Active microwave remote sensing research program plan. Recommendations of the Earth Resources Synthetic Aperture Radar Task Force. [application areas: vegetation canopies, surface water, surface morphology, rocks and soils, and man-made structures

NASA Technical Reports Server (NTRS)

1980-01-01

A research program plan developed by the Office of Space and Terrestrial Applications to provide guidelines for a concentrated effort to improve the understanding of the measurement capabilities of active microwave imaging sensors, and to define the role of such sensors in future Earth observations programs is outlined. The focus of the planned activities is on renewable and non-renewable resources. Five general application areas are addressed: (1) vegetation canopies, (2) surface water, (3) surface morphology, (4) rocks and soils, and (5) man-made structures. Research tasks are described which, when accomplished, will clearly establish the measurement capabilities in each area, and provide the theoretical and empirical results needed to specify and justify satellite systems using imaging radar sensors for global observations.

Evaluating the capacity of GF-4 satellite data for estimating fractional vegetation cover

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, Q.; Ren, H.; Zhang, T.; Sun, Y.

2016-12-01

Fractional vegetation cover (FVC) is a crucial parameter for many agricultural, environmental, meteorological and ecological applications, which is of great importance for studies on ecosystem structure and function. The Chinese GaoFen-4 (GF-4) geostationary satellite designed for the purpose of environmental and ecological observation was launched in December 29, 2015, and official use has been started by Chinese Government on June 13, 2016. Multi-spectral images with spatial resolution of 50 m and high temporal resolution, could be acquired by the sensor on GF-4 satellite on the 36000 km-altitude orbit. To take full advantage of the outstanding performance of GF-4 satellite, this study evaluated the capacity of GF-4 satellite data for monitoring FVC. To the best of our knowledge, this is the first research about estimating FVC from GF-4 satellite images. First, we developed a procedure for preprocessing GF-4 satellite data, including radiometric calibration and atmospheric correction, to acquire surface reflectance. Then single image and multi-temporal images were used for extracting the endmembers of vegetation and soil, respectively. After that, dimidiate pixel model and square model based on vegetation indices were used for estimating FVC. Finally, the estimation results were comparatively analyzed with FVC estimated by other existing sensors. The experimental results showed that satisfying accuracy of FVC estimation could be achieved from GF-4 satellite images using dimidiate pixel model and square model based on vegetation indices. What's more, the multi-temporal images increased the probability to find pure vegetation and soil endmembers, thus the characteristic of high temporal resolution of GF-4 satellite images improved the accuracy of FVC estimation. This study demonstrated the capacity of GF-4 satellite data for monitoring FVC. The conclusions reached by this study are significant for improving the accuracy and spatial-temporal resolution of existing FVC products, which provides a basis for the studies on ecosystem structure and function using remote sensing data acquired by GF-4 satellite.

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.

Satellite-based peatland mapping: potential of the MODIS sensor.

Treesearch

D. Pflugmacher; O.N. Krankina; W.B. Cohen

2006-01-01

Peatlands play a major role in the global carbon cycle but are largely overlooked in current large-scale vegetation mapping efforts. In this study, we investigated the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to capture extent and distribution of peatlands in the St. Petersburg region of Russia.

Estimating Achievable Accuracy for Global Imaging Spectroscopy Measurement of Non-Photosynthetic Vegetation Cover

NASA Astrophysics Data System (ADS)

Dennison, P. E.; Kokaly, R. F.; Daughtry, C. S. T.; Roberts, D. A.; Thompson, D. R.; Chambers, J. Q.; Nagler, P. L.; Okin, G. S.; Scarth, P.

2016-12-01

Terrestrial vegetation is dynamic, expressing seasonal, annual, and long-term changes in response to climate and disturbance. Phenology and disturbance (e.g. drought, insect attack, and wildfire) can result in a transition from photosynthesizing "green" vegetation to non-photosynthetic vegetation (NPV). NPV cover can include dead and senescent vegetation, plant litter, agricultural residues, and non-photosynthesizing stem tissue. NPV cover is poorly captured by conventional remote sensing vegetation indices, but it is readily separable from substrate cover based on spectral absorption features in the shortwave infrared. We will present past research motivating the need for global NPV measurements, establishing that mapping seasonal NPV cover is critical for improving our understanding of ecosystem function and carbon dynamics. We will also present new research that helps determine a best achievable accuracy for NPV cover estimation. To test the sensitivity of different NPV cover estimation methods, we simulated satellite imaging spectrometer data using field spectra collected over mixtures of NPV, green vegetation, and soil substrate. We incorporated atmospheric transmittance and modeled sensor noise to create simulated spectra with spectral resolutions ranging from 10 to 30 nm. We applied multiple methods of NPV estimation to the simulated spectra, including spectral indices, spectral feature analysis, multiple endmember spectral mixture analysis, and partial least squares regression, and compared the accuracy and bias of each method. These results prescribe sensor characteristics for an imaging spectrometer mission with NPV measurement capabilities, as well as a "Quantified Earth Science Objective" for global measurement of NPV cover. Copyright 2016, all rights reserved.

Impact of Sensor Degradation on the MODIS NDVI Time Series

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

Analysis of airborne imaging spectrometer data for the Ruby Mountains, Montana, by use of absorption-band-depth images

NASA Technical Reports Server (NTRS)

Brickey, David W.; Crowley, James K.; Rowan, Lawrence C.

1987-01-01

Airborne Imaging Spectrometer-1 (AIS-1) data were obtained for an area of amphibolite grade metamorphic rocks that have moderate rangeland vegetation cover. Although rock exposures are sparse and patchy at this site, soils are visible through the vegetation and typically comprise 20 to 30 percent of the surface area. Channel averaged low band depth images for diagnostic soil rock absorption bands. Sets of three such images were combined to produce color composite band depth images. This relative simple approach did not require extensive calibration efforts and was effective for discerning a number of spectrally distinctive rocks and soils, including soils having high talc concentrations. The results show that the high spectral and spatial resolution of AIS-1 and future sensors hold considerable promise for mapping mineral variations in soil, even in moderately vegetated areas.

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

PubMed

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

2017-04-15

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

The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography

NASA Astrophysics Data System (ADS)

Blair, J. Bryan; Rabine, David L.; Hofton, Michelle A.

The Laser Vegetation Imaging Sensor (LVIS) is an airborne, scanning laser altimeter, designed and developed at NASA's Goddard Space Flight Center (GSFC). LVIS operates at altitudes up to 10 km above ground, and is capable of producing a data swath up to 1000 m wide nominally with 25-m wide footprints. The entire time history of the outgoing and return pulses is digitised, allowing unambiguous determination of range and return pulse structure. Combined with aircraft position and attitude knowledge, this instrument produces topographic maps with dm accuracy and vertical height and structure measurements of vegetation. The laser transmitter is a diode-pumped Nd:YAG oscillator producing 1064 nm, 10 ns, 5 mJ pulses at repetition rates up to 500 Hz. LVIS has recently demonstrated its ability to determine topography (including sub-canopy) and vegetation height and structure on flight missions to various forested regions in the US and Central America. The LVIS system is the airborne simulator for the Vegetation Canopy Lidar (VCL) mission (a NASA Earth remote sensing satellite due for launch in year 2000), providing simulated data sets and a platform for instrument proof-of-concept studies. The topography maps and return waveforms produced by LVIS provide Earth scientists with a unique data set allowing studies of topography, hydrology, and vegetation with unmatched accuracy and coverage.

A special vegetation index for the weed detection in sensor based precision agriculture.

PubMed

Langner, Hans-R; Böttger, Hartmut; Schmidt, Helmut

2006-06-01

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).

Multi-Sensor Mud Detection

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.; Matthies, Larry H.

2010-01-01

Robust mud detection is a critical perception requirement for Unmanned Ground Vehicle (UGV) autonomous offroad navigation. A military UGV stuck in a mud body during a mission may have to be sacrificed or rescued, both of which are unattractive options. There are several characteristics of mud that may be detectable with appropriate UGV-mounted sensors. For example, mud only occurs on the ground surface, is cooler than surrounding dry soil during the daytime under nominal weather conditions, is generally darker than surrounding dry soil in visible imagery, and is highly polarized. However, none of these cues are definitive on their own. Dry soil also occurs on the ground surface, shadows, snow, ice, and water can also be cooler than surrounding dry soil, shadows are also darker than surrounding dry soil in visible imagery, and cars, water, and some vegetation are also highly polarized. Shadows, snow, ice, water, cars, and vegetation can all be disambiguated from mud by using a suite of sensors that span multiple bands in the electromagnetic spectrum. Because there are military operations when it is imperative for UGV's to operate without emitting strong, detectable electromagnetic signals, passive sensors are desirable. JPL has developed a daytime mud detection capability using multiple passive imaging sensors. Cues for mud from multiple passive imaging sensors are fused into a single mud detection image using a rule base, and the resultant mud detection is localized in a terrain map using range data generated from a stereo pair of color cameras.

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

USGS Publications Warehouse

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

2005-01-01

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

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...

Overview of the Shuttle Imaging Radar-B preliminary scientific results

NASA Technical Reports Server (NTRS)

Elachi, C.; Cimino, J.; Settle, M.

1986-01-01

Data collected with the Shuttle Imaging Radar-B (SIR-B) on the October 5, 1985 Shuttle mission are discussed. The design and capabilities of the sensor which operates in a fixed illumination geometry and has incidence angles between 15 and 60 deg with 1 deg increments are described. Problems encountered with the SIR-B during the mission are examined. the The radar stereo imaging capability of the sensor was verified and three-dimensional images of the earth surface were obtained. The oceanography experiments provided significant data on ocean wave and internal wave patterns, oil spills, and ice zones. The geological images revealed that the sensor can evaluate penetration effect in dry soil from buried receivers and the existence of subsurface dry channels in the Egyptian desert was validated. The use of multiincidence angle imaging to classify terrain units and derive vegetation maps and the development of terrain maps are confirmed.

GEOScan: A GEOScience Facility From Space

NASA Astrophysics Data System (ADS)

Dyrud, L. P.; Fentzke, J. T.; Anderson, B. J.; Bishop, R. L.; Bust, G. S.; Cahoy, K.; Erlandson, R. E.; Fish, C. S.; Gunter, B. C.; Hall, F. G.; Hilker, T.; Lorentz, S. R.; Mazur, J. E.; Murphy, S. D.; Mustard, J. F.; O'Brien, P. P.; Slagowski, S.; Trenberth, K. E.; Wiscombe, W. J.

2012-12-01

GEOScan is a proposed globally networked orbiting facility that will provide revolutionary, massively dense global geosciences observations. Major scientific research projects are typically conducted using two approaches: community facilities, or investigator led focused missions. GEOScan is a new concept in space science, blending the PI mission and community facility models: it is PI-led, but it carries sensors that are the result of a grass-roots competition, and, uniquely, it preserves open slots for sensors which are purposely not yet decided. The goal is threefold: first, to select sensors that maximize science value for the greatest number of scientific disciplines, second, to target science questions that cannot be answered without simultaneous global space-based measurements, and third to reap the cost advantages of scale manufacturing for space instrumentation. The relatively small size, mass, and power requirements of the GEOScan sensor suite would make it an ideal hosted payload aboard a global constellation of communication satellites, such as Iridium NEXT's 66-satellite constellation or as hosted small-sat payload. Each GEOScan sensor suite consists of 6 instruments: a Radiometer to measure Earth's total outgoing radiation; a GPS Compact Total Electron Content Sensor to image Earth's plasma environment and gravity field; a MicroCam Multispectral Imager to provide the first uniform, instantaneous image of Earth and measure global cloud cover, vegetation, land use, and bright aurora; a Radiation Belt Mapping System (dosimeter) to measure energetic electron and proton distributions; a Compact Earth Observing Spectrometer to measure aerosol-atmospheric composition and vegetation; and MEMS Accelerometers to deduce non-conservative forces aiding gravity and neutral drag studies. These instruments, employed in a constellation, can provide major breakthroughs in Earth and Geospace science, as well as offering a low-cost technology demonstration for operational weather, climate, and land-imaging.

Inter-Comparison of MODIS and VIIRS Vegetation Indices Using One-Year Global Data

NASA Astrophysics Data System (ADS)

Miura, T.; Muratsuchi, J.; Obata, K.; Kato, A.; Vargas, M.; Huete, A. R.

2016-12-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor series of the Joint Polar Satellite System program is slated to continue the highly calibrated data stream initiated with the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. A number of geophysical products are being/to be produced from VIIRS data, including the "Top-of-the-Atmosphere (TOA)" Normalized Difference Vegetation Index (NDVI), "Top-of-Canopy (TOC)" Enhanced Vegetation Index (EVI), and TOC NDVI. In this study, we cross-compared vegetation indices (VIs) from the first VIIRS sensor aboard the Suomi National Polar-orbiting Partnership satellite with the Aqua MODIS counterparts using one-year global data. This study was aimed at developing a thorough understanding of radiometric compatibility between the two VI datasets across globe, seasons, a range of viewing angle, and land cover types. VIIRS and MODIS VI data of January-December 2015 were obtained at monthly intervals when their orbital tracks coincided. These data were projected and spatially-aggregated into a .0036-degree grid while screening for cloud and aerosol contaminations using their respective quality flags. VIIRS-MODIS observation pairs with near-identical sun-target-view angles were extracted from each of these monthly image pairs for cross-comparison. The four VIs of TOA NDVI, TOC NDVI, TOC EVI, and TOC EVI2 (a two-band version of the EVI) were analyzed. Between MODIS and VIIRS, TOA NDVI, TOC NDVI, and TOC EVI2 had very small overall mean differences (MD) of .014, .013, and .013 VI units, respectively, whereas TOC EVI had a slightly larger overall MD of 0.023 EVI units attributed to the disparate blue bands of the two sensors. These systematic differences were consistent across the one-year period. With respect to sun-target-viewing geometry, MDs were also consistent across the view zenith angle range, but always lower for forward- than backward-viewing geometry. MDs showed large land cover dependencies for TOA NDVI and TOC NDVI, varying 10 folds from .002 for forests to .02 for sparsely-vegetated areas. They were consistent across land cover types for TOC EVI and TOC EVI2. Future studies should address the impact of sun-target-view geometry on corss-sensor VI comparisons.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

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.

Intelligent image processing for vegetation classification using multispectral LANDSAT data

NASA Astrophysics Data System (ADS)

Santos, Stewart R.; Flores, Jorge L.; Garcia-Torales, G.

2015-09-01

We propose an intelligent computational technique for analysis of vegetation imaging, which are acquired with multispectral scanner (MSS) sensor. This work focuses on intelligent and adaptive artificial neural network (ANN) methodologies that allow segmentation and classification of spectral remote sensing (RS) signatures, in order to obtain a high resolution map, in which we can delimit the wooded areas and quantify the amount of combustible materials present into these areas. This could provide important information to prevent fires and deforestation of wooded areas. The spectral RS input data, acquired by the MSS sensor, are considered in a random propagation remotely sensed scene with unknown statistics for each Thematic Mapper (TM) band. Performing high-resolution reconstruction and adding these spectral values with neighbor pixels information from each TM band, we can include contextual information into an ANN. The biggest challenge in conventional classifiers is how to reduce the number of components in the feature vector, while preserving the major information contained in the data, especially when the dimensionality of the feature space is high. Preliminary results show that the Adaptive Modified Neural Network method is a promising and effective spectral method for segmentation and classification in RS images acquired with MSS sensor.

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.

3D Vegetation Mapping Using UAVSAR, LVIS, and LIDAR Data Acquisition Methods

NASA Technical Reports Server (NTRS)

Calderon, Denice

2011-01-01

The overarching objective of this ongoing project is to assess the role of vegetation within climate change. Forests capture carbon, a green house gas, from the atmosphere. Thus, any change, whether, natural (e.g. growth, fire, death) or due to anthropogenic activity (e.g. logging, burning, urbanization) may have a significant impact on the Earth's carbon cycle. Through the use of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA's Laser Vegetation Imaging Sensor (LVIS), which are airborne Light Detection and Ranging (LIDAR) remote sensing technologies, we gather data to estimate the amount of carbon contained in forests and how the content changes over time. UAVSAR and LVIS sensors were sent all over the world with the objective of mapping out terrain to gather tree canopy height and biomass data; This data is in turn used to correlate vegetation with the global carbon cycle around the world.

Unmanned Aircraft Systems Used over Western U.S. Rangelands to Characterize Terrestrial Ecosystems

NASA Astrophysics Data System (ADS)

Rango, A.

2015-12-01

New remote sensing methods to quantify terrestrial ecosystems have developed rapidly over the past 10 years. New platforms with improved aeronautical capabilities have become known as Unmanned Aircraft Systems (UAS). In addition to the new aircraft, sensors are becoming smaller and some can fit into limited payload bays. The miniaturization process is well underway, but much remains to be done. Rather than using a wide variety of sensors, a limited number of instruments is recommended. At the moment we fly 2-3 instruments (digital SLR camera, 6-band multispectral camera, and single video camera). Our flights are primarily over low population density western U.S. rangeland with objectives to assess rangeland health, active erosion, vegetation change, phenology, livestock movement, and vegetation type consumed by grazing animals. All of our UAS flights are made using a serpentine flight path with overlapping images at an altitude of 700 ft (215 m). This altitude allows hyperspatial imagery with a resolution of 5-15 cm depending upon the sensor being used, and it allows determination of vegetation type based on the plant structure and vegetation geometries, or by multispectral analysis. In addition to advances in aircraft and sensor technology, image processing software has become more sophisticated. Future development is necessary, and we can expect improvement in sensors, aircraft, data collection, and application to terrestrial ecosystems. Of 17 ARS research laboratories across the country four laboratories are interested in future UAS applications and another 13 already have at least one UAS. In 2015 the Federal Aviation Administration proposed a framework of recommendations that would allow routine use of certain small UAS (those weighing less than 55 lb (25 kg)). Although these new regulations will provide increased flexibility in how flights are made, other operations will still require the use of a Certificate of Authorization.

Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.

USGS Publications Warehouse

Brown, Jesslyn F.; Miura, T.; Wardlow, B.; Gu, Yingxin

2011-01-01

Droughts occur repeatedly in the United States resulting in billions of dollars of damage. Monitoring and reporting on drought conditions is a necessary function of government agencies at multiple levels. A team of Federal and university partners developed a drought decision- support tool with higher spatial resolution relative to traditional climate-based drought maps. The Vegetation Drought Response Index (VegDRI) indicates general canopy vegetation condition assimilation of climate, satellite, and biophysical data via geospatial modeling. In VegDRI, complementary drought-related data are merged to provide a comprehensive, detailed representation of drought stress on vegetation. Time-series data from daily polar-orbiting earth observing systems [Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS)] providing global measurements of land surface conditions are ingested into VegDRI. Inter-sensor compatibility is required to extend multi-sensor data records; thus, translations were developed using overlapping observations to create consistent, long-term data time series. 

On the Role of Urban and Vegetative Land Cover in the Identification of Tornado Damage Using Dual-Resolution Multispectral Satellite Imagery

NASA Astrophysics Data System (ADS)

Kingfield, D.; de Beurs, K.

2014-12-01

It has been demonstrated through various case studies that multispectral satellite imagery can be utilized in the identification of damage caused by a tornado through the change detection process. This process involves the difference in returned surface reflectance between two images and is often summarized through a variety of ratio-based vegetation indices (VIs). Land cover type plays a large contributing role in the change detection process as the reflectance properties of vegetation can vary based on several factors (e.g. species, greenness, density). Consequently, this provides the possibility for a variable magnitude of loss, making certain land cover regimes less reliable in the damage identification process. Furthermore, the tradeoff between sensor resolution and orbital return period may also play a role in the ability to detect catastrophic loss. Moderate resolution imagery (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS)) provides relatively coarse surface detail with a higher update rate which could hinder the identification of small regions that underwent a dynamic change. Alternatively, imagery with higher spatial resolution (e.g. Landsat) have a longer temporal return period between successive images which could result in natural recovery underestimating the absolute magnitude of damage incurred. This study evaluates the role of land cover type and sensor resolution on four high-end (EF3+) tornado events occurring in four different land cover groups (agriculture, forest, grassland, urban) in the spring season. The closest successive clear images from both Landsat 5 and MODIS are quality controlled for each case. Transacts of surface reflectance across a homogenous land cover type both inside and outside the damage swath are extracted. These metrics are synthesized through the calculation of six different VIs to rank the calculated change metrics by land cover type, sensor resolution and VI.

A 868MHz-based wireless sensor network for ground truthing of soil moisture for a hyperspectral remote sensing campaign - design and preliminary results

NASA Astrophysics Data System (ADS)

Näthe, Paul; Becker, Rolf

2014-05-01

Soil moisture and plant available water are important environmental parameters that affect plant growth and crop yield. Hence, they are significant parameters for vegetation monitoring and precision agriculture. However, validation through ground-based soil moisture measurements is necessary for accessing soil moisture, plant canopy temperature, soil temperature and soil roughness with airborne hyperspectral imaging systems in a corresponding hyperspectral imaging campaign as a part of the INTERREG IV A-Project SMART INSPECTORS. At this point, commercially available sensors for matric potential, plant available water and volumetric water content are utilized for automated measurements with smart sensor nodes which are developed on the basis of open-source 868MHz radio modules, featuring a full-scale microcontroller unit that allows an autarkic operation of the sensor nodes on batteries in the field. The generated data from each of these sensor nodes is transferred wirelessly with an open-source protocol to a central node, the so-called "gateway". This gateway collects, interprets and buffers the sensor readings and, eventually, pushes the data-time series onto a server-based database. The entire data processing chain from the sensor reading to the final storage of data-time series on a server is realized with open-source hardware and software in such a way that the recorded data can be accessed from anywhere through the internet. It will be presented how this open-source based wireless sensor network is developed and specified for the application of ground truthing. In addition, the system's perspectives and potentials with respect to usability and applicability for vegetation monitoring and precision agriculture shall be pointed out. Regarding the corresponding hyperspectral imaging campaign, results from ground measurements will be discussed in terms of their contributing aspects to the remote sensing system. Finally, the significance of the wireless sensor network for the application of ground truthing shall be determined.

