap transformation coefficients matrix of HJ will enhance the application effect of HJ multispectral remote sensing data in their application fields.

Remote sensing techniques for conservation and management of natural vegetation ecosystems

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Verdesio, J. J.; Dossantos, J. R.

1981-01-01

The importance of using remote sensing techniques, in the visible and near-infrared ranges, for mapping, inventory, conservation and management of natural ecosystems is discussed. Some examples realized in Brazil or other countries are given to evaluate the products from orbital platform (MSS and RBV imagery of LANDSAT) and aerial level (photography) for ecosystems study. The maximum quantitative and qualitative information which can be obtained from each sensor, at different level, are discussed. Based on the developed experiments it is concluded that the remote sensing technique is a useful tool in mapping vegetation units, estimating biomass, forecasting and evaluation of fire damage, disease detection, deforestation mapping and change detection in land-use. In addition, remote sensing techniques can be used in controling implantation and planning natural/artificial regeneration.
An intercomparison of three remote sensing-based energy balance models using large aperture scintillometer measurements over a wheat-corn production region

USDA-ARS?s Scientific Manuscript database

This paper compares three remote sensing-based models for estimating evapotranspiration (ET), namely the Surface Energy Balance System (SEBS), the Two-Source Energy Balance (TSEB) model, and the surface Temperature-Vegetation index Triangle (TVT). The models used as input MODIS/TERRA products and gr...
Study on the Feasibility of RGB Substitute CIR for Automatic Removal Vegetation Occlusion Based on Ground Close-Range Building Images

NASA Astrophysics Data System (ADS)

Li, C.; Li, F.; Liu, Y.; Li, X.; Liu, P.; Xiao, B.

2012-07-01

Building 3D reconstruction based on ground remote sensing data (image, video and lidar) inevitably faces the problem that buildings are always occluded by vegetation, so how to automatically remove and repair vegetation occlusion is a very important preprocessing work for image understanding, compute vision and digital photogrammetry. In the traditional multispectral remote sensing which is achieved by aeronautics and space platforms, the Red and Near-infrared (NIR) bands, such as NDVI (Normalized Difference Vegetation Index), are useful to distinguish vegetation and clouds, amongst other targets. However, especially in the ground platform, CIR (Color Infra Red) is little utilized by compute vision and digital photogrammetry which usually only take true color RBG into account. Therefore whether CIR is necessary for vegetation segmentation or not has significance in that most of close-range cameras don't contain such NIR band. Moreover, the CIE L*a*b color space, which transform from RGB, seems not of much interest by photogrammetrists despite its powerfulness in image classification and analysis. So, CIE (L, a, b) feature and support vector machine (SVM) is suggested for vegetation segmentation to substitute for CIR. Finally, experimental results of visual effect and automation are given. The conclusion is that it's feasible to remove and segment vegetation occlusion without NIR band. This work should pave the way for texture reconstruction and repair for future 3D reconstruction.
VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

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

Serrato, M.; Jungho, I.; Jensen, J.

2012-01-17

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using threemore » different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.« less
Carbon stock and carbon turnover in boreal and temperate forests - Integration of remote sensing data and global vegetation models

NASA Astrophysics Data System (ADS)

Thurner, Martin; Beer, Christian; Carvalhais, Nuno; Forkel, Matthias; Tito Rademacher, Tim; Santoro, Maurizio; Tum, Markus; Schmullius, Christiane

2016-04-01

Long-term vegetation dynamics are one of the key uncertainties of the carbon cycle. There are large differences in simulated vegetation carbon stocks and fluxes including productivity, respiration and carbon turnover between global vegetation models. Especially the implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current models and their importance at global scale is highly uncertain. These shortcomings have been due to the lack of spatially extensive information on vegetation carbon stocks, which cannot be provided by inventory data alone. Instead, we recently have been able to estimate northern boreal and temperate forest carbon stocks based on radar remote sensing data. Our spatially explicit product (0.01° resolution) shows strong agreement to inventory-based estimates at a regional scale and allows for a spatial evaluation of carbon stocks and dynamics simulated by global vegetation models. By combining this state-of-the-art biomass product and NPP datasets originating from remote sensing, we are able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests along spatial gradients. We observe an increasing turnover rate with colder winter temperatures and longer winters in boreal forests, suggesting frost damage and the trade-off between frost adaptation and growth being important mortality processes in this ecosystem. In contrast, turnover rate increases with climatic conditions favouring drought and insect outbreaks in temperate forests. Investigated global vegetation models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce observation-based spatial climate - turnover rate relationships only to a limited extent. While most of the models compare relatively well in terms of NPP, simulated vegetation carbon stocks are severely biased compared to our biomass dataset. Current limitations lead to considerable uncertainties in the estimated vegetation carbon turnover, contributing substantially to the forest feedback to climate change. Our results are the basis for improving mortality concepts in models and estimating their impact on the land carbon balance.
Tracking diurnal changes of photosynthesis and evapotranspiration using fluorescence, gas exchange and hyperspectral remote sensing measurements

NASA Astrophysics Data System (ADS)

Wang, S.; Zhang, L.; Guanter, L.; Huang, C.

2017-12-01

Photosynthesis and evapotranspiration (ET) are the two most important activities of vegetation and make a great contribution to carbon, water and energy exchanges. Remote sensing provides opportunities for monitoring these processes across time and space. This study focuses on tracking diurnal changes of photosynthesis and evapotranspiration over soybean using multiple measurement techniques. Diurnal changes of both remote sensing-based indicators, including active and passive chlorophyll fluorescence and biophysical-related parameters, including photosynthesis rate (photo) and leaf stomatal conductance (cond), were observed. Results showed that both leaf-level steady-state fluorescence (Fs) and canopy-level solar-induced chlorophyll fluorescence were linearly correlated to photosynthetically active radiation (PAR) during the daytime. A double-peak diurnal change curve was observed for leaf-level photo and cond but not for Fs or SIF. Photo and cond showed a strong nonlinear (second-order) correlation, indicating that photosynthesis, which might be remotely sensed by SIF, has the opportunity to track short-term changes of ET. Results presented in this report will be helpful for better understanding the relationship between remote-sensing-based indices and vegetation's biophysical processes.
Hyperspectral Remote Sensing of Vegetation:Knowledge Gain and Knowledge Gap after 40 years of research

NASA Astrophysics Data System (ADS)

Thenkabail, P. S.; Huete, A. R.

2012-12-01

This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher: Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advancements in processing large volumes of hyperspectral data have generated tremendous interest in expanding the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband spectral data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) the ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reduced uncertainty in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) the ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing wavebands and indices with greater sensitivity for analyzing vegetation characteristics. Current state of knowledge on hyperspectral narrowbands (HNBs) for agricultural and vegetation studies inferred from the Book entitled hyperspectral remote sensing of vegetation by Thenkabail et al., 2011. Six study areas of the World for which we have extensive data from field spectroradiometers for 8 major world crops (wheat, corn, rice, barley, soybeans, pulses, and cotton). Approx. 10,500 such data points will be used in crop modeling and in building spectral libraries.
Evapotranspiration from combined reflected solar and emitted terrestrial radiation - Preliminary FIFE results from AVHRR data

NASA Technical Reports Server (NTRS)

Goward, S. N.; Hope, A. S.

1989-01-01

The relation between remotely sensed spectral vegetation indices and thermal IR measurements is studied. Land surface evapotranspiration is evaluated based on this relationship. Analysis of the AVHRR data, obtained in Kansas in 1987, reveal a strong correlation between the spectral vegetation indices and surface temperature and this relation covaries with surface moisture conditions. It is noted that the relation between remotely sensed measurements of canopy green foliage and surface temperature is useful for examining variations in the interface thermal inertia and energy balance Bowen ratio.
[Extracting black soil border in Heilongjiang province based on spectral angle match method].

PubMed

Zhang, Xin-Le; Zhang, Shu-Wen; Li, Ying; Liu, Huan-Jun

2009-04-01

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.
How does spatial and temporal resolution of vegetation index impact crop yield estimation?

USDA-ARS?s Scientific Manuscript database

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing data have long been used in crop yield estimation for decades. The process-based approach uses light use efficiency model to estimate crop yield. Vegetation index (VI) ...
A multi-characteristic based algorithm for classifying vegetation in a plateau area: Qinghai Lake watershed, northwestern China

NASA Astrophysics Data System (ADS)

Ma, Weiwei; Gong, Cailan; Hu, Yong; Li, Long; Meng, Peng

2015-10-01

Remote sensing technology has been broadly recognized for its convenience and efficiency in mapping vegetation, particularly in high-altitude and inaccessible areas where there are lack of in-situ observations. In this study, Landsat Thematic Mapper (TM) images and Chinese environmental mitigation satellite CCD sensor (HJ-1 CCD) images, both of which are at 30m spatial resolution were employed for identifying and monitoring of vegetation types in a area of Western China——Qinghai Lake Watershed(QHLW). A decision classification tree (DCT) algorithm using multi-characteristic including seasonal TM/HJ-1 CCD time series data combined with digital elevation models (DEMs) dataset, and a supervised maximum likelihood classification (MLC) algorithm with single-data TM image were applied vegetation classification. Accuracy of the two algorithms was assessed using field observation data. Based on produced vegetation classification maps, it was found that the DCT using multi-season data and geomorphologic parameters was superior to the MLC algorithm using single-data image, improving the overall accuracy by 11.86% at second class level and significantly reducing the "salt and pepper" noise. The DCT algorithm applied to TM /HJ-1 CCD time series data geomorphologic parameters appeared as a valuable and reliable tool for monitoring vegetation at first class level (5 vegetation classes) and second class level(8 vegetation subclasses). The DCT algorithm using multi-characteristic might provide a theoretical basis and general approach to automatic extraction of vegetation types from remote sensing imagery over plateau areas.
Large-Scale Variation in Forest Carbon Turnover Rate and its Relation to Climate - Remote Sensing vs. Global Vegetation Models

NASA Astrophysics Data System (ADS)

Carvalhais, N.; Thurner, M.; Beer, C.; Forkel, M.; Rademacher, T. T.; Santoro, M.; Tum, M.; Schmullius, C.

2015-12-01

While vegetation productivity is known to be strongly correlated to climate, there is a need for an improved understanding of the underlying processes of vegetation carbon turnover and their importance at a global scale. This shortcoming has been due to the lack of spatially extensive information on vegetation carbon stocks, which we recently have been able to overcome by a biomass dataset covering northern boreal and temperate forests originating from radar remote sensing. Based on state-of-the-art products on biomass and NPP, we are for the first time able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests. The implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current global vegetation models. In contrast to our observation-based findings, investigated models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce spatial climate - turnover rate relationships only to a limited extent. While most of the models compare relatively well to observation-based NPP, simulated vegetation carbon stocks are severely biased compared to our biomass dataset. Current limitations lead to considerable uncertainties in the estimated vegetation carbon turnover, contributing substantially to the forest feedback to climate change. Our results are the basis for improving mortality concepts in global vegetation models and estimating their impact on the land carbon balance.
Soil moisture status estimation over Three Gorges area with Landsat TM data based on temperature vegetation dryness index

NASA Astrophysics Data System (ADS)

Xu, Lina; Niu, Ruiqing; Li, Jiong; Dong, Yanfang

2011-12-01

Soil moisture is the important indicator of climate, hydrology, ecology, agriculture and other parameters of the land surface and atmospheric interface. Soil moisture plays an important role on the water and energy exchange at the land surface/atmosphere interface. Remote sensing can provide information on large area quickly and easily, so it is significant to do research on how to monitor soil moisture by remote sensing. This paper presents a method to assess soil moisture status using Landsat TM data over Three Gorges area in China based on TVDI. The potential of Temperature- Vegetation Dryness Index (TVDI) from Landsat TM data in assessing soil moisture was investigated in this region. After retrieving land surface temperature and vegetation index a TVDI model based on the features of Ts-NDVI space is established. And finally, soil moisture status is estimated according to TVDI. It shows that TVDI has the advantages of stability and high accuracy to estimating the soil moisture status.
Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Bolten, John; Crow, Wade

2012-01-01

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.
Monitoring crop coefficient of orange orchards using energy balance and the remote sensed NDVI

NASA Astrophysics Data System (ADS)

Consoli, Simona; Cirelli, Giuseppe Luigi; Toscano, Attilio

2006-09-01

The structure of vegetation is paramount in regulating the exchange of mass and energy across the biosphereatmosphere interface. In particular, changes in vegetation density affected the partitioning of incoming solar energy into sensible and latent heat fluxes that may result in persistent drought through reductions in agricultural productivity and in the water resources availability. Limited research with citrus orchards has shown improvements to irrigation scheduling due to better water-use estimation and more appropriate timing of irrigation when crop coefficient (Kc) estimate, derived from remotely sensed multispectral vegetation indices (VIs), are incorporated into irrigation-scheduling algorithms. The purpose of this article is the application of an empirical reflectance-based model for the estimation of Kc and evapotranspiration fluxes (ET) using ground observations on climatic data and high-resolution VIs from ASTER TERRA satellite imagery. The remote sensed Kc data were used in developing the relationship with the normalized difference vegetation index (NDVI) for orange orchards during summer periods. Validation of remote sensed data on ET, Kc and vegetation features was deal through ground data observations and the resolution of the energy balance to derive latent heat flux density (λE), using measures of net radiation (Rn) and soil heat flux density (G) and estimate of sensible heat flux density (H) from high frequency temperature measurements (Surface Renewal technique). The chosen case study is that of an irrigation area covered by orange orchards located in Eastern Sicily, Italy) during the irrigation seasons 2005 and 2006.
Effectiveness of Vegetation Index Transformation for Land Use Identifying and Mapping in the Area of Oil palm Plantation based on SPOT-6 Imagery (Case Study: PT.Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu)

NASA Astrophysics Data System (ADS)

Setyowati, H. A.; S, S. H. Murti B.; Sukentyas, E. S.

2016-11-01

The reflection of land surface, atmosphere and vegetation conditions affect the reflectance value of the object is recorded on remote sensing image so that it can affect the outcome of information extraction from remote sensing imagery one multispectral classification. This study aims to assess the ability of the transformation of generic vegetation index (Wide Dynamic Range Vegetation Index), the vegetation index transformation that is capable reducing the influence of the atmosphere (Atmospherically Resistant Vegetation Index), and the transformation of vegetation index that is capable of reducing the influence of the background soil (Second Modified Soil Adjusted Vegetation Index) for the identification and mapping of land use in the oil palm plantation area based on SPOT-6 archived on June 13, 2013 from LAPAN. The study area selected oil palm plantations PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu, Riau Province. The method is using the transformation of the vegetation index ARVI, MSAVI2, and WDRVI. Sample selection method used was stratified random sampling. The test method used mapping accuracy of the confusion matrix. The results showed that the best transformation of the vegetation index for the identification and mapping of land use in the plantation area is ARVI transformation with a total of accuracy is 96%. Accuracy of mapping land use settlements 100%, replanting 82.35%, 81.25% young oil palm, old oil palm 99.46%, 100% bush, body of water 100%, and 100% bare-soil.
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
Effects of corn stalk orientation and water content on passive microwave sensing of soil moisture

NASA Technical Reports Server (NTRS)

Oneill, P. E.; Blanchard, B. J.; Wang, J. R.; Gould, W. I.; Jackson, T. J.

1984-01-01

A field experiment was conducted utilizing artificial arrangements of plant components during the summer of 1982 to examine the effects of corn canopy structure and plant water content on microwave emission. Truck-mounted microwave radiometers at C (5 GHz) and L (1.4 GHz) band sensed vertically and horizontally polarized radiation concurrent with ground observations of soil moisture and vegetation parameters. Results indicate that the orientation of cut stalks and the distribution of their dielectric properties through the canopy layer can influence the microwave emission measured from a vegetation/soil scene. The magnitude of this effect varies with polarization and frequency and with the amount of water in the plant, disappearing at low levels of vegetation water content. Although many of the canopy structures and orientations studied in this experiment are somewhat artificial, they serve to improve understanding of microwave energy interactions within a vegetation canopy and to aid in the development of appropriate physically based vegetation models.
A National Crop Progress Monitoring System Based on NASA Earth Science Results

NASA Astrophysics Data System (ADS)

Di, L.; Yu, G.; Zhang, B.; Deng, M.; Yang, Z.

2011-12-01

Crop progress is an important piece of information for food security and agricultural commodities. Timely monitoring and reporting are mandated for the operation of agricultural statistical agencies. Traditionally, the weekly reporting issued by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) is based on reports from the knowledgeable state and county agricultural officials and farmers. The results are spatially coarse and subjective. In this project, a remote-sensing-supported crop progress monitoring system is being developed intensively using the data and derived products from NASA Earth Observing satellites. Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 product - MOD09 (Surface Reflectance) is used for deriving daily normalized vegetation index (NDVI), vegetation condition index (VCI), and mean vegetation condition index (MVCI). Ratio change to previous year and multiple year mean can be also produced on demand. The time-series vegetation condition indices are further combined with the NASS' remote-sensing-derived Cropland Data Layer (CDL) to estimate crop condition and progress crop by crop. To facilitate the operational requirement and increase the accessibility of data and products by different users, each component of the system has being developed and implemented following open specifications under the Web Service reference model of Open Geospatial Consortium Inc. Sensor observations and data are accessed through Web Coverage Service (WCS), Web Feature Service (WFS), or Sensor Observation Service (SOS) if available. Products are also served through such open-specification-compliant services. For rendering and presentation, Web Map Service (WMS) is used. A Web-service based system is set up and deployed at dss.csiss.gmu.edu/NDVIDownload. Further development will adopt crop growth models, feed the models with remotely sensed precipitation and soil moisture information, and incorporate the model results with vegetation-index time series for crop progress stage estimation.
Remotely sensed vegetation moisture as explanatory variable of Lyme borreliosis incidence

NASA Astrophysics Data System (ADS)

Barrios, J. M.; Verstraeten, W. W.; Maes, P.; Clement, J.; Aerts, J. M.; Farifteh, J.; Lagrou, K.; Van Ranst, M.; Coppin, P.

2012-08-01

The strong correlation between environmental conditions and abundance and spatial spread of the tick Ixodes ricinus is widely documented. I. ricinus is in Europe the main vector of the bacterium Borrelia burgdorferi, the pathogen causing Lyme borreliosis (LB). Humidity in vegetated systems is a major factor in tick ecology and its effects might translate into disease incidence in humans. Time series of two remotely sensed indices with sensitivity to vegetation greenness and moisture were tested as explanatory variables of LB incidence. Wavelet-based multiresolution analysis allowed the examination of these signals at different temporal scales in study sites in Belgium, where increases in LB incidence were reported in recent years. The analysis showed the potential of the tested indices for disease monitoring, the usefulness of analyzing the signal in different time frames and the importance of local characteristics of the study area for the selection of the vegetation index.

Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review

USDA-ARS?s Scientific Manuscript database

New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of...
Accounting for non-photosynthetic vegetation in remote-sensing-based estimates of carbon flux in wetlands

USGS Publications Warehouse

Schile, Lisa M.; Byrd, Kristin B.; Windham-Myers, Lisamarie; Kelly, Maggi

2013-01-01

Monitoring productivity in coastal wetlands is important due to their high carbon sequestration rates and potential role in climate change mitigation. We tested agricultural- and forest-based methods for estimating the fraction of absorbed photosynthetically active radiation (f APAR), a key parameter for modelling gross primary productivity (GPP), in a restored, managed wetland with a dense litter layer of non-photosynthetic vegetation, and we compared the difference in canopy light transmission between a tidally influenced wetland and the managed wetland. The presence of litter reduced correlations between spectral vegetation indices and f APAR. In the managed wetland, a two-band vegetation index incorporating simulated World View-2 or Hyperion green and near-infrared bands, collected with a field spectroradiometer, significantly correlated with f APAR only when measured above the litter layer, not at the ground where measurements typically occur. Measures of GPP in these systems are difficult to capture via remote sensing, and require an investment of sampling effort, practical methods for measuring green leaf area and accounting for background effects of litter and water.
Desertification Assessment and Monitoring Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Z.; del Barrio, G.; Li, X.

2016-08-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired during the last two years could be summarized as follows:(1) Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) were estimated in Otindag sandy land by comparison of the pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, Automated Monte Carlo Unmixing Analysis, AutoMCU) methods, based on GF-1 data and field measured spectral library.(2) Based on GF-1 data, SMA was applied to solve vegetation cover and transitional sandy land detection in Zhenglan Banner, Inner Mongolia, China.(3) By defined a new indictor, Moisture-responded NPP(MNPP), a new method for identification of degraded lands was put forward, and the land degradation in Xinlin Gol league, Inner Mongolia Autonomous Region, China was assessed preliminarily. (4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China (PEDC) was assessed.
Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Friedl, Mark A.; Schaaf, Crystal B.

2006-12-01

In the last two decades the availability of global remote sensing data sets has provided a new means of studying global patterns and dynamics in vegetation. The vast majority of previous work in this domain has used data from the Advanced Very High Resolution Radiometer, which until recently was the primary source of global land remote sensing data. In recent years, however, a number of new remote sensing data sources have become available that have significantly improved the capability of remote sensing to monitor global ecosystem dynamics. In this paper, we describe recent results using data from NASA's Moderate Resolution Imaging Spectroradiometer to study global vegetation phenology. Using a novel new method based on fitting piecewise logistic models to time series data from MODIS, key transition dates in the annual cycle(s) of vegetation growth can be estimated in an ecologically realistic fashion. Using this method we have produced global maps of seven phenological metrics at 1-km spatial resolution for all ecosystems exhibiting identifiable annual phenologies. These metrics include the date of year for (1) the onset of greenness increase (greenup), (2) the onset of greenness maximum (maturity), (3) the onset of greenness decrease (senescence), and (4) the onset of greenness minimum (dormancy). The three remaining metrics are the growing season minimum, maximum, and summation of the enhanced vegetation index derived from MODIS. Comparison of vegetation phenology retrieved from MODIS with in situ measurements shows that these metrics provide realistic estimates of the four transition dates identified above. More generally, the spatial distribution of phenological metrics estimated from MODIS data is qualitatively realistic, and exhibits strong correspondence with temperature patterns in mid- and high-latitude climates, with rainfall seasonality in seasonally dry climates, and with cropping patterns in agricultural areas.
A comparison between active and passive sensing of soil moisture from vegetated terrains

NASA Technical Reports Server (NTRS)

Fung, A. K.; Eom, H. J.

1985-01-01

A comparison between active and passive sensing of soil moisture over vegetated areas is studied via scattering models. In active sensing three contributing terms to radar backscattering can be identified: (1) the ground surface scatter term; (2) the volume scatter term representing scattering from the vegetation layer; and (3) the surface volume scatter term accounting for scattering from both surface and volume. In emission three sources of contribution can also be identified: (1) surface emission; (2) upward volume emission from the vegetation layer; and (3) downward volume emission scattered upward by the ground surface. As ground moisture increases, terms (1) and (3) increase due to increase in permittivity in the active case. However, in passive sensing, term (1) decreases but term (3) increases for the same reason. This self compensating effect produces a loss in sensitivity to change in ground moisture. Furthermore, emission from vegetation may be larger than that from the ground. Hence, the presence of vegetation layer causes a much greater loss of sensitivity to passive than active sensing of soil moisture.
A comparison between active and passive sensing of soil moisture from vegetated terrains

NASA Technical Reports Server (NTRS)

Fung, A. K.; Eom, H. J.

1984-01-01

A comparison between active and passive sensing of soil moisture over vegetated areas is studied via scattering models. In active sensing three contributing terms to radar backscattering can be identified: (1) the ground surface scatter term; (2) the volume scatter term representing scattering from the vegetation layer; and (3) the surface volume scatter term accounting for scattering from both surface and volume. In emission three sources of contribution can also be identified: (1) surface emission; (2) upward volume emission from the vegetation layer; and (3) downward volume emission scattered upward by the ground surface. As ground moisture increases, terms (1) and (3) increase due to increase in permittivity in the active case. However, in passive sensing, term (1) decreases but term (3) increases for the same reason. This self conpensating effect produces a loss in sensitivity to change in ground moisture. Furthermore, emission from vegetation may be larger than that from the ground. Hence, the presence of vegetation layer causes a much greater loss of sensitivity to passive than active sensing of soil moisture.
Hyperspectral remote sensing tools for quantifying plant litter and invasive species in arid ecosystems

USGS Publications Warehouse

Nagler, Pamela L.; Sridhar, B.B. Maruthi; Olsson, Aaryn Dyami; Glenn, Edward P.; van Leeuwen, Willem J.D.; Thenkabail, Prasad S.; Huete, Alfredo; Lyon, John G.

2012-01-01

Green vegetation can be distinguished using visible and infrared multi-band and hyperspectral remote sensing methods. The problem has been in identifying and distinguishing the non-photosynthetically active radiation (PAR) landscape components, such as litter and soils, and from green vegetation. Additionally, distinguishing different species of green vegetation is challenging using the relatively few bands available on most satellite sensors. This chapter focuses on hyperspectral remote sensing characteristics that aim to distinguish between green vegetation, soil, and litter (or senescent vegetation). Quantifying litter by remote sensing methods is important in constructing carbon budgets of natural and agricultural ecosystems. Distinguishing between plant types is important in tracking the spread of invasive species. Green leaves of different species usually have similar spectra, making it difficult to distinguish between species. However, in this chapter we show that phenological differences between species can be used to detect some invasive species by their distinct patterns of greening and dormancy over an annual cycle based on hyperspectral data. Both applications require methods to quantify the non-green cellulosic fractions of plant tissues by remote sensing even in the presence of soil and green plant cover. We explore these methods and offer three case studies. The first concerns distinguishing surface litter from soil using the Cellulose Absorption Index (CAI), as applied to no-till farming practices where plant litter is left on the soil after harvest. The second involves using different band combinations to distinguish invasive saltcedar from agricultural and native riparian plants on the Lower Colorado River. The third illustrates the use of the CAI and NDVI in time-series analyses to distinguish between invasive buffelgrass and native plants in a desert environment in Arizona. Together the results show how hyperspectral imagery can be applied to solve problems that are not amendable to solution by the simple band combinations normally used in remote sensing.
Effect of metal stress on the thermal infrared emission of soybeans: A greenhouse experiment - Possible utility in remote sensing

NASA Technical Reports Server (NTRS)

Suresh, R.; Schwaller, M. R.; Foy, C. D.; Weidner, J. R.; Schnetzler, C. S.

1989-01-01

Manganese-sensitive forest and manganese-tolerant lee soybean cultivars were subjected to differential manganese stress in loring soil in a greenhouse experiment. Leaf temperature measurements were made using thermistors for forest and lee. Manganese-stressed plants had higher leaf temperatures than control plants in both forest and lee. Results of this experiment have potential applications in metal stress detection using remote sensing thermal infrared data over large areas of vegetation. This technique can be useful in reconnaissance mineral exploration in densely-vegetated regions where conventional ground-based methods are of little help.
Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa.

PubMed

Ardö, Jonas

2015-12-01

Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types. Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship. Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.
Three examples of applied remote sensing of vegetation

NASA Technical Reports Server (NTRS)

Rouse, J. W., Jr.; Benton, A. R., Jr.; Toler, R. W.; Haas, R. H.

1975-01-01

Cause studies in which remote sensing techniques were adapted to assist in the solution of particular problem situations in Texas involving vegetation are described. In each case, the final sensing technique developed for operational use by the concerned organizations employed photographic sensors which were optimized through studies of the spectral reflectance characteristics of the vegetation species and background conditions unique to the problem being considered. The three examples described are: (1) Assisting Aquatic Plant Monitoring and Control; (2) Improving Vegetation Utilization in Urban Planning; and (3) Enforcing the Quarantine of Diseased Crops.
Northern Everglades, Florida, satellite image map

USGS Publications Warehouse

Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.
Research on Method of Interactive Segmentation Based on Remote Sensing Images

NASA Astrophysics Data System (ADS)

Yang, Y.; Li, H.; Han, Y.; Yu, F.

2017-09-01

In this paper, we aim to solve the object extraction problem in remote sensing images using interactive segmentation tools. Firstly, an overview of the interactive segmentation algorithm is proposed. Then, our detailed implementation of intelligent scissors and GrabCut for remote sensing images is described. Finally, several experiments on different typical features (water area, vegetation) in remote sensing images are performed respectively. Compared with the manual result, it indicates that our tools maintain good feature boundaries and show good performance.
Modelisation de l'architecture des forets pour ameliorer la teledetection des attributs forestiers

NASA Astrophysics Data System (ADS)

Cote, Jean-Francois

The quality of indirect measurements of canopy structure, from in situ and satellite remote sensing, is based on knowledge of vegetation canopy architecture. Technological advances in ground-based, airborne or satellite remote sensing can now significantly improve the effectiveness of measurement programs on forest resources. The structure of vegetation canopy describes the position, orientation, size and shape of elements of the canopy. The complexity of the canopy in forest environments greatly limits our ability to characterize forest structural attributes. Architectural models have been developed to help the interpretation of canopy structural measurements by remote sensing. Recently, the terrestrial LiDAR systems, or TLiDAR (Terrestrial Light Detection and Ranging), are used to gather information on the structure of individual trees or forest stands. The TLiDAR allows the extraction of 3D structural information under the canopy at the centimetre scale. The methodology proposed in my Ph.D. thesis is a strategy to overcome the weakness in the structural sampling of vegetation cover. The main objective of the Ph.D. is to develop an architectural model of vegetation canopy, called L-Architect (LiDAR data to vegetation Architecture), and to focus on the ability to document forest sites and to get information on canopy structure from remote sensing tools. Specifically, L-Architect reconstructs the architecture of individual conifer trees from TLiDAR data. Quantitative evaluation of L-Architect consisted to investigate (i) the structural consistency of the reconstructed trees and (ii) the radiative coherence by the inclusion of reconstructed trees in a 3D radiative transfer model. Then, a methodology was developed to quasi-automatically reconstruct the structure of individual trees from an optimization algorithm using TLiDAR data and allometric relationships. L-Architect thus provides an explicit link between the range measurements of TLiDAR and structural attributes of individual trees. L-Architect has finally been applied to model the architecture of forest canopy for better characterization of vertical and horizontal structure with airborne LiDAR data. This project provides a mean to answer requests of detailed canopy architectural data, difficult to obtain, to reproduce a variety of forest covers. Because of the importance of architectural models, L-Architect provides a significant contribution for improving the capacity of parameters' inversion in vegetation cover for optical and lidar remote sensing. Mots-cles: modelisation architecturale, lidar terrestre, couvert forestier, parametres structuraux, teledetection.
Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013

USDA-ARS?s Scientific Manuscript database

Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and funct...
Discrete return lidar-based prediction of leaf area index in two conifer forests

Treesearch

Jennifer L. R. Jensen; Karen S. Humes; Lee A. Vierling; Andrew T. Hudak

2008-01-01

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical...
Utility of an image-based canopy reflectance modeling tool for remote estimation and LAI and leaf chlorophyll content at regional scales

USDA-ARS?s Scientific Manuscript database

Radiance data recorded by remote sensors function as a unique source for monitoring the terrestrial biosphere and vegetation dynamics at a range of spatial and temporal scales. A key challenge is to relate the remote sensing signal to critical variables describing land surface vegetation canopies su...
An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi-arid region with MISR imagery

USDA-ARS?s Scientific Manuscript database

Multi-angle remote sensing has been proved useful for mapping vegetation community types in desert regions. Based on Multi-angle Imaging Spectro-Radiometer (MISR) multi-angular images, this study compares roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with th...
Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

USGS Publications Warehouse

Poitras, Travis; Villarreal, Miguel; Waller, Eric K.; Nauman, Travis; Miller, Mark E.; Duniway, Michael C.

2018-01-01

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.
Improving the prediction of African savanna vegetation variables using time series of MODIS products

NASA Astrophysics Data System (ADS)

Tsalyuk, Miriam; Kelly, Maggi; Getz, Wayne M.

2017-09-01

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.
Remote sensing of Sonoran Desert vegetation structure and phenology with ground-based LiDAR

USGS Publications Warehouse

Sankey, Joel B.; Munson, Seth M.; Webb, Robert H.; Wallace, Cynthia S.A.; Duran, Cesar M.

2015-01-01

Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics at high spatial and temporal resolution. We determined the effectiveness of LiDAR to detect intra-annual variability in vegetation structure at a long-term Sonoran Desert monitoring plot dominated by cacti, deciduous and evergreen shrubs. Monthly repeat LiDAR scans of perennial plant canopies over the course of one year had high precision. LiDAR measurements of canopy height and area were accurate with respect to total station survey measurements of individual plants. We found an increase in the number of LiDAR vegetation returns following the wet North American Monsoon season. This intra-annual variability in vegetation structure detected by LiDAR was attributable to a drought deciduous shrub Ambrosia deltoidea, whereas the evergreen shrub Larrea tridentata and cactus Opuntia engelmannii had low variability. Benefits of using LiDAR over traditional methods to census desert plants are more rapid, consistent, and cost-effective data acquisition in a high-resolution, 3-dimensional context. We conclude that repeat LiDAR measurements can be an effective method for documenting ecosystem response to desert climatology and drought over short time intervals and at detailed-local spatial scale.

Observed positive vegetation-rainfall feedbacks in the Sahel dominated by a moisture recycling mechanism

DOE PAGES

Yu, Yan; Notaro, Michael; Wang, Fuyao; ...

2017-11-30

Classic, model-based theory of land-atmosphere interactions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo mechanism has been convincingly demonstrated using observational data. Here, we present observational evidence for the region’s proposed positive vegetation-rainfall feedback on the seasonal to interannual time scale, and find that it is associated with a moisture recycling mechanism, rather than the classic albedo-based mechanism. Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late and post-monsoon periods favor enhanced evapotranspiration, precipitable water, convective activity and rainfall, indicative ofmore » amplified moisture recycling. The identified modest low-level cooling and anomalous atmospheric subsidence in response to positive vegetation greenness anomalies are counter to the responses expected through the classic vegetation-albedo feedback mechanism. The observational analysis further reveals enhanced dust emissions in response to diminished Sahel vegetation growth, potentially contributing to the positive vegetation-rainfall feedback.« less
Observed positive vegetation-rainfall feedbacks in the Sahel dominated by a moisture recycling mechanism

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

Yu, Yan; Notaro, Michael; Wang, Fuyao

Classic, model-based theory of land-atmosphere interactions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo mechanism has been convincingly demonstrated using observational data. Here, we present observational evidence for the region’s proposed positive vegetation-rainfall feedback on the seasonal to interannual time scale, and find that it is associated with a moisture recycling mechanism, rather than the classic albedo-based mechanism. Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late and post-monsoon periods favor enhanced evapotranspiration, precipitable water, convective activity and rainfall, indicative ofmore » amplified moisture recycling. The identified modest low-level cooling and anomalous atmospheric subsidence in response to positive vegetation greenness anomalies are counter to the responses expected through the classic vegetation-albedo feedback mechanism. The observational analysis further reveals enhanced dust emissions in response to diminished Sahel vegetation growth, potentially contributing to the positive vegetation-rainfall feedback.« less
Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review.

PubMed

Maes, W H; Steppe, K

2012-08-01

As evaporation of water is an energy-demanding process, increasing evapotranspiration rates decrease the surface temperature (Ts) of leaves and plants. Based on this principle, ground-based thermal remote sensing has become one of the most important methods for estimating evapotranspiration and drought stress and for irrigation. This paper reviews its application in agriculture. The review consists of four parts. First, the basics of thermal remote sensing are briefly reviewed. Second, the theoretical relation between Ts and the sensible and latent heat flux is elaborated. A modelling approach was used to evaluate the effect of weather conditions and leaf or vegetation properties on leaf and canopy temperature. Ts increases with increasing air temperature and incoming radiation and with decreasing wind speed and relative humidity. At the leaf level, the leaf angle and leaf dimension have a large influence on Ts; at the vegetation level, Ts is strongly impacted by the roughness length; hence, by canopy height and structure. In the third part, an overview of the different ground-based thermal remote sensing techniques and approaches used to estimate drought stress or evapotranspiration in agriculture is provided. Among other methods, stress time, stress degree day, crop water stress index (CWSI), and stomatal conductance index are discussed. The theoretical models are used to evaluate the performance and sensitivity of the most important methods, corroborating the literature data. In the fourth and final part, a critical view on the future and remaining challenges of ground-based thermal remote sensing is presented.
Characterizing decadal-scale vegetation and ecohydrologic change associated with the Mozambican civil war via multiple-sensor remote sensing datasets

NASA Astrophysics Data System (ADS)

Flores, A. N.; Lakshmi, V.; Al-Barakat, R.; Maksimowicz, M.

2016-12-01

Terrestrial vegetation controls the partitioning of incoming energy into latent and sensible heat fluxes and precipitation into runoff and infiltration. Humans modify terrestrial vegetation in direct and indirect ways, impacting the components of the surface water and energy balance. Although ecohydrologic impacts of land modification due to agriculture and deforestation have been studied extensively, impacts of civil conflict on regional ecohydrology have received comparatively less study. Remote sensing provides a unique opportunity to investigate potential impacts of this civil conflict on terrestrial vegetation communities and the surface water and energy balance. During the Mozambican civil war (1977-1992) many agricultural fields went fallow and large herbivore populations collapsed due to poaching. The extent of these impacts on changes in regional water and energy balance and the spatiotemporal scale of those changes, however, is largely unknown. We use remote sensing data from multiple satellite platforms to diagnose and characterize changes in terrestrial vegetation and ecohydrology in Mozambique. The Advanced very High Resolution Radiometer (AVHRR) sensor has been integral to many NOAA satellite platforms and provides long-term continuous data that can document terrestrial vegetation change during most of the Mozambican civil war period. More recently, the Tropical Rainfall Measurement Mission provides microwave-based estimates of precipitation from 1997 onward, affording the ability to explore associations between precipitation and vegetation in the post-bellum period. In this work we explore application of graph theory methods for characterizing spatial and temporal patterns in vegetation and precipitation. This work is important to advancing fundamental understanding of coupled human-environment systems through characterizing potential impacts of civil conflict (which may become more frequent and widespread with climate change) on regional ecohydrology.
A Remotely Sensed Global Terrestrial Drought Severity Index

NASA Astrophysics Data System (ADS)

Mu, Q.; Zhao, M.; Kimball, J. S.; McDowell, N. G.; Running, S. W.

2012-12-01

Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse eco-social impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. We developed a method to generate a near-real-time remotely sensed Drought Severity Index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly and annual frequencies. The new DSI integrates and exploits information from current operational satellite based terrestrial evapotranspiration (ET) and Vegetation greenness Index (NDVI) products, which are sensitive to vegetation water stress. Specifically, our approach determines the annual DSI departure from its normal (2000-2011) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). The DSI corresponded favorably (r=0.43) with the precipitation based Palmer Drought Severity Index (PDSI), while both indices captured similar wetting and drying patterns. The DSI was also correlated with satellite based vegetation net primary production (NPP) records, indicating that the combined use of these products may be useful for assessing water supply and ecosystem interactions, including drought impacts on crop yields and forest productivity. The remotely-sensed global terrestrial DSI enhances capabilities for near-real-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods.
Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

NASA Technical Reports Server (NTRS)

Kimes, Daniel S.; Nelson, Ross F.

1998-01-01

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.
Surveillance and Control of Malaria Transmission Using Remotely Sensed Meteorological and Environmental Parameters

NASA Technical Reports Server (NTRS)

Kiang, R.; Adimi, F.; Nigro, J.

2007-01-01

Meteorological and environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters can most conveniently be obtained using remote sensing. Selected provinces and districts in Thailand and Indonesia are used to illustrate how remotely sensed meteorological and environmental parameters may enhance the capabilities for malaria surveillance and control. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.
Global response of the growing season to soil moisture and topography

NASA Astrophysics Data System (ADS)

Guevara, M.; Arroyo, C.; Warner, D. L.; Equihua, J.; Lule, A. V.; Schwartz, A.; Taufer, M.; Vargas, R.

2017-12-01

Soil moisture has a direct influence in plant productivity. Plant productivity and its greenness can be inferred by remote sensing with higher spatial detail than soil moisture. The objective was to improve the coarse scale of currently available satellite soil moisture estimates and identify areas of strong coupling between the interannual variability soil moisture and the maximum greenness vegetation fraction (MGVF) at the global scale. We modeled, cross-validated and downscaled remotely sensed soil moisture using machine learning and digital terrain analysis across 23 years (1991-2013) of available data. Improving the accuracy (0.69-0.87 % of cross-validated explained variance) and the spatial detail (from 27 to 15km) of satellite soil moisture, we filled temporal gaps of information across vegetated areas where satellite soil moisture does not work properly. We found that 7.57% of global vegetated area shows strong correlation with our downscaled product (R2>0.5, Fig. 1). We found a dominant positive response of vegetation greenness to topography-based soil moisture across water limited environments, however, the tropics and temperate environments of higher latitudes showed a sparse negative response. We conclude that topography can be used to effectively improve the spatial detail of globally available remotely sensed soil moisture, which is convenient to generate unbiased comparisons with global vegetation dynamics, and better inform land and crop modeling efforts.
Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity monitoring

NASA Astrophysics Data System (ADS)

Nieland, Simon; Kleinschmit, Birgit; Förster, Michael

2015-05-01

Ontology-based applications hold promise in improving spatial data interoperability. In this work we use remote sensing-based biodiversity information and apply semantic formalisation and ontological inference to show improvements in data interoperability/comparability. The proposed methodology includes an observation-based, "bottom-up" engineering approach for remote sensing applications and gives a practical example of semantic mediation of geospatial products. We apply the methodology to three different nomenclatures used for remote sensing-based classification of two heathland nature conservation areas in Belgium and Germany. We analysed sensor nomenclatures with respect to their semantic formalisation and their bio-geographical differences. The results indicate that a hierarchical and transparent nomenclature is far more important for transferability than the sensor or study area. The inclusion of additional information, not necessarily belonging to a vegetation class description, is a key factor for the future success of using semantics for interoperability in remote sensing.
Characterization of extreme years in Central Europe between 2000 and 2016 according to specific vegetation characteristics based on Earth Observatory data

NASA Astrophysics Data System (ADS)

Kern, Anikó; Marjanović, Hrvoje; Barcza, Zoltán

2017-04-01

Extreme weather events frequently occur in Central Europe, affecting the state of the vegetation in large areas. Droughts and heat-waves affect all plant functional types, but the response of the vegetation is not uniform and depends on other parameters, plant strategies and the antecedent meteorological conditions as well. Meteorologists struggle with the definition of extreme events and selection of years that can be considered as extreme in terms of meteorological conditions due to the large variability of the meteorological parameters both in time and space. One way to overcome this problem is the definition of extreme weather based on its observed effect on plant state. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Leaf Area Index (LAI), the Fraction of Photosynthetically Active Radiation (FPAR) and the Gross Primary Production (GPP) are different measures of the land vegetation derived from remote sensing data, providing information about the plant state, but it is less known how weather anomalies affect these measures. We used the vegetation related official products created from the measurements of the MODerate resolution Imaging Spectroradiometer (MODIS) on board satellite Terra to select and characterize the extreme years in Central European countries during the 2000-2016 time period. The applied Collection-6 MOD13 NDVI/EVI, MOD15 LAI/FPAR and MOD17 GPP datasets have 500 m × 500 m spatial resolution covering the region of the Carpathian-Basin. After quality and noise filtering (and temporal interpolation in case of MOD13) 8-day anomaly values were derived to investigate the different years. The freely available FORESEE meteorological database was used to study climate variability in the region. Daily precipitation and maximum/minimum temperature fields at 1/12° × 1/12° grid were resampled to the 8-day temporal and 500 m × 500 m spatial resolution of the MODIS products. To discriminate the different behavior of the various plant functional types MODIS (MCD12) and CORINE (CLC2012) land cover datasets were applied and handled together. Based on the determination of the reliable pixels with different plant types the response of broadleaf forests, coniferous forests, grasslands and croplands were discriminated and investigated. Characteristic time periods were selected based on the remote sensing data to define anomalies, and then the meteorological data were used to define critical time periods within the year that has the strongest effect on the observed anomalies. Similarities/dissimilarities between the behaviors of the different remotely sensed measures are also studied to elucidate the consistency of the indices. The results indicate that the diverse remote sensing indices typically co-vary but reveal strong plant functional type dependency. The study suggest that the selection of extreme years based on annual data is not the best choice, as shorter time periods within the years explain the anomalies to a higher degree than annual data. The results can be used to select anomalous years outside of the satellite era as well. Keywords: Remote sensing, meteorology; extreme years; MODIS, NDVI; EVI; LAI; FPAR; GPP; phenology
[Vegetation index estimation by chlorophyll content of grassland based on spectral analysis].

PubMed

Xiao, Han; Chen, Xiu-Wan; Yang, Zhen-Yu; Li, Huai-Yu; Zhu, Han

2014-11-01

Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters' estimation, vegetation growth status' evaluation and grassland ecological environment change's monitoring.
Implication of remotely sensed data to incorporate land cover effect into a linear reservoir-based rainfall-runoff model

USDA-ARS?s Scientific Manuscript database

This study investigates the effect of land use on the Geomorphological Cascade of unequal Linear Reservoirs (GCUR) model. We use the Normalized Difference Vegetation Index (NDVI) derived from remotely sensed data as a measure of land use. Our approach has two important aspects: (i) it considers the ...
Analysis of terrestrial conditions and dynamics

NASA Technical Reports Server (NTRS)

Goward, S. N. (Principal Investigator)

1984-01-01

Land spectral reflectance properties for selected locations, including the Goddard Space Flight Center, the Wallops Flight Facility, a MLA test site in Cambridge, Maryland, and an acid test site in Burlington, Vermont, were measured. Methods to simulate the bidirectional reflectance properties of vegetated landscapes and a data base for spatial resolution were developed. North American vegetation patterns observed with the Advanced Very High Resolution Radiometer were assessed. Data and methods needed to model large-scale vegetation activity with remotely sensed observations and climate data were compiled.
The use of satellite data for monitoring temporal and spatial patterns of fire: a comprehensive review

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2009-04-01

Remotely sensed (RS) data can fruitfully support both research activities and operative monitoring of fire at different temporal and spatial scales with a synoptic view and cost effective technologies. "The contribution of remote sensing (RS) to forest fires may be grouped in three categories, according to the three phases of fire management: (i) risk estimation (before fire), (ii) detection (during fire) and (iii) assessment (after fire)" Chuvieco (2006). Relating each phase, wide research activities have been conducted over the years. (i) Risk estimation (before fire) has been mainly based on the use of RS data for (i) monitoring vegetation stress and assessing variations in vegetation moisture content, (ii) fuel type mapping, at different temporal and spatial scales from global, regional down to a local scale (using AVHRR, MODIS, TM, ASTER, Quickbird images and airborne hyperspectral and LIDAR data). Danger estimation has been mainly based on the use of AVHRR (onborad NOAA), MODIS (onboard TERRA and AQUA), VEGETATION (onboard SPOT) due to the technical characteristics (i.e. spectral, spatial and temporal resolution). Nevertheless microwave data have been also used for vegetation monitoring. (ii) Detection: identification of active fires, estimation of fire radiative energy and fire emission. AVHRR was one of the first satellite sensors used for setting up fire detection algorithms. The availbility of MODIS allowed us to obtain global fire products free downloaded from NASA web site. Sensors onboard geostationary satellite platforms, such as GOES, SEVIRI, have been used for fire detection, to obtain a high temporal resolution (at around 15 minutes) monitoring of active fires. (iii) Post fire damage assessment includes: burnt area mapping, fire emission, fire severity, vegetation recovery, fire resilience estimation, and, more recently, fire regime characterization. Chuvieco E. L. Giglio, C. Justice, 2008 Global charactrerization of fire activity: toward defining fire regimes from Earth observation data Global Change Biology vo. 14. doi: 10.1111/j.1365-2486.2008.01585.x 1-15, Chuvieco E., P. Englefield, Alexander P. Trishchenko, Yi Luo Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2381-2396 Chuvieco Emilio 2006, Remote Sensing of Forest Fires: Current limitations and future prospects in Observing Land from Space: Science, Customers and Technology, Advances in Global Change Research Vol. 4 pp 47-51 De Santis A., E. Chuvieco Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models, Remote Sensing of Environment, Volume 108, Issue 4, 29 June 2007, Pages 422-435. De Santis A., E. Chuvieco, Patrick J. Vaughan, Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models, Remote Sensing of Environment, Volume 113, Issue 1, 15 January 2009, Pages 126-136 García M., E. Chuvieco, H. Nieto, I. Aguado Combining AVHRR and meteorological data for estimating live fuel moisture content Remote Sensing of Environment, Volume 112, Issue 9, 15 September 2008, Pages 3618-3627 Ichoku C., L. Giglio, M. J. Wooster, L. A. Remer Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2950-2962. Lasaponara R. and Lanorte, On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape Ecological Modelling Volume 204, Issues 1-2, 24 May 2007, Pages 79-84 Lasaponara R., A. Lanorte, S. Pignatti,2006 Multiscale fuel type mapping in fragmented ecosystems: preliminary results from Hyperspectral MIVIS and Multispectral Landsat TM data, Int. J. Remote Sens., vol. 27 (3) pp. 587-593. Lasaponara R., V. Cuomo, M. F. Macchiato, and T. Simoniello, 2003 .A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection.n International Journal of Remote Sensing, vol. 24, No 8, 1723-1749. Minchella A., F. Del Frate, F. Capogna, S. Anselmi, F. Manes Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas Remote Sensing of Environment, In Press Næsset E., T. Gobakken Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 3079-3090 Peterson S. H, Dar A. Roberts, Philip E. Dennison Mapping live fuel moisture with MODIS data: A multiple regression approach, Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4272-4284. Schroeder Wilfrid, Elaine Prins, Louis Giglio, Ivan Csiszar, Christopher Schmidt, Jeffrey Morisette, Douglas Morton Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2711-2726 Shi J., T. Jackson, J. Tao, J. Du, R. Bindlish, L. Lu, K.S. Chen Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4285-4300 Tansey, K., Grégoire, J-M., Defourny, P., Leigh, R., Pekel, J-F., van Bogaert, E. and Bartholomé, E., 2008 A New, Global, Multi-Annual (2000-2007) Burnt Area Product at 1 km Resolution and Daily Intervals Geophysical Research Letters, VOL. 35, L01401, doi:10.1029/2007GL031567, 2008. Telesca L. and Lasaponara R., 2006; "Pre-and Post- fire Behaviural trends revealed in satellite NDVI time series" Geophysical Research Letters,., 33, L14401, doi:10.1029/2006GL026630 Telesca L. and Lasaponara R 2005 Discriminating Dynamical Patterns in Burned and Unburned Vegetational Covers by Using SPOT-VGT NDVI Data. Geophysical Research Letters,, 32, L21401, doi:10.1029/2005GL024391. Telesca L. and Lasaponara R. Investigating fire-induced behavioural trends in vegetation covers , Communications in Nonlinear Science and Numerical Simulation, 13, 2018-2023, 2008 Telesca L., A. Lanorte and R. Lasaponara, 2007. Investigating dynamical trends in burned and unburned vegetation covers by using SPOT-VGT NDVI data. Journal of Geophysics and Engineering, Vol. 4, pp. 128-138, 2007 Telesca L., R. Lasaponara, and A. Lanorte, Intra-annual dynamical persistent mechanisms in Mediterranean ecosystems revealed SPOT-VEGETATION Time Series, Ecological Complexity, 5, 151-156, 2008 Verbesselt, J., Somers, B., Lhermitte, S., Jonckheere, I., van Aardt, J., and Coppin, P. (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sensing of Environment 108: 357-368. Zhang X., S. Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897 Zhang X., Shobha Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897
Monitoring vegetation recovery in fire-affected areas using temporal profiles of spectral signal from time series MODIS and LANDSAT satellite images

NASA Astrophysics Data System (ADS)

Georgopoulou, Danai; Koutsias, Nikos

2015-04-01

Vegetation phenology is an important element of vegetation characteristics that can be useful in vegetation monitoring especially when satellite remote sensing observations are used. In that sense temporal profiles extracted from spectral signal of time series MODIS and LANDSAT satellite images can be used to characterize vegetation phenology and thus to be helpful for monitoring vegetation recovery in fire-affected areas. The aim of this study is to explore the vegetation recovery pattern of the catastrophic wildfires that occurred in Peloponnisos, southern Greece, in 2007. These fires caused the loss of 67 lives and were recognized as the most extreme natural disaster in the country's recent history. Satellite remote sensing data from MODIS and LANDSAT satellites in the period from 2000 to 2014 were acquired and processed to extract the temporal profiles of the spectral signal for selected areas within the fire-affected areas. This dataset and time period analyzed together with the time that these fires occurred gave the opportunity to create temporal profiles seven years before and seven years after the fire. The different scale of the data used gave us the chance to understand how vegetation phenology and therefore the recovery patterns are influenced by the spatial resolution of the satellite data used. Different metrics linked to key phenological events have been created and used to assess vegetation recovery in the fire-affected areas. Our analysis was focused in the main land cover types that were mostly affected by the 2007 wildland fires. Based on CORINE land-cover maps these were agricultural lands highly interspersed with large areas of natural vegetation followed by sclerophyllous vegetation, transitional woodland shrubs, complex cultivation patterns and olive groves. Apart of the use of the original spectral data we estimated and used vegetation indices commonly found in vegetation studies as well as in burned area mapping studies. In this study we explore the strength and the use of these time series satellite data to characterize vegetation phenology as an a aid to monitor vegetation recovery in fire affected-areas. In a recent study we found that the original spectral channels, based on which these indices are estimated, are sensitive to external vegetation parameters such as the spectral reflectance of the background soil. In such cases, the influence of the soil in the reflectance values is different in the various spectral regions depending on its type. The use of such indices is also justified according to a recent study on the sensitivity of spectral reflectance values to different burn and vegetation ratios, who concluded that the Near Infrared (NIR) and Short-Wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas. Additionally, it has been found that semi-burned classes are spectrally more consistent to their different fractions of scorched and non-scorched vegetation, than the original spectral channels based on which these indices are estimated.
Information extraction with object based support vector machines and vegetation indices

NASA Astrophysics Data System (ADS)

Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

2016-07-01

Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.
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.
South Florida Everglades: satellite image map

USGS Publications Warehouse

Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

2001-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.
UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING

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

Nancy F. Glenn; Jessica J. Mitchell; Matthew O. Anderson

2012-06-01

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis shouldmore » be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).« less
Chlorophyll Fluorescence Better Captures Seasonal and Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of Southwestern North America

NASA Astrophysics Data System (ADS)

Smith, W. K.; Biederman, J. A.; Scott, R. L.; Moore, D. J. P.; He, M.; Kimball, J. S.; Yan, D.; Hudson, A.; Barnes, M. L.; MacBean, N.; Fox, A. M.; Litvak, M. E.

2018-01-01

Satellite remote sensing provides unmatched spatiotemporal information on vegetation gross primary productivity (GPP). Yet understanding of the relationship between GPP and remote sensing observations and how it changes with factors such as scale, biophysical constraint, and vegetation type remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have characteristic high spatiotemporal variability and are under-represented by long-term field measurements. Here we utilize an eddy covariance (EC) data synthesis for southwestern North America in an assessment of how accurately satellite-derived vegetation proxies capture seasonal to interannual GPP dynamics across dryland gradients. We evaluate the enhanced vegetation index, solar-induced fluorescence (SIF), and the photochemical reflectivity index. We find evidence that SIF is more accurately capturing seasonal GPP dynamics particularly for evergreen-dominated EC sites and more accurately estimating the full magnitude of interannual GPP dynamics for all dryland EC sites. These results suggest that incorporation of SIF could significantly improve satellite-based GPP estimates.

Evidence of a robust relationship between solar-induced chlorophyll fluorescence and gross primary productivity across dryland ecosystems of southwestern North America

NASA Astrophysics Data System (ADS)

Smith, W. K.; Biederman, J. A.; Scott, R. L.; Moore, D. J.; Kimball, J. S.; He, M.; Yan, D.; Hudson, A.; Barnes, M.; MacBean, N.; Fox, A. M.; Litvak, M. E.