Potential and Limitations of Low-Cost Unmanned Aerial Systems for Monitoring Altitudinal Vegetation Phenology in the Tropics

NASA Astrophysics Data System (ADS)

Silva, T. S. F.; Torres, R. S.; Morellato, P.

2017-12-01

Vegetation phenology is a key component of ecosystem function and biogeochemical cycling, and highly susceptible to climatic change. Phenological knowledge in the tropics is limited by lack of monitoring, traditionally done by laborious direct observation. Ground-based digital cameras can automate daily observations, but also offer limited spatial coverage. Imaging by low-cost Unmanned Aerial Systems (UAS) combines the fine resolution of ground-based methods with and unprecedented capability for spatial coverage, but challenges remain in producing color-consistent multitemporal images. We evaluated the applicability of multitemporal UAS imaging to monitor phenology in tropical altitudinal grasslands and forests, answering: 1) Can very-high resolution aerial photography from conventional digital cameras be used to reliably monitor vegetative and reproductive phenology? 2) How is UAS monitoring affected by changes in illumination and by sensor physical limitations? We flew imaging missions monthly from Feb-16 to Feb-17, using a UAS equipped with an RGB Canon SX260 camera. Flights were carried between 10am and 4pm, at 120-150m a.g.l., yielding 5-10cm spatial resolution. To compensate illumination changes caused by time of day, season and cloud cover, calibration was attempted using reference targets and empirical models, as well as color space transformations. For vegetative phenological monitoring, multitemporal response was severely affected by changes in illumination conditions, strongly confounding the phenological signal. These variations could not be adequately corrected through calibration due to sensor limitations. For reproductive phenology, the very-high resolution of the acquired imagery allowed discrimination of individual reproductive structures for some species, and its stark colorimetric differences to vegetative structures allowed detection of the reproductive timing on the HSV color space, despite illumination effects. We conclude that reliable vegetative phenology monitoring may exceed the capabilities of consumer cameras, but reproductive phenology can be successfully monitored for species with conspicuous reproductive structures. Further research is being conducted to improve calibration methods and information extraction through machine learning.

The earth's surface studied from space; Proceedings of Workshop II of the COSPAR 25th Plenary Meeting, Graz, Austria, June 25-July 7, 1984

NASA Technical Reports Server (NTRS)

Ungar, S. G. (Editor)

1985-01-01

Consideration is given to: Landsat image data quality studies; a preliminary evaluation of Landsat-4 Thematic Mapper (TM) data for mineral exploration; and the early evaluation of TM data for mapping forest, agricultural and soil resources. Among other topics discussed are: shortwave infrared detection of vegetation; SPOT image quality and post-launch assessment; an evaluation of SPOT HRV simulation data for Corps of Engineers applications; and the application potential of SPOT imagery for topographic mapping. Consideration is also given to: verification studies of MOS-1 sensors; multiple sensor geocoded data; and the utility of proposed sensors for coastal engineering studies.

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 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.

Study on Remote Sensing Image Characteristics of Ecological Land: Case Study of Original Ecological Land in the Yellow River Delta

NASA Astrophysics Data System (ADS)

An, G. Q.

2018-04-01

Takes the Yellow River Delta as an example, this paper studies the characteristics of remote sensing imagery with dominant ecological functional land use types, compares the advantages and disadvantages of different image in interpreting ecological land use, and uses research results to analyse the changing trend of ecological land in the study area in the past 30 years. The main methods include multi-period, different sensor images and different seasonal spectral curves, vegetation index, GIS and data analysis methods. The results show that the main ecological land in the Yellow River Delta included coastal beaches, saline-alkaline lands, and water bodies. These lands have relatively distinct spectral and texture features. The spectral features along the beach show characteristics of absorption in the green band and reflection in the red band. This feature is less affected by the acquisition year, season, and sensor type. Saline-alkali land due to the influence of some saline-alkaline-tolerant plants such as alkali tent, Tamarix and other vegetation, the spectral characteristics have a certain seasonal changes, winter and spring NDVI index is less than the summer and autumn vegetation index. The spectral characteristics of a water body generally decrease rapidly with increasing wavelength, and the reflectance in the red band increases with increasing sediment concentration. In conclusion, according to the spectral characteristics and image texture features of the ecological land in the Yellow River Delta, the accuracy of image interpretation of such ecological land can be improved.

Relation of NDVI obtained from different remote sensing at different space and resolutions sensors in Spanish Dehesas

NASA Astrophysics Data System (ADS)

Escribano Rodríguez, Juan; Tarquis, Ana M.; Saa-Requejo, Antonio; Díaz-Ambrona, Carlos G. H.

2015-04-01

Satellite data are an important source of information and serve as monitoring crops on large scales. There are several indexes, but the most used for monitoring vegetation is NDVI (Normalized Difference Vegetation Index), calculated from the spectral bands of red (RED) and near infrared (NIR), obtaining the value according to relationship: [(NIR - RED) / (NIR + RED)]. During the years 2010-2013 monthly monitoring was conducted in three areas of Spain (Salamanca, Caceres and Cordoba). Pasture plots were selected and satellite images of two different sensors, DEIMOS-1 and MODIS were obtained. DEIMOS-1 is based on the concept Microsat-100 from Surrey. It is designed for imaging the Earth with a resolution good enough to study terrestrial vegetation cover (20x20 m), although with a wide range of visual field (600 km) to get those images with high temporal resolution. By contrast, MODIS images present a much lower spatial resolution (500x500 m). Indices obtained from both sensors to the same area and date are compared and the results show r2 = 0.56; r2 = 0.65 and r2 = 0.90 for the areas of Salamanca, Cáceres and Cordoba respectively. According to the results obtained show that the NDVI obtained by MODIS is slightly larger than that obtained by the sensor for DEIMOS for same time and area. References J.A. Escribano, C.G.H. Diaz-Ambrona, L. Recuero, M. Huesca, V. Cicuendez, A. Palacios-Orueta y A.M. Tarquis. Aplicacion de Indices de Vegetacion para evaluar la falta de produccion de pastos y montaneras en dehesas. I Congreso Iberico de la Dehesa y el Montado. 6-7 Noviembre, 2013, Badajoz. J.A. Escribano Rodriguez, A.M. Tarquis, C.G. Hernandez Diaz-Ambrona. Pasture Drought Insurance Based on NDVI and SAVI. Geophysical Research Abstracts, 14, EGU2012-13945, 2012. EGU General Assembly 2012. Juan Escribano Rodriguez, Carmelo Alonso, Ana Maria Tarquis, Rosa Maria Benito, Carlos Hernandez Diaz-Ambrona. Comparison of NDVI fields obtained from different remote sensors. Geophysical Research Abstracts, 15, EGU2013-14153, 2013. EGU General Assembly 2013 Juan Escribano, Carlos G.H. Díaz-Ambrona, Laura Recuero, Margarita Huesca, Victor Cicuendez, Alicia Palacios, and Ana M. Tarquis. Application of Vegetation Indices to Estimate Acorn Production at Iberian Peninsula. Geophysical Research Abstracts, 16, EGU2014-16428, 2014. EGU General Assembly 2014. Acknowledgements This work was partially supported by ENESA under project P10 0220C-823

Soybean varieties discrimination using non-imaging hyperspectral sensor

NASA Astrophysics Data System (ADS)

da Silva Junior, Carlos Antonio; Nanni, Marcos Rafael; Shakir, Muhammad; Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; Cezar, Everson; de Gois, Givanildo; Lima, Mendelson; Wojciechowski, Julio Cesar; Shiratsuchi, Luciano Shozo

2018-03-01

Infrared region of electromagnetic spectrum has remarkable applications in crop studies. Infrared along with Red band has been used to develop certain vegetation indices. These indices like NDVI, EVI provide important information on any crop physiological stages. The main objective of this research was to discriminate 4 different soybean varieties (BMX Potência, NA5909, FT Campo Mourão and Don Mario) using non-imaging hyperspectral sensor. The study was conducted in four agricultural areas in the municipality of Deodápolis (MS), Brazil. For spectral analysis, 2400 field samples were taken from soybean leaves by means of FieldSpec 3 JR spectroradiometer in the range from 350 to 2500 nm. The data were evaluated through multivariate analysis with the whole set of spectral curves isolated by blue, green, red and near infrared wavelengths along with the addition of vegetation indices like (Enhanced Vegetation Index - EVI, Normalized Difference Vegetation Index - NDVI, Green Normalized Difference Vegetation Index - GNDVI, Soil-adjusted Vegetation Index - SAVI, Transformed Vegetation Index - TVI and Optimized Soil-Adjusted Vegetation Index - OSAVI). A number of the analysis performed where, discriminant (60 and 80% of the data), simulated discriminant (40 and 20% of data), principal component (PC) and cluster analysis (CA). Discriminant and simulated discriminant analyze presented satisfactory results, with average global hit rates of 99.28 and 98.77%, respectively. The results obtained by PC and CA revealed considerable associations between the evaluated variables and the varieties, which indicated that each variety has a variable that discriminates it more effectively in relation to the others. There was great variation in the sample size (number of leaves) for estimating the mean of variables. However, it was possible to observe that 200 leaves allow to obtain a maximum error of 2% in relation to the mean.

Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

USGS Publications Warehouse

Ji, Lei; Zhang, Li; Rover, Jennifer R.; Wylie, Bruce K.; Chen, Xuexia

2014-01-01

In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

Multispectral data processing from unmanned aerial vehicles: application in precision agriculture using different sensors and platforms

NASA Astrophysics Data System (ADS)

Piermattei, Livia; Bozzi, Carlo Alberto; Mancini, Adriano; Tassetti, Anna Nora; Karel, Wilfried; Pfeifer, Norbert

2017-04-01

Unmanned aerial vehicles (UAVs) in combination with consumer grade cameras have become standard tools for photogrammetric applications and surveying. The recent generation of multispectral, cost-efficient and lightweight cameras has fostered a breakthrough in the practical application of UAVs for precision agriculture. For this application, multispectral cameras typically use Green, Red, Red-Edge (RE) and Near Infrared (NIR) wavebands to capture both visible and invisible images of crops and vegetation. These bands are very effective for deriving characteristics like soil productivity, plant health and overall growth. However, the quality of results is affected by the sensor architecture, the spatial and spectral resolutions, the pattern of image collection, and the processing of the multispectral images. In particular, collecting data with multiple sensors requires an accurate spatial co-registration of the various UAV image datasets. Multispectral processed data in precision agriculture are mainly presented as orthorectified mosaics used to export information maps and vegetation indices. This work aims to investigate the acquisition parameters and processing approaches of this new type of image data in order to generate orthoimages using different sensors and UAV platforms. Within our experimental area we placed a grid of artificial targets, whose position was determined with differential global positioning system (dGPS) measurements. Targets were used as ground control points to georeference the images and as checkpoints to verify the accuracy of the georeferenced mosaics. The primary aim is to present a method for the spatial co-registration of visible, Red-Edge, and NIR image sets. To demonstrate the applicability and accuracy of our methodology, multi-sensor datasets were collected over the same area and approximately at the same time using the fixed-wing UAV senseFly "eBee". The images were acquired with the camera Canon S110 RGB, the multispectral cameras Canon S110 NIR and S110 RE and with the multi-camera system Parrot Sequoia, which is composed of single-band cameras (Green, Red, Red Edge, NIR and RGB). Imagery from each sensor was georeferenced and mosaicked with the commercial software Agisoft PhotoScan Pro and different approaches for image orientation were compared. To assess the overall spatial accuracy of each dataset the root mean square error was computed between check point coordinates measured with dGPS and coordinates retrieved from georeferenced image mosaics. Additionally, image datasets from different UAV platforms (i.e. DJI Phantom 4Pro, DJI Phantom 3 professional, and DJI Inspire 1 Pro) were acquired over the same area and the spatial accuracy of the orthoimages was evaluated.

Comparison of Huanjing and Landsat satellite remote sensing of the spatial heterogeneity of Qinghai-Tibet alpine grassland

NASA Astrophysics Data System (ADS)

Wang, Junbang; Sun, Wenyi

2014-11-01

Remote sensing is widely applied in the study of terrestrial primary production and the global carbon cycle. The researches on the spatial heterogeneity in images with different sensors and resolutions would improve the application of remote sensing. In this study two sites on alpine meadow grassland in Qinghai, China, which have distinct fractal vegetation cover, were used to test and analyze differences between Normalized Difference Vegetation Index (NDVI) and enhanced vegetation index (EVI) derived from the Huanjing (HJ) and Landsat Thematic Mapper (TM) sensors. The results showed that: 1) NDVI estimated from HJ were smaller than the corresponding values from TM at the two sites whereas EVI were almost the same for the two sensors. 2) The overall variance represented by HJ data was consistently about half of that of Landsat TM although their nominal pixel size is approximately 30m for both sensors. The overall variance from EVI is greater than that from NDVI. The difference of the range between the two sensors is about 6 pixels at 30m resolution. The difference of the range in which there is not more corrective between two vegetation indices is about 1 pixel. 3) The sill decreased when pixel size increased from 30m to 1km, and then decreased very quickly when pixel size is changed to 250m from 30m or 90m but slowly when changed from 250m to 500m. HJ can capture this spatial heterogeneity to some extent and this study provides foundations for the use of the sensor for validation of net primary productivity estimates obtained from ecosystem process models.

Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images

NASA Astrophysics Data System (ADS)

Chiang, Y.; Chen, K.

2013-12-01

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

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

USGS Publications Warehouse

Chen, X.; Vierling, Lee; Deering, D.

2005-01-01

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes. ?? 2005 Elsevier Inc. All rights reserved.

A multimodal image sensor system for identifying water stress in grapevines

NASA Astrophysics Data System (ADS)

Zhao, Yong; Zhang, Qin; Li, Minzan; Shao, Yongni; Zhou, Jianfeng; Sun, Hong

2012-11-01

Water stress is one of the most common limitations of fruit growth. Water is the most limiting resource for crop growth. In grapevines, as well as in other fruit crops, fruit quality benefits from a certain level of water deficit which facilitates to balance vegetative and reproductive growth and the flow of carbohydrates to reproductive structures. A multi-modal sensor system was designed to measure the reflectance signature of grape plant surfaces and identify different water stress levels in this paper. The multi-modal sensor system was equipped with one 3CCD camera (three channels in R, G, and IR). The multi-modal sensor can capture and analyze grape canopy from its reflectance features, and identify the different water stress levels. This research aims at solving the aforementioned problems. The core technology of this multi-modal sensor system could further be used as a decision support system that combines multi-modal sensory data to improve plant stress detection and identify the causes of stress. The images were taken by multi-modal sensor which could output images in spectral bands of near-infrared, green and red channel. Based on the analysis of the acquired images, color features based on color space and reflectance features based on image process method were calculated. The results showed that these parameters had the potential as water stress indicators. More experiments and analysis are needed to validate the conclusion.

Characterizing sub-arctic peatland vegeation using height estimates from structure from motion and an unmanned aerial system (UAS)

NASA Astrophysics Data System (ADS)

Palace, M. W.; DelGreco, J.; Herrick, C.; Sullivan, F.; Varner, R. K.

2017-12-01

The collapse of permafrost, due to thawing, changes landscape topography, hydrology and vegetation. Changes in plant species composition influence methane production pathways and methane emission rates. The complex spatial heterogeneity of vegetation composition across peatlands proves important in quantifying methane emissions. Effort to characterize vegetation across these permafrost peatlands has been conducted with varied success, with difficulty seen in estimating some cover types that are at opposite ends of the permafrost collapse transition, ie palsa/tall shrub and tall graminoid. This is because some of the species are the same (horsetail) and some of the species have similar structure (horsetail/Carex spp.). High resolution digital elevation maps, developed with airborne LIght Detection And Ranging (lidar) have provided insight into some wetland attributes, but lidar collection is costly and requires extensive data processing effort. Lidar information also lacks the spectral information that optical sensors provide. We used an inexpensive Unmanned Aerial Vehicle (UAV) with an optical sensor to image a mire in northern Sweden (Stordalen Mire) in 2015. We collected 700 overlapping images that were stitched together using Structure from Motion (SfM). SfM analysis also provided, due to parallax, the ability to develop a height map of vegetation. This height map was used, along with textural analysis, to develop an artificial neural network to predict five vegetation cover types. Using 200 training points, we found improvements in our prediction of these cover types. We suggest that using the digital height model from SfM provides useful information in remotely sensing vegetation across a permafrost collapsing region that exhibit resulting changes in vegetation composition. The ability to rapidly and inexpensively deploy such a UAV system provides the opportunity to examine multiple sites with limited personnel effort in remote areas.

Imaging Laser Altimetry in the Amazon: Mapping Large Areas of Topography, Vegetation Height and Structure, and Biomass

NASA Technical Reports Server (NTRS)

Blair, J. Bryan; Nelson, B.; dosSantos, J.; Valeriano, D.; Houghton, R.; Hofton, M.; Lutchke, S.; Sun, Q.

2002-01-01

A flight mission of NASA GSFC's Laser Vegetation Imaging Sensor (LVIS) is planned for June-August 2003 in the Amazon region of Brazil. The goal of this flight mission is to map the vegetation height and structure and ground topography of a large area of the Amazon. This data will be used to produce maps of true ground topography, vegetation height, and estimated above-ground biomass and for comparison with and potential calibration of Synthetic Aperture Radar (SAR) data. Approximately 15,000 sq. km covering various regions of the Amazon will be mapped. The LVIS sensor has the unique ability to accurately sense the ground topography beneath even the densest of forest canopies. This is achieved by using a high signal-to-noise laser altimeter to detect the very weak reflection from the ground that is available only through small gaps in between leaves and between tree canopies. Often the amount of ground signal is 1% or less of the total returned echo. Once the ground elevation is identified, that is used as the reference surface from which we measure the vertical height and structure of the vegetation. Test data over tropical forests have shown excellent correlation between LVIS measurements and biomass, basal area, stem density, ground topography, and canopy height. Examples of laser altimetry data over forests and the relationships to biophysical parameters will be shown. Also, recent advances in the LVIS instrument will be discussed.

A Simple and Effective Image Normalization Method to Monitor Boreal Forest Change in a Siberian Burn Chronosequence across Sensors and across Time

NASA Astrophysics Data System (ADS)

Chen, X.; Vierling, L. A.; Deering, D. W.

2004-12-01

Satellite data offer unique perspectives for monitoring and quantifying land cover change, however, the radiometric consistency among co-located multi-temporal images is difficult to maintain due to variations in sensors and atmosphere. To detect accurate landscape change using multi-temporal images, we developed a new relative radiometric normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on 9 June 1990 (Landsat 4), 20 June 2000, and 26 August 2001 (Landsat 7) for analyses over boreal forests near the Siberian city of Krasnoyarsk. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Reduced Simple Ratio (RSR) were investigated in the normalization study. The temporally invariant cluster (TIC) centers were identified through a point density map of the base image and the target image and a normalization regression line was created through all TIC centers. The target image digital data were then converted using the regression function so that the two images could be compared using the resulting common radiometric scale. We found that EVI was very sensitive to vegetation structure and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. NDVI was a very effective vegetation index to reduce the influence of shadow, while EVI was very sensitive to shadowing. After normalization, correlations of NDVI and EVI with field collected total Leaf Area Index (LAI) data in 2000 and 2001 were significantly improved; the r-square values in these regressions increased from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI ¡°cancellation effect¡± where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared with some previous relative normalization methods, this new method can avoid subjective selection of a normalization regression line. It does not require high level programming and statistical analyses, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare.

Spatial and temporal remote sensing data fusion for vegetation monitoring

USDA-ARS?s Scientific Manuscript database

The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery...

The NDVI: Back to basics

USDA-ARS?s Scientific Manuscript database

Ease of access to satellite sensor imagery and image products has driven the use of remote sensing data in many disciplines, including landscape ecology, forestry, environmental and wildlife management, agriculture, and epidemiology. A common format of these data is as vegetation indices and of thes...

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.

German Radar Observation Shuttle Experiment (ROSE)

NASA Technical Reports Server (NTRS)

Sleber, A. J.; Hartl, P.; Haydn, R.; Hildebrandt, G.; Konecny, G.; Muehlfeld, R.

1984-01-01

The success of radar sensors in several different application areas of interest depends on the knowledge of the backscatter of radar waves from the targets of interest, the variance of these interaction mechanisms with respect to changing measurement parameters, and the determination of the influence of he measuring systems on the results. The incidence-angle dependency of the radar cross section of different natural targets is derived. Problems involved by the combination of data gained with different sensors, e.g., MSS-, TM-, SPOTand SAR-images are analyzed. Radar cross-section values gained with ground-based radar spectrometers and spaceborne radar imaging, and non-imaging scatterometers and spaceborne radar images from the same areal target are correlated. The penetration of L-band radar waves into vegetated and nonvegetated surfaces is analyzed.

A forestry GIS-based study on evaluating the potential of imaging spectroscopy in mapping forest land fertility

NASA Astrophysics Data System (ADS)

Mõttus, Matti; Takala, Tuure

2014-12-01

Fertility, or the availability of nutrients and water, controls forest productivity. It affects its carbon sequestration, and thus the forest's effect on climate, as well as its commercial value. Although the availability of nutrients cannot be measured directly using remote sensing methods, fertility alters several vegetation traits detectable from the reflectance spectra of the forest stand, including its pigment content and water stress. However, forest reflectance is also influenced by other factors, such as species composition and stand age. Here, we present a case study demonstrating how data obtained using imaging spectroscopy is correlated with site fertility. The study was carried out in Hyytiälä, Finland, in the southern boreal forest zone. We used a database of state-owned forest stands including basic forestry variables and a site fertility index. To test the suitability of imaging spectroscopy with different spatial and spectral resolutions for site fertility mapping, we performed two airborne acquisitions using different sensor configurations. First, the sensor was flown at a high altitude with high spectral resolution resulting in a pixel size in the order of a tree crown. Next, the same area was flown to provide reflectance data with sub-meter spatial resolution. However, to maintain usable signal-to-noise ratios, several spectral channels inside the sensor were combined, thus reducing spectral resolution. We correlated a number of narrowband vegetation indices (describing canopy biochemical composition, structure, and photosynthetic activity) on site fertility. Overall, site fertility had a significant influence on the vegetation indices but the strength of the correlation depended on dominant species. We found that high spatial resolution data calculated from the spectra of sunlit parts of tree crowns had the strongest correlation with site fertility.