2017-12-01

Satellite remote sensing provides unmatched spatiotemporal information on multiple facets of vegetation dynamics including seasonal to interannual total photosynthesis, termed gross primary productivity (GPP). Yet, our understanding of the relationship between GPP and remote sensing observations - and how this relationship changes with scale, biophysical constraint, vegetation type, etc. - remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have high spatial and temporal variability and are under-represented by long-term, continuous field measurements. Here, utilizing a new synthesis of eddy covariance flux tower data for southwestern North America, we present a first assessment of the ability of novel satellite remote sensing vegetation proxies to accurately capture seasonal to interannual GPP dynamics across the region. We evaluate the greenness-based Enhanced Vegetation Index (EVI) and emerging proxies linked to plant physiological function, Solar-Induced Fluorescence (SIF) and Photochemical Reflectivity Index (PRI). We find that SIF observations more consistently correlate with seasonal GPP dynamics (R = 0.90) compared to EVI (R = 0.85) and PRI (R = 0.78). More, we find that SIF observations are also more sensitive to interannual GPP variability (linear slope = 0.80) relative to EVI (linear slope = 0.63) and PRI (linear slope = 0.35). This is likely due to increased sensitivity of SIF to GPP during periods of decoupling between greenness and photosynthesis due to water-limitation / stomatal closure. Conversely, EVI and PRI observations better capture spatial GPP variability between flux tower sites. These results suggest that combinations of these independent vegetation growth proxies could yield synergistic improvements in satellite-based GPP estimates.
Comparison of inversion accuracy of soil copper content from vegetation indices under different spectral resolution

NASA Astrophysics Data System (ADS)

Sun, Zhongqing; Shang, Kun; Jia, Lingjun

2018-03-01

Remote sensing inversion of heavy metal in vegetation leaves is generally based on the physiological characteristics of vegetation spectrum under heavy metal stress, and empirical models with vegetation indices are established to inverse the heavy metal content of vegetation leaves. However, the research of inversion of heavy metal content in vegetation-covered soil is still rare. In this study, Pulang is chosen as study area. The regression model of a typical heavy metal element, copper (Cu), is established with vegetation indices. We mainly investigate the inversion accuracies of Cu element in vegetation-covered soil by different vegetation indices according to specific spectral resolutions of ASD (Analytical Spectral Device) and Hyperion data. The inversion results of soil copper content in the vegetation-covered area shows a good accuracy, and the vegetation indices under ASD spectral resolution correspond to better results.
Photopolarimetric properties of leaf and vegetation covers over a wide range of measurement directions

NASA Astrophysics Data System (ADS)

Sun, Zhongqiu; Peng, Zhiyan; Wu, Di; Lv, Yunfeng

2018-02-01

The optical scattering property of the target is the essential signal for passive remote sensing applications. To deepen our understanding of the light reflected from vegetation, we present results of photopolarimetric laboratory measurements from single leaf and two vegetation covers (planophile and erectophile) over a wide range of viewing directions. The bidirectional polarized reflectance factor (BPRF) was used to characterize the polarization property of our samples. We observed positive and negative polarization (-BPRFQ) of all samples in the forward scattering and backward scattering directions, respectively. Based on the comparison of the BPRF among single leaf, planophile vegetation and erectophile vegetation, our measurements demonstrate that the orientation of the leaf is a key factor in describing the amount of polarization in the forward scattering direction. Our measurements also validated certain model results stating that (1) specular reflection generates a portion of polarization in the forward scattering direction and diffuses scattering of polarized light in all hemisphere directions, (2) BPRFU is anti-symmetric in the principal plane from a recent study in which the authors simulated the polarized reflectance of vegetation cover using the vector radiative transfer theory. These photopolarimetric measurement results, which can be completely explained by the theoretical results, are useful in remote sensing applications to vegetation.
Assimilation of remote sensing data into a process-based ecosystem model for monitoring changes of soil water content in croplands

NASA Astrophysics Data System (ADS)

Ju, Weimin; Gao, Ping; Wang, Jun; Li, Xianfeng; Chen, Shu

2008-10-01

Soil water content (SWC) is an important factor affecting photosynthesis, growth, and final yields of crops. The information on SWC is of importance for mitigating the reduction of crop yields caused by drought through proper agricultural water management. A variety of methodologies have been developed to estimate SWC at local and regional scales, including field sampling, remote sensing monitoring and model simulations. The reliability of regional SWC simulation depends largely on the accuracy of spatial input datasets, including vegetation parameters, soil and meteorological data. Remote sensing has been proved to be an effective technique for controlling uncertainties in vegetation parameters. In this study, the vegetation parameters (leaf area index and land cover type) derived from the Moderate Resolution Imaging Spectrometer (MODIS) were assimilated into a process-based ecosystem model BEPS for simulating the variations of SWC in croplands of Jiangsu province, China. Validation shows that the BEPS model is able to capture 81% and 83% of across-site variations of SWC at 10 and 20 cm depths during the period from September to December, 2006 when a serous autumn drought occurred. The simulated SWC responded the events of rainfall well at regional scale, demonstrating the usefulness of our methodology for SWC and practical agricultural water management at large scales.
Method and system for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Lewis, Mark David (Inventor); Seal, Michael R. (Inventor); Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor)

2007-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.
Method and apparatus for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor); Seal, Michael R. (Inventor); Lewis, Mark David (Inventor)

2004-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.
Arctic Tundra Greening and Browning at Circumpolar and Regional Scales

NASA Astrophysics Data System (ADS)

Epstein, H. E.; Bhatt, U. S.; Walker, D. A.; Raynolds, M. K.; Yang, X.

2017-12-01

Remote sensing data have historically been used to assess the dynamics of arctic tundra vegetation. Until recently the scientific literature has largely described the "greening" of the Arctic; from a remote sensing perspective, an increase in the Normalized Difference Vegetation Index (NDVI), or a similar satellite-based vegetation index. Vegetation increases have been heterogeneous throughout the Arctic, and were reported to be up to 25% in certain areas over a 30-year timespan. However, more recently, arctic tundra vegetation dynamics have gotten more complex, with observations of more widespread tundra "browning" being reported. We used a combination of remote sensing data, including the Global Inventory Monitoring and Modeling System (GIMMS), as well as higher spatial resolution Landsat data, to evaluate the spatio-temporal patterns of arctic tundra vegetation dynamics (greening and browning) at circumpolar and regional scales over the past 3-4 decades. At the circumpolar scale, we focus on the spatial heterogeneity (by tundra subzone and continent) of tundra browning over the past 5-15 years, followed by a more recent recovery (greening since 2015). Landsat time series allow us to evaluate the landscape-scale heterogeneity of tundra greening and browning for northern Alaska and the Yamal Peninsula in northwestern Siberia, Russia. Multi-dataset analyses reveal that tundra greening and browning (i.e. increases or decreases in the NDVI respectively) are generated by different sets of processes. Tundra greening is largely a result of either climate warming, lengthening of the growing season, or responses to disturbances, such as fires, landslides, and freeze-thaw processes. Browning on the other hand tends to be more event-driven, such as the shorter-term decline in vegetation due to fire, insect defoliation, consumption by larger herbivores, or extreme weather events (e.g. winter warming or early summer frost damage). Browning can also be caused by local or regional cooling, or changes in the snow regime (e.g. depth, timing of melt). The spatio-temporal dynamics of tundra vegetation are only now beginning to get serious attention from the scientific community and the continual use of remote sensing data across spatial scales allows us to monitor these dynamics and elucidate their controls.
Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

NASA Technical Reports Server (NTRS)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.
An algorithm for retrieving rock-desertification from multispectral remote sensing images

NASA Astrophysics Data System (ADS)

Xia, Xueqi; Tian, Qingjiu; Liao, Yan

2009-06-01

Rock-desertification is a typical environmental and ecological problem in Southwest China. As remote sensing is an important means of monitoring spatial variation of rock-desertification, a method is developed for measurement and information retrieval of rock-desertification from multi-spectral high-resolution remote sensing images. MNF transform is applied to 4-band IKONOS multi-spectral remotely sensed data to reduce the number of spectral dimensions to three. In the 3-demension endmembers are extracted and analyzed. It is found that various vegetations group into a line defined as "vegetation line", in which "dark vegetations", such as coniferous forest and broadleaf forest, continuously change to "bright vegetations", such as grasses. It is presumed that is caused by deferent proportion of shadow mixed in leaves or branches in various types of vegetation. Normalized distance between the endmember of rocks and the vegetation line is defined as Geometric Rock-desertification Index (GRI), which was used to scale rock-desertification. The case study with ground truth validation in Puding, Guizhou province showed successes and the advantages of this method.
Development of a Global Evaporative Stress Index Based on Thermal and Microwave LST towards Improved Monitoring of Agricultural Drought

NASA Astrophysics Data System (ADS)

Hain, C.; Anderson, M. C.; Otkin, J.; Holmes, T. R.; Gao, F.

2017-12-01

This presentation will describe the development of a global agricultural monitoring tool, with a focus on providing early warning of developing vegetation stress for agricultural decision-makers and stakeholders at relatively high spatial resolution (5-km). The tool is based on remotely sensed estimates of evapotranspiration, retrieved via energy balance principals using observations of land surface temperature. The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET generated with the ALEXI surface energy balance model. The LST inputs to ESI have been shown to provide early warning information about the development of vegetation stress with stress-elevated canopy temperatures observed well before a decrease in greenness is detected in remotely sensed vegetation indices. As a diagnostic indicator of actual ET, the ESI requires no information regarding antecedent precipitation or soil moisture storage capacity - the current available moisture to vegetation is deduced directly from the remotely sensed LST signal. This signal also inherently accounts for both precipitation and non-precipitation related inputs/sinks to the plant-available soil moisture pool (e.g., irrigation) which can modify crop response to rainfall anomalies. Independence from precipitation data is a benefit for global agricultural monitoring applications due to sparseness in existing ground-based precipitation networks, and time delays in public reporting. Several enhancements to the current ESI framework will be addressed as requested from project stakeholders: (a) integration of "all-sky" MW Ka-band LST retrievals to augment "clear-sky" thermal-only ESI in persistently cloudy regions; (b) operational production of ESI Rapid Change Indices which provide important early warning information related to onset of actual vegetation stress; and (c) assessment of ESI as a predictor of global yield anomalies; initial studies have shown the ability of intra-seasonal ESI to provide an early indication of at-harvest agricultural yield anomalies.
Measuring grassland structure for recovery of grassland species at risk

NASA Astrophysics Data System (ADS)

Guo, Xulin; Gao, Wei; Wilmshurst, John

2005-09-01

An action plan for recovering species at risk (SAR) depends on an understanding of the plant community distribution, vegetation structure, quality of the food source and the impact of environmental factors such as climate change at large scale and disturbance at small scale, as these are fundamental factors for SAR habitat. Therefore, it is essential to advance our knowledge of understanding the SAR habitat distribution, habitat quality and dynamics, as well as developing an effective tool for measuring and monitoring SAR habitat changes. Using the advantages of non-destructive, low cost, and high efficient land surface vegetation biophysical parameter characterization, remote sensing is a potential tool for helping SAR recovery action. The main objective of this paper is to assess the most suitable techniques for using hyperspectral remote sensing to quantify grassland biophysical characteristics. The challenge of applying remote sensing in semi-arid and arid regions exists simply due to the lower biomass vegetation and high soil exposure. In conservation grasslands, this problem is enhanced because of the presence of senescent vegetation. Results from this study demonstrated that hyperspectral remote sensing could be the solution for semi-arid grassland remote sensing applications. Narrow band raw data and derived spectral vegetation indices showed stronger relationships with biophysical variables compared to the simulated broad band vegetation indices.
Classification of Liss IV Imagery Using Decision Tree Methods

NASA Astrophysics Data System (ADS)

Verma, Amit Kumar; Garg, P. K.; Prasad, K. S. Hari; Dadhwal, V. K.

2016-06-01

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.
Remote sensing information sciences research group

NASA Technical Reports Server (NTRS)

Estes, John E.; Smith, Terence; Star, Jeffrey L.

1988-01-01

Research conducted under this grant was used to extend and expand existing remote sensing activities at the University of California, Santa Barbara in the areas of georeferenced information systems, matching assisted information extraction from image data and large spatial data bases, artificial intelligence, and vegetation analysis and modeling. The research thrusts during the past year are summarized. The projects are discussed in some detail.
Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

NASA Astrophysics Data System (ADS)

Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

2017-03-01

Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.
Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...
Phenology-based, remote sensing of post-burn disturbance windows in rangelands

USGS Publications Warehouse

Sankeya, Joel B.; Wallace, Cynthia S.A.; Ravi, Sujith

2013-01-01

Wildland fire activity has increased in many parts of the world in recent decades. Ecological disturbance by fire can accelerate ecosystem degradation processes such as erosion due to combustion of vegetation that otherwise provides protective cover to the soil surface. This study employed a novel ecological indicator based on remote sensing of vegetation greenness dynamics (phenology) to estimate variability in the window of time between fire and the reemergence of green vegetation. The indicator was applied as a proxy for short-term, post-fire disturbance windows in rangelands; where a disturbance window is defined as the time required for an ecological or geomorphic process that is altered to return to pre-disturbance levels. We examined variability in the indicator determined for time series of MODIS and AVHRR NDVI remote sensing data for a database of ∼100 historical wildland fires, with associated post-fire reseeding treatments, that burned 1990–2003 in cold desert shrub steppe of the Great Basin and Columbia Plateau of the western USA. The indicator-based estimates of disturbance window length were examined relative to the day of the year that fires burned and seeding treatments to consider effects of contemporary variability in fire regime and management activities in this environment. A key finding was that contemporary changes of increased length of the annual fire season could have indirect effects on ecosystem degradation, as early season fires appeared to result in longer time that soils remained relatively bare of the protective cover of vegetation after fires. Also important was that reemergence of vegetation did not occur more quickly after fire in sites treated with post-fire seeding, which is a strategy commonly employed to accelerate post-fire vegetation recovery and stabilize soil. Future work with the indicator could examine other ecological factors that are dynamic in space and time following disturbance – such as nutrient cycling, carbon storage, microbial community composition, or soil hydrology – as a function of disturbance windows, possibly using simulation modeling and historical wildfire information.
Hyperspectral remote sensing of vegetation and agricultural crops: knowledge gain and knowledge gap after 40 years of research

USGS Publications Warehouse

Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo; Edited by Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

2011-01-01

The focus of this chapter was to summarize the advances made over last 40+ years, as reported in various chapters of this book, in understanding, modeling, and mapping terrestrial vegetation using hyperspectral remote sensing (or imaging spectroscopy) using sensors that are ground-based, truck-mounted, airborne, and spaceborne. As we have seen in various chapters of this book and synthesized in this chapter, the advances made include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracies (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (b), (e) ability to access stress resulting from causes such as management practices, pests and disease, water deficit or excess; , and (f) establishing more sensitive wavebands and indices to detect plant water\\moisture content. The advent of spaceborne hyperspectral sensors (e.g., NASA’s Hyperion, ESA’s PROBA, and upcoming NASA’s HyspIRI) and numerous methods and techniques espoused in this book to overcome Hughes phenomenon or data redundancy when handling large volumes of hyperspectral data have generated tremendous interest in advancing our hyperspectral applications knowledge base over larger spatial extent such as region, nation, continent, and globe.
Temporal variation (seasonal and interannual) of vegetation indices of maize and soybeans across multiple years in central Iowa

NASA Astrophysics Data System (ADS)

Prueger, J. H.; Hatfield, J. L.

2015-09-01

Remotely sensed reflectance parameters from corn and soybean surfaces can be correlated to crop production. Surface reflectance of a typical Upper Midwest corn /soybean region in central Iowa across multiple years reveal subtle dynamics in vegetative surface response to a continually varying climate. From 2006 through 2014 remotely sensed data have been acquired over production fields of corn and soybeans in central IA, U.S.A. with the fields alternating between corn and soybeans. The data have been acquired using ground-based radiometers with 16 wavebands covering the visible, near infrared, shortwave infrared wavebands and combined into a series of vegetative indices. These data were collected on clear days with the goal of collecting data at a minimum of once per week from prior to planting until after fall tillage operations. Within each field, five sites were established and sampled during the year to reduce spatial variation and allow for an assessment of changes in the vegetative indices throughout the growing season. Ancillary data collected for each crop included the phenological stage at each sampling date along with biomass sampled at the onset of the reproductive stage and at physiological maturity. Evaluation of the vegetative indices for the different years revealed that patterns were related to weather effects on corn and soybean growth. Remote sensing provides a method to evaluate changes within and among growing seasons to assess crop growth and development as affected by differences in weather variability.
Stomatal conductance, canopy temperature, and leaf area index estimation using remote sensing and OBIA techniques

Treesearch

S. Panda; D.M. Amatya; G. Hoogenboom

2014-01-01

Remotely sensed images including LANDSAT, SPOT, NAIP orthoimagery, and LiDAR and relevant processing tools can be used to predict plant stomatal conductance (gs), leaf area index (LAI), and canopy temperature, vegetation density, albedo, and soil moisture using vegetation indices like normalized difference vegetation index (NDVI) or soil adjusted...
Exploring the control of land-atmospheric oscillations over terrestrial vegetation productivity

NASA Astrophysics Data System (ADS)

Depoorter, Mathieu; Green, Julia; Gentine, Pierre; Liu, Yi; van Eck, Christel; Regnier, Pierre; Dorigo, Wouter; Verhoest, Niko; Miralles, Diego

2015-04-01

Vegetation dynamics play an important role in the climate system due to their control on the carbon, energy and water cycles. The spatiotemporal variability of vegetation is regulated by internal climate variability as well as natural and anthropogenic forcing mechanisms, including fires, land use, volcano eruptions or greenhouse gas emissions. Ocean-atmospheric oscillations, affect the fluxes of heat and water over continents, leading to anomalies in radiation, precipitation or temperature at widely separated locations (i.e. teleconnections); an effect of ocean-atmospheric oscillations on terrestrial primary productivity can therefore be expected. While different studies have shown the general importance of internal climate variability for global vegetation dynamics, the control by particular teleconnections over the regional growth and decay of vegetation is still poorly understood. At continental to global scales, satellite remote sensing offers a feasible approach to enhance our understanding of the main drivers of vegetation variability. Traditional studies of the multi-decadal variability of global vegetation have been usually based on the normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR), which extends back to the early '80s. There are, however, some limitations to NDVI observations; arguably the most important of these limitations is that from the plant physiology perspective the index does not have a well-defined meaning, appearing poorly correlated to vegetation productivity. On the other hand, recently developed records from other remotely-sensed properties of vegetation, like fluorescence or microwave vegetation optical depth, have proven a significantly better correspondence to above-ground biomass. To enhance our understanding of the controls of ocean-atmosphere oscillations over vegetation, we propose to explore the link between climate oscillation extremes and net primary productivity over the last two decades. The co-variability of a range of climate oscillation indices and newly-derived records of fluorescence and vegetation optical depth is analyzed using a statistical framework based on correlations, bootstrapping and Empirical Orthogonal Functions (EOFs). Results will enable us to characterize regional hotspots where particular climatic oscillations control vegetation productivity, as well as allowing us to underpin the climatic variables behind this control.

UAV-LiDAR accuracy and comparison to Structure from Motion photogrammetry

NASA Astrophysics Data System (ADS)

Kucharczyk, M.; Hugenholtz, C.; Zou, X.; Nesbit, P. R.; Barchyn, T.

2016-12-01

We compare the spatial accuracy of a UAV-LiDAR system with Structure from Motion (SfM) photogrammetry. UAV-based LiDAR remote sensing potentially offers advantages over SfM photogrammetry in vegetated terrain, particularly with respect to canopy penetration and related measurements of ground surface elevation and vegetation height; however, little quantitative evidence has been presented to date. To address this, we performed a case study at a field site in Alberta, Canada with six different land cover types: short grass, tall grass, short shrubs, tall shrubs, deciduous trees, and coniferous trees. Both UAV datasets were acquired on the same day. The SfM dataset was derived from images acquired by a senseFly eBee fixed-wing UAV equipped with a 16.1 megapixel RGB camera. The UAV-LiDAR system is a proprietary design that consists of a single-rotor helicopter (2-m rotor diameter) equipped with a Riegl VUX-1UAV laser scanner, KVH 1750 inertial measurement unit, and dual NovAtel GNSS receivers. We measured vegetation height from at least 30 samples in each land cover type and acquired check point measurements to determine horizontal and vertical accuracy. Vegetation height was measured manually for grasses and shrubs with a level staff, and with a total station for trees. Coordinates of horizontal and vertical check points were surveyed with real-time kinematic (RTK) GNSS. We followed standard methods for computing horizontal and vertical accuracies based on the 2015 guidelines from the American Society of Photogrammetry and Remote Sensing. Results will be presented at the AGU Fall Meeting.
Using remotely sensed vegetation indices to model ecological pasture conditions in Kara-Unkur watershed, Kyrgyzstan

NASA Astrophysics Data System (ADS)

Masselink, Loes; Baartman, Jantiene; Verbesselt, Jan; Borchardt, Peter

2017-04-01

Kyrgyzstan has a long history of nomadic lifestyle in which pastures play an important role. However, currently the pastures are subject to severe grazing-induced degradation. Deteriorating levels of biomass, palatability and biodiversity reduce the pastures' productivity. To counter this and introduce sustainable pasture management, up-to-date information regarding the ecological conditions of the pastures is essential. This research aimed to investigate the potential of a remote sensing-based methodology to detect changing ecological pasture conditions in the Kara-Unkur watershed, Kyrgyzstan. The relations between Vegetation Indices (VIs) from Landsat ETM+ images and biomass, palatability and species richness field data were investigated. Both simple and multiple linear regression (MLR) analyses, including terrain attributes, were applied. Subsequently, trends of these three pasture conditions were mapped using time series analysis. The results show that biomass is most accurately estimated by a model including the Modified Soil Adjusted Vegetation Index (MSAVI) and a slope factor (R2 = 0.65, F = 0.0006). Regarding palatability, a model including the Enhanced Vegetation Index (EVI), Northness Index, Near Infrared (NIR) and Red band was most accurate (R2 = 0.61, F = 0.0160). Species richness was most accurately estimated by a model including Topographic Wetness Index (TWI), Eastness Index and estimated biomass (R2 = 0.81, F = 0.0028). Subsequent trend analyses of all three estimated ecological pasture conditions presented very similar trend patterns. Despite the need for a more robust validation, this study confirms the high potential of a remote sensing based methodology to detect changing ecological pasture conditions.
The Effect of Incident Light Polarization on Vegetation Bidirectional Reflectance Factor

NASA Technical Reports Server (NTRS)

Georgiev, Georgi T.; Thome, Kurt; Ranson, Kurtis J.; King, Michael D.; Butler, James J.

2010-01-01

The Laboratory-based Bidirectional Reflectance Factor (BRF) polarization study of vegetation is presented in this paper. The BRF was measured using a short-arc Xenon lamp/monochromator assembly producing an incoherent, tunable light source with a well-defined spectral bandpass at visible and near-infrared wavelengths of interest at 470 nm and 870 nm and coherent light source at 1.656 microns. All vegetation samples were measured using P and S linearly polarized incident light over a range of incident and scatter angles. By comparing these results, we quantitatively examine how the BRF of the samples depends on the polarization of the incident light. The differences are significant, depend strongly on the incident and scatter angles, and can be as high as 120% at 67 deg incident and 470nm. The global nature of Earth's processes requires consistent long-term calibration of all instruments involved in data retrieval. The BRF defines the reflection characteristics of Earth surface. It provides the reflectance of a target in a specific direction as a function of illumination and viewing geometry. The BRF is a function of wavelength and reflects the structural and optical properties of the surface. Various space and airborne radiometric and imaging remote sensing instruments are used in the remote sensing characterization of vegetation canopies and soils, oceans, or especially large pollution sources. The satellite data is validated through comparison with airborne, ground-based and laboratory-based data in an effort to fully understand the vegetation canopy reflectance, The Sun's light is assumed to be unpolarized at the top of the atmosphere; however it becomes polarized to some degree due to atmospheric effects by the time it reaches the vegetation canopy. Although there are numerous atmospheric correction models, laboratory data is needed for model verification and improvement.
[Application of hyperspectral remote sensing in research on ecological boundary in north farming-pasturing transition in China].

PubMed

Wang, Hong-Mei; Wang, Kun; Xie, Ying-Zhong

2009-06-01

Studies of ecological boundaries are important and have become a rapidly evolving part of contemporary ecology. The ecotones are dynamic and play several functional roles in ecosystem dynamics, and the changes in their locations can be used as an indicator of environment changes, and for these reasons, ecotones have recently become a focus of investigation of landscape ecology and global climate change. As the interest in ecotone increases, there is an increased need for formal techniques to detect it. Hence, to better study and understand the functional roles and dynamics of ecotones in ecosystem, we need quantitative methods to characterize them. In the semi-arid region of northern China, there exists a farming-pasturing transition resulting from grassland reclamation and deforestation. With the fragmentation of grassland landscape, the structure and function of the grassland ecosystem are changing. Given this perspective; new-image processing approaches are needed to focus on transition themselves. Hyperspectral remote sensing data, compared with wide-band remote sensing data, has the advantage of high spectral resolution. Hyperspectral remote sensing can be used to visualize transitional zones and to detect ecotone based on surface properties (e. g. vegetation, soil type, and soil moisture etc). In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation classifications, vegetation growth state, estimating the canopy biochemical characteristics, soil moisture, soil organic matter etc are reviewed in detail. Finally the paper involves further application of hyperspectral remote sensing information in research on local climate in ecological boundary in north farming-pasturing transition in China.
Integrating satellite remote sensing data and field data to predict rangeland structural indicators at the continental scale

NASA Astrophysics Data System (ADS)

Zhang, J.; Okin, G.

2016-12-01

Rangelands provide a variety of important ecosystem goods and services across drylands globally. They are also the most important emitters of dust across the globe. Field data collection based on points does not represent spatially continuous information about surface variables and, given the vast size of the world's rangelands, cannot cover even a small fraction of their area. Remote sensing is potentially a labor- and time-saving method to observe important rangeland vegetation variables at both temporal and spatial scales. Information on vegetation cover, bare gap size, and plant height provide key rangeland vegetation variables in arid and semiarid rangelands, in part because they strongly impact dust emission and determine wildlife habitat characteristics. This study reports on relationships between remote sensing in the reflected solar spectrum and field measures related to these three variables, and shows how these relationships can be extended to produce spatially and temporally continuous datasets coupled with quantitative estimates of error. Field data for this study included over 3,800 Assessment, Inventory, and Monitoring (AIM) measurements on Bureau of Land Management (BLM) lands throughout the western US. Remote sensing data were derived from MODIS nadir BRDF-adjusted reflectance (NBAR) and Landsat 8 OLI surface reflectance. Normalized bare gap size, total foliar cover, herbaceous cover and herbaceous height exhibit the greatest predictability from remote sensing variables with physically-reasonable relationships between remote sensing variables and field measures. Data fields produced using these relationships across the western US exhibit good agreement with independent high-resolution imagery.
Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.
Vegetation and soils field research data base: Experiment summaries

NASA Technical Reports Server (NTRS)

Biehl, L. L.; Daughtry, C. S. T.; Bauer, M. E.

1984-01-01

Understanding of the relationships between the optical, spectral characteristics and important biological-physical parameters of earth-surface features can best be obtained by carefully controlled studies over fields and plots where complete data describing the condition of targets are attainable and where frequent, timely spectral measurement can be obtained. Development of a vegetation and soils field research data base was initiated in 1972 at Purdue University's Laboratory for Applications of Remote Sensing and expanded in the fall of 1974 by NASA as part of LACIE. Since then, over 250,000 truck-mounted and helicopter-borne spectrometer/multiband radiometer observations have been obtained of more than 50 soil series and 20 species of crops, grasses, and trees. These data are supplemented by an extensive set of biophysical and meteorological data acquired during each mission. The field research data form one of the most complete and best-documented data sets acquired for agricultural remote sensing research. Thus, they are well-suited to serve as a data base for research to: (1) quantiatively determine the relationships of spectral and biophysical characteristics of vegetation, (2) define future sensor systems, and (3) develop advanced data analysis techniques.
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.
Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR

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

Kumar, Jitendra; HargroveJr., William Walter; Norman, Steven P

Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify andmore » analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.« less
Phenological studies to improve the accuracy of remote sensing data in a diverse pasture

NASA Astrophysics Data System (ADS)

Gecse, Bernadett; Petrás, Dóra; Kertész, Péter; Koncz, Péter; Fóti, Szilvia; Balogh, János

2017-04-01

Remote sensing has been used widely to map nearly all types of vegetation of the Earth. This technique is excessively valuable because it can determine the distribution and the health condition of the vegetation, however it is rarely used in diverse vegetation. Remote sensing is challenged in diverse vegetation, where there is a high variability in the rate and intensity of flowering, greening/senescing among plants. We hypothesised that the interpretation of images of diverse vegetation could become more accurate if the growth and distribution of the dominant plant species are also considered. Our first goal was to establish a monitoring protocol as how to capture the main phenological changes of a diverse (over 80 species per hectare) pasture and reveal the ratio of the production of dominant species to the total biomass production. Our second goal was to answer how flowering influences (i.e. to what extent) the correlation coefficient between airborne Normalized Difference Vegetation Index (NDVI) and biomass. To monitor the phenological changes we measured leaf area index (LAI), estimated the cover of flowers (%), and performed vegetation survey in permanent quadrates (15) during eight measurement campaigns. We also selected 20 dominant species, based on the experience of previous years, which have visually dominant flowers in the area. For these species besides the cover of plant species (%) the number of flowering individuals, the number of flowers and other plant traits were recorded in permanent plots during measurement campaigns. In these plots 10 individuals per species were selected to measure the area and biomass of their leaves, shoots and flowers in the lab. Our results from the biomass production estimations show that biomass of the five most dominant species provided 68% of the total biomass production. We analyzed the connection between the percentage of flowering coverage and the correlation of NDVI and biomass. The data indicated that after June, when many species started to bloom there was a sharp deterioration in the correlation coefficient (r2=0.65 in early May, r2=0.15 in mid-June). The use of remote sensing data for biomass estimations in a diverse grassland is restricted to the spring period.
Vegetation productivity responses to drought on tribal lands in the four corners region of the Southwest USA

NASA Astrophysics Data System (ADS)

El-Vilaly, Mohamed Abd Salam; Didan, Kamel; Marsh, Stuart E.; van Leeuwen, Willem J. D.; Crimmins, Michael A.; Munoz, Armando Barreto

2018-03-01

For more than a decade, the Four Corners Region has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources while exacerbating soil erosion. These persistent droughts threaten ecosystem services, agriculture, and livestock activities, and expose the hypersensitivity of this region to inter-annual climate variability and change. Much of the intermountainWestern United States has sparse climate and vegetation monitoring stations, making fine-scale drought assessments difficult. Remote sensing data offers the opportunity to assess the impacts of the recent droughts on vegetation productivity across these areas. Here, we propose a drought assessment approach that integrates climate and topographical data with remote sensing vegetation index time series. Multisensor Normalized Difference Vegetation Index (NDVI) time series data from 1989 to 2010 at 5.6 km were analyzed to characterize the vegetation productivity changes and responses to the ongoing drought. A multi-linear regression was applied to metrics of vegetation productivity derived from the NDVI time series to detect vegetation productivity, an ecosystem service proxy, and changes. The results show that around 60.13% of the study area is observing a general decline of greenness ( p<0.05), while 3.87% show an unexpected green up, with the remaining areas showing no consistent change. Vegetation in the area show a significant positive correlation with elevation and precipitation gradients. These results, while, confirming the region's vegetation decline due to drought, shed further light on the future directions and challenges to the region's already stressed ecosystems. Whereas the results provide additional insights into this isolated and vulnerable region, the drought assessment approach used in this study may be adapted for application in other regions where surface-based climate and vegetation monitoring record is spatially and temporally limited.
Monitoring land cover changes by remote sensing in north west Egypt

NASA Astrophysics Data System (ADS)

Richards, Timothy Steven

The Mediterratiean coastal strip of Egypt is a semi-arid environment which supports a variety of agricultural practices ranging from irrigated sedentary agriculture to semi-nomadic pastoralism. The sedentarisation of the nomadic Bedouin coupled with an increase in population of both people and livestock and a decrease in the extent of the rangelands, has resulted in severe pressure being exerted upon the environment. Satellite remote sensing of vegetation offers the potential to aid regional management by complementing conventional techniques of vegetation mapping and monitoring. This thesis examines the different techniques available for vegetation mapping using visible and near infrared spectral wave bands. The different techniques available for vegetation mapping using remotely sensed data are reviewed and discussed with reference to semi-arid environments. The underlying similarity of many of the techniques is emphasised and their individual merits discussed. The spectral feature-space of Landsat data of two representative study areas in northern Egypt is explored and examined using graphical techniques and principal components analysis. Hand held radiometric field data are also presented for individual soil types within the region. It is proposed that by using reference data for individual soil types, improved estimates of vegetation cover can be ascertained. A number of radiometric corrections are applied to the digital Landsat data in order to convert the arbitrary digital values of the different spectral bands into physical values of reflectance. The effect of this standardization on the principal components is examined. The stratified approach to vegetation mapping which was explored using the field data is applied in turn to the digital Landsat images. Whilst the stratified approach was not found to offer significant advantages over the non-stratified approach in this case, the analysis does serve to provide an accurate datum against which to measure vegetation. In conclusion a satellite based system for operational vegetation monitoring is proposed.
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.
A LAI inversion algorithm based on the unified model of canopy bidirectional reflectance distribution function for the Heihe River Basin

NASA Astrophysics Data System (ADS)

Ma, B.; Li, J.; Fan, W.; Ren, H.; Xu, X.

2017-12-01

Leaf area index (LAI) is one of the important parameters of vegetation canopy structure, which can represent the growth condition of vegetation effectively. The accuracy, availability and timeliness of LAI data can be improved greatly, which is of great importance to vegetation-related research, such as the study of atmospheric, land surface and hydrological processes to obtain LAI by remote sensing method. Heihe River Basin is the inland river basin in northwest China. There are various types of vegetation and all kinds of terrain conditions in the basin, so it is helpful for testing the accuracy of the model under the complex surface and evaluating the correctness of the model to study LAI in this area. On the other hand, located in west arid area of China, the ecological environment of Heihe Basin is fragile, LAI is an important parameter to represent the vegetation growth condition, and can help us understand the status of vegetation in the Heihe River Basin. Different from the previous LAI inversion models, the BRDF (bidirectional reflectance distribution function) unified model can be applied for both continuous vegetation and discrete vegetation, it is appropriate to the complex vegetation distribution. LAI is the key input parameter of the model. We establish the inversion algorithm that can exactly retrieve LAI using remote sensing image based on the unified model. First, we determine the vegetation type through the vegetation classification map to obtain the corresponding G function, leaf and surface reflectivity. Then, we need to determine the leaf area index (LAI), the aggregation index (ζ) and the sky scattered light ratio (β) range and the value of the interval, entering all the parameters into the model to calculate the corresponding reflectivity ρ and establish the lookup table of different vegetation. Finally, we can invert LAI on the basis of the established lookup table. The principle of inversion is least squares method. We have produced 1 km LAI products from 2000 to 2014, once every 8 days. The results show that the algorithm owns good stability and can effectively invert LAI in areas with very complex vegetation and terrain conditions.
Passive Microwave Remote Sensing of Soil Moisture

NASA Technical Reports Server (NTRS)

Njoku, Eni G.; Entekhabi, Dara

1996-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.
Circumpolar Arctic vegetation: a hierarchic review and roadmap toward an internationally consistent approach to survey, archive and classify tundra plot data

Treesearch

D A Walker; F J A Daniels; I Alsos; U S Bhatt; A L Breen; M Buchhorn; H Bultmann; L A Druckenmiller; M E Edwards; D Ehrich; H E Epstein; William Gould; R A Ims; H Meltofte; M K Raynolds; J Sibik; S S Talbot; P J Webber

2016-01-01

Satellite-derived remote-sensing products are providing a modern circumpolar perspective of Arctic vegetation and its changes, but this new view is dependent on a long heritage of ground-based observations in the Arctic. Several products of the Conservation of Arctic Flora and Fauna are key to our current understanding.Wereview aspects of the PanArctic Flora, the...
Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system

NASA Astrophysics Data System (ADS)

Shi, Yeyin; Murray, Seth C.; Rooney, William L.; Valasek, John; Olsenholler, Jeff; Pugh, N. Ace; Henrickson, James; Bowden, Ezekiel; Zhang, Dongyan; Thomasson, J. Alex

2016-05-01

Recent development of unmanned aerial systems has created opportunities in automation of field-based high-throughput phenotyping by lowering flight operational cost and complexity and allowing flexible re-visit time and higher image resolution than satellite or manned airborne remote sensing. In this study, flights were conducted over corn and sorghum breeding trials in College Station, Texas, with a fixed-wing unmanned aerial vehicle (UAV) carrying two multispectral cameras and a high-resolution digital camera. The objectives were to establish the workflow and investigate the ability of UAV-based remote sensing for automating data collection of plant traits to develop genetic and physiological models. Most important among these traits were plant height and number of plants which are currently manually collected with high labor costs. Vegetation indices were calculated for each breeding cultivar from mosaicked and radiometrically calibrated multi-band imagery in order to be correlated with ground-measured plant heights, populations and yield across high genetic-diversity breeding cultivars. Growth curves were profiled with the aerial measured time-series height and vegetation index data. The next step of this study will be to investigate the correlations between aerial measurements and ground truth measured manually in field and from lab tests.
Urban area thermal monitoring: Liepaja case study using satellite and aerial thermal data

NASA Astrophysics Data System (ADS)

Gulbe, Linda; Caune, Vairis; Korats, Gundars

2017-12-01

The aim of this study is to explore large (60 m/pixel) and small scale (individual building level) temperature distribution patterns from thermal remote sensing data and to conclude what kind of information could be extracted from thermal remote sensing on regular basis. Landsat program provides frequent large scale thermal images useful for analysis of city temperature patterns. During the study correlation between temperature patterns and vegetation content based on NDVI and building coverage based on OpenStreetMap data was studied. Landsat based temperature patterns were independent from the season, negatively correlated with vegetation content and positively correlated with building coverage. Small scale analysis included spatial and raster descriptor analysis for polygons corresponding to roofs of individual buildings for evaluating insulation of roofs. Remote sensing and spatial descriptors are poorly related to heat consumption data, however, thermal aerial data median and entropy can help to identify poorly insulated roofs. Automated quantitative roof analysis has high potential for acquiring city wide information about roof insulation, but quality is limited by reference data quality and information on building types, and roof materials would be crucial for further studies.
Vegetation Water Content Mapping in a Diverse Agricultural Landscape: National Airborne Field Experiment 2006

NASA Technical Reports Server (NTRS)

Cosh, Michael H.; Jing Tao; Jackson, Thomas J.; McKee, Lynn; O'Neill, Peggy

2011-01-01

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.
Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotely-sensed data.

PubMed

Rotela, Camilo H; Spinsanti, Lorena I; Lamfri, Mario A; Contigiani, Marta S; Almirón, Walter R; Scavuzzo, Carlos M

2011-11-01

In response to the first human outbreak (January May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.

Validation of Distributed Soil Moisture: Airborne Polarimetric SAR vs. Ground-based Sensor Networks

NASA Astrophysics Data System (ADS)

Jagdhuber, T.; Kohling, M.; Hajnsek, I.; Montzka, C.; Papathanassiou, K. P.

2012-04-01

The knowledge of spatially distributed soil moisture is highly desirable for an enhanced hydrological modeling in terms of flood prevention and for yield optimization in combination with precision farming. Especially in mid-latitudes, the growing agricultural vegetation results in an increasing soil coverage along the crop cycle. For a remote sensing approach, this vegetation influence has to be separated from the soil contribution within the resolution cell to extract the actual soil moisture. Therefore a hybrid decomposition was developed for estimation of soil moisture under vegetation cover using fully polarimetric SAR data. The novel polarimetric decomposition combines a model-based decomposition, separating the volume component from the ground components, with an eigen-based decomposition of the two ground components into a surface and a dihedral scattering contribution. Hence, this hybrid decomposition, which is based on [1,2], establishes an innovative way to retrieve soil moisture under vegetation. The developed inversion algorithm for soil moisture under vegetation cover is applied on fully polarimetric data of the TERENO campaign, conducted in May and June 2011 for the Rur catchment within the Eifel/Lower Rhine Valley Observatory. The fully polarimetric SAR data were acquired in high spatial resolution (range: 1.92m, azimuth: 0.6m) by DLR's novel F-SAR sensor at L-band. The inverted soil moisture product from the airborne SAR data is validated with corresponding distributed ground measurements for a quality assessment of the developed algorithm. The in situ measurements were obtained on the one hand by mobile FDR probes from agricultural fields near the towns of Merzenhausen and Selhausen incorporating different crop types and on the other hand by distributed wireless sensor networks (SoilNet clusters) from a grassland test site (near the town of Rollesbroich) and from a forest stand (within the Wüstebach sub-catchment). Each SoilNet cluster incorporates around 150 wireless measuring devices on a grid of approximately 30ha for distributed soil moisture sensing. Finally, the comparison of both distributed soil moisture products results in a discussion on potentials and limitations for obtaining soil moisture under vegetation cover with high resolution fully polarimetric SAR. [1] S.R. Cloude, Polarisation: applications in remote sensing. Oxford, Oxford University Press, 2010. [2] Jagdhuber, T., Hajnsek, I., Papathanassiou, K.P. and Bronstert, A.: A Hybrid Decomposition for Soil Moisture Estimation under Vegetation Cover Using Polarimetric SAR. Proc. of the 5th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, ESA-ESRIN, Frascati, Italy, January 24-28, 2011, p.1-6.
Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

PubMed Central

Callister, Kate E.; Griffioen, Peter A.; Avitabile, Sarah C.; Haslem, Angie; Kelly, Luke T.; Kenny, Sally A.; Nimmo, Dale G.; Farnsworth, Lisa M.; Taylor, Rick S.; Watson, Simon J.; Bennett, Andrew F.; Clarke, Michael F.

2016-01-01

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km2 study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery. PMID:27029046
Biochemical processes in sagebrush ecosystems: Interactions with terrain

NASA Technical Reports Server (NTRS)

Matson, P. (Principal Investigator); Reiners, W.; Strong, L.

1985-01-01

The objectives of a biogeochemical study of sagebrush ecosystems in Wyoming and their interactions with terrain are as follows: to describe the vegetational pattern on the landscape and elucidate controlling variables, to measure the soil properties and chemical cycling properties associated with the vegetation units, to associate soil properties with vegetation properties as measured on the ground, to develop remote sensing capabilities for vegetation and surface characteristics of the sagebrush landscape, to develop a system of sensing snow cover and indexing seasonal soil to moisture; and to develop relationships between temporal Thematic Mapper (TM) data and vegetation phenological state.
A new multi-sensor integrated index for drought monitoring

NASA Astrophysics Data System (ADS)

Jiao, W.; Wang, L.; Tian, C.

2017-12-01

Drought is perceived as one of the most expensive and least understood natural disasters. The remote-sensing-based integrated drought indices, which integrate multiple variables, could reflect the drought conditions more comprehensively than single drought indices. However, most of current remote-sensing-based integrated drought indices focus on agricultural drought (i.e., deficit in soil moisture), their application in monitoring meteorological drought (i.e., deficit in precipitation) was limited. More importantly, most of the remote-sensing-based integrated drought indices did not take into consideration of the spatially non-stationary nature of the related variables, so such indices may lose essential local details when integrating multiple variables. In this regard, we proposed a new mathematical framework for generating integrated drought index for meteorological drought monitoring. The geographically weighted regression (GWR) model and principal component analysis were used to composite Moderate-resolution Imaging Spectroradiometer (MODIS) based temperature condition index (TCI), the Vegetation Index based on the Universal Pattern Decomposition method (VIUPD) based vegetation condition index (VCI), tropical rainfall measuring mission (TRMM) based Precipitation Condition Index (PCI) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) based soil moisture condition index (SMCI). We called the new remote-sensing-based integrated drought index geographical-location-based integrated drought index (GLIDI). We examined the utility of the GLIDI for drought monitoring in various climate divisions across the continental United States (CONUS). GLIDI showed high correlations with in-situ drought indices and outperformed most other existing drought indices. The results also indicate that the performance of GLIDI is not affected by environmental factors such as land cover, precipitation, temperature and soil conditions. As such, the GLIDI has considerable potential for drought monitoring across various environmental conditions.
Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study

PubMed Central

2014-01-01

Background Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Methods Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. Results During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. Conclusions In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season. PMID:24927747
Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study.

PubMed

Nygren, David; Stoyanov, Cristina; Lewold, Clemens; Månsson, Fredrik; Miller, John; Kamanga, Aniset; Shiff, Clive J

2014-06-13

Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season.
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.
Repression of Meiotic Genes by Antisense Transcription and by Fkh2 Transcription Factor in Schizosaccharomyces pombe

PubMed Central

Chen, Huei-Mei; Rosebrock, Adam P.; Khan, Sohail R.; Futcher, Bruce; Leatherwood, Janet K.

2012-01-01

In S. pombe, about 5% of genes are meiosis-specific and accumulate little or no mRNA during vegetative growth. Here we use Affymetrix tiling arrays to characterize transcripts in vegetative and meiotic cells. In vegetative cells, many meiotic genes, especially those induced in mid-meiosis, have abundant antisense transcripts. Disruption of the antisense transcription of three of these mid-meiotic genes allowed vegetative sense transcription. These results suggest that antisense transcription represses sense transcription of meiotic genes in vegetative cells. Although the mechanism(s) of antisense mediated transcription repression need to be further explored, our data indicates that RNAi machinery is not required for repression. Previously, we and others used non-strand specific methods to study splicing regulation of meiotic genes and concluded that 28 mid-meiotic genes are spliced only in meiosis. We now demonstrate that the “unspliced” signal in vegetative cells comes from the antisense RNA, not from unspliced sense RNA, and we argue against the idea that splicing regulates these mid-meiotic genes. Most of these mid-meiotic genes are induced in mid-meiosis by the forkhead transcription factor Mei4. Interestingly, deletion of a different forkhead transcription factor, Fkh2, allows low levels of sense expression of some mid-meiotic genes in vegetative cells. We propose that vegetative expression of mid-meiotic genes is repressed at least two independent ways: antisense transcription and Fkh2 repression. PMID:22238674
Evaluating Evapotranspiration of Pine Forest, Switchgrass, and Pine- Switchgrass Intercroppings using Remote Sensing and Ground-based Methods

NASA Astrophysics Data System (ADS)

Amatya, D. M.; Panda, S.; Chescheir, G. M.; Nettles, J. E.; Appelboom, T.; Skaggs, R. W.

2011-12-01

Vast areas of the land in the Southeastern United States are under pine forests managed primarily for timber and related byproducts. Evapotranspiration (ET) is the major loss in the water balance of this forest ecosystem. A long-term (1988-2008) study to evaluate hydrologic and nutrient balance during a life cycle of a pine stand was just completed. The study used both monitoring and modeling approaches to evaluate hydrologic and water quality effects of silvicultural and water management treatments on three 25 ha experimental watersheds in eastern North Carolina (NC). The research was extended in 2009 to include a dedicated energy crop, switchgrass (Panicum virgatum), by adding an adjacent 25 ha watershed. These multiple watersheds are being used to evaluate the hydrologic and water quality effects of switchgrass alone, young pine with natural understory, and young pine with switchgrass intercropping compared to the control (pine stand with a natural understory). The biofuels study has been further expanded to two other southern states, Alabama (AL) and Mississippi (MS). Each has five small watersheds (< 25 ha size) consisting of the above treatments and an additional woody biomass removal treatment. In this presentation we provide methods for estimating ET for these treatment watersheds in all three states (NC, AL, and MS) using remote sensing based spatial high resolution multispectral satellite imagery data with ground truthing, where possible, together with sensor technology. This technology is making ET parameter estimation a reality for various crops and vegetation surfaces. Slope-based vegetation indices like Normalized Difference Vegetation Index (NDVI) and Green Vegetation Index (GVI) and distance-based vegetation indices like Soil Adjusted Vegetation Index (SAVI) and Perpendicular Vegetation Index (PVI) will be developed using the R and NIR bands, vegetation density, and background soil reflectance as necessary. Landsat and high resolution aerial imageries of vegetation and soils will be used. IDRISI Taiga software will be used for the indices development. The forested vegetation health will be correlated to the leaf chlorophyll content for determining the vegetation health with a subsequent derivation of available plant water for radiation. Models will be developed to correlate the plant and soil available water to different vegetation indices. Correlation models will also be developed to obtain information on climatic parameters like surface air temperature, net radiation, albedo, soil moisture content, and stomatal water availability from Landsat imageries. On-site weather parameters used for the PET estimates will be combined with other vegetation parameters like leaf area index (LAI) obtained using LIDAR data and NAIP orthophotos of different seasons. That will also help detect the upper and understory vegetation. The LIDAR data will be processed to obtain the volume of vegetation to correctly estimate the total ET for each treatment.
Procedures for woody vegetation surveys in the Kazgail rural council area, Kordofan, Sudan

USGS Publications Warehouse

Falconer, Allan; Cross, Matthew D.; Orr, Donald G.

1990-01-01

Efforts to reforest parts of the Kordofan Province of Sudan are receiving support from international development agencies. These efforts include planning and implementing reforestation activities that require the collection of natural resources and socioeconomic data, and the preparation of base maps. A combination of remote sensing, geographic information system and global positioning systems procedures are used in this study to meet these requirements.Remote sensing techniques were used to provide base maps and to guide the compilation of vegetation resources maps. These techniques provided a rapid and efficient method for documenting available resources. Pocket‐sized global positioning system units were used to establish the location of field data collected for mapping and resource analysis. A microcomputer data management system tabulated and displayed the field data. The resulting system for data analysis, management, and planning has been adopted for the mapping and inventory of the Gum Belt of Sudan.
Vegetation burn severity mapping using Landsat-8 and WorldView-2

USGS Publications Warehouse

Wu, Zhuoting; Middleton, Barry R.; Hetzler, Robert; Vogel, John M.; Dye, Dennis G.

2015-01-01

We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.
Collection and analysis of remotely sensed data from the Rhode River Estuary Watershed

NASA Technical Reports Server (NTRS)

Jenkins, D. W.; Williamson, F. S. L.

1973-01-01

The remote sensing study to survey the Rhode River watershed for spray irrigation with secondarily treated sewage is reported. The standardization of Autumn coloration changes with Munsell color chips is described along with the mapping of old field vegetation for the spray irrigation project. The interpretation and verification of salt marsh vegetation by remote sensing of the water shed is discussed.
Evaluation of Multispectral Based Radiative Transfer Model Inversion to Estimate Leaf Area Index in Wheat

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based o...
PRELIMINARY INVESTIGATION OF SUBMERGED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL REMOTE SENSING

EPA Science Inventory

The use of airborne hyperspectral remote sensing imagery for automated mapping of submersed aquatic vegetation in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery, together with in-situ spectral refl...
Vegetation mapping of the Mond Protected Area of Bushehr Province (south-west Iran).

PubMed

Mehrabian, Ahmadreza; Naqinezhad, Alireza; Mahiny, Abdolrassoul Salman; Mostafavi, Hossein; Liaghati, Homan; Kouchekzadeh, Mohsen

2009-03-01

Arid regions of the world occupy up to 35% of the earth's surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.
Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 1; Theory

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Schull, Mitchell A.; Samanta, Arindam; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramakrishna R.; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRRmode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.
A tool for NDVI time series extraction from wide-swath remotely sensed images

NASA Astrophysics Data System (ADS)

Li, Zhishan; Shi, Runhe; Zhou, Cong

2015-09-01

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.
A Comparison of AIS Data with Other Aircraft and Ground Data for the Geobotanical Discrimination of Rock Types in Southwest Oregon

NASA Technical Reports Server (NTRS)

Mouat, D. A.

1985-01-01

The use of remote sensing techniques for the geobotanical discrimination of rock types is predicated upon a number of factors. These include an understanding of vegetation response to environmental (especially geochemical) conditions, the establishment of correlations between those vegetation factors and environmental factors, and the use of appropriate remote sensing techniques to discriminate the vegetation.
Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces

NASA Technical Reports Server (NTRS)

Myneni, Ranga B.; Asrar, Ghassem; Tanre, Didier; Choudhury, Bhaskar J.

1992-01-01

1D and 3D radiative-transfer models have been used to investigate the problem of remotely sensed determination of vegetated land surface-absorbed and reflected solar radiation. Calculations were conducted for various illumination conditions to determine surface albedo, soil- and canopy-absorbed photosynthetically active and nonactive radiation, and normalized difference vegetation index. Simple predictive models are developed on the basis of the relationships among these parameters.
Earth Observation for monitoring phenology for european land use and ecosystems over 1998-2011

NASA Astrophysics Data System (ADS)

Ceccherini, Guido; Gobron, Nadine

2013-04-01

Long-term measurements of plant phenology have been used to track vegetation responses to climate change but are often limited to particular species and locations and may not represent synoptic patterns. Given the limitations of working directly with in-situ data, many researchers have instead used available satellite remote sensing. Remote sensing extends the possible spatial coverage and temporal range of phenological assessments of environmental change due to the greater availability of observations. Variations and trends of vegetation dynamics are important because they alter the surface carbon, water and energy balance. For example, the net ecosystem CO2 exchange of vegetation is strongly linked to length of the growing season: extentions and decreases in length of growing season modify carbon uptake and the amount of CO2 in the atmosphere. Advances and delays in starting of growing season also affect the surface energy balance and consequently transpiration. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a key climate variable identified by Global Terrestrial Observing System (GTOS) that can be monitored from space. This dimensionless variable - varying between 0 and 1- is directly linked to the photosynthetic activity of vegetation, and therefore, can monitor changes in phenology. In this study, we identify the spatio/temporal patterns of vegetation dynamics using a long-term remotely sensed FAPAR dataset over Europe. Our aim is to provide a quantitative analysis of vegetation dynamics relevant to climate studies in Europe. As part of this analysis, six vegetation phenological metrics have been defined and made routinely in Europe. Over time, such metrics can track simple, yet critical, impacts of climate change on ecosystems. Validation has been performed through a direct comparison against ground-based data over ecological sites. Subsequently, using the spatio/temporal variability of this suite of metrics, we classify areas with similar vegetation dynamics. This permits assessment of variations and trends of vegetation dynamics over Europe. Statistical tests to assess the significance of temporal changes are used to evaluate trends in the metrics derived from the recorded time series of the FAPAR.

Mapping rice extent map with crop intensity in south China through integration of optical and microwave images based on google earth engine

NASA Astrophysics Data System (ADS)

Zhang, X.; Wu, B.; Zhang, M.; Zeng, H.

2017-12-01

Rice is one of the main staple foods in East Asia and Southeast Asia, which has occupied more than half of the world's population with 11% of cultivated land. Study on rice can provide direct or indirect information on food security and water source management. Remote sensing has proven to be the most effective method to monitoring the cropland in large scale by using temporary and spectral information. There are two main kinds of satellite have been used to mapping rice including microwave and optical. Rice, as the main crop of paddy fields, the main feature different from other crops is flooding phenomenon at planning stage (Figure 1). Microwave satellites can penetrate through clouds and efficiency on monitoring flooding phenomenon. Meanwhile, the vegetation index based on optical satellite can well distinguish rice from other vegetation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. Google has collected large number of remote sensing satellite data around the world, which providing researchers with the possibility of doing application by using multi-source remote sensing data in a large area. In this work, we map rice planting area in south China through integration of Landsat-8 OLI, Sentienl-2, and Sentinel-1 Synthetic Aperture Radar (SAR) images. The flowchart is shown in figure 2. First, a threshold method the VH polarized backscatter from SAR sensor and vegetation index including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from optical sensor were used the classify the rice extent map. The forest and water surface extent map provided by earth engine were used to mask forest and water. To overcome the problem of the "salt and pepper effect" by Pixel-based classification when the spatial resolution increased, we segment the optical image and use the pixel- based classification results to merge the object-oriented segmentation data, and finally get the rice extent map. At last, by using the time series analysis, the peak count was obtained for each rice area to ensure the crop intensity. In this work, the rice ground point from a GVG crowdsourcing smartphone and rice area statistical results from National Bureau of Statistics were used to validate and evaluate our result.
Mid Term Progress Report: Desertification Assessment and Monitoring in China Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Zhihai; del Barrio, Gabriel; Li, Xiaosong; Wang, Bengyu; Puigdefabregas, Juan; Sanjuan, Maria E.; Bai, Lina; Wu, Junjun; Sun, Bin; Li, Changlong

2014-11-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired since2012could be summarized as follows: (1)Photosynthetic vegetation(PV)and non-photosynthetic vegetation(NPV)fraction were retrieved separately through utilizing Auto Monte Carlo Unmixing technique (AutoMCU), based on BJ-1 data and field measured spectral library. (2) The accuracy of sandy land classification was as high as81.52%when the object-oriented method and Support Vector Machine (SVM) classifiers were used. (3) A new Monthly net primary productivity (NPP)dataset from 2002 to 2010 for the whole China were established with Envisat-MERIS fraction of absorbed photosynthetically active radiation (FPAR) data. (4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China(PEDC) was assessed preliminarily.
Mid Term Progress Report: Desertification Assessment and Monitoring in China Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Zhihai; del Barrio, Gabriel; Li, Xiaosong; Wang, Wengyu; Puigdefabregas, Juan; Sanjuan, Maria E.; Bai, Lina; Wu, Junjun; Sun, Bin; Li, Changlong

2014-11-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired since 2012 could be summarized as follows:(1) Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) fraction were retrieved separately through utilizing Auto Monte Carlo Unmixing technique (AutoMCU), based on BJ-1 data and field measured spectral library.(2) The accuracy of sandy land classification was as high as 81.52% when the object-oriented method and Support Vector Machine (SVM) classifiers were used.(3) A new Monthly net primary productivity (NPP) dataset from 2002 to 2010 for the whole China were established with Envisat-MERIS fraction of absorbed photosynthetically active radiation (FPAR) data.(4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China (PEDC) was assessed preliminarily.
[An object-based information extraction technology for dominant tree species group types].