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.

DMSP Special Sensor Microwave/Imager Calibration/Validation

DTIC Science & Technology

1991-05-20

degrade , regardless of the algorithm, seems to be about 2 mm/hr. SSMAI wind speed retrieval accuracy in... the U.S. and ;ome rangeland of the western U.S. at peak vegetation cover. 2. The " cerrado " vegetation regior, of central Brazil. These are savanna tye...presented; with and without the 85 GIIz channels as a con.;_tcuence of the degradation of the 85 GHz channels on the SSM/I on DMSP F-8. The primary data

Preliminary evaluation of the airborne imaging spectrometer for vegetation analysis in the Klamath National Forest of northeastern California

NASA Technical Reports Server (NTRS)

Strahler, A. H.; Woodcock, C. E.; Avila, F. X.

1985-01-01

The experiences and results associated with a project entitled Preliminary Evaluation of the Airborne Imaging Spectrometer for Vegetation Analysis is documented. The primary goal of the project was to provide ground truth, manual interpretation, and computer processing of data from an experimental flight of the Airborne Infrared Spectrometer (AIS) to determine the extent to which high spectral resolution remote sensing could differentiate among plant species, and especially species of conifers, for a naturally vegetated test site. Through the course of the research, JPL acquired AIS imagery of the test areas in the Klamath National Forest, northeastern California, on two overflights of both the Dock Well and Grass Lake transects. Over the next year or so, three generations of data was also received: first overflight, second overflight, and reprocessed second overflight. Two field visits were made: one trip immediately following the first overflight to note snow conditions and temporally-related vegetation states at the time of the sensor overpass; and a second trip about six weeks later, following acquisition of prints of the images from the first AIS overpass.

Monitoring, analysis and classification of vegetation and soil data collected by a small and lightweight hyperspectral imaging system

NASA Astrophysics Data System (ADS)

Mönnig, Carsten

2014-05-01

The increasing precision of modern farming systems requires a near-real-time monitoring of agricultural crops in order to estimate soil condition, plant health and potential crop yield. For large sized agricultural plots, satellite imagery or aerial surveys can be used at considerable costs and possible time delays of days or even weeks. However, for small to medium sized plots, these monitoring approaches are cost-prohibitive and difficult to assess. Therefore, we propose within the INTERREG IV A-Project SMART INSPECTORS (Smart Aerial Test Rigs with Infrared Spectrometers and Radar), a cost effective, comparably simple approach to support farmers with a small and lightweight hyperspectral imaging system to collect remotely sensed data in spectral bands in between 400 to 1700nm. SMART INSPECTORS includes the whole remote sensing processing chain of small scale remote sensing from sensor construction, data processing and ground truthing for analysis of the results. The sensors are mounted on a remotely controlled (RC) Octocopter, a fixed wing RC airplane as well as on a two-seated Autogyro for larger plots. The high resolution images up to 5cm on the ground include spectra of visible light, near and thermal infrared as well as hyperspectral imagery. The data will be analyzed using remote sensing software and a Geographic Information System (GIS). The soil condition analysis includes soil humidity, temperature and roughness. Furthermore, a radar sensor is envisaged for the detection of geomorphologic, drainage and soil-plant roughness investigation. Plant health control includes drought stress, vegetation health, pest control, growth condition and canopy temperature. Different vegetation and soil indices will help to determine and understand soil conditions and plant traits. Additional investigation might include crop yield estimation of certain crops like apples, strawberries, pasture land, etc. The quality of remotely sensed vegetation data will be tested with ground truthing tools like a spectrometer, visual inspection and ground control panel. The soil condition will also be monitored with a wireless sensor network installed on the examined plots of interest. Provided with this data, a farmer can respond immediately to potential threats with high local precision. In this presentation, preliminary results of hyperspectral images of distinctive vegetation cover and soil on different pasture test plots are shown. After an evaluation period, the whole processing chain will offer farmers a unique, near real- time, low cost solution for small to mid-sized agricultural plots in order to easily assess crop and soil quality and the estimation of harvest. SMART INSPECTORS remotely sensed data will form the basis for an input in a decision support system which aims to detect crop related issues in order to react quickly and efficiently, saving fertilizer, water or pesticides.

Wedge imaging spectrometer: application to drug and pollution law enforcement

NASA Astrophysics Data System (ADS)

Elerding, George T.; Thunen, John G.; Woody, Loren M.

1991-08-01

The Wedge Imaging Spectrometer (WIS) represents a novel implementation of an imaging spectrometer sensor that is compact and rugged and, therefore, suitable for use in drug interdiction and pollution monitoring activities. With performance characteristics equal to comparable conventional imaging spectrometers, it would be capable of detecting and identifying primary and secondary indicators of drug activities and pollution events. In the design, a linear wedge filter is mated to an area array of detectors to achieve two-dimensional sampling of the combined spatial/spectral information passed by the filter. As a result, the need for complex and delicate fore optics is avoided, and the size and weight of the instrument are approximately 50% that of comparable sensors. Spectral bandwidths can be controlled to provide relatively narrow individual bandwidths over a broad spectrum, including all visible and infrared wavelengths. This sensor concept has been under development at the Hughes Aircraft Co. Santa Barbara Research Center (SBRC), and hardware exists in the form of a brassboard prototype. This prototype provides 64 spectral bands over the visible and near infrared region (0.4 to 1.0 micrometers ). Implementation issues have been examined, and plans have been formulated for packaging the sensor into a test-bed aircraft for demonstration of capabilities. Two specific areas of utility to the drug interdiction problem are isolated: (1) detection and classification of narcotic crop growth areas and (2) identification of coca processing sites, cued by the results of broad-area survey and collateral information. Vegetation stress and change-detection processing may also be useful in detecting active from dormant airfields. For pollution monitoring, a WIS sensor could provide data with fine spectral and spatial resolution over suspect areas. On-board or ground processing of the data would isolate the presence of polluting effluents, effects on vegetation caused by airborne or other pollutants, or anomalous ground conditions indicative of buried or dumped toxic materials.

Solfatara Crater Seen Through Hyperspectral Dais Sensor Data In The Tir Region: Temperature Map and Spectral Emissivity Image For Mineralogical Species Identification.

NASA Astrophysics Data System (ADS)

Merucci, L.; Buongiorno, M. F.; Teggi, S.; Bogliolo, M. P.

Temperature map and spectral emissivity have been retrieved by means of the TIR re- gion data collected by the DAIS airborne hyperspectral sensor on the Solfatara, Campi Flegrei, Italy, during the July 27, 1997 flight. During the 7915 DAIS flight a contem- poraneous field campaign was carried out in order to measure the surface temperature in the Solfatara crater and a radiosonde has been launched to measure the local at- mospheric profile. A normalized vegetation index filter has been used to select in the Solfatara crater scene the areas not covered by vegetation upon which the temperature and emissivity retrieval algorithms have been applied. The atmospheric contribute has been estimated by means of the MODTRAN radiative transfer code. The temperature map has been finally validated with the field measurements and the spectral emissivity image has been compared with the spectra available for the mineralogical species that cover the Solfatara crater.

Three-dimensional estimates of tree canopies: Scaling from high-resolution UAV data to satellite observations

NASA Astrophysics Data System (ADS)

Sankey, T.; Donald, J.; McVay, J.

2015-12-01

High resolution remote sensing images and datasets are typically acquired at a large cost, which poses big a challenge for many scientists. Northern Arizona University recently acquired a custom-engineered, cutting-edge UAV and we can now generate our own images with the instrument. The UAV has a unique capability to carry a large payload including a hyperspectral sensor, which images the Earth surface in over 350 spectral bands at 5 cm resolution, and a lidar scanner, which images the land surface and vegetation in 3-dimensions. Both sensors represent the newest available technology with very high resolution, precision, and accuracy. Using the UAV sensors, we are monitoring the effects of regional forest restoration treatment efforts. Individual tree canopy width and height are measured in the field and via the UAV sensors. The high-resolution UAV images are then used to segment individual tree canopies and to derive 3-dimensional estimates. The UAV image-derived variables are then correlated to the field-based measurements and scaled to satellite-derived tree canopy measurements. The relationships between the field-based and UAV-derived estimates are then extrapolated to a larger area to scale the tree canopy dimensions and to estimate tree density within restored and control forest sites.

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.

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.

Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor

NASA Astrophysics Data System (ADS)

Matongera, Trylee Nyasha; Mutanga, Onisimo; Dube, Timothy; Sibanda, Mbulisi

2017-05-01

Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = -10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor.

Comparison of Landsat MSS and merged MSS/RBV data for analysis of natural vegetation

NASA Technical Reports Server (NTRS)

Roller, N. E. G.; Cox, S.

1980-01-01

Improved resolution could make satellite remote sensing data more useful for surveys of natural vegetation. Although improved satellite/sensor systems appear to be several years away, one potential interim solution to the problem of achieving greater resolution without sacrificing spectral sensitivity is through the merging of Landsat RBV and MSS data. This paper describes the results of a study performed to obtain a preliminary evaluation of the usefulness of two types of products that can be made by merging Landsat RBV and MSS data. The products generated were a false color composite image and a computer recognition map. Of these two products, the false color composite image appears to be the most useful.

Analysis of relationships between land surface temperature and land use changes in the Yellow River Delta

NASA Astrophysics Data System (ADS)

Ning, Jicai; Gao, Zhiqiang; Meng, Ran; Xu, Fuxiang; Gao, Meng

2018-06-01

This study analyzed land use and land cover changes and their impact on land surface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of land surface temperature, a mono-window algorithm was applied to retrieve the regional land surface temperature. The results showed bilinear correlation between land surface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the land surface temperature and the vegetation indices are correlated negatively, and vice versa. Land surface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.

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

Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

NASA Astrophysics Data System (ADS)

Parekh, R. A.; Mehta, R. L.; Vyas, A.

2016-10-01

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9thDec. 2012, 28thJan. 2013 and 22nd Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

Using High-Resolution, Regional-Scale Data to Characterize Floating Aquatic Nuisance Vegetation in Coastal Louisiana Navigation Channels

DTIC Science & Technology

2014-01-01

Comparison of footprints from various image sensors used in this study . Landsat (blue) is in the upper left panel, SPOT (yellow) is in the upper right...the higher resolution sensors evaluated as part of this study are limited to four spectral bands. Moderate resolution processing. ArcGIS ...moderate, effective useful coverage may be much more limited for a scene that includes significant amounts of water. Throughout the study period, SPOT 4

The use of UAVs for monitoring land degradation

NASA Astrophysics Data System (ADS)

Themistocleous, Kyriacos

2017-10-01

Land degradation is one of the causes of desertification of drylands in the Mediterranean. UAVs can be used to monitor and document the various variables that cause desertification in drylands, including overgrazing, aridity, vegetation loss, etc. This paper examines the use of UAVs and accompanying sensors to monitor overgrazing, vegetation stress and aridity in the study area. UAV images can be used to generate digital elevation models (DEMs) to examine the changes in microtopography as well as ortho-photos were used to detect changes in vegetation patterns. The combined data of the digital elevation models and the orthophotos can be used to identify the mechanisms for desertification in the study area.

a Novel Ihs-Ga Fusion Method Based on Enhancement Vegetated Area

NASA Astrophysics Data System (ADS)

Niazi, S.; Mokhtarzade, M.; Saeedzadeh, F.

2015-12-01

Pan sharpening methods aim to produce a more informative image containing the positive aspects of both source images. However, the pan sharpening process usually introduces some spectral and spatial distortions in the resulting fused image. The amount of these distortions varies highly depending on the pan sharpening technique as well as the type of data. Among the existing pan sharpening methods, the Intensity-Hue-Saturation (IHS) technique is the most widely used for its efficiency and high spatial resolution. When the IHS method is used for IKONOS or QuickBird imagery, there is a significant color distortion which is mainly due to the wavelengths range of the panchromatic image. Regarding the fact that in the green vegetated regions panchromatic gray values are much larger than the gray values of intensity image. A novel method is proposed which spatially adjusts the intensity image in vegetated areas. To do so the normalized difference vegetation index (NDVI) is used to identify vegetation areas where the green band is enhanced according to the red and NIR bands. In this way an intensity image is obtained in which the gray values are comparable to the panchromatic image. Beside the genetic optimization algorithm is used to find the optimum weight parameters in order to gain the best intensity image. Visual and statistical analysis proved the efficiency of the proposed method as it significantly improved the fusion quality in comparison to conventional IHS technique. The accuracy of the proposed pan sharpening technique was also evaluated in terms of different spatial and spectral metrics. In this study, 7 metrics (Correlation Coefficient, ERGAS, RASE, RMSE, SAM, SID and Spatial Coefficient) have been used in order to determine the quality of the pan-sharpened images. Experiments were conducted on two different data sets obtained by two different imaging sensors, IKONOS and QuickBird. The result of this showed that the evaluation metrics are more promising for our fused image in comparison to other pan sharpening methods.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.

PubMed

Andújar, Dionisio; Rueda-Ayala, Victor; Moreno, Hugo; Rosell-Polo, Joan Ramón; Escolá, Alexandre; Valero, Constantino; Gerhards, Roland; Fernández-Quintanilla, César; Dorado, José; Griepentrog, Hans-Werner

2013-10-29

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12-14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

PubMed Central

Andújar, Dionisio; Rueda-Ayala, Victor; Moreno, Hugo; Rosell-Polo, Joan Ramón; Escolà, Alexandre; Valero, Constantino; Gerhards, Roland; Fernández-Quintanilla, César; Dorado, José; Griepentrog, Hans-Werner

2013-01-01

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying. PMID:24172283

Characterization of Landsat-7 to Landsat-8 Reflective Wavelength and Normalized Difference Vegetation Index Continuity

NASA Technical Reports Server (NTRS)

Roy, D. P.; Kovalskyy, V.; Zhang, H. K.; Vermote, E. F.; Yan, L.; Kumar, S. S.; Egorov, A.

2016-01-01

At over 40 years, the Landsat satellites provide the longest temporal record of space-based land surface observations, and the successful 2013 launch of the Landsat-8 is continuing this legacy. Ideally, the Landsat data record should be consistent over the Landsat sensor series. The Landsat-8 Operational Land Imager (OLI) has improved calibration, signal to noise characteristics, higher 12-bit radiometric resolution, and spectrally narrower wavebands than the previous Landsat-7 Enhanced Thematic Mapper (ETM+). Reflective wavelength differences between the two Landsat sensors depend also on the surface reflectance and atmospheric state which are difficult to model comprehensively. The orbit and sensing geometries of the Landsat- 8 OLI and Landsat-7 ETM+ provide swath edge overlapping paths sensed only one day apart. The overlap regions are sensed in alternating backscatter and forward scattering orientations so Landsat bi-directional reflectance effects are evident but approximately balanced between the two sensors when large amounts of time series data are considered. Taking advantage of this configuration a total of 59 million 30m corresponding sensor observations extracted from 6,317 Landsat-7 ETM+ and Landsat-8 OLI images acquired over three winter and three summer months for all the conterminous United States (CONUS) are compared. Results considering different stages of cloud and saturation filtering, and filtering to reduce one day surface state differences, demonstrate the importance of appropriate per-pixel data screening. Top of atmosphere (TOA) and atmospherically corrected surface reflectance for the spectrally corresponding visible, near infrared and shortwave infrared bands, and derived normalized difference vegetation index (NDVI), are compared and their differences quantified. On average the OLI TOA reflectance is greater than the ETM+ TOA reflectance for all bands, with greatest differences in the near-infrared (NIR) and the shortwave infrared bands due to the quite different spectral response functions between the sensors. The atmospheric correction reduces the mean difference in the NIR and shortwave infrared but increases the mean difference in the visible bands. Regardless of whether TOA or surface reflectance are used to generate NDVI, on average, for vegetated soil and vegetation surfaces (0 = NDVI = 1), the OLI NDVI is greater than the ETM+ NDVI. Statistical functions to transform between the comparable sensor bands and sensor NDVI values are presented so that the user community may apply them in their own research to improve temporal continuity between the Landsat-7 ETM+ and Landsat-8 OLI sensor data. The transformation functions were developed using ordinary least squares (OLS) regression and were fit quite reliably (r2 values is greater than 0.7 for the reflectance data and greater than 0.9 for the NDVI data, p-values less than 0.0001).

Registration of Laser Scanning Point Clouds and Aerial Images Using either Artificial or Natural Tie Features

NASA Astrophysics Data System (ADS)

Rönnholm, P.; Haggrén, H.

2012-07-01

Integration of laser scanning data and photographs is an excellent combination regarding both redundancy and complementary. Applications of integration vary from sensor and data calibration to advanced classification and scene understanding. In this research, only airborne laser scanning and aerial images are considered. Currently, the initial registration is solved using direct orientation sensors GPS and inertial measurements. However, the accuracy is not usually sufficient for reliable integration of data sets, and thus the initial registration needs to be improved. A registration of data from different sources requires searching and measuring of accurate tie features. Usually, points, lines or planes are preferred as tie features. Therefore, the majority of resent methods rely highly on artificial objects, such as buildings, targets or road paintings. However, in many areas no such objects are available. For example in forestry areas, it would be advantageous to be able to improve registration between laser data and images without making additional ground measurements. Therefore, there is a need to solve registration using only natural features, such as vegetation and ground surfaces. Using vegetation as tie features is challenging, because the shape and even location of vegetation can change because of wind, for example. The aim of this article was to compare registration accuracies derived by using either artificial or natural tie features. The test area included urban objects as well as trees and other vegetation. In this area, two registrations were performed, firstly, using mainly built objects and, secondly, using only vegetation and ground surface. The registrations were solved applying the interactive orientation method. As a result, using artificial tie features leaded to a successful registration in all directions of the coordinate system axes. In the case of using natural tie features, however, the detection of correct heights was difficult causing also some tilt errors. The planimetric registration was accurate.

Fall's Changing Colors

NASA Technical Reports Server (NTRS)

2002-01-01

As the clouds allowed during the past two months, the Sea-viewing Wide field-of-View Sensor (SeaWiFS) recorded the changing colors of eastern U.S. and Canadian vegetation. This series of true-color images from the fall of 2000 shows the deciduous forests of the region change from dark green to bright red and orange, and begin to drop their leaves. Image provided by the SeaWiFS Project, NASA/Goddard Space Flight Center, and ORBIMAGE

Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China

NASA Astrophysics Data System (ADS)

Zhang, Zhiming; de Wulf, Robert R.; van Coillie, Frieke M. B.; Verbeke, Lieven P. C.; de Clercq, Eva M.; Ou, Xiaokun

2011-01-01

Mapping of vegetation using remote sensing in mountainous areas is considerably hampered by topographic effects on the spectral response pattern. A variety of topographic normalization techniques have been proposed to correct these illumination effects due to topography. The purpose of this study was to compare six different topographic normalization methods (Cosine correction, Minnaert correction, C-correction, Sun-canopy-sensor correction, two-stage topographic normalization, and slope matching technique) for their effectiveness in enhancing vegetation classification in mountainous environments. Since most of the vegetation classes in the rugged terrain of the Lancang Watershed (China) did not feature a normal distribution, artificial neural networks (ANNs) were employed as a classifier. Comparing the ANN classifications, none of the topographic correction methods could significantly improve ETM+ image classification overall accuracy. Nevertheless, at the class level, the accuracy of pine forest could be increased by using topographically corrected images. On the contrary, oak forest and mixed forest accuracies were significantly decreased by using corrected images. The results also showed that none of the topographic normalization strategies was satisfactorily able to correct for the topographic effects in severely shadowed areas.

Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat

PubMed Central

Yao, Xinfeng; Yao, Xia; Jia, Wenqing; Tian, Yongchao; Ni, Jun; Cao, Weixing; Zhu, Yan

2013-01-01

Various sensors have been used to obtain the canopy spectral reflectance for monitoring above-ground plant nitrogen (N) uptake in winter wheat. Comparison and intercalibration of spectral reflectance and vegetation indices derived from different sensors are important for multi-sensor data fusion and utilization. In this study, the spectral reflectance and its derived vegetation indices from three ground-based sensors (ASD Field Spec Pro spectrometer, CropScan MSR 16 and GreenSeeker RT 100) in six winter wheat field experiments were compared. Then, the best sensor (ASD) and its normalized difference vegetation index (NDVI (807, 736)) for estimating above-ground plant N uptake were determined (R2 of 0.885 and RMSE of 1.440 g·N·m−2 for model calibration). In order to better utilize the spectral reflectance from the three sensors, intercalibration models for vegetation indices based on different sensors were developed. The results indicated that the vegetation indices from different sensors could be intercalibrated, which should promote application of data fusion and make monitoring of above-ground plant N uptake more precise and accurate. PMID:23462622

Demonstration of Airborne Wide Area Assessment Technologies at Pueblo Precision Bombing Ranges, Colorado. Hyperspectral Imaging, Version 2.0

DTIC Science & Technology

2007-09-27

the spatial and spectral resolution ...variety of geological and vegetation mapping efforts, the Hymap sensor offered the best available combination of spectral and spatial resolution , signal... The limitations of the technology currently relate to spatial and spectral resolution and geo- correction accuracy. Secondly, HSI datasets

Time series analysis of satellite multi-sensors imagery to study the recursive abnormal grow of floating macrophyte in the lake victoria (central Africa)

NASA Astrophysics Data System (ADS)

Fusilli, Lorenzo; Cavalli, Rosa Maria; Laneve, Giovanni; Pignatti, Stefano; Santilli, Giancarlo; Santini, Federico

2010-05-01

Remote sensing allows multi-temporal mapping and monitoring of large water bodies. The importance of remote sensing for wetland and inland water inventory and monitoring at all scales was emphasized several times by the Ramsar Convention on Wetlands and from EU projects like SALMON and ROSALMA, e.g. by (Finlayson et al., 1999) and (Lowry and Finlayson, 2004). This paper aims at assessing the capability of time series of satellite imagery to provide information suitable for enhancing the understanding of the temporal cycles shown by the macrophytes growing in order to support the monitor and management of the lake Victoria water resources. The lake Victoria coastal areas are facing a number of challenges related to water resource management which include growing population, water scarcity, climate variability and water resource degradation, invasive species, water pollution. The proliferation of invasive plants and aquatic weeds, is of growing concern. In particular, let us recall some of the problems caused by the aquatic weeds growing: Ø interference with human activities such as fishing, and boating; Ø inhibition or interference with a balanced fish population; Ø fish killing due to removal of too much oxygen from the water; Ø production of quiet water areas that are ideal for mosquito breeding. In this context, an integrated use of medium/high resolution images from sensors like MODIS, ASTER, LANDSAT/TM and whenever available CHRIS offers the possibility of creating a congruent time series allowing the analysis of the floating vegetation dynamic on an extended temporal basis. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution, further its spatial resolution can results not always adequate to map the extension of floating plants. Therefore, the integrated use of sensors with different spatial resolution, were used to map across seasons the evolution of the phenomena. The integrated use of satellite resources allowed the estimate of the temporal variability of physical parameters that were used to i) sample the spatio-temporal distribution of the whole floating vegetation (i.e. native vegetation and weed) and ii) assess the seasonal recurrence of the abnormal weeds grow, as well as, their possible relation with the hydrological regimes of the rivers. The paper describes how the 2000 - 2009 MODIS images time series, were analysed (navigated and processed) to derive i) the map the floating vegetation on the test area and ii) identify the areas more interested by the growing iii) to discriminate, whenever possible, according to the spectral and spatial resolution of the sensor applied (i.e. LANDSAT, ASTER, CHRIS), the different vegetation species in order to discriminate the weeds from the floating vegetation. The spectral identification of the different species was performed by exploiting the results of a field campaign performed in the past along the Kenyan coastal areas devoted to define a data base of spectral signatures of the main species. Spectral information was treated to define indexes and spectral analysis procedure customized to multispectral high resolution satellite data. Moreover, the results of the images time series has been analysed to identify a possible definition of the temporal occurrence of the floating vegetation growing considering both the natural phenomenological cycles and the conditions related to the abnormal growing. These results, whenever related to ancillary hydrological information (e.g. the amount of rain), they have shown that the synergy of MODIS images time series with lower temporal frequency time series imagery is a powerful tool to monitor the lake Victoria ecosystem and to follow the floating vegetation extension and even to foresee the possibility to set up a model for the abnormal vegetation growing.