PubMed

Tian, Tian; Fan, Wen-yi; Lu, Wei; Xiao, Xiang

2015-06-01

Information extraction for dominant tree group types is difficult in remote sensing image classification, howevers, the object-oriented classification method using high spatial resolution remote sensing data is a new method to realize the accurate type information extraction. In this paper, taking the Jiangle Forest Farm in Fujian Province as the research area, based on the Quickbird image data in 2013, the object-oriented method was adopted to identify the farmland, shrub-herbaceous plant, young afforested land, Pinus massoniana, Cunninghamia lanceolata and broad-leave tree types. Three types of classification factors including spectral, texture, and different vegetation indices were used to establish a class hierarchy. According to the different levels, membership functions and the decision tree classification rules were adopted. The results showed that the method based on the object-oriented method by using texture, spectrum and the vegetation indices achieved the classification accuracy of 91.3%, which was increased by 5.7% compared with that by only using the texture and spectrum.
Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin-up.

PubMed

Hanan, Erin J; Tague, Christina; Choate, Janet; Liu, Mingliang; Kolden, Crystal; Adam, Jennifer

2018-03-24

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections. © 2018 by the Ecological Society of America.
New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data

PubMed Central

Yang, Bao; He, Minhui; Shishov, Vladimir; Tychkov, Ivan; Vaganov, Eugene; Rossi, Sergio; Ljungqvist, Fredrik Charpentier; Bräuning, Achim; Grießinger, Jussi

2017-01-01

Phenological responses of vegetation to climate, in particular to the ongoing warming trend, have received much attention. However, divergent results from the analyses of remote sensing data have been obtained for the Tibetan Plateau (TP), the world’s largest high-elevation region. This study provides a perspective on vegetation phenology shifts during 1960–2014, gained using an innovative approach based on a well-validated, process-based, tree-ring growth model that is independent of temporal changes in technical properties and image quality of remote sensing products. Twenty composite site chronologies were analyzed, comprising about 3,000 trees from forested areas across the TP. We found that the start of the growing season (SOS) has advanced, on average, by 0.28 d/y over the period 1960–2014. The end of the growing season (EOS) has been delayed, by an estimated 0.33 d/y during 1982–2014. No significant changes in SOS or EOS were observed during 1960–1981. April–June and August–September minimum temperatures are the main climatic drivers for SOS and EOS, respectively. An increase of 1 °C in April–June minimum temperature shifted the dates of xylem phenology by 6 to 7 d, lengthening the period of tree-ring formation. This study extends the chronology of TP phenology farther back in time and reconciles the disparate views on SOS derived from remote sensing data. Scaling up this analysis may improve understanding of climate change effects and related phenological and plant productivity on a global scale. PMID:28630302
New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data

NASA Astrophysics Data System (ADS)

Yang, Bao; He, Minhui; Shishov, Vladimir; Tychkov, Ivan; Vaganov, Eugene; Rossi, Sergio; Charpentier Ljungqvist, Fredrik; Bräuning, Achim; Grießinger, Jussi

2017-07-01

Phenological responses of vegetation to climate, in particular to the ongoing warming trend, have received much attention. However, divergent results from the analyses of remote sensing data have been obtained for the Tibetan Plateau (TP), the world’s largest high-elevation region. This study provides a perspective on vegetation phenology shifts during 1960-2014, gained using an innovative approach based on a well-validated, process-based, tree-ring growth model that is independent of temporal changes in technical properties and image quality of remote sensing products. Twenty composite site chronologies were analyzed, comprising about 3,000 trees from forested areas across the TP. We found that the start of the growing season (SOS) has advanced, on average, by 0.28 d/y over the period 1960-2014. The end of the growing season (EOS) has been delayed, by an estimated 0.33 d/y during 1982-2014. No significant changes in SOS or EOS were observed during 1960-1981. April-June and August-September minimum temperatures are the main climatic drivers for SOS and EOS, respectively. An increase of 1 °C in April-June minimum temperature shifted the dates of xylem phenology by 6 to 7 d, lengthening the period of tree-ring formation. This study extends the chronology of TP phenology farther back in time and reconciles the disparate views on SOS derived from remote sensing data. Scaling up this analysis may improve understanding of climate change effects and related phenological and plant productivity on a global scale.
Monitoring ecosystem reclamation recovery using optical remote sensing: Comparison with field measurements and eddy covariance.

PubMed

Chasmer, L; Baker, T; Carey, S K; Straker, J; Strilesky, S; Petrone, R

2018-06-12

Time series remote sensing vegetation indices derived from SPOT 5 data are compared with vegetation structure and eddy covariance flux data at 15 dry to wet reclamation and reference sites within the Oil Sands region of Alberta, Canada. This comprehensive analysis examines the linkages between indicators of ecosystem function and change trajectories observed both at the plot level and within pixels. Using SPOT imagery, we find that higher spatial resolution datasets (e.g. 10 m) improves the relationship between vegetation indices and structural measurements compared with interpolated (lower resolution) pixels. The simple ratio (SR) vegetation index performs best when compared with stem density-based indicators (R 2  = 0.65; p < 0.00), while the normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) are most comparable to foliage indicators (leaf area index (LAI) and canopy cover (R 2  = 0.52-0.78; p > 0.02). Fluxes (net ecosystem production (NEP) and gross ecosystem production (GEP)) are most related to NDVI and SAVI when these are interpolated to larger 20 m × 20 m pixels (R 2  = 0.44-0.50; p < 0.00). As expected, decreased sensitivity of NDVI is problematic for sites with LAI > 3 m 2  m -2 , making this index more appropriate for newly regenerating reclamation areas. For sites with LAI < 3 m 2  m -2 , trajectories of vegetation change can be mapped over time and are within 2.7% and 3.3% of annual measured LAI changes observed at most sites. This study demonstrates the utility of remote sensing in combination with field and eddy covariance data for monitoring and scaling of reclaimed and reference site productivity within and beyond the Oil Sands Region of western Canada. Copyright © 2018 Elsevier B.V. All rights reserved.
Salinity modeling by remote sensing in central and southern Iraq

NASA Astrophysics Data System (ADS)

Wu, W.; Mhaimeed, A. S.; Platonov, A.; Al-Shafie, W. M.; Abbas, A. M.; Al-Musawi, H. H.; Khalaf, A.; Salim, K. A.; Chrsiten, E.; De Pauw, E.; Ziadat, F.

2012-12-01

Salinization, leading to a significant loss of cultivated land and crop production, is one of the most active land degradation phenomena in the Mesopotamian region in Iraq. The objectives of this study (under the auspices of ACIAR and Italian Government) are to investigate the possibility to use remote sensing technology to establish salinity-sensitive models which can be further applied to local and regional salinity mapping and assessment. Case studies were conducted in three pilot sites namely Musaib, Dujaila and West Garraf in the central and southern Iraq. Fourteen spring (February - April), seven June and four summer Landsat ETM+ images in the period 2009-2012, RapidEye data (April 2012), and 95 field EM38 measurements undertaken in this spring and summer, 16 relevant soil laboratory analysis result (Dujaila) were employed in this study. The procedure we followed includes: (1) Atmospheric correction using FLAASH model; (2) Multispectral transformation of a set of vegetation and non-vegetation indices such as GDVI (Generalized Difference Vegetation Index), NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), SARVI (Soil Adjusted and Atmospherically Resistant Vegetation Index), NDII (Normalized Difference Infrared Index), Principal Components and surface temperature (T); (3) Derivation of the spring maximum (Musaib) and annual maximum (Dujaila and West Garraf) value in each pixel of each index of the observed period to avoid problems related to crop rotation (e.g. fallow) and the SLC-Off gaps in ETM+ images; (4) Extraction of the values of each vegetation and non-vegetation index corresponding to the field sampling locations (about 3 to 5 controversial samples very close to the roads or located in fallow were excluded); and (5) Coupling remote sensing indices with the available EM38 and soil electrical conductivity (EC) data using multiple linear least-square regression model at the confidence level of 95% in a stepwise (forward) manner. The results reveal that soil salinity and EM38 readings are negatively correlated with the different vegetation indices, especially, GDVI and NDVI, and positively correlated with T. The models obtained for the pilot sites are presented in Table 1. Although we are still waiting for more laboratory analytical result and satellite imagery for more comprehensive analysis, it is clearly possible to build up salinity models by remote sensing, on which further salinity mapping and assessment can be based. It is also noted that among all the vegetation indices, GDVI is the best salinity indicator followed by NDVI and T. RapidEye image shows lower correlation with EM38 measurements and EC because fallow and crop rotation issue cannot be sorted out by one acquisition image.Table 1: Salinity models obtained from the pilot sitesNote: EMV- Vertical reading of EM38, EC - Electrical conductivity in dS/m
[Effects of climate and land use change on the changes of vegetation coverage in farming-pastoral ecotone of Northern China].

PubMed

Liu, Jun-Hui; Gao, Ji-Xi

2008-09-01

Based on the remote sensing images and the meteorological data in 1986 and 2000, and by using the model of extracting vegetation coverage, the spatiotemporal changes of vegetation coverage in the farming-pastoral ecotone of Northern China in 1986-2000 were studied, with the effects of climate and land use change on the changes analyzed. The results showed that in this ecotone, the area with lower vegetation coverage was increasing, while that with higher vegetation coverage was in adverse. The regions with increasing vegetation coverage were mainly in the east of northeast section, the west of north section, and the west of northwest section of the ecotone, while the vegetation coverage in the other sections was obviously degraded. The vegetation coverage were positively correlated with precipitation and aridity index, but negatively correlated with temperature. The change direction and extent of the vegetation coverage varied with land use types.
Remote sensing sensors and applications in environmental resources mapping and modeling

USGS Publications Warehouse

Melesse, Assefa M.; Weng, Qihao; Thenkabail, Prasad S.; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.
Space-Derived Phenology, Retrieval and Use for Drought and Food Security Monitoring

NASA Astrophysics Data System (ADS)

Meroni, M.; Kayitakire, F.; Rembold, F.; Urbano, F.; Schucknecht, A.; LEO, O.

2014-12-01

Monitoring vegetation conditions is a critical activity for assessing food security in Africa. Rural populations relying on rain-fed agriculture and livestock grazing are highly exposed to large seasonal and inter-annual fluctuations in water availability. Monitoring the state, evolution, and productivity of vegetation, crops and pastures in particular, is important to conduct food emergency responses and plan for a long-term, resilient, development strategy in this area. The timing of onset, the duration, and the intensity of vegetation growth can be retrieved from space observations and used for food security monitoring to assess seasonal vegetation development and forecast the likely seasonal outcome when the season is ongoing. In this contribution we present a set of phenology-based remote sensing studies in support to food security analysis. Key phenological indicators are retrieved using a model-fit approach applied to SOPT-VEGETATION FAPAR time series. Remote-sensing phenology is first used to estimate i) the impact of the drought in the Horn of Africa, ii) crop yield in Tunisia and, iii) rangeland biomass production in Niger. Then the impact of the start and length of vegetation growing period on the total biomass production is assessed over the Sahel. Finally, a probabilistic approach using phenological information to forecast the occurrence of an end-of-season biomass production deficit is applied over the Sahel to map hot-spots of drought-related risk.
Ground-based Remote Sensing for Quantifying Subsurface and Surface Co-variability to Scale Arctic Ecosystem Functioning

NASA Astrophysics Data System (ADS)

Oktem, R.; Wainwright, H. M.; Curtis, J. B.; Dafflon, B.; Peterson, J.; Ulrich, C.; Hubbard, S. S.; Torn, M. S.

2016-12-01

Predicting carbon cycling in Arctic requires quantifying tightly coupled surface and subsurface processes including permafrost, hydrology, vegetation and soil biogeochemistry. The challenge has been a lack of means to remotely sense key ecosystem properties in high resolution and over large areas. A particular challenge has been characterizing soil properties that are known to be highly heterogeneous. In this study, we exploit tightly-coupled above/belowground ecosystem functioning (e.g., the correlations among soil moisture, vegetation and carbon fluxes) to estimate subsurface and other key properties over large areas. To test this concept, we have installed a ground-based remote sensing platform - a track-mounted tram system - along a 70 m transect in the ice-wedge polygonal tundra near Barrow, Alaska. The tram carries a suite of near-surface remote sensing sensors, including sonic depth, thermal IR, NDVI and multispectral sensors. Joint analysis with multiple ground-based measurements (soil temperature, active layer soil moisture, and carbon fluxes) was performed to quantify correlations and the dynamics of above/belowground processes at unprecedented resolution, both temporally and spatially. We analyzed the datasets with particular focus on correlating key subsurface and ecosystem properties with surface properties that can be measured by satellite/airborne remote sensing over a large area. Our results provided several new insights about system behavior and also opens the door for new characterization approaches. We documented that: (1) soil temperature (at >5 cm depth; critical for permafrost thaw) was decoupled from soil surface temperature and was influenced strongly by soil moisture, (2) NDVI and greenness index were highly correlated with both soil moisture and gross primary productivity (based on chamber flux data), and (3) surface deformation (which can be measured by InSAR) was a good proxy for thaw depth dynamics at non-inundated locations.
Data needs and data bases for climate studies

NASA Technical Reports Server (NTRS)

Matthews, Elaine

1986-01-01

Two complementary global digital data bases of vegetation and land use, compiled at 1 deg resolution from published sources for use in climate studies, are discussed. The data bases were implemented, in several individually tailored formulations, in a series of climate related applications including: land-surface prescriptions in three-dimensional general circulation models, global biogeochemical cycles (CO2, methane), critical-area mapping for satellite monitoring of land-cover change, and large-scale remote sensing of surface reflectance. The climate applications are discussed with reference to data needs, and data availability from traditional and remote sensing sources.
Validation of satellite data through the remote sensing techniques and the inclusion of them into agricultural education pilot programs

NASA Astrophysics Data System (ADS)

Papadavid, Georgios; Kountios, Georgios; Bournaris, T.; Michailidis, Anastasios; Hadjimitsis, Diofantos G.

2016-08-01

Nowadays, the remote sensing techniques have a significant role in all the fields of agricultural extensions as well as agricultural economics and education but they are used more specifically in hydrology. The aim of this paper is to demonstrate the use of field spectroscopy for validation of the satellite data and how combination of remote sensing techniques and field spectroscopy can have more accurate results for irrigation purposes. For this reason vegetation indices are used which are mostly empirical equations describing vegetation parameters during the lifecycle of the crops. These numbers are generated by some combination of remote sensing bands and may have some relationship to the amount of vegetation in a given image pixel. Due to the fact that most of the commonly used vegetation indices are only concerned with red-near-infrared spectrum and can be divided to perpendicular and ratio based indices the specific goal of the research is to illustrate the effect of the atmosphere to those indices, in both categories. In this frame field spectroscopy is employed in order to derive the spectral signatures of different crops in red and infrared spectrum after a campaign of ground measurements. The main indices have been calculated using satellite images taken at interval dates during the whole lifecycle of the crops by using a GER 1500 spectro-radiomete. These indices was compared to those extracted from satellite images after applying an atmospheric correction algorithm -darkest pixel- to the satellite images at a pre-processing level so as the indices would be in comparable form to those of the ground measurements. Furthermore, there has been a research made concerning the perspectives of the inclusion of the above mentioned remote satellite techniques to agricultural education pilot programs.
A Sensitivity Analysis of NDWI and SRWI to Different types of Vegetation Moisture

NASA Astrophysics Data System (ADS)

Chai, Linna; Chen, Zhizhong

2017-04-01

There are many definitions of vegetation moisture, such as fuel moisture content (FMC), gravimetric water content (GWC), relative water content (RWC), leaf water content (LWC), canopy water content (CWC) and vegetation water content (VWC). They were introduced because of different applications. For example, FMC is with superiority in monitoring wildfire potential, and GWC responses well to determine whether the plant is in health. RWC is suitable for estimating vegetation water stress. LWC and CWC are often used in optical remote sensing and are always related to equivalent water thickness (EWT). For VWC, the main application is for improving retrievals of soil moisture content from microwave sensors. For optical remote sensing technique, the absorption features of liquid water in plant leaves are readily detectable by spectroscopy. Spectral reflectance at 970nm, 1200nm, 1450nm, 1930nm and 2500nm are the basis of numerous remote-sensing indices that could be used in estimating vegetation moisture. Foregoing studies have introduced different spectral indices based on these bands to retrieve vegetation moisture. These spectral indices often fall into two categories, one is Normalized Different Water Index (NDWI), and the other is Simple Ratio Water Index (SRWI). NDWIs take the form of normalized difference spectral index, while SRWIs are in the form of ratio type. They were calculated from different combinations of spectral channels. Since the sensitivities to vegetation moisture of reflectance at different spectral channel are distinguished from each other, the capabilities of these NDWIs and SRWIs in estimating different types of vegetation moisture will be distinguished from one to one. In this work, based on in-situ measurements collected in the north China plain from wheat and corn (Fig. 1), a sensitivity analysis of NDWI and SRWI to different types of vegetation moisture, such as VWC, FMC and GWC, was carried out. They were calculated from different combinations of spectral channels of MODIS and Landsat-8 OLI. Result shows that: 1) NDWI and SRWI are more sensitive to VWC than to FMC and GWC; 2) SRWI and NDWI calculated from reflectances of green band at about 550nm and shortwave infrared band at about 1240nm often yielded relatively higher correlation coefficients with VWC; 3) For a fixed two-band combination, SRWI shows a slight superiority to NDWI. PIC Fig.1 The north China plain and the experimental area with corn and winter wheat sample locations A detailed description to this study work will be demonstrated in the fullpaper.
Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.
Equipment and techniques for low-altitude aerial sensing of water-vapor concentration and movement

USGS Publications Warehouse

Howell, R.L.

1969-01-01

Progress in the development of equipment and techniques for making rapid measurements of moisture movement through the atmosphere over a large area is described. Airborne sensing elements measure relative humidity, temperature, and air currents. These data are telemetered to a ground-based station and recorded. A radar unit tracks the aircraft and electronically plots its position on a base map of the area being studied. Thus the distribution of atmospheric conditions can be directly related to the underlying terrain and vegetation features. ?? 1969 American Elsevier Publishing Company, Inc.
Application of remote sensing in regional scale estimates of vegetation carbon budgets: The Belfix project

NASA Technical Reports Server (NTRS)

Veroustraete, Frank; Patyn, Johan; Myneni, R. B.

1994-01-01

A concept for coupling the remote sensing derived fraction of the absorbed photosynthetic active radiation (FAPAR) with a functional ecosystem model was developed. The study was named the Belfix procedure. The quantification of changes in carbon dynamics at the ecosystem level is a key issue in studies of global climatic change effects at the vegetation atmosphere interface. An operational procedure, for the determination of carbon fluxes at the regional scale (Belgian territory), is presented. The approach allows for the determination of the sink function of vegetation for carbon (dioxide). The phyto- and litter mass, photosynthetic assimilation, autotroph and heterotroph carbon fluxes and net ecosystem exchange (NEE) of carbon, were evaluated. The results suggest that a single solution can be obtained for ecosystem rates and states, applying an iterative procedure, based on minimizing the change in maximal seasonal green phytomass in function of yearly FAPAR temporal profiles. Total phytomass values obtained are in close range with those obtained by ground sampling.
Data and techniques for studying the urban heat island effect in Johannesburg

NASA Astrophysics Data System (ADS)

Hardy, C. H.; Nel, A. L.

2015-04-01

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg's residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

Global Urban Mapping and Modeling for Sustainable Urban Development

NASA Astrophysics Data System (ADS)

Zhou, Y.; Li, X.; Asrar, G.; Yu, S.; Smith, S.; Eom, J.; Imhoff, M. L.

2016-12-01

In the past several decades, the world has experienced fast urbanization, and this trend is expected to continue for decades to come. Urbanization, one of the major land cover and land use changes (LCLUC), is becoming increasingly important in global environmental changes, such as urban heat island (UHI) growth and vegetation phenology change. Better scientific insights and effective decision-making unarguably require reliable science-based information on spatiotemporal changes in urban extent and their environmental impacts. In this study, we developed a globally consistent 20-year urban map series to evaluate the time-reactive nature of global urbanization from the nighttime lights remote sensing data, and projected future urban expansion in the 21st century by employing an integrated modeling framework (Zhou et al. 2014, Zhou et al. 2015). We then evaluated the impacts of urbanization on building energy use and vegetation phenology that affect both ecosystem services and human health. We extended the modeling capability of building energy use in the Global Change Assessment Model (GCAM) with consideration of UHI effects by coupling the remote sensing based urbanization modeling and explored the impact of UHI on building energy use. We also investigated the impact of urbanization on vegetation phenology by using an improved phenology detection algorithm. The derived spatiotemporal information on historical and potential future urbanization and its implications in building energy use and vegetation phenology will be of great value in sustainable urban design and development for building energy use and human health (e.g., pollen allergy), especially when considered together with other factors such as climate variability and change. Zhou, Y., S. J. Smith, C. D. Elvidge, K. Zhao, A. Thomson & M. Imhoff (2014) A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment, 147, 173-185. Zhou, Y., S. J. Smith, K. Zhao, M. Imhoff, A. Thomson, B. Bond-Lamberty, G. R. Asrar, X. Zhang, C. He & C. D. Elvidge (2015) A global map of urban extent from nightlights. Environmental Research Letters, 10, 054011.
Monitoring of Vegetation Impact Due to Trampling on Cadillac Mountain Summit Using High Spatial Resolution Remote Sensing Data Sets

NASA Astrophysics Data System (ADS)

Kim, Min-Kook; Daigle, John J.

2012-11-01

Cadillac Mountain—the highest peak along the eastern seaboard of the United States—is a major tourist destination in Acadia National Park, Maine. Managing vegetation impact due to trampling on the Cadillac Mountain summit is extremely challenging because of the large number of visitors and the general open nature of landscape in this fragile subalpine environmental setting. Since 2000, more intensive management strategies—based on placing physical barriers and educational messages for visitors—have been employed to protect threatened vegetation, decrease vegetation impact, and enhance vegetation recovery in the vicinity of the summit loop trail. The primary purpose of this study was to evaluate the effect of the management strategies employed. For this purpose, vegetation cover changes between 2001 and 2007 were detected using multispectral high spatial resolution remote sensing data sets. A normalized difference vegetation index was employed to identify the rates of increase and decrease in the vegetation areas. Three buffering distances (30, 60, and 90 m) from the edges of the trail were used to define multiple spatial extents of the site, and the same spatial extents were employed at a nearby control site that had no visitors. No significant differences were detected between the mean rates of vegetation increase and decrease at the experimental site compared with a nearby control site in the case of a small spatial scale (≤30 m) comparison (in all cases P > 0.05). However, in the medium (≤60 m) and large (≤90 m) spatial scales, the rates of increased vegetation were significantly greater and rates of decreased vegetation significantly lower at the experimental site compared with the control site (in all cases P < 0.001). Research implications are explored that relate to the spatial extent of the radial patterns of impact of trampling on vegetation at the site level. Management implications are explored in terms of the spatial strategies used to decrease the impact of trampling on vegetation.
Monitoring of vegetation impact due to trampling on Cadillac Mountain summit using high spatial resolution remote sensing data sets.

PubMed

Kim, Min-Kook; Daigle, John J

2012-11-01

Cadillac Mountain--the highest peak along the eastern seaboard of the United States--is a major tourist destination in Acadia National Park, Maine. Managing vegetation impact due to trampling on the Cadillac Mountain summit is extremely challenging because of the large number of visitors and the general open nature of landscape in this fragile subalpine environmental setting. Since 2000, more intensive management strategies--based on placing physical barriers and educational messages for visitors--have been employed to protect threatened vegetation, decrease vegetation impact, and enhance vegetation recovery in the vicinity of the summit loop trail. The primary purpose of this study was to evaluate the effect of the management strategies employed. For this purpose, vegetation cover changes between 2001 and 2007 were detected using multispectral high spatial resolution remote sensing data sets. A normalized difference vegetation index was employed to identify the rates of increase and decrease in the vegetation areas. Three buffering distances (30, 60, and 90 m) from the edges of the trail were used to define multiple spatial extents of the site, and the same spatial extents were employed at a nearby control site that had no visitors. No significant differences were detected between the mean rates of vegetation increase and decrease at the experimental site compared with a nearby control site in the case of a small spatial scale (≤30 m) comparison (in all cases P > 0.05). However, in the medium (≤60 m) and large (≤90 m) spatial scales, the rates of increased vegetation were significantly greater and rates of decreased vegetation significantly lower at the experimental site compared with the control site (in all cases P < 0.001). Research implications are explored that relate to the spatial extent of the radial patterns of impact of trampling on vegetation at the site level. Management implications are explored in terms of the spatial strategies used to decrease the impact of trampling on vegetation.
[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.
Remote Sensing of Vegetation Nitrogen Content for Spatially Explicit Carbon and Water Cycle Estimation

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Miller, J. R.; Chen, J. M.

2009-05-01

Foliage nitrogen concentration is a determinant of photosynthetic capacity of leaves, thereby an important input to ecological models for estimating terrestrial carbon and water budgets. Recently, spectrally continuous airborne hyperspectral remote sensing imagery has proven to be useful for retrieving an important related parameter, total chlorophyll content at both leaf and canopy scales. Thus remote sensing of vegetation biochemical parameters has promising potential for improving the prediction of global carbon and water balance patterns. In this research, we explored the feasibility of estimating leaf nitrogen content using hyperspectral remote sensing data for spatially explicit estimation of carbon and water budgets. Multi-year measurements of leaf biochemical contents of seven major boreal forest species were carried out in northeastern Ontario, Canada. The variation of leaf chlorophyll and nitrogen content in response to various growth conditions, and the relationship between them,were investigated. Despite differences in plant type (deciduous and evergreen), leaf age, stand growth conditions and developmental stages, leaf nitrogen content was strongly correlated with leaf chlorophyll content on a mass basis during the active growing season (r2=0.78). With this general correlation, leaf nitrogen content was estimated from leaf chlorophyll content at an accuracy of RMSE=2.2 mg/g, equivalent to 20.5% of the average measured leaf nitrogen content. Based on this correlation and a hyperspectral remote sensing algorithm for leaf chlorophyll content retrieval, the spatial variation of leaf nitrogen content was inferred from the airborne hyperspectral remote sensing imagery acquired by Compact Airborne Spectrographic Imager (CASI). A process-based ecological model Boreal Ecosystem Productivity Simulator (BEPS) was used for estimating terrestrial carbon and water budgets. In contrast to the scenario with leaf nitrogen content assigned as a constant value without differentiation between and within vegetation types for calculating the photosynthesis rate, we incorporated the spatial distribution of leaf nitrogen content in the model to estimate net primary productivity and evaportranspiration of boreal ecosystem. These regional estimates of carbon and water budgets with and without N mapping are compared, and the importance of this leaf biochemistry information derived from hyperspectral remote sensing in regional mapping of carbon and water fluxes is quantitatively assessed. Keywords: Remote Sensing, Leaf Nitrogen Content, Spatial Distribution, Carbon and Water Budgets, Estimation
The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE)

PubMed Central

Tian, Xin; Li, Zengyuan; Chen, Erxue; Liu, Qinhuo; Yan, Guangjian; Wang, Jindi; Niu, Zheng; Zhao, Shaojie; Li, Xin; Pang, Yong; Su, Zhongbo; van der Tol, Christiaan; Liu, Qingwang; Wu, Chaoyang; Xiao, Qing; Yang, Le; Mu, Xihan; Bo, Yanchen; Qu, Yonghua; Zhou, Hongmin; Gao, Shuai; Chai, Linna; Huang, Huaguo; Fan, Wenjie; Li, Shihua; Bai, Junhua; Jiang, Lingmei; Zhou, Ji

2015-01-01

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques. PMID:26332035
Field based plastic contamination sensing

USDA-ARS?s Scientific Manuscript database

The United States has a long-held reputation of being a dependable source of high quality, contaminant-free cotton. Recently, increased incidence of plastic contamination from sources such as shopping bags, vegetable mulch, surface irrigation tubing, and module covers has threatened the reputation o...
Estimating soil organic and aboveground woody carbon stock in a protected dry Miombo ecosystem, Zimbabwe: Landsat 8 OLI data applications

NASA Astrophysics Data System (ADS)

Dube, Timothy; Muchena, Richard; Masocha, Mhosisi; Shoko, Cletah

2018-06-01

Accurate and reliable soil organic carbon stock estimation is critical in understanding forest role to regional carbon cycles. So far, the total carbon pool in dry Miombo ecosystems is often under-estimated. In that regard this study sought to model the relationship between the aboveground woody carbon pool and the soil carbon pool, using both ground-based and remote sensing methods. To achieve this objective, the Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and the Soil Adjusted Vegetation Index (SAVI) computed from the newly launched Landsat 8 OLI satellite data were used. Correlation and regression analysis were used to relate Soil Organic Carbon (S.O.C), aboveground woody carbon and remotely sensed vegetation indices. Results showed a soil organic carbon in the upper soil layer (0-15 cm) was positively correlated with aboveground woody carbon and this relationship was significant (r = 0.678; P < 0.05) aboveground carbon. However, there were no significant correlations (r = -0.11, P > 0.05) between SOC in the deeper soil layer (15-30 cm) and aboveground woody carbon. These findings imply that (relationship between aboveground woody carbon and S.O.C) aboveground woody carbon stocks can be used as a proxy to estimate S.O.C in the top soil layer (0-15 cm) in dry Miombo ecosystems. Overall, these findings underscore the potential and significance of remote sensing data in understanding savanna ecosystems contribution to the global carbon cycle.
Remote Sensing-based Models of Soil Vulnerability to Compaction and Erosion from Off-highway Vehicles

NASA Astrophysics Data System (ADS)

Villarreal, M. L.; Webb, R. H.; Norman, L.; Psillas, J.; Rosenberg, A.; Carmichael, S.; Petrakis, R.; Sparks, P.

2014-12-01

Intensive off-road vehicle use for immigration, smuggling, and security of the United States-Mexico border has prompted concerns about long-term human impacts on sensitive desert ecosystems. To help managers identify areas susceptible to soil erosion from vehicle disturbances, we developed a series of erosion potential models based on factors from the Revised Universal Soil Loss Equation (RUSLE), with particular focus on the management factor (P-factor) and vegetation cover (C-factor). To better express the vulnerability of soils to human disturbances, a soil compaction index (applied as the P-factor) was calculated as the difference in saturated hydrologic conductivity (Ks) between disturbed and undisturbed soils, which was then scaled up to remote sensing-based maps of vehicle tracks and digital soils maps. The C-factor was improved using a satellite-based vegetation index, which was better correlated with estimated ground cover (r2 = 0.77) than data derived from regional land cover maps (r2 = 0.06). RUSLE factors were normalized to give equal weight to all contributing factors, which provided more management-specific information on vulnerable areas where vehicle compaction of sensitive soils intersects with steep slopes and low vegetation cover. Resulting spatial data on vulnerability and erosion potential provide land managers with information to identify critically disturbed areas and potential restoration sites where off-road driving should be restricted to reduce further degradation.
Vegetation shifts observed in arctic tundra 17 years after fire

USGS Publications Warehouse

Barrett, Kirsten; Rocha, Adrian V.; van de Weg, Martine Janet; Shaver, Gaius

2012-01-01

With anticipated climate change, tundra fires are expected to occur more frequently in the future, but data on the long-term effects of fire on tundra vegetation composition are scarce. This study addresses changes in vegetation structure that have persisted for 17 years after a tundra fire on the North Slope of Alaska. Fire-related shifts in vegetation composition were assessed from remote-sensing imagery and ground observations of the burn scar and an adjacent control site. Early-season remotely sensed imagery from the burn scar exhibits a low vegetation index compared with the control site, whereas the late-season signal is slightly higher. The range and maximum vegetation index are greater in the burn scar, although the mean annual values do not differ among the sites. Ground observations revealed a greater abundance of moss in the unburned site, which may account for the high early growing season normalized difference vegetation index (NDVI) anomaly relative to the burn. The abundance of graminoid species and an absence of Betula nana in the post-fire tundra sites may also be responsible for the spectral differences observed in the remotely sensed imagery. The partial replacement of tundra by graminoid-dominated ecosystems has been predicted by the ALFRESCO model of disturbance, climate and vegetation succession.
Improved Mapping of Carbon, Water and Energy Land-Surface Fluxes Using Remotely Sensed Indicators of Canopy Light Use Efficiency

NASA Astrophysics Data System (ADS)

Schull, M. A.; Anderson, M. C.; Kustas, W.; Cammalleri, C.; Houborg, R.

2012-12-01

A light-use-efficiency (LUE) based model of canopy resistance has been embedded into a thermal-based Two-Source Energy Balance (TSEB) model to facilitate coupled simulations of transpiration and carbon assimilation. The model assumes that deviations of the observed canopy LUE from a nominal stand-level value (LUEn - typically indexed by vegetation class) are due to varying conditions of light, humidity, CO2 concentration and leaf temperature. The deviations are accommodated by adjusting an effective LUE that responds to the varying conditions. The challenge to monitoring fluxes on a larger scale is to capture the physiological responses due to changing conditions. This challenge can be met using remotely sensed leaf chlorophyll (Cab). Since Cab is a vital pigment for absorbing light for use in photosynthesis, it has been recognized as a key parameter for quantifying photosynthetic functioning that are sensitive to these conditions. Recent studies have shown that it is sensitive to changes in LUE, which defines how efficiently a plant can assimilate carbon dioxide (CO2) given the absorbed Photosynthetically Active Radiation (PAR) and is therefore useful for monitoring carbon fluxes. We investigate the feasibility of leaf chlorophyll to capture these variations in LUEn using remotely sensed data. To retrieve Cab from remotely sensed data we use REGFLEC, a physically based tool that translates at-sensor radiances in the green, red and NIR spectral regions from multiple satellite sensors into realistic maps of LAI and Cab. Initial results show that Cab is exponentially correlated to light use efficiency. Incorporating nominal light use efficiency estimated from Cab is shown to improve fluxes of carbon, water and energy most notably in times of stressed vegetation. The result illustrates that Cab is sensitive to changes in plant physiology and can capture plant stress needed for improved estimation of fluxes. The observed relationship and initial results demonstrate the need for integrating remotely sensed Cab to facilitate improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.
Utilization of satellite remote sensing data on land surface characteristics in water and heat balance component modeling for vegetation covered territories

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Uspensky, Alexander; Startseva, Zoya; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2010-05-01

The model of vertical water and heat transfer in the "soil-vegetation-atmosphere" system (SVAT) for vegetation covered territory has been developed, allowing assimilating satellite remote sensing data on land surface condition as well as accounting for heterogeneities of vegetation and meteorological characteristics. The model provides the calculation of water and heat balance components (such as evapotranspiration Ev, soil water content W, sensible and latent heat fluxes and others ) as well as vertical soil moisture and temperature distributions, temperatures of soil surface and foliage, land surface brightness temperature for any time interval within vegetation season. To describe the landscape diversity soil constants and leaf area index LAI, vegetation cover fraction B, and other vegetation characteristics are used. All these values are considered to be the model parameters. Territory of Kursk region with square about 15 thousands km2 situated in the Black Earth zone of Central Russia was chosen for investigation. Satellite-derived estimates of land surface characteristics have been constructed under cloud-free condition basing AVHRR/NOAA, MODIS/EOS Terra and EOS Aqua, SEVIRI/Meteosat-8, -9 data. The developed technologies of AVHRR data thematic processing have been refined providing the retrieval of surface skin brightness temperature Tsg, air foliage temperature Ta, efficient surface temperature Ts.eff and emissivity E, as well as derivation of vegetation index NDVI, B, and LAI. The linear regression estimators for Tsg, Ta and LAI have been built using representative training samples for 2003-2009 vegetation seasons. The updated software package has been applied for AVHRR data thematic processing to generate named remote sensing products for various dates of the above vegetation seasons. The error statistics of Ta, Ts.eff and Тsg derivation has been investigated for various samples using comparison with in-situ measurements that has given RMS errors in the range 2.0-2.6, 2.5-3.7, and 3.5-4.9°C respectively. The dataset of remote sensing products has been compiled on the base of special technology using Internet resources, that includes MODIS-based estimates of land surface temperature (LST) Tsg, E, NDVI, LAI for the region of interest and the same vegetation seasons. Two types of MODIS-based Тsg and E estimates have been extracted from LP DAAC web-site (for separate dates of 2003-2009 time period): LST/E Daily L3 product (MOD11В1) with spatial resolution ~ 4.8 km and LST/E 5-Min L2 product (MOD11_L2) with spatial resolution ~ 1 km. The verification of Tsg estimates has been performed via comparison with analogous and collocated AVHRR-based ones. Along with this the sample of SEVIRI-based Tsg and E estimates has been accumulated for the Kursk area and surrounding territories for the time interval of several days during 2009 vegetation season. To retrieve Тsg and Е from SEVIRI/Meteosat-8, -9 data the new method has been developed. Being designed as the combination of well-known Split Window Technique and Two Temperature Method algorithms it provides the derivation of Тsg from SEVIRI/Meteosat-9 measurements carried out at three successive times (classified as 100% cloud-free) and covering the region under consideration without accurate a priory knowledge of E. Comparison of the SEVIRI-based Tsg retrievals with the independent collocated Tsg estimates gives the values of RMS deviation in the range of 0.9-1.4°C and it proves (indirectly) the efficiency of proposed approach. To assimilate satellite-derived estimates of vegetation characteristics and LST in the SVAT model some procedures have been developed. These procedures have included: 1) the replacement of LAI and B ground and point-wise estimates by their AVHRR- or MODIS-based analogues. The efficiency of such approach has been proved through comparison between satellite-derived and ground-based seasonal time behaviors of LAI and B, between satellite-derived, modeled, and in-situ measured temperatures as well as through comparison the modeled and actual values of evapotranspiration Ev and soil water content W for one meter soil layer. The discrepancies between mentioned temperatures do not exceed the RMS errors of satellite-derived estimates Ta, Ts.eff and Tsg while the modeled and measured values of Ev and W have been found close to each other within their standard estimation error; 2) the treating AVHRR- or MODIS-based LST as the input model variable within the SVAT model instead their standard ground-based estimates if the satisfactory time-matching of satellite and ground-based observations takes place. The SEVIRI-derived Tsg can be also used for these aims. Permissibility of such replacement has been verified while comparing remote sensed, modeled and ground-based temperatures as well as calculated and measured values of W and Ev. The SEVIRI-based Tsg estimates were found to be very informative and useful due to their high temporal resolution. Moreover the approach has been developed to account for space variability of vegetation cover parameters and meteorological characteristics. This approach includes the development of algorithms and programs for entering AVHRR- and MODIS-derived LAI and B, all named satellite-based LSTs as well as ground-based precipitation, air temperature and humidity data prepared by Inverse Distance Weighted Average Method into the model in each calculation grid unit. The calculations of vertical water and heat fluxes, soil water and heat contents and other water and heat balance components for Kursk region have been carried out with the help of the SVAT model using fields of AVHRR/3- and MODIS-derived LAI and B and AVHRR/3-, MODIS, and SEVIRI-derived LST for various vegetation seasons of 2003-2009. The acceptable accuracy levels of above values assessment have been achieved under all scenarios of parameter and input model variable specification. Thus, the results of this study confirm the opportunity of using area distributed satellite-derived estimates of land surface characteristics for the model calculations of water and heat balance components for large territories especially under the lack of ground observation data. The present study was carried out with support of the Russian Foundation of Basic Researches - grant N 10-05-00807.
Controls on sinuosity in the sparsely vegetated Fossálar River, southern Iceland

NASA Astrophysics Data System (ADS)

Ielpi, Alessandro

2017-06-01

Vegetation exerts strong controls on fluvial sinuosity, providing bank stability and buffering surface runoff. These controls are manifest in densely vegetated landscapes, whereas sparsely vegetated fluvial systems have been so far overlooked. This study integrates remote sensing and gauging records of the meandering to wandering Fossálar River, a relatively steep-sloped (< 2.5%) Icelandic river featuring well-developed point bars (79%-85% of total active bar surface) despite the lack of thick, arborescent vegetation. Over four decades, fluctuations in the sinuosity index (1.15-1.43) and vegetation cover (63%-83%) are not significantly correlated (r = 0.28, p > 0.05), suggesting that relationships between the two are mediated by intervening variables and uncertain lag times. By comparison, discharge regime and fluvial planform show direct correlation over monthly to yearly time scales, with stable discharge stages accompanying the accretion of meander bends and peak floods related to destructive point-bar reworking. Rapid planform change is aided by the unconsolidated nature of unrooted alluvial banks, with recorded rates of lateral channel-belt migration averaging 18 m/yr. Valley confinement and channel mobility also control the geometry and evolution of individual point bars, with the highest degree of spatial geomorphic variability recorded in low-gradient stretches where lateral migration is unimpeded. Point bars in the Fossálar River display morphometric values comparable to those of other sparsely vegetated rivers, suggesting shared scalar properties. This conjecture prompts the need for more sophisticated integrations between remote sensing and gauging records on modern rivers lacking widespread plant life. While a large volume of experimental and field-based work maintains that thick vegetation has a critical role in limiting braiding, thus favouring sinuosity, this study demonstrates the stronger controls of discharge regime and alluvial morphology on sparsely vegetated sinuous rivers.
Monitoring vegetation phenology using MODIS

USGS Publications Warehouse

Zhang, Xiayong; Friedl, Mark A.; Schaaf, Crystal B.; Strahler, Alan H.; Hodges, John C.F.; Gao, Feng; Reed, Bradley C.; Huete, Alfredo

2003-01-01

Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.
A Rapidly Prototyped Vegetation Dryness Index Developed for Wildfire Risk Assessment at Stennis Space Center

NASA Technical Reports Server (NTRS)

Ross, Kenton; Graham, William D.; Prados, Donald; Spruce, Joseph

2006-01-01

A remote sensing index was developed to allow improved monitoring of vegetation dryness conditions on a regional basis. This remote sensing index was rapidly prototyped at Stennis Space Center in response to drought conditions in the local area in spring 2006.
Chlorophyll fluorescence better captures seasonal and interannual gross primary productivity dynamics across dryland ecosystems of southwestern North America

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing provides unmatched spatiotemporal information on vegetation gross primary productivity (GPP). Yet, understanding of the relationship between GPP and remote sensing observations and how it changes as a function of factors such as scale, biophysical constraint, and vegetation ...
Landscape characterization of peridomestic risk for Lyme disease using satellite imagery

NASA Technical Reports Server (NTRS)

Dister, S. W.; Fish, D.; Bros, S. M.; Frank, D. H.; Wood, B. L.

1997-01-01

Remotely sensed characterizations of landscape composition were evaluated for Lyme disease exposure risk on 337 residential properties in two communities of suburban Westchester County, New York. Properties were categorized as no, low, or high risk based on seasonally adjusted densities of Ixodes scapularis nymphs, determined by drag sampling during June and July 1990. Spectral indices based on Landsat Thematic Mapper data provided relative measures of vegetation structure and moisture (wetness), as well as vegetation abundance (greenness). A geographic information system (GIS) was used to spatially quantify and relate the remotely sensed landscape variables to risk category. A comparison of the two communities showed that Chappaqua, which had more high-risk properties (P < 0.001), was significantly greener and wetter than Armonk (P < 0.001). Furthermore, within Chappaqua, high-risk properties were significantly greener and wetter than lower-risk properties in this community (P < 0.01). The high-risk properties appeared to contain a greater proportion of broadleaf trees, while lower-risk properties were interpreted as having a greater proportion of nonvegetative cover and/or open lawn. The ability to distinguish these fine scale differences among communities and individual properties illustrates the efficiency of a remote sensing/GIS-based approach for identifying peridomestic risk of Lyme disease over large geographic areas.
Wind erosion in semiarid landscapes: Predictive models and remote sensing methods for the influence of vegetation

NASA Technical Reports Server (NTRS)

Musick, H. Brad

1993-01-01

The objectives of this research are: to develop and test predictive relations for the quantitative influence of vegetation canopy structure on wind erosion of semiarid rangeland soils, and to develop remote sensing methods for measuring the canopy structural parameters that determine sheltering against wind erosion. The influence of canopy structure on wind erosion will be investigated by means of wind-tunnel and field experiments using structural variables identified by the wind-tunnel and field experiments using model roughness elements to simulate plant canopies. The canopy structural variables identified by the wind-tunnel and field experiments as important in determining vegetative sheltering against wind erosion will then be measured at a number of naturally vegetated field sites and compared with estimates of these variables derived from analysis of remotely sensed data.
CubeSats in Hydrology: Ultrahigh-Resolution Insights Into Vegetation Dynamics and Terrestrial Evaporation

NASA Astrophysics Data System (ADS)

McCabe, M. F.; Aragon, B.; Houborg, R.; Mascaro, J.

2017-12-01

Satellite-based remote sensing has generally necessitated a trade-off between spatial resolution and temporal frequency, affecting the capacity to observe fast hydrological processes and rapidly changing land surface conditions. An avenue for overcoming these spatiotemporal restrictions is the concept of using constellations of satellites, as opposed to the mission focus exemplified by the more conventional space-agency approach to earth observation. Referred to as CubeSats, these platforms offer the potential to provide new insights into a range of earth system variables and processes. Their emergence heralds a paradigm shift from single-sensor launches to an operational approach that envisions tens to hundreds of small, lightweight, and comparatively inexpensive satellites placed into a range of low earth orbits. Although current systems are largely limited to sensing in the optical portion of the electromagnetic spectrum, we demonstrate the opportunity and potential that CubeSats present the hydrological community via the retrieval of vegetation dynamics and terrestrial evaporation and foreshadow future sensing capabilities.
New developments of a knowledge based system (VEG) for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Harrison, P. A.; Harrison, P. R.

1992-01-01

An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).

A dataset mapping the potential biophysical effects of vegetation cover change

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-02-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.
A dataset mapping the potential biophysical effects of vegetation cover change

PubMed Central

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-01-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes. PMID:29461538
An evaluation of thematic mapper simulator data for the geobotanical discrimination of rock types in Southwest Oregon

NASA Technical Reports Server (NTRS)

Weinstock, K. J.; Morrissey, L. A.

1984-01-01

Rock type identification may be assisted by the use of remote sensing of associated vegetation, particularly in areas of dense vegetative cover where surface materials are not imaged directly by the sensor. The geobotanical discrimination of ultramafic parent materials was investigated and analytical techniques for lithologic mapping and mineral exploration were developed. The utility of remotely sensed data to discriminate vegetation types associated with ultramafic parent materials in a study area in southwest Oregon were evaluated. A number of specific objectives were identified, which include: (1) establishment of the association between vegetation and rock types; (2) examination of the spectral separability of vegetation types associated with rock types; (3) determination of the contribution of each TMS band for discriminating vegetation associated with rock types and (4) comparison of analytical techniques for spectrally classifying vegetation.
Inventory and monitoring of natural vegetation and related resources in an arid environment: A comprehensive evaluation of ERTS-1 imagery. [Arizona

NASA Technical Reports Server (NTRS)

Schrumpf, B. J. (Principal Investigator); Johnson, J. R.; Mouat, D. A.; Pyott, W. T.

1974-01-01

The author has identified the following significant results. A vegetation classification, with 31 types and compatible with remote sensing applications, was developed for the test site. Terrain features can be used to discriminate vegetation types. Elevation and macrorelief interpretations were successful on ERTS photos, although for macrorelief, high sun angle stereoscopic interpretations were better than low sun angle monoscopic interpretations. Using spectral reflectivity, several vegetation types were characterized in terms of patterns of signature change. ERTS MSS digital data were used to discriminate vegetation classes at the association level and at the alliance level when image contrasts were high or low, respectively. An imagery comparison technique was developed to test image complexity and image groupability. In two stage sampling of vegetation types, ERTS plus high altitude photos were highly satisfactory for estimating kind and extent of types present, and for providing a mapping base.
Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes.

PubMed

Leong, Misha; Roderick, George K

2015-01-01

Global change has led to shifts in phenology, potentially disrupting species interactions such as plant-pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators.
Non-Lambertian effects on remote sensing of surface reflectance and vegetation index

NASA Technical Reports Server (NTRS)

Lee, T. Y.; Kaufman, Y. J.

1986-01-01

This paper discusses the effects of non-Lambertian reflection from a homogeneous surface on remote sensing of the surface reflectance and vegetation index from a satellite. Remote measurement of the surface characteristics is perturbed by atmospheric scattering of sun light. This scattering tends to smooth the angular dependence of non-Lambertian surface reflectances, an effect that is not present in the case of Lambertian surfaces. This effect is calculated to test the validity of a Lambertian assumption used in remote sensing. For the three types of vegetations considered in this study, the assumption of Lambertian surface can be used satisfactorily in the derivation of surface reflectance from remotely measured radiance for a view angle outside the backscattering region. Within the backscattering region, however, the use of the assumption can result in a considerable error in the derived surface reflectance. Accuracy also deteriorates with increasing solar zenith angle. The angular distribution of the surface reflectance derived from remote measurements is smoother than that at the surface. The effect of surface non-Lambertianity on remote sensing of vegetation index is very weak. Since the effect is similiar in the visible and near infrared part of the solar spectrum for the vegetations treated in this study, it is canceled in deriving the vegetation index. The effect of the diffuse skylight on surface reflectance measurements at ground level is also discussed.
Remote Sensing of Vegetation Recovery from Disturbance in Drylands

NASA Astrophysics Data System (ADS)

Poitras, T. B.; Villarreal, M. L.; Waller, E.; Duniway, M.; Nauman, T.

2016-12-01

Characteristics of dryland ecosystems such as climatic extremes and water limitations render semi-arid regions vulnerable to disturbance and slow to recover. Land surface monitoring over time through the use of remote sensing may have potential for identifying dryland ecosystem recovery after anthropogenic and natural disturbance. However, semi-arid vegetation cover is challenging to measure using remote sensing techniques due to low vegetation cover and confusion between bright and variable soils and non-photosynthetic vegetation (NPV). We therefore evaluated the ability of various multispectral indices to distinguish bare ground from total vegetation cover, in order to determine those that can detect changes over time in heavily disturbed sites. We calculated nine spectral indices from Landsat TM using Google Earth Engine (March through October, 2006 through 2008) and tested relationships between index values and ground measurements from long-term monitoring data collected in and around Canyonlands National Park in Utah. We also tested multivariate models, with some showing improvement under cross-validation. We found that indices that included shortwave infrared bands and soil brightness were important for capturing gradients in bare ground, and vegetation cover was best quantified with near-infrared bands. These results will be used to help assess the landscape-scale impacts of oil and gas development in dryland ecosystems and to measure response to restoration efforts. Keywords: remote sensing, landsat, drylands
The Effectiveness of Hydrothermal Alteration Mapping based on Hyperspectral Data in Tropical Region

NASA Astrophysics Data System (ADS)

Muhammad, R. R. D.; Saepuloh, A.

2016-09-01

Hyperspectral remote sensing could be used to characterize targets at earth's surface based on their spectra. This capability is useful for mapping and characterizing the distribution of host rocks, alteration assemblages, and minerals. Contrary to the multispectral sensors, the hyperspectral identifies targets with high spectral resolution. The Wayang Windu Geothermal field in West Java, Indonesia was selected as the study area due to the existence of surface manifestation and dense vegetation environment. Therefore, the effectiveness of hyperspectral remote sensing in tropical region was targeted as the study objective. The Spectral Angle Mapper (SAM) method was used to detect the occurrence of clay minerals spatially from Hyperion data. The SAM references of reflectance spectra were obtained from field observation at altered materials. To calculate the effectiveness of hyperspectral data, we used multispectral data from Landsat-8. The comparison method was conducted by comparing the SAM's rule images from Hyperion and Landsat-8, resulting that hyperspectral was more accurate than multispectral data. Hyperion SAM's rule images showed lower value compared to Landsat-8, the significant number derived from using Hyperion was about 24% better. This inferred that the hyperspectral remote sensing is preferable for mineral mapping even though vegetation covered study area.
Groundwater dependant vegetation identified by remote sensing in the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Gouveia, Célia; Pascoa, Patrícia; Kurz-Besson, Cathy

2017-04-01

Groundwater Dependant Ecosystems (GDEs) are defined as ecosystems whose composition, structure, and function depend on the water supplies from groundwater aquifers. Within GDEs, phreatophytes are terrestrial plants relying on groundwater through deep rooting. They can be found worldwide but are mostly adapted to environments facing scarce water availability or recurrent drought periods mainly in semi-arid to arid climate geographical areas, such as the Mediterranean basin. We present a map of the potential distribution of GDEs over the Iberian Peninsula (IP) obtained by remote sensing and identifying hotspots corresponding to the most vulnerable areas for rainfed vegetation facing the risk of desertification. The characterization of GDEs was assessed by remote sensing (RS), using CORINE land-cover information and the Normalized Difference Vegetation Index (NDVI) from VEGETATION recorded between 1998 and 2014 with a resolution of 1km. The methodology based on Gou et al (2015) relied on three approaches to map GDEs over the IP by: i) Detecting vegetation remaining green during the dry periods, since GDEs are more likely to show high NDVI values during summer of dry years; ii) Spotting vegetation with low seasonal changes since GDEs are more prone to have the lowest NDVI standard deviation along an entire year, and iii) Discriminating vegetation with low inter-annual variability since GDEs areas should provide the lowest NDVI changes between extreme wet and dry years. A geospatial analysis was performed to gather the potential area of GDEs (obtained with NDVI), vegetation land cover types (CORINE land cover) and climatic variables (temperature, precipitation and the Standardized Precipitation-Evapotranspiration Index SPEI). This analysis allowed the identification of hotspots of the most vulnerable areas for rainfed vegetation regarding water scarcity over the Iberian Peninsula, where protection measures should be urgently applied to sustain rainfed ecosystem and agro-systems and biodiversity in the near future. Keywords: NDVI, CORINE, SPEI, Groundwater, Mediterranean vegetation, Phreatophyte species. Reference: Gou S., Susana Gonzales S., and Gretchen R. Miller G. R. (2015). Mapping Potential Groundwater-Dependent Ecosystems for Sustainable Management. Groundwater 53, 99-110. Acknowledgements: This work was supported by the project PIEZAGRO (PTDC/AAG-REC/7046/2014) funded by the Fundação para a Ciência e a Tecnologia, Portugal.
Combining high fidelity simulations and real data for improved small-footprint waveform lidar assessment of vegetation structure (Invited)

NASA Astrophysics Data System (ADS)

van Aardt, J. A.; Wu, J.; Asner, G. P.

2010-12-01

Our understanding of vegetation complexity and biodiversity, from a remote sensing perspective, has evolved from 2D species diversity to also include 3D vegetation structural diversity. Attempts at using image-based approaches for structural assessment have met with reasonable success, but 3D remote sensing technologies, such as radar and light detection and ranging (lidar), are arguably more adept at sensing vegetation structure. While radar-derived structure metrics tend to break down at high biomass levels, novel waveform lidar systems present us with new opportunities for detailed and scalable structural characterization of vegetation. These sensors digitize the entire backscattered energy profile at high spatial and vertical resolutions and often at off-nadir angles. Research teams at Rochester Institute of Technology (RIT) and Carnegie Institution for Science have been using airborne data from the Carnegie Airborne Observatory (CAO) to assess vegetation structure and variation in savanna ecosystems in and around the Kruger National Park, South Africa. It quickly became evident that (i) pre-processing of small-footprint waveform data is a critical step prior to testing scientific hypotheses, (ii) a number of assumptions of how vegetation structure is expressed in these 3D signals need to be evaluated, and very importantly (iii) we need to re-evaluate our linkages between coarse in-field measurements, e.g., volume, biomass, leaf area index (LAI), and metrics derived from waveform lidar. Research has progressed to the stage where we have evaluated various pre-processing steps, e.g., convolution via the Wiener filter, Richardson-Lucy, and non-negative least squares algorithms, and the coupling of waveform voxels to tree structure in a simulation environment. This was done in the MODTRAN-based Digital Imaging and Remote Sensing Image Generation (DIRSIG) simulation environment, developed at RIT. We generated "truth" cross-section datasets of detailed virtual trees in this environment and evaluated inversion approaches to tree structure estimation. Various outgoing pulse widths, tree structures, and a noise component were included as part of the simulation effort. Results, for example, have shown that the Richardson-Lucy algorithm outperforms other approaches in terms of retrieval of known structural information, that our assumption regarding the position of the ground surface needs re-evaluation, and has shed light on herbaceous biomass and waveform interactions and the impact of outgoing pulse width on assessments. These efforts have gone a long way in providing a solid foundation for analysis and interpretation of actual waveform data from the savanna study area. We expect that newfound knowledge with respect to waveform-target interactions from these simulations will also aid efforts to reconstruct 3D trees from real data and better describe associated structural diversity. Results will be presented at the conference.
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.
Study on environment detection and appraisement of mining area with RS

NASA Astrophysics Data System (ADS)

Yang, Fengjie; Hou, Peng; Zhou, Guangzhu; Li, Qingting; Wang, Jie; Cheng, Jianguang

2006-12-01

In this paper, the big coal mining area Yanzhou is selected as the typical research area. According to the special dynamic change characteristic of the environment in the mining area, the environmental dynamic changes are timely monitored with the remote sensing detection technology. Environmental special factors, such as vegetation, water, air, land-over, are extracted by the professional remote sensing image processing software, then the spatial information is managed and analyzed in the geographical information system (GIS) software. As the result, the dynamic monitor and query for change information is achieved, and the special environmental factor dynamic change maps are protracted. On the base of the data coming from the remote sensing image, GIS and the traditional environment monitoring, the environmental quality is appraised with the method of indistinct matrix analysis, the multi-index and the analytical hierarchy process. At last, those provide the credible science foundation for the local environment appraised and the sustained development. In addition, this paper apply the hyper spectrum graphs by the FieldSpec Pro spectroradiometer, together with the analytical data from environmental chemical, to study the growth of vegetation which were seed in the land-over consisting of gangue, which is a new method to study the impact to vegetation that are growing in the soil.
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.
Extracting Vegetation Coverage in Dry-hot Valley Regions Based on Alternating Angle Minimum Algorithm

NASA Astrophysics Data System (ADS)

Y Yang, M.; Wang, J.; Zhang, Q.