Seasonal vegetation characteristics of the United States

USGS Publications Warehouse

Reed, Bradley C.; Yang, Limin

1997-01-01

The U.S. Geological Survey's EROS Data Center has created a prototype 1‐km resolution data base of vegetation seasonal characteristics. The characteristics are derived from time‐series NDVI data collected by the AVHRR satellite sensor. Information covering the 5 years 1989–1993 is included in the data base. Although quantitative validation of the seasonal characteristics cannot be made until several evaluation efforts are completed, general observations are possible by viewing images of the seasonal parameters. Figures 2 through 8 show several examples of the seasonal characteristics data base.

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.

Object-based landslide mapping on satellite images from different sensors

NASA Astrophysics Data System (ADS)

Hölbling, Daniel; Friedl, Barbara; Eisank, Clemens; Blaschke, Thomas

2015-04-01

Several studies have proven that object-based image analysis (OBIA) is a suitable approach for landslide mapping using remote sensing data. Mostly, optical satellite images are utilized in combination with digital elevation models (DEMs) for semi-automated mapping. The ability of considering spectral, spatial, morphometric and contextual features in OBIA constitutes a significant advantage over pixel-based methods, especially when analysing non-uniform natural phenomena such as landslides. However, many of the existing knowledge-based OBIA approaches for landslide mapping are rather complex and are tailored to specific data sets. These restraints lead to a lack of transferability of OBIA mapping routines. The objective of this study is to develop an object-based approach for landslide mapping that is robust against changing input data with different resolutions, i.e. optical satellite imagery from various sensors. Two study sites in Taiwan were selected for developing and testing the landslide mapping approach. One site is located around the Baolai village in the Huaguoshan catchment in the southern-central part of the island, the other one is a sub-area of the Taimali watershed in Taitung County near the south-eastern Pacific coast. Both areas are regularly affected by severe landslides and debris flows. A range of very high resolution (VHR) optical satellite images was used for the object-based mapping of landslides and for testing the transferability across different sensors and resolutions: (I) SPOT-5, (II) Formosat-2, (III) QuickBird, and (IV) WorldView-2. Additionally, a digital elevation model (DEM) with 5 m spatial resolution and its derived products (e.g. slope, plan curvature) were used for supporting the semi-automated mapping, particularly for differentiating source areas and accumulation areas according to their morphometric characteristics. A focus was put on the identification of comparatively stable parameters (e.g. relative indices), which could be transferred to the different satellite images. The presence of bare ground was assumed to be an evidence for the occurrence of landslides. For separating vegetated from non-vegetated areas the Normalized Difference Vegetation Index (NDVI) was primarily used. Each image was divided into two respective parts based on an automatically calculated NDVI threshold value in eCognition (Trimble) software by combining the homogeneity criterion of multiresolution segmentation and histogram-based methods, so that heterogeneity is increased to a maximum. Expert knowledge models, which depict features and thresholds that are usually used by experts for digital landslide mapping, were considered for refining the classification. The results were compared to the respective results from visual image interpretation (i.e. manually digitized reference polygons for each image), which were produced by an independent local expert. By that, the spatial overlaps as well as under- and over-estimated areas were identified and the performance of the approach in relation to each sensor was evaluated. The presented method can complement traditional manual mapping efforts. Moreover, it contributes to current developments for increasing the transferability of semi-automated OBIA approaches and for improving the efficiency of change detection approaches across multi-sensor imagery.

A manual for inexpensive methods of analyzing and utilizing remote sensor data

NASA Technical Reports Server (NTRS)

Elifrits, C. D.; Barr, D. J.

1978-01-01

Instructions are provided for inexpensive methods of using remote sensor data to assist in the completion of the need to observe the earth's surface. When possible, relative costs were included. Equipment need for analysis of remote sensor data is described, and methods of use of these equipment items are included, as well as advantages and disadvantages of the use of individual items. Interpretation and analysis of stereo photos and the interpretation of typical patterns such as tone and texture, landcover, drainage, and erosional form are described. Similar treatment is given to monoscopic image interpretation, including LANDSAT MSS data. Enhancement techniques are detailed with respect to their application and simple techniques of creating an enhanced data item. Techniques described include additive and subtractive (Diazo processes) color techniques and enlargement of photos or images. Applications of these processes, including mappings of land resources, engineering soils, geology, water resources, environmental conditions, and crops and/or vegetation, are outlined.

Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach

PubMed Central

Kooistra, Lammert; Bergsma, Aldo; Chuma, Beatus; de Bruin, Sytze

2009-01-01

This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources. PMID:22574019

Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.

Detection of Green up Phenomenon in Amazon Forests Using Spaceborne Solar-induced Fluorescence

NASA Astrophysics Data System (ADS)

Chen, S.; Chen, X.; Chen, J.; Cao, X.

2016-12-01

The role of Amazon forests in the global carbon budget still remains uncertain. The critical issue is whether tropical forest productivity is more limited by sunlight or rainfall. Recent studies using satellite data have challenged the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions because of the adding effects of variations in sun-sensor geometry. To reducing uncertainties in knowing the sensitivity of Amazon rainforests to dry season droughts, we evaluated a newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll for the seasonal green-up phenomenon, providing for the first time a direct measurement related to vegetation photosynthetic activity as well as unaffected by sun-sensor geometry. Moreover, NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) products (the enhanced vegetation index (EVI) and leaf area index (LAI)) and Landsat Operational Land Imager (OLI) data are also compared to evaluate this phenomenon. Here we show that the green up of Amazon forests in the study area around manas site did show in SIF of chlorophyll data in 2015 drought resulted from seasonal changes. The EVI has more apparent green up phenomenon than the NDVI data both in MODIS and OLI data, suggesting that the EVI can better reflect near-infrared (NIR) and LAI information of vegetation. The OLI data is less influenced by variations caused by bidirectional reflectance effect. In addition, SIF of chlorophyll data shows well correlation relationship with the EVI, LAI and NDVI, suggesting that the SIF of chlorophyll data present well quality to capture the characteristics of the phenology of vegetation.

Variation of MODIS reflectance and vegetation indices with viewing geometry and soybean development.

PubMed

Breunig, Fábio M; Galvão, Lênio S; Formaggio, Antônio R; Epiphanio, José C N

2012-06-01

Directional effects introduce a variability in reflectance and vegetation index determination, especially when large field-of-view sensors are used (e.g., Moderate Resolution Imaging Spectroradiometer - MODIS). In this study, we evaluated directional effects on MODIS reflectance and four vegetation indices (Normalized Difference Vegetation Index - NDVI; Enhanced Vegetation Index - EVI; Normalized Difference Water Index - NDWI(1640) and NDWI(2120)) with the soybean development in two growing seasons (2004-2005 and 2005-2006). To keep the reproductive stage for a given cultivar as a constant factor while varying viewing geometry, pairs of images obtained in close dates and opposite view angles were analyzed. By using a non-parametric statistics with bootstrapping and by normalizing these indices for angular differences among viewing directions, their sensitivities to directional effects were studied. Results showed that the variation in MODIS reflectance between consecutive phenological stages was generally smaller than that resultant from viewing geometry for closed canopies. The contrary was observed for incomplete canopies. The reflectance of the first seven MODIS bands was higher in the backscattering. Except for the EVI, the other vegetation indices had larger values in the forward scattering direction. Directional effects decreased with canopy closure. The NDVI was lesser affected by directional effects than the other indices, presenting the smallest differences between viewing directions for fixed phenological stages.

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

USGS Publications Warehouse

Boyte, Stephen; Wylie, Bruce K.; Rigge, Matthew B.; Dahal, Devendra

2018-01-01

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact.

PubMed

Khanna, Shruti; Santos, Maria J; Ustin, Susan L; Shapiro, Kristen; Haverkamp, Paul J; Lay, Mui

2018-02-12

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact

PubMed Central

Santos, Maria J.; Ustin, Susan L.; Haverkamp, Paul J.; Lay, Mui

2018-01-01

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills. PMID:29439504

A comparison between the first four thematic mapper reflective bands and other satellite sensor systems for vegetational monitoring

NASA Technical Reports Server (NTRS)

Tucker, C. J.

1978-01-01

The first four Landsat-D thematic mapper sensors were evaluated and compared to the RBV and MSS sensors from Landsats-1, 2, and 3, Colvocoresses' proposed 'operational Landsat' three band system, and the French SPOT three band system using simulation/integration techniques and in situ collected spectral reflectance data. Sensors were evaluated by their ability to discriminate vegetation biomass, chlorophyll concentration, and leaf water content. The thematic mapper and SPOT bands were superior in a spectral resolution context to the other three sensor systems for vegetational applications. Significant improvements are expected for vegetational analyses from Landsat-D thematic mapper and SPOT imagery over MSS and RBV imagery.

Vegetation species composition and canopy architecture information expressed in leaf water absorption measured in the 1000 nm and 2200 spectral region by an imaging spectrometer

NASA Technical Reports Server (NTRS)

Green, Robert O.; Roberts, Dar A.

1995-01-01

Plant species composition and plant architectural attributes are critical parameters required for the measuring, monitoring, and modeling of terrestrial ecosystems. Remote sensing is commonly cited as an important tool for deriving vegetation properties at an appropriate scale for ecosystem studies, ranging from local to regional and even synoptic scales. Classical approaches rely on vegetation indices such as the normalized difference vegetation index (NDVI) to estimate biophysical parameters such as leaf area index or intercepted photosynthetically active radiation (IPAR). Another approach is to apply a variety of classification schemes to map vegetation and thus extrapolate fine-scale information about specific sites to larger areas of similar composition. Imaging spectrometry provides additional information that is not obtainable through broad-band sensors and that may provide improved inputs both to direct biophysical estimates as well as classification schemes. Some of this capability has been demonstrated through improved discrimination of vegetation, estimates of canopy biochemistry, and liquid water estimates from vegetation. We investigate further the potential of leaf water absorption estimated from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data as a means for discriminating vegetation types and deriving canopy architectural information. We expand our analysis to incorporate liquid water estimates from two spectral regions, the 1000-nm region and the 2200-nm region. The study was conducted in the vicinity of Jasper Ridge, California, which is located on the San Francisco peninsula to the west of the Stanford University campus. AVIRIS data were acquired over Jasper Ridge, CA, on June 2, 1992, at 19:31 UTC. Spectra from three sites in this image were analyzed. These data are from an area of healthy grass, oak woodland, and redwood forest, respectively. For these analyses, the AVIRIS-measured upwelling radiance spectra for the entire Jasper Ridge scene were transformed to apparent surface reflectance using a radiative transfer code-based inversion algorithm.

An evaluation of the first four LANDSAT-D thematic mapper reflective sensors for monitoring vegetation: A comparison with other satellite sensor systems

NASA Technical Reports Server (NTRS)

Tucker, C. J.

1978-01-01

The first four LANDSAT-D thematic mapper sensors were evaluated and compared to: the return beam vidicon (RBV) and multispectral scanners (MSS) sensors from LANDSATS 1, 2, and 3; Colvocoresses' proposed 'operational LANDSAT' three band system; and the French SPOT three band system using simulation/intergration techniques and in situ collected spectral reflectance data. Sensors were evaluated by their ability to discriminate vegetation biomass, chlorophyll concentration, and leaf water content. The thematic mapper and SPOT bands were found to be superior in a spectral resolution context to the other three sensor systems for vegetational applications. Significant improvements are expected for most vegetational analyses from LANDSAT-D thematic mapper and SPOT imagery over MSS and RBV imagery.

A methodology for investigating interdependencies between measured throughfall, meteorological variables and canopy structure on a small catchment.

NASA Astrophysics Data System (ADS)

Maurer, Thomas; Gustavos Trujillo Siliézar, Carlos; Oeser, Anne; Pohle, Ina; Hinz, Christoph

2016-04-01

In evolving initial landscapes, vegetation development depends on a variety of feedback effects. One of the less understood feedback loops is the interaction between throughfall and plant canopy development. The amount of throughfall is governed by the characteristics of the vegetation canopy, whereas vegetation pattern evolution may in turn depend on the spatio-temporal distribution of throughfall. Meteorological factors that may influence throughfall, while at the same time interacting with the canopy, are e.g. wind speed, wind direction and rainfall intensity. Our objective is to investigate how throughfall, vegetation canopy and meteorological variables interact in an exemplary eco-hydrological system in its initial development phase, in which the canopy is very heterogeneous and rapidly changing. For that purpose, we developed a methodological approach combining field methods, raster image analysis and multivariate statistics. The research area for this study is the Hühnerwasser ('Chicken Creek') catchment in Lower Lusatia, Brandenburg, Germany, where after eight years of succession, the spatial distribution of plant species is highly heterogeneous, leading to increasingly differentiated throughfall patterns. The constructed 6-ha catchment offers ideal conditions for our study due to the rapidly changing vegetation structure and the availability of complementary monitoring data. Throughfall data were obtained by 50 tipping bucket rain gauges arranged in two transects and connected via a wireless sensor network that cover the predominant vegetation types on the catchment (locust copses, dense sallow thorn bushes and reeds, base herbaceous and medium-rise small-reed vegetation, and open areas covered by moss and lichens). The spatial configuration of the vegetation canopy for each measurement site was described via digital image analysis of hemispheric photographs of the canopy using the ArcGIS Spatial Analyst, GapLight and ImageJ software. Meteorological data from two on-site weather stations (wind direction, wind speed, air temperature, air humidity, insolation, soil temperature, precipitation) were provided by the 'Research Platform Chicken Creek' (https://www.tu-cottbus.de/projekte/en/oekosysteme/startseite.html). Data were combined and multivariate statistical analysis (PCA, cluster analysis, regression trees) were conducted using the R-software to i) obtain statistical indices describing the relevant characteristics of the data and ii) to identify the determining factors for throughfall intensity. The methodology is currently tested and results will be presented. Preliminary evaluation of the image analysis approach showed only marginal, systematic deviation of results for the different software tools applied, which makes the developed workflow a viable tool for canopy characterization. Results from this study will have a broad spectrum of possible applications, for instance the development / calibration of rainfall interception models, the incorporation into eco-hydrological models, or to test the fault tolerance of wireless rainfall sensor networks.

Full-Waveform, Wide-Swath Lidar Imaging of Forested and Urban Areas in Leaf-On Conditions: Development, Results and Future Direction

NASA Astrophysics Data System (ADS)

Blair, B.; Hofton, M.; Rabine, D.; Welch, W.; Ramos, L.; Padden, P.

2003-12-01

Full-Waveform lidar measurements provide unprecedented views of the vertical and horizontal structure of vegetation and the topography of the Earth's surface. Utilizing a high signal-to-noise ratio lidar system, larger than typical laser footprints (10-20 m), and the recorded time history of interaction between a short-duration (10 ns) pulse of laser light and the surface of the Earth, full-waveform lidar is able to simultaneously image sub-canopy topography as well as the vertical structure of any overlying vegetation. These data reveal the true 3-D vegetation structure in leaf-on conditions enabling important biophysical parameters such as above-ground biomass to be estimated with unprecedented accuracy. An airborne lidar mission was conducted July-August 2003 in support of the North America Carbon Program. NASA's Laser Vegetation Imaging Sensor (LVIS) was used to image approximately 2,000 sq. km in Maine, New Hampshire, Massachusetts and Maryland. Areas with available ground and other data were included (e.g., experimental forests, FLUXNET sites) in order to facilitate as many bio- and geophysical investigations as possible. Data collected included ground elevation and canopy height measurements for each laser footprint, as well as the vertical distribution of intercepted surfaces. Data will be publicly distributed within 6-12 months of collection. Further details of the mission, including the lidar system technology, the locations of the mapped areas, and examples of the numerous data products that can be derived from the return waveform data products will be presented. Future applications including detection of ground and vegetation canopy changes and a spaceborne implementation of wide-swath, full-waveform imaging lidar will also be discussed.

Full-Waveform, Wide-Swath Lidar Imaging of Forested and Urban Areas in Leaf-On Conditions: Development, Results and Future Direction

NASA Technical Reports Server (NTRS)

Blair, James B.; Hofton, M.; Rabine, David; Welch, Wayne; Ramos, Luis; Padden, Phillip

2003-01-01

Full-Waveform lidar measurements provide unprecedented views of the vertical and horizontal structure of vegetation and the topography of the Earth s surface. Utilizing a high signal-to-noise ratio lidar system, larger than typical laser footprints (10-20 m), and the recorded time history of interaction between a short-duration (approx. 10 ns) pulse of laser light and the surface of the Earth, full-waveform lidar is able to simultaneously image sub-canopy topography as well as the vertical structure of any overlying vegetation. These data reveal the true 3-D vegetation structure in leaf-on conditions enabling important biophysical parameters such as above-ground biomass to be estimated with unprecedented accuracy. An airborne lidar mission was conducted July-August 2003 in support of the North America Carbon Program. NASA s Laser Vegetation Imaging Sensor (LVIS) was used to image approximately 2,000 km$^2$ in Maine, New Hampshire, Massachusetts and Maryland. Areas with available ground and other data were included (e.g., experimental forests, FLUXNET sites) in order to facilitate as many bio- and geophysical investigations as possible. Data collected included ground elevation and canopy height measurements for each laser footprint, as well as the vertical distribution of intercepted surfaces. Data will be publicly distributed within 6- 12 months of collection. Further details of the mission, including the lidar system technology, the locations of the mapped areas, and examples of the numerous data products that can be derived from the return waveform data products will be presented. Future applications including detection of ground and vegetation canopy changes and a spaceborne implementation of wide-swath, full-waveform imaging lidar will also be discussed.

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

PubMed

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

2017-08-11

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

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

PubMed Central

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

2017-01-01

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

Full-waveform, Laser Altimeter Measurements of Vegetation Vertical Structure and Sub-canopy Topography in Support of the North American Carbon Program

NASA Technical Reports Server (NTRS)

Blair, B.; Hofton, M.; Rabine, D.; Padden, P.; Rhoads, J.

2004-01-01

Full-waveform, scanning laser altimeters (i.e. lidar) provide a unique and precise view of the vertical and horizontal structure of vegetation across wide swaths. These unique laser altimeters systems are able to simultaneously image sub-canopy topography and the vertical structure of any overlying vegetation. These data reveal the true 3-D distribution of vegetation in leaf-on conditions enabling important biophysical parameters such as canopy height and aboveground biomass to be estimated with unprecedented accuracy. An airborne lidar mission was conducted in the summer of 2003 in support of preliminary studies for the North America Carbon Program. NASA's Laser Vegetation Imaging Sensor (LVIS) was used to image approximately 2,000 sq km in Maine, New Hampshire, Massachusetts and Maryland. Areas with available ground and other data were included (e.g., experimental forests, FLUXNET sites) in order to facilitate numerous bio- and geophysical investigations. Data collected included ground elevation and canopy height measurements for each laser footprint, as well as the vertical distribution of intercepted surfaces (i.e. the return waveform). Data are currently available at the LVIS website (http://lvis.gsfc.nasa.gov/). Further details of the mission, including the lidar system technology, the locations of the mapped areas, and examples of the numerous data products that can be derived from the return waveform data products are available on the website and will be presented. Future applications including potential fusion with other remote sensing data sets and a spaceborne implementation of wide-swath, full-waveform imaging lidar will also be discussed.

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.

Role of Satellite Sensors in Groundwater Exploration

PubMed Central

Mukherjee, Saumitra

2008-01-01

Spatial as well as spectral resolution has a very important role to play in water resource management. It was a challenge to explore the groundwater and rainwater harvesting sites in the Aravalli Quartzite-Granite-Pegmatite Precambrian terrain of Delhi, India. Use of only panchromatic sensor data of IRS-1D satellite with 5.8-meter spatial resolution has the potential to infer lineaments and faults in this hard rock area. It is essential to identify the location of interconnected lineaments below buried pediment plains in the hard rock area for targeting sub-surface water resources. Linear Image Self Scanning sensor data of the same satellite with 23.5-meter resolution when merged with the panchromatic data has produced very good results in delineation of interconnected lineaments over buried pediment plains as vegetation anomaly. These specific locations of vegetation anomaly were detected as dark red patches in various hard rock areas of Delhi. Field investigation was carried out on these patches by resistivity and magnetic survey in parts of Jawaharlal Nehru University (JNU), Indira Gandhi national Open University, Research and Referral Hospital and Humayuns Tomb areas. Drilling was carried out in four locations of JNU that proved to be the most potential site with ground water discharge ranging from 20,000 to 30,000 liters per hour with 2 to 4 meters draw down. Further the impact of urbanization on groundwater recharging in the terrain was studied by generating Normalized difference Vegetation Index (NDVI) map which was possible to generate by using the LISS-III sensor of IRS-1D satellite. Selection of suitable sensors has definitely a cutting edge on natural resource exploration and management including groundwater. PMID:27879808

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.