2017-07-01

Vegetation coverage is one of the most important indicators for ecological environment change, and is also an effective index for the assessment of land degradation and desertification. The dry-hot valley regions have sparse surface vegetation, and the spectral information about the vegetation in such regions usually has a weak representation in remote sensing, so there are considerable limitations for applying the commonly-used vegetation index method to calculate the vegetation coverage in the dry-hot valley regions. Therefore, in this paper, Alternating Angle Minimum (AAM) algorithm of deterministic model is adopted for selective endmember for pixel unmixing of MODIS image in order to extract the vegetation coverage, and accuracy test is carried out by the use of the Landsat TM image over the same period. As shown by the results, in the dry-hot valley regions with sparse vegetation, AAM model has a high unmixing accuracy, and the extracted vegetation coverage is close to the actual situation, so it is promising to apply the AAM model to the extraction of vegetation coverage in the dry-hot valley regions.
The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness

USGS Publications Warehouse

Tadesse, T.; Wardlow, B.; Hayes, M.; Svoboda, M.; Brown, J.

2010-01-01

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of "historical patterns" (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation's response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.
Hyperspectral remote sensing of foliar nitrogen content

USDA-ARS?s Scientific Manuscript database

A strong positive correlation between vegetation canopy Bidirectional Reflectance Factor (BRF) in the Near'InfraRed (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional...
A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape.

PubMed

Lausch, Angela; Pause, Marion; Merbach, Ines; Zacharias, Steffen; Doktor, Daniel; Volk, Martin; Seppelt, Ralf

2013-02-01

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the "One Sensor at Different Scales" (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R (2) of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.
Multi-Frequency Investigation into Scattering from Vegetation over the Growth Cycle

NASA Technical Reports Server (NTRS)

Lang, R. H.; Kurum, M.; O'Neill, P. E.; Joseph, A. T.; Deshpande, M. D.; Cosh, M. H.

2016-01-01

In this investigation, we aim to collect and use time-series multi-frequency microwave data over winter wheat during entire growth cycle to characterize vegetation dynamics and to quantify its effects on soil moisture retrievals. We plan to incorporate C-band radar and VHF receiver within the existing L-band radarradiometer system called ComRAD (SMAPs ground based simulator). With C-bands ability to sense vegetation details and VHFs root-zone soil moisture within ComRADs footprint, we will be able to test our discrete scatterer vegetation models and parameters at various surface conditions. The purpose of this study is to determine optical depth and effective scattering albedo of vegetation of a given type (i.e. winter wheat) at various stages of growth that are need to refine soil moisture retrieval algorithms being developed for the SMAP mission.
A remote sensing tool to monitor and predict epidemiologic outbreaks of Hanta virus infections and Lyme disease

NASA Astrophysics Data System (ADS)

Barrios, J. M.

2009-04-01

Lyme disease and Hanta virus infection are the result of the conjunction of several climatic and ecological conditions. Although both affections have different causal agents, they share an important characteristic which is the fact that rodents play an important role in the contagium. One of the most important agents in the dispersion of these diseases is the bank vole (Clethrionomys glareoulus). The bank vole is a common host for both, the Borrelia bacteria which via the ticks (Ixodes ricinus) reaches the human body and causes the Lyme disease, and the Nephropatia epidemica which is caused by Puumala Hantavirus and affects kidneys in humans. The prefered habitat of bank voles is broad-leaf forests with an important presence of beeches (Fagus sylvatica) and oaks (Quercus sp.) and a relatively dense low vegetation layer. These vegetation systems are common in West-Europe and their dynamics have a great influence in the bank voles population and, therefore, in the spreading of the infections this study is concerned about. The fact that the annual seed production is not stable in time has an important effect in bank voles population and, as it has been described in other studies, in the number of reported cases of Hanta virus infections and Lyme disease. The years in which an abundant production of seeds is observed are referred to as mast years which are believed to obey to cyclic patterns and to certain climatological characteristics of the preceding years. Statistical analysis have confirmed the correlation in the behaviour of the number of infected cases and the presence of mast years. This project aims at the design of a remote sensing based system (INFOPRESS - INFectious disease Outbreak Prediction REmote Sensing based System) that should enable local and national health care instances to predict and locate the occurrence of infection outbreaks and design policies to counteract undesired effects. The predictive capabilities of the system are based on the understanding and modelling of the interactions between relevant climatic parameters (temperature, humidity, precipitation) and the main features of vegetation systems which host the vectors and determine the survival and infectious potential of the causal agents. Among the most important study subjects in this research initiative one can mention the time series analysis of vegetation parameters derived from satellite remote sensing and its relatation to climatic time series and historical records of infected cases; with special attention to the assessment of remotely sensed evidences of the mast phenomenon. These analysis will constitute important buildind bricks in the construction of the INFOPRESS system in what concerns the assessment of the potentials of satellite remote sensing as information source for the prediction of infection outbreaks. The bank voles habitat description will also be supported by on-gound remote sensing techniques, specially Lidar technology and soil humidity modelling. These measurements are to be coupled to bank voles and ticks epidemiologic features obtained from field capturing and lab analysis.
Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

PubMed Central

Melesse, Assefa M.; Weng, Qihao; S.Thenkabail, Prasad; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling. PMID:28903290

Assessing Crop Coefficients for Natural Vegetated Areas Using Satellite Data and Eddy Covariance Stations.

PubMed

Corbari, Chiara; Ravazzani, Giovanni; Galvagno, Marta; Cremonese, Edoardo; Mancini, Marco

2017-11-18

The Food and Agricultural Organization (FAO) method for potential evapotranspiration assessment is based on the crop coefficient, which allows one to relate the reference evapotranspiration of well irrigated grass to the potential evapotranspiration of specific crops. The method was originally developed for cultivated species based on lysimeter measurements of potential evapotranspiration. Not many applications to natural vegetated areas exist due to the lack of available data for these species. In this paper we investigate the potential of using evapotranspiration measurements acquired by micrometeorological stations for the definition of crop coefficient functions of natural vegetated areas and extrapolation to ungauged sites through remotely sensed data. Pastures, deciduous and evergreen forests have been considered and lower crop coefficient values are found with respect to FAO data.
High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision

Treesearch

Jonathan P. Dandois; Erle C. Ellis

2013-01-01

High spatial resolution three-dimensional (3D) measurements of vegetation by remote sensing are advancing ecological research and environmental management. However, substantial economic and logistical costs limit this application, especially for observing phenological dynamics in ecosystem structure and spectral traits. Here we demonstrate a new aerial remote sensing...
Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems

Treesearch

Alistair M. S. Smith; Crystal A. Kolden; Wade T. Tinkham; Alan F. Talhelm; John D. Marshall; Andrew T. Hudak; Luigi Boschetti; Michael J. Falkowski; Jonathan A. Greenberg; John W. Anderson; Andrew Kliskey; Lilian Alessa; Robert F. Keefe; James R. Gosz

2014-01-01

Climate change is altering the species composition, structure, and function of vegetation in natural terrestrial ecosystems. These changes can also impact the essential ecosystem goods and services derived from these ecosystems. Following disturbances, remote-sensing datasets have been used to monitor the disturbance and describe antecedent conditions as a means of...
Remotely Sensed Northern Vegetation Response to Changing Climate: Growing Season and Productivity Perspective

NASA Technical Reports Server (NTRS)

Ganguly, S.; Park, Taejin; Choi, Sungho; Bi, Jian; Knyazikhin, Yuri; Myneni, Ranga

2016-01-01

Vegetation growing season and maximum photosynthetic state determine spatiotemporal variability of seasonal total gross primary productivity of vegetation. Recent warming induced impacts accelerate shifts on growing season and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we first investigate how vegetation growing season and maximum photosynthesis state are evolved and how such components contribute on inter-annual variation of seasonal total gross primary productivity. Furthermore, seasonally different response of northern vegetation to changing temperature and water availability is also investigated. We utilized both long-term remotely sensed data to extract larger scale growing season metrics (growing season start, end and duration) and productivity (i.e., growing season summed vegetation index, GSSVI) for answering these questions. We find that regionally diverged growing season shift and maximum photosynthetic state contribute differently characterized productivity inter-annual variability and trend. Also seasonally different response of vegetation gives different view of spatially varying interaction between vegetation and climate. These results highlight spatially and temporally varying vegetation dynamics and are reflective of biome-specific responses of northern vegetation to changing climate.
Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)

USGS Publications Warehouse

Marshall, Michael T.; Thenkabail, Prasad S.; Biggs, Trent; Post, Kirk

2016-01-01

Evapotranspiration (ET) is an important component of micro- and macro-scale climatic processes. In agriculture, estimates of ET are frequently used to monitor droughts, schedule irrigation, and assess crop water productivity over large areas. Currently, in situ measurements of ET are difficult to scale up for regional applications, so remote sensing technology has been increasingly used to estimate crop ET. Ratio-based vegetation indices retrieved from optical remote sensing, like the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index, and Enhanced Vegetation Index are critical components of these models, particularly for the partitioning of ET into transpiration and soil evaporation. These indices have their limitations, however, and can induce large model bias and error. In this study, micrometeorological and spectroradiometric data collected over two growing seasons in cotton, maize, and rice fields in the Central Valley of California were used to identify spectral wavelengths from 428 to 2295 nm that produced the highest correlation to and lowest error with ET, transpiration, and soil evaporation. The analysis was performed with hyperspectral narrowbands (HNBs) at 10 nm intervals and multispectral broadbands (MSBBs) commonly retrieved by Earth observation platforms. The study revealed that (1) HNB indices consistently explained more variability in ET (ΔR2 = 0.12), transpiration (ΔR2 = 0.17), and soil evaporation (ΔR2 = 0.14) than MSBB indices; (2) the relationship between transpiration using the ratio-based index most commonly used for ET modeling, NDVI, was strong (R2 = 0.51), but the hyperspectral equivalent was superior (R2 = 0.68); and (3) soil evaporation was not estimated well using ratio-based indices from the literature (highest R2 = 0.37), but could be after further evaluation, using ratio-based indices centered on 743 and 953 nm (R2 = 0.72) or 428 and 1518 nm (R2 = 0.69).
Coherence Effects in L-Band Active and Passive Remote Sensing of Quasi-Periodic Corn Canopies

NASA Technical Reports Server (NTRS)

Utku, Cuneyt; Lang, Roger H.

2011-01-01

Due to their highly random nature, vegetation canopies can be modeled using the incoherent transport theory for active and passive remote sensing applications. Agricultural vegetation canopies however are generally more structured than natural vegetation. The inherent row structure in agricultural canopies induces coherence effects disregarded by the transport theory. The objective of this study is to demonstrate, via Monte-Carlo simulations, these coherence effects on L-band scattering and thermal emission from corn canopies consisting of only stalks.
Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China

NASA Astrophysics Data System (ADS)

Wu, Hao; Ye, Lu-Ping; Shi, Wen-Zhong; Clarke, Keith C.

2014-10-01

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.
FORUM: A Suggestion for an Improved Vegetation Scheme for Local and Global Mapping and Monitoring.

PubMed

ADAMS

1999-01-01

/ Understanding of global ecological problems is at least partly dependent on clear assessments of vegetation change, and such assessment is always dependent on the use of a vegetation classification scheme. Use of satellite remotely sensed data is the only practical means of carrying out any global-scale vegetation mapping exercise, but if the resulting maps are to be useful to most ecologists and conservationists, they must be closely tied to clearly defined features of vegetation on the ground. Furthermore, much of the mapping that does take place involves more local-scale description of field sites; for purposes of cost and practicality, such studies usually do not involve remote sensing using satellites. There is a need for a single scheme that integrates the smallest to the largest scale in a way that is meaningful to most environmental scientists. Existing schemes are unsatisfactory for this task; they are ambiguous, unnecessarily complex, and their categories do not correspond to common-sense definitions. In response to these problems, a simple structural-physiognomically based scheme with 23 fundamental categories is proposed here for mapping and monitoring on any scale, from local to global. The fundamental categories each subdivide into more specific structural categories for more detailed mapping, but all the categories can be used throughout the world and at any scale, allowing intercomparison between regions. The next stage in the process will be to obtain the views of as many people working in as many different fields as possible, to see whether the proposed scheme suits their needs and how it should be modified. With a few modifications, such a scheme could easily be appended to an existing land cover classification scheme, such as the FAO system, greatly increasing the usefulness and accessability of the results of the landcover classification. KEY WORDS: Vegetation scheme; Mapping; Monitoring; Land cover
Hyperspectral remote sensing of vegetation: knowledge gain and knowledge gap after 50 years of research (Conference Presentation)

NASA Astrophysics Data System (ADS)

Thenkabail, Prasad S.

2017-04-01

This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher:Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advances made in processing when handling large volumes of hyperspectral data have generated tremendous interest in advancing the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing more sensitive wavebands and indices to study vegetation characteristics. The presentation will discuss topics such as: (1) hyperspectral sensors and their characteristics, (2) methods of overcoming the Hughes phenomenon, (3) characterizing biophysical and biochemical properties, (4) advances made in using hyperspectral data in modeling evapotranspiration or actual water use by plants, (5) study of phenology, light use efficiency, and gross primary productivity, (5) improved accuracies in species identification and land cover classifications, and (6) applications in precision farming.
View angle effects on relationships between leaf area index in wheat and vegetation indices

NASA Astrophysics Data System (ADS)

Chen, H.; Li, W.; Huang, W.; Niu, Z.

2016-12-01

The effects of plant types and view angles on the canopy-reflected spectrum can not be ignored in the estimation of leaf area index (LAI) using remote sensing vegetation indices. While vegetation indices derived from nadir-viewing remote sensors are insufficient in leaf area index (LAI) estimation because of its misinterpretation of structural characteristecs, vegetation indices derived from multi-angular remote sensors have potential to improve detection of LAI. However, view angle effects on relationships between these indices and LAI for low standing crops (i.e. wheat) has not been fully evaluated and thus limits them to applied for consistent and accurate monitoring of vegetation. View angles effects of two types of winter wheat (wheat 411, erectophile; and wheat 9507, planophile) on relationship between LAI and spectral reflectance are assessed and compared in this study. An evaluation is conducted with in-situ measurements of LAI and bidirectional reflectance in the principal plane from -60° (back-scattering direction ) ot 60° (forward scattering direction) in the growth cycle of winter wheat. A variety of vegetation indices (VIs) published are calculated by BRDF. Additionally, all combinations of the bands are used in order to calculate Normalized difference Spectral Indices (NDSI) and Simple Subtraction Indices (SSI). The performance of the above indices along with raw reflectance and reflectance derivatives on LAI estimation are examined based on a linearity comparison. The results will be helpful in further developing multi-angle remote sensing models for accurate LAI evaluation.
Effects of vegetation canopy structure on remotely sensed canopy temperatures. [inferring plant water stress and yield

NASA Technical Reports Server (NTRS)

Kimes, D. S.

1979-01-01

The effects of vegetation canopy structure on thermal infrared sensor response must be understood before vegetation surface temperatures of canopies with low percent ground cover can be accurately inferred. The response of a sensor is a function of vegetation geometric structure, the vertical surface temperature distribution of the canopy components, and sensor view angle. Large deviations between the nadir sensor effective radiant temperature (ERT) and vegetation ERT for a soybean canopy were observed throughout the growing season. The nadir sensor ERT of a soybean canopy with 35 percent ground cover deviated from the vegetation ERT by as much as 11 C during the mid-day. These deviations were quantitatively explained as a function of canopy structure and soil temperature. Remote sensing techniques which determine the vegetation canopy temperature(s) from the sensor response need to be studied.
Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery.

PubMed

Martinuzzi, Sebastián; Ramos-González, Olga M; Muñoz-Erickson, Tischa A; Locke, Dexter H; Lugo, Ariel E; Radeloff, Volker C

2018-04-01

Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter (<1 m) resolution aerial imagery, and identify social-ecological relationships of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution (0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3) investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planning in tropical cities, and emphasizes the need for more studies in tropical cities. © 2017 by the Ecological Society of America.
Development of indicators of vegetation recovery based on time series analysis of SPOT Vegetation data

NASA Astrophysics Data System (ADS)

Lhermitte, S.; Tips, M.; Verbesselt, J.; Jonckheere, I.; Van Aardt, J.; Coppin, Pol

2005-10-01

Large-scale wild fires have direct impacts on natural ecosystems and play a major role in the vegetation ecology and carbon budget. Accurate methods for describing post-fire development of vegetation are therefore essential for the understanding and monitoring of terrestrial ecosystems. Time series analysis of satellite imagery offers the potential to quantify these parameters with spatial and temporal accuracy. Current research focuses on the potential of time series analysis of SPOT Vegetation S10 data (1999-2001) to quantify the vegetation recovery of large-scale burns detected in the framework of GBA2000. The objective of this study was to provide quantitative estimates of the spatio-temporal variation of vegetation recovery based on remote sensing indicators. Southern Africa was used as a pilot study area, given the availability of ground and satellite data. An automated technique was developed to extract consistent indicators of vegetation recovery from the SPOT-VGT time series. Reference areas were used to quantify the vegetation regrowth by means of Regeneration Indices (RI). Two kinds of recovery indicators (time and value- based) were tested for RI's of NDVI, SR, SAVI, NDWI, and pure band information. The effects of vegetation structure and temporal fire regime features on the recovery indicators were subsequently analyzed. Statistical analyses were conducted to assess whether the recovery indicators were different for different vegetation types and dependent on timing of the burning season. Results highlighted the importance of appropriate reference areas and the importance of correct normalization of the SPOT-VGT data.
Regional Impacts of Woodland Expansion on Nitrogen Oxide Emissions from Texas Savannahs: Combining Field, Modeling and Remote Sensing Approaches

NASA Technical Reports Server (NTRS)

Asner, Gregory P. (Principal Investigator)

2003-01-01

Woody encroachment has contributed to documented changes world-wide and locally in the southwestern U.S. Specifically, in North Texas rangelands encroaching mesquite (Prosopis glandulosa var. glandulosa) a known N-fixing species has caused changes in aboveground biomass. While measurements of aboveground plant production are relatively common, measures of soil N availability are scarce and vary widely. N trace gas emissions (nitric and nitrous oxide) flom soils reflect patterns in current N cycling rates and availability as they are stimulated by inputs of organic and inorganic N. Quantification of N oxide emissions from savanna soils may depend upon the spatial distribution of woody plant canopies, and specifically upon the changes in N availability and cycling and subsequent N trace gas production as influenced by the shift from herbaceous to woody vegetation type. The main goal of this research was to determine whether remotely sensible parameters of vegetation structure and soil type could be used to quantify biogeochemical changes in N at local, landscape and regional scales. To accomplish this goal, field-based measurements of N trace gases were carried out between 2000-2001, encompassing the acquisition of imaging spectrometer data from the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) on September 29, 2001. Both biotic (vegetation type and soil organic N) and abiotic (soil type, soil pH, temperature, soil moisture, and soil inorganic N) controls were analyzed for their contributions to observed spatial and temporal variation in soil N gas fluxes. These plot level studies were used to develop relationships between spatially extensive, field-based measurements of N oxide fluxes and remotely sensible aboveground vegetation and soil properties, and to evaluate the short-term controls over N oxide emissions through intensive field wetting experiments. The relationship between N oxide emissions, remotely-sensed parameters (vegetation cover, and soil type), and physical controls (soil moisture, and temperature) permitted the regional scale quantification of soil N oxides emissions. Landscape scale analysis linking N oxide emissions with cover change revealed an alleviation from N limitation following mesquite invasion. This study demonstrated the advantage of using N trace gases as a measure of ecosystem N availability combined with remote sensing to characterize the spatial heterogeneity in ecosystem parameters at a scale commensurate with field-based measurements of these properties. Woody vegetation encroachment provided an opportunity to capitalize on detection of the remotely-sensible parameter of woody cover as it relates to belowground biogeochemical processes that determine N trace gas production. The first spatially-explicit estimates of NO flux were calculated based on Prosopis fractional cover derived from high resolution remote sensing estimates of fractional woody cover (< 4 m) for a 120 sq km region of North Texas. An assessment of both N stocks and fluxes from the study revealed an alleviation of N limitation at this site experiencing recent woody encroachment. Many arid and semi-arid regions of the world are experiencing woody invasions, often of N-fixing species. The issue of woody encroachment is in the center of an ecological and political debate. Improving the links between biogeochemical processes and remote sensing of ecosystem properties will improve our understanding of biogeochemical processes at the regional scale, thus providing a means to address issues of land-use and land-cover change.
Use of geostationary satellite imagery in optical and thermal bands for the estimation of soil moisture status and land evapotranspiration

NASA Astrophysics Data System (ADS)

Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.

2009-04-01

For water and agricultural management, there is an increasing demand to monitor the soil water status and the land evapotranspiration. In the framework of the LSA-SAF project (http://landsaf.meteo.pt), we are developing an energy balance model forced by remote sensing products, i.e. radiation components and vegetation parameters, to monitor in quasi real-time the evapotranspiration rate over land (Gellens-Meulenberghs et al, 2007; Ghilain et al, 2008). The model is applied over the full MSG disk, i.e. including Europe and Africa. Meteorological forcing, as well as the soil moisture status, is provided by the forecasts of the ECMWF model. Since soil moisture is computed by a forecast model not dedicated to the monitoring of the soil water status, inadequate soil moisture input can occur, and can cause large effects on evapotranspiration rates, especially over semi-arid or arid regions. In these regions, a remotely sensed-based method for the soil moisture retrieval can therefore be preferable, to avoid too strong dependency in ECMWF model estimates. Among different strategies, remote sensing offers the advantage of monitoring large areas. Empirical methods of soil moisture assessment exist using remotely sensed derived variables either from the microwave bands or from the thermal bands. Mainly polar orbiters are used for this purpose, and little attention has been paid to the new possibilities offered by geosynchronous satellites. In this contribution, images of the SEVIRI instrument on board of MSG geosynchronous satellites are used. Dedicated operational algorithms were developed for the LSA-SAF project and now deliver images of land surface temperature (LST) every 15-minutes (Trigo et al, 2008) and vegetations indices (leaf area index, LAI; fraction of vegetation cover, FVC; fraction of absorbed photosynthetically active radiation, FAPAR) every day (Garcia-Haro et al, 2005) over Africa and Europe. One advantage of using products derived from geostationary satellites is the close monitoring of the diurnal variation of the land surface temperature. This feature reinforced the statistical strength of empirical methods. An empirical method linking land surface morning heating rates and the fraction of the vegetation cover, also known as a ‘Triangle method' (Gillies et al, 1997) is examined. This method is expected to provide an estimation of a root-zone soil moisture index. The sensitivity of the method to wind speed, soil type, vegetation type and climatic region is explored. Moreover, the impact of the uncertainty of LST and FVC on the resulting soil moisture estimates is assessed. A first impact study of using remotely sensed soil moisture index in the energy balance model is shown and its potential benefits for operational monitoring of evapotranspiration are outlined. References García-Haro, F.J., F. Camacho-de Coca, J. Meliá, B. Martínez (2005) Operational derivation of vegetation products in the framework of the LSA SAF project. Proceedings of the EUMETSAT Meteorological Satellite Conference Dubrovnik (Croatia) 19-23 Septembre. Gellens-Meulenberghs, F., Arboleda, A., Ghilain, N. (2007) Towards a continuous monitoring of evapotranspiration based on MSG data. Proceedings of the symposium on Remote Sensing for Environmental Monitoring and Change Detection. IAHS series. IUGG, Perugia, Italy, July 2007, 7 pp. Ghilain, N., Arboleda, A. and Gellens-Meulenberghs, F., (2008) Improvement of a surface energy balance model by the use of MSG-SEVIRI derived vegetation parameters. Proceedings of the 2008 EUMETSAT meteorological satellite data user's conference, Darmstadt, Germany, 8th-12th September, 7 pp. Gillies R.R., Carlson T.N., Cui J., Kustas W.P. and Humes K. (1997), Verification of the triangle method for obtaining surface soil water content and energy fluxes from remote measurements of Normalized Difference Vegetation Index (NDVI) and surface radiant temperature, International Journal of Remote Sensing, 18, pp. 3145-3166. Trigo, I.F., Monteiro I.T., Olesen F. and Kabsch E. (2008) An assessment of remotely sensed land surface temperature. Journal of Geophysical Research, 113, D17108, doi:10.1029/2008JD010035.
Mapping wildfire susceptibility in Southern California using live and dead fractions of vegetation derived from Multiple Endmember Spectral Mixture Analysis of MODIS imagery

NASA Astrophysics Data System (ADS)

Schneider, P.; Roberts, D. A.

2008-12-01

Wildfire is a significant natural disturbance mechanism in Southern California. Assessing spatial patterns of wildfire susceptibility requires estimates of the live and dead fractions of vegetation. The Fire Potential Index (FPI), which is currently the only operationally computed fire susceptibility index incorporating remote sensing data, estimates such fractions using a relative greenness measure based on time series of vegetation index images. This contribution assesses the potential of Multiple Endmember Spectral Mixture Analysis (MESMA) for deriving such fractions from single MODIS images without the need for a long remote sensing time series, and investigates the applicability of such MESMA-derived fractions for mapping dynamic fire susceptibility in Southern California. Endmembers for MESMA were selected from a library of reference endmembers using Constrained Reference Endmember Selection (CRES), which uses field estimates of fractions to guide the selection process. Fraction images of green vegetation, non-photosynthetic vegetation, soil, and shade were then computed for all available 16-day MODIS composites between 2000 and 2006 using MESMA. Initial results indicate that MESMA of MODIS imagery is capable of providing reliable estimates of live and dead vegetation fraction. Validation against in situ observations in the Santa Ynez Mountains near Santa Barbara, California, shows that the average fraction error for two tested species was around 10%. Further validation of MODIS-derived fractions was performed against fractions from high-resolution hyperspectral data. It was shown that the fractions derived from data of both sensors correlate with R2 values greater than 0.95. MESMA-derived live and dead vegetation fractions were subsequently tested as a substitute to relative greenness in the FPI algorithm. FPI was computed for every day between 2000 and 2006 using the derived fractions. Model performance was then tested by extracting FPI values for historical fire events and random no-fire events in Southern California for the same period and developing a logistic regression model. Preliminary results show that an FPI based on MESMA-derived fractions has the potential to deliver similar performance as the traditional FPI but requiring a greatly reduced data volume and using an approach based on physical rather than empirical relationships.
High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors

USGS Publications Warehouse

Nouri, Hamideh; Beecham, Simon; Anderson, Sharolyn; Nagler, Pamela

2014-01-01

Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index) for shrubs (r2 = 0.66) and trees (r2 = 0.63). However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p > 0.05) and the lowest one was for turf (r2 = 0.88, p > 0.05). In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI) from MODIS was employed. The results revealed a significant positive relationship between ETMODIS and ETWV2 (r2 = 0.9857, p > 0.05). This indicates that the relationship between NDVI using high resolution WorldView-2 imagery and ground-based validation approaches could provide an effective predictive tool for determining ET rates from unstressed mixed urban landscape plantings.
Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Bahrawi, Jarbou A.

2017-03-01

Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.
The use of remote sensing and GIS data in the NW Forest Plan aquatic assessment

NASA Astrophysics Data System (ADS)

Hirsch, C.; Gordon, S. N.; Eldred, P.

2017-12-01

The NW Forest Plan is an integrated set of standards and guidelines covering 24 m ac of federal lands on the west side of Washington, Oregon, and northern California. The associated the Aquatic and Riparian Effectiveness Monitoring Program (AREMP) assesses watershed condition and trend on these lands at two levels: upslope/riparian based on GIS and remote sensing data, and inchannel, based on field data collection. AREMP uses a multicriteria evaluation approach similar to WCF, but upslope attributes are restricted to roads and vegetation because these are the only ones which can be reliably backcasted to estimate trend since the start of the Plan in 1994. AREMP has developed a context-sensitive road evaluation system in response to studies showing that a large percentage of road sediment delivery to streams comes from a small percentage of the overall road network, and we are currently testing integration with the GRAIP-lite road sediment tool. AREMP's evaluation of vegetation data, based on Landsat imagery, also uses context derived from the distributions of least-human-disturbed conditions by 16 major forest types. A major question remains in how to evaluate vegetation given the importance of natural disturbances in renewing aquatic habitat. A common challenge across all of our data sources is how to assess uncertainty in data values and locational accuracy.
Remote Sensing-Based Detection and Spatial Pattern Analysis for Geo-Ecological Niche Modeling of Tillandsia SPP. In the Atacama, Chile

NASA Astrophysics Data System (ADS)

Wolf, N.; Siegmund, A.; del Río, C.; Osses, P.; García, J. L.

2016-06-01

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called `fog oases' dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

Spectral Unmixing of Vegetation, Soil and Dry Carbon in Arid Regions: Comparing Multispectral and Hyperspectral Observations

NASA Technical Reports Server (NTRS)

Asner, Gregory P.; Heidebrecht, Kathleen B.

2001-01-01

Remote sensing of vegetation cover and condition is critically needed to understand the impacts of land use and climate variability in and and semi-arid regions. However, remote sensing of vegetation change in these environments is difficult for several reasons. First, individual plant canopies are typically small and do not reach the spatial scale of typical Landsat-like satellite image pixels. Second, the phenological status and subsequent dry carbon (or non-photosynthetic) fraction of plant canopies varies dramatically in both space and time throughout and and semi-arid regions. Detection of only the 'green' part of the vegetation using a metric such as the normalized difference vegetation index (NDVI) thus yields limited information on the presence and condition of plants in these ecosystems. Monitoring of both photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) is needed to understand a range of ecosystem characteristics including vegetation presence, cover and abundance, physiological and biogeochemical functioning, drought severity, fire fuel load, disturbance events and recovery from disturbance.
Characterizing meadow vegetation with multitemporal Landsat thematic mapper remote sensing.

Treesearch

Alan A. Ager; Karen E. Owens

2004-01-01

Wet meadows are important biological components in the Blue Mountains of eastern Oregon. Many meadows in the Blue Mountains and elsewhere in the Western United States are in a state of change owing to grazing, mining, logging, road development, and other factors. This project evaluated the utility of remotely sensed data to characterize and monitor meadow vegetation...
A multi-frequency radiometric measurement of soil moisture content over bare and vegetated fields

NASA Technical Reports Server (NTRS)

Wang, J. R.; Schmugge, T. J.; Gould, W. I.; Glazar, W. S.; Fuchs, J. E.; Mcmurtrey, J. E., III

1982-01-01

An experiment on soil moisture remote sensing was conducted during July to September 1981 on bare, grass, and alfalfa fields at frequencies of 0.6, 1.4, 5.0, and 10.6 GHz with radiometers mounted on mobile towers. The results confirm the frequency dependence of sensitivity reduction due to the presence of vegetation cover. For the type of vegetated fields reported here, the vegetation effect is appreciable even at 0.6 GHz. Measurements over bare soil show that when the soil is wet, the measured brightness temperature is lowest at 5.0 GHz and highest at 0.6 GHz, a result contrary to the expectation based on the estimated dielectric permittivity of soil-water mixtures and the current radiative transfer model in that frequency range.
Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States

PubMed Central

Swetnam, Tyson L.; Gillan, Jeffrey K.; Sankey, Temuulen T.; McClaran, Mitchel P.; Nichols, Mary H.; Heilman, Philip; McVay, Jason

2018-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft. PMID:29379511
Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States.

PubMed

Swetnam, Tyson L; Gillan, Jeffrey K; Sankey, Temuulen T; McClaran, Mitchel P; Nichols, Mary H; Heilman, Philip; McVay, Jason

2017-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.
Mapping Post-Fire Vegetation Recovery at Different Lithologies of Taygetos mt (greece) with Multi-Temporal Remote Sensing Data

NASA Astrophysics Data System (ADS)

Vassilakis, Emmanuel; Mallinis, George; Christopoulou, Anastasia; Farangitakis, Georgios-Pavlos; Papanikolaou, Ioannis; Arianoutsou, Margarita

2017-04-01

Mt Taygetos (2407m), located at southern Peloponnese (Greece) suffered a large fire during the summer of 2007. The fire burned approximately 45% of the area covered by the endemic Greek fir (Abies cephalonica) and Black Pine (Pinus nigra) forest ecosystems. The aim of the current study is to examine the potential differences on post-fire vegetation recovery imposed by the lithology as well as the geomorphology of the given area over sites of the same climatic and landscape conditions (elevation, aspect, slope etc.). The main lithologies consist of carbonate, permeable, not easily erodible formations (limestones and marbles) and clastic, impermeable (schists, slate and flysch) erodible ones. A time-series of high spatial resolution satellite images were interpreted, analyzed and compared in order to detect changes in vegetation coverage which could prioritize areas of interest for fieldwork campaigns. The remote sensing datasets were acquired before (Ikonos-2), a few months after (Quickbird-2) and some years after (Worldview-3) the 2007 fire. High resolution Digital Elevation Model was used for the ortho-rectification and co-registration of the remote sensing data, but also for the extraction of the mountainous landscape characteristics. The multi-temporal image dataset was analyzed through GEographic-Object Based Image Analysis (GEOBIA). Objects corresponding to different vegetation types through time were identified through spectral and textural features. The classification results were combined with basic layers such as lithological outcrops, pre-fire vegetation, landscape morphology etc., supplementing a spatial geodatabase used for classifying burnt areas with varying post-fire plant community recovery. We validated the results of the classification during fieldwork and found that at a local scale, where the landscape features are quite similar, the bedrock type proves to be an important factor for vegetation recovery, as it clearly defines the soil generation along with its properties. Plant species recovery seems to be controlled by the local lithology as it was found weaker in plots overlying limestones and marbles, comparing to that observed over schists, even for the same species. In conclusion, post-fire vegetation recovery seems to be a complex process controlled not only from species biology, but also from the geological features.
Dustfall Effect on Hyperspectral Inversion of Chlorophyll Content - a Laboratory Experiment

NASA Astrophysics Data System (ADS)

Chen, Yuteng; Ma, Baodong; Li, Xuexin; Zhang, Song; Wu, Lixin

2018-04-01

Dust pollution is serious in many areas of China. It is of great significance to estimate chlorophyll content of vegetation accurately by hyperspectral remote sensing for assessing the vegetation growth status and monitoring the ecological environment in dusty areas. By using selected vegetation indices including Medium Resolution Imaging Spectrometer Terrestrial Chlorophyll Index (MTCI) Double Difference Index (DD) and Red Edge Position Index (REP), chlorophyll inversion models were built to study the accuracy of hyperspectral inversion of chlorophyll content based on a laboratory experiment. The results show that: (1) REP exponential model has the most stable accuracy for inversion of chlorophyll content in dusty environment. When dustfall amount is less than 80 g/m2, the inversion accuracy based on REP is stable with the variation of dustfall amount. When dustfall amount is greater than 80 g/m2, the inversion accuracy is slightly fluctuation. (2) Inversion accuracy of DD is worst among three models. (3) MTCI logarithm model has high inversion accuracy when dustfall amount is less than 80 g/m2; When dustfall amount is greater than 80 g/m2, inversion accuracy decreases regularly and inversion accuracy of modified MTCI (mMTCI) increases significantly. The results provide experimental basis and theoretical reference for hyperspectral remote sensing inversion of chlorophyll content.
Estimating Gross Primary Production in Cropland with High Spatial and Temporal Scale Remote Sensing Data

NASA Astrophysics Data System (ADS)

Lin, S.; Li, J.; Liu, Q.

2018-04-01

Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50 % (R2 = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE < 3 gC/m2/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.
Spatiotemporal Analysis for Wildlife-Vehicle Based on Accident Statistics of the County Straubing-Bogen in Lower Bavaria

NASA Astrophysics Data System (ADS)

Pagany, R.; Dorner, W.

2016-06-01

During the last years the numbers of wildlife-vehicle-collisions (WVC) in Bavaria increased considerably. Despite the statistical registration of WVC and preventive measures at areas of risk along the roads, the number of such accidents could not be contained. Using geospatial analysis on WVC data of the last five years for county Straubing-Bogen, Bavaria, a small-scale methodology was found to analyse the risk of WVC along the roads in the investigated area. Various indicators were examined, which may be related to WVC. The risk depends on the time of the day and year which shows correlations in turn to the traffic density and wildlife population. Additionally the location of the collision depends on the species and on different environmental parameters. Accidents seem to correlate with the land use left and right of the street. Land use data and current vegetation were derived from remote sensing data, providing information of the general land use, also considering the vegetation period. For this a number of hot spots was selected to identify potential dependencies between land use, vegetation and season. First results from these hotspots show, that WVCs do not only depend on land use, but may show a correlation with the vegetation period. With regard to agriculture and seasonal as well as annual changes this indicates that warnings will fail due to their static character in contrast to the dynamic situation of land use and resulting risk for WVCs. This shows that there is a demand for remote sensing data with a high spatial and temporal resolution as well as a methodology to derive WVC warnings considering land use and vegetation. With remote sensing data, it could become possible to classify land use and calculate risk levels for WVC. Additional parameters, derived from remote sensed data that could be considered are relief and crops as well as other parameters such as ponds, natural and infrastructural barriers that could be related to animal behaviour and should be considered by future research.
Combining remote sensing and watershed modeling for regional-scale carbon cycling studies in disturbance-prone systems

NASA Astrophysics Data System (ADS)

Hanan, E. J.; Tague, C.; Choate, J.; Liu, M.; Adam, J. C.

2016-12-01

Disturbance is a major force regulating C dynamics in terrestrial ecosystems. Evaluating future C balance in disturbance-prone systems requires understanding the underlying mechanisms that drive ecosystem processes over multiple scales of space and time. Simulation modeling is a powerful tool for bridging these scales, however, model projections are limited by large uncertainties in the initial state of vegetation C and N stores. Watershed models typically use one of two methods to initialize these stores. Spin up involves running a model until vegetation reaches steady state based on climate. This "potential" state however assumes the vegetation across the entire watershed has reached maturity and has a homogeneous age distribution. Yet to reliably represent C and N dynamics in disturbance-prone systems, models should be initialized to reflect their non-equilibrium conditions. Alternatively, remote sensing of a single vegetation parameter (typically leaf area index; LAI) can be combined with allometric relationships to allocate C and N to model stores and can reflect non-steady-state conditions. However, allometric relationships are species and region specific and do not account for environmental variation, thus resulting in C and N stores that may be unstable. To address this problem, we developed a new approach for initializing C and N pools using the watershed-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spinup with the spatial fidelity of remote sensing. Unlike traditional spin up, this approach supports non-homogeneous stand ages. We tested our approach in a pine-dominated watershed in central Idaho, which partially burned in July of 2000. We used LANDSAT and MODIS data to calculate LAI across the watershed following the 2000 fire. We then ran three sets of simulations using spin up, direct measurements, and the combined approach to initialize vegetation C and N stores, and compared our results to remotely sensed LAI following the simulation period. Model estimates of C, N, and water fluxes varied depending on which approach was used. The combined approach provided the best LAI estimates after 10 years of simulation. This method shows promise for improving projections of C, N, and water fluxes in disturbance-prone watersheds.
[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

PubMed

Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

2009-10-01

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.
New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

NASA Astrophysics Data System (ADS)

Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, Arijit; Singh, Sarnam; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, Ch. Sudhakar; Gupta, Stutee; Pujar, Girish; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, Poonam; Singh, J. S.; Chitale, Vishwas; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, Deepak; Karnatak, Harish; Saran, Sameer; Giriraj, A.; Padalia, Hitendra; Kale, Manish; Nandy, Subrato; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, Chiranjibi; Singh, D. K.; Devagiri, G. M.; Talukdar, Gautam; Panigrahy, Rabindra K.; Singh, Harnam; Sharma, J. R.; Haridasan, K.; Trivedi, Shivam; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, Madhura; Nagabhatla, Nidhi; Prasad, Nupoor; Tripathi, O. P.; Prasad, P. Rama Chandra; Dash, Pushpa; Qureshi, Qamer; Tripathi, S. K.; Ramesh, B. R.; Gowda, Balakrishnan; Tomar, Sanjay; Romshoo, Shakil; Giriraj, Shilpa; Ravan, Shirish A.; Behera, Soumit Kumar; Paul, Subrato; Das, Ashesh Kumar; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, Uma; Menon, A. R. R.; Srivastava, Gaurav; Neeti; Sharma, Subrat; Mohapatra, U. B.; Peddi, Ashok; Rashid, Humayun; Salroo, Irfan; Krishna, P. Hari; Hajra, P. K.; Vergheese, A. O.; Matin, Shafique; Chaudhary, Swapnil A.; Ghosh, Sonali; Lakshmi, Udaya; Rawat, Deepshikha; Ambastha, Kalpana; Malik, Akhtar H.; Devi, B. S. S.; Gowda, Balakrishna; Sharma, K. C.; Mukharjee, Prashant; Sharma, Ajay; Davidar, Priya; Raju, R. R. Venkata; Katewa, S. S.; Kant, Shashi; Raju, Vatsavaya S.; Uniyal, B. P.; Debnath, Bijan; Rout, D. K.; Thapa, Rajesh; Joseph, Shijo; Chhetri, Pradeep; Ramachandran, Reshma M.

2015-07-01

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge's life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).
Assessing and monitoring of urban vegetation using multiple endmember spectral mixture analysis

NASA Astrophysics Data System (ADS)

Zoran, M. A.; Savastru, R. S.; Savastru, D. M.

2013-08-01

During last years urban vegetation with significant health, biological and economical values had experienced dramatic changes due to urbanization and human activities in the metropolitan area of Bucharest in Romania. We investigated the utility of remote sensing approaches of multiple endmember spectral mixture analysis (MESMA) applied to IKONOS and Landsat TM/ETM satellite data for estimating fractional cover of urban/periurban forest, parks, agricultural vegetation areas. Because of the spectral heterogeneity of same physical features of urban vegetation increases with the increase of image resolution, the traditional spectral information-based statistical method may not be useful to classify land cover dynamics from high resolution imageries like IKONOS. So we used hierarchy tree classification method in classification and MESMA for vegetation land cover dynamics assessment based on available IKONOS high-resolution imagery of Bucharest town. This study employs thirty two endmembers and six hundred and sixty spectral models to identify all Earth's features (vegetation, water, soil, impervious) and shade in the Bucharest area. The mean RMS error for the selected vegetation land cover classes range from 0.0027 to 0.018. The Pearson correlation between the fraction outputs from MESMA and reference data from all IKONOS images 1m panchromatic resolution data for urban/periurban vegetation were ranging in the domain 0.7048 - 0.8287. The framework in this study can be applied to other urban vegetation areas in Romania.
Polarimetric Remote Sensing of Atmospheric Particulate Pollutants

NASA Astrophysics Data System (ADS)

Li, Z.; Zhang, Y.; Hong, J.

2018-04-01

Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF), whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.
High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.
Estimation of Spatial Trends in LAI in Heterogeneous Semi-arid Ecosystems using Full Waveform Lidar

NASA Astrophysics Data System (ADS)

Glenn, N. F.; Ilangakoon, N.; Spaete, L.; Dashti, H.

2017-12-01

Leaf area index (LAI) is a key structural trait that is defined by the plant functional type (PFT) and controlled by prevailing climate- and human-driven ecosystem stresses. Estimates of LAI using remote sensing techniques are limited by the uncertainties of vegetation inter and intra-gap fraction estimates; this is especially the case in sparse, low stature vegetated ecosystems. Small footprint full waveform lidar digitizes the total amount of return energy with the direction information as a near continuous waveform at a high vertical resolution (1 ns). Thus waveform lidar provides additional data matrices to capture vegetation gaps as well as PFTs that can be used to constrain the uncertainties of LAI estimates. In this study, we calculated a radiometrically calibrated full waveform parameter called backscatter cross section, along with other data matrices from the waveform to estimate vegetation gaps across plots (10 m x 10 m) in a semi-arid ecosystem in the western US. The LAI was then estimated using empirical relationships with directional gap fraction. Full waveform-derived gap fraction based LAI showed a high correlation with field observed shrub LAI (R2 = 0.66, RMSE = 0.24) compared to discrete return lidar based LAI (R2 = 0.01, RMSE = 0.5). The data matrices derived from full waveform lidar classified a number of deciduous and evergreen tree species, shrub species, and bare ground with an overall accuracy of 89% at 10 m. A similar analysis was performed at 1m with overall accuracy of 80%. The next step is to use these relationships to map the PFTs LAI at 10 m spatial scale across the larger study regions. The results show the exciting potential of full waveform lidar to identify plant functional types and LAI in low-stature vegetation dominated semi-arid ecosystems, an ecosystem in which many other remote sensing techniques fail. These results can be used to assess ecosystem state, habitat suitability as well as to constrain model uncertainties in vegetation dynamic models with a combination of other remote sensing techniques. Multi-spatial resolution (1 m and 10 m) studies provide basic information on the applicability and detection thresholds of future global satellite sensors designed at coarser spatial resolutions (e.g. GEDI, ICESat-2) in semi-arid ecosystems.
Applying Lidar and High-Resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic

NASA Astrophysics Data System (ADS)

Greaves, Heather E.

Climate change is disproportionately affecting high northern latitudes, and the extreme temperatures, remoteness, and sheer size of the Arctic tundra biome have always posed challenges that make application of remote sensing technology especially appropriate. Advances in high-resolution remote sensing continually improve our ability to measure characteristics of tundra vegetation communities, which have been difficult to characterize previously due to their low stature and their distribution in complex, heterogeneous patches across large landscapes. In this work, I apply terrestrial lidar, airborne lidar, and high-resolution airborne multispectral imagery to estimate tundra vegetation characteristics for a research area near Toolik Lake, Alaska. Initially, I explored methods for estimating shrub biomass from terrestrial lidar point clouds, finding that a canopy-volume based algorithm performed best. Although shrub biomass estimates derived from airborne lidar data were less accurate than those from terrestrial lidar data, algorithm parameters used to derive biomass estimates were similar for both datasets. Additionally, I found that airborne lidar-based shrub biomass estimates were just as accurate whether calibrated against terrestrial lidar data or harvested shrub biomass--suggesting that terrestrial lidar potentially could replace destructive biomass harvest. Along with smoothed Normalized Differenced Vegetation Index (NDVI) derived from airborne imagery, airborne lidar-derived canopy volume was an important predictor in a Random Forest model trained to estimate shrub biomass across the 12.5 km2 covered by our lidar and imagery data. The resulting 0.80 m resolution shrub biomass maps should provide important benchmarks for change detection in the Toolik area, especially as deciduous shrubs continue to expand in tundra regions. Finally, I applied 33 lidar- and imagery-derived predictor layers in a validated Random Forest modeling approach to map vegetation community distribution at 20 cm resolution across the data collection area, creating maps that will enable validation of coarser maps, as well as study of fine-scale ecological processes in the area. These projects have pushed the limits of what can be accomplished for vegetation mapping using airborne remote sensing in a challenging but important region; it is my hope that the methods explored here will illuminate potential paths forward as landscapes and technologies inevitably continue to change.
Spatial and spectral resolution necessary for remotely sensed vegetation studies

NASA Technical Reports Server (NTRS)

Rock, B. N.

1982-01-01

An outline is presented of the required spatial and spectral resolution needed for accurate vegetation discrimination and mapping studies as well as for determination of state of health (i.e., detection of stress symptoms) of actively growing vegetation. Good success was achieved in vegetation discrimination and mapping of a heterogeneous forest cover in the ridge and valley portion of the Appalachians using multispectral data acquired with a spatial resolution of 15 m (IFOV). A sensor system delivering 10 to 15 m spatial resolution is needed for both vegetation mapping and detection of stress symptoms. Based on the vegetation discrimination and mapping exercises conducted at the Lost River site, accurate products (vegetation maps) are produced using broad-band spectral data ranging from the .500 to 2.500 micron portion of the spectrum. In order of decreasing utility for vegetation discrimination, the four most valuable TM simulator VNIR bands are: 6 (1.55 to 1.75 microns), 3 (0.63 to 0.69 microns), 5 (1.00 to 1.30 microns) and 4 (0.76 to 0.90 microns).
Response of Alpine Grassland Vegetation Phenology to Snow Accumulation and Melt in Namco Basin

NASA Astrophysics Data System (ADS)

Chen, S.; Cui, X.; Liang, T.

2018-04-01

Snow/ice accumulation and melt, as a vital part of hydrological processes, is close related with vegetation activities. Taking Namco basin for example, based on multisource remote sensing data and the ground observation data of temperature and precipitation, phenological information was extracted by S-G filtering and dynamic threshold method. Daily snow cover fraction was calculated with daily cloud-free snow cover maps. Evolution characteristics of grassland vegetation greening, growth length and daily snow cover fraction and their relationship were analyzed from 2001 to 2013. The results showed that most of grassland vegetation had advanced greening and prolong growth length trend in Namco basin. There were negative correlations between snow cover fraction and vegetation greening or growth length. The response of vegetation phenology to snow cover fraction is more sensitive than that to temperature in spring. Meanwhile, vegetation growth condition turned worse with advanced greening and prolong growth length. To a certain extent, our research reveals the relationship between grassland vegetation growth cycle and snow in alpine ecosystem. It has provided reference to research the response mechanism of alpine grassland ecosystem to climate changes.
Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators

NASA Astrophysics Data System (ADS)

Gouveia, C. M.; Trigo, R. M.; Beguería, S.; Vicente-Serrano, S. M.

2017-04-01

The present work analyzes the drought impacts on vegetation over the entire Mediterranean basin, with the purpose of determining the vegetation communities, regions and seasons at which vegetation is driven by drought. Our approach is based on the use of remote sensing data and a multi-scalar drought index. Correlation maps between fields of monthly Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation-Evapotranspiration Index (SPEI) at different time scales (1-24 months) were computed for representative months of winter (Feb), spring (May), summer (Aug) and fall (Nov). Results for the period from 1982 to 2006 show large areas highly controlled by drought, although presenting high spatial and seasonal differences, with a maximum influence in August and a minimum in February. The highest correlation values are observed in February for 3 months' time scale and in May for 6 and 12 months. The higher control of drought on vegetation in February and May is obtained mainly over the drier vegetation communities (Mediterranean Dry and Desertic) at shorter time scales (3 to 9 months). Additionally, in February the impact of drought on vegetation is lower for Temperate Oceanic and Continental vegetation types and takes place at longer time scales (18-24). The dependence of drought time-scale response with water balance, as obtained through a simple difference between precipitation and reference evapotranspiration, varies with vegetation communities. During February and November low water balance values correspond to shorter time scales over dry vegetation communities, whereas high water balance values implies longer time scales over Temperate Oceanic and Continental areas. The strong control of drought on vegetation observed for Mediterranean Dry and Desertic vegetation types located over areas with high negative values of water balance emphasizes the need for an early warning drought system covering the entire Mediterranean basin. We are confident that these results will provide a useful tool for drought management plans and play a relevant role in mitigating the impact of drought episodes.
Desertification, resilience, and re-greening in the African Sahel - a matter of the observation period?

NASA Astrophysics Data System (ADS)

Kusserow, Hannelore

2017-12-01

Since the turn of the millennium various scientific publications have been discussing a re-greening of the Sahel after the 1980s drought mainly based on coarse-resolution satellite data. However, the author's own field studies suggest that the situation is far more complex and that both paradigms, the encroaching Sahara and the re-greening Sahel, need to be questioned.
This paper discusses the concepts of desertification, resilience, and re-greening by addressing four main aspects: (i) the relevance of edaphic factors for a vegetation re-greening, (ii-iii) the importance of the selected observation period in the debate on Sahel greening or browning, and (iv) modifications in the vegetation pattern as possible indicators of ecosystem changes (shift from originally diffuse to contracted vegetation patterns).
The data referred to in this paper cover a time period of more than 150 years and include the author's own research results from the early 1980s until today. A special emphasis, apart from fieldwork data and remote sensing data, is laid on the historical documents.
The key findings summarised at the end show the following: (i) vegetation recovery predominantly depends on soil types; (ii) when discussing Sahel greening vs. Sahel browning, the majority of research papers only focus on post-drought conditions. Taking pre-drought conditions (before the 1980s) into account, however, is essential to fully understand the situation. Botanical investigations and remote-sensing-based time series clearly show a substantial decline in woody species diversity and cover density compared to pre-drought conditions; (iii) the self-organised patchiness of vegetation is considered to be an important indicator of ecosystem changes.
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.
Post-fire vegetation dynamics in Portugal

NASA Astrophysics Data System (ADS)

Gouveia, C.; Dacamara, C. C.; Trigo, R. M.

2009-04-01

The number of fires and the extent of the burned surface in Mediterranean Europe have increased significantly during the last three decades. This may be due either to modifications in land-use (e.g. land abandonment and fuel accumulation) or to climatic changes (e.g. reduction of fuel humidity), both factors leading to an increase of fire risk and fire spread. As in the Mediterranean ecosystems, fires in Portugal have an intricate effect on vegetation regeneration due to the complexity of landscape structures as well as to the different responses of vegetation to the variety of fire regimes. A thorough evaluation of vegetation recovery after fire events becomes therefore crucial in land management. In the above mentioned context remote sensing plays an important role because of its ability to monitor and characterise post-fire vegetation dynamics. A number of fire recovery studies, based on remote sensing, have been conducted in regions characterised by Mediterranean climates and the use of NDVI to monitor plant regeneration after fire events was successfully tested (Díaz-Delgado et al., 1998). In particular, several studies have shown that rapid regeneration occurs within the first 2 years after the fire occurrences, with distinct recovery rates according to the geographical facing of the slopes (Pausas and Vallejo, 1999). In 2003 Portugal was hit by the most devastating sequence of large fires, responsible by a total burnt area of 450 000 ha (including 280 000 ha of forest), representing about 5% of the Portuguese mainland (Trigo et al., 2006). The aim of the present work is to assess and monitor the vegetation behaviour over Portugal following the 2003 fire episodes. For this purpose we have used the regional fields of the Normalized Difference Vegetation Index (NDVI) as obtained from the VEGETATION-SPOT5 instrument, from 1999 to 2008. We developed a methodology to identify large burnt scars in Portugal for the 2003 fire season. The vegetation dynamics was then analysed for some selected areas and a regression model of post-fire recovery was fitted to the recorded values of NDVI. The model allowed characterising the dynamics of the regeneration process. It was found that recovery rates depend on geographical location, fire intensity/severity and type of vegetation cover. Díaz-Delgado, R., Salvador, R. and Pons, X., 1998: Monitoring of plant community regeneration after fire by remote sensing. In L. Traboud (Ed.), Fire management and landscape ecology (pp. 315-324). International Association of Wildland Fire, Fairfield, WA. Pausas, G.J. and Vallejo, V.R., 1999: The role of fire in European Mediterranean Ecosystems. In: E. Chuvieco (Ed.), Remote sensing of large wildfires in the European Mediterranean basin (pp. 3-16). Springer-Verlag. Trigo R.M., Pereira J.M.C., Pereira M.G., Mota B., Calado M.T., DaCamara C.C., Santo F.E., 2006: Atmospheric conditions associated with the exceptional fire season of 2003 in Portugal. International Journal of Climatology 26 (13): 1741-1757 NOV 15 2006.
Preliminary study of near surface detections at geothermal field using optic and SAR imageries

NASA Astrophysics Data System (ADS)

Kurniawahidayati, Beta; Agoes Nugroho, Indra; Syahputra Mulyana, Reza; Saepuloh, Asep

2017-12-01

Current remote sensing technologies shows that surface manifestation of geothermal system could be detected with optical and SAR remote sensing, but to assess target beneath near the surface layer with the surficial method needs a further study. This study conducts a preliminary result using Optic and SAR remote sensing imagery to detect near surface geothermal manifestation at and around Mt. Papandayan, West Java, Indonesia. The data used in this study were Landsat-8 OLI/TIRS for delineating geothermal manifestation prospect area and an Advanced Land Observing Satellite(ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) level 1.1 for extracting lineaments and their density. An assumption was raised that the lineaments correlated with near surface structures due to long L-band wavelength about 23.6 cm. Near surface manifestation prospect area are delineated using visual comparison between Landsat 8 RGB True Colour Composite band 4,3,2 (TCC), False Colour Composite band 5,6,7 (FCC), and lineament density map of ALOS PALSAR. Visual properties of ground object were distinguished from interaction of the electromagnetic radiation and object whether it reflect, scatter, absorb, or and emit electromagnetic radiation based on characteristic of their molecular composition and their macroscopic scale and geometry. TCC and FCC composite bands produced 6 and 7 surface manifestation zones according to its visual classification, respectively. Classified images were then compared to a Normalized Different Vegetation Index (NDVI) to obtain the influence of vegetation at the ground surface to the image. Geothermal area were classified based on vegetation index from NDVI. TCC image is more sensitive to the vegetation than FCC image. The later composite produced a better result for identifying visually geothermal manifestation showed by detail-detected zones. According to lineament density analysis high density area located on the peak of Papandayan overlaid with zone 1 and 2 of FCC. Comparing to the extracted lineament density, we interpreted that the near surface manifestation is located at zone 1 and 2 of FCC image.
Remote sensing techniques for monitoring drought hazards: an intercomparison (Invited)

NASA Astrophysics Data System (ADS)

Brown, J. F.; Anderson, M. C.; Wardlow, B. D.; Svoboda, M. D.