Waveform LiDAR across forest biomass gradients

NASA Astrophysics Data System (ADS)

Montesano, P. M.; Nelson, R. F.; Dubayah, R.; Sun, G.; Ranson, J.

2011-12-01

Detailed information on the quantity and distribution of aboveground biomass (AGB) is needed to understand how it varies across space and changes over time. Waveform LiDAR data is routinely used to derive the heights of scattering elements in each illuminated footprint, and the vertical structure of vegetation is related to AGB. Changes in LiDAR waveforms across vegetation structure gradients can demonstrate instrument sensitivity to land cover transitions. A close examination of LiDAR waveforms in footprints across a forest gradient can provide new insight into the relationship of vegetation structure and forest AGB. In this study we use field measurements of individual trees within Laser Vegetation Imaging Sensor (LVIS) footprints along transects crossing forest to non-forest gradients to examine changes in LVIS waveform characteristics at sites with low (< 50Mg/ha) AGB. We relate field AGB measurements to original and adjusted LVIS waveforms to detect the forest AGB interval along a forest - non-forest transition in which the LVIS waveform lose the ability to discern differences in AGB. Our results help identify the lower end the forest biomass range that a ~20m footprint waveform LiDAR can detect, which can help infer accumulation of biomass after disturbances and during forest expansion, and which can guide the use of LiDAR within a multi-sensor fusion biomass mapping approach.

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

NASA Technical Reports Server (NTRS)

Reith, Ernest; Roberts, Dar A.; Prentiss, Dylan

2001-01-01

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Real-time detection of natural objects using AM-coded spectral matching imager

NASA Astrophysics Data System (ADS)

Kimachi, Akira

2004-12-01

This paper describes application of the amplitude-modulation (AM)-coded spectral matching imager (SMI) to real-time detection of natural objects such as human beings, animals, vegetables, or geological objects or phenomena, which are much more liable to change with time than artificial products while often exhibiting characteristic spectral functions associated with some specific activity states. The AM-SMI produces correlation between spectral functions of the object and a reference at each pixel of the correlation image sensor (CIS) in every frame, based on orthogonal amplitude modulation (AM) of each spectral channel and simultaneous demodulation of all channels on the CIS. This principle makes the SMI suitable to monitoring dynamic behavior of natural objects in real-time by looking at a particular spectral reflectance or transmittance function. A twelve-channel multispectral light source was developed with improved spatial uniformity of spectral irradiance compared to a previous one. Experimental results of spectral matching imaging of human skin and vegetable leaves are demonstrated, as well as a preliminary feasibility test of imaging a reflective object using a test color chart.

Real-time detection of natural objects using AM-coded spectral matching imager

NASA Astrophysics Data System (ADS)

Kimachi, Akira

2005-01-01

This paper describes application of the amplitude-modulation (AM)-coded spectral matching imager (SMI) to real-time detection of natural objects such as human beings, animals, vegetables, or geological objects or phenomena, which are much more liable to change with time than artificial products while often exhibiting characteristic spectral functions associated with some specific activity states. The AM-SMI produces correlation between spectral functions of the object and a reference at each pixel of the correlation image sensor (CIS) in every frame, based on orthogonal amplitude modulation (AM) of each spectral channel and simultaneous demodulation of all channels on the CIS. This principle makes the SMI suitable to monitoring dynamic behavior of natural objects in real-time by looking at a particular spectral reflectance or transmittance function. A twelve-channel multispectral light source was developed with improved spatial uniformity of spectral irradiance compared to a previous one. Experimental results of spectral matching imaging of human skin and vegetable leaves are demonstrated, as well as a preliminary feasibility test of imaging a reflective object using a test color chart.

Quantifying Fractional Ground Cover on the Climate Sensitive High Plains Using AVIRIS and Landsat TM Data

NASA Technical Reports Server (NTRS)

Warner, Amanda Susan

2002-01-01

The High Plains is an economically important and climatologically sensitive region of the United States and Canada. The High Plains contain 100,000 sq km of Holocene sand dunes and sand sheets that are currently stabilized by natural vegetation. Droughts and the larger threat of global warming are climate phenomena that could cause depletion of natural vegetation and make this region susceptible to sand dune reactivation. This thesis is part of a larger study that is assessing the effect of climate variability on the natural vegetation that covers the High Plains using Landsat 5 and Landsat 7 data. The question this thesis addresses is how can fractional vegetation cover be mapped with the Landsat instruments using linear spectral mixture analysis and to what accuracy. The method discussed in this thesis made use of a high spatial and spectral resolution sensor called AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) and field measurements to test vegetation mapping in three Landsat 7 sub-scenes. Near-simultaneous AVIRIS images near Ft. Morgan, Colorado and near Logan, New Mexico were acquired on July 10, 1999 and September 30, 1999, respectively. The AVIRIS flights preceded Landsat 7 overpasses by approximately one hour. These data provided the opportunity to test spectral mixture algorithms with AVIRIS and to use these data to constrain the multispectral mixed pixels of Landsat 7. The comparisons of mixture analysis between the two instruments showed that AVIRIS endmembers can be used to unmix Landsat 7 data with good estimates of soil cover, and reasonable estimates of non-photosynthetic vegetation and green vegetation. Landsat 7 derived image endmembers correlate with AVIRIS fractions, but the error is relatively large and does not give a precise estimate of cover.

Science synergism study for EOS on evolution of desert surfaces

NASA Technical Reports Server (NTRS)

Farr, Tom G.

1987-01-01

The effectiveness of EOS data as a basis for the study of desert surfaces' evolution is presently evaluated for both long and short term geomorphic evolution. Attention is given to the usefulness of such sensor systems planned for EOS as MODIS for regional vegetation distribution/variability monitoring, HIRIS for visible-near IR observations, TIMS for lithological identification, HMMR and SSMI for soil characteristics, LASA for atmospheric profiles, SAR for surface roughness, ALT for two-dimensional topography, ACR for the calibration of imaging sensors, and ERBE for climate modeling and regional surface albedo variation determinations.

PASTIS 57: Autonomous light sensors for PAI continuous monitoring. Principles, calibration and application to vegetation phenology

NASA Astrophysics Data System (ADS)

Lecerf, R.; Baret, F.; Hanocq, J.; Marloie, O.; Rautiainen, M.; Mottus, M.; Heiskanen, J.; Stenberg, P.

2010-12-01

The LAI (Leaf Area Index) is a key variable to analyze and model vegetation and its interactions with atmosphere and soils. The LAI maps derived from remote sensing images are often validated with non-destructive LAI measures obtained from digital hemispherical photography, LAI-2000 or ceptometer instruments. These methods are expensive and time consuming particularly when human intervention is needed. Consequently it is difficult to acquire overlapping field data and remotely sensed LAI. There is a need of a cheap, autonomous, easy to use ground system to measure foliage development and senescence at least with a daily frequency in order to increase the number of validation sites where vegetation phenology is continuously monitored. A system called PASTIS-57 (PAI Autonomous System from Transmittance Instantaneous Sensors oriented at 57°) devoted to PAI (Plant Area Index) ground measurements was developed to answer this need. PASTIS-57 consists in 6 sensors plugged on one logger that record data with a sampling rate of 1 to few minutes (tunable) with up to 3 months autonomy (energy and data storage). The sensors are plugged to the logger with 2x10m wires, 2x6m wires and 2x2m wires. The distance between each sensor was determined to obtain a representative spatial sampling over a 20m pixel corresponding to an Elementary Sampling Unit (ESU). The PASTIS-57 sensors are made of photodiodes that measure the incoming light in the blue wavelength to maximize the contrast between vegetation and sky and limit multiple scattering effects in the canopy. The diodes are oriented to the north to avoid direct sun light and point to a zenithal angle of 57° to minimize leaf angle distribution and plant clumping effects. The field of view of the diodes was set to ± 20° to take into consideration vegetation cover heterogeneity and to minimize environmental effects. The sensors were calibrated after recording data on a clear view site during a week. After calibration, the sensors were installed on several study sites including a boreal forest in Finland and an agricultural area in southern France. On each study site, several ESUs were equipped with 2 to 4 systems. The sensors were installed along an East-West line and were pointing to the north. A reference system was set up to monitor unobstructed incident radiation field. The results show that the transmitted light recorded by sensors depends on gap fraction and may be used to measure the PAI (Plant Area Index). The time series acquired with the PASTIS-57 show strong correlation with plant phenology. The PAI values were then derived from the measured gap fractions. Advantages and limitations of the system are finally discussed with emphasis on potential operational use within networks of sites.

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.

Learning for Autonomous Navigation

NASA Technical Reports Server (NTRS)

Angelova, Anelia; Howard, Andrew; Matthies, Larry; Tang, Benyang; Turmon, Michael; Mjolsness, Eric

2005-01-01

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

Mapping Variation in Vegetation Functioning with Imaging Spectroscopy

NASA Astrophysics Data System (ADS)

Townsend, P. A.; Couture, J. J.; Kruger, E. L.; Serbin, S.; Singh, A.

2015-12-01

Imaging spectroscopy (otherwise known as hyperspectral remote sensing) offers the potential to characterize the spatial and temporal variation in biophysical and biochemical properties of vegetation that can be costly or logistically difficult to measure comprehensively using traditional methods. A number of recent studies have illustrated the capacity for imaging spectroscopy data, such as from NASA's AVIRIS sensor, to empirically estimate functional traits related to foliar chemistry and physiology (Singh et al. 2015, Serbin et al. 2015). Here, we present analyses that illustrate the implications of those studies to characterize within-field or -stand variability in ecosystem functioning. In agricultural ecosystems, within-field photosynthetic capacity can vary by 30-50%, likely due to within-field variations in water availability and soil fertility. In general, the variability of foliar traits is lower in forests than agriculture, but can still be significant. Finally, we demonstrate that functional trait variability at the stand scale is strongly related to vegetation diversity. These results have two significant implications: 1) reliance on a small number of field samples to broadly estimate functional traits likely underestimates variability in those traits, and 2) if trait estimations from imaging spectroscopy are reliable, such data offer the opportunity to greatly increase the density of measurements we can use to predict ecosystem function.

Detecting subtle environmental change: a multi-temporal airborne imaging spectroscopy approach

NASA Astrophysics Data System (ADS)

Yule, Ian J.; Pullanagari, Reddy R.; Kereszturi, G.

2016-10-01

Airborne and satellite hyperspectral remote sensing is a key technology to observe finite change in ecosystems and environments. The role of such sensors will improve our ability to monitor and mitigate natural and agricultural environments on a much larger spatial scale than can be achieved using field measurements such as soil coring or proximal sensors to estimate the chemistry of vegetation. Hyperspectral sensors for commentarial and scientific activities are increasingly available and cost effective, providing a great opportunity to measure and detect changes in the environment and ecosystem. This can be used to extract critical information to develop more advanced management practices. In this research, we provide an overview of the data acquisition, processing and analysis of airborne, full-spectrum hyperspectral imagery from a small-scale aerial mapping project in hill-country farms in New Zealand, using an AISA Fenix sensor (Specim, Finland). The imagery has been radiometrically and atmospherically corrected, georectified and mosaicked. The hyperspectral data cube was then spectrally and spatially smoothed using Savitzky-Golay and median filter, respectively. The mosaicked imagery used to calculate bio-chemical properties of surface vegetation, such as pasture. Ground samples (n = 200) were collected a few days after the over-flight are used to develop a calibration model using partial least squares regression method. In-leaf nitrogen, potassium and phosphorous concentration were calculated using the reflectance values from the airborne hyperspectral imagery. In total, three surveys of an example property have been acquired that show changes in the pattern of availability of a major element in vegetation canopy, in this case nitrogen.

Microwave remote sensing of soil moisture, volume 1. [Guymon, Oklahoma and Dalhart, Texas

NASA Technical Reports Server (NTRS)

Mcfarland, M. J. (Principal Investigator); Theis, S. W.; Rosenthal, W. D.; Jones, C. L.

1982-01-01

Multifrequency sensor data from NASA's C-130 aircraft were used to determine which of the all weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. The perpendicular vegetation index (PVI) as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture. A linear equation was developed to estimate percent field capacity as a function of L-band emissivity and the vegetation index. The prediction algorithm improves the estimation of moisture significantly over predictions from L-band emissivity alone.

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

USGS Publications Warehouse

Stow, Douglas A.; Hope, Allen; McGuire, David; Verbyla, David; Gamon, John A.; Huemmrich, Fred; Houston, Stan; Racine, Charles H.; Sturm, Matthew; Tape, Ken D.; Hinzman, Larry D.; Yoshikawa, Kenji; Tweedie, Craig E.; Noyle, Brian; Silapaswan, Cherie; Douglas, David C.; Griffith, Brad; Jia, Gensuo; Howard E. Epstein,; Walker, Donald A.; Daeschner, Scott; Petersen, Aaron; Zhou, Liming; Myneni, Ranga B.

2004-01-01

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

Multispectral imaging with vertical silicon nanowires

PubMed Central

Park, Hyunsung; Crozier, Kenneth B.

2013-01-01

Multispectral imaging is a powerful tool that extends the capabilities of the human eye. However, multispectral imaging systems generally are expensive and bulky, and multiple exposures are needed. Here, we report the demonstration of a compact multispectral imaging system that uses vertical silicon nanowires to realize a filter array. Multiple filter functions covering visible to near-infrared (NIR) wavelengths are simultaneously defined in a single lithography step using a single material (silicon). Nanowires are then etched and embedded into polydimethylsiloxane (PDMS), thereby realizing a device with eight filter functions. By attaching it to a monochrome silicon image sensor, we successfully realize an all-silicon multispectral imaging system. We demonstrate visible and NIR imaging. We show that the latter is highly sensitive to vegetation and furthermore enables imaging through objects opaque to the eye. PMID:23955156

Open source software and low cost sensors for teaching UAV science

NASA Astrophysics Data System (ADS)

Kefauver, S. C.; Sanchez-Bragado, R.; El-Haddad, G.; Araus, J. L.

2016-12-01

Drones, also known as UASs (unmanned aerial systems), UAVs (Unmanned Aerial Vehicles) or RPAS (Remotely piloted aircraft systems), are both useful advanced scientific platforms and recreational toys that are appealing to younger generations. As such, they can make for excellent education tools as well as low-cost scientific research project alternatives. However, the process of taking pretty pictures to remote sensing science can be daunting if one is presented with only expensive software and sensor options. There are a number of open-source tools and low cost platform and sensor options available that can provide excellent scientific research results, and, by often requiring more user-involvement than commercial software and sensors, provide even greater educational benefits. Scale-invariant feature transform (SIFT) algorithm implementations, such as the Microsoft Image Composite Editor (ICE), which can create quality 2D image mosaics with some motion and terrain adjustments and VisualSFM (Structure from Motion), which can provide full image mosaicking with movement and orthorectification capacities. RGB image quantification using alternate color space transforms, such as the BreedPix indices, can be calculated via plugins in the open-source software Fiji (http://fiji.sc/Fiji; http://github.com/george-haddad/CIMMYT). Recent analyses of aerial images from UAVs over different vegetation types and environments have shown RGB metrics can outperform more costly commercial sensors. Specifically, Hue-based pixel counts, the Triangle Greenness Index (TGI), and the Normalized Green Red Difference Index (NGRDI) consistently outperformed NDVI in estimating abiotic and biotic stress impacts on crop health. Also, simple kits are available for NDVI camera conversions. Furthermore, suggestions for multivariate analyses of the different RGB indices in the "R program for statistical computing", such as classification and regression trees can allow for a more approachable interpretation of results in the classroom.

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.

Simulating Visible/Infrared Imager Radiometer Suite Normalized Difference Vegetation Index Data Using Hyperion and MODIS

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; Russell, Jeffrey; Ryan, Robert E.

2006-01-01

The success of MODIS (the Moderate Resolution Imaging Spectrometer) in creating unprecedented, timely, high-quality data for vegetation and other studies has created great anticipation for data from VIIRS (the Visible/Infrared Imager Radiometer Suite). VIIRS will be carried onboard the joint NASA/Department of Defense/National Oceanic and Atmospheric Administration NPP (NPOESS (National Polar-orbiting Operational Environmental Satellite System) Preparatory Project). Because the VIIRS instruments will have lower spatial resolution than the current MODIS instruments 400 m versus 250 m at nadir for the channels used to generate Normalized Difference Vegetation Index data, scientists need the answer to this question: how will the change in resolution affect vegetation studies? By using simulated VIIRS measurements, this question may be answered before the VIIRS instruments are deployed in space. Using simulated VIIRS products, the U.S. Department of Agriculture and other operational agencies can then modify their decision support systems appropriately in preparation for receipt of actual VIIRS data. VIIRS simulations and validations will be based on the ART (Application Research Toolbox), an integrated set of algorithms and models developed in MATLAB(Registerd TradeMark) that enables users to perform a suite of simulations and statistical trade studies on remote sensing systems. Specifically, the ART provides the capability to generate simulated multispectral image products, at various scales, from high spatial hyperspectral and/or multispectral image products. The ART uses acquired ( real ) or synthetic datasets, along with sensor specifications, to create simulated datasets. For existing multispectral sensor systems, the simulated data products are used for comparison, verification, and validation of the simulated system s actual products. VIIRS simulations will be performed using Hyperion and MODIS datasets. The hyperspectral and hyperspatial properties of Hyperion data will be used to produce simulated MODIS and VIIRS products. Hyperion-derived MODIS data will be compared with near-coincident MODIS collects to validate both spectral and spatial synthesis, which will ascertain the accuracy of converting from MODIS to VIIRS. MODIS-derived VIIRS data is needed for global coverage and for the generation of time series for regional and global investigations. These types of simulations will have errors associated with aliasing for some scene types. This study will help quantify these errors and will identify cases where high-quality, MODIS-derived VIIRS data will be available.

Monitoring vegetation cover in the postfire in Tavira - São Brás de Alportel (southern Portugal)

NASA Astrophysics Data System (ADS)

Ramos-Simões, Nuno A.; Granja-Martins, Fernando M.; Neto-Paixão, Helena M.; Jordán, Antonio; Zavala, Lorena M.

2014-05-01

1. INTRODUCTION Often, restoration of areas affected by fire faces lack of knowledge of how ecosystems respond to the action of fire. Depending on environmental conditions, structure and diversity of the vegetation or the severity of the fire, burnt systems can provide responses ranging from spontaneous recovery in a relatively short time to onset of severe degradation processes. For this reason, it is necessary to monitor the evolution of post-burned in the fire, in order to plan effective strategies for restoring systems and soil erosion control. In order to assess soil erosion risk, this research aims to is to analyse the evolution of vegetation cover in a Mediterranean burnt forest soil, using vegetation indexes derived from Landsat-7 (Thematic Mapper sensor-TM) and Landsat-8 (Operation Land Imager sensor, OLI). 2. METHODS This study was carried out in a forest area affected by a wildfire by 18-22 July 2012. The study area is located within the coordinates 37o 9' - 37o 21' N and 7o 40' - 7o 53' W, including part of the municipalities of Tavira and São Brás de Alportel (southern Portugal). The relief in the studied area has an irregular topography. Soils are shallow and develop mainly metamorphic rocks (as slates or quartzite) and igneous rocks, which produce acidic and nutrient-poor soils, poorly developed in depth. The wildfire was one of the most important fires in Portugal during the recent years, and affected more than 24000 ha. Vegetation is dominated by cork oak (Quercus suber) ,holm oaks (Quercus ilex), strawberry tree (Arbutus unedo) and sclerophyllous vegetation (mostly formed by Quercus coccifera and Rosmarinus officinalis). These species are adapted to acidic-poor soils and show a great capability of resprouting and germination after fire. The study area is poorly developed, with cork and timber harvesting and other forest products or tourism as main economic activities. The area shows a highly fragmented urban fabric with the sparse infrastructures. In recent years, migration processes have further aggravated the economic situation in this region. Landsat 7 and Landsat 8 images were used for this study (April 2012, December 2012, March 2013 and November 2013). Images were corrected for the scattering effect by extraction of black objects for near infrared bands and correction by linear regression for the red bands. Several vegetation indexes were used, such as, vegetation ratio, NDVI, the perpendicular vegetation index with assessment of distance to soil, PVI, WDVI, PVI3, and vegetation indexes based on orthogonal transformation of bands (Tasselled Cap) and principal component analysis (PCA). After studying the correlations between indexes by PCA, the Tasselled Cap-green index was selected as the most accurate one. Presence/absence of vegetation and land use were monitored to select the best parameter to study the evolution of vegetation. The evolution of the vegetation was compared with the CORINE Land Cover map (2006) and validated in field visits in January 2014. 3. RESULTS For the study area, results show a positive evolution of vegetation in the burned area during the months following to burning. Recovery of natural-native vegetation is more intense than anthropic vegetation types, with sclerophyllous vegetation showing the most intense evolution after burning.

Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors

NASA Astrophysics Data System (ADS)

Sepuru, Terrence Koena; Dube, Timothy

2018-07-01

In this study, we determine the most suitable multispectral sensor that can accurately detect and map eroded areas from other land cover types in Sekhukhune rural district, Limpopo Province, South Africa. Specifically, the study tested the ability of multi-date (wet and dry season) Landsat 8 OLI and Sentinel-2 MSI images in detecting and mapping eroded areas. The implementation was done, using a robust non-parametric classification ensemble: Discriminant Analysis (DA). Three sets of analysis were applied (Analysis 1: Spectral bands as independent dataset; Analysis 2: Spectral vegetation indices as independent and Analysis 3: Combined spectral bands and spectral vegetation indices). Overall classification accuracies ranging between 80% to 81.90% for MSI and 75.71%-80.95% for OLI were derived for the wet and dry season, respectively. The integration of spectral bands and spectral vegetation indices showed that Sentinel-2 (OA = 83, 81%), slightly performed better than Landsat 8, with 82, 86%. The use of bands and vegetation indices as independent dataset resulted in slightly weaker results for both sensors. Sentinel-2 MSI bands located in the NIR (0.785-0.900 μm), red edge (0.698-0.785 μm) and SWIR (1.565-2.280 μm) regions were selected as the most optimal for discriminating degraded soils from other land cover types. However, for Landsat 8OLI, only the SWIR (1.560-2.300 μm), NIR (0.845-0.885 μm) region were selected as the best regions. Of the eighteen spectral vegetation indices computed, NDVI and SAVI and SAVI and Global Environmental Monitoring Index (GEMI) were ranked selected as the most suitable for detecting and mapping soil erosion. Additionally, SRTM DEM derived information illustrates that for both sensors eroded areas occur on sites that are 600 m and 900 m of altitude with similar trends observed in both dry and wet season maps. Findings of this work emphasize the importance of free and readily available new generation sensors in continuous landscape-scale soil erosion monitoring. Besides, such information can help to identify hotspots and potentially vulnerable areas, as well as aid in developing possible control and mitigation measures.

Use of EO-1 Hyperion Data for Inter-Sensor Calibration of Vegetation Indices

NASA Technical Reports Server (NTRS)

Huete, Alfredo; Miura, Tomoaki; Kim, HoJin; Yoshioka, Hiroki

2004-01-01

Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been launched and their derived products are increasingly being used in regional and global vegetation studies. The increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multiresolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group of sensors that have produced a long-term, archived data set of the Earth s surface, at fine resolution and since 1972, capable of being processed into useful information for global change studies (Hall et al., 1991).

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.

Garden City, Kansas

NASA Image and Video Library

2017-12-08

Center pivot irrigation systems create red circles of healthy vegetation in this image of croplands near Garden City, Kansas. This image was acquired by Landsat 7’s Enhanced Thematic Mapper plus (ETM+) sensor on September 25, 2000. This is a false-color composite image made using near infrared, red, and green wavelengths. The image has also been sharpened using the sensor’s panchromatic band. Credit: NASA/GSFC/Landsat NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Join us on Facebook

Wetland Vegetation Integrity Assessment with Low Altitude Multispectral Uav Imagery

NASA Astrophysics Data System (ADS)

Boon, M. A.; Tesfamichael, S.

2017-08-01

The use of multispectral sensors on Unmanned Aerial Vehicles (UAVs) was until recently too heavy and bulky although this changed in recent times and they are now commercially available. The focus on the usage of these sensors is mostly directed towards the agricultural sector where the focus is on precision farming. Applications of these sensors for mapping of wetland ecosystems are rare. Here, we evaluate the performance of low altitude multispectral UAV imagery to determine the state of wetland vegetation in a localised spatial area. Specifically, NDVI derived from multispectral UAV imagery was used to inform the determination of the integrity of the wetland vegetation. Furthermore, we tested different software applications for the processing of the imagery. The advantages and disadvantages we experienced of these applications are also shortly presented in this paper. A JAG-M fixed-wing imaging system equipped with a MicaScene RedEdge multispectral camera were utilised for the survey. A single surveying campaign was undertaken in early autumn of a 17 ha study area at the Kameelzynkraal farm, Gauteng Province, South Africa. Structure-from-motion photogrammetry software was used to reconstruct the camera position's and terrain features to derive a high resolution orthoretified mosaic. MicaSense Atlas cloud-based data platform, Pix4D and PhotoScan were utilised for the processing. The WET-Health level one methodology was followed for the vegetation assessment, where wetland health is a measure of the deviation of a wetland's structure and function from its natural reference condition. An on-site evaluation of the vegetation integrity was first completed. Disturbance classes were then mapped using the high resolution multispectral orthoimages and NDVI. The WET-Health vegetation module completed with the aid of the multispectral UAV products indicated that the vegetation of the wetland is largely modified ("D" PES Category) and that the condition is expected to deteriorate (change score) in the future. However a lower impact score were determined utilising the multispectral UAV imagery and NDVI. The result is a more accurate estimation of the impacts in the wetland.

A multi-sensor method for in-situ quantification of multiple biodiversity and ecosystem service indicators in wetland vegetation

NASA Astrophysics Data System (ADS)

Zlinszky, András; Prager, Katharina; Koma, Zsófia

2017-04-01

Biodiversity and ecosystem services are in the focus of biogeosciences research and conservation management worldwide. However, their quantification is notoriously difficult. Since full coverage of biodiversity and/or ecosystem services is unfeasible due to their complexity, indicators are recommended: biophysical quantities that are measureable and are expected to be closely related to biodiversity or to ecosystem processes. Nevertheless, many biodiversity and ecosystem service assessments are based on upscaling very few (if any) in-situ measurements using models driven by basic land cover data. Also, many assessments select only a single or very few indicators, which then does not enable analysis of trade-offs and interconnections. Here we propose a system of simple yet reliable field measurements, based on basic sensors, measurements, imaging and sampling technology, suitable for quantitatively representing many components of biodiversity and ecosystem services in emergent wetland vegetation. Along a transect from open water to the shore, sampling stations are laid out that include water temperature, air temperature and humidity sensors, zenith facing photographs and pole contact counts of vegetation in height intervals. Additionally, for some of these stations, small quadrats of vegetation are harvested, separated to individual species and weighed in height intervals above ground/water. Underwater surface of vegetation is estimated by counting stalks and registering average diameter. Finally, decomposition is quantified by leaving a standard amount of biomass in a plastic net bag and re-weighing it a year later. This system allows measuring alpha and beta diversity together with vertical structural diversity, leaf area (as a proxy of shading and pollution absorbtion), biomass (as a proxy of carbon sequestration), underwater surface (as a proxy of fish population sustaining), microclimate influence and soil provision. The necessary tools are temperature and humidity sensors, field scales, pruning shears, plastic net bags, measuring poles (for water depth), a digital camera and a GPS; all small and lightweight enough to be carried and operated by one person under wetland field conditions. Additionally, such measurements are suitable for remote sensing-based direct upscaling of biophysical parameters to create area-covering maps of biodiversity and ecosystem service indicators.

A comparative interregional analysis of selected data from LANDSAT-1 and EREP for the inventory and monitoring of natural ecosystems

NASA Technical Reports Server (NTRS)

Poulton, C. E.

1975-01-01

Comparative statistics were presented on the capability of LANDSAT-1 and three of the Skylab remote sensing systems (S-190A, S-190B, S-192) for the recognition and inventory of analogous natural vegetations and landscape features important in resource allocation and management. Two analogous regions presenting vegetational zonation from salt desert to alpine conditions above the timberline were observed, emphasizing the visual interpretation mode in the investigation. An hierarchical legend system was used as the basic classification of all land surface features. Comparative tests were run on image identifiability with the different sensor systems, and mapping and interpretation tests were made both in monocular and stereo interpretation with all systems except the S-192. Significant advantage was found in the use of stereo from space when image analysis is by visual or visual-machine-aided interactive systems. Some cost factors in mapping from space are identified. The various image types are compared and an operational system is postulated.

Global-scale analysis of vegetation indices for moderate resolution monitoring of terrestrial vegetation

NASA Astrophysics Data System (ADS)

Huete, Alfredo R.; Didan, Kamel; van Leeuwen, Willem J. D.; Vermote, Eric F.

1999-12-01

Vegetation indices have emerged as important tools in the seasonal and inter-annual monitoring of the Earth's vegetation. They are radiometric measures of the amount and condition of vegetation. In this study, the Sea-viewing Wide Field-of-View sensor (SeaWiFS) is used to investigate coarse resolution monitoring of vegetation with multiple indices. A 30-day series of SeaWiFS data, corrected for molecular scattering and absorption, was composited to cloud-free, single channel reflectance images. The normalized difference vegetation index (NDVI) and an optimized index, the enhanced vegetation index (EVI), were computed over various 'continental' regions. The EVI had a normal distribution of values over the continental set of biomes while the NDVI was skewed toward higher values and saturated over forested regions. The NDVI resembled the skewed distributions found in the red band while the EVI resembled the normal distributions found in the NIR band. The EVI minimized smoke contamination over extensive portions of the tropics. As a result, major biome types with continental regions were discriminable in both the EVI imagery and histograms, whereas smoke and saturation considerably degraded the NDVI histogram structure preventing reliable discrimination of biome types.

Imaging Radar Applications in the Death Valley Region

NASA Technical Reports Server (NTRS)

Farr, Tom G.

1996-01-01

Death Valley has had a long history as a testbed for remote sensing techniques (Gillespie, this conference). Along with visible-near infrared and thermal IR sensors, imaging radars have flown and orbited over the valley since the 1970's, yielding new insights into the geologic applications of that technology. More recently, radar interferometry has been used to derive digital topographic maps of the area, supplementing the USGS 7.5' digital quadrangles currently available for nearly the entire area. As for their shorter-wavelength brethren, imaging radars were tested early in their civilian history in Death Valley because it has a variety of surface types in a small area without the confounding effects of vegetation. In one of the classic references of these early radar studies, in a semi-quantitative way the response of an imaging radar to surface roughness near the radar wavelength, which typically ranges from about 1 cm to 1 m was explained. This laid the groundwork for applications of airborne and spaceborne radars to geologic problems in and regions. Radar's main advantages over other sensors stems from its active nature- supplying its own illumination makes it independent of solar illumination and it can also control the imaging geometry more accurately. Finally, its long wavelength allows it to peer through clouds, eliminating some of the problems of optical sensors, especially in perennially cloudy and polar areas.

Fusion of Laser Altimetry Data with Dems Derived from Stereo Imaging Systems

NASA Astrophysics Data System (ADS)

Schenk, T.; Csatho, B. M.; Duncan, K.

2016-06-01

During the last two decades surface elevation data have been gathered over the Greenland Ice Sheet (GrIS) from a variety of different sensors including spaceborne and airborne laser altimetry, such as NASA's Ice Cloud and land Elevation Satellite (ICESat), Airborne Topographic Mapper (ATM) and Laser Vegetation Imaging Sensor (LVIS), as well as from stereo satellite imaging systems, most notably from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Worldview. The spatio-temporal resolution, the accuracy, and the spatial coverage of all these data differ widely. For example, laser altimetry systems are much more accurate than DEMs derived by correlation from imaging systems. On the other hand, DEMs usually have a superior spatial resolution and extended spatial coverage. We present in this paper an overview of the SERAC (Surface Elevation Reconstruction And Change detection) system, designed to cope with the data complexity and the computation of elevation change histories. SERAC simultaneously determines the ice sheet surface shape and the time-series of elevation changes for surface patches whose size depends on the ruggedness of the surface and the point distribution of the sensors involved. By incorporating different sensors, SERAC is a true fusion system that generates the best plausible result (time series of elevation changes) a result that is better than the sum of its individual parts. We follow this up with an example of the Helmheim gacier, involving ICESat, ATM and LVIS laser altimetry data, together with ASTER DEMs.

Earth Surface Monitoring with COSI-Corr, Techniques and Applications

NASA Astrophysics Data System (ADS)

Leprince, S.; Ayoub, F.; Avouac, J.

2009-12-01

Co-registration of Optically Sensed Images and Correlation (COSI-Corr) is a software package developed at the California Institute of Technology (USA) for accurate geometrical processing of optical satellite and aerial imagery. Initially developed for the measurement of co-seismic ground deformation using optical imagery, COSI-Corr is now used for a wide range of applications in Earth Sciences, which take advantage of the software capability to co-register, with very high accuracy, images taken from different sensors and acquired at different times. As long as a sensor is supported in COSI-Corr, all images between the supported sensors can be accurately orthorectified and co-registered. For example, it is possible to co-register a series of SPOT images, a series of aerial photographs, as well as to register a series of aerial photographs with a series of SPOT images, etc... Currently supported sensors include the SPOT 1-5, Quickbird, Worldview 1 and Formosat 2 satellites, the ASTER instrument, and frame camera acquisitions from e.g., aerial survey or declassified satellite imagery. Potential applications include accurate change detection between multi-temporal and multi-spectral images, and the calibration of pushbroom cameras. In particular, COSI-Corr provides a powerful correlation tool, which allows for accurate estimation of surface displacement. The accuracy depends on many factors (e.g., cloud, snow, and vegetation cover, shadows, temporal changes in general, steadiness of the imaging platform, defects of the imaging system, etc...) but in practice, the standard deviation of the measurements obtained from the correlation of mutli-temporal images is typically around 1/20 to 1/10 of the pixel size. The software package also includes post-processing tools such as denoising, destriping, and stacking tools to facilitate data interpretation. Examples drawn from current research in, e.g., seismotectonics, glaciology, and geomorphology will be presented. COSI-Corr is developed in IDL (Interactive Data Language), integrated under the user friendly interface ENVI (Environment for Visualizing Images), and is distributed free of charge for academic research purposes.

Monitoring Land Surface Soil Moisture from Space with in-Situ Sensors Validation: The Huntsville Example

NASA Technical Reports Server (NTRS)

Wu, Steve Shih-Tseng

1997-01-01

Based on recent advances in microwave remote sensing of soil moisture and in pursuit of research interests in areas of hydrology, soil climatology, and remote sensing, the Center for Hydrology, Soil Climatology, and Remote Sensing (HSCARS) conducted the Huntsville '96 field experiment in Huntsville, Alabama from July 1-14, 1996. We, researchers at the Global Hydrology and Climate Center's MSFC/ES41, are interested in using ground-based microwave sensors, to simulate land surface brightness signatures of those spaceborne sensors that were in operation or to be launched in the near future. The analyses of data collected by the Advanced Microwave Precipitation Radiometer (AMPR) and the C-band radiometer, which together contained five frequencies (6.925,10.7,19.35, 37.1, and 85.5 GHz), and with concurrent in-situ collection of surface cover conditions (surface temperature, surface roughness, vegetation, and surface topology) and soil moisture content, would result in a better understanding of the data acquired over land surfaces by the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR), because these spaceborne sensors contained these five frequencies. This paper described the approach taken and the specific objective to be accomplished in the Huntsville '97 field experiment.

Time Series of Images to Improve Tree Species Classification

NASA Astrophysics Data System (ADS)

Miyoshi, G. T.; Imai, N. N.; de Moraes, M. V. A.; Tommaselli, A. M. G.; Näsi, R.

2017-10-01

Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26 %.

A Multi-Frequency Polarimetric SAR Sensors Analysis over the UNESCO Archaeological Site of Djebel Barkal (Sudan)

NASA Astrophysics Data System (ADS)

Patruno, Jolanda; Dore, Nicole; Pottier, Eric; Crespi, Mattia

2013-08-01

Differences in vegetation growth and in soil moisture content generate ground anomalies which can be linked to subsurface anthropic structures. Such evidences have been studied by means of aerial photographs and of historical II World War acquisitions first, and of very high spatial resolution of optical satellites later. This work aims to exploit the technique of SAR Polarimetry for the detection of surface and subsurface archaeological structures, comparing ALOS P ALSAR L-band (central frequency 1.27 GHz), with RADARSAT-2 C-band sensor (central frequency 5.405 GHz). The great potential of the two polarimetric sensors with different frequency for the detection of archaeological remains has been demonstrated thanks to the sand penetration capability of both C-band and L- band sensors. The choice to analyze radar sensors is based on their 24-hour observations, independent from Sun illumination and meteorological conditions and on the electromagnetic properties of the target they could provide, information not derivable from optical images.

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.

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.

A new approach for fast indexing of hyperspectral image data for knowledge retrieval and mining

NASA Astrophysics Data System (ADS)

Clowers, Robert; Dua, Sumeet

2005-11-01

Multispectral sensors produce images with a few relatively broad wavelength bands. Hyperspectral remote sensors, on the other hand, collect image data simultaneously in dozens or hundreds of narrow and adjacent spectral bands. These measurements make it possible to derive a continuous spectrum for each image cell, generating an image cube across multiple spectral components. Hyperspectral imaging has sound applications in a variety of areas such as mineral exploration, hazardous waste remediation, mapping habitat, invasive vegetation, eco system monitoring, hazardous gas detection, mineral detection, soil degradation, and climate change. This image has a strong potential for transforming the imaging paradigms associated with several design and manufacturing processes. In this paper, we describe a novel approach for fast indexing of multi-dimensional hyperspectral image data, especially for data mining applications. The index exploits the spectral and spatial relationships embedded in these image sets. The index will be employed for knowledge retrieval applications that require fast information interpretation approaches. The index can also be deployed in real-time mission-critical domains, as it is shown to exhibit speed with high degrees of dimensionality associated with the data. The strength of this index in terms of degree of false dismissals and false alarms will also be demonstrated. The paper will highlight some common applications of this imaging computational paradigm and will conclude with directions for future improvement and investigation.

Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors

NASA Technical Reports Server (NTRS)

Holben, Brent N.; Shimabukuro, Yosio E.

1993-01-01

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55-3.95 micron channel was used with the two reflective channels 0.58-0.68 micron and 0.725-1.1 micron to run a constrained least squares model to generate fraction images for an area in the west central region of Brazil. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse spatial resolution data for global studies.

Tracking of Environment Changes by Exploitation of Suomi-NPP VIIRS Data

NASA Astrophysics Data System (ADS)

Ibrahim, W.; Greene, E.; van Poollen, C.; Cumpton, D.

2017-12-01

NOAA's next-generation environmental satellite system, Joint Polar Satellite System (JPSS), replaces the current Polar-orbiting Operational Environmental Satellites. JPSS satellites carry sensors which collect meteorological, oceanographic, climatological, and solar-geophysical observations of the earth, atmosphere, and space. The first JPSS satellite, Suomi National Polar-orbiting Partnership (S-NPP), was launched in 2011. The JPSS ground system is the Common Ground System (CGS), and provides command, control, and communications (C3) and data processing (DP). S-NPP satellite includes the Visible Infrared Imaging Radiometer Suite (VIIRS), a 22-band scanning radiometer that provides top-of-atmosphere radiances and reflectances at a range of visible and infrared frequencies. Data collected from VIIRS are output by CGS DP into Raw Data Records (RDRs; Level-0), Sensor Data Records (SDRs; Level-1B) and Environmental Data Records (EDRs; Level-1C). This paper presents a methodology of monitoring and tracking impact of weather conditions on environment changes by exploitation of data from S-NPP VIIRS products. Three different products created from VIIRS data, SDR M-band True-Color (TC) composite visible imagery RGB (M5, M4 and M3), SDR M-band Natural-Color (NC) composite imagery RGB (M10, M7 and M5) and Vegetation Index (VI) EDR, are used to analyze the change in springtime vegetation and snowpack in California, USA, over four years from the height of the drought in 2014 to its end in 2017. While the TC composite images are more appealing to the human observer, utilization of the NC composite images allows for tracking and monitoring the changes in the snowpack in the Sierra Nevada, the reappearance of bodies of water and the changes in the vegetation composite. The VI product uses NDVI to characterize the vegetation temporally. By combining multiple VIIRS products, complex scenes can be visualized and analyzed temporally and spatially more accurately than just using a single product. Assimilation of both imagery and EDR products allows for a better characterization of impact of weather conditions on environment changes. This method can be expanded to characterize impact of weather conditions on environment changes in sea ice, snow, forest, agricultural land, population centers, etc.

The 2011 Eco3D Flight Campaign: Vegetation Structure and Biomass Estimation from Simultaneous SAR, Lidar and Radiometer Measurements

NASA Technical Reports Server (NTRS)

Fatoyinbo, Temilola; Rincon, Rafael; Harding, David; Gatebe, Charles; Ranson, Kenneth Jon; Sun, Guoqing; Dabney, Phillip; Roman, Miguel

2012-01-01

The Eco3D campaign was conducted in the Summer of 2011. As part of the campaign three unique and innovative NASA Goddard Space Flight Center airborne sensors were flown simultaneously: The Digital Beamforming Synthetic Aperture Radar (DBSAR), the Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) and the Cloud Absorption Radiometer (CAR). The campaign covered sites from Quebec to Southern Florida and thereby acquired data over forests ranging from Boreal to tropical wetlands. This paper describes the instruments and sites covered and presents the first images resulting from the campaign.

Study of sensor spectral responses and data processing algorithms and architectures for onboard feature identification

NASA Technical Reports Server (NTRS)

Huck, F. O.; Davis, R. E.; Fales, C. L.; Aherron, R. M.

1982-01-01

A computational model of the deterministic and stochastic processes involved in remote sensing is used to study spectral feature identification techniques for real-time onboard processing of data acquired with advanced earth-resources sensors. Preliminary results indicate that: Narrow spectral responses are advantageous; signal normalization improves mean-square distance (MSD) classification accuracy but tends to degrade maximum-likelihood (MLH) classification accuracy; and MSD classification of normalized signals performs better than the computationally more complex MLH classification when imaging conditions change appreciably from those conditions during which reference data were acquired. The results also indicate that autonomous categorization of TM signals into vegetation, bare land, water, snow and clouds can be accomplished with adequate reliability for many applications over a reasonably wide range of imaging conditions. However, further analysis is required to develop computationally efficient boundary approximation algorithms for such categorization.

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2011-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system".

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2009-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''.

Early On-Orbit Performance of the Visible Infrared Imaging Radiometer Suite Onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite

NASA Technical Reports Server (NTRS)

Cao, Changyong; DeLuccia, Frank J.; Xiong, Xiaoxiong; Wolfe, Robert; Weng, Fuzhong

2014-01-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key environmental remote-sensing instruments onboard the Suomi National Polar-Orbiting Partnership spacecraft, which was successfully launched on October 28, 2011 from the Vandenberg Air Force Base, California. Following a series of spacecraft and sensor activation operations, the VIIRS nadir door was opened on November 21, 2011. The first VIIRS image acquired signifies a new generation of operational moderate resolution-imaging capabilities following the legacy of the advanced very high-resolution radiometer series on NOAA satellites and Terra and Aqua Moderate-Resolution Imaging Spectroradiometer for NASA's Earth Observing system. VIIRS provides significant enhancements to the operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for 23 environmental data records including aerosol, cloud properties, fire, albedo, snow and ice, vegetation, sea surface temperature, ocean color, and nigh-time visible-light-related applications. Preliminary results from the on-orbit verification in the postlaunch check-out and intensive calibration and validation have shown that VIIRS is performing well and producing high-quality images. This paper provides an overview of the onorbit performance of VIIRS, the calibration/validation (cal/val) activities and methodologies used. It presents an assessment of the sensor initial on-orbit calibration and performance based on the efforts from the VIIRS-SDR team. Known anomalies, issues, and future calibration efforts, including the long-term monitoring, and intercalibration are also discussed.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Characterization techniques for incorporating backgrounds into DIRSIG

NASA Astrophysics Data System (ADS)

Brown, Scott D.; Schott, John R.