2009-12-01

Drought events are frequently described using many different terms; for example, recurring climate phenomena, creeping natural hazards, agricultural disasters, and moisture deficiencies. In addition, droughts operate at many different spatial and temporal scales and affect different societal sectors, making them quite challenging to monitor, map, and assess impacts. Because of these factors, determining drought severity often requires using a convergence of evidence assisted by an analysis of multiple drought indicators. Frequent optical and thermal observations collected by daily polar-orbiting and geostationary satellites allow for regular monitoring of the land surface. In recent decades, with the launching of more advanced sensors and the maturation of remote sensing techniques, a variety of tools have been designed for improved understanding and tracking of drought events as they are occurring. These applications are intended to provide key decision makers with timely geospatial drought information to support various drought planning and mitigation activities. Two such tools highlighted in this study, are the Vegetation Drought Response Index (VegDRI) and the Evaporative Stress Index (ESI). While both indices incorporate satellite-based inputs, they are involved in different modeling approaches and observations from different parts of the electromagnetic spectrum. The VegDRI is a hybrid remote sensing and climate based indicator of drought-induced vegetation stress that combines satellite-based vegetation index observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors with climate-based drought index data and other biophysical parameters (such as land use/land cover type and soil characteristics). VegDRI provides near real-time vegetation drought severity information at relatively higher spatial resolution (1-km2) than traditional climatic drought indices such as the Standardized Precipitation Index (SPI) or the U.S. Drought Monitor (USDM), which tend to depicted broad-scale spatial drought patterns. . The ESI is an indicator of anomalous land-surface evaporation and soil moisture deficiency. The ESI is related to the ratio of actual-to-potential evapotranspiration (ET), where actual ET is estimated with a thermal-infrared (TIR) based surface energy balance algorithm. The ESI product is generated in near-real time at 10-km2 resolution over the continental U.S. using TIR imagery from the Geostationary Operational Environmental Satellites (GOES). Because it does not use precipitation data as an input, it is a valuable complement to existing precipitation-based indices and is readily portable to data-poor regions with sparse ground-based rainfall monitoring networks. In this study, we present an intercomparison of the VegDRI and the ESI for the 2009 growing season, highlighting weekly, monthly, and seasonal patterns of moisture flux from soils and vegetation.
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.
Grassland Aboveground Biomass in Inner Mongolia: Dynamics (2001-2016) and Driving force

NASA Astrophysics Data System (ADS)

Li, F.; Zeng, Y.; Chen, J.; Wu, B.

2017-12-01

Plant biomass is the most critical measure of carbon stored in an ecosystem, yet it remains imprecisely modeled for many terrestrial biomes. This lack of modeling capacity for biomass and its change through time and space has impeded scientists from making headway concerning issues in the geographic and social sciences. Satellite remote sensing techniques excel at detecting changes in the Earth's surface; however, accurate estimates of biomass for the heterogeneous biome landscapes based on remote sensing techniques are few and far between, which has led to many repetitive studies. Here, we argued that our ability to assess biomass in a heterogeneous landscape using satellite remote sensing techniques would be effectively enhanced through a stratification of landscapes, i.e homogenizing landscapes. Specifically, above-ground biomass (AGB) for an extended heterogeneous grassland biome over the entirety of Inner Mongolia during the past 16 years (2001-2016) was explored using remote sensing time series data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Massive and extensive in-situ measurement AGB data and pure vegetation index (PVI) models, developed from normal remote sensing vegetation indices such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were highlighted in the accomplishment of this study. Taking into full consideration the landscape heterogeneity for the grassland biome over Inner Mongolia, we achieved a series of AGB models with high R2 (>0.85) and low RMSE ( 20.85 g/m2). The total average amount of fresh AGB for the entirety of Inner Mongolia grasslands over the past 16 years was estimated as 87 Tg with an inter-annual standard deviation of 9 Tg. Overall, the grassland AGB for Inner Mongolia increased sporadically. We found that the dynamics of AGB in the grassland biome of Inner Mongolia were substantially dominated by variation in precipitation despite the accommodation of a huge population of livestock in this area over the past few decades. Concerning the production of grassland AGB for the future, we emphasized that the impacts of the frequently warming-drying climate associated with climate change across the Mongolia Plateau should be paid more attention.
Revealing livestock effects on bunchgrass vegetation with Landsat ETM+ data across a grazing season

NASA Astrophysics Data System (ADS)

Jansen, Vincent S.

Remote sensing provides monitoring solutions for more informed grazing management. To investigate the ability to detect the effects of cattle grazing on bunchgrass vegetation with Landsat Enhanced Thematic Mapper Plus (ETM+) data, we conducted a study on the Zumwalt Prairie in northeastern Oregon across a gradient of grazing intensities. Biophysical vegetation data was collected on vertical structure, biomass, and cover at three different time periods during the grazing season: June, August, and October 2012. To relate these measures to the remotely sensed Landsat ETM+ data, Pearson's correlations and multiple regression models were computed. Using the best models, predicted vegetation metrics were then mapped across the study area. Results indicated that models using common vegetation indices had the ability to discern different levels of grazing across the study area. Results can be distributed to land managers to help guide grassland conservation by improving monitoring of bunchgrass vegetation for sustainable livestock management.
Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology

USGS Publications Warehouse

Tappan, G. Gray; Moore, Donald G.; Knauseberger, Walter I.

1991-01-01

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.
A vegetation mapping strategy for conifer forests by combining airborne LiDAR data and aerial imagery

Treesearch

Yanjun Su; Qinghua Guo; Danny L. Fry; Brandon M. Collins; Maggi Kelly; Jacob P. Flanagan; John J. Battles

2016-01-01

Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure...
Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index

Treesearch

Philip E. Dennison; Dar A. Roberts; Sommer R. Thorgusen; Jon C. Regelbrugge; David Weise; Christopher Lee

2003-01-01

Live fuel moisture, an important determinant of fire danger in Mediterranean ecosystems, exhibits seasonal changes in response to soil water availability. Both drought stress indices based on meteorological data and remote sensing indices based on vegetation water absorption can be used to monitor live fuel moisture. In this study, a cumulative water balance index (...
Analyzing long-term changes in vegetation with geographic information system and remotely sensed data

Treesearch

Louis. R. Iverson; Paul. G. Risser; Paul. G. Risser

1987-01-01

Geographic information systems and remote sensing techniques are powerful tools in the analysis of long-term changes in vegetation and land use, especially because spatial information from two or more time intervals can be compared more readily than by manual methods. A primary restriction is the paucity of data that has been digitized from earlier periods. The...
Dynamic stratification of the landscape of Mexico: analysis of vegetation patterns observed with multitemporal remotely sensed images

Treesearch

Franz Mora; Louis R. Iverson; Louis R. Iverson

1997-01-01

Rapid deforestation in Mexico, when coupled with poor access to current and consistent ecological information across the country underscores the need for an ecological classification system that can be readily updated as new data become available. In this study, regional vegetation resources in Mexico were evaluated using remotely sensed information. Multitemporal...
Thermal remote sensing of active vegetation fires and biomass burning events [Chapter 18

Treesearch

Martin J. Wooster; Gareth Roberts; Alistair M.S. Smith; Joshua Johnston; Patrick Freeborn; Stefania Amici; Andrew T. Hudak

2013-01-01

Thermal remote sensing is widely used in the detection, study, and management of biomass burning occurring in open vegetation fires. Such fires may be planned for land management purposes, may occur as a result of a malicious or accidental ignition by humans, or may result from lightning or other natural phenomena. Under suitable conditions, fires may spread rapidly...
Evaluation of SPOT imagery for the estimation of grassland biomass

NASA Astrophysics Data System (ADS)

Dusseux, P.; Hubert-Moy, L.; Corpetti, T.; Vertès, F.

2015-06-01

In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R2 values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting.
Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, G.; Yule, I. J.

2017-06-01

New Zealand farming relies heavily on grazed pasture for feeding livestock; therefore it is important to provide high quality palatable grass in order to maintain profitable and sustainable grassland management. The presence of non-photosynthetic vegetation (NPV) such as dead vegetation in pastures severely limits the quality and productivity of pastures. Quantifying the fraction of dead vegetation in mixed pastures is a great challenge even with remote sensing approaches. In this study, a high spatial resolution with pixel resolution of 1 m and spectral resolution of 3.5-5.6 nm imaging spectroscopy data from AisaFENIX (380-2500 nm) was used to assess the fraction of dead vegetation component in mixed pastures on a hill country farm in New Zealand. We used different methods to retrieve dead vegetation fraction from the spectra; narrow band vegetation indices, full spectrum based partial least squares (PLS) regression and feature selection based PLS regression. Among all approaches, feature selection based PLS model exhibited better performance in terms of prediction accuracy (R2CV = 0.73, RMSECV = 6.05, RPDCV = 2.25). The results were consistent with validation data, and also performed well on the external test data (R2 = 0.62, RMSE = 8.06, RPD = 2.06). In addition, statistical tests were conducted to ascertain the effect of topographical variables such as slope and aspect on the accumulation of the dead vegetation fraction. Steep slopes (>25°) had a significantly (p < 0.05) higher amount of dead vegetation. In contrast, aspect showed non-significant impact on dead vegetation accumulation. The results from the study indicate that AisaFENIX imaging spectroscopy data could be a useful tool for mapping the dead vegetation fraction accurately.
Validation for Vegetation Green-up Date Extracted from GIMMS NDVI and NDVI3g Using Variety of Methods

NASA Astrophysics Data System (ADS)

Chang, Q.; Jiao, W.

2017-12-01

Phenology is a sensitive and critical feature of vegetation change that has regarded as a good indicator in climate change studies. So far, variety of remote sensing data sources and phenology extraction methods from satellite datasets have been developed to study the spatial-temporal dynamics of vegetation phenology. However, the differences between vegetation phenology results caused by the varies satellite datasets and phenology extraction methods are not clear, and the reliability for different phenology results extracted from remote sensing datasets is not verified and compared using the ground observation data. Based on three most popular remote sensing phenology extraction methods, this research calculated the Start of the growing season (SOS) for each pixels in the Northern Hemisphere for two kinds of long time series satellite datasets: GIMMS NDVIg (SOSg) and GIMMS NDVI3g (SOS3g). The three methods used in this research are: maximum increase method, dynamic threshold method and midpoint method. Then, this study used SOS calculated from NEE datasets (SOS_NEE) monitored by 48 eddy flux tower sites in global flux website to validate the reliability of six phenology results calculated from remote sensing datasets. Results showed that both SOSg and SOS3g extracted by maximum increase method are not correlated with ground observed phenology metrics. SOSg and SOS3g extracted by the dynamic threshold method and midpoint method are both correlated with SOS_NEE significantly. Compared with SOSg extracted by the dynamic threshold method, SOSg extracted by the midpoint method have a stronger correlation with SOS_NEE. And, the same to SOS3g. Additionally, SOSg showed stronger correlation with SOS_NEE than SOS3g extracted by the same method. SOS extracted by the midpoint method from GIMMS NDVIg datasets seemed to be the most reliable results when validated with SOS_NEE. These results can be used as reference for data and method selection in future's phenology study.
Assessment and prediction of land ecological environment quality change based on remote sensing-a case study of the Dongting lake area in China

NASA Astrophysics Data System (ADS)

Hu, Wenmin; Wang, Zhongcheng; Li, Chunhua; Zhao, Jin; Li, Yi

2018-02-01

Multi-source remote sensing data is rarely used for the comprehensive assessment of land ecologic environment quality. In this study, a digital environmental model was proposed with the inversion algorithm of land and environmental factors based on the multi-source remote sensing data, and a comprehensive index (Ecoindex) was applied to reconstruct and predict the land environment quality of the Dongting Lake Area to assess the effect of human activities on the environment. The main finding was that with the decrease of Grade I and Grade II quality had a decreasing tendency in the lake area, mostly in suburbs and wetlands. Atmospheric water vapour, land use intensity, surface temperature, vegetation coverage, and soil water content were the main driving factors. The cause of degradation was the interference of multi-factor combinations, which led to positive and negative environmental agglomeration effects. Positive agglomeration, such as increased rainfall and vegetation coverage and reduced land use intensity, could increase environmental quality, while negative agglomeration resulted in the opposite. Therefore, reasonable ecological restoration measures should be beneficial to limit the negative effects and decreasing tendency, improve the land ecological environment quality and provide references for macroscopic planning by the government.
Study of Burn Scar Extraction Automatically Based on Level Set Method using Remote Sensing Data

PubMed Central

Liu, Yang; Dai, Qin; Liu, JianBo; Liu, ShiBin; Yang, Jin

2014-01-01

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model. PMID:24503563

Some insights on grassland health assessment based on remote sensing.

PubMed

Xu, Dandan; Guo, Xulin

2015-01-29

Grassland ecosystem is one of the largest ecosystems, which naturally occurs on all continents excluding Antarctica and provides both ecological and economic functions. The deterioration of natural grassland has been attracting many grassland researchers to monitor the grassland condition and dynamics for decades. Remote sensing techniques, which are advanced in dealing with the scale constraints of ecological research and provide temporal information, become a powerful approach of grassland ecosystem monitoring. So far, grassland health monitoring studies have mostly focused on different areas, for example, productivity evaluation, classification, vegetation dynamics, livestock carrying capacity, grazing intensity, natural disaster detecting, fire, climate change, coverage assessment and soil erosion. However, the grassland ecosystem is a complex system which is formed by soil, vegetation, wildlife and atmosphere. Thus, it is time to consider the grassland ecosystem as an entity synthetically and establish an integrated grassland health monitoring system to combine different aspects of the complex grassland ecosystem. In this review, current grassland health monitoring methods, including rangeland health assessment, ecosystem health assessment and grassland monitoring by remote sensing from different aspects, are discussed along with the future directions of grassland health assessment.
Some Insights on Grassland Health Assessment Based on Remote Sensing

PubMed Central

Xu, Dandan; Guo, Xulin

2015-01-01

Grassland ecosystem is one of the largest ecosystems, which naturally occurs on all continents excluding Antarctica and provides both ecological and economic functions. The deterioration of natural grassland has been attracting many grassland researchers to monitor the grassland condition and dynamics for decades. Remote sensing techniques, which are advanced in dealing with the scale constraints of ecological research and provide temporal information, become a powerful approach of grassland ecosystem monitoring. So far, grassland health monitoring studies have mostly focused on different areas, for example, productivity evaluation, classification, vegetation dynamics, livestock carrying capacity, grazing intensity, natural disaster detecting, fire, climate change, coverage assessment and soil erosion. However, the grassland ecosystem is a complex system which is formed by soil, vegetation, wildlife and atmosphere. Thus, it is time to consider the grassland ecosystem as an entity synthetically and establish an integrated grassland health monitoring system to combine different aspects of the complex grassland ecosystem. In this review, current grassland health monitoring methods, including rangeland health assessment, ecosystem health assessment and grassland monitoring by remote sensing from different aspects, are discussed along with the future directions of grassland health assessment. PMID:25643060
Assessing Nitrogen Status of Dryland Wheat Using the Canopy Chlorophyll Content Index

USDA-ARS?s Scientific Manuscript database

Ground-based, active light sensing relies upon the Normalized Difference Vegetation Index (NDVI) for assessing crop nitrogen (N) response and applying N fertilizer. However, NDVI may not work well in semiarid environments where biomass and yields depend upon plant water. This study evaluated the C...
Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region.

PubMed

Song, Youngkeun; Njoroge, John B; Morimoto, Yukihiro

2013-05-01

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E ~ 1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period.
Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen)

NASA Astrophysics Data System (ADS)

Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello

2013-01-01

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.
Fuel loads and fuel type mapping

USGS Publications Warehouse

Chuvieco, Emilio; Riaño, David; Van Wagtendonk, Jan W.; Morsdof, Felix; Chuvieco, Emilio

2003-01-01

Correct description of fuel properties is critical to improve fire danger assessment and fire behaviour modeling, since they guide both fire ignition and fire propagation. This chapter deals with properties of fuel that can be considered static in short periods of time: biomass loads, plant geometry, compactness, etc. Mapping these properties require a detail knowledge of vegetation vertical and horizontal structure. Several systems to classify the great diversity of vegetation characteristics in few fuel types are described, as well as methods for mapping them with special emphasis on those based on remote sensing images.
Impact of 3D Canopy Structure on Remote Sensing Vegetation Index and Solar Induced Chlorophyll Fluorescence

NASA Astrophysics Data System (ADS)

Zeng, Y.; Berry, J. A.; Jing, L.; Qinhuo, L.

2017-12-01

Terrestrial ecosystem plays a critical role in removing CO2 from atmosphere by photosynthesis. Remote sensing provides a possible way to monitor the Gross Primary Production (GPP) at the global scale. Vegetation Indices (VI), e.g., NDVI and NIRv, and Solar Induced Fluorescence (SIF) have been widely used as a proxy for GPP, while the impact of 3D canopy structure on VI and SIF has not be comprehensively studied yet. In this research, firstly, a unified radiative transfer model for visible/near-infrared reflectance and solar induced chlorophyll fluorescence has been developed based on recollision probability and directional escape probability. Then, the impact of view angles, solar angles, weather conditions, leaf area index, and multi-layer leaf angle distribution (LAD) on VI and SIF has been studied. Results suggest that canopy structure plays a critical role in distorting pixel-scale remote sensing signal from leaf-scale scattering. In thin canopy, LAD affects both of the remote sensing estimated GPP and real GPP, while in dense canopy, SIF variations are mainly due to canopy structure, instead of just due to physiology. At the microscale, leaf angle reflects the plant strategy to light on the photosynthesis efficiency, and at the macroscale, a priori knowledge of leaf angle distribution for specific species can improve the global GPP estimation by remote sensing.
Prototype simulates remote sensing spectral measurements on fruits and vegetables

NASA Astrophysics Data System (ADS)

Hahn, Federico

1998-09-01

A prototype was designed to simulate spectral packinghouse measurements in order to simplify fruit and vegetable damage assessment. A computerized spectrometer is used together with lenses and an externally controlled illumination in order to have a remote sensing simulator. A laser is introduced between the spectrometer and the lenses in order to mark the zone where the measurement is being taken. This facilitates further correlation work and can assure that the physical and remote sensing measurements are taken in the same place. Tomato ripening and mango anthracnose spectral signatures are shown.
Remote sensing of water and nitrogen stress in broccoli

NASA Astrophysics Data System (ADS)

Elsheikha, Diael-Deen Mohamed

Remote sensing is being used in agriculture for crop management. Ground based remote sensing data acquisition system was used for collection of high spatial and temporal resolution data for irrigated broccoli crop. The system was composed of a small cart that ran back and forth on a rail system that was mounted on a linear move irrigation system. The cart was equipped with a sensor that had 4 discrete wavelengths; 550 nm, 660 nm, 720 nm, and 810 nm, and an infrared thermometer, all had 10 nm bandwidth. A global positioning system was used to indicate the cart position. The study consisted of two parts; the first was to evaluate remotely sensed reflectance and indices in broccoli during the growing season, and determine whether remotely sensed indices or standard deviation of indices can distinguish between nitrogen and water stress in broccoli, and the second part of the study was to evaluate remotely sensed indices and standard deviation of remotely sensed indices in broccoli during daily changes in solar zenith angle. Results indicated that nitrogen was detected using Ratio Vegetation index, RVI, Normalized Difference Vegetation Index, NDVI, Canopy Chlorophyll Concentration Index, CCCI, and also using the reflectance in the Near-Infrared, NIR, bands. The Red reflectance band capability of showing stress was not as clear as the previous indices and bands reflectance. The Canopy Chlorophyll Concentration Index, CCCI, was the most successful index. The Crop Water Stress Index was able to detect water stress but it was highly affected by the solar zenith angle change along the day.
Remote sensing of vegetation canopy photosynthetic and stomatal conductance efficiencies

NASA Technical Reports Server (NTRS)

Myneni, R. B.; Ganapol, B. D.; Asrar, G.

1992-01-01

The problem of remote sensing the canopy photosynthetic and stomatal conductance efficiencies is investigated with the aid of one- and three-dimensional radiative transfer methods coupled to a semi-empirical mechanistic model of leaf photosynthesis and stomatal conductance. Desertlike vegetation is modeled as clumps of leaves randomly distributed on a bright dry soil with partial ground cover. Normalized difference vegetation index (NDVI), canopy photosynthetic (Ep), and stomatal efficiencies (Es) are calculated for various geometrical, optical, and illumination conditions. The contribution of various radiative fluxes to estimates of Ep is evaluated and the magnitude of errors in bulk canopy formulation of problem parameters are quantified. The nature and sensitivity of the relationship between Ep and Es to NDVI is investigated, and an algorithm is proposed for use in operational remote sensing.
Integrating UAV and orbital remote sensing for spatiotemporal assessment of coastal vegetation health following hurricane events

NASA Astrophysics Data System (ADS)

Bernardes, S.; Madden, M.; Jordan, T.; Knight, A.; Aragon, A.

2017-12-01

Hurricane impacts often include the total or partial removal of vegetation due to strong winds (e.g., uprooted trees and broken trunks and limbs). Those impacts can usually be quickly assessed following hurricanes, by using established field and remote sensing methods. Conversely, impacts on vegetation health may present challenges for identification and assessment, as they are disconnected in time from the hurricane event and may be less evident. For instance, hurricanes may promote drastic increases in salinity of water available to roots and may increase exposure of aerial parts to salt spray. Derived stress conditions can negatively impact biological processes and may lead to plant decline and death. Large areas along the coast of the United States have been affected by hurricanes and show such damage (vegetation browning). Those areas may continue to be impacted, as climate projections indicate that hurricanes may become more frequent and intense, resulting from the warming of ocean waters. This work uses remote sensing tools and techniques to record and assess impacts resulting from recent hurricanes at Sapelo Island, a barrier island off the coast of the State of Georgia, United States. Analyses included change detection at the island using time series of co-registered Sentinel 2 and Landsat images. A field campaign was conducted in September 2017, which included flying three UAVs over the island and collecting high-overlap 20-megapixel RGB images at two spatial resolutions (1 and 2 inches/pixel). A five-band MicaSense RedEdge camera, a downwelling radiation sensor and calibration panel were used to collect calibrated multispectral images of multiple vegetation types, including healthy vegetation and vegetation affected by browning. Drone images covering over 600 acres were then analyzed for vegetation status and damage, with emphasis to vegetation removal and browning resulting from salinity alterations and salt spray. Results from images acquired by drones were then scaled-up to Sentinel 2 and Landsat spatial/spectral resolutions and tested using a control area. The work evaluated limits of detectability of vegetation damage using orbital systems and addresses changes in damage over time following hurricanes, including the spatiotemporal representation of damage severity in the affected areas.
Measurement of Ecosystem Metabolism across Climatic and Vegetation Gradients in California

NASA Astrophysics Data System (ADS)

DuBois, S.; Serbin, S.; Desai, A. R.; Kruger, E.; Kingdon, C.; Goulden, M.; Townsend, P. A.

2013-12-01

Terrestrial ecosystem models require information on vegetation structure, phenology, demographics, biochemistry, radiation properties, and physiology in order to accurately simulate the responses of ecosystem functioning to global change and disturbances. These models generally depend on a small number of intensive, fine-scaled point-based measurements from eddy covariance towers, detailed vegetation surveys, literature values, and site-scale data assimilation techniques to improve model calibration. However, the limited geographic and/or temporal scope of measurements can lead to inadequate model generalizations of modeled carbon (C), water, and energy fluxes across broad regions and relevant time periods. Remote sensing approaches, particularly imaging spectroscopy (IS) and thermal infrared (TIR) data, have the potential to provide the broad-scale spatial and temporal dynamics in many important vegetation properties related to ecosystem functioning. As part of the ongoing NASA HyspIRI Airborne Campaign (http://hyspiri.jpl.nasa.gov/airborne) we are assessing the potential of IS+TIR to generate spatially explicit estimates of two important parameters characterizing plant photosynthetic capacity: the maximum rate of CO2 carboxylation by RuBisCo (Vcmax), and the maximum rate of electron transport required for the regeneration of RuBP needed in Calvin Cycle processes (Jmax). These estimates are based on recent evidence that both properties can be predicted at the leaf level using spectroscopy techniques (Ainsworth et al. 2013 [http://tinyurl.com/n5xnzjg]; Serbin et al. 2012 [http://tinyurl.com/mhocmlz]). It follows that estimation of these variables from remotely sensed IS+TIR (i.e. AVIRIS & MASTER) could facilitate the prediction of seasonal C assimilation across large areas using data from the anticipated HyspIRI satellite mission. Our research focuses on two climate-elevation transects in California, which span a vegetation gradient from coastal sage and chaparral to oak woodlands and closed-canopy coniferous forests, as well as agro-ecosystems located throughout the Central and Imperial Valleys. We are also comparing remotely sensed estimates of ecosystem photosynthetic capacity with C flux data from a series of 10 eddy covariance towers. Results from the 2013 field season highlight the large range in sampled vegetation structure, optical properties (i.e. reflectance and transmittance) and physiology (i.e. Vcmax, Jmax, and cholorphyll fluorescence). Using approaches similar to Serbin et al. (2012) we have confirmed the ability of spectroscopy to estimate Vcmax and Jmax across these diverse and structurally complex vegetation types. Ecosystem products, such as gross primary productivity, estimated from flux towers highlight the relationship between climatic parameters and vegetation productivity. Multiple data-years allow this relationship to be examined under various climatic forcings including drought and heat stress. Based on these preliminary results, our next step is to scale leaf-level information to AVIRIS footprints using radiative transfer and statistical modeling approaches with ecosystem modeling in order to assess the IS data products against flux tower observations.
Fruit and Vegetable Quality Assessment via Dielectric Sensing

PubMed Central

El Khaled, Dalia; Novas, Nuria; Gazquez, Jose A.; Garcia, Rosa M.; Manzano-Agugliaro, Francisco

2015-01-01

The demand for improved food quality has been accompanied by a technological boost. This fact enhances the possibility of improving the quality of horticultural products, leading towards healthier consumption of fruits and vegetables. A better electrical characterization of the dielectric properties of fruits and vegetables is required for this purpose. Moreover, a focused study of dielectric spectroscopy and advanced dielectric sensing is a highly interesting topic. This review explains the dielectric property basics and classifies the dielectric spectroscopy measurement techniques. It comprehensively and chronologically covers the dielectric experiments explored for fruits and vegetables, along with their appropriate sensing instrumentation, analytical modelling methods and conclusions. An in-depth definition of dielectric spectroscopy and its usefulness in the electric characterization of food materials is presented, along with the various sensor techniques used for dielectric measurements. The collective data are tabulated in a summary of the dielectric findings in horticultural field investigations, which will facilitate more advanced and focused explorations in the future. PMID:26131680
Use of Normalized Difference Vegetation Index (NDVI) habitat models to predict breeding birds on the San Pedro River, Arizona

USGS Publications Warehouse

McFarland, Tiffany Marie; van Riper, Charles

2013-01-01

Successful management practices of avian populations depend on understanding relationships between birds and their habitat, especially in rare habitats, such as riparian areas of the desert Southwest. Remote-sensing technology has become popular in habitat modeling, but most of these models focus on single species, leaving their applicability to understanding broader community structure and function largely untested. We investigated the usefulness of two Normalized Difference Vegetation Index (NDVI) habitat models to model avian abundance and species richness on the upper San Pedro River in southeastern Arizona. Although NDVI was positively correlated with our bird metrics, the amount of explained variation was low. We then investigated the addition of vegetation metrics and other remote-sensing metrics to improve our models. Although both vegetation metrics and remotely sensed metrics increased the power of our models, the overall explained variation was still low, suggesting that general avian community structure may be too complex for NDVI models.
Inter-annual Variability in Tundra Phenology Captured with Digital Photography

NASA Astrophysics Data System (ADS)

Melendez, M.; Vargas, S. A.; Tweedie, C. E.

2012-12-01

The need to improve multi-scale phenological monitoring of arctic terrestrial ecosystems has been a persistent research challenge. Although there has been a range of advances in remote sensing capacities over the past decade, these present costly, and sometimes logistically challenging and technically demanding solutions for arctic terrestrial ecosystems. In this poster and undergraduate research project, we demonstrate how seasonal and inter-annual variability in landscape phenology can be derived for multiple tundra ecosystems using a low-cost and low-tech kite aerial photography (KAP) system that has been developed as a contribution to the US Arctic Observing Network. Seasonal landscape phenology was observed over the Networked Info-Mechanical Systems (NIMS) grids (2 x 50 meters) located in Barrow and Atqasuk, Alaska using imagery acquired with KAP and analyzed for a range of greenness indices. Preliminary results showed that the 2G-RB greenness index correlated the best with NDVI values calculated from ground based hyperspectral reflectance measurements. 2012 had the highest 2G-RB greenness index values for both Barrow and Atqasuk sites, which correlated well with NDVI values acquired from ground-based hyperspectral reflectance measurements. Wet vegetation types showed the most interannual variability at the Atqasuk site based on the 2G-RB greenness index while in Barrow the moist vegetation types showed the most interannual variability. These results show that vegetation indices similar to those acquired from hyperspectral remote sensing platforms can be derived using low-cost and low-tech techniques. Further analysis using these same techniques is required in order to link relatively small scale vegetation dynamics measured with KAP with those documented at large scales using satellite imagery.
Shorter Harvest Cycles Counteract Increasing Annual Productivity in Industrial Plantation Forests: Trends from Three Decades of Remote Sensing in Southeastern Brazil

NASA Astrophysics Data System (ADS)

McMahon, D.; Jackson, R. B.

2017-12-01

Plantation forestry can produce woody biomass many times faster than native vegetation, particularly in the tropical regions where plantations have expanded rapidly in the past three decades. However, activists and practitioners have raised concerns over the sustainability of intensive plantations, suggesting that changes to soil properties may inhibit vegetation growth after multiple harvest cycles. We use a 32-year time series of remotely sensed vegetation indices derived from Landsat data, coupled with recent geospatial and wood volume data from plantation companies, to identify trends in management and vegetation productivity in thousands of individual eucalyptus plantation stands. We find that peak vegetation index values at canopy closure, which are correlated with annual wood volume increment, increase over successive harvest cycles, while the length of each cycle decreases. These opposing trends suggest that the number of harvests required to produce a given wood volume peaks around the second harvest cycle and then declines, likely due to refinement of management practices. Across the region, vegetation index data do not support the hypothesized decrease in productivity over multiple harvest cycles. Additional field data and ongoing soil analyses will complement the remote sensing approach to quantifying plantations' long-term effects on the land they occupy.
A remote sensing tool to monitor and predict epidemiologic outbreaks of Hanta virus infections and Lyme disease

NASA Astrophysics Data System (ADS)

Barrios, M.; Verstraeten, W. W.; Amipour, S.; Wambacq, J.; Aerts, J.-M.; Maes, P.; Berckmans, D.; Lagrou, K.; van Ranst, M.; Coppin, P.

2009-04-01

Lyme disease and Hanta virus infection are the result of the conjunction of several climatic and ecological conditions. Although both affections have different causal agents, they share an important characteristic which is the fact that rodents play an important role in the contagion. One of the most important agents in the dispersion of these diseases is the bank vole (Clethrionomys glareoulus). The bank vole is a common host for both, the Borrelia bacteria which via the ticks (Ixodes ricinus) reaches the human body and causes the Lyme disease, and the Nephropatia epidemica which is caused by Puumala Hantavirus and affects kidneys in humans. The prefered habitat of bank voles is broad-leaf forests with an important presence of beeches (Fagus sylvatica) and oaks (Quercus sp.) and a relatively dense low vegetation layer. These vegetation systems are common in West-Europe and their dynamics have a great influence in the bank voles population and, therefore, in the spreading of the infections this study is concerned about. The fact that the annual seed production is not stable in time has an important effect in bank voles population and, as it has been described in other studies, in the number of reported cases of Hanta virus infections and Lyme disease. The years in which an abundant production of seeds is observed are referred to as mast years which are believed to obey to cyclic patterns and to certain climatologically characteristics of the preceding years. Statistical analysis have confirmed the correlation in the behaviour of the number of infected cases and the presence of mast years. This project aims at the design of a remote sensing based system (INFOPRESS - INFectious disease Outbreak Prediction REmote Sensing based System) that should enable local and national health care instances to predict and locate the occurrence of infection outbreaks and design policies to counteract undesired effects. The predictive capabilities of the system are based on the understanding and modelling of the interactions between relevant climatic parameters (temperature, humidity, precipitation) and the main features of vegetation systems which host the vectors and determine the survival and infectious potential of the causal agents. Among the most important study subjects in this research initiative one can mention the time series analysis of vegetation parameters derived from satellite remote sensing and its relation to climatic time series and historical records of infected cases; with special attention to the assessment of remotely sensed evidences of the mast phenomenon. This analysis will constitute important buildind bricks in the construction of the INFOPRESS system in what concerns the assessment of the potentials of satellite remote sensing as information source for the prediction of infection outbreaks. The bank voles habitat description will also be supported by on-ground remote sensing techniques, specially LiDAR technology and soil humidity modelling. These measurements are to be coupled to bank voles epidemiologic features obtained from field capturing and lab analysis in which the presence of Hanta virus will be assessed.
Research Advances on Radiation Transfer Modeling and Inversion for Multi-Scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.

2011-09-01

At first, research advances on radiation transfer modeling on multi-scale remote sensing data are presented: after a general overview of remote sensing radiation transfer modeling, several recent research advances are presented, including leaf spectrum model (dPROS-PECT), vegetation canopy BRDF models, directional thermal infrared emission models(TRGM, SLEC), rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed. The land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation etc. are taken as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is designed and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China will be introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.
A probabilistic assessment of the likelihood of vegetation drought under varying climate conditions across China.

PubMed

Liu, Zhiyong; Li, Chao; Zhou, Ping; Chen, Xiuzhi

2016-10-07

Climate change significantly impacts the vegetation growth and terrestrial ecosystems. Using satellite remote sensing observations, here we focus on investigating vegetation dynamics and the likelihood of vegetation-related drought under varying climate conditions across China. We first compare temporal trends of Normalized Difference Vegetation Index (NDVI) and climatic variables over China. We find that in fact there is no significant change in vegetation over the cold regions where warming is significant. Then, we propose a joint probability model to estimate the likelihood of vegetation-related drought conditioned on different precipitation/temperature scenarios in growing season across China. To the best of our knowledge, this study is the first to examine the vegetation-related drought risk over China from a perspective based on joint probability. Our results demonstrate risk patterns of vegetation-related drought under both low and high precipitation/temperature conditions. We further identify the variations in vegetation-related drought risk under different climate conditions and the sensitivity of drought risk to climate variability. These findings provide insights for decision makers to evaluate drought risk and vegetation-related develop drought mitigation strategies over China in a warming world. The proposed methodology also has a great potential to be applied for vegetation-related drought risk assessment in other regions worldwide.
A probabilistic assessment of the likelihood of vegetation drought under varying climate conditions across China

PubMed Central

Liu, Zhiyong; Li, Chao; Zhou, Ping; Chen, Xiuzhi

2016-01-01

Climate change significantly impacts the vegetation growth and terrestrial ecosystems. Using satellite remote sensing observations, here we focus on investigating vegetation dynamics and the likelihood of vegetation-related drought under varying climate conditions across China. We first compare temporal trends of Normalized Difference Vegetation Index (NDVI) and climatic variables over China. We find that in fact there is no significant change in vegetation over the cold regions where warming is significant. Then, we propose a joint probability model to estimate the likelihood of vegetation-related drought conditioned on different precipitation/temperature scenarios in growing season across China. To the best of our knowledge, this study is the first to examine the vegetation-related drought risk over China from a perspective based on joint probability. Our results demonstrate risk patterns of vegetation-related drought under both low and high precipitation/temperature conditions. We further identify the variations in vegetation-related drought risk under different climate conditions and the sensitivity of drought risk to climate variability. These findings provide insights for decision makers to evaluate drought risk and vegetation-related develop drought mitigation strategies over China in a warming world. The proposed methodology also has a great potential to be applied for vegetation-related drought risk assessment in other regions worldwide. PMID:27713530

Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest China.

PubMed

Yue, Yuemin; Wang, Kelin; Zhang, Bing; Chen, Zhengchao; Jiao, Quanjun; Liu, Bo; Chen, Hongsong

2010-01-01

Remote sensing of local environmental conditions is not accessible if substrates are covered with vegetation. This study explored the relationship between vegetation spectra and karst eco-geo-environmental conditions. Hyperspectral remote sensing techniques showed that there were significant differences between spectral features of vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative spectra could delineate karst and non-karst vegetation groups. Canonical correspondence analysis (CCA) successfully assessed to what extent the variation of vegetation spectral features can be explained by associated eco-geo-environmental variables, and it was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst region. Our study indicates that vegetation spectra is tightly linked to eco-geo-environmental conditions and CCA is an effective means of studying the relationship between vegetation spectral features and eco-geo-environmental variables. Employing a combination of spectral and spatial analysis, it is anticipated that hyperspectral imagery can be used in interpreting or mapping eco-geo-environmental conditions covered with vegetation in karst region.
A method of extracting impervious surface based on rule algorithm

NASA Astrophysics Data System (ADS)

Peng, Shuangyun; Hong, Liang; Xu, Quanli

2018-02-01

The impervious surface has become an important index to evaluate the urban environmental quality and measure the development level of urbanization. At present, the use of remote sensing technology to extract impervious surface has become the main way. In this paper, a method to extract impervious surface based on rule algorithm is proposed. The main ideas of the method is to use the rule-based algorithm to extract impermeable surface based on the characteristics and the difference which is between the impervious surface and the other three types of objects (water, soil and vegetation) in the seven original bands, NDWI and NDVI. The steps can be divided into three steps: 1) Firstly, the vegetation is extracted according to the principle that the vegetation is higher in the near-infrared band than the other bands; 2) Then, the water is extracted according to the characteristic of the water with the highest NDWI and the lowest NDVI; 3) Finally, the impermeable surface is extracted based on the fact that the impervious surface has a higher NDWI value and the lowest NDVI value than the soil.In order to test the accuracy of the rule algorithm, this paper uses the linear spectral mixed decomposition algorithm, the CART algorithm, the NDII index algorithm for extracting the impervious surface based on six remote sensing image of the Dianchi Lake Basin from 1999 to 2014. Then, the accuracy of the above three methods is compared with the accuracy of the rule algorithm by using the overall classification accuracy method. It is found that the extraction method based on the rule algorithm is obviously higher than the above three methods.
Assessment of wetland productive capacity from a remote-sensing-based model - A NASA/NMFS joint research project

NASA Technical Reports Server (NTRS)

Butera, M. K.; Frick, A. L.; Browder, J.

1983-01-01

NASA and the U.S. National Marine Fisheries Service have undertaken the development of Landsat Thematic Mapper (TM) technology for the evaluation of the usefulness of wetlands to estuarine fish and shellfish production. Toward this end, a remote sensing-based Productive Capacity model has been developed which characterizes the biological and hydrographic features of a Gulf Coast Marsh to predict detrital export. Regression analyses of TM simulator data for wetland plant production estimation are noted to more accurately estimate the percent of total vegetative cover than biomass, indicating that a nonlinear relationship may be involved.
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.
Tropical forest plantation biomass estimation using RADARSAT-SAR and TM data of south china

NASA Astrophysics Data System (ADS)

Wang, Chenli; Niu, Zheng; Gu, Xiaoping; Guo, Zhixing; Cong, Pifu

2005-10-01

Forest biomass is one of the most important parameters for global carbon stock model yet can only be estimated with great uncertainties. Remote sensing, especially SAR data can offers the possibility of providing relatively accurate forest biomass estimations at a lower cost than inventory in study tropical forest. The goal of this research was to compare the sensitivity of forest biomass to Landsat TM and RADARSAT-SAR data and to assess the efficiency of NDVI, EVI and other vegetation indices in study forest biomass based on the field survey date and GIS in south china. Based on vegetation indices and factor analysis, multiple regression and neural networks were developed for biomass estimation for each species of the plantation. For each species, the better relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed for the species. The relationship between predicted and measured biomass derived from vegetation indices differed between species. This study concludes that single band and many vegetation indices are weakly correlated with selected forest biomass. RADARSAT-SAR Backscatter coefficient has a relatively good logarithmic correlation with forest biomass, but neither TM spectral bands nor vegetation indices alone are sufficient to establish an efficient model for biomass estimation due to the saturation of bands and vegetation indices, multiple regression models that consist of spectral and environment variables improve biomass estimation performance. Comparing with TM, a relatively well estimation result can be achieved by RADARSAT-SAR, but all had limitations in tropical forest biomass estimation. The estimation results obtained are not accurate enough for forest management purposes at the forest stand level. However, the approximate volume estimates derived by the method can be useful in areas where no other forest information is available. Therefore, this paper provides a better understanding of relationships of remote sensing data and forest stand parameters used in forest parameter estimation models.
Impact of sensory-based food education in kindergarten on willingness to eat vegetables and berries

PubMed Central

Hoppu, Ulla; Prinz, Mira; Ojansivu, Pauliina; Laaksonen, Oskar; Sandell, Mari A.

2015-01-01

Background Children use all of their senses when exploring new foods, and sensory-based food education provides new possibilities for promoting healthy dietary habits. Objective To evaluate the effect of sensory-based food education activities on children's willingness to eat test samples of selected vegetables and berries. Design Two kindergartens in Hanko, Finland, participated in the study and the subjects were children aged 3–6 years, divided in the intervention (n=44) and control (n=24) kindergarten. In the intervention kindergarten, five sensory-based food education sessions focusing on vegetables and berries were implemented, once per week for 5 weeks. A tasting protocol was performed with the children at baseline and after the intervention. The willingness to eat (5 different vegetables and 3 Finnish berries) was categorised. Parents also filled in a questionnaire on the children's food preferences at home. Results In the intervention kindergarten, the willingness to eat the samples increased significantly (p≤0.001, Wilcoxon and Friedman), while in the control kindergarten, no significant change was observed when all of the test samples were taken into account. The parental report of their children's preferences and children's actual eating of the test samples corresponded relatively weakly. Conclusions Sensory-based food education activities may promote a willingness to eat vegetables and berries. Child-centred test methods are important for evaluating the effects of dietary interventions among children. PMID:26652259
Experimental evidence for spring and autumn windows for the detection of geobotanical anomalies through the remote sensing of overlying vegetation

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Masuoka, E. J.; Bell, R.; Nelson, R. F.; Larsen, C. A.; Hooker, L. K.; Troensegaard, K. W.

1985-01-01

It is pointed out that in many regions of the world, vegetation is the predominant factor influencing variation in reflected energy in the 0.4-2.5 micron region of the spectrum. Studies have, therefore, been conducted regarding the utility of remote sensing for detecting changes in vegetation which could be related to the presence of mineralization. The present paper provides primarily a report on the results of the second year of a multiyear study of geobotanical-remote-sensing relationships as developed over areas of sulfide mineralization. The field study has a strong experimental design basis. It is proceeded by first delineating the boundaries of a large geographic region which satisfied a set of previously enumerated field-site criteria. Within this region, carefully selected pairs of mineralized and nonmineralized test sites were examined over the growing season. The experiment is to provide information about the spectral and temporal resolutions required for remote-sensing-geobotanical exploration. The obtained results are evaluated.
Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 1: Measurement of Evapotranspiration at the Environmental Research Center and Determination of Priestley-taylor Parameter

NASA Technical Reports Server (NTRS)

Kotada, K.; Nakagawa, S.; Kai, K.; Yoshino, M. M.; Takeda, K.; Seki, K.

1985-01-01

In order to study the distribution of evapotranspiration in the humid region using remote sensing technology, the parameter (alpha) in the Priestley-Taylor model was determined. The daily means of the parameter alpha = 1.14 can be available from summer to autumn and alpha = to approximately 2.0 in winter. The results of the satellite and the airborne sensing done on 21st and 22nd January, 1983, are described. Using the vegetation distribution in the Tsukuba Academic New Town, as well as the radiation temperature obtained by remote sensing and the radiation data observed at the ground surface, the evapotranspiration was calculated for each vegetation type by the Priestley-Taylor method. The daily mean evapotranspiration on 22nd January, 1983, was approximately 0.4 mm/day. The differences in evapotranspiration between the vegetation types were not detectable, because the magnitude of evapotranspiration is very little in winter.
Combining machine learning and remotely sensed bandratios to investigate chlorophyll content and photosynthetic processes

NASA Astrophysics Data System (ADS)

Gholizadeh, Hamed

Photosynthesis in aquatic and terrestrial ecosystems is the key component of the food chain and the most important driver of the global carbon cycle. Therefore, estimation of photosynthesis at large spatial scales is of great scientific importance and can only practically be achieved by remote sensing data and techniques. In this dissertation, remotely sensed information and techniques, as well as field measurements, are used to improve current approaches of assessing photosynthetic processes. More specifically, three topics are the focus here: (1) investigating the application of spectral vegetation indices as proxies for terrestrial chlorophyll in a mangrove ecosystem, (2) evaluating and improving one of the most common empirical ocean-color algorithms (OC4), and (3) developing an improved approach based on sunlit-to-shaded scaled photochemical reflectance index (sPRI) ratios for detecting drought signals in a deciduous forest at eastern United States. The results indicated that although the green normalized difference vegetation index (GNDVI) is an efficient proxy for terrestrial chlorophyll content, there are opportunities to improve the performance of vegetation indices by optimizing the band weights. In regards to the second topic, we concluded that the parameters of the OC4 algorithm and similar empirical models should be tuned regionally and the addition of sea-surface temperature makes the global ocean-color approaches more valid. Results obtained from the third topic showed that considering shaded and sunlit portions of the canopy (i.e., two-leaf models instead of single big leaf models) and taking into account the divergent stomatal behavior of the species (i.e. isohydric and anisohydric) can improve the capability of sPRI in detecting drought. In addition to investigating the photosynthetic processes, the other common theme of the three research topics is the evaluation of "off- the-shelf" solutions to remote-sensing problems. Although widely used approaches such as normalized difference vegetation index (NDVI) are easy to apply and are often efficient choices in remote sensing applications, the use of these approaches should be justified and their shortcomings need to be considered in the context of the research application. When developing new remote sensing approaches, special attention should be paid to (1) initial data analysis such as statistical data transformations (e.g. Tukey ladder-of-powers transformation) and (2) rigorous validation design by creating separate training and validation data sets preferably using both field measurements and satellite-based data. Developing a sound approach and applying a rigorous validation methodology go hand in hand. In sum, all approaches have advantages and disadvantages or as George Box puts it, "all models are wrong but some are useful".
Coupled topographic and vegetation patterns in coastal dunes: Remote sensing observations and ecomorphodynamic implications

NASA Astrophysics Data System (ADS)

Yousefi Lalimi, F.; Silvestri, S.; Moore, L. J.; Marani, M.

2017-01-01

Vegetation plays a key role in stabilizing coastal dunes and barrier islands by mediating sand transport, deposition, and erosion. Dune topography, in turn, affects vegetation growth, by determining local environmental conditions. However, our understanding of vegetation and dune topography as coupled and spatially extensive dynamical systems is limited. Here we develop and use remote sensing analyses to quantitatively characterize coastal dune ecotopographic patterns by simultaneously identifying the spatial distribution of topographic elevation and vegetation biomass. Lidar-derived leaf area index and hyperspectral-derived normalized difference vegetation index patterns yield vegetation distributions at the whole-system scale which are in agreement with each other and with field observations. Lidar-derived concurrent quantifications of biomass and topography show that plants more favorably develop on the landward side of the foredune crest and that the foredune crestline marks the position of an ecotone, which is interpreted as the result of a sheltering effect sharply changing local environmental conditions. We conclude that the position of the foredune crestline is a chief ecomorphodynamic feature resulting from the two-way interaction between vegetation and topography.
A review of remote sensing and grasslands literature

NASA Technical Reports Server (NTRS)

Tappan, G.; Kinsler, M. C. (Principal Investigator)

1982-01-01

Studies between 1971 and 1980 dealing with remote sensing of rangelands/grasslands in the multispectral band are summarized and evaluated. Vegetation and soil reflectance properties are described. In the majority of the studies, the effect of the reflectance of green rangelands vegetation on the reflectance from the total scene is the primary concern. Developments in technique are summarized and recommendations for further research are presented.
How useful are meteorological drought indicators to assess agricultural drought impacts across Europe?

NASA Astrophysics Data System (ADS)

Bachmair, Sophie; Tanguy, Maliko; Hannaford, Jamie; Stahl, Kerstin

2016-04-01

Drought monitoring and early warning (M&EW) is an important component of agricultural and silvicultural risk management. Meteorological indicators such as the Standardized Precipitation Index (SPI) are widely used in operational M&EW systems and for drought hazard assessment. Meteorological drought yet does not necessarily equate to agricultural drought given differences in drought susceptibility, e.g. crop-specific vulnerability, soil water holding capacity, irrigation and other management practices. How useful are meteorological indicators such as SPI to assess agricultural drought? Would the inclusion of vegetation indicators into drought M&EW systems add value for the agricultural sector? To answer these questions, it is necessary to investigate the link between meteorological indicators and agricultural impacts of drought. Crop yield or loss data is one source of information for drought impacts, yet mostly available as aggregated data at the annual scale. Remotely sensed vegetation stress data offer another possibility to directly assess agricultural impacts with high spatial and temporal resolution and are already used by some M&EW systems. At the same time, reduced crop yield and satellite-based vegetation stress potentially suffer from multi-causality. The aim of this study is therefore to investigate the relation between meteorological drought indicators and agricultural drought impacts for Europe, and to intercompare different agricultural impact variables. As drought indicators we used SPI and the Standardized Precipitation Evaporation Index (SPEI) for different accumulation periods. The focus regarding drought impact variables was on remotely sensed vegetation stress derived from MODIS NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) data, but the analysis was complemented with crop yield data and text-based information from the European Drought Impact report Inventory (EDII) for selected countries. A correlation analysis between meteorological drought indicators and remotely sensed vegetation stress at the EU NUTS3 region level revealed a high correlation between the two types of indicators for many regions; however some spatial variability was observed in (i) strength of correlation, (ii) performance of SPI versus SPEI, and (iii) best linked SPI/SPEI time scale. We additionally explored whether geographic properties like climate, soil texture, land use, and location explain the observed spatial patterns. Our study revealed that climatically dryer areas (water limited) showed high correlations between SPI/SPEI and vegetation stress, whereas the wettest parts of Europe (radiation limited regions) showed negative correlations especially for short accumulation periods, suggesting that for these regions, short droughts could actually be beneficial for vegetation growth. These findings suggest that relying solely on meteorological indicators for agricultural risk assessment in some regions might be inadequate. Overall, such information may help to tailor agricultural drought M&EW systems to specific regions.
Agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)

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

Archibald, Richard K; Filippi, Anthony M; Bhaduri, Budhendra L

Extracting endmembers from remotely sensed images of vegetated areas can present difficulties. In this research, we applied a recently developed endmember-extraction algorithm based on Support Vector Machines (SVMs) to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically and effectively estimate endmembers; synthetic data and a geologicmore » scene were previously analyzed. Here we compared the efficacies of the SVM-BEE and N-FINDR algorithms in extracting endmembers from a predominantly agricultural scene. SVM-BEE was able to estimate vegetation and other endmembers for all classes in the image, which N-FINDR failed to do. Classifications based on SVM-BEE endmembers were markedly more accurate compared with those based on N-FINDR endmembers.« less
Drought index driven by L-band microwave soil moisture data

NASA Astrophysics Data System (ADS)

Bitar, Ahmad Al; Kerr, Yann; Merlin, Olivier; Cabot, François; Choné, Audrey; Wigneron, Jean-Pierre

2014-05-01

Drought is considered in many areas across the globe as one of the major extreme events. Studies do not all agree on the increase of the frequency of drought events over the past 60 years [1], but they all agree that the impact of droughts has increased and the need for efficient global monitoring tools has become most than ever urgent. Droughts are monitored through drought indexes, many of which are based on precipitation (Palmer index(s), PDI…), on vegetation status (VDI) or on surface temperatures. They can also be derived from climate prediction models outputs. The GMO has selected the (SPI) Standardized Precipitation Index as the reference index for the monitoring of drought at global scale. The drawback of this index is that it is directly dependent on global precipitation products that are not accurate over global scale. On the other hand, Vegetation based indexes show the a posteriori effect of drought, since they are based on NDVI. In this study, we choose to combine the surface soil moisture from microwave sensor with climate data to access a drought index. The microwave data are considered from the SMOS (Soil Moisture and Ocean Salinity) mission at L-Band (1.4 Ghz) interferometric radiometer from ESA (European Space Agency) [2]. Global surface soil moisture maps with 3 days coverage for ascending 6AM and descending 6PM orbits SMOS have been delivered since January 2010 at a 40 km nominal resolution. We use in this study the daily L3 global soil moisture maps from CATDS (Centre Aval de Traitement des Données SMOS) [3,4]. We present a drought index computed by a double bucket hydrological model driven by operational remote sensing data and ancillary datasets. The SPI is also compared to other drought indicators like vegetation indexes and Palmer drought index. Comparison of drought index to vegetation indexes from AVHRR and MODIS over continental United States show that the drought index can be used as an early warning system for drought monitoring as the water shortage can be sensed several weeks before the vegetation dryness occures. Keywords: SMOS, microwave, level 4, soil moisture, drought, precipitation, hydrological model, vegetation index
Monitoring the state of vegetation in Hungary using 15 years long MODIS Data

NASA Astrophysics Data System (ADS)

Kern, Anikó; Bognár, Péter; Pásztor, Szilárd; Barcza, Zoltán; Timár, Gábor; Lichtenberger, János; Ferencz, Csaba

2015-04-01

Monitoring the state and health of the vegetation is essential to understand causes and severity of environmental change and to prepare for the negative effects of climate change on plant growth and productivity. Satellite remote sensing is the fundamental tool to monitor and study the changes of vegetation activity in general and to understand its relationship with the climate fluctuations. Vegetation indices and other vegetation related measures calculated from remotely sensed data are widely used to monitor and characterize the state of the terrestrial vegetation. Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are among the most popular indices that can be calculated from measurements of the MODerate resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS-AM1/Terra and EOS-PM1/Aqua satellites (since 1999 and 2002 respectively). Based on the available, 15 years long MODIS data (2000-2014) the vegetation characteristics of Hungary was investigated in our research, primarily using vegetation indices. The MODIS NDVI and EVI (both part of the so-called MOD13 product of NASA) are freely available with a finest spatial resolution of 250 meters and a temporal resolution of 16 days since 2000/2002 (for Terra and Aqua respectively). The accuracy, the spatial resolution and temporal continuity of the MODIS products makes these datasets highly valuable despite of its relatively short temporal coverage. NDVI is also calculated routinely from the raw MODIS data collected by the receiving station of Eötvös Loránd University. In order to characterize vegetation activity and its variability within the Carpathian Basin the area-averaged annual cycles and their interannual variability were determined. The main aim was to find those years that can be considered as extreme according to specific indices. Using archive meteorological data the effects of extreme weather on vegetation activity and growth were investigated with emphasis on drought and heat waves. Te relationship between anomalies of vegetation characteristics and crop yield decrease in agricultural regions were characterised as well. The mean NDVI values of Hungary during the 15 years reveal the behaviour of the vegetation in the country, where the main land cover types (forest, agriculture and grassland) were distinguished as well. NDVI anomalies are analyzed separately for the main land cover types. Deviations from the potential maximum vegetation greenness are also calculated for the entire time period.
Pasture Drought Insurance Based on NDVI and SAVI

NASA Astrophysics Data System (ADS)

Escribano Rodríguez, J. A.; Tarquis, A. M.; Hernandez Díaz-Ambrona, C. G.

2012-04-01

Drought is a complex phenomenon, which is difficult to define. The term is used to refer to deficiency in rainfall, soil moisture, vegetation greenness, ecological conditions or socio economic conditions, and different drought types can be inferred. In this study, drought is considered as a period when the pasture growth is low in regard to long-term average conditions. The extensive livestock production is based on the natural resources available. The good management practices concurs the maximum livestock nutrition needs with the maximum pasture availability. Therefore, early drought detection and impact assessment on the amount of pasture biomass are important in several areas in Spain, whose economy strongly depends on livestock production. The use of remote sensing data presents a number of advantages when determining drought impact on vegetation. The information covers the whole of a territory and the repetition of images provides multi-temporal measurements. In addition, vegetation indexes, being NDVI (normalized difference vegetation index) and SAVI (soil-adjusted vegetation index) the most common ones, obtainedfrom satellite data allow areas affected by droughts to be identified. These indices are being used for estimation of vegetation photosynthesis activity and monitoring drought. The present study shows the application of these vegetation indices for pasture drought monitoring in three places in Spain and their correlation with several field measurements. During 2010 and 2011 three locations, El Cubo de Don Sancho (Salamanca), Trujillo (Cáceres) and Pozoblanco (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 of the chosen places.This satellite 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. It has 6 cameras in red, green and near infrared bands, equivalent to Landsat ones. A discussion on the correlations found between field measurements and both vegetation index considering seasonal pattern and location are presented. 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. AGL2010-21501/AGR is greatly appreciated.
Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability.

PubMed

Tagesson, Torbern; Fensholt, Rasmus; Guiro, Idrissa; Rasmussen, Mads Olander; Huber, Silvia; Mbow, Cheikh; Garcia, Monica; Horion, Stéphanie; Sandholt, Inge; Holm-Rasmussen, Bo; Göttsche, Frank M; Ridler, Marc-Etienne; Olén, Niklas; Lundegard Olsen, Jørgen; Ehammer, Andrea; Madsen, Mathias; Olesen, Folke S; Ardö, Jonas

2015-01-01

The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350-1800 nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land-atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (~3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of ~-7.5 g C m(-2) day(-1) during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350-1800 nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models. © 2014 John Wiley & Sons Ltd.
Status of the undisturbed mangroves at Brunei Bay, East Malaysia: a preliminary assessment based on remote sensing and ground-truth observations

PubMed Central

Izzaty Horsali, Nurul Amira; Mat Zauki, Nurul Ashikin; Otero, Viviana; Nadzri, Muhammad Izuan; Ibrahim, Sulong; Husain, Mohd-Lokman; Dahdouh-Guebas, Farid

2018-01-01

Brunei Bay, which receives freshwater discharge from four major rivers, namely Limbang, Sundar, Weston and Menumbok, hosts a luxuriant mangrove cover in East Malaysia. However, this relatively undisturbed mangrove forest has been less scientifically explored, especially in terms of vegetation structure, ecosystem services and functioning, and land-use/cover changes. In the present study, mangrove areal extent together with species composition and distribution at the four notified estuaries was evaluated through remote sensing (Advanced Land Observation Satellite—ALOS) and ground-truth (Point-Centred Quarter Method—PCQM) observations. As of 2010, the total mangrove cover was found to be ca. 35,183.74 ha, of which Weston and Menumbok occupied more than two-folds (58%), followed by Sundar (27%) and Limbang (15%). The medium resolution ALOS data were efficient for mapping dominant mangrove species such as Nypa fruticans, Rhizophora apiculata, Sonneratia caseolaris, S. alba and Xylocarpus granatum in the vicinity (accuracy: 80%). The PCQM estimates found a higher basal area at Limbang and Menumbok—suggestive of more mature vegetation, compared to Sundar and Weston. Mangrove stand structural complexity (derived from the complexity index) was also high in the order of Limbang > Menumbok > Sundar > Weston and supporting the perspective of less/undisturbed vegetation at two former locations. Both remote sensing and ground-truth observations have complementarily represented the distribution of Sonneratia spp. as pioneer vegetation at shallow river mouths, N. fruticans in the areas of strong freshwater discharge, R. apiculata in the areas of strong neritic incursion and X. granatum at interior/elevated grounds. The results from this study would be able to serve as strong baseline data for future mangrove investigations at Brunei Bay, including for monitoring and management purposes locally at present. PMID:29479500
Prospects for phenological monitoring in an arid southwestern U.S. rangeland using field observations with hyperspatial and moderate resolution imagery

NASA Astrophysics Data System (ADS)

Browning, D. M.; Laliberte, A. S.; Rango, A.; Herrick, J. E.

2009-12-01

Relating field observations of plant phenological events to remotely sensed depictions of land surface phenology remains a challenge to the vertical integration of data from disparate sources. This research conducted at the Jornada Basin Long-Term Ecological Research site in southern New Mexico capitalizes on legacy datasets pertaining to reproductive phenology and biomass and hyperspatial imagery. Large amounts of exposed bare soil and modest cover from shrubs and grasses in these arid and semi-arid ecosystems challenge the integration of field observations of phenology and remotely sensed data to monitor changes in land surface phenology. Drawing on established field protocols for reproductive phenology, hyperspatial imagery (4 cm), and object-based image analysis, we explore the utility of two approaches to scale detailed observations (i.e., field and 4 cm imagery) to the extent of long-term field plots (50 x 50m) and moderate resolution Landsat Thematic Mapper (TM) imagery (30 x 30m). Very high resolution color-infrared imagery was collected June 2007 across 15 LTER study sites that transect five distinct vegetation communities along a continuum of grass to shrub dominance. We examined two methods for scaling spectral vegetation indices (SVI) at 4 cm resolution: pixel averaging and object-based integration. Pixel averaging yields the mean SVI value for all pixels within the plot or TM pixel. Alternatively, the object-based method is based on a weighted average of SVI values that correspond to discrete image objects (e.g., individual shrubs or grass patches). Object-based image analysis of 4 cm imagery provides a detailed depiction of ground cover and allows us to extract species-specific contributions to upscaled SVI values. The ability to discern species- or functional-group contributions to remotely sensed signals of vegetation greenness can greatly enhance the design of field sampling protocols for phenological research. Furthermore, imagery from unmanned aerial vehicles (UAV) is a cost-effective and increasingly available resource and generation of UAV mosaics has been accomplished so that larger study areas can be addressed. This technology can provide a robust basis for scaling relationships for phenology-based research applications.
Potential of remotely-sensed data for mapping sediment connectivity pathways and their seasonal changes in dryland environments

NASA Astrophysics Data System (ADS)

Foerster, Saskia; Wilczok, Charlotte; Brosinsky, Arlena; Kroll, Anja; Segl, Karl; Francke, Till

2014-05-01

Many drylands are characterized by strong erosion in headwater catchments, where connectivity processes play an important role in the redistribution of water and sediments. Sediment connectivity relates to the physical transfer of sediment through a drainage basin (Bracken and Croke 2007). The identification of sediment source areas and the way they connect to the channel network are essential to environmental management (Reid et al. 2007), especially where high erosion and sediment delivery rates occur. Vegetation cover and its spatial and temporal pattern is one of the main factors affecting sediment connectivity. This is particularly true for patchy vegetation covers typical for dryland environments. While many connectivity studies are based on field-derived data, the potential of remotely-sensed data for sediment connectivity analyses has not yet been fully exploited. Recent advances in remote sensing allow for quantitative, spatially explicit, catchment-wide derivation of surface information to be used in connectivity analyses. These advances include a continuous increase in spatial image resolution to comprise processes at the plot to hillslope to catchment scale, an increase in the temporal resolution to cover seasonal and long-term changes and an increase in the spectral resolution enabling the discrimination of dry and green vegetation fractions from soil surfaces in heterogeneous dryland landscapes. The utilization of remotely-sensed data for connectivity studies raises questions on what type of information is required, how scale of sediment flux and image resolution match, how the connectivity information can be incorporated into water and sediment transport models and how this improves model predictions. The objective of this study is to demonstrate the potential of remotely-sensed data for mapping sediment connectivity pathways and their seasonal change at the example of a mesoscale dryland catchment in the Spanish Pyrenees. Here, sediment connectivity pathways have been mapped for two adjacent sub-catchments (approx. 70 km²) of the Isábena River in different seasons using a quantitative connectivity index based on fractional vegetation cover and topography data. Fractional cover of green and dry vegetation, bare soil and rock were derived by applying a Multiple Endmember Spectral Mixture Analysis approach applied to a hyperspectral image dataset. Sediment connectivity was mapped using the Index of Connectivity (Borselli et al. 2008), in which the effect of land cover on runoff and sediment fluxes is expressed by a spatially distributed weighing factor (in this study, the cover and management factor of the RUSLE). The resulting connectivity maps show that areas behave very differently with regard to connectivity, depending on the land cover but also on the spatial distribution of vegetation abundances and topographic barriers. Most parts of the catchment show higher connectivity values in summer than in spring. The studied sub-catchments show a slightly different connectivity behaviour reflecting the different land cover proportions and their spatial configuration. Future work includes the incorporation of sediment connectivity information into a hydrological model (WASA-SED, Mueller et al. 2010) to better reflect connectivity processes and testing the sensitivity of the model to different input data.

Extracting Forest Canopy Characteristics from Remote Sensing Imagery: Implications for Sentinel-2 Mission

NASA Astrophysics Data System (ADS)

Gholizadeh, Asa; Kopaekova, Veronika; Rogass, Christian; Mielke, Christian; Misurec, Jan

2016-08-01

Systematic quantification and monitoring of forest biophysical and biochemical variables is required to assess the response of ecosystems to climate change. Remote sensing has been introduced as a time and cost- efficient way to carry out large scale monitoring of vegetation parameters. Red-Edge Position (REP) is a hyperspectrally detectable parameter which is sensitive to vegetation Chl. In the current study, REP was modelled for the Norway spruce forest canopy resampled to HyMap and Sentinel-2 spectral resolution as well as calculated from the real HyMap and Sentinel-2 simulated data. Different REP extraction methods (4PLI, PF, LE, 4PLIH and 4PLIS) were assessed. The study showed the way for effective utilization of the forthcoming hyper and superspectral remote sensing sensors from orbit to monitor vegetation attributes.
A simple method to estimate vegetation indices and crop canopy factors using field spectroscopy for solanum tuberosum during the whole phenological cycle

NASA Astrophysics Data System (ADS)

Perdikou, S.; Papadavid, G.; Hadjimitsis, M.; Hadjimitsis, D.; Neofytou, N.

2013-08-01

Field spectroscopy is a part of the remote sensing techniques and very important for studies in agriculture. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data of the spring potatoes for estimating spectral vegetation indices (SVI's). A field campaign was undertaken from September to the end of November 2012 for the collection of spectro-radiometric measurements. The study area was in the Mandria Village in Paphos district in Cyprus. This paper demonstrates how crop canopy factors can be statistically related to remotely sensed data, namely vegetation indices. The paper is a part of an EU cofounded project regarding estimating crop water requirements using remote sensing techniques and informing the farmers through 3G smart telephony.
Soil Moisture Estimate under Forest using a Semi-empirical Model at P-Band

NASA Astrophysics Data System (ADS)

Truong-Loi, M.; Saatchi, S.; Jaruwatanadilok, S.

2013-12-01

In this paper we show the potential of a semi-empirical algorithm to retrieve soil moisture under forests using P-band polarimetric SAR data. In past decades, several remote sensing techniques have been developed to estimate the surface soil moisture. In most studies associated with radar sensing of soil moisture, the proposed algorithms are focused on bare or sparsely vegetated surfaces where the effect of vegetation can be ignored. At long wavelengths such as L-band, empirical or physical models such as the Small Perturbation Model (SPM) provide reasonable estimates of surface soil moisture at depths of 0-5cm. However for densely covered vegetated surfaces such as forests, the problem becomes more challenging because the vegetation canopy is a complex scattering environment. For this reason there have been only few studies focusing on retrieving soil moisture under vegetation canopy in the literature. Moghaddam et al. developed an algorithm to estimate soil moisture under a boreal forest using L- and P-band SAR data. For their studied area, double-bounce between trunks and ground appear to be the most important scattering mechanism. Thereby, they implemented parametric models of radar backscatter for double-bounce using simulations of a numerical forest scattering model. Hajnsek et al. showed the potential of estimating the soil moisture under agricultural vegetation using L-band polarimetric SAR data and using polarimetric-decomposition techniques to remove the vegetation layer. Here we use an approach based on physical formulation of dominant scattering mechanisms and three parameters that integrates the vegetation and soil effects at long wavelengths. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the Distorted Born Approximation (DBA). The simplified model has three equations and three unknowns, preserving the three dominant scattering mechanisms of volume, double-bounce and surface for three polarized backscattering coefficients: σHH, σVV and σHV. The inversion process, which is not an ill-posed problem, uses the non-linear optimization method of Levenberg-Marquardt and estimates the three model parameters: vegetation aboveground biomass, average soil moisture and surface roughness. The model analytical formulation will be first recalled and sensitivity analyses will be shown. Then some results obtained with real SAR data will be presented and compared to ground estimates.
Analysis of Vegetation Within A Semi-Arid Urban Environment Using High Spatial Resolution Airborne Thermal Infrared Remote Sensing Data

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Ridd, Merrill K.

1998-01-01

High spatial resolution (5 m) remote sensing data obtained using the airborne Thermal Infrared Multispectral Scanner (TIMS) sensor for daytime and nighttime have been used to measure thermal energy responses for 2 broad classes and 10 subclasses of vegetation typical of the Salt Lake City, Utah urban landscape. Polygons representing discrete areas corresponding to the 10 subclasses of vegetation types have been delineated from the remote sensing data and are used for analysis of upwelling thermal energy for day, night, and the change in response between day and night or flux, as measured by the TIMS. These data have been used to produce three-dimensional graphs of energy responses in W/ sq m for day, night, and flux, for each urban vegetation land cover as measured by each of the six channels of the TIMS sensor. Analysis of these graphs provides a unique perspective for both viewing and understanding thermal responses, as recorded by the TIMS, for selected vegetation types common to Salt Lake City. A descriptive interpretation is given for each of the day, night, and flux graphs along with an analysis of what the patterns mean in reference to the thermal properties of the vegetation types surveyed in this study. From analyses of these graphs, it is apparent that thermal responses for vegetation can be highly varied as a function of the biophysical properties of the vegetation itself, as well as other factors. Moreover, it is also seen where vegetation, particularly trees, has a significant influence on damping or mitigating the amount of thermal radiation upwelling into the atmosphere across the Salt Lake City urban landscape. Published by Elsevier Science Ltd.
An investigation of vegetation and other Earth resource/feature parameters using LANDSAT and other remote sensing data. 1: LANDSAT. 2: Remote sensing of volcanic emissions. [New England forest and emissions from Mt. St. Helens and Central American volcanoes

NASA Technical Reports Server (NTRS)

Birnie, R. W.; Stoiber, R. E. (Principal Investigator)

1981-01-01

A fanning technique based on a simplistic physical model provided a classification algorithm for mixture landscapes. Results of applications to LANDSAT inventory of 1.5 million acres of forest land in Northern Maine are presented. Signatures for potential deer year habitat in New Hampshire were developed. Volcanic activity was monitored in Nicaragua, El Salvador, and Guatemala along with the Mt. St. Helens eruption. Emphasis in the monitoring was placed on the remote sensing of SO2 concentrations in the plumes of the volcanoes.
Cover estimations using object-based image analysis rule sets developed across multiple scales in pinyon-juniper woodlands

USDA-ARS?s Scientific Manuscript database

Numerous studies have been conducted that evaluate the utility of remote sensing for monitoring and assessing vegetation and ground cover to support land management decisions and complement ground-measurements. However, few land cover comparisons have been made using high-resolution imagery and obj...
Advances in the Two Source Energy Balance (TSEB) model using very high resolution remote sensing data in vineyards

USDA-ARS?s Scientific Manuscript database

The thermal-based Two Source Energy Balance (TSEB) model partitions the water and energy fluxes from vegetation and soil components providing thus the ability for estimating soil evaporation (E) and canopy transpiration (T) separately. However, it is crucial for ET partitioning to retrieve reliable ...
EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) Comparisons Across mMltiple Spatial Scales RSAD Oral Poster based session

EPA Science Inventory

Leaf Area Index (LAI) is an important parameter in assessing vegetation structure for characterizing forest canopies over large areas at broad spatial scales using satellite remote sensing data. However, satellite-derived LAI products can be limited by obstructed atmospheric cond...
Thermal remote sensing approach combined with field spectroscopy for detecting underground structures intended for defence and security purposes in Cyprus

NASA Astrophysics Data System (ADS)

Melillos, George; Themistocleous, Kyriacos; Hadjimitsis, Diofantos G.

2018-04-01

The purpose of this paper is to present the results obtained from unmanned aerial vehicle (UAV) using multispectral with thermal imaging sensors and field spectroscopy campaigns for detecting underground structures. Airborne thermal prospecting is based on the principle that there is a fundamental difference between the thermal characteristics of underground structures and the environment in which they are structure. This study aims to combine the flexibility and low cost of using an airborne drone with the accuracy of the registration of a thermal digital camera. This combination allows the use of thermal prospection for underground structures detection at low altitude with high-resolution information. In addition vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR), were utilized for the development of a vegetation index-based procedure aiming at the detection of underground military structures by using existing vegetation indices or other in-band algorithms. The measurements were taken at the following test areas such as: (a) vegetation area covered with the vegetation (barley), in the presence of an underground military structure (b) vegetation area covered with the vegetation (barley), in the absence of an underground military structure. It is important to highlight that this research is undertaken at the ERATOSTHENES Research Centre which received funding to be transformed to an EXcellence Research Centre for Earth SurveiLlance and Space-Based MonItoring Of the EnviRonment (Excelsior) from the HORIZON 2020 Widespread-04-2017: Teaming Phase 1(Grant agreement no: 763643).
Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments.

PubMed

Wang, Xuelei; Wang, Qiao; Yang, Shengtian; Zheng, Donghai; Wu, Chuanqing; Mannaerts, C M

2011-06-01

Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling. Copyright © 2011 Elsevier B.V. All rights reserved.
Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

USGS Publications Warehouse

West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-01-01

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

PubMed

West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-10-11

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.
Evaluation of spatial, radiometric and spectral Thematic Mapper performance for coastal studies

NASA Technical Reports Server (NTRS)

Klemas, V.; Ackleson, S. G.; Hardisky, M. A.

1985-01-01

On 31 March 1983, the University of Delaware's Center for Remote Sensing initiated a study to evaluate the spatial, radiometric and spectral performance of the LANDSAT Thematic Mapper for coastal and estuarine studies. The investigation was supported by Contract NAS5-27580 from the NASA Goddard Space Flight Center. The research was divided into three major subprojects: (1) a comparison of LANDSAT TM to MSS imagery for detecting submerged aquatic vegetation in Chesapeake Bay; (2) remote sensing of submerged aquatic vegetation - a radiative transfer approach; and (3) remote sensing of coastal wetland biomass using Thematic Mapper wavebands.
Assessment of Vegetation Responses and Sensitivity to the Millennium Drought in Australia

NASA Astrophysics Data System (ADS)

Jiao, T.; Williams, C. A.

2017-12-01

During the period from 1997 to 2009, Australia experienced one of the most severe and persistent drought known as the Millennium Drought (MD). Major water shortages were reported across the Australian continent as well as a great many tree mortality during and post this drought event. Given the projection of hotter and drier conditions for much of the continent (Hughes 2003), it is critical to analyze the impacts of climate extremes like MD as an indicator of possible impacts of future trends. A few drought assessments have been performed for the MD but their utilization of single-source Remote sensing data like vegetation indices makes it difficult to produce a comprehensive understanding of drought responses for diverse ecosystems in Australia. Furthermore, methods adopted in past drought assessments did not distinguish vegetation responses to drought events with different intensity, duration and sequence, which are critically important in determining the magnitude of vegetation responses to drought. Here, multi-source remote sensing datasets and an event-based drought assessment method were employed to assess the impacts of MD on vegetation in Australia in terms of the magnitude and sensitivity. Vegetation variables examined include fraction of photosynthetically absorbed radiation (Fpar), vegetation optical depth (VOD) and aboveground biomass (AGB). Drought indicators were calculated based on precipitation and potential evapotranspiration. Results show that most of Eastern Australia experienced abnormal water deficit during the MD and drought intensity was greatest in humid regions. The decline in aboveground biomass (ABC) demonstrates consistent variation with drought intensity across aridity levels. Drought impacts on Fpar and VOD were greatest at intermediate dryness and for woodier ecosystems, with impacts appearing in Fpar before VOD. Drought sensitivity was also greatest at intermediate dryness and for woodier ecosystems. The small difference in drought sensitivity, in terms of Fpar and VOD, across biomes suggests that trees, shrubs, and herbaceous are equally vulnerable to canopy dieback while the high drought sensitivity of trees as shown in ABC implies that a large amount of carbon could be released to the atmosphere if intense and long-duration drought occurs in forested areas.
DroughtView: Satellite Based Drought Monitoring and Assessment

NASA Astrophysics Data System (ADS)

Hartfield, K. A.; Van Leeuwen, W. J. D.; Crimmins, M.; Marsh, S. E.; Torrey, Y.; Rahr, M.; Orr, B. J.

2014-12-01

Drought is an ever growing concern within the United States and Mexico. Extended periods of below-average precipitation can adversely affect agricultural production and ecosystems, impact local water resources and create conditions prime for wildfire. DroughtView (www.droughtview.arizona.edu) is a new on-line resource for scientists, natural resource managers, and the public that brings a new perspective to remote-sensing based drought impact assessment that is not currently available. DroughtView allows users to monitor the impact of drought on vegetation cover for the entire continental United States and the northern regions of Mexico. As a spatially and temporally dynamic geospatial decision support tool, DroughtView is an excellent educational introduction to the relationship between remotely sensed vegetation condition and drought. The system serves up Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data generated from 250 meter 16-day composite Moderate-resolution Imaging Spectroradiometer (MODIS) imagery from 2000 to the present. Calculation of difference from average, previous period and previous year greenness products provide the user with a proxy for drought conditions and insight on the secondary impacts of drought, such as wildfire. The various image products and overlays are served up via the ArcGIS Server platform. DroughtView serves as a useful tool to introduce and teach vegetation time series analysis to those unfamiliar with the science. High spatial resolution imagery is available as a reference layer to locate points of interest, zoom in and export images for implementation in reports and presentations. Animation of vegetation time series allows users to examine ecosystem disturbances and climate data is also available to examine the relationship between precipitation, temperature and vegetation. The tool is mobile friendly allowing users to access the system while in the field. The systems capabilities and applications will be demonstrated live during the poster session. Expansion of DroughtView includes future plans to add snow products, phenology data and climate scenarios. Extension of the spatial coverage of the data to other parts of the world is also planned.
Hyperspectral remote sensing application for monitoring and preservation of plant ecosystems

NASA Astrophysics Data System (ADS)

Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas; Petrov, Nikolay; Stoev, Antoniy

Remote sensing technologies have advanced significantly at last decade and have improved the capability to gather information about Earth’s resources and environment. They have many applications in Earth observation, such as mapping and updating land-use and cover, weather forecasting, biodiversity determination, etc. Hyperspectral remote sensing offers unique opportunities in the environmental monitoring and sustainable use of natural resources. Remote sensing sensors on space-based platforms, aircrafts, or on ground, are capable of providing detailed spectral, spatial and temporal information on terrestrial ecosystems. Ground-based sensors are used to record detailed information about the land surface and to create a data base for better characterizing the objects which are being imaged by the other sensors. In this paper some applications of two hyperspectral remote sensing techniques, leaf reflectance and chlorophyll fluorescence, for monitoring and assessment of the effects of adverse environmental conditions on plant ecosystems are presented. The effect of stress factors such as enhanced UV-radiation, acid rain, salinity, viral infections applied to some young plants (potato, pea, tobacco) and trees (plums, apples, paulownia) as well as of some growth regulators were investigated. Hyperspectral reflectance and fluorescence data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (450-850 nm and 600-900 nm), respectively. The differences between the reflectance data of healthy (control) and injured (stressed) plants were assessed by means of statistical (Student’s t-criterion), first derivative, and cluster analysis and calculation of some vegetation indices in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm). Fluorescence spectra were analyzed at five characteristic wavelengths located at the maximums of the emitted radiation and at the forefronts and rear slopes. The strong relationship, which was found between the results from the two remote sensing techniques and some biochemical and serological analyses (stress markers, DAS-ELISA test), indicates the importance of hyperspectral reflectance and fluorescence techniques for conducting, easily and without damage, rapid health condition assessments of vegetation. This study fills in the existed spectral data base and exemplifies the benefits of integrating remote sensing, Earth observation, plant physiology, ecology, and conducting of interdisciplinary investigations of terrestrial ecosystems.
Microbiota on spoiled vegetables and their characterization.

PubMed

Lee, Dong Hwan; Kim, Jin-Beom; Kim, Mihyun; Roh, Eunjung; Jung, Kyusuk; Choi, Minseon; Oh, Changsik; Choi, Jaehyuk; Yun, Jongchul; Heu, Sunggi

2013-08-01

Spoilage causes vegetables to deteriorate and develop unpleasant characteristics. Approximately 30 % of fresh vegetables are lost to spoilage, mainly due to colonization by bacteria. In the present study, a total of 44 bacterial isolates were obtained from a number of spoiled vegetables. The isolates were identified and classified into 20 different species of 14 genera based on fatty acid composition, biochemical tests, and 16S rDNA sequence analyses. Pseudomonas spp. were the species most frequently isolated from the spoiled vegetables. To evaluate the spoilage ability of each species, a variety of fresh vegetables were treated with each isolate and their degree of maceration was observed. In addition, the production of plant cell wall-degrading enzymes (PCWDEs), such as cellulase, xylanase, pectate lyase, and polygalacturonase, was compared among isolates to investigate their potential associations with spoilage. Strains that produce more PCWDEs cause spoilage on more diverse plants, and pectinase may be the most important enzyme among PCWDEs for vegetable spoilage. Most gram-negative spoilage bacteria produced acylated homoserine lactone, a quorum-sensing signal molecule, suggesting that it may be possible to use this compound effectively to prevent or slow down the spoilage of vegetables contaminated with diverse bacteria.
Vegetation Monitoring by Remote Sensing Technology for Uninhabited Islands of the Xisha Islands

NASA Astrophysics Data System (ADS)

Li, L.; Guo, Y.; Wu, X.

2018-04-01

The Xisha islands are tropical coral islands in the south sea of China, with special ecological environment. As far away from the inland, they are more sensitive to climate change than inland, and are looked as the window to reflect global environment changes. Since Sansha city established, some of islands were developed. The uninhabited islands are decreasing. To discover the changes of uninhabited islands become more impending. In order to find out the natural status of uninhabited islands, monitoring four years vegetation change of 2002, 2010, 2013 and 2016. In addition, monitoring the typical uninhabited island and sandbar vegetation by making the most of existed high resolution remote sensing data, nine years from 2002 to 2013 and six months in 2012. The results show that the sandbars are in stable growth stage, especially after 2010, the vegetation start appeared. Meanwhile, analysis the vegetation variation of the uninhabited islands and sandbars.
Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

NASA Astrophysics Data System (ADS)

Mayes, Marc; Mustard, John; Melillo, Jerry; Neill, Christopher; Nyadzi, Gerson

2017-08-01

In sub-Saharan Africa (SSA), tropical dry forests and savannas cover over 2.5 million km2 and support livelihoods for millions in fast-growing nations. Intensifying land use pressures have driven rapid changes in tree cover structure (basal area, biomass) that remain poorly characterized at regional scales. Here, we posed the hypothesis that tree cover structure related strongly to senesced and non-photosynthetic (NPV) vegetation features in a SSA tropical dry forest landscape, offering improved means for satellite remote sensing of tree cover structure compared to vegetation greenness-based methods. Across regrowth miombo woodland sites in Tanzania, we analyzed relationships among field data on tree structure, land cover, and satellite indices of green and NPV features based on spectral mixture analyses and normalized difference vegetation index calculated from Landsat 8 data. From satellite-field data relationships, we mapped regional basal area and biomass using NPV and greenness-based metrics, and compared map performances at landscape scales. Total canopy cover related significantly to stem basal area (r 2 = 0.815, p < 0.01) and biomass (r 2 = 0.635, p < 0.01), and NPV dominated ground cover (> 60%) at all sites. From these two conditions emerged a key inverse relationship: skyward exposure of NPV ground cover was high at sites with low tree basal area and biomass, and decreased with increasing stem basal area and biomass. This pattern scaled to Landsat NPV metrics, which showed strong inverse correlations to basal area (Pearson r = -0.85, p < 0.01) and biomass (r = -0.86, p < 0.01). Biomass estimates from Landsat NPV-based maps matched field data, and significantly differentiated landscape gradients in woody biomass that greenness metrics failed to track. The results suggest senesced vegetation metrics at Landsat scales are a promising means for improved monitoring of tree structure across disturbance and ecological gradients in African and other tropical dry forests.
On validating remote sensing simulations using coincident real data

NASA Astrophysics Data System (ADS)

Wang, Mingming; Yao, Wei; Brown, Scott; Goodenough, Adam; van Aardt, Jan

2016-05-01

The remote sensing community often requires data simulation, either via spectral/spatial downsampling or through virtual, physics-based models, to assess systems and algorithms. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is one such first-principles, physics-based model for simulating imagery for a range of modalities. Complex simulation of vegetation environments subsequently has become possible, as scene rendering technology and software advanced. This in turn has created questions related to the validity of such complex models, with potential multiple scattering, bidirectional distribution function (BRDF), etc. phenomena that could impact results in the case of complex vegetation scenes. We selected three sites, located in the Pacific Southwest domain (Fresno, CA) of the National Ecological Observatory Network (NEON). These sites represent oak savanna, hardwood forests, and conifer-manzanita-mixed forests. We constructed corresponding virtual scenes, using airborne LiDAR and imaging spectroscopy data from NEON, ground-based LiDAR data, and field-collected spectra to characterize the scenes. Imaging spectroscopy data for these virtual sites then were generated using the DIRSIG simulation environment. This simulated imagery was compared to real AVIRIS imagery (15m spatial resolution; 12 pixels/scene) and NEON Airborne Observation Platform (AOP) data (1m spatial resolution; 180 pixels/scene). These tests were performed using a distribution-comparison approach for select spectral statistics, e.g., established the spectra's shape, for each simulated versus real distribution pair. The initial comparison results of the spectral distributions indicated that the shapes of spectra between the virtual and real sites were closely matched.

Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method

NASA Astrophysics Data System (ADS)

He, Yaqian; Bo, Yanchen; Chai, Leilei; Liu, Xiaolong; Li, Aihua

2016-08-01

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bias.
Preliminary determination of geothermal working area based on Thermal Infrared and Synthetic Aperture Radar (SAR) remote sensing

NASA Astrophysics Data System (ADS)

Agoes Nugroho, Indra; Kurniawahidayati, Beta; Syahputra Mulyana, Reza; Saepuloh, Asep

2017-12-01

Remote sensing is one of the methods for geothermal exploration. This method can be used to map the geological structures, manifestations, and predict the geothermal potential area. The results from remote sensing were used as guidance for the next step exploration. Analysis of target in remote sensing is an efficient method to delineate geothermal surface manifestation without direct contact to the object. The study took a place in District Merangin, Jambi Province, Indonesia. The area was selected due to existing of Merangin volcanic complex composed by Mounts Sumbing and Hulunilo with surface geothermal manifestations presented by hot springs and hot pools. The location of surface manifestations could be related with local and regional structures of Great Sumatra Fault. The methods used in this study were included identification of volcanic products, lineament extraction, and lineament density quantification. The objective of this study is to delineate the potential zones for sitting the geothermal working site based on Thermal Infrared and Synthetic Aperture Radar (SAR) sensors. The lineament-related to geological structures, was aimed for high lineament density, is using ALOS - PALSAR (Advanced Land Observing Satellite - The Phased Array type L-band Synthetic Aperture Radar) level 1.1. The Normalized Difference Vegetation Index (NDVI) analysis was used to predict the vegetation condition using Landsat 8 OLI-TIRS (The Operational Land Imager - Thermal Infrared Sensor). The brightness temperature was extracted from TIR band to estimate the surface temperature. Geothermal working area identified based on index overlay method from extracted parameter of remote sensing data was located at the western part of study area (Graho Nyabu area). This location was identified because of the existence of high surface temperature about 30°C, high lineament density about 4 - 4.5 km/km2 and low NDVI values less than 0.3.
Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Roberts, Dar A.; Adams, John B.; Smith, Milton O.

1993-01-01

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.
Comparison of two satellite imaging platforms for evaluating quasi-circular vegetation patch in the Yellow River Delta, China

NASA Astrophysics Data System (ADS)

Liu, Qingsheng; Liang, Li; Liu, Gaohuan; Huang, Chong

2017-09-01

Vegetation often exists as patch in arid and semi-arid region throughout the world. Vegetation patch can be effectively monitored by remote sensing images. However, not all satellite platforms are suitable to study quasi-circular vegetation patch. This study compares fine (GF-1) and coarse (CBERS-04) resolution platforms, specifically focusing on the quasicircular vegetation patches in the Yellow River Delta (YRD), China. Vegetation patch features (area, shape) were extracted from GF-1 and CBERS-04 imagery using unsupervised classifier (K-Means) and object-oriented approach (Example-based feature extraction with SVM classifier) in order to analyze vegetation patterns. These features were then compared using vector overlay and differencing, and the Root Mean Squared Error (RMSE) was used to determine if the mapped vegetation patches were significantly different. Regardless of K-Means or Example-based feature extraction with SVM classification, it was found that the area of quasi-circular vegetation patches from visual interpretation from QuickBird image (ground truth data) was greater than that from both of GF-1 and CBERS-04, and the number of patches detected from GF-1 data was more than that of CBERS-04 image. It was seen that without expert's experience and professional training on object-oriented approach, K-Means was better than example-based feature extraction with SVM for detecting the patch. It indicated that CBERS-04 could be used to detect the patch with area of more than 300 m2, but GF-1 data was a sufficient source for patch detection in the YRD. However, in the future, finer resolution platforms such as Worldview are needed to gain more detailed insight on patch structures and components and formation mechanism.
Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

PubMed

Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.
Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

PubMed Central

LI, GUIYING; LU, DENGSHENG; MORAN, EMILIO; HETRICK, SCOTT

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes. PMID:22368311
Satellite-based GNSS-R observations from TDS-1 for soil moisture studies in agricultural vegetation landscapes

NASA Astrophysics Data System (ADS)

Liu, P. W.; Clarizia, M. P.; Judge, J.; Camps, A.; Ruf, C. S.; Bongiovanni, T. E.

2015-12-01

Soil moisture (SM) is a critical factor governing the water and energy fluxes at the land surface that are important for near-term climate forecasting, drought monitoring, crop yield estimation, and better water resources management. Remotely sensed observations at microwave frequencies are the most sensitive to changes of water in the soil. Particularly, frequencies at L-band (1-2 GHz) have been widely used for SM studies under the vegetated land covers because of their minimal atmospheric interference and attenuation by vegetation, allowing observations from the soil surface. In addition to current satellite based microwave sensors, such as the Soil Moisture Active Passive (SMAP) missions, the Global Navigation Satellite System-Reflectometry technique is capable of observing the GNSS signal reflected from the terrain that contains combined information of soil and vegetation characteristics. The technique has recently attracted attention for global SM monitoring because its receiver is small in size and light weight and can be on board the low orbit, small satellites with low power consumption and low cost. Therefore the GNSS-R remote sensing may lead to affordable multi-satellite constellations that enable improved temporal resolution for highly dynamic hydrologic conditions. The current UK Technology Demonstration Satellite (TDS-1) has been providing global GNSS-R observations since September 2014 for experimental purposes and the receiver is accessed and operated for 2 days during every 8-day cycle. In the near future, the NASA Cyclone GNSS (CYGNSS) mission, scheduled to be launched in 2016, will consist of 8 satellites observing GPS L1 signal at a frequency of 1.5754 GHz with a spatial resolution of 10-25 km and a temporal resolution of < 12 hours. The goal of this study is to understand the impacts of SM and characteristics of agricultural vegetation on the forward scattering mechanisms of satellite-based GNSS-R observations. The GNSS-R observations from TDS-1 will be used to investigate the correlation of the GNSS-R signals to SMAP, ALOS-2 PALSAR-2, vegetation indices from MODIS, and the in-situ SM observations in an agricultural region in the Midwestern US. This study will provide insights into SM estimation in the agricultural region using satellite-based GNSS-R observations.
Using Remote Sensing to Estimate Crop Water Use to Improve Irrigation Water Management

NASA Astrophysics Data System (ADS)

Reyes-Gonzalez, Arturo

Irrigation water is scarce. Hence, accurate estimation of crop water use is necessary for proper irrigation managements and water conservation. Satellite-based remote sensing is a tool that can estimate crop water use efficiently. Several models have been developed to estimate crop water requirement or actual evapotranspiration (ETa) using remote sensing. One of them is the Mapping EvapoTranspiration at High Resolution using Internalized Calibration (METRIC) model. This model has been compared with other methods for ET estimations including weighing lysimeters, pan evaporation, Bowen Ratio Energy Balance System (BREBS), Eddy Covariance (EC), and sap flow. However, comparison of METRIC model outputs to an atmometer for ETa estimation has not yet been attempted in eastern South Dakota. The results showed a good relationship between ETa estimated by the METRIC model and estimated with atmometer (r2 = 0.87 and RMSE = 0.65 mm day-1). However, ETa values from atmometer were consistently lower than ET a values from METRIC. The verification of remotely sensed estimates of surface variables is essential for any remote-sensing study. The relationships between LAI, Ts, and ETa estimated using the remote sensing-based METRIC model and in-situ measurements were established. The results showed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m -2, Ts had r2 = 0.87 and RMSE 1.24 °C and ETa presented r2= 0.89 and RMSE = 0.71 mm day -1. Estimation of ETa using energy balance method can be challenging and time consuming. Thus, there is a need to develop a simple and fast method to estimate ETa using minimum input parameters. Two methods were used, namely 1) an energy balance method (EB method) that used input parameters of the Landsat image, weather data, a digital elevation map, and a land cover map and 2) a Kc-NDVI method that use two input parameters: the Landsat image and weather data. A strong relationship was found between the two methods with r2 of 0.97 and RMSE of 0.37 mm day -1. Hence, the Kc-NDVI method performed well for ET a estimations, indicating that Kc-NDVI method can be a robust and reliable method to estimate ETa in a short period of time. Estimation of crop evapotranspiration (ETc) using satellite remote sensing-based vegetation index such as the Normalized Difference Vegetation Index (NDVI). The NDVI was calculated using near-infrared and red wavebands. The relationship between NDVI and tabulated Kc's was used to generate Kc maps. ETc maps were developed as an output of Kc maps multiplied by reference evapotranspiration (ETr). Daily ETc maps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude that ETc maps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water use at regional and field scales.
Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

PubMed

Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel

2017-09-11

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.
[Study on the nutrition of alpine meadow based on hyperspectral data].

PubMed

Wang, Xun; Liu, Shu-Jie; Jia, Hai-Feng; Chai, Sha-Tuo; Dang, An-Rong; Liu, Xue-Hua; Hao, Li-Zhuang; Cui, Zhan-Hong

2012-10-01

Remote sensing monitoring of alpine grassland nutritional status is a key factor of grassland reasonable utilization, also a difficulty for dynamic vegetation monitoring. The present paper studies the correlations between vegetation nutrition and hyperspectral data. The results showed that two band ratio models have a significant correlation with biomass, air-DM, P, CF, and CP. MAXR models have a significant correlation with most of nutrition index when selected wavebands equaled five. On the whole, the MAXR model precedes two band ratio models. Using MAXR models to estimate air-DM, P and CF can obtain higher accuracy.
A Pattern-Based Definition of Urban Context Using Remote Sensing and GIS

PubMed Central

Benza, Magdalena; Weeks, John R.; Stow, Douglas A.; López-Carr, David; Clarke, Keith C.

2016-01-01

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user’s accuracy (commission errors) being lower than producer’s accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient. PMID:27867227
A Pattern-Based Definition of Urban Context Using Remote Sensing and GIS.

PubMed

Benza, Magdalena; Weeks, John R; Stow, Douglas A; López-Carr, David; Clarke, Keith C

2016-09-15

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user's accuracy (commission errors) being lower than producer's accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient.
Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

NASA Astrophysics Data System (ADS)

Shew, A. M.; Ghosh, A.

2017-10-01

Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.
Use of remote sensing techniques for geological hazard surveys in vegetated urban regions. [multispectral imagery for lithological mapping

NASA Technical Reports Server (NTRS)

Stow, S. H.; Price, R. C.; Hoehner, F.; Wielchowsky, C.

1976-01-01

The feasibility of using aerial photography for lithologic differentiation in a heavily vegetated region is investigated using multispectral imagery obtained from LANDSAT satellite and aircraft-borne photography. Delineating and mapping of localized vegetal zones can be accomplished by the use of remote sensing because a difference in morphology and physiology results in different natural reflectances or signatures. An investigation was made to show that these local plant zones are affected by altitude, topography, weathering, and gullying; but are controlled by lithology. Therefore, maps outlining local plant zones were used as a basis for lithologic map construction.
A technique for the determination of Louisiana marsh salinity zone from vegetation mapped by multispectral scanner data: A comparison of satellite and aircraft data

NASA Technical Reports Server (NTRS)

Butera, M. K.

1977-01-01

Vegetation in selected study areas on the Louisiana coast was mapped using low altitude aircraft and satellite (LANDSAT) multispectral scanner data. Fresh, brackish, and saline marshes were then determined from the remotely sensed presence of dominant indicator plant associations. Such vegetational classifications were achieved from data processed through a standard pattern recognition computer program. The marsh salinity zone maps from the aircraft and satellite data compared favorably within the broad salinity regimes. The salinity zone boundaries determined by remote sensing compared favorably with those interpolated from line-transect field observations from an earlier year.
Relationships among vegetation structure, canopy composition, and avian richness patterns across an aspen-conifer forest gradient

Treesearch

Charles E. Swift; Kerri T. Vierling; Andrew T. Hudak; Lee A. Vierling

2017-01-01

Ecologists have a long-term interest in understanding the relative influence of vegetation composition and vegetation structure on avian diversity. LiDAR remote sensing is useful in studying local patterns of avian diversity because it characterizes fine-scale vegetation structure across broad extents. We used LiDAR, aerial and satellite imagery, and avian field data...
The Use of Commercial Remote Sensing Predicting Helicopter Brownout Conditions

DTIC Science & Technology

2007-09-01

and is not partial to the number of times an individual can visit the site and update or supply new information. (Shewfelt 2006) (U) An example of...leaves as well as promote new growth of more green vegetation.(Shewfelt 2006) This green leaf vegetation relation to moisture plays a vital role in...Sensing in Predicting Helicopter Brownout Conditions. September 2006 (Top Secret). Tan, Kim H., First Edition, Enviromental Soil Science Marcel
East Europe Report: Political, Sociological and Military Affairs, No. 2193

DTIC Science & Technology

1983-09-06

want to become "good" people, perform humanistic deeds, be useful, not senselessly vegetate. Values such as a sense of duty, a sense of honor, but...produc- tion, primarily in the production of vegetables, conservation of pastures, preservation of livestock, and broadly interpreted social- existential ...by three fourths of the respondents to have self-government active in social- existential matters (an explanation to this might be, the lack of trade
UAV-based high-throughput phenotyping in legume crops

NASA Astrophysics Data System (ADS)

Sankaran, Sindhuja; Khot, Lav R.; Quirós, Juan; Vandemark, George J.; McGee, Rebecca J.

2016-05-01

In plant breeding, one of the biggest obstacles in genetic improvement is the lack of proven rapid methods for measuring plant responses in field conditions. Therefore, the major objective of this research was to evaluate the feasibility of utilizing high-throughput remote sensing technology for rapid measurement of phenotyping traits in legume crops. The plant responses of several chickpea and peas varieties to the environment were assessed with an unmanned aerial vehicle (UAV) integrated with multispectral imaging sensors. Our preliminary assessment showed that the vegetation indices are strongly correlated (p<0.05) with seed yield of legume crops. Results endorse the potential of UAS-based sensing technology to rapidly measure those phenotyping traits.
The LANDFIRE Refresh strategy: updating the national dataset

USGS Publications Warehouse

Nelson, Kurtis J.; Connot, Joel A.; Peterson, Birgit E.; Martin, Charley

2013-01-01

The LANDFIRE Program provides comprehensive vegetation and fuel datasets for the entire United States. As with many large-scale ecological datasets, vegetation and landscape conditions must be updated periodically to account for disturbances, growth, and natural succession. The LANDFIRE Refresh effort was the first attempt to consistently update these products nationwide. It incorporated a combination of specific systematic improvements to the original LANDFIRE National data, remote sensing based disturbance detection methods, field collected disturbance information, vegetation growth and succession modeling, and vegetation transition processes. This resulted in the creation of two complete datasets for all 50 states: LANDFIRE Refresh 2001, which includes the systematic improvements, and LANDFIRE Refresh 2008, which includes the disturbance and succession updates to the vegetation and fuel data. The new datasets are comparable for studying landscape changes in vegetation type and structure over a decadal period, and provide the most recent characterization of fuel conditions across the country. The applicability of the new layers is discussed and the effects of using the new fuel datasets are demonstrated through a fire behavior modeling exercise using the 2011 Wallow Fire in eastern Arizona as an example.

Remote sensing of vegetation structure using computer vision

NASA Astrophysics Data System (ADS)

Dandois, Jonathan P.

High-spatial resolution measurements of vegetation structure are needed for improving understanding of ecosystem carbon, water and nutrient dynamics, the response of ecosystems to a changing climate, and for biodiversity mapping and conservation, among many research areas. Our ability to make such measurements has been greatly enhanced by continuing developments in remote sensing technology---allowing researchers the ability to measure numerous forest traits at varying spatial and temporal scales and over large spatial extents with minimal to no field work, which is costly for large spatial areas or logistically difficult in some locations. Despite these advances, there remain several research challenges related to the methods by which three-dimensional (3D) and spectral datasets are joined (remote sensing fusion) and the availability and portability of systems for frequent data collections at small scale sampling locations. Recent advances in the areas of computer vision structure from motion (SFM) and consumer unmanned aerial systems (UAS) offer the potential to address these challenges by enabling repeatable measurements of vegetation structural and spectral traits at the scale of individual trees. However, the potential advances offered by computer vision remote sensing also present unique challenges and questions that need to be addressed before this approach can be used to improve understanding of forest ecosystems. For computer vision remote sensing to be a valuable tool for studying forests, bounding information about the characteristics of the data produced by the system will help researchers understand and interpret results in the context of the forest being studied and of other remote sensing techniques. This research advances understanding of how forest canopy and tree 3D structure and color are accurately measured by a relatively low-cost and portable computer vision personal remote sensing system: 'Ecosynth'. Recommendations are made for optimal conditions under which forest structure measurements should be obtained with UAS-SFM remote sensing. Ultimately remote sensing of vegetation by computer vision offers the potential to provide an 'ecologist's eye view', capturing not only canopy 3D and spectral properties, but also seeing the trees in the forest and the leaves on the trees.
Research Advances on Radiation Transfer Modeling and Inversion for Multi-scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.; Li, J.; Du, Y.; Wen, J.; Zhong, B.; Wang, K.

2011-12-01

As the remote sensing data accumulating, it is a challenge and significant issue how to generate high accurate and consistent land surface parameter product from the multi source remote observation and the radiation transfer modeling and inversion methodology are the theoretical bases. In this paper, recent research advances and unresolved issues are presented. At first, after a general overview, recent research advances on multi-scale remote sensing radiation transfer modeling are presented, including leaf spectrum model, vegetation canopy BRDF models, directional thermal infrared emission models, rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed, taking the land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is suggested and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China are introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.
Health condition assessment for vegetation exposed to heavy metal pollution through airborne hyperspectral data.

PubMed

Banerjee, Bikram Pratap; Raval, Simit; Zhai, Hao; Cullen, Patrick Joseph

2017-11-03

Recent advancements in hyperspectral remote sensing technology now provide improved diagnostic capabilities to assess vegetation health conditions. This paper uses a set of 13 vegetation health indices related to chlorophyll, xanthophyll, blue/green/red ratio and structure from airborne hyperspectral reflectance data collected around a derelict mining area in Yerranderie, New South Wales, Australia. The studied area has ten historic mine shafts with a legacy of heavy metals and acidic contamination in a pristine ecosystem now recognised as Great Blue Mountain World Heritage Area. The forest is predominantly comprised of different species of Eucalyptus trees. In addition to the airborne survey, ground-based spectra of the tree leaves were collected along the two accessible heavy metal contaminated pathways. The stream networks in the area were classified and the geospatial patterns of vegetation health were analysed along the Tonalli River, a major water tributary flowing through the National Park. Despite the inflow of contaminated water from the near-mine streams, the measured vegetation health indices along Tonalli River were found to remain unchanged. The responses of the vegetation health indices between the near-mine and away-mine streams were found similar. Based on the along-stream and inter-stream analysis of the spectral indices of vegetation health, no significant impact of the heavy metal pollution could be noticed. The results indicate the possibility of the vegetation having developed immunity towards the high levels of heavy metal pollution over a century of exposure.
Remote Sensing Center

NASA Technical Reports Server (NTRS)

1973-01-01

The applications are reported of new remote sensing techniques for earth resources surveys and environmental monitoring. Applications discussed include: vegetation systems, environmental monitoring, and plant protection. Data processing systems are described.
LEAF AREA INDEX CHANGE DETECTION OF UNDERSTORY VEGETATION IN THE ALBEMARLE-PAMLICO BASIN USING IKOMOS AND LANDSAT ETM+ SATELLITE DATA

EPA Science Inventory

The advent of remotely sensed data from satellite platforms has enabled the research community to examine vegetative spatial distributions over regional and global scales. This assessment of ecosystem condition through the synoptic monitoring of terrestrial vegetation extent, bio...
LEAF AREA INDEX (LAI) CHANGES DETECTION OF UNDERSTORY VEGETATION IN THE ALBEMARLE-PAMLICO BASIN IKONOS AND LANDSAT ETM+ SATELLITE DATA

EPA Science Inventory

The advent of remotely sensed data from satellite platforms has enabled the research community to examine vegetative spatial distributions over regional and global scales. This assessment of ecosystem condition through the synoptic monitoring of terrestrial vegetation extent, bio...
Daily monitoring of vegetation conditions and evapotranspiration at field scale by fusing multi-satellite images

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires frequent remote sensing observations. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for vegetation monitoring. The medium spatial resolution (10-100m) sensors are su...
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.
Attribution and Characterisation of Sclerophyll Forested Landscapes Over Large Areas

NASA Astrophysics Data System (ADS)

Jones, Simon; Soto-Berelov, Mariela; Suarez, Lola; Wilkes, Phil; Woodgate, Will; Haywood, Andrew

2016-06-01

This paper presents a methodology for the attribution and characterisation of Sclerophyll forested landscapes over large areas. First we define a set of woody vegetation data primitives (e.g. canopy cover, leaf area index (LAI), bole density, canopy height), which are then scaled-up using multiple remote sensing data sources to characterise and extract landscape woody vegetation features. The advantage of this approach is that vegetation landscape features can be described from composites of these data primitives. The proposed data primitives act as building blocks for the re-creation of past woody characterisation schemes as well as allowing for re-compilation to support present and future policy and management and decision making needs. Three main research sites were attributed; representative of different sclerophyll woody vegetated systems (Box Iron-bark forest; Mountain Ash forest; Mixed Species foothills forest). High resolution hyperspectral and full waveform LiDAR data was acquired over the three research sites. At the same time, land management agencies (Victorian Department of Environment, Land Water and Planning) and researchers (RMIT, CRC for Spatial Information and CSIRO) conducted fieldwork to collect structural and functional measurements of vegetation, using traditional forest mensuration transects and plots, terrestrial lidar scanning and high temporal resolution in-situ autonomous laser (VegNet) scanners. Results are presented of: 1) inter-comparisons of LAI estimations made using ground based hemispherical photography, LAI 2200 PCA, CI-110 and terrestrial and airborne laser scanners; 2) canopy height and vertical canopy complexity derived from airborne LiDAR validated using ground observations; and, 3) time-series characterisation of land cover features. 1. Accuracy targets for remotely sensed LAI products to match within ground based estimates are ± 0.5 LAI or a 20% maximum (CEOS/GCOS) with new aspirational targets of 5%). In this research we conducted a total of 67 ground-based method-to-method pairwise comparisons across 11 plots in five sites, incorporating the previously mentioned LAI methods. Out of the 67 comparisons, 29 had an RMSE ≥ 0.5 LAIe. This has important implications for the validation of remotely sensed products since ground based techniques themselves exhibit LAI variations greater than internationally recommended guidelines for satellite product accuracies. 2. Two methods of canopy height derivation are proposed and tested over a large area (4 Million Ha). 99th percentile maximum height achieved a RMSE of 6.6%, whilst 95th percentile dominant height a RMSE = 10.3%. Vertical canopy complexity (i.e. the number of forest layers of strata) was calculated as the local maxima of vegetation density within the LiDAR canopy profile and determined using a cubic spline smoothing of Pgap. This was then validated against in-situ and LiDAR observations of canopy strata with an RMSE 0.39 canopy layers. 3. Preliminary results are presented of landcover characterisation using LandTrendr analysis of Landsat LEDAPS data. kNN is then used to link these features to a dense network of 800 field plots sites.
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.
Quantifying Vegetation Composition, Structure and Dynamics in Selected Australian Ecosystems: Science to Management

NASA Astrophysics Data System (ADS)

Phinn, S. R.; Scarth, P.; Armston, J.; Witte, C.; Danaher, T.; Flood, N.; Gill, T.; Lucas, R.

2011-12-01

Management of Australian ecosystems is carried out by state governments using information derived from satellite image data. The state of Queensland covers approximately 1.8 x 10^6 km^2 and uses satellite remote sensing and field survey programs to support legislated environmental monitoring, management and compliance activities.This poster outlines how the Joint Remote Sensing Research Program(JRSRP)delivered satellite image based data sets to address these activities by mapping foliage projective cover, vegetation height and biomass. Foliage projective cover (FPC), the vertically projected percentage cover of photosynthetic foliage of all strata, is produced from Landsat TM/ETM data using 88 scenes and over 1700 field sites. The JRSRP enabled government staff to be seconded to a university research group to work on the project, and the university provided postdoctoral and graduate student support. The JRSRP activities focussed on geometric and topographic corrections, BRDF corrections and time-series based approaches for correcting the archive of field survey and Landsat TM/ETM+ images. This has now progressed to a program using the entire Landsat TM/ETM+ archive on an annual basis and annual state-wide field survey data. The Landsat TM/ETM+ calibrations have been a critical input to the Landsat program's global vicarious calibration activities. Vegetation height is a critical parameter required for a range of state-wide activities and can be mapped accurately from field plots to regional areas using airborne Lidar. To develop statewide height estimates, an approach was developed using Icesat and existing vegetation community maps. By aggregating the spaceborne Icesat full waveform data within the mapped vegetation structure polygons it was possible to retrieve vegetation vertical structure information continuously across the landscape. This was used to derive mean canopy and understorey height, depth and density across Queensland, which was validated using airborne lidar data provided by the JRSRP. Biomass mapping is emerging as a critical environmental parameter for local, state and national agencies in Australia. Staff from JRSRP developed an approach with University of Aberystwyth in Wales, through JAXA's Kyoto and Carbon initiative, for acquiring ALOS PALSAR L-band image data, conducting geometric and radiometric corrections, and normalising for significant scene to scene differences in soil and vegetation moisture content. This pre-processing of 31 image strip time-series generated state-wide mosaics for Queensland that were then used with 1815 field survey sites collected across the state to produce a state-wide biomass estimation model for L-HV data, providing estimates for both remnant and non-remnant forests, with saturation at 263 Mg.Ha^-1 for 20% estimation error. The Joint Remote Sensing Research Program has enabled a sound approach to research and development for validated operational applications.
Comparison of remote sensing indices for monitoring of desert cienegas

USGS Publications Warehouse

Wilson, Natalie R.; Norman, Laura M.; Villarreal, Miguel; Gass, Leila; Tiller, Ron; Salywon, Andrew

2016-01-01

This research considers the applicability of different vegetation indices at 30 m resolution for mapping and monitoring desert wetland (cienega) health and spatial extent through time at Cienega Creek in southeastern Arizona, USA. Multiple stressors including the risk of decadal-scale drought, the effects of current and predicted global warming, and continued anthropogenic pressures threaten aquatic habitats in the southwest and cienegas are recognized as important sites for conservation and restoration efforts. However, cienegas present a challenge to satellite-imagery based analysis due to their small size and mixed surface cover of open water, exposed soils, and vegetation. We created time series of five well-known vegetation indices using annual Landsat Thematic Mapper (TM) images retrieved during the April–June dry season, from 1984 to 2011 to map landscape-level distribution of wetlands and monitor the temporal dynamics of individual sites. Indices included the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII). One topographic index, the Topographic Wetness Index (TWI), was analyzed to examine the utility of topography in mapping distribution of cienegas. Our results indicate that the NDII, calculated using Landsat TM band 5, outperforms the other indices at differentiating cienegas from riparian and upland sites, and was the best means to analyze change. As such, it offers a critical baseline for future studies that seek to extend the analysis of cienegas to other regions and time scales, and has broader applicability to the remote sensing of wetland features in arid landscapes.
Characterization of Vegetation Change in a Sub-Arctic Mire using Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

DelGreco, J. L.; McArthur, K. J.; Palace, M. W.; Herrick, C.; Garnello, A.; Finnell, D.; McCalley, C. K.; Anderson, S. M.; Varner, R. K.