2000-07-01

The appearance of operation hyperspectral imaging spectrometers in both solar and thermal regions has lead to the development of a variety of spectral detection algorithms. The development and testing of these algorithms requires well characterized field collection campaigns that can be time and cost prohibitive. Radiometrically robust synthetic image generation (SIG) environments that can generate appropriate images under a variety of atmospheric conditions and with a variety of sensors offers an excellent supplement to reduce the scope of the expensive field collections. In addition, SIG image products provide the algorithm developer with per-pixel truth, allowing for improved characterization of the algorithm performance. To meet the needs of the algorithm development community, the image modeling community needs to supply synthetic image products that contain all the spatial and spectral variability present in real world scenes, and that provide the large area coverage typically acquired with actual sensors. This places a heavy burden on synthetic scene builders to construct well characterized scenes that span large areas. Several SIG models have demonstrated the ability to accurately model targets (vehicles, buildings, etc.) Using well constructed target geometry (from CAD packages) and robust thermal and radiometry models. However, background objects (vegetation, infrastructure, etc.) dominate the percentage of real world scene pixels and utilizing target building techniques is time and resource prohibitive. This paper discusses new methods that have been integrated into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to characterize backgrounds. The new suite of scene construct types allows the user to incorporate both terrain and surface properties to obtain wide area coverage. The terrain can be incorporated using a triangular irregular network (TIN) derived from elevation data or digital elevation model (DEM) data from actual sensors, temperature maps, spectral reflectance cubes (possible derived from actual sensors), and/or material and mixture maps. Descriptions and examples of each new technique are presented as well as hybrid methods to demonstrate target embedding in real world imagery.

Correcting the influence of vegetation on surface soil moisture indices by using hyperspectral artificial 3D-canopy models

NASA Astrophysics Data System (ADS)

Spengler, D.; Kuester, T.; Frick, A.; Scheffler, D.; Kaufmann, H.

2013-10-01

Surface soil moisture content is one of the key variables used for many applications especially in hydrology, meteorology and agriculture. Hyperspectral remote sensing provides effective methodologies for mapping soil moisture content over a broad area by different indices such as NSMI [1,2] and SMGM [3]. Both indices can achieve a high accuracy for non-vegetation influenced soil samples, but their accuracy is limited in case of the presence of vegetation. Since, the increase of the vegetation cover leads to non-linear variations of the indices. In this study a new methodology for moisture indices correcting the influence of vegetation is presented consisting of several processing steps. First, hyperspectral reflectance data are classified in terms of crop type and growth stage. Second, based on these parameters 3D plant models from a database used to simulate typical canopy reflectance considering variations in the canopy structure (e.g. plant density and distribution) and the soil moisture content for actual solar illumination and sensor viewing angles. Third, a vegetation correction function is developed, based on the calculated soil moisture indices and vegetation indices of the simulated canopy reflectance data. Finally this function is applied on hyperspectral image data. The method is tested on two hyperspectral image data sets of the AISA DUAL at the test site Fichtwald in Germany. The results show a significant improvements compared to solely use of NSMI index. Up to a vegetation cover of 75 % the correction function minimise the influences of vegetation cover significantly. If the vegetation is denser the method leads to inadequate quality to predict the soil moisture content. In summary it can be said that applying the method on weakly to moderately overgrown with vegetation locations enables a significant improvement in the quantification of soil moisture and thus greatly expands the scope of NSMI.

Pesticide residue quantification analysis by hyperspectral imaging sensors

NASA Astrophysics Data System (ADS)

Liao, Yuan-Hsun; Lo, Wei-Sheng; Guo, Horng-Yuh; Kao, Ching-Hua; Chou, Tau-Meu; Chen, Junne-Jih; Wen, Chia-Hsien; Lin, Chinsu; Chen, Hsian-Min; Ouyang, Yen-Chieh; Wu, Chao-Cheng; Chen, Shih-Yu; Chang, Chein-I.

2015-05-01

Pesticide residue detection in agriculture crops is a challenging issue and is even more difficult to quantify pesticide residue resident in agriculture produces and fruits. This paper conducts a series of base-line experiments which are particularly designed for three specific pesticides commonly used in Taiwan. The materials used for experiments are single leaves of vegetable produces which are being contaminated by various amount of concentration of pesticides. Two sensors are used to collected data. One is Fourier Transform Infrared (FTIR) spectroscopy. The other is a hyperspectral sensor, called Geophysical and Environmental Research (GER) 2600 spectroradiometer which is a batteryoperated field portable spectroradiometer with full real-time data acquisition from 350 nm to 2500 nm. In order to quantify data with different levels of pesticide residue concentration, several measures for spectral discrimination are developed. Mores specifically, new measures for calculating relative power between two sensors are particularly designed to be able to evaluate effectiveness of each of sensors in quantifying the used pesticide residues. The experimental results show that the GER is a better sensor than FTIR in the sense of pesticide residue quantification.

Multi-Scale Fractal Analysis of Image Texture and Pattern

NASA Technical Reports Server (NTRS)

Emerson, Charles W.; Lam, Nina Siu-Ngan; Quattrochi, Dale A.

1999-01-01

Analyses of the fractal dimension of Normalized Difference Vegetation Index (NDVI) images of homogeneous land covers near Huntsville, Alabama revealed that the fractal dimension of an image of an agricultural land cover indicates greater complexity as pixel size increases, a forested land cover gradually grows smoother, and an urban image remains roughly self-similar over the range of pixel sizes analyzed (10 to 80 meters). A similar analysis of Landsat Thematic Mapper images of the East Humboldt Range in Nevada taken four months apart show a more complex relation between pixel size and fractal dimension. The major visible difference between the spring and late summer NDVI images is the absence of high elevation snow cover in the summer image. This change significantly alters the relation between fractal dimension and pixel size. The slope of the fractal dimension-resolution relation provides indications of how image classification or feature identification will be affected by changes in sensor spatial resolution.

Multi-Scale Fractal Analysis of Image Texture and Pattern

NASA Technical Reports Server (NTRS)

Emerson, Charles W.; Lam, Nina Siu-Ngan; Quattrochi, Dale A.

1999-01-01

Analyses of the fractal dimension of Normalized Difference Vegetation Index (NDVI) images of homogeneous land covers near Huntsville, Alabama revealed that the fractal dimension of an image of an agricultural land cover indicates greater complexity as pixel size increases, a forested land cover gradually grows smoother, and an urban image remains roughly self-similar over the range of pixel sizes analyzed (10 to 80 meters). A similar analysis of Landsat Thematic Mapper images of the East Humboldt Range in Nevada taken four months apart show a more complex relation between pixel size and fractal dimension. The major visible difference between the spring and late summer NDVI images of the absence of high elevation snow cover in the summer image. This change significantly alters the relation between fractal dimension and pixel size. The slope of the fractal dimensional-resolution relation provides indications of how image classification or feature identification will be affected by changes in sensor spatial resolution.

Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest

NASA Astrophysics Data System (ADS)

Tian, Jinyan; Wang, Le; Li, Xiaojuan; Gong, Huili; Shi, Chen; Zhong, Ruofei; Liu, Xiaomeng

2017-09-01

Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2). In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R2 = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R2 = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation.

Using Small Drone (UAS) Imagery to Bridge the Gap Between Field- and Satellite-Based Measurements of Vegetation Structure and Change

NASA Astrophysics Data System (ADS)

Mayes, M. T.; Estes, L. D.; Gago, X.; Debats, S. R.; Caylor, K. K.; Manfreda, S.; Oudemans, P.; Ciraolo, G.; Maltese, A.; Nadal, M.; Estrany, J.

2016-12-01

Leaf area is an important ecosystem variable that relates to vegetation biomass, productivity, water and nutrient use in natural and agricultural systems globally. Since the 1980s, optical satellite image-based estimates of leaf area based on indices such as Normalized Difference Vegetation Index (NDVI) have greatly improved understanding of vegetation structure, function, and responses to disturbance at landscape (10^3 km2) to continental (10^6 km2) spatial scales. However, at landscape scales, satellites have failed to capture many leaf area patterns indicative of vegetation succession, crop types, stress and other conditions important for ecological processes. Small drones (UAS - unmanned aerial systems) offer new means for assessing leaf area and vegetation structure at higher spatial resolutions (<1 m) and land cover features such as substrate exposure that may affect estimates of vegetation structure in satellite data. Yet it is unclear how differences in spatial and spectral resolution between UAS and satellite data affect their relationships to each other, and to common field measurements of leaf area (e.g. LiCOR photosensors) and land cover. Constraining these relationships is important for leveraging UAS data to improve scaling of field data on leaf area and biomass to satellite data from Landsat, Sentinel-2, and increasing numbers of commercial sensors. Here, we quantify relationships among field, UAS and satellite estimates of vegetation leaf area and biomass in three case study landscapes spanning semi-arid Mediterranean (Matera, Southern Italy and Mallorca, Spain) and North American temperate ecosystems (New Jersey, USA). We assess how land cover and sensor spectral characteristics affect UAS and satellite-derived NDVI, leaf-area and biomass estimates. Then, we assess the fidelity of UAS, WorldView-2, and Landsat leaf-area and biomass estimates to field-measured landscape changes and variability, including vegetation recovery from fire (Mallorca), and leaf-area and biomass variability due to orchard type and agro-ecosystem management (Matera, New Jersey). Finally, we highlight promising ways forward for improving field data collection and the use of UAS observations to monitor vegetation leaf-area and biomass change at landscape scales in natural and agricultural systems.

A study to explore the use of orbital remote sensing to determine native arid plant distribution. [Arizona

NASA Technical Reports Server (NTRS)

Mcginnies, W. G. (Principal Investigator); Conn, J. S.; Haase, E. F.; Lepley, L. K.; Musick, H. B.; Foster, K. E.

1975-01-01

The author has identified the following significant results. Research results include a method for determining the reflectivities of natural areas from ERTS data taking into account sun angle and atmospheric effects on the radiance seen by the satellite sensor. Ground truth spectral signature data for various types of scenes, including ground with and without annuals, and various shrubs were collected. Large areas of varnished desert pavement are visible and mappable on ERTS and high altitude aircraft imagery. A large scale and a small scale vegetation pattern were found to be correlated with presence of desert pavement. A comparison of radiometric data with video recordings shows quantitatively that for most areas of desert vegetation, soils are the most influential factor in determining the signature of a scene. Additive and subtractive image processing techniques were applied in the dark room to enhance vegetational aspects of ERTS.

Wetland Feature Extraction in Poyang Lake from Muti-Sensor and Multi-Temporal Images

NASA Astrophysics Data System (ADS)

Zhang, Li; Desnos, Yves-Louis; Wang, Yeqiao; Chen, Xiaoling; Zmuda, Andy; Yesou, Herve

2016-08-01

Under the high dynamic hydrological variations and impacts from human activities, the nature wetlands of Poyang Lake face major challenges in biodiversity decline and wetland degradation. Variations of Poyang Lake wetlands are difficult to map by a single source or one time remote sensing imagery because the landscape is dominated by herbaceous vegetation and aquatic macrophytes which are altered and controlled by the water level. This study selected and combined time series NDVI, Green Ratio Vegetation Index (GRVI) and Modified Normalized Different Water Index (MNDWI), Backscattering coefficients(σ0) (VV&VH mode), Shannon Entropy (SE) and H/α wishart classification value derived from Sentinel 1A and Sentinel 2A to investigate the spatial-temporal variation of wetlands in autumn and spring growing season with discussions about the possibility of monitoring the wetland vegetation by C-band dual-pol datasets.

Adjusting Spectral Indices for Spectral Response Function Differences of Very High Spatial Resolution Sensors Simulated from Field Spectra

PubMed Central

Cundill, Sharon L.; van der Werff, Harald M. A.; van der Meijde, Mark

2015-01-01

The use of data from multiple sensors is often required to ensure data coverage and continuity, but differences in the spectral characteristics of sensors result in spectral index values being different. This study investigates spectral response function effects on 48 spectral indices for cultivated grasslands using simulated data of 10 very high spatial resolution sensors, convolved from field reflectance spectra of a grass covered dike (with varying vegetation condition). Index values for 48 indices were calculated for original narrow-band spectra and convolved data sets, and then compared. The indices Difference Vegetation Index (DVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI2) and Soil-Adjusted Vegetation Index (SAVI), which include the difference between the near-infrared and red bands, have values most similar to those of the original spectra across all 10 sensors (1:1 line mean 1:1R2 > 0.960 and linear trend mean ccR2 > 0.997). Additionally, relationships between the indices’ values and two quality indicators for grass covered dikes were compared to those of the original spectra. For the soil moisture indicator, indices that ratio bands performed better across sensors than those that difference bands, while for the dike cover quality indicator, both the choice of bands and their formulation are important. PMID:25781511

Geodetic Imaging Lidar: Applications for high-accuracy, large area mapping with NASA's upcoming high-altitude waveform-based airborne laser altimetry Facility

NASA Astrophysics Data System (ADS)

Blair, J. B.; Rabine, D.; Hofton, M. A.; Citrin, E.; Luthcke, S. B.; Misakonis, A.; Wake, S.

2015-12-01

Full waveform laser altimetry has demonstrated its ability to capture highly-accurate surface topography and vertical structure (e.g. vegetation height and structure) even in the most challenging conditions. NASA's high-altitude airborne laser altimeter, LVIS (the Land Vegetation, and Ice Sensor) has produced high-accuracy surface maps over a wide variety of science targets for the last 2 decades. Recently NASA has funded the transition of LVIS into a full-time NASA airborne Facility instrument to increase the amount and quality of the data and to decrease the end-user costs, to expand the utilization and application of this unique sensor capability. Based heavily on the existing LVIS sensor design, the Facility LVIS instrument includes numerous improvements for reliability, resolution, real-time performance monitoring and science products, decreased operational costs, and improved data turnaround time and consistency. The development of this Facility instrument is proceeding well and it is scheduled to begin operations testing in mid-2016. A comprehensive description of the LVIS Facility capability will be presented along with several mission scenarios and science applications examples. The sensor improvements included increased spatial resolution (footprints as small as 5 m), increased range precision (sub-cm single shot range precision), expanded dynamic range, improved detector sensitivity, operational autonomy, real-time flight line tracking, and overall increased reliability and sensor calibration stability. The science customer mission planning and data product interface will be discussed. Science applications of the LVIS Facility include: cryosphere, territorial ecology carbon cycle, hydrology, solid earth and natural hazards, and biodiversity.

Chlorophyll Meters Aid Plant Nutrient Management

NASA Technical Reports Server (NTRS)

2009-01-01

On December 7, 1972, roughly 5 hours and 6 minutes after launch, the crew of Apollo 17 took one of history s most famous photographs. The brilliant image of the fully illuminated Earth, the African and Antarctic continents peering out from behind swirling clouds, came to be known as the Blue Marble. Today, Earth still sometimes goes by the Blue Marble nickname, but as the satellites comprising NASA s Earth Observing System (EOS) scan the planet daily in ever greater resolutions, it is often the amount of green on the planet that is a focus of researchers attention. Earth s over 400,000 known plant species play essential roles in the planet s health: They absorb carbon dioxide and release the oxygen we breathe, help manage the Earth s temperature by absorbing and reflecting sunlight, provide food and habitats for animals, and offer building materials, medication, and sustenance for humans. As part of NASA s efforts to study our own planet along with the universe around it, the Agency s EOS satellites have been accumulating years of valuable data about Earth s vegetation (not to mention its land features, oceans, and atmosphere) since the first EOS satellite launched in 1997. Among the powerful sensors used is the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra and Aqua satellites. MODIS sweeps the entire Earth every few days, beaming back information gathered across 36 bands of visible and infrared light, yielding images that let scientists track how much of Earth is green over the course of seasons and years. Monitoring the density and distribution of vegetation on Earth provides a means of determining everything from the impact of natural and human-induced climate change to the potential outbreak of disease. (Goddard Space Flight Center and U.S. Department of Defense researchers have determined, for example, that vegetation density can be used to pinpoint regions of heavy rainfall in Africa regions ripe for outbreaks of rainfall-correlated diseases like mosquito-borne Rift Valley fever.) While the Space Agency is continually seeking to upgrade the power and scope of its satellite sensors, it is also finding ways to bring that potent information-gathering capacity down to Earth. Scientists at Stennis Space Center developed one such tool that is placing some of those sensor capabilities in the hands of farmers and agricultural researchers on the ground.

Use of UAVs for Remote Measurement of Vegetation Canopy Variables

NASA Astrophysics Data System (ADS)

Rango, A.; Laliberte, A.; Herrick, J.; Steele, C.; Bestelmeyer, B.; Chopping, M. J.

2006-12-01

Remote sensing with different sensors has proven useful for measuring vegetation canopy variables at scales ranging from landscapes down to individual plants. For use at landscape scales, such as desert grasslands invaded by shrubs, it is possible to use multi-angle imagery from satellite sensors, such as MISR and CHRIS/Proba, with geometric optical models to retrieve fractional woody plant cover. Vegetation community states can be mapped using visible and near infrared ASTER imagery at 15 m resolution. At finer scales, QuickBird satellite imagery with approximately 60 cm resolution and piloted aircraft photography with 25-80 cm resolution can be used to measure shrubs above a critical size. Tests conducted with the QuickBird data in the Jornada basin of southern New Mexico have shown that 87% of all shrubs greater than 2 m2 were detected whereas only about 29% of all shrubs less than 2 m2 were detected, even at these high resolutions. Because there is an observational gap between satellite/aircraft measurements and ground observations, we have experimented with Unmanned Aerial Vehicles (UAVs) producing digital photography with approximately 5 cm resolution. We were able to detect all shrubs greater than 2 m2, and we were able to map small subshrubs indicative of rangeland deterioration, as well as remnant grass patches, for the first time. None of these could be identified on the 60 cm resolution data. Additionally, we were able to measure canopy gaps, shrub patterns, percent bare soil, and vegetation cover over mixed rangeland vegetation. This approach is directly applicable to rangeland health monitoring, and it provides a quantitative way to assess shrub invasion over time and to detect the depletion or recovery of grass patches. Further, if the UAV images have sufficient overlap, it may be possible to exploit the stereo viewing capabilities to develop a digital elevation model from the orthophotos, with a potential for extracting canopy height. We envision two parallel routes for investigation: one which emphasizes utilization of the most technically advanced passive and active space and aircraft sensors (e.g., LIDAR, radar, Hyperion, ASTER, QuickBird follow-on) for modeling research, and a second which emphasizes minimization of costs and maximization of simplicity for monitoring purposes utilizing inexpensive sensors such as digital cameras on UAVs for arid and semiarid rangelands. The use of UAVs will provide management agencies a way to assess various vegetation canopy variables for a very reasonable cost.

Monitoring change in mountainous dry-heath vegetation at a regional scale using multitemporal Landsat TM data.

PubMed

Nordberg, Maj-Liz; Evertson, Joakim

2003-12-01

Vegetation cover-change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Satellite sensors like Landsat TM offer the advantages of wide spatial coverage while providing land-cover information. This facilitates the monitoring of surface processes. This study discusses change detection in mountainous dry-heath communities in Jämtland County, Sweden, using satellite data. Landsat-5 TM and Landsat-7 ETM+ data from 1984, 1994 and 2000, respectively, were used. Different change detection methods were compared after the images had been radiometrically normalized, georeferenced and corrected for topographic effects. For detection of the classes change--no change the NDVI image differencing method was the most accurate with an overall accuracy of 94% (K = 0.87). Additional change information was extracted from an alternative method called NDVI regression analysis and vegetation change in 3 categories within mountainous dry-heath communities were detected. By applying a fuzzy set thresholding technique the overall accuracy was improved from of 65% (K = 0.45) to 74% (K = 0.59). The methods used generate a change product showing the location of changed areas in sensitive mountainous heath communities, and it also indicates the extent of the change (high, moderate and unchanged vegetation cover decrease). A total of 17% of the dry and extremely dry-heath vegetation within the study area has changed between 1984 and 2000. On average 4% of the studied heath communities have been classified as high change, i.e. have experienced "high vegetation cover decrease" during the period. The results show that the low alpine zone of the southern part of the study area shows the highest amount of "high vegetation cover decrease". The results also show that the main change occurred between 1994 and 2000.

Spatio-temporal pattern of eco-environmental parameters in Jharia coalfield, India

NASA Astrophysics Data System (ADS)

Saini, V.; Gupta, R. P.; Arora, M. K.

2015-10-01

Jharia coal-field holds unequivocal importance in the Indian context as it is the only source of prime coking coal in the country. The coalfield is also known for its infamous coal mine fires which have been burning since last more than a century. Haphazard mining over a century has led to eco-environmental changes to a large extent such as changes in vegetation distribution and widespread development of surface and subsurface fires. This article includes the spatiotemporal study of remote sensing derived eco-environmental parameters like vegetation index (NDVI), tasseled cap transformation (TCT) and temperature distribution in fire areas. In order to have an estimate of the temporal variations of NDVI over the years, a study has been carried out on two subsets of the Jharia coalfield using Landsat images of 1972 (MSS), 1992 (TM), 1999 (ETM+) and 2013 (OLI). To assess the changes in brightness and greenness over the year s, difference images have been calculated using the 1992 (TM) and 2013 (OLI) images. Radiance images derived from thermal bands have been used to calculate at-sensor brightness temperature over a 23 year period from 1991 to 2013. It has been observed that during the years 1972 to 2013, moderate to dense vegetation has decreased drastically due to the intense mining going on in the area. TCT images show the areas that have undergone changes in both brightness and greenness from 1992 to 2013. Surface temperature data obtained shows a constant increase from 1991 to 2013 apparently due to coal fires. The utility of remote sensing data in such EIA studies has been emphasized.