2015-12-01

Climate change is impacting northern ecosystems through the thawing of the permafrost, which has resulted in changes to plant communities and greenhouse gas emissions, such as carbon dioxide (CO2) and methane (CH4). These greenhouse gases are of concern due to their potential feedbacks which create a warmer climate, thus increasing permafrost thawing. Our study focuses on how vegetation type differs in areas that have been impacted by thawing permafrost at Stordalen Mire located in Abisko, Sweden. To estimate change in vegetation communities, field-based measurements combined with remotely sensed image data was used. 75 randomized square-meter plots were measured for vegetation composition and classified into one of five site-types, each representing a different stage of permafrost degradation. New high-resolution imagery (1 cm) was collected using Unmanned Aerial Vehicles (UAV) providing insight into the spatial patterning, characterizations, and changes of these communities. The UAV imagery was georectified using high precision GPS points collected across the mire. The imagery was then examined using a neural network analysis to estimate cover type across the mire. This 2015 cover type classification was then compared to previous UAV imagery taken on July 2014 to analyze changes in vegetation distribution as an indication of permafrost thaw. Hummock sites represent intact permafrost and have lost 21.5% coverage since 2014, while tall gramminoid sites, which indicate fully thawed sites, have increased coverage by 12.1%. A discriminate function analysis showed that site types can be differentiated based on species composition, thus showing that vegetation differs significantly across the thaw gradient. Using average flux rates of CH4 from each cover type reported previously, the percent of CH4 emitted over the mire was estimated for 2014 and 2015. Comparing both estimates, CH4 emissions increased with a flux change of 5604.5 g CH4/day. Our estimates of vegetation change may be used to parameterize simulation models and create future scenarios of how the vegetation cover will change in response to climate change. Data from this study will also help to explain how the ecology of the subarctic peatlands, now a carbon sink, may be on its way to changing into a source of carbon.
Numerical study of hydrodynamic and salinity transport processes in the Pink Beach wetlands of the Liao River estuary, China

NASA Astrophysics Data System (ADS)

Qiao, Huiting; Zhang, Mingliang; Jiang, Hengzhi; Xu, Tianping; Zhang, Hongxing

2018-06-01

Interaction studies of vegetation within flow environments are essential for the determination of bank protection, morphological characteristics and ecological conditions for wetlands. This paper uses the MIKE 21 hydrodynamic and salinity model to simulate the hydrodynamic characteristics and salinity transport processes in the Pink Beach wetlands of the Liao River estuary. The effect of wetland plants on tidal flow in wetland areas is represented by a varying Manning coefficient in the bottom friction term. Acquisition of the vegetation distribution is based on Landsat TM satellites by remote sensing techniques. Detailed comparisons between field observation and simulated results of water depth, salinity and tidal currents are presented in the vegetated domain of the Pink Beach wetlands. Satisfactory results were obtained from simulations of both flow characteristics and salinity concentration, with or without vegetation. A numerical experiment was conducted based on variations in vegetation density, and compared with the tidal currents in non-vegetated areas; the computed current speed decreased remarkably with an increase in vegetation density. The impact of vegetation on water depth and salinity was simulated, and the findings revealed that wetland vegetation has an insignificant effect on the water depth and salinity in this wetland domain. Several stations (from upstream to downstream) in the Pink Beach wetlands were selected to estimate the longitudinal variation of salinity under different river runoff conditions; the results showed that salinity concentration decreases with an increase in river runoff. This study can consequently help increase the understanding of favourable salinity conditions for particular vegetation growth in the Pink Beach wetlands of the Liao River estuary. The results also provide crucial guidance for related interaction studies of vegetation, flow and salinity in other wetland systems.
Biomass and health based forest cover delineation using spectral un-mixing

Treesearch

Mohan Tiruveedhula; Joseph Fan; Ravi R. Sadasivuni; Surya S. Durbha; David L. Evans

2009-01-01

Remote sensing is a well-suited source of information on various forest characteristics such as forest cover type, leaf area, biomass, and health. The use of appropriate layers helps to quantify the variables of interest. For example, normalized difference vegetation index (NDVI) and greenness help explain variability in biomass as well as health of forests....
Using remote sensing to monitor post-fire watershed recovery as a tool for management

Treesearch

Jess Clark; Marc Stamer; Kevin Cooper; Carolyn Napper; Terri Hogue; Alicia Kinoshita

2013-01-01

Post-fire watershed recovery is influenced by numerous variables but one of the most important factors is the rate of re-establishment of vegetative cover. Burned Area Emergency Response (BAER) teams, along with other agencies (Natural Resource Conservation Service, state, counties, cities, etc.), prescribe temporary post-fire mitigation treatments based on expected...
Potential of Sentinel Satellites for Schistosomiasis Monitoring

NASA Astrophysics Data System (ADS)

Li, C.-R.; Tang, L.-L.; Niu, H.-B.; Zhou, X.-N.; Liu, Z.-Y.; Ma, L.-L.; Zhou, Y.-S.

2012-04-01

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis is the unique intermediate host of Schistosoma, and hence monitoring and controlling of the number of oncomelania is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to oncomelania breeding and reproduction, such as temperature, moisture, vegetation, soil, and rainfall, and can also provide the efficient information to determine the location, area, and spread tendency of oncomelania. Many studies show that the correlation coefficient between oncomelania densities and remote sensing environmental factors depends largely on suitable and high quality remote sensing data used in retrieve environmental factors. Research achievements on retrieving environmental factors (which are related to the living, multiplying and transmission of oncomelania) by multi-source remote data are shown firstly, including: (a) Vegetation information (e.g., Modified Soil-Adjusted Vegetation Index, Normalized Difference Moisture Index, Fractional Vegetation Cover) extracted from optical remote sensing data, such as Landsat TM, HJ-1A/HSI image; (b) Surface temperature retrieval from Thermal Infrared (TIR) and passive-microwave remote sensing data; (c) Water region, soil moisture, forest height retrieval from synthetic aperture radar data, such as Envisat SAR, DLR's ESAR image. Base on which, the requirements of environmental factor accuracy for schistosomiasis monitoring will be analyzed and summarized. Our work on applying remote sensing technique to schistosomiasis monitoring is then presented. The fuzzy information theory is employed to analyze the sensitivity and feasibility relation between oncomelania densities and environmental factors. Then a mechanism model of predicting oncomelania distribution and densities is developed. The new model is validated with field data of Dongting Lake and the dynamic monitoring of schistosomiasis breeding in Dongting Lake region is presented. Finally, emphasis are placed on analyzing the potential of Sentinel satellites for schistosomiasis monitoring. The requirements of optical high resolution data on spectral resolution, spatial resolution, radiometric resolution/accuracy, as well as the requirements of synthetic aperture radar data on operation frequency, spatial resolution, polarization, radiometric accuracy, repeat cycle are presented and then compared with the parameters of Sentinel satellites. The parameters of Sentinel satellites are also compared with those of available remote satellites, such as Envisat, Landsat, whose data are being used for schistosomiasis monitoring. The application potential of Sentinel satellites for the schistosomiasis monitoring will be concluded in the end, which will benefit for the mission operation, model development, etc.
Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image.

PubMed

Rapinel, Sébastien; Clément, Bernard; Magnanon, Sylvie; Sellin, Vanessa; Hubert-Moy, Laurence

2014-11-01

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers. Copyright © 2014 Elsevier Ltd. All rights reserved.
[The new method monitoring crop water content based on NIR-Red spectrum feature space].

PubMed

Cheng, Xiao-juan; Xu, Xin-gang; Chen, Tian-en; Yang, Gui-jun; Li, Zhen-hai

2014-06-01

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.
[Analysis of spectral features based on water content of desert vegetation].

PubMed

Zhao, Zhao; Li, Xia; Yin, Ye-biao; Tang, Jin; Zhou, Sheng-bin

2010-09-01

By using HR-768 field-portable spectroradiometer made by the Spectra Vista Corporation (SVC) of America, the hyper-spectral data of nine types of desert plants were measured, and the water content of corresponding vegetation was determined by roasting in lab. The continuum of measured hyperspectral data was removed by using ENVI, and the relationship between the water content of vegetation and the reflectance spectrum was analyzed by using correlation coefficient method. The result shows that the correlation between the bands from 978 to 1030 nm and water content of vegetation is weak while it is better for the bands from 1133 to 1266 nm. The bands from 1374 to 1534 nm are the characteristic bands because of the correlation between them and water content is the best. By using cluster analysis and according to the water content, the vegetation could be marked off into three grades: high (>70%), medium (50%-70%) and low (<50%). The research reveals the relationship between water content of desert vegetation and hyperspectral data, and provides basis for the analysis of area in desert and the monitoring of desert vegetation by using remote sensing data.

Regional Drought Monitoring Based on Multi-Sensor Remote Sensing

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Im, Jungho; Park, Seonyoung

2014-05-01

Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety of land cover types. Remote sensing data from the Tropical Rainfall Measuring Mission satellite (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) sensors were obtained for the period from 2000 to 2012, and observation data from 99 weather stations, 441 streamflow gauges, as well as the gridded observation data from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE) were obtained for validation. The objective blends of multiple indicators helped better assessment of various types of drought, and can be useful for drought early warning system. Since the improved SDCI is based on remotely sensed data, it can be easily applied to regions with limited or no observation data for drought assessment and monitoring.
Quantifying Uncertainties from Presence Data Sampling Methods for Species Distribution Modeling: Focused on Vegetation.

NASA Astrophysics Data System (ADS)

Sung, S.; Kim, H. G.; Lee, D. K.; Park, J. H.; Mo, Y.; Kil, S.; Park, C.

2016-12-01

The impact of climate change has been observed throughout the globe. The ecosystem experiences rapid changes such as vegetation shift, species extinction. In these context, Species Distribution Model (SDM) is one of the popular method to project impact of climate change on the ecosystem. SDM basically based on the niche of certain species with means to run SDM present point data is essential to find biological niche of species. To run SDM for plants, there are certain considerations on the characteristics of vegetation. Normally, to make vegetation data in large area, remote sensing techniques are used. In other words, the exact point of presence data has high uncertainties as we select presence data set from polygons and raster dataset. Thus, sampling methods for modeling vegetation presence data should be carefully selected. In this study, we used three different sampling methods for selection of presence data of vegetation: Random sampling, Stratified sampling and Site index based sampling. We used one of the R package BIOMOD2 to access uncertainty from modeling. At the same time, we included BioCLIM variables and other environmental variables as input data. As a result of this study, despite of differences among the 10 SDMs, the sampling methods showed differences in ROC values, random sampling methods showed the lowest ROC value while site index based sampling methods showed the highest ROC value. As a result of this study the uncertainties from presence data sampling methods and SDM can be quantified.
Climatic drivers of vegetation based on wavelet analysis

NASA Astrophysics Data System (ADS)

Claessen, Jeroen; Martens, Brecht; Verhoest, Niko E. C.; Molini, Annalisa; Miralles, Diego

2017-04-01

Vegetation dynamics are driven by climate, and at the same time they play a key role in forcing the different bio-geochemical cycles. As climate change leads to an increase in frequency and intensity of hydro-meteorological extremes, vegetation is expected to respond to these changes, and subsequently feed back on their occurrence. This response can be analysed using time series of different vegetation diagnostics observed from space, in the optical (e.g. Normalised Difference Vegetation Index (NDVI), Solar Induced Fluorescence (SIF)) and microwave (Vegetation Optical Depth (VOD)) domains. In this contribution, we compare the climatic drivers of different vegetation diagnostics, based on a monthly global data-cube of 24 years at a 0.25° resolution. To do so, we calculate the wavelet coherence between each vegetation-related observation and observations of air temperature, precipitation and incoming radiation. The use of wavelet coherence allows unveiling the scale-by-scale response and sensitivity of the diverse vegetation indices to their climatic drivers. Our preliminary results show that the wavelet-based statistics prove to be a suitable tool for extracting information from different vegetation indices. Going beyond traditional methods based on linear correlations, the application of wavelet coherence provides information about: (a) the specific periods at which the correspondence between climate and vegetation dynamics is larger, (b) the frequencies at which this correspondence occurs (e.g. monthly or seasonal scales), and (c) the time lag in the response of vegetation to their climate drivers, and vice versa. As expected, areas of high rainfall volumes are characterised by a strong control of radiation and temperature over vegetation. Furthermore, precipitation is the most important driver of vegetation variability over short terms in most regions of the world - which can be explained by the rapid response of leaf development towards available water content - while at seasonal scales the vegetative response is dominated by solar radiation in most regions. At the higher latitudes, the trends in all vegetation diagnostics agree with the hypothesis of a greening pattern explained by the increase in temperature. At the same time, substantial differences can be observed between the responses of the different vegetation indices as well. As an example, the VOD - thought to be a close proxy for vegetation water content - shows a larger sensitivity to precipitation than traditional optical indices like the NDVI. Our findings help to further understand the physical attributes of vegetation that each remotely-sensed vegetation index is responding to in order to optimize their use in global bio-geoscience research.
Biodiversity and agriculture in dynamic landscapes: Integrating ground and remotely-sensed baseline surveys.

PubMed

Gillison, Andrew N; Asner, Gregory P; Fernandes, Erick C M; Mafalacusser, Jacinto; Banze, Aurélio; Izidine, Samira; da Fonseca, Ambrósio R; Pacate, Hermenegildo

2016-07-15

Sustainable biodiversity and land management require a cost-effective means of forecasting landscape response to environmental change. Conventional species-based, regional biodiversity assessments are rarely adequate for policy planning and decision making. We show how new ground and remotely-sensed survey methods can be coordinated to help elucidate and predict relationships between biodiversity, land use and soil properties along complex biophysical gradients that typify many similar landscapes worldwide. In the lower Zambezi valley, Mozambique we used environmental, gradient-directed transects (gradsects) to sample vascular plant species, plant functional types, vegetation structure, soil properties and land-use characteristics. Soil fertility indices were derived using novel multidimensional scaling of soil properties. To facilitate spatial analysis, we applied a probabilistic remote sensing approach, analyzing Landsat 7 satellite imagery to map photosynthetically active and inactive vegetation and bare soil along each gradsect. Despite the relatively low sample number, we found highly significant correlations between single and combined sets of specific plant, soil and remotely sensed variables that permitted testable spatial projections of biodiversity and soil fertility across the regional land-use mosaic. This integrative and rapid approach provides a low-cost, high-return and readily transferable methodology that permits the ready identification of testable biodiversity indicators for adaptive management of biodiversity and potential agricultural productivity. Copyright © 2016 Elsevier Ltd. All rights reserved.
Hookworm Infection and Environmental Factors in Mbeya Region, Tanzania: A Cross-Sectional, Population-Based Study

PubMed Central

Riess, Helene; Clowes, Petra; Kroidl, Inge; Kowuor, Dickens O.; Nsojo, Anthony; Mangu, Chacha; Schüle, Steffen A.; Mansmann, Ulrich; Geldmacher, Christof; Mhina, Seif; Maboko, Leonard; Hoelscher, Michael; Saathoff, Elmar

2013-01-01

Background Hookworm disease is one of the most common infections and cause of a high disease burden in the tropics and subtropics. Remotely sensed ecological data and model-based geostatistics have been used recently to identify areas in need for hookworm control. Methodology Cross-sectional interview data and stool samples from 6,375 participants from nine different sites in Mbeya region, south-western Tanzania, were collected as part of a cohort study. Hookworm infection was assessed by microscopy of duplicate Kato-Katz thick smears from one stool sample from each participant. A geographic information system was used to obtain remotely sensed environmental data such as land surface temperature (LST), vegetation cover, rainfall, and elevation, and combine them with hookworm infection data and with socio-demographic and behavioral data. Uni- and multivariable logistic regression was performed on sites separately and on the pooled dataset. Principal Findings Univariable analyses yielded significant associations for all ecological variables. Five ecological variables stayed significant in the final multivariable model: population density (odds ratio (OR) = 0.68; 95% confidence interval (CI) = 0.63–0.73), mean annual vegetation density (OR = 0.11; 95% CI = 0.06–0.18), mean annual LST during the day (OR = 0.81; 95% CI = 0.75–0.88), mean annual LST during the night (OR = 1.54; 95% CI = 1.44–1.64), and latrine coverage in household surroundings (OR = 1.02; 95% CI = 1.01–1.04). Interaction terms revealed substantial differences in associations of hookworm infection with population density, mean annual enhanced vegetation index, and latrine coverage between the two sites with the highest prevalence of infection. Conclusion/Significance This study supports previous findings that remotely sensed data such as vegetation indices, LST, and elevation are strongly associated with hookworm prevalence. However, the results indicate that the influence of environmental conditions can differ substantially within a relatively small geographic area. The use of large-scale associations as a predictive tool on smaller scales is therefore problematic and should be handled with care. PMID:24040430
State resource management and role of remote sensing. [California

NASA Technical Reports Server (NTRS)

Johnson, H. D.

1981-01-01

Remote sensing by satellite can provide valuable information to state officials when making decisions regarding resources management. Portions of California's investment for Prosperity program which seem likely candidates for remote sensing include: (1) surveying vegetation type, age, and density in forests and wildlife habitats; (2) controlling fires through chaparal management; (3) monitoring wetlands and measuring ocean biomass; (4) eliminating ground water overdraught; (5) locating crops in overdraught areas, assessing soil erosion and the areas of poorly drained soils and those affected by salt; (6) monitoring coastal lands and resources; (7) changes in landscapes for recreational purposes; (8) inventorying irrigated lands; (9) classifying ground cover; (10) monitoring farmland conversion; and (11) supplying data for a statewide computerized farmlands data base.
HABSEED: a Simple Spatially Explicit Meta-Populations Model Using Remote Sensing Derived Habitat Quality Data

NASA Astrophysics Data System (ADS)

Heumann, B. W.; Guichard, F.; Seaquist, J. W.

2005-05-01

The HABSEED model uses remote sensing derived NPP as a surrogate for habitat quality as the driving mechanism for population growth and local seed dispersal. The model has been applied to the Sahel region of Africa. Results show that the functional response of plants to habitat quality alters population distribution. Plants more tolerant of medium quality habitat have greater distributions to the North while plants requiring only the best habitat are limited to the South. For all functional response types, increased seed production results in diminishing returns. Functional response types have been related to life history tradeoffs and r-K strategies based on the results. Results are compared to remote sensing derived vegetation land cover.
Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China

PubMed Central

Shao, Zhenfeng; Zhang, Linjing

2016-01-01

Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass. PMID:27338378
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.
Advanced Ecosystem Mapping Techniques for Large Arctic Study Domains Using Calibrated High-Resolution Imagery

NASA Astrophysics Data System (ADS)

Macander, M. J.; Frost, G. V., Jr.

2015-12-01

Regional-scale mapping of vegetation and other ecosystem properties has traditionally relied on medium-resolution remote sensing such as Landsat (30 m) and MODIS (250 m). Yet, the burgeoning availability of high-resolution (<=2 m) imagery and ongoing advances in computing power and analysis tools raises the prospect of performing ecosystem mapping at fine spatial scales over large study domains. Here we demonstrate cutting-edge mapping approaches over a ~35,000 km² study area on Alaska's North Slope using calibrated and atmospherically-corrected mosaics of high-resolution WorldView-2 and GeoEye-1 imagery: (1) an a priori spectral approach incorporating the Satellite Imagery Automatic Mapper (SIAM) algorithms; (2) image segmentation techniques; and (3) texture metrics. The SIAM spectral approach classifies radiometrically-calibrated imagery to general vegetation density categories and non-vegetated classes. The SIAM classes were developed globally and their applicability in arctic tundra environments has not been previously evaluated. Image segmentation, or object-based image analysis, automatically partitions high-resolution imagery into homogeneous image regions that can then be analyzed based on spectral, textural, and contextual information. We applied eCognition software to delineate waterbodies and vegetation classes, in combination with other techniques. Texture metrics were evaluated to determine the feasibility of using high-resolution imagery to algorithmically characterize periglacial surface forms (e.g., ice-wedge polygons), which are an important physical characteristic of permafrost-dominated regions but which cannot be distinguished by medium-resolution remote sensing. These advanced mapping techniques provide products which can provide essential information supporting a broad range of ecosystem science and land-use planning applications in northern Alaska and elsewhere in the circumpolar Arctic.
Nationwide classification of forest types of India using remote sensing and GIS.

PubMed

Reddy, C Sudhakar; Jha, C S; Diwakar, P G; Dadhwal, V K

2015-12-01

India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth's (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36% of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km(2) (34.80%) followed by 2,07,649 km(2) (33.19%) under tropical moist deciduous forests, 48,295 km(2) (7.72%) under tropical semi-evergreen forests and 47,192 km(2) (7.54%) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.
Modelling Sensor and Target effects on LiDAR Waveforms

NASA Astrophysics Data System (ADS)

Rosette, J.; North, P. R.; Rubio, J.; Cook, B. D.; Suárez, J.

2010-12-01

The aim of this research is to explore the influence of sensor characteristics and interactions with vegetation and terrain properties on the estimation of vegetation parameters from LiDAR waveforms. This is carried out using waveform simulations produced by the FLIGHT radiative transfer model which is based on Monte Carlo simulation of photon transport (North, 1996; North et al., 2010). The opportunities for vegetation analysis that are offered by LiDAR modelling are also demonstrated by other authors e.g. Sun and Ranson, 2000; Ni-Meister et al., 2001. Simulations from the FLIGHT model were driven using reflectance and transmittance properties collected from the Howland Research Forest, Maine, USA in 2003 together with a tree list for a 200m x 150m area. This was generated using field measurements of location, species and diameter at breast height. Tree height and crown dimensions of individual trees were calculated using relationships established with a competition index determined for this site. Waveforms obtained by the Laser Vegetation Imaging Sensor (LVIS) were used as validation of simulations. This provided a base from which factors such as slope, laser incidence angle and pulse width could be varied. This has enabled the effect of instrument design and laser interactions with different surface characteristics to be tested. As such, waveform simulation is relevant for the development of future satellite LiDAR sensors, such as NASA’s forthcoming DESDynI mission (NASA, 2010), which aim to improve capabilities of vegetation parameter estimation. ACKNOWLEDGMENTS We would like to thank scientists at the Biospheric Sciences Branch of NASA Goddard Space Flight Center, in particular to Jon Ranson and Bryan Blair. This work forms part of research funded by the NASA DESDynI project and the UK Natural Environment Research Council (NE/F021437/1). REFERENCES NASA, 2010, DESDynI: Deformation, Ecosystem Structure and Dynamics of Ice. http://desdyni.jpl.nasa.gov/ (accessed May 2010). NI-MEISTER, W., JUPP, D. L. B. and DUBAYAH, R., 2001, Modeling Lidar Waveforms in Heterogeneous and Discrete Canopies. IEEE Transactions on Geoscience and Remote Sensing, 39 (9): 1943-1958. NORTH, P. R. J., 1996, Three-Dimensional Forest Light Interaction Model Using a Monte Carlo Method. IEEE Transactions on Geoscience and Remote Sensing, 34 (4): 946-956. NORTH, P. R. J., ROSETTE, J. A. B., SUÁREZ, J. C. and LOS, S. O., 2010, A Monte Carlo radiative transfer model of satellite waveform lidar. International Journal of Remote Sensing, 31 (5): 1343-1358. SUN, G. and RANSON, K. J., 2000, Modeling lidar returns from forest canopies. IEEE Transactions on Geoscience and Remote Sensing, 38 (6): 2617-2626.
The Future of Remote Sensing from Space: Civilian Satellite Systems and Applications.

DTIC Science & Technology

1993-07-01

image shows abundant (dark green) vegetation across the Amazon of South America, while lack of vegetation (black areas) is seen across the Sahara Desert...primarily through the space shuttle and space station Freedom programs.25 Hence, if NASA’s overall budget remains flat or includes only modest growth... remain the primary collector of satellite remote sensing data for both meteorolog- ical and climate monitoring efforts through the decade of the 1990s
Remote sensing in Michigan for land resource management: Highway impact assessment

NASA Technical Reports Server (NTRS)

1972-01-01

An existing section of M-14 freeway constructed in 1964 and a potential extension from Ann Arbor to Plymouth, Michigan provided an opportunity for investigating the potential uses of remote sensing techniques in providing projective information needed for assessing the impact of highway construction. Remote sensing data included multispectral scanner imagery and aerial photography. Only minor effects on vegetation, soils, and land use were found to have occurred in the existing corridor. Adverse changes expected to take place in the corridor proposed for extension of the freeway can be minimized by proper design of drainage ditches and attention to good construction practices. Remote sensing can be used to collect and present many types of data useful for highway impact assessment on land use, vegetation categories and species, soil properties and hydrologic characteristics.
Spectroscopic Methods of Remote Sensing for Vegetation Characterization

NASA Astrophysics Data System (ADS)

Kokaly, R. F.

2013-12-01

Imaging spectroscopy (IS), often referred to as hyperspectral remote sensing, is one of the latest innovations in a very long history of spectroscopy. Spectroscopic methods have been used for understanding the composition of the world around us, as well as, the solar system and distant parts of the universe. Continuous sampling of the electromagnetic spectrum in narrow bands is what separates IS from previous forms of remote sensing. Terrestrial imaging spectrometers often have hundreds of channels that cover the wavelength range of reflected solar radiation, including the visible, near-infrared (NIR), and shortwave infrared (SWIR) regions. In part due to the large number of channels, a wide variety of methods have been applied to extract information from IS data sets. These can be grouped into several broad classes, including: multi-channel indices, statistical procedures, full spectrum mixing models, and spectroscopic methods. Spectroscopic methods carry on the more than 150 year history of laboratory-based spectroscopy applied to material identification and characterization. Spectroscopic methods of IS relate the positions and shapes of spectral features resolved by airborne and spaceborne sensors to the biochemical and physical composition of vegetation in a pixel. The chlorophyll 680nm, water 980nm, water 1200nm, SWIR 1700nm, SWIR 2100nm, and SWIR 2300nm features have been the subject of study. Spectral feature analysis (SFA) involves isolating such an absorption feature using continuum removal (CR) and calculating descriptors of the feature, such as center position, depth, width, area, and asymmetry. SFA has been applied to quantify pigment and non-pigment biochemical concentrations in leaves, plants, and canopies. Spectral feature comparison (SFC) utilizes CR of features in each pixel's spectrum and linear regression with continuum-removed features in reference spectra in a library of known vegetation types to map vegetation species and communities. SFC has been applied to map the distributions of minerals in soils and rocks; however, its application to characterize vegetation cover has been less widespread than SFA. Using IS data and the USGS Processing Routines in IDL for Spectroscopic Measurements (PRISM; http://pubs.usgs.gov/of/2011/1155/), this talk will examine requirements for and limitations in applying SFA and SFC to characterize vegetation. A time series of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) data collected in the marshes of Louisiana following the Deepwater Horizon oil spill will be used to examine the impact of varying leaf water content on the shapes of the SWIR 1700, 2100, and 2300 nm features and the implications of these changes on vegetation identification and biochemical estimation. The USGS collection of HyMap data over Afghanistan, the largest terrestrial coverage of IS data to date, will be used to demonstrate the characterization of vegetation in arid and semi-arid regions, in which chlorophyll absorption is often weak and soil and rock mineral absorption features overlap vegetation features. Hyperion data, overlapping the HyMap data, will be presented to illustrate the complications that arise when signal-to-noise is low. The benefits of and challenges to applying a spectroscopic remote sensing approach to imaging spectrometer data will be discussed.
Ground Albedo Neutron Sensing (GANS) for Measurement of Integral Soil Water Content at the Small Catchment Scale

NASA Astrophysics Data System (ADS)

Rivera Villarreyes, C.; Baroni, G.; Oswald, S. E.

2012-12-01

Soil water content at the plot or hill-slope scale is an important link between local vadose zone hydrology and catchment hydrology. One largest initiative to cover the measuring gap of soil moisture between point scale and remote sensing observations is the COSMOS network (Zreda et al., 2012). Here, cosmic-ray neutron sensing, which may be more precisely named ground albedo neutron sensing (GANS), is applied. The measuring principle is based on the crucial role of hydrogen as neutron moderator compared to others landscape materials. Soil water content contained in a footprint of ca. 600 m diameter and a depth ranging down to a few decimeters is inversely correlated to the neutron flux at the air-ground interface. This approach is now implemented, e.g. in USA (Zreda et al., 2012) and Germany (Rivera Villarreyes et al., 2011), based on its simple installation and integral measurement of soil moisture at the small catchment scale. The present study performed Ground Albedo Neutron Sensing on farmland at two locations in Germany under different vegetative situations (cropped and bare field) and different seasonal conditions (summer, autumn and winter). Ground albedo neutrons were measured at (i) a farmland close to Potsdam and Berlin cropped with corn in 2010, sunflower in 2011 and winter rye in 2012, and (ii) a mountainous farmland catchment (Schaefertal, Harz Mountains) since middle 2011. In order to test this methodology, classical soil moisture devices and meteorological data were used for comparison. Moreover, several calibration approaches, role of vegetation cover and transferability of calibration parameters to different times and locations were also evaluated. Observations suggest that GANS can overcome the lack of data for hydrological processes at the intermediate scale. Soil moisture from GANS compared quantitatively with mean values derived from a network of classical devices under vegetated and non- vegetated conditions. The GANS approach responded well to precipitation events through summer and autumn, but soil water content estimations were affected by water stored in snow and partly biomass. Thus, when calibration parameters were transferred to different crops (e.g. from sunflower to rye), the changes in biomass water will have to be considered. Finally, these results imply that GANS measurements can be a reliable ground-truthing possibility as well as additional constraint for hydrological models. References (1) Rivera Villarreyes, C.A., Baroni, G., and Oswald, S.E. (2011): Integral quantification of seasonal soil moisture changes in farmland by cosmic-ray neutrons, Hydrol. Earth Syst. Sci., 15, 3843-3859. (2) Rivera Villarreyes, C.A., Baroni, G., and Oswald, S.E. (2012): Evaluation of the Ground Albedo Neutron Sensing (GANS) method for soil moisture estimations in different crop fields (in preparation for Hydrological Processes). (3) Zreda, M., Shuttleworth, W.J., Zeng, X., Zweck, C., Desilets, D., Franz, T., Rosolem, R., and Ferre, T.P.A. (2012): COSMOS: The COsmic-ray Soil Moisture Observing System. Hydrol. Earth Syst. Sci. Discuss., 9, 4505-4551.
Remote Sensing of Wildland Fire-Induced Risk Assessment at the Community Level.

PubMed

Ahmed, M Razu; Rahaman, Khan Rubayet; Hassan, Quazi K

2018-05-15

Wildland fires are some of the critical natural hazards that pose a significant threat to the communities located in the vicinity of forested/vegetated areas. In this paper, our overall objective was to study the structural damages due to the 2016 Horse River Fire (HRF) that happened in Fort McMurray (Alberta, Canada) by employing primarily very high spatial resolution optical satellite data, i.e., WorldView-2. Thus, our activities included the: (i) estimation of the structural damages; and (ii) delineation of the wildland-urban interface (WUI) and its associated buffers at certain intervals, and their utilization in assessing potential risks. Our proposed method of remote sensing-based estimates of the number of structural damages was compared with the ground-based information available from the Planning and Development Recovery Committee Task Force of Regional Municipality of Wood Buffalo (RMWB); and found a strong linear relationship (i.e., r² value of 0.97 with a slope of 0.97). Upon delineating the WUI and its associated buffer zones at 10 m, 30 m, 50 m, 70 m and 100 m distances; we found existence of vegetation within the 30 m buffers from the WUI for all of the damaged structures. In addition, we noticed that the relevant authorities had removed vegetation in some areas between 30 m and 70 m buffers from the WUI, which was proven to be effective in order to protect the structures in the adjacent communities. Furthermore, we mapped the wildland fire-induced vulnerable areas upon considering the WUI and its associated buffers. Our analysis revealed that approximately 30% of the areas within the buffer zones of 10 m and 30 m were vulnerable due to the presence of vegetation; in which, approximately 7% were burned during the 2016 HRF event that led the structural damages. Consequently, we suggest to remove the existing vegetation within these critical zones and also monitor the region at a regular interval in order to reduce the wildland fire-induced risk.
Land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China

NASA Astrophysics Data System (ADS)

Cheng, Tao; Zhang, Jialong; Zheng, Xinyan; Yuan, Rujin

2018-03-01

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.
Remote sensing for control of tsetse flies

NASA Technical Reports Server (NTRS)

Giddings, L. E.

1976-01-01

Remotely sensed information is discussed which has potential for aiding in the control or eradication of tsetse flies. Data are available from earth resources meteorological, and manned satellites, from airborne sensors, and possibly from data collection platforms. A new zone discrimination technique, based on data from meteorological satellites may also allow the identification of zones hospitable to one or another species of tsetse. For background, a review is presented of the vegetation of Tanzania and Zanzibar, and illustrations presented of automatic processing of data from these areas. In addition, a review is presented of the applicability of temperature data to tsetse areas.
Monitoring vegetation dynamics in the Amazon with RapidScat

NASA Astrophysics Data System (ADS)

van Emmerik, Tim; Steele-Dunne, Susan; Paget, Aaron C.; van de Giesen, Nick

2017-04-01

Several studies affiliated diurnal variations in radar backscatter over the Amazon [1,2] with vegetation water stress. Recent studies on tree and corn canopies [3,4] have demonstrated that during periods of low soil moisture availability, the total radar backscatter is primarily sensitive to changes in leaf water content, highlighting the potential of radar for water stress detection. The RapidScat mission (Ku-band, 13.4GHz), mounted on the International Space Station, observes the Earth in a non-sun-synchronous orbit [5]. This unique orbit allows for reconstructing diurnal cycles of radar backscatter. We hypothesize that the state of the canopy is a significant portion of the diurnal variations observed in the radar backscatter. Recent, yet inconclusive, analyses support the theory of the impact of vegetation water content on diurnal variation in RapidScat radar backscatter over the Amazon and Congo. Linking ground measurements of canopy dynamics to radar backscatter will allow further exploration of the possibilities for monitoring vegetation dynamics. Our presentation focuses of two parts. First, we reconstruct diurnal cycles of RapidScat backscatter over the Amazon, and study its variation over time. Second, we analyze the pre-dawn backscatter over time. The water content at this time of day is a measure of water stress, and might therefore be visible in the backscatter time series. References [1] Frolking, S., et al.: "Tropical forest backscatter anomaly evident in SeaWinds scatterometer morning overpass data during 2005 drought in Amazonia", Remote Sensing of Environment, 2011. [2] Jaruwatanadilok, S., and B. Stiles: "Trends and variation in Ku-band backscatter of natural targets on land observed in QuikSCAT data", IEEE Transactions on Geoscience and Remote Sensing , 2014. [3] Steele-Dunne, S., et al.: "Using diurnal variation in backscatter to detect vegetation water stress", IEEE Transactions on Geoscience and Remote Sensing, 2012. [4] van Emmerik, T., et al.: "Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress", IEEE Transactions on Geoscience and Remote Sensing, 2015. [5] Paget, A., et al.: "RapidScat Diurnal Cycles Over Land", IEEE Transactions on Geoscience and Remote Sensing, 2016.

Remote sensing of the Canadian Arctic: Modelling biophysical variables

NASA Astrophysics Data System (ADS)

Liu, Nanfeng

It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic overestimation of 0.08, which was attributed to PAR absorption by soil that could not be excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial resolution remote sensing VIs can be used to successfully model Arctic biophysical variables. The methods and results presented in this research provided a guide for future studies aiming to model other Arctic biophysical variables through remote sensing data.
An approach for detecting five typical vegetation types on the Chinese Loess Plateau using Landsat TM data.

PubMed

Wang, Zhi-Jie; Jiao, Ju-Ying; Lei, Bo; Su, Yuan

2015-09-01

Remote sensing can provide large-scale spatial data for the detection of vegetation types. In this study, two shortwave infrared spectral bands (TM5 and TM7) and one visible spectral band (TM3) of Landsat 5 TM data were used to detect five typical vegetation types (communities dominated by Bothriochloa ischaemum, Artemisia gmelinii, Hippophae rhamnoides, Robinia pseudoacacia, and Quercus liaotungensis) using 270 field survey data in the Yanhe watershed on the Loess Plateau. The relationships between 200 field data points and their corresponding radiance reflectance were analyzed, and the equation termed the vegetation type index (VTI) was generated. The VTI values of five vegetation types were calculated, and the accuracy was tested using the remaining 70 field data points. The applicability of VTI was also tested by the distribution of vegetation type of two small watersheds in the Yanhe watershed and field sample data collected from other regions (Ziwuling Region, Huangling County, and Luochuan County) on the Loess Plateau. The results showed that the VTI can effectively detect the five vegetation types with an average accuracy exceeding 80 % and a representativeness above 85 %. As a new approach for monitoring vegetation types using remote sensing at a larger regional scale, VTI can play an important role in the assessment of vegetation restoration and in the investigation of the spatial distribution and community diversity of vegetation on the Loess Plateau.
A forward-looking, national-scale remote sensing-based model of tidal marsh aboveground carbon stocks

NASA Astrophysics Data System (ADS)

Holmquist, J. R.; Byrd, K. B.; Ballanti, L.; Nguyen, D.; Simard, M.; Windham-Myers, L.; Thomas, N.

2017-12-01

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our goal was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). To meet this objective we developed the first national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest algorithm we tested Sentinel-1 radar backscatter metrics and Landsat vegetation indices as predictors of biomass. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n=409, RMSE=310 g/m2, 10.3% normalized RMSE), successfully predicted biomass and carbon for a range of marsh plant functional types defined by height, leaf angle and growth form. Model error was reduced by scaling field measured biomass by Landsat fraction green vegetation derived from object-based classification of National Agriculture Imagery Program imagery. We generated 30m resolution biomass maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map for each region. With a mean plant %C of 44.1% (n=1384, 95% C.I.=43.99% - 44.37%) we estimated mean aboveground carbon densities (Mg/ha) and total carbon stocks for each wetland type for each region. Louisiana palustrine emergent marshes had the highest C density (2.67 ±0.08 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ±0.06 Mg/ha). This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included for the first time in the 2018 U.S. EPA Greenhouse Gas Inventory for coastal wetlands. As technical barriers have been reduced through the availability of free post-processed satellite data, cloud computing platforms and open source software, this approach can potentially be applied globally as well.
Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

NASA Technical Reports Server (NTRS)

Huemmrich, Karl Fred; Gamon, John A.; Tweedie, Craig E.; Campbell, Petya K. Entcheva; Landis, David R.; Middleton, Elizabeth M.

2013-01-01

Non-vascular plants (lichens and mosses) are significant components of tundra landscapes and may respond to climate change differently from vascular plants affecting ecosystem carbon balance. Remote sensing provides critical tools for monitoring plant cover types, as optical signals provide a way to scale from plot measurements to regional estimates of biophysical properties, for which spatial-temporal patterns may be analyzed. Gas exchange measurements were collected for pure patches of key vegetation functional types (lichens, mosses, and vascular plants) in sedge tundra at Barrow, AK. These functional types were found to have three significantly different values of light use efficiency (LUE) with values of 0.013 plus or minus 0.0002, 0.0018 plus or minus 0.0002, and 0.0012 plus or minus 0.0001 mol C mol (exp -1) absorbed quanta for vascular plants, mosses and lichens, respectively. Discriminant analysis of the spectra reflectance of these patches identified five spectral bands that separated each of these vegetation functional types as well as nongreen material (bare soil, standing water, and dead leaves). These results were tested along a 100 m transect where midsummer spectral reflectance and vegetation coverage were measured at one meter intervals. Along the transect, area-averaged canopy LUE estimated from coverage fractions of the three functional types varied widely, even over short distances. The patch-level statistical discriminant functions applied to in situ hyperspectral reflectance data collected along the transect successfully unmixed cover fractions of the vegetation functional types. The unmixing functions, developed from the transect data, were applied to 30 m spatial resolution Earth Observing-1 Hyperion imaging spectrometer data to examine variability in distribution of the vegetation functional types for an area near Barrow, AK. Spatial variability of LUE was derived from the observed functional type distributions. Across this landscape, a fivefold variation in tundra LUE was observed. LUE calculated from the functional type cover fractions was also correlated to a spectral vegetation index developed to detect vegetation chlorophyll content. The concurrence of these alternate methods suggest that hyperspectral remote sensing can distinguish functionally distinct vegetation types and can be used to develop regional estimates of photosynthetic LUE in tundra landscapes.
Individual based, long term monitoring of acacia trees in hyper arid zone: Integration of a field survey and a remote sensing approach

NASA Astrophysics Data System (ADS)

Isaacson, Sivan; Blumberg, Dan G.; Ginat, Hanan; Shalmon, Benny

2013-04-01

Vegetation in hyper arid zones is very sparse as is. Monitoring vegetation changes in hyper arid zones is important because any reduction in the vegetation cover in these areas can lead to a considerable reduction in the carrying capacity of the ecological system. This study focuses on the impact of climate fluctuations on the acacia population in the southern Arava valley, Israel. The period of this survey includes a sequence of dry years with no flashfloods in most of the plots that ended in two years with vast floods. Arid zone acacia trees play a significant role in the desert ecosystem by moderating the extreme environmental conditions including radiation, temperature, humidity and precipitation. The trees also provide nutrients for the desert dwellers. Therefore, acacia trees in arid zones are considered to be `keystone species', because they have major influence over both plants and animal species, i.e., biodiversity. Long term monitoring of the acacia tree population in this area can provide insights into long term impacts of climate fluctuations on ecosystems in arid zones. Since 2000, a continuous yearly based survey on the three species of acacia population in seven different plots is conducted in the southern Arava (established by Shalmon, ecologist of the Israel nature and parks authority). The seven plots representing different ecosystems and hydrological regimes. A yearly based population monitoring enabled us to determine the mortality and recruitment rate of the acacia populations as well as growing rates of individual trees. This survey provides a unique database of the acacia population dynamics during a sequence of dry years that ended in a vast flood event during the winter of 2010. A lack of quantitative, nondestructive methods to estimate and monitor stress status of the acacia trees, led us to integrate remote sensing tools (ground and air-based) along with conventional field measurements in order to develop a long term monitoring of acacia trees in hyper arid zones. This study includes further work on the development of ground based remote sensing as a new tool to monitor stress indicators as part of long term ecological research. Since acacia trees are long lived, we were able to identify individual trees in satellite images from 1968 (corona) and expand our monitoring "into the past". Remote sensing expands the spatial and temporal database and is thus a powerful tool for long term monitoring in arid zones, where access is limited and long-term ground data are rare.
Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements.

PubMed

Chavana-Bryant, Cecilia; Malhi, Yadvinder; Wu, Jin; Asner, Gregory P; Anastasiou, Athanasios; Enquist, Brian J; Cosio Caravasi, Eric G; Doughty, Christopher E; Saleska, Scott R; Martin, Roberta E; Gerard, France F

2017-05-01

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (P mass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (N mass ) and carbon (C mass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R 2 = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R 2 = 0.07-0.73; %RMSE = 7-38) and multiple (R 2 = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.
Applications of Remote Sensing and In-Situ Measurements for the Purpose of Understanding Lateral Carbon Fluxes between Tidal Marshes and Connected Estuarine Waters

NASA Astrophysics Data System (ADS)

Van, U. A.; Lamb, B. T.

2016-12-01

Wetlands are biologically diverse ecosystems that provide a number of ecosystems services, including flood protection, erosion prevention, and carbon sequestration. Wetlands often act as carbon sinks because the abundant plant life in wetlands does not decompose easily in the saturated conditions, leading to carbon accumulating in wetland soils. Due to the motion of tides, however, this stored carbon can be transported to the adjacent estuary. Our study site is in the northwestern shore of the Chesapeake Bay, focusing on the Kirkpatrick Marsh and the adjacent Rhode River estuary. The goal of this project is to use remotely sensed data and in situ measurements to understand carbon fluxes between the Kirkpatrick marsh and the Rhode river estuary. Satellite earth images are obtained from the Optical Land Imager (OLI) sensor aboard the Landsat 8 satellite through the USGS Earth Explorer online interface. Landsat imagery is then processed using various spatial analysis tools to calculate for vegetation indices such as Normalized Density Vegetation Index (NDVI), Transformed Vegetation Index (TVI) and Green Normalized Density Vegetation Index (GNDVI). One goal of this project is to compare the vegetation data obtained from the different indices and find out which index can optimize the wide categorization of vegetation over the wetland. We evaluated lesser known vegetation indices (TVI and GNDVI) to compare to NDVI. Preliminary results have shown TVI to be most effective when compared against NDVI and has a correlating factor of 0.987. In addition to using marsh vegetation indices, we are using water quality indices such as the Red/Green index to compare to in-situ water samples in the Rhode River. A YSI EXO2 sensor sits at the marsh-estuary interface and continuously measures water parameters such as turbidity, depth, fDOM and chlorophyll-A. We are attempting to understand if the marsh vegetation indices, water quality indices (remote sensing), and in-situ measurements of water quality are related to one another. Initial comparison between remotely sensed NDVI data and in-situ fDOM data have a correlating factor of 0.93. Understanding the processes affecting carbon cycling within wetlands is pivotal to knowing how to manage them in the future.
Towards an improved LAI collection protocol via simulated field-based PAR sensing

DOE PAGES

Yao, Wei; Van Leeuwen, Martin; Romanczyk, Paul; ...

2016-07-14

In support of NASA’s next-generation spectrometer—the Hyperspectral Infrared Imager (HyspIRI)—we are working towards assessing sub-pixel vegetation structure from imaging spectroscopy data. Of particular interest is Leaf Area Index (LAI), which is an informative, yet notoriously challenging parameter to efficiently measure in situ. While photosynthetically-active radiation (PAR) sensors have been validated for measuring crop LAI, there is limited literature on the efficacy of PAR-based LAI measurement in the forest environment. This study (i) validates PAR-based LAI measurement in forest environments, and (ii) proposes a suitable collection protocol, which balances efficiency with measurement variation, e.g., due to sun flecks and various-sized canopymore » gaps. A synthetic PAR sensor model was developed in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and used to validate LAI measurement based on first-principles and explicitly-known leaf geometry. Simulated collection parameters were adjusted to empirically identify optimal collection protocols. Furthermore, these collection protocols were then validated in the field by correlating PAR-based LAI measurement to the normalized difference vegetation index (NDVI) extracted from the “classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) data (R 2 was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3–5 ( m 2/m 2)).« less
A remote sensing protocol for identifying rangelands with degraded productive capacity

Treesearch

Matthew C. Reeves; L. Scott Bagget

2014-01-01

Rangeland degradation is a growing problem throughout the world. An assessment process for com-paring the trend and state of vegetation productivity to objectively derived reference conditions wasdeveloped. Vegetation productivity was estimated from 2000 to 2012 using annual maximum Normalized Difference Vegetation Index (NDVI) from the MODIS satellite platform. Each...
Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson

USGS Publications Warehouse

Wallace, Cynthia S.A.; Advised by Marsh, Stuart E.

2002-01-01

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created.Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition.Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population.Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations.The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.
Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras

NASA Astrophysics Data System (ADS)

Naito, Hiroki; Ogawa, Satoshi; Valencia, Milton Orlando; Mohri, Hiroki; Urano, Yutaka; Hosoi, Fumiki; Shimizu, Yo; Chavez, Alba Lucia; Ishitani, Manabu; Selvaraj, Michael Gomez; Omasa, Kenji

2017-03-01

Application of field based high-throughput phenotyping (FB-HTP) methods for monitoring plant performance in real field conditions has a high potential to accelerate the breeding process. In this paper, we discuss the use of a simple tower based remote sensing platform using modified single-lens reflex cameras for phenotyping yield traits in rice under different nitrogen (N) treatments over three years. This tower based phenotyping platform has the advantages of simplicity, ease and stability in terms of introduction, maintenance and continual operation under field conditions. Out of six phenological stages of rice analyzed, the flowering stage was the most useful in the estimation of yield performance under field conditions. We found a high correlation between several vegetation indices (simple ratio (SR), normalized difference vegetation index (NDVI), transformed vegetation index (TVI), corrected transformed vegetation index (CTVI), soil-adjusted vegetation index (SAVI) and modified soil-adjusted vegetation index (MSAVI)) and multiple yield traits (panicle number, grain weight and shoot biomass) across a three trials. Among all of the indices studied, SR exhibited the best performance in regards to the estimation of grain weight (R2 = 0.80). Under our tower-based field phenotyping system (TBFPS), we identified quantitative trait loci (QTL) for yield related traits using a mapping population of chromosome segment substitution lines (CSSLs) and a single nucleotide polymorphism data set. Our findings suggest the TBFPS can be useful for the estimation of yield performance during early crop development. This can be a major opportunity for rice breeders whom desire high throughput phenotypic selection for yield performance traits.
A multi-frequency radiometric measurement of soil moisture content over bare and vegetated fields

NASA Technical Reports Server (NTRS)

Wang, J. R.; Schmugge, T. J.; Mcmurtrey, J. E., III; Gould, W. I.; Glazar, W. S.; Fuchs, J. E. (Principal Investigator)

1981-01-01

A USDA Beltsville Agricultural Research Center site was used for an experiment in which soil moisture remote sensing over bare, grass, and alfalfa fields was conducted over a three-month period using 0.6 GHz, 1.4 GHz, and 10.6 GHz Dicke-type microwave radiometers mounted on mobile towers. Ground truth soil moisture content and ambient air and sil temperatures were obtained concurrently with the radiometric measurements. Biomass of the vegetation cover was sampled about once a week. Soil density for each of the three fields was measured several times during the course of the experiment. Results of the radiometric masurements confirm the frequency dependence of moisture sensing sensitivity reduction reported earlier. Observations over the bare, wet field show that the measured brightness temperature is lowest at 5.0 GHz and highest of 0.6 GHz frequency, a result contrary to expectation based on the estimated dielectric permittivity of soil water mixtures and current radiative transfer model in that frequency range.
Remote sensing monitoring study of ecological environment change in Qingtu Lake

NASA Astrophysics Data System (ADS)

Han, Tao; Wang, Dawei; Jiang, Youyan; Qian, Li; Chen, Lei; Hao, Xiaocui

2018-03-01

Based on the Environmental Mitigation Satellite (HJ-1) data, this paper has carried on the remote sensing monitoring to change of the surrounding vegetation and water area of the Qingtu Lake since 2009. The result shows that the average area of water has increased by 3.59 square kilometres annually since the reappearance of the waters with the Qingtu Lake in 2010. The area of Qingtu Lake and surrounding vegetation cover has presented an average increase of 1.09 square kilometres per year. Since 2010, the precipitation of the Qingtu Lake and its surrounding area in Minqin county have a significant increase in the trend, the average increase rate of 6.0 mm/year. Compared to 2010 years ago, the average precipitation increased 36.4 mm. And it shows that the change of the Qingtu Lake underlying surface has a positive feedback effect to local heavy rainfall according to the comparative analysis of the precipitation observation in the surrounding weather station.
VEG: An intelligent workbench for analysing spectral reflectance data

NASA Technical Reports Server (NTRS)

Harrison, P. Ann; Harrison, Patrick R.; Kimes, Daniel S.

1994-01-01

An Intelligent Workbench (VEG) was developed for the systematic study of remotely sensed optical data from vegetation. A goal of the remote sensing community is to infer the physical and biological properties of vegetation cover (e.g. cover type, hemispherical reflectance, ground cover, leaf area index, biomass, and photosynthetic capacity) using directional spectral data. VEG collects together, in a common format, techniques previously available from many different sources in a variety of formats. The decision as to when a particular technique should be applied is nonalgorithmic and requires expert knowledge. VEG has codified this expert knowledge into a rule-based decision component for determining which technique to use. VEG provides a comprehensive interface that makes applying the techniques simple and aids a researcher in developing and testing new techniques. VEG also provides a classification algorithm that can learn new classes of surface features. The learning system uses the database of historical cover types to learn class descriptions of one or more classes of cover types.
Classification of simple vegetation types using POLSAR image data

NASA Technical Reports Server (NTRS)

Freeman, A.

1993-01-01

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.
Diurnal variations in maize and soybean vegetation indices from continuous measurements of ground-based spectral reflectance

NASA Astrophysics Data System (ADS)

Arkebauer, T. J.; Walter-Shea, E. A.

2017-12-01

Vegetation indices, based on canopy spectral reflectance, are widely used to infer physical and biological characteristics of vegetation. Understanding the changes in remotely sensed signals as vegetation responds to its changing environment is essential for full assessment of canopy structure and function. Canopy-level reflectance has been measured at Nebraska AmeriFlux sites US-Ne1, US-Ne2 and US-Ne3 for most years since flux measurements were initiated in 2001. Tower-mounted spectral sensors provided 10-minute averaged reflectance (in PAR and NIR spectral regions) every half hour through the growing season for maize and soybean. Canopy reflectance varied over diurnal and seasonal time periods which led to variations in vegetation indices. One source of variation is due to the interaction of incident solar radiant energy with canopy structure (e.g., reflectance varies with changes in solar zenith angle and direct beam fraction, vegetative fraction, and leaf angle distribution). Another source of variation results from changes in canopy function (e.g., fluctuations in gross primary production and invocation of photoprotective mechanisms with plant stress). We present here a series of diurnal "patterns" of vegetation indices (including Normalized Difference Vegetation Index and Chlorophyll Index) for maize and soybean under mostly clear sky conditions. We demonstrate that diurnal patterns change as the LAI of the canopy changes through the course of the growing season in a somewhat predictable pattern from plant emergence (low vegetative cover) through peak green LAI (full vegetation cover). However, there are changes in the diurnal pattern that we have yet to fully understand; this variation in pattern may indicate variation in canopy function. Initially, we have explored the pattern changes qualitatively and are currently developing more quantitative approaches.
A mathematical characterization of vegetation effect on microwave remote sensing from the Earth

NASA Technical Reports Server (NTRS)

Choe, Y.; Tsang, L.

1983-01-01

In passive microwave remote sensing of the earth, a theoretical model that utilizes the radiative transfer equations was developed to account for the volume scattering effects of the vegetation canopy. Vegetation canopies such as alfalfa, sorghum, and corn are simulated by a layer of ellipsoidal scatterers and cylindrical structures. The ellipsoidal scatterers represent the leaves of vegetation and are randomly positioned and oriented. The orientation of ellipsoids is characterized by a probability density function of Eulerian angles of rotation. The cylindrical structures represent the stalks of vegetation and their radii are assumed to be much smaller than their lengths. The underlying soil is represented by a half-space medium with a homogeneous permittivity and uniform temperature profile. The radiative transfer quations are solved by a numerical method using a Gaussian quadrature formula to compute both the vertical and horizontal polarized brightness temperature as a function of observation angle. The theory was applied to the interpretation of experimental data obtained from sorghum covered fields near College Station, Texas.
The response of vegetation to geochemical conditions

NASA Technical Reports Server (NTRS)

Mouat, D. A.

1983-01-01

An understanding of the factors of vegetation response to changes in the geochemistry of the environment may give exploration geologists and other researchers an additional and effective tool for rock type discrimination. The factors of vegetation response can be grouped into three principal categories: structural or morphological factors, taxonomic factors which include indicator flora as well as vegetation assemblages, and spectral factors which represent the manner in which the vegetation interacts with electromagnetic radiation. The response of these factors over areas of anomalous mineralization is often unique and may be due to nutrient deficiencies and/or imbalances, toxicity and stress caused by anomalous mineral concentrations in the soil, low water retention, and plant competition. The successful use of geobotanical techniques results from the integration of the geobotanical observations with other techniques. The use of remote sensing in such a program must be predicated on those factors which can be discriminated within the constraints of the spatial, spectral, radiometric, and temporal resolutions of the sensing system and with appropriate analytical techniques.
The research of road and vehicle information extraction algorithm based on high resolution remote sensing image

NASA Astrophysics Data System (ADS)

Zhou, Tingting; Gu, Lingjia; Ren, Ruizhi; Cao, Qiong

2016-09-01

With the rapid development of remote sensing technology, the spatial resolution and temporal resolution of satellite imagery also have a huge increase. Meanwhile, High-spatial-resolution images are becoming increasingly popular for commercial applications. The remote sensing image technology has broad application prospects in intelligent traffic. Compared with traditional traffic information collection methods, vehicle information extraction using high-resolution remote sensing image has the advantages of high resolution and wide coverage. This has great guiding significance to urban planning, transportation management, travel route choice and so on. Firstly, this paper preprocessed the acquired high-resolution multi-spectral and panchromatic remote sensing images. After that, on the one hand, in order to get the optimal thresholding for image segmentation, histogram equalization and linear enhancement technologies were applied into the preprocessing results. On the other hand, considering distribution characteristics of road, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to suppress water and vegetation information of preprocessing results. Then, the above two processing result were combined. Finally, the geometric characteristics were used to completed road information extraction. The road vector extracted was used to limit the target vehicle area. Target vehicle extraction was divided into bright vehicles extraction and dark vehicles extraction. Eventually, the extraction results of the two kinds of vehicles were combined to get the final results. The experiment results demonstrated that the proposed algorithm has a high precision for the vehicle information extraction for different high resolution remote sensing images. Among these results, the average fault detection rate was about 5.36%, the average residual rate was about 13.60% and the average accuracy was approximately 91.26%.
UAV-based remote sensing of the Heumoes landslide, Austria Vorarlberg

NASA Astrophysics Data System (ADS)

Niethammer, U.; Joswig, M.

2009-04-01

The Heumoes landslide, is located in the eastern Vorarlberg Alps, Austria, 10 km southeast of Dornbirn. The extension of the landslide is about 2000 m in west to east direction and about 500 m at its widest extent in north to south direction. It occurs between an elevation of 940 m in the east and 1360 m in the west, slope angles of more than 60 % can be observed as well as almost flat areas. Its total volume is estimated to be 9.400.000 cubic meters and its average velocities amount to some centimeter per year. Surface signatures or 'photolineations' of creeping landslides, e.g. fractures and rupture lines in sediments and street pavings, and vegetation contrasts by changes of water table in shallow vegetation in principle can be resolved by remote sensing. The necessary ground cell resolution of few centimeters, however, generally can't be achieved by routine areal or satellite imagery. The fast technological progress of unmanned areal vehicles (UAV) and the reduced payload by miniaturized optical cameras now allow for UAV remote sensing applications that are below the high financial limits of military intelligence. Even with 'low-cost' equipment, the necessary centimeter-scale ground cell resolution can be achieved by adapting the flight altitude to some ten to one hundred meters. Operated by scientists experienced in remote-control flight models, UAV remote sensing can now be performed routinely, and campaign-wise after any significant event of, e.g., heavy rainfall, or partial mudflow. We have investigated a concept of UAV-borne remote sensing based on motorized gliders, and four-propeller helicopters or 'quad-rotors'. Several missions were flown over the Heumoes landslide. Between 2006 and 2008 three series UAV-borne photographs of the Heumoes landslide were taken and could be combined to orto-mosaics of the slope area within few centimeters ground cell resolution. We will present the concept of our low cost quad-rotor UAV system and first results of the image-processing based evaluation of the acquired images to characterize spatial and temporal details of landslide behaviour. We will also sketch first schemes of joint interpretation or 'data fusion' of UAV-based remote sensing with the results from geophysical mapping of underground distribution of soil moisture and fracture processes (Walter & Joswig, EGU 2009).

Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant.

PubMed

Rascher, U; Alonso, L; Burkart, A; Cilia, C; Cogliati, S; Colombo, R; Damm, A; Drusch, M; Guanter, L; Hanus, J; Hyvärinen, T; Julitta, T; Jussila, J; Kataja, K; Kokkalis, P; Kraft, S; Kraska, T; Matveeva, M; Moreno, J; Muller, O; Panigada, C; Pikl, M; Pinto, F; Prey, L; Pude, R; Rossini, M; Schickling, A; Schurr, U; Schüttemeyer, D; Verrelst, J; Zemek, F

2015-12-01

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress. © 2015 John Wiley & Sons Ltd.
Changing spring phenology dates in the Three-Rivers Headwater Region of the Tibetan Plateau during 1960-2013

NASA Astrophysics Data System (ADS)

Yu, Shuang; Xia, Jiangjiang; Yan, Zhongwei; Yang, Kun

2018-01-01

The variation of the vegetation growing season in the Three-Rivers Headwater Region of the Tibetan Plateau has recently become a controversial topic. One issue is that the estimated local trend in the start of the vegetation growing season (SOS) based on remote sensing data is easily affected by outliers because this data series is short. In this study, we determine that the spring minimum temperature is the most influential factor for SOS. The significant negative linear relationship between the two variables in the region is evaluated using Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index data for 2000-13. We then reconstruct the SOS time series based on the temperature data for 1960-2013. The regional mean SOS shows an advancing trend of 1.42 d (10 yr)-1 during 1960-2013, with the SOS occurring on the 160th and 151st days in 1960 and 2013, respectively. The advancing trend enhances to 6.04 d (10 yr)-1 during the past 14 years. The spatiotemporal variations of the reconstructed SOS data are similar to those deduced from remote sensing data during the past 14 years. The latter exhibit an even larger regional mean trend of SOS [7.98 d (10 yr-1)] during 2000-13. The Arctic Oscillation is found to have significantly influenced the changing SOS, especially for the eastern part of the region, during 2000-13.
Rethinking Chlorophyll Responses To Stress: Fluorescence and Flectance Remote Sensing in a Coastal Environment

DTIC Science & Technology

2010-11-15

fluorescence emission of vegetation for mapping vegetation stress as chlorophyll content and/or carotenoid content changes. 1. REPORT DATE (DD-MM-YYYY...that estimate fluorescence emission of vegetation for mapping vegetation stress as chlorophyll content and/or carotenoid content changes...not related to changes in chlorophyll content or the carotenoids /chlorophyll ratio. PRI is an indicator of chronic salinity stress and may be used as
A vegetational and ecological resource analysis from space and high flight photography

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Faulkner, D. P.; Schrumpf, B. J.

1970-01-01

A hierarchial classification of vegetation and related resources is considered that is applicable to convert remote sensing data in space and aerial synoptic photography. The numerical symbolization provides for three levels of vegetational classification and three levels of classification of environmental features associated with each vegetational class. It is shown that synoptic space photography accurately projects how urban sprawl affects agricultural land use areas and ecological resources.
Inferring nutrient loading of estuarine systems by remote sensing of aquatic vegetation

NASA Technical Reports Server (NTRS)

Anderson, R. R.

1978-01-01

THe use of remote sensing to record algal and vascular aquatic plant growths in estuarine waters is discussed. A technique is proposed that uses a combination of data to hierarchically classify watersheds with regard to severity of potential pollution. Specific nonpoint sources of nutrients in tributaries of the watershed are identified with lower altitude photography of vegetation and selected ground sampling. It is concluded that excessive growths of some aquatic plants may be related to nutrient pollution.
Remote sensing applications to forest vegetation classification and conifer vigor loss due to dwarf mistletoe

NASA Technical Reports Server (NTRS)

Douglass, R. W.; Meyer, M. P.; French, D. W.

1972-01-01

Criteria was established for practical remote sensing of vegetation stress and mortality caused by dwarf mistletoe infections in black spruce subboreal forest stands. The project was accomplished in two stages: (1) A fixed tower-tramway site in an infected black spruce stand was used for periodic multispectral photo coverage to establish basic film/filter/scale/season/weather parameters; (2) The photographic combinations suggested by the tower-tramway tests were used in low, medium, and high altitude aerial photography.
Oleoresin, Chemistry and Spectral Reflectance in "Stressed" Lodgepole and White Bark Pine, Mammoth Mountain, California

NASA Technical Reports Server (NTRS)

Hickey, James C.; Birnie, Richard W.; Zhao, Mei-Xun

2001-01-01

Development of methods to identify the physical and chemical character of materials on the earth's surface is one of the foci of hyperspectral remote sensing activities. Enhancing the ability to elucidate changes in foliar chemistry that relate to the health of a plant is a benefit to plant physiologists, foresters, and plant ecologists, as well as geologist and environmental scientists. Vegetation covers the landscape throughout the temperate and tropical regions of the earth. The existence of vegetation in these areas presents special problems to remote sensing systems since geologic bedrock and alteration zones are masked. At times, however, alterations in the soil/sediment geochemical environment result in foliar chemical changes that are detectable via remote sensing. Examples include monitoring of chlorophyll reflectance/fluorescence and equivalent water thickness indices as indicators of drought-induced plant stress. Another processing and interpretation approach used with hyperspectral data has been principal components analysis (PCA). Rowan et al. used PCA to identify absorption feature patterns obtained from vegetated areas with discrete bedrock geology or mineralization as the substrate. Many researchers highlight the need to advance our ability for hyperspectral imaging in vegetated areas as a near-term priority.
Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013

NASA Astrophysics Data System (ADS)

Anderson, Martha C.; Zolin, Cornelio A.; Hain, Christopher R.; Semmens, Kathryn; Tugrul Yilmaz, M.; Gao, Feng

2015-07-01

Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003-2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought indicators, and used in concert may provide a more reliable evaluation of natural and managed ecosystem response to variable climate regimes.
Development and Analysis of Global, High-Resolution Diagnostic Metrics for Vegetation Monitoring, Yield Estimation and Famine Mitigation

NASA Astrophysics Data System (ADS)

Anderson, B. T.; Zhang, P.; Myneni, R.

2008-12-01

Drought, through its impact on food scarcity and crop prices, can have significant economic, social, and environmental impacts - presently, up to 36 countries and 73 million people are facing food crises around the globe. Because of these adverse affects, there has been a drive to develop drought and vegetation- monitoring metrics that can quantify and predict human vulnerability/susceptibility to drought at high- resolution spatial scales over the entire globe. Here we introduce a new vegetation-monitoring index utilizing data derived from satellite-based instruments (the Moderate Resolution Imaging Spectroradiometer - MODIS) designed to identify the vulnerability of vegetation in a particular region to climate variability during the growing season. In addition, the index can quantify the percentage of annual grid-point vegetation production either gained or lost due to climatic variability in a given month. When integrated over the growing season, this index is shown to be better correlated with end-of-season crop yields than traditional remotely-sensed or meteorological indices. In addition, in-season estimates of the index, which are available in near real-time, provide yield forecasts comparable to concurrent in situ objective yield surveys, which are only available in limited regions of the world. Overall, the cost effectiveness and repetitive, near-global view of earth's surface provided by this satellite-based vegetation monitoring index can potentially improve our ability to mitigate human vulnerability/susceptibility to drought and its impacts upon vegetation and agriculture.
Development of remote sensing based site specific weed management for Midwest mint production

NASA Astrophysics Data System (ADS)

Gumz, Mary Saumur Paulson

Peppermint and spearmint are high value essential oil crops in Indiana, Michigan, and Wisconsin. Although the mints are profitable alternatives to corn and soybeans, mint production efficiency must improve in order to allow industry survival against foreign produced oils and synthetic flavorings. Weed control is the major input cost in mint production and tools to increase efficiency are necessary. Remote sensing-based site-specific weed management offers potential for decreasing weed control costs through simplified weed detection and control from accurate site specific weed and herbicide application maps. This research showed the practicability of remote sensing for weed detection in the mints. Research was designed to compare spectral response curves of field grown mint and weeds, and to use these data to develop spectral vegetation indices for automated weed detection. Viability of remote sensing in mint production was established using unsupervised classification, supervised classification, handheld spectroradiometer readings and spectral vegetation indices (SVIs). Unsupervised classification of multispectral images of peppermint production fields generated crop health maps with 92 and 67% accuracy in meadow and row peppermint, respectively. Supervised classification of multispectral images identified weed infestations with 97% and 85% accuracy for meadow and row peppermint, respectively. Supervised classification showed that peppermint was spectrally distinct from weeds, but the accuracy of these measures was dependent on extensive ground referencing which is impractical and too costly for on-farm use. Handheld spectroradiometer measurements of peppermint, spearmint, and several weeds and crop and weed mixtures were taken over three years from greenhouse grown plants, replicated field plots, and production peppermint and spearmint fields. Results showed that mints have greater near infrared (NIR) and lower green reflectance and a steeper red edge slope than all weed species. These distinguishing characteristics were combined to develop narrow band and broadband spectral vegetation indices (SVIs, ratios of NIR/green reflectance), that were effective in differentiating mint from key weed species. Hyperspectral images of production peppermint and spearmint fields were then classified using SVI-based classification. Narrowband and broadband SVIs classified early season peppermint and spearmint with 64 to 100% accuracy compared to 79 to 100% accuracy for supervised classification of multispectral images of the same fields. Broadband SVIs have potential for use as an automated spectral indicator for weeds in the mints since they require minimal ground referencing and can be calculated from multispectral imagery which is cheaper and more readily available than hyperspectral imagery. This research will allow growers to implement remote sensing based site specific weed management in mint resulting in reduced grower input costs and reduced herbicide entry into the environment and will have applications in other specialty and meadow crops.
Land Use and Land Cover (LULC) Change Detection in Islamabad and its Comparison with Capital Development Authority (CDA) 2006 Master Plan

NASA Astrophysics Data System (ADS)

Hasaan, Zahra

2016-07-01

Remote sensing is very useful for the production of land use and land cover statistics which can be beneficial to determine the distribution of land uses. Using remote sensing techniques to develop land use classification mapping is a convenient and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region. In Islamabad city and surrounding the land use has been changing, every day new developments (urban, industrial, commercial and agricultural) are emerging leading to decrease in vegetation cover. The purpose of this work was to develop the land use of Islamabad and its surrounding area that is an important natural resource. For this work the eCognition Developer 64 computer software was used to develop a land use classification using SPOT 5 image of year 2012. For image processing object-based classification technique was used and important land use features i.e. Vegetation cover, barren land, impervious surface, built up area and water bodies were extracted on the basis of object variation and compared the results with the CDA Master Plan. The great increase was found in built-up area and impervious surface area. On the other hand vegetation cover and barren area followed a declining trend. Accuracy assessment of classification yielded 92% accuracies of the final land cover land use maps. In addition these improved land cover/land use maps which are produced by remote sensing technique of class definition, meet the growing need of legend standardization.
Predictive modeling of hazardous waste landfill total above-ground biomass using passive optical and LIDAR remotely sensed data

NASA Astrophysics Data System (ADS)

Hadley, Brian Christopher

This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.
Remote sensing of earth terrain

NASA Technical Reports Server (NTRS)

Kong, Jin AU; Yueh, Herng-Aung; Shin, Robert T.

1991-01-01

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others.
Potential for Small Unmanned Aircraft Systems applications for identifying groundwater-surface water exchange in a meandering river reach

USGS Publications Warehouse

Pai, H.; Malenda, H.; Briggs, Martin A.; Singha, K.; González-Pinzón, R.; Gooseff, M.; Tyler, S.W.; ,

2017-01-01

The exchange of groundwater and surface water (GW-SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here, we describe the use of a suite of high spatial-resolution remote-sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW-SW exchange. sUAS provided centimeter-scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS-based vegetation imagery, vegetation-top elevation, and normalized difference vegetation index (NDVI) mapping indicated GW-SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite-based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.
Potential for Small Unmanned Aircraft Systems Applications for Identifying Groundwater-Surface Water Exchange in a Meandering River Reach

NASA Astrophysics Data System (ADS)

Pai, H.; Malenda, H. F.; Briggs, M. A.; Singha, K.; González-Pinzón, R.; Gooseff, M. N.; Tyler, S. W.

2017-12-01

The exchange of groundwater and surface water (GW-SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here we describe the use of a suite of high spatial resolution remote sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW-SW exchange. sUAS provided centimeter-scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS-based vegetation imagery, vegetation-top elevation, and normalized difference vegetation index mapping indicated GW-SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite-based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW "shortcutting" through meander necks, which was corroborated by temperature data at the riverbed interface.
Cubesats and drones: bridging the spatio-temporal divide for enhanced earth observation

NASA Astrophysics Data System (ADS)

McCabe, M. F.; Aragon, B.; Parkes, S. D.; Mascaro, J.; Houborg, R.

2017-12-01

In just the last few years, a range of advances in remote sensing technologies have enabled an unprecedented opportunity in earth observation. Parallel developments in cubesats and unmanned aerial vehicles (UAVs) have overcome one of the outstanding challenges in observing the land surface: the provision of timely retrievals at a spatial resolution that is sufficiently detailed to make field-level decisions. Planet cubesats have revolutionized observing capacity through their objective of near daily global retrieval. These nano-satellite systems provide high resolution (approx. 3 m) retrievals in red-green-blue and near-infrared wavelengths, offering capacity to develop vegetation metrics for both hydrological and precision agricultural applications. Apart from satellite based advances, nearer to earth technology is being exploited for a range of observation needs. UAVs provide an adaptable platform from which a variety of sensing systems can be deployed. Combinations of optical, thermal, multi- and hyper-spectral systems allow for the estimation of a range of land surface variables, including vegetation structure, vegetation health, land surface temperature and evaporation. Here we explore some of these exciting developments in the context of agricultural hydrology, providing examples of cubesat and UAV imagery that has been used to inform upon crop health and water use. An investigation of the spatial and temporal advantage of these complementary systems is undertaken, with examples of multi-day high-resolution vegetation dynamics from cubesats presented alongside diurnal-cycle responses derived from multiple within-day UAV flights.
Modeling spatial patterns of wildfire susceptibility in southern California: Applications of MODIS remote sensing data and mesoscale numerical weather models

NASA Astrophysics Data System (ADS)

Schneider, Philipp

This dissertation investigates the potential of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and mesoscale numerical weather models for mapping wildfire susceptibility in general and for improving the Fire Potential Index (FPI) in southern California in particular. The dissertation explores the use of the Visible Atmospherically Resistant Index (VARI) from MODIS data for mapping relative greenness (RG) of vegetation and subsequently for computing the FPI. VARI-based RG was validated against in situ observations of live fuel moisture. The results indicate that VARI is superior to the previously used Normalized Difference Vegetation Index (NDVI) for computing RG. FPI computed using VARI-based RG was found to outperform the traditional FPI when validated against historical fire detections using logistic regression. The study further investigates the potential of using Multiple Endmember Spectral Mixture Analysis (MESMA) on MODIS data for estimating live and dead fractions of vegetation. MESMA fractions were compared against in situ measurements and fractions derived from data of a high-resolution, hyperspectral sensor. The results show that live and dead fractions obtained from MODIS using MESMA are well correlated with the reference data. Further, FPI computed using MESMA-based green vegetation fraction in lieu of RG was validated against historical fire occurrence data. MESMA-based FPI performs at a comparable level to the traditional NDVI-based FPI, but can do so using a single MODIS image rather than an extensive remote sensing time series as required for the RG approach. Finally this dissertation explores the potential of integrating gridded wind speed data obtained from the MM5 mesoscale numerical weather model in the FPI. A new fire susceptibility index, the Wind-Adjusted Fire Potential Index (WAFPI), was introduced. It modifies the FPI algorithm by integrating normalized wind speed. Validating WAFPI against historical wildfire events using logistic regression indicates that gridded data sets of wind speed are a valuable addition to the FPI as they can significantly increase the probability range of the fitted model and can further increase the model's discriminatory power over that of the traditional FPI.
An Investigation of New Snow Water Equivalence Sensing Modalities

NASA Astrophysics Data System (ADS)

Frolik, J.; Skalka, C.; Wemple, B.

2008-12-01

It is well known that snowpack is highly variable and influenced by a range of factors, including topography and vegetation cover. As such, single point measurements may be viewed as being inadequate to characterize snowpack in a given area. Thus motivated by the desire for distributed sensing, this work presents results of a proof-of-concept investigation for new, low-cost, snow water equivalence (SWE) sensors based on the attenuation of microwave and gamma radiation. First, our work considers the attenuation of microwave signals at 2.4 GHz and 5 GHz due to an accumulating snowpack. These frequencies coincide with those used for common wireless networks and thus our proposed sensor can leverage existing hardware designs which are low-cost and power efficient. Second, we present attenuation data for radiation energy occurring between 500 keV and 1 MeV. These results were obtained utilizing a radiation detector based on Cadmium Zinc Telluride (CZT) technology. The proposed sensor will leverage recent investments such CZT based designs for homeland security applications. We contend that sensors based on these modalities will be low-cost and low-energy and thus readily integrated with wireless sensor network hardware for distributed monitoring. In addition, these sensors will be compact and thus can be placed in locations not feasible for current SWE sensor designs (e.g., snow pillows) or in locations too dangerous for snow course measurements (e.g., areas prone to avalanche). Since neither sensing methods requires contact with the snowpack, these modalities are also immune to snow bridging effects which plague existing designs. We also present preliminary findings of work conducted in a mountainous forested setting in northern New England which examines the influence of forest vegetation on snowpack.
Water stress index for alkaline fen habitat based on UAV and continuous tower measurements of canopy infrared temperature

NASA Astrophysics Data System (ADS)

Ciężkowski, Wojciech; Jóźwiak, Jacek; Chormański, Jarosław; Szporak-Wasilewska, Sylwia; Kleniewska, Małgorzata

2017-04-01

This study is focused on developing water stress index for alkaline fen, to evaluate water stress impact on habitat protected within Natura 2000 network: alkaline fens (habitat code:7230). It is calculated based on continuous measurements of air temperature, relative humidity and canopy temperature from meteorological tower and several UAV flights for canopy temperature registration. Measurements were taken during the growing season in 2016 in the Upper Biebrza Basin in north-east Poland. Firstly methodology of the crop water stress index (CWSI) determination was used to obtained non-water stress base line based on continuous measurements (NWSBtower). Parameters of NWSBtower were directly used to calculate spatial variability of CWSI for UAV thermal infrared (TIR) images. Then for each UAV flight day at least 3 acquisition were performed to define NWSBUAV. NWSBUAV was used to calculate canopy waters stress for whole image relative to the less stressed areas. The spatial distribution of developed index was verified using remotely sensed indices of vegetation health. Results showed that in analysed area covered by sedge-moss vegetation NWSB cannot be used directly. The proposed modification of CWSI allows identifying water stress in alkaline fen habitats and was called as Sedge-Moss Water Stress Index (SMWSI). The study shows possibility of usage remotely sensed canopy temperature data to detect areas exposed to the water stress on wetlands. This research has been carried out under the Biostrateg Programme of the Polish National Centre for Research and Development (NCBiR), project No.: DZP/BIOSTRATEG-II/390/2015: The innovative approach supporting monitoring of non-forest Natura 2000 habitats, using remote sensing methods (HabitARS).
The impact of in-canopy wind attenuation formulations onheat flux estimation using the remote sensing-based two-source model for an open orchard canopy in southern Italy

USDA-ARS?s Scientific Manuscript database

For open orchard and vineyard canopies containing significant fractions of exposed soil (>50%), typical of Mediterranean agricultural regions, the energy balance of the vegetation elements is strongly influenced by heat exchange with the bare soil/substrate. For these agricultural systems a “two-sou...

Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

Treesearch

Veronika Leitold; Michael Keller; Douglas C Morton; Bruce D Cook; Yosio E Shimabukuro

2015-01-01

Background: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas...
A temporal analysis of urban forest carbon storage using remote sensing

Treesearch

Soojeong Myeong; David J. Nowak; Michael J. Duggin

2006-01-01

Quantifying the carbon storage, distribution, and change of urban trees is vital to understanding the role of vegetation in the urban environment. At present, this is mostly achieved through ground study. This paper presents a method based on the satellite image time series, which can save time and money and greatly speed the process of urban forest carbon storage...
[Hyperspectral remote sensing in monitoring the vegetation heavy metal pollution].

PubMed

Li, Na; Lü, Jian-sheng; Altemann, W

2010-09-01

Mine exploitation aggravates the environment pollution. The large amount of heavy metal element in the drainage of slag from the mine pollutes the soil seriously, doing harm to the vegetation growing and human health. The investigation of mining environment pollution is urgent, in which remote sensing, as a new technique, helps a lot. In the present paper, copper mine in Dexing was selected as the study area and China sumac as the study plant. Samples and spectral data in field were gathered and analyzed in lab. The regression model from spectral characteristics for heavy metal content was built, and the feasibility of hyperspectral remote sensing in environment pollution monitoring was testified.
Exploring the capacity of radar remote sensing to estimate wetland marshes water storage.

PubMed

Grings, F; Salvia, M; Karszenbaum, H; Ferrazzoli, P; Kandus, P; Perna, P

2009-05-01

This paper focuses on the use of radar remote sensing for water storage estimation in wetland marshes of the Paraná River Delta in Argentina. The approach followed is based on the analysis of a temporal set of ENVISAT ASAR data which includes images acquired under different polarizations and incidence angles as well as different environmental conditions (water level, precipitation, and vegetation condition). Two marsh species, named junco and cortadera, were monitored. This overall data set gave us the possibility of studying and understanding the basic interactions between the radar, the soil under different flood conditions, and the vegetation structure. The comprehension of the observed features was addressed through electromagnetic models developed for these ecosystems. The procedure used in this work to estimate water level within marshes combines a direct electromagnetic model, field work data specifically obtained to feed the model, the actual ASAR measurements and a well known retrieval scheme based on a cost function. Results are validated with water level evaluations at specific points. A map showing an estimation of the water storage capacity and its error in junco and cortadera areas for the date where the investigation was done is also presented.
Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

2001-01-01

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.
American Society for Photogrammetry and Remote Sensing and ACSM, Fall Convention, Reno, NV, Oct. 4-9, 1987, ASPRS Technical Papers

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

Not Available

1987-01-01

Recent advances in remote-sensing technology and applications are examined in reviews and reports. Topics addressed include the use of Landsat TM data to assess suspended-sediment dispersion in a coastal lagoon, the use of sun incidence angle and IR reflectance levels in mapping old-growth coniferous forests, information-management systems, Large-Format-Camera soil mapping, and the economic potential of Landsat TM winter-wheat crop-condition assessment. Consideration is given to measurement of ephemeral gully erosion by airborne laser ranging, the creation of a multipurpose cadaster, high-resolution remote sensing and the news media, the role of vegetation in the global carbon cycle, PC applications in analytical photogrammetry,more » multispectral geological remote sensing of a suspected impact crater, fractional calculus in digital terrain modeling, and automated mapping using GP-based survey data.« less
Research on Land Use Changes in Panjin City Basing on Remote Sensing Data

NASA Astrophysics Data System (ADS)

Ding, Hua; Li, Ru Ren; Shuang Sun, Li; Wang, Xin; Liu, Yu Mei

2018-05-01

Taking Landsat remote sensing image as the main data source, the research on land use changes in Panjin City in 2005 to 2015 is made with the support of remote sensing platform and GIS platform in this paper; the range of land use changes and change rate are analyzed through the classification of remote sensing image; the dynamic analysis on land changes is made with the help of transfer matrix of land use type; the quantitative calculation on all kinds of dynamic change features of land changes is made by utilizing mathematical model; and the analysis on driving factors of land changes of image is made at last. The research results show that, in recent ten years, the area of cultivated land in Panjin City decreased, the area of vegetation increased, and meanwhile the area of road increased drastically, the settlement place decreased than ever, and water area changed slightly.
Image Texture Predicts Avian Density and Species Richness

PubMed Central

Wood, Eric M.; Pidgeon, Anna M.; Radeloff, Volker C.; Keuler, Nicholas S.

2013-01-01

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas. PMID:23675463
Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology

NASA Astrophysics Data System (ADS)

Themistocleous, K.; Agapiou, A.; Cuca, B.; Hadjimitsis, D. G.

2015-04-01

Remote sensing has open up new dimensions in archaeological research. Although there has been significant progress in increasing the resolution of space/aerial sensors and image processing, the detection of the crop (and soil marks) formations, which relate to buried archaeological remains, are difficult to detect since these marks may not be visible in the images if observed over different period or at different spatial/spectral resolution. In order to support the improvement of earth observation remote sensing technologies specifically targeting archaeological research, a better understanding of the crop/soil marks formation needs to be studied in detail. In this paper the contribution of both Unmanned Aerial Systems as well ground spectroradiometers is discussed in a variety of examples applied in the eastern Mediterranean region (Cyprus and Greece) as well in Central Europe (Hungary). In- situ spectroradiometric campaigns can be applied for the removal of atmospheric impact to simultaneous satellite overpass images. In addition, as shown in this paper, the systematic collection of ground truth data prior to the satellite/aerial acquisition can be used to detect the optimum temporal and spectral resolution for the detection of stress vegetation related to buried archaeological remains. Moreover, phenological studies of the crops from the area of interest can be simulated to the potential sensors based on their Relative Response Filters and therefore prepare better the satellite-aerial campaigns. Ground data and the use of Unmanned Aerial Systems (UAS) can provide an increased insight for studying the formation of crop and soil marks. New algorithms such as vegetation indices and linear orthogonal equations for the enhancement of crop marks can be developed based on the specific spectral characteristics of the area. As well, UAS can be used for remote sensing applications in order to document, survey and model cultural heritage and archaeological sites.
Linkages between Land Surface Phenology Metrics and Natural and Anthropogenic Events in Drylands (Invited)

NASA Astrophysics Data System (ADS)

de Beurs, K.; Brown, M. E.; Ahram, A.; Walker, J.; Henebry, G. M.

2013-12-01

Tracking vegetation dynamics across landscapes using remote sensing, or 'land surface phenology,' is a key mechanism that allows us to understand ecosystem changes. Land surface phenology models rely on vegetation information from remote sensing, such as the datasets derived from the Advanced Very High Resolution Radiometer (AVHRR), the newer MODIS sensors on Aqua and Terra, and sometimes the higher spatial resolution Landsat data. Vegetation index data can aid in the assessment of variables such as the start of season, growing season length and overall growing season productivity. In this talk we use Landsat, MODIS and AVHRR data and derive growing season metrics based on land surface phenology models that couple vegetation indices with satellite derived accumulated growing degreeday and evapotranspiration estimates. We calculate the timing and the height of the peak of the growing season and discuss the linkage of these land surface phenology metrics with natural and anthropogenic changes on the ground in dryland ecosystems. First we will discuss how the land surface phenology metrics link with annual and interannual price fluctuations in 229 markets distributed over Africa. Our results show that there is a significant correlation between the peak height of the growing season and price increases for markets in countries such as Nigeria, Somalia and Niger. We then demonstrate how land surface phenology metrics can improve models of post-conflict resolution in global drylands. We link the Uppsala Conflict Data Program's dataset of political, economic and social factors involved in civil war termination with an NDVI derived phenology metric and the Palmer Drought Severity Index (PDSI). An analysis of 89 individual conflicts in 42 dryland countries (totaling 892 individual country-years of data between 1982 and 2005) revealed that, even accounting for economic and political factors, countries that have higher NDVI growth following conflict have a lower risk of reverting to civil war. Finally, the patchy and heterogeneous arrangement of vegetation in dryland areas sometimes complicates the extraction of phenological signals using existing remote sensing data. We conclude by demonstrating how the phenological analysis of a range of dryland land cover classes benefits from the availability of synthetic images at Landsat spatial resolution and MODIS time intervals.
Comparison of Uncalibrated Rgbvi with Spectrometer-Based Ndvi Derived from Uav Sensing Systems on Field Scale

NASA Astrophysics Data System (ADS)

Bareth, G.; Bolten, A.; Gnyp, M. L.; Reusch, S.; Jasper, J.

2016-06-01

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers' field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.
A Forward GPS Multipath Simulator Based on the Vegetation Radiative Transfer Equation Model

PubMed Central

Wu, Xuerui; Jin, Shuanggen; Xia, Junming

2017-01-01

Global Navigation Satellite Systems (GNSS) have been widely used in navigation, positioning and timing. Nowadays, the multipath errors may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), i.e., GPS-Multipath Reflectometry (GPS-MR). However, bistatic scattering properties and the relation between GPS observables and geophysical parameters are not clear, e.g., vegetation. In this paper, a new element on bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that the wheat will decrease the amplitudes of GPS multipath observables (SNR, phase and code), if we increase the vegetation moisture contents or the scatters sizes (stem or leaf). Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with previous results for environmental parameter detections by GPS-MR. PMID:28587255
Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

NASA Astrophysics Data System (ADS)

Mundava, C.; Helmholz, P.; Schut, A. G. T.; Corner, R.; McAtee, B.; Lamb, D. W.

2014-09-01

The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011-2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites; (2) separate relationships per site group; (3) multiple regressions including selected vegetation indices per site group; and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the Open plains and Bunch grasses sites, but not for Spinifex sites. When rainfall and elevation data are included, cross validation improved slightly with a Q2 of 0.49-0.72 for Open plains and Bunch grasses sites respectively. Cross validation results for total AGB were moderately accurate (Q2 of 0.41) for Open plains but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors.
Polarization Remote Sensing Physical Mechanism, Key Methods and Application

NASA Astrophysics Data System (ADS)

Yang, B.; Wu, T.; Chen, W.; Li, Y.; Knjazihhin, J.; Asundi, A.; Yan, L.

2017-09-01

China's long-term planning major projects "high-resolution earth observation system" has been invested nearly 100 billion and the satellites will reach 100 to 2020. As to 2/3 of China's area covered by mountains it has a higher demand for remote sensing. In addition to light intensity, frequency, phase, polarization is also the main physical characteristics of remote sensing electromagnetic waves. Polarization is an important component of the reflected information from the surface and the atmospheric information, and the polarization effect of the ground object reflection is the basis of the observation of polarization remote sensing. Therefore, the effect of eliminating the polarization effect is very important for remote sensing applications. The main innovations of this paper is as follows: (1) Remote sensing observation method. It is theoretically deduced and verified that the polarization can weaken the light in the strong light region, and then provide the polarization effective information. In turn, the polarization in the low light region can strengthen the weak light, the same can be obtained polarization effective information. (2) Polarization effect of vegetation. By analyzing the structure characteristics of vegetation, polarization information is obtained, then the vegetation structure information directly affects the absorption of biochemical components of leaves. (3) Atmospheric polarization neutral point observation method. It is proved to be effective to achieve the ground-gas separation, which can achieve the effect of eliminating the atmospheric polarization effect and enhancing the polarization effect of the object.
Do BRDF effects dominate seasonal changes in tower-based remote sensing imagery?

NASA Astrophysics Data System (ADS)

Nagol, J. R.; Morton, D. C.; Rubio, J.; Cook, B. D.; Rishmawi, K.

2014-12-01

In situ remote sensing complements data from airborne and space-based sensors, in particular for intensive study sites where optical imagery can be paired with detailed ground and tower measurements. The characteristics of tower-mounted imaging systems are quite different from the nadir viewing geometry of other remote sensing platforms. In particular, tower-mounted systems are quite sensitive to artifacts of seasonal and diurnal sun angle variations. Most systems are oriented in a fixed north or south direction (depending on latitude), placing them in the principal plane at solar noon. The strength of the BRDF (Bidirectional Reflectance Distribution Function) effect is strongest for images acquired at that time. Phenological metrics derived from tower based oblique angle imaging systems are particularly prone to BRDF effects, as shadowing within and between tree crowns varies seasonally. For sites in the northern hemisphere, the fraction of sunlit and shaded vegetation declines following the June solstice to leaf senescence in September. Correcting tower-based remote sensing imagery for artifacts of BRDF is critical to isolate real changes in canopy phenology and reflectance. Here, we used airborne lidar data from NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT) to develop a 3D forest scene for Harvard Forest in the Discrete Anisotrophic Radiative Transfer (DART) model. Our objective was to model the contribution of changes in shadowing and illumination to observations of changes in greenness from the Phenocam image time series at the Harvard Forest site. Diurnal variability in canopy greenness from the Phenocam time series provides an independent evaluation of BRDF effects from changes in illumination and sun-sensor geometries. The overall goal of this work is to develop a look-up table solution to correct major components of BRDF for tower-mounted imaging systems such as Phenocam, based on characteristics of the forest structure (forest height, canopy rugosity, fractional cover, and composition) and viewing geometry of the sensor. Given the sensitivity of tower-based systems to BRDF effects, efforts to correct artifacts of BRDF in phenology time series is critical to isolate seasonal changes in vegetation reflectance.
Review of FEWS NET Biophysical Monitoring Requirements

NASA Technical Reports Server (NTRS)

Ross, K. W.; Brown, Molly E.; Verdin, J.; Underwood, L. W.

2009-01-01

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users: it focused upon operational requirements to determine additional useful remote sensing data and; subsequently, beneficial FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world, enabling a robust set of professional perspectives to be gathered and analyzed rapidly. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard: that high resolution remote sensing products continue to be in demand, and that rainfall and vegetation products were valued as data that provide actionable food security information.
Low-cost multispectral imaging for remote sensing of lettuce health

NASA Astrophysics Data System (ADS)

Ren, David D. W.; Tripathi, Siddhant; Li, Larry K. B.

2017-01-01

In agricultural remote sensing, unmanned aerial vehicle (UAV) platforms offer many advantages over conventional satellite and full-scale airborne platforms. One of the most important advantages is their ability to capture high spatial resolution images (1-10 cm) on-demand and at different viewing angles. However, UAV platforms typically rely on the use of multiple cameras, which can be costly and difficult to operate. We present the development of a simple low-cost imaging system for remote sensing of crop health and demonstrate it on lettuce (Lactuca sativa) grown in Hong Kong. To identify the optimal vegetation index, we recorded images of both healthy and unhealthy lettuce, and used them as input in an expectation maximization cluster analysis with a Gaussian mixture model. Results from unsupervised and supervised clustering show that, among four widely used vegetation indices, the blue wide-dynamic range vegetation index is the most accurate. This study shows that it is readily possible to design and build a remote sensing system capable of determining the health status of lettuce at a reasonably low cost (
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
Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data: a case study of Xuzhou, China

NASA Astrophysics Data System (ADS)

Tan, Kun; Zhou, Songyang; Li, Erzhu; Du, Peijun

2015-06-01

An improved Carnegie Ames Stanford Approach (CASA) model based on two kinds of remote sensing (RS) data, Landsat Enhanced Thematic Mapper Plus (ETM +) and Moderate Resolution Imaging Spectro-radiometer (MODIS), and climate variables were applied to estimate the Net Primary Productivity (NPP) of Xuzhou in June of each year from 2001 to 2010. The NPP of the study area decreased as the spatial scale increased. The average NPP of terrestrial vegetation in Xuzhou showed a decreasing trend in recent years, likely due to changes in climate and environment. The study area was divided into four sub-regions, designated as highest, moderately high, moderately low, and lowest in NPP. The area designated as the lowest sub-region in NPP increased with expanding scale, indicating that the NPP distribution varied with different spatial scales. The NPP of different vegetation types was also significantly influenced by scale. In particular, the NPP of urban woodland produced lower estimates because of mixed pixels. Similar trends in NPP were observed with different RS data. In addition, expansion of residential areas and reduction of vegetated areas were the major reasons for NPP change. Land cover changes in urban areas reduced NPP, which could chiefly be attributed to human-induced disturbance.

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

NASA Astrophysics Data System (ADS)

Ruiz-Pérez, Guiomar; Koch, Julian; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

2017-12-01

Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment - the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.
Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?

Treesearch

Yulong Zhang; Conghe Song; Lawrence E. Band; Ge Sun; Junxiang Li

2017-01-01

Accurately monitoring global vegetation dynamics with modern remote sensing is critical for understanding the functions and processes of the biosphere and its interactions with the planetary climate. The MODerate resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for this purpose. To date, theMODIS teamhad released...
Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic data from Landsat and MODIS BRDF/albedo product

USDA-ARS?s Scientific Manuscript database

Climate warming over the past half century has led to observable changes in vegetation phenology and growing season length; which can be measured globally using remote sensing derived vegetation indices. Previous studies in mid- and high northern latitude systems show temperature driven earlier spri...
A COMPARISON OF THE SALINITY REGIME ALONG THE TEXAS COAST WITH TERRESTRIAL VEGETATION GREENNESS AND WATER USE IN THE GALVESTON BAY WATERSHED USING REMOTING SENSING

EPA Science Inventory

Variability in vegetation greenness was determined for the Galveston Bay watershed using biweekly Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Very High Resolution Radiometer (A VHRR) flown on NOAA satellites. NDVI variability was compared with reg...
TERRESTRIAL VEGETATION GREENNESS OF THE LOWER GALVESTON BAY WATERSHED FROM SATELLITE REMOTE SENSING AND ITS RELATION TO WATER AND THE SALINITY REGIME OF THE GALVESTON BAY ESTUARY

EPA Science Inventory

Spatial and temporal variability of vegetation greenness have been determined for coastal Texas using biweekly Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR). Results are presented on relationships between grou...
Construction of an unmanned aerial vehicle remote sensing system for crop monitoring

NASA Astrophysics Data System (ADS)

Jeong, Seungtaek; Ko, Jonghan; Kim, Mijeong; Kim, Jongkwon

2016-04-01

We constructed a lightweight unmanned aerial vehicle (UAV) remote sensing system and determined the ideal method for equipment setup, image acquisition, and image processing. Fields of rice paddy (Oryza sativa cv. Unkwang) grown under three different nitrogen (N) treatments of 0, 50, or 115 kg/ha were monitored at Chonnam National University, Gwangju, Republic of Korea, in 2013. A multispectral camera was used to acquire UAV images from the study site. Atmospheric correction of these images was completed using the empirical line method, and three-point (black, gray, and white) calibration boards were used as pseudo references. Evaluation of our corrected UAV-based remote sensing data revealed that correction efficiency and root mean square errors ranged from 0.77 to 0.95 and 0.01 to 0.05, respectively. The time series maps of simulated normalized difference vegetation index (NDVI) produced using the UAV images reproduced field variations of NDVI reasonably well, both within and between the different N treatments. We concluded that the UAV-based remote sensing technology utilized in this study is potentially an easy and simple way to quantitatively obtain reliable two-dimensional remote sensing information on crop growth.
International Symposium on Remote Sensing of Environment, Third Thematic Conference: Remote Sensing for Exploration Geology, Colorado Springs, CO, April 16-19, 1984, Proceedings. Volumes 1 & 2

NASA Technical Reports Server (NTRS)

1985-01-01

A photogeologic and remote sensing model of porphyry type mineral sytems is considered along with a Landsat application to development of a tectonic model for hydrocarbon exploration of Devonian shales in west-central Virginia, remote sensing and the funnel philosophy, Landsat-based tectonic and metallogenic synthesis of the southwest United States, and an evolving paradigm for computer vision. Attention is given to the neotectonics of the Tibetan plateau deduced from Landsat MSS image interpretation, remote sensing in northern Arizona, the use of an airborne laser system for vegetation inventories and geobotanical prospecting, an evaluation of Thematic Mapper data for hydrocarbon exploration in low-relief basins, and an evaluation of the information content of high spectral resolution imagery. Other topics explored are related to a major source of new radar data for exploration research, the accuracy of geologic maps produced from Landsat data, and an approach for the geometric rectification of radar imagery.
Land Cover Classification of the Jornada Experimental Range with Simulated HyspIRI Data

NASA Astrophysics Data System (ADS)

Thorp, K. R.; French, A. N.

2011-12-01

The proposed NASA mission, HyspIRI, would facilitate the use of hyperspectral satellite remote sensing images for monitoring a variety of Earth system processes. We utilized four years of AVIRIS data of the USDA Jornada Experimental Range in southern New Mexico to simulate the visible and near-infrared bands of the planned HyspIRI satellite. Vegetation dynamics at Jornada has been the subject of several recent studies due to concerns of invasive plant species encroaching on native rangeland grasses. Our objective was to assess the added value of simulated HyspIRI images to appropriately classify rangeland vegetation. The AVIRIS images were georeferenced to an orthophoto of the region and 's6' was implemented for atmospheric correction. Images were resampled to simulate HyspIRI wavebands in the visible and near-infrared. Supervised image classification based on observed spectra of rangeland vegetation species was used to map spatial vegetation cover class and temporal dynamics over four years. Forthcoming results will identify the added value of hyperspectral images, as compared to broadband images, for monitoring vegetation dynamics at Jornada.
An Intercomparison of Vegetation Products from Satellite-based Observations used for Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Vreugdenhil, Mariette; de Jeu, Richard; Wagner, Wolfgang; Dorigo, Wouter; Hahn, Sebastian; Bloeschl, Guenter

2013-04-01

Vegetation and its water content affect active and passive microwave soil moisture retrievals and need to be taken into account in such retrieval methodologies. This study compares the vegetation parameterisation that is used in the TU-Wien soil moisture retrieval algorithm to other vegetation products, such as the Vegetation Optical Depth (VOD), Net Primary Production (NPP) and Leaf Area Index (LAI). When only considering the retrieval algorithm for active microwaves, which was developed by the TU-Wien, the effect of vegetation on the backscattering coefficient is described by the so-called slope [1]. The slope is the first derivative of the backscattering coefficient in relation to the incidence angle. Soil surface backscatter normally decreases quite rapidly with the incidence angle over bare or sparsely vegetated soils, whereas the contribution of dense vegetation is fairly uniform over a large range of incidence angles. Consequently, the slope becomes less steep with increasing vegetation. Because the slope is a derivate of noisy backscatter measurements, it is characterised by an even higher level of noise. Therefore, it is averaged over several years assuming that the state of the vegetation doesn't change inter-annually. The slope is compared to three dynamic vegetation products over Australia, the VOD, NPP and LAI. The VOD was retrieved from AMSR-E passive microwave data using the VUA-NASA retrieval algorithm and provides information on vegetation with a global coverage of approximately every two days [2]. LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. In this study LAI is used from the Geoland2 products derived from SPOT Vegetation*. The NPP is the net rate at which plants build up carbon through photosynthesis and is a model-based estimate from the BiosEquil model [3, 4]. Results show that VOD and slope correspond reasonably well over vegetated areas, whereas in arid areas, where the microwave signals mostly stem from the soil surface and deeper soil layers, they are negatively correlated. A second comparison of monthly values of both vegetation parameters to modelled NPP data shows that particularly over dry areas the VOD corresponds better to the NPP, with r=0.79 for VOD-NPP and r=-0.09 for slope-NPP. 1. Wagner, W., et al., A Study of Vegetation Cover Effects on ERS Scatterometer Data. IEEE Transactions on Geoscience and Remote Sensing, 1999. 37(2): p. 938-948. 2. Owe, M., R. de Jeu, and J. Walker, A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. Geoscience and Remote Sensing, IEEE Transactions on, 2001. 39(8): p. 1643-1654. 3. Raupach, M.R., et al., Balances of Water, Carbon, Nitrogen and Phosphorus in Australian Landscapes: (1) Project Description and Results, 2001, Sustainable Minerals Institute, CSIRO Land and Water. 4. Raupach, M.R., et al., Balances of Water, Carbon, Nitrogen and Phosporus in Australian Landscapes: (2) Model Formulation and Testing, 2001, Sustainable Minerals Institute, CSIRO Land and Water. * These products are the joint property of INRA, CNES and VITO under copyright of Geoland2. They are generated from the SPOT VEGETATION data under copyright CNES and distribution by VITO.
Proxies for soil organic carbon derived from remote sensing

NASA Astrophysics Data System (ADS)

Rasel, S. M. M.; Groen, T. A.; Hussin, Y. A.; Diti, I. J.

2017-07-01

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.
[Monitoring of soil salinization in Northern Tarim Basin, Xinjiang of China in dry and wet seasons based on remote sensing].

PubMed

Yao, Yuan; Ding, Jian-Li; Zhang, Fang; Wang, Gang; Jiang, Hong-Nan

2013-11-01

Soil salinization is one of the most important eco-environment problems in arid area, which can not only induce land degradation, inhibit vegetation growth, but also impede regional agricultural production. To accurately and quickly obtain the information of regional saline soils by using remote sensing data is critical to monitor soil salinization and prevent its further development. Taking the Weigan-Kuqa River Delta Oasis in the northern Tarim River Basin of Xinjiang as test object, and based on the remote sensing data from Landsat-TM images of April 15, 2011 and September 22, 2011, in combining with the measured data from field survey, this paper extracted the characteristic variables modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), and the third principal component from K-L transformation (K-L-3). The decision tree method was adopted to establish the extraction models of soil salinization in the two key seasons (dry and wet seasons) of the study area, and the classification maps of soil salinization in the two seasons were drawn. The results showed that the decision tree method had a higher discrimination precision, being 87.2% in dry season and 85.3% in wet season, which was able to be used for effectively monitoring the dynamics of soil salinization and its spatial distribution, and to provide scientific basis for the comprehensive management of saline soils in arid area and the rational utilization of oasis land resources.
Developing a novel UAV (Unmanned Aerial Vehicle) helicopter platform for very high resolution environmental monitoring of catchment processes

NASA Astrophysics Data System (ADS)

Freer, J. E.; Richardson, T.; Yang, Z.

2012-12-01

Recent advances in remote sensing and geographic information has led the way for the development of hyperspectral sensors and cloud scanning LIDAR (Light Detection And Ranging). Both these technologies can be used to sense environmental processes and capture detailed spatial information, they are often deployed in ground, aircraft and satellite based systems. Hyperspectral remote sensing, also known as imaging spectroscopy, is a relatively new technology that is currently being investigated by researchers and scientists with regard to the detection and identification of landscapes, terrestrial vegetation, and manmade materials and backgrounds. There are many applications that could take advantages of hyperspectral remote sensing coupled to detailed surface feature mapping using LIDAR. This embryonic project involves developing the engineering solutions and post processing techniques needed to realise an ultra high resolution helicopter based environmental sensing platform which can fly at lower altitudes than aircraft systems and can be deployed more frequently. We aim to present this new technology platform in this special session (the only one of it's kind in the UK). Initial applications are planned on a range of environmental sensing problems that would benefit from such complex and detailed data.We look forward to being able to display and discuss this initiative with colleagues and any potential interest in future collaborative projects.
Developing a novel UAV (Unmanned Aerial Vehicle) helicopter platform for very high resolution environmental monitoring of catchment processes

NASA Astrophysics Data System (ADS)

Freer, J.; Richardson, T. S.

2012-04-01

Recent advances in remote sensing and geographic information has led the way for the development of hyperspectral sensors and cloud scanning LIDAR (Light Detection And Ranging). Both these technologies can be used to sense environmental processes and capture detailed spatial information, they are often deployed in ground, aircraft and satellite based systems. Hyperspectral remote sensing, also known as imaging spectroscopy, is a relatively new technology that is currently being investigated by researchers and scientists with regard to the detection and identification of landscapes, terrestrial vegetation, and manmade materials and backgrounds. There are many applications that could take advantages of hyperspectral remote sensing coupled to detailed surface feature mapping using LIDAR. This embryonic project involves developing the engineering solutions and post processing techniques needed to realise an ultra high resolution helicopter based environmental sensing platform which can fly at lower altitudes than aircraft systems and can be deployed more frequently. We aim to display this new technology platform in this special session (the only one of it's kind in the UK). Initial applications are planned on a range of environmental sensing problems that would benefit from such complex and detailed data. We look forward to being able to display and discuss this initiative with colleagues and any potential interest in future collaborative projects.
Applying vegetation indices to detect high water table zones in humid warm-temperate regions using satellite remote sensing

NASA Astrophysics Data System (ADS)

Koide, Kaoru; Koike, Katsuaki

2012-10-01

This study developed a geobotanical remote sensing method for detecting high water table zones using differences in the conditions of forest trees induced by groundwater supply in a humid warm-temperate region. A new vegetation index (VI) termed added green band NDVI (AgbNDVI) was proposed to discriminate the differences. The AgbNDVI proved to be more sensitive to water stress on green vegetation than existing VIs, such as SAVI and EVI2, and possessed a strong linear correlation with the vegetation fraction. To validate a proposed vegetation index method, a 23 km2 study area was selected in the Tono region of Gifu prefecture, central Japan. The AgbNDVI values were calculated from atmospheric corrected SPOT HRV data. To correctly extract high VI points, the influence factors on forest tree growth were identified using the AgbNDVI values, DEM and forest type data; the study area was then divided into 555 domains chosen from a combination of the influence factors and forest types. Thresholds for extracting high VI points were defined for each domain based on histograms of AgbNDVI values. By superimposing the high VI points on topographic and geologic maps, most high VI points are clearly located on either concave or convex slopes, and are found to be proximal to geologic boundaries—particularly the boundary between the Pliocene gravel layer and the Cretaceous granite, which should act as a groundwater flow path. In addition, field investigations support the correctness of the high VI points, because they are located around groundwater seeps and in high water table zones where the growth increments and biomass of trees are greater than at low VI points.
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.
Analysing spatio-temporal land degradation dynamics in dry rangelands using landscape metrics and satellite time series data

NASA Astrophysics Data System (ADS)

von Keyserlingk, Jennifer; Paton, Eva Nora; Förster, Saskia; Bronstert, Axel

2017-04-01

Many of the dry rangelands of Southern Europe are threatened by land degradation. This process not only reduces the land's ecological functioning, but also its capacity to provide ecosystem goods and services for local land users. In rangelands, one important aspect is vegetation degradation, which reduces the land's capacity to support livestock. Thus, there is an urgent need to understand the complex dynamics and drivers of land degradation. In the past, both have been difficult to study due to the extensive spatial and temporal scales involved. In the last decade, a large number of remotely sensed imageries has become available for free, which enables a new approach to this topic. The aim of this research is to study land degradation as a multidimensional process incorporating its spatial and temporal components. We developed a methodological approach that makes use of long-term satellite Landsat data. Here, we use imagery of a typical degraded Mediterranean rangeland in Southern Cyprus (Randi Forest) for the years 1998-2015. We have chosen the NDVI as a proxy for vegetation greenness and applied different spatial landscape metrics to calculate changes in vegetation patterns over time. Further, we applied a time-series based approach (BFAST) on selected pixels, to look for sudden changes and trends in the vegetation dynamics. The results promoted our knowledge on how land degradation dynamics in Mediterranean rangelands can be captured through spatio-temporal vegetation dynamics and allowed us to select the most suitable metrics for further analysis. In the long-term, we aim at using Landsat satellite data covering 30 years. To gain a functional understanding of land degradation, we want to overlay our results from the remotely sensed data with results of an eco-hydrological model (SWAT).
Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.
Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate

NASA Astrophysics Data System (ADS)

Chirouze, J.; Boulet, G.; Jarlan, L.; Fieuzal, R.; Rodriguez, J. C.; Ezzahar, J.; Er-Raki, S.; Bigeard, G.; Merlin, O.; Garatuza-Payan, J.; Watts, C.; Chehbouni, G.

2014-03-01

Instantaneous evapotranspiration rates and surface water stress levels can be deduced from remotely sensed surface temperature data through the surface energy budget. Two families of methods can be defined: the contextual methods, where stress levels are scaled on a given image between hot/dry and cool/wet pixels for a particular vegetation cover, and single-pixel methods, which evaluate latent heat as the residual of the surface energy balance for one pixel independently from the others. Four models, two contextual (S-SEBI and a modified triangle method, named VIT) and two single-pixel (TSEB, SEBS) are applied over one growing season (December-May) for a 4 km × 4 km irrigated agricultural area in the semi-arid northern Mexico. Their performance, both at local and spatial standpoints, are compared relatively to energy balance data acquired at seven locations within the area, as well as an uncalibrated soil-vegetation-atmosphere transfer (SVAT) model forced with local in situ data including observed irrigation and rainfall amounts. Stress levels are not always well retrieved by most models, but S-SEBI as well as TSEB, although slightly biased, show good performance. The drop in model performance is observed for all models when vegetation is senescent, mostly due to a poor partitioning both between turbulent fluxes and between the soil/plant components of the latent heat flux and the available energy. As expected, contextual methods perform well when contrasted soil moisture and vegetation conditions are encountered in the same image (therefore, especially in spring and early summer) while they tend to exaggerate the spread in water status in more homogeneous conditions (especially in winter). Surface energy balance models run with available remotely sensed products prove to be nearly as accurate as the uncalibrated SVAT model forced with in situ data.
Comparative analysis of remotely-sensed data products via ecological niche modeling of avian influenza case occurrences in Middle Eastern poultry.

PubMed

Bodbyl-Roels, Sarah; Peterson, A Townsend; Xiao, Xiangming

2011-03-28

Ecological niche modeling integrates known sites of occurrence of species or phenomena with data on environmental variation across landscapes to infer environmental spaces potentially inhabited (i.e., the ecological niche) to generate predictive maps of potential distributions in geographic space. Key inputs to this process include raster data layers characterizing spatial variation in environmental parameters, such as vegetation indices from remotely sensed satellite imagery. The extent to which ecological niche models reflect real-world distributions depends on a number of factors, but an obvious concern is the quality and content of the environmental data layers. We assessed ecological niche model predictions of H5N1 avian flu presence quantitatively within and among four geographic regions, based on models incorporating two means of summarizing three vegetation indices derived from the MODIS satellite. We evaluated our models for predictive ability using partial ROC analysis and GLM ANOVA to compare performance among indices and regions. We found correlations between vegetation indices to be high, such that they contain information that overlaps broadly. Neither the type of vegetation index used nor method of summary affected model performance significantly. However, the degree to which model predictions had to be transferred (i.e., projected onto landscapes and conditions not represented on the landscape of training) impacted predictive strength greatly (within-region model predictions far out-performed models projected among regions). Our results provide the first quantitative tests of most appropriate uses of different remotely sensed data sets in ecological niche modeling applications. While our testing did not result in a decisive "best" index product or means of summarizing indices, it emphasizes the need for careful evaluation of products used in modeling (e.g. matching temporal dimensions and spatial resolution) for optimum performance, instead of simple reliance on large numbers of data layers.
[Analysis of vegetation spatial and temporal variations in Qinghai Province based on remote sensing].

PubMed

Wang, Li-wen; Wei, Ya-xing; Niu, Zheng

2008-06-01

1 km MODIS NDVI time series data combining with decision tree classification, supervised classification and unsupervised classification was used to classify land cover type of Qinghai Province into 14 classes. In our classification system, sparse grassland and sparse shrub were emphasized, and their spatial distribution locations were labeled. From digital elevation model (DEM) of Qinghai Province, five elevation belts were achieved, and we utilized geographic information system (GIS) software to analyze vegetation cover variation on different elevation belts. Our research result shows that vegetation cover in Qinghai Province has been improved in recent five years. Vegetation cover area increases from 370047 km2 in 2001 to 374576 km2 in 2006, and vegetation cover rate increases by 0.63%. Among five grade elevation belts, vegetation cover ratio of high mountain belt is the highest (67.92%). The area of middle density grassland in high mountain belt is the largest, of which area is 94 003 km2. Increased area of dense grassland in high mountain belt is the greatest (1280 km2). During five years, the biggest variation is the conversion from sparse grassland to middle density grassland in high mountain belt, of which area is 15931 km2.

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