Effects of Telecoupling on Global Vegetation Dynamics

NASA Astrophysics Data System (ADS)

Viña, A.; Liu, J.

2016-12-01

With the ever increasing trend in telecoupling processes, such as international trade, all countries around the world are becoming more interdependent. However, the effects of this growing interdependence on vegetation (e.g., shifts in the geographic extent and distribution) remain unknown even though vegetation dynamics are crucially important for food production, carbon sequestration, provision of other ecosystem services, and biodiversity conservation. In this study we evaluate the effects of international trade on the spatio-temporal trajectories of vegetation at national and global scales, using vegetation index imagery collected over more than three decades by the Advanced Very High Resolution Radiometer (AVHRR) satellite sensor series together with concurrent national and international data on international trade (and its associated movement of people, goods, services and information). The spatio-temporal trajectories of vegetation are obtained using the scale of fluctuation technique, which is based on the decomposition of the AVHRR image time series to obtain information on its spatial dependence structure over time. Similar to the correlation length, the scale of fluctuation corresponds to the range over which fluctuations in the vegetation index are spatially correlated. Results indicate that global vegetation has changed drastically over the last three decades. These changes are not uniform across space, with hotspots in active trading countries. This study not only has direct implications for understanding global vegetation dynamics, but also sheds important insights on the complexity of human-nature interactions across telecoupled systems.

Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data

PubMed Central

Scharlemann, Jörn P. W.; Benz, David; Hay, Simon I.; Purse, Bethan V.; Tatem, Andrew J.; Wint, G. R. William; Rogers, David J.

2008-01-01

Background Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling. PMID:18183289

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data.

PubMed

Scharlemann, Jörn P W; Benz, David; Hay, Simon I; Purse, Bethan V; Tatem, Andrew J; Wint, G R William; Rogers, David J

2008-01-09

Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

Spatio - Temporal Variation of Aerosol and its relation to vegetation cover over mega-city New Delhi

NASA Astrophysics Data System (ADS)

Pandey, Alok; Pravesh Kumar, Ram; Berwal, Shivesh; Kumar, Krishan; Kumar, Ritesh

2016-07-01

MODerate resolution Imaging Spectro-radiometer (MODIS) on board NASA's Terra and Aqua satellite Level 2 Aerosol optical depth (AOD) data is used for aerosol study and LISS III sensor on board Indian Remote Sensing (IRS) Satellite procured from the National Remote Sensing Center (NRSC), Hyderabad, India was used for vegetation cover estimation over New Delhi and its surrounding regions. Lowest AOD was found in the spring and winter season where highest AOD in summer months. Different dates representing different seasons LISS III imageries were used for generation of land-cover maps for vegetation study. The land cover maps reveal that most of the surrounding areas of Delhi are covered with vegetation in the month of March. By the month of May-June herbs are cut or dry from most of the region surrounding Delhi and the land cover in the surrounding areas changes to bare soil. During the rainy season (July to September) the vegetation cover over Delhi and the surrounding areas increases significantly. In November - December there is dispersed vegetation cover over New Delhi and its surrounding regions depending upon the age of the newly sown crop and ornamental plants. We found that, there is statistically significant negative correlation between AOD and Vegetation in every season over New Delhi.

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

NASA Technical Reports Server (NTRS)

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

2008-01-01

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

Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management.

PubMed

Torres-Sánchez, Jorge; López-Granados, Francisca; De Castro, Ana Isabel; Peña-Barragán, José Manuel

2013-01-01

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).

Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management

PubMed Central

Torres-Sánchez, Jorge; López-Granados, Francisca; De Castro, Ana Isabel; Peña-Barragán, José Manuel

2013-01-01

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches). PMID:23483997

IN SITU ESTIMATES OF FOREST LAI FOR MODIS DATA VALIDATION

EPA Science Inventory

Satellite remote sensor data are commonly used to assess ecosystem conditions through synoptic monitoring of terrestrial vegetation extent, biomass, and seasonal dynamics. Two commonly used vegetation indices that can be derived from various remote sensor systems include the Norm...

Configuration and Specifications of AN Unmanned Aerial Vehicle for Precision Agriculture

NASA Astrophysics Data System (ADS)

Erena, M.; Montesinos, S.; Portillo, D.; Alvarez, J.; Marin, C.; Fernandez, L.; Henarejos, J. M.; Ruiz, L. A.

2016-06-01

Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated, generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for farmers.

Integration of airborne optical and thermal imagery for archaeological subsurface structures detection: the Arpi case study (Italy)

NASA Astrophysics Data System (ADS)

Bassani, C.; Cavalli, R. M.; Fasulli, L.; Palombo, A.; Pascucci, S.; Santini, F.; Pignatti, S.

2009-04-01

The application of Remote Sensing data for detecting subsurface structures is becoming a remarkable tool for the archaeological observations to be combined with the near surface geophysics [1, 2]. As matter of fact, different satellite and airborne sensors have been used for archaeological applications, such as the identification of spectral anomalies (i.e. marks) related to the buried remnants within archaeological sites, and the management and protection of archaeological sites [3, 5]. The dominant factors that affect the spectral detectability of marks related to manmade archaeological structures are: (1) the spectral contrast between the target and background materials, (2) the proportion of the target on the surface (relative to the background), (3) the imaging system characteristics being used (i.e. bands, instrument noise and pixel size), and (4) the conditions under which the surface is being imaged (i.e. illumination and atmospheric conditions) [4]. In this context, just few airborne hyperspectral sensors were applied for cultural heritage studies, among them the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer), the CASI (Compact Airborne Spectrographic Imager), the HyMAP (Hyperspectral MAPping) and the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer). Therefore, the application of high spatial/spectral resolution imagery arise the question on which is the trade off between high spectral and spatial resolution imagery for archaeological applications and which spectral region is optimal for the detection of subsurface structures. This paper points out the most suitable spectral information useful to evaluate the image capability in terms of spectral anomaly detection of subsurface archaeological structures in different land cover contexts. In this study, we assess the capability of MIVIS and CASI reflectances and of ATM and MIVIS emissivities (Table 1) for subsurface archaeological prospection in different sites of the Arpi archaeological area (southern Italy). We identify, for the selected sites, three main land cover overlying the buried structures: (a) photosynthetic (i.e. green low vegetation), (b) non-photosynthetic vegetation (i.e. yellow, dry low vegetation), and (c) dry bare soil. Afterwards, we analyse the spectral regions showing an inherent potential for the archaeological detection as a function of the land cover characteristics. The classified land cover units have been used in a spectral mixture analysis to assess the land cover fractional abundance surfacing the buried structures (i.e. mark-background system). The classification and unmixing results for the CASI, MIVIS and ATM remote sensing data processing showed a good accordance both in the land cover units and in the subsurface structures identification. The integrated analysis of the unmixing results for the three sensors allowed us to establish that for the land cover characterized by green and dry vegetation (occurrence higher than 75%), the visible and near infrared (VNIR) spectral regions better enhance the buried man-made structures. In particular, if the structures are covered by more than 75% of vegetation the two most promising wavelengths for their detection are the chlorophyll peak at 0.56 m (Visible region) and the red edge region (0.67 to 0.72 m; NIR region). This result confirms that the variation induced by the subsurface structures (e.g., stone walls, tile concentrations, pavements near the surface, road networks) to the natural vegetation growth and/or colour (i.e., for different stress factors) is primarily detectable by the chlorophyll peak and the red edge region applied for the vegetation stress detection. Whereas, if dry soils cover the structures (occurrence higher than 75%), both the VNIR and thermal infrared (TIR) regions are suitable to detect the subsurface structures. This work demonstrates that airborne reflectances and emissivities data, even though at different spatial/spectral resolutions and acquisition time represent an effective and rapid tool to detect subsurface structures within different land cover contexts. As concluding results, this study reveals that the airborne multi/hyperspectral image processing can be an effective and cost-efficient tool to perform a preliminary analysis of those areas where large cultural heritage assets prioritising and localizing the sites where to apply near surface geophysics surveys. Spectral Region Spectral Resolution ( m )Spectral Range ( m) Spatial Resolution (m)IFOV (deg) ATM VIS-NIR SWIR-TIR (tot 12 ch) variable from 24 to 3100 0.42 - 1150 2 0.143 CASI VNIR (48 ch.) 0.01 0.40-0.94 2 0.115 MIVIS VNIR (28ch.) 0.02 (VIS) 0.05 (NIR) 0.43-0.83 (VIS) 1.15-1.55 (NIR) 6 - 7 0.115 SWIR (64ch.) 0.09 1.983-2.478 TIR (10ch.) 0.34-0.54 8.180-12.700 Table 1. Characteristics of airborne sensors used for the Arpi test area. 1 References 2 [1] Beck, A., Philip, G., Abdulkarim, M. and Donoghue, D., 2007. Evaluation of Corona and Ikonos high resolution satellite imagery for archaeological prospection in western Syria. Antiquity, 81: 161-175. 3 [2] Altaweel, M., 2005. The Use of ASTER Satellite Imagery in Archaeological Contexts. Archaeological Prospection, 12: 151- 166. 4 [3] Cavalli, R.M.; Colosi, F.; Palombo, A.; Pignatti, S.; Poscolieri, M. Remote hyperspectral imagery as a support to archaeological prospection. J. of Cultural Heritage 2007, 8, 272-283. 5 [4] Kucukkaya, A.G. Photogrammetry and remote sensing in archaeology. J. Quant. Spectrosc. Radiat. Transfer 2004, 97(1-3), 83-97. [5] Rowlands, A.; Sarris, A. Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing. J. of Archaeological Science 2007, 34, 795-803.

Comparison of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Earth Observing One (EO-1) Advanced Land Imager

NASA Technical Reports Server (NTRS)

Pedelty, Jeffrey A.; Morisette, Jeffrey T.; Smith, James A.

2004-01-01

We compare images from the Enhanced Thematic Mapper Plus (ETM+) sensor on Landsat-7 and the Advanced Land Imager (ALI) instrument on Earth Observing One (EO-1) over a test site in Rochester, New York. The site contains a variety of features, ranging from water of varying depths, deciduous/coniferous forest, and grass fields, to urban areas. Nearly coincident cloud-free images were collected one minute apart on 25 August 2001. We also compare images of a forest site near Howland, Maine, that were collected on 7 September, 2001. We atmospherically corrected each pair of images with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmosphere model, using aerosol optical thickness and water vapor column density measured by in situ Cimel sun photometers within the Aerosol Robotic Network (AERONET), along with ozone density derived from the Total Ozone Mapping Spectrometer (TOMS) on the Earth Probe satellite. We present true-color composites from each instrument that show excellent qualitative agreement between the multispectral sensors, along with grey-scale images that demonstrate a significantly improved ALI panchromatic band. We quantitatively compare ALI and ETM+ reflectance spectra of a grassy field in Rochester and find < or equal to 6% differences in the visible/near infrared and approx. 2% differences in the short wave infrared. Spectral comparisons of forest sites in Rochester and Howland yield similar percentage agreement except for band 1, which has very low reflectance. Principal component analyses and comparison of normalized difference vegetation index histograms for each sensor indicate that the ALI is able to reproduce the information content in the ETM+ but with superior signal-to-noise performance due to its increased 12-bit quantization.

GPS-aided inertial technology and navigation-based photogrammetry for aerial mapping the San Andreas fault system

USGS Publications Warehouse

Sanchez, Richard D.; Hudnut, Kenneth W.

2004-01-01

Aerial mapping of the San Andreas Fault System can be realized more efficiently and rapidly without ground control and conventional aerotriangulation. This is achieved by the direct geopositioning of the exterior orientation of a digital imaging sensor by use of an integrated Global Positioning System (GPS) receiver and an Inertial Navigation System (INS). A crucial issue to this particular type of aerial mapping is the accuracy, scale, consistency, and speed achievable by such a system. To address these questions, an Applanix Digital Sensor System (DSS) was used to examine its potential for near real-time mapping. Large segments of vegetation along the San Andreas and Cucamonga faults near the foothills of the San Bernardino and San Gabriel Mountains were burned to the ground in the California wildfires of October-November 2003. A 175 km corridor through what once was a thickly vegetated and hidden fault surface was chosen for this study. Both faults pose a major hazard to the greater Los Angeles metropolitan area and a near real-time mapping system could provide information vital to a post-disaster response.

Plant stress analysis technology deployment

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

Ebadian, M.A.

1998-01-01

Monitoring vegetation is an active area of laser-induced fluorescence imaging (LIFI) research. The Hemispheric Center for Environmental Technology (HCET) at Florida International University (FIU) is assisting in the transfer of the LIFI technology to the agricultural private sector through a market survey. The market survey will help identify the key eco-agricultural issues of the nations that could benefit from the use of sensor technologies developed by the Office of Science and Technology (OST). The principal region of interest is the Western Hemisphere, particularly, the rapidly growing countries of Latin America and the Caribbean. The analysis of needs will assure thatmore » the focus of present and future research will center on economically important issues facing both hemispheres. The application of the technology will be useful to the agriculture industry for airborne crop analysis as well as in the detection and characterization of contaminated sites by monitoring vegetation. LIFI airborne and close-proximity systems will be evaluated as stand-alone technologies and additions to existing sensor technologies that have been used to monitor crops in the field and in storage.« less

MUNSELL COLOR ANALYSIS OF LANDSAT COLOR-RATIO-COMPOSITE IMAGES OF LIMONITIC AREAS IN SOUTHWEST NEW MEXICO.

USGS Publications Warehouse

Kruse, Fred A.

1984-01-01

Green areas on Landsat 4/5 - 4/6 - 6/7 (red - blue - green) color-ratio-composite (CRC) images represent limonite on the ground. Color variation on such images was analyzed to determine the causes of the color differences within and between the green areas. Digital transformation of the CRC data into the modified cylindrical Munsell color coordinates - hue, value, and saturation - was used to correlate image color characteristics with properties of surficial materials. The amount of limonite visible to the sensor is the primary cause of color differences in green areas on the CRCs. Vegetation density is a secondary cause of color variation of green areas on Landsat CRC images. Digital color analysis of Landsat CRC images can be used to map unknown areas. Color variations of green pixels allows discrimination among limonitic bedrock, nonlimonitic bedrock, nonlimonitic alluvium, and limonitic alluvium.

Shuttle imaging radar views the Earth from Challenger: The SIR-B experiment

NASA Technical Reports Server (NTRS)

Ford, J. P.; Cimino, J. B.; Holt, B.; Ruzek, M. R.

1986-01-01

In October 1984, SIR-B obtained digital image data of about 6.5 million km2 of the Earth's surface. The coverage is mostly of selected experimental test sites located between latitudes 60 deg north and 60 deg south. Programmed adjustments made to the look angle of the steerable radar antenna and to the flight attitude of the shuttle during the mission permitted collection of multiple-incidence-angle coverage or extended mapping coverage as required for the experiments. The SIR-B images included here are representative of the coverage obtained for scientific studies in geology, cartography, hydrology, vegetation cover, and oceanography. The relations between radar backscatter and incidence angle for discriminating various types of surfaces, and the use of multiple-incidence-angle SIR-B images for stereo measurement and viewing, are illustrated with examples. Interpretation of the images is facilitated by corresponding images or photographs obtained by different sensors or by sketch maps or diagrams.

Research of BRDF effects on remote sensing imagery

NASA Astrophysics Data System (ADS)

Nina, Peng; Kun, Wang; Tao, Li; Yang, Pan

2011-08-01

The gray distribution and contrast of the optical satellite remote sensing imagery in the same kind of ground surface acquired by sensor is quite different, it depends not only on the satellite's observation and the sun incidence orientation but also the structural and optical properties of the surface. Therefore, the objectives of this research are to analyze the different BRDF characters of soil, vegetation, water and urban surface and also their BRDF effects on the quality of satellite image through 6S radiative transfer model. Furthermore, the causation of CCD blooming and spilling by ground reflectance is discussed by using QUICKBIRD image data and the corresponding ground image data. The general conclusion of BRDF effects on remote sensing imagery is proposed.

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

NASA Astrophysics Data System (ADS)

Franceschini, M. H. D.; Demattê, J. A. M.; da Silva Terra, F.; Vicente, L. E.; Bartholomeus, H.; de Souza Filho, C. R.

2015-06-01

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS - Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.

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.

Evaluation of airborne image data for mapping riparian vegetation within the Grand Canyon

USGS Publications Warehouse

Davis, Philip A.; Staid, Matthew I.; Plescia, Jeffrey B.; Johnson, Jeffrey R.

2002-01-01

This study examined various types of remote-sensing data that have been acquired during a 12-month period over a portion of the Colorado River corridor to determine the type of data and conditions for data acquisition that provide the optimum classification results for mapping riparian vegetation. Issues related to vegetation mapping included time of year, number and positions of wavelength bands, and spatial resolution for data acquisition to produce accurate vegetation maps versus cost of data. Image data considered in the study consisted of scanned color-infrared (CIR) film, digital CIR, and digital multispectral data, whose resolutions from 11 cm (photographic film) to 100 cm (multispectral), that were acquired during the Spring, Summer, and Fall seasons in 2000 for five long-term monitoring sites containing riparian vegetation. Results show that digitally acquired data produce higher and more consistent classification accuracies for mapping vegetation units than do film products. The highest accuracies were obtained from nine-band multispectral data; however, a four-band subset of these data, that did not include short-wave infrared bands, produced comparable mapping results. The four-band subset consisted of the wavelength bands 0.52-0.59 µm, 0.59-0.62 µm, 0.67-0.72 µm, and 0.73-0.85 µm. Use of only three of these bands that simulate digital CIR sensors produced accuracies for several vegetation units that were 10% lower than those obtained using the full multispectral data set. Classification tests using band ratios produced lower accuracies than those using band reflectance for scanned film data; a result attributed to the relatively poor radiometric fidelity maintained by the film scanning process, whereas calibrated multispectral data produced similar classification accuracies using band reflectance and band ratios. This suggests that the intrinsic band reflectance of the vegetation is more important than inter-band reflectance differences in attaining high mapping accuracies. These results also indicate that radiometrically calibrated sensors that record a wide range of radiance produce superior results and that such sensors should be used for monitoring purposes. When texture (spatial variance) at near-infrared wavelength is combined with spectral data in classification, accuracy increased most markedly (20-30%) for the highest resolution (11-cm) CIR film data, but decreased in its effect on accuracy in lower-resolution multi-spectral image data; a result observed in previous studies (Franklin and McDermid 1993, Franklin et al. 2000, 2001). While many classification unit accuracies obtained from the 11-cm film CIR band with texture data were in fact higher than those produced using the 100-cm, nine-band multispectral data with texture, the 11-cm film CIR data produced much lower accuracies than the 100-cm multispectral data for the more sparsely populated vegetation units due to saturation of picture elements during the film scanning process in vegetation units with a high proportion of alluvium. Overall classification accuracies obtained from spectral band and texture data range from 36% to 78% for all databases considered, from 57% to 71% for the 11-cm film CIR data, and from 54% to 78% for the 100-cm multispectral data. Classification results obtained from 20-cm film CIR band and texture data, which were produced by applying a Gaussian filter to the 11-cm film CIR data, showed increases in accuracy due to texture that were similar to those observed using the original 11-cm film CIR data. This suggests that data can be collected at the lower resolution and still retain the added power of vegetation texture. Classification accuracies for the riparian vegetation units examined in this study do not appear to be influenced by season of data acquisition, although data acquired under direct sunlight produced higher overall accuracies than data acquired under overcast conditions. The latter observation, in addition to the importance of band reflectance for classification, implies that data should be acquired near summer solstice when sun elevation and reflectance is highest and when shadows cast by steep canyon walls are minimized.

Scaling dimensions in spectroscopy of soil and vegetation

NASA Astrophysics Data System (ADS)

Malenovský, Zbyněk; Bartholomeus, Harm M.; Acerbi-Junior, Fausto W.; Schopfer, Jürg T.; Painter, Thomas H.; Epema, Gerrit F.; Bregt, Arnold K.

2007-05-01

The paper revises and clarifies definitions of the term scale and scaling conversions for imaging spectroscopy of soil and vegetation. We demonstrate a new four-dimensional scale concept that includes not only spatial but also the spectral, directional and temporal components. Three scaling remote sensing techniques are reviewed: (1) radiative transfer, (2) spectral (un)mixing, and (3) data fusion. Relevant case studies are given in the context of their up- and/or down-scaling abilities over the soil/vegetation surfaces and a multi-source approach is proposed for their integration. Radiative transfer (RT) models are described to show their capacity for spatial, spectral up-scaling, and directional down-scaling within a heterogeneous environment. Spectral information and spectral derivatives, like vegetation indices (e.g. TCARI/OSAVI), can be scaled and even tested by their means. Radiative transfer of an experimental Norway spruce ( Picea abies (L.) Karst.) research plot in the Czech Republic was simulated by the Discrete Anisotropic Radiative Transfer (DART) model to prove relevance of the correct object optical properties scaled up to image data at two different spatial resolutions. Interconnection of the successive modelling levels in vegetation is shown. A future development in measurement and simulation of the leaf directional spectral properties is discussed. We describe linear and/or non-linear spectral mixing techniques and unmixing methods that demonstrate spatial down-scaling. Relevance of proper selection or acquisition of the spectral endmembers using spectral libraries, field measurements, and pure pixels of the hyperspectral image is highlighted. An extensive list of advanced unmixing techniques, a particular example of unmixing a reflective optics system imaging spectrometer (ROSIS) image from Spain, and examples of other mixture applications give insight into the present status of scaling capabilities. Simultaneous spatial and temporal down-scaling by means of a data fusion technique is described. A demonstrative example is given for the moderate resolution imaging spectroradiometer (MODIS) and LANDSAT Thematic Mapper (TM) data from Brazil. Corresponding spectral bands of both sensors were fused via a pyramidal wavelet transform in Fourier space. New spectral and temporal information of the resultant image can be used for thematic classification or qualitative mapping. All three described scaling techniques can be integrated as the relevant methodological steps within a complex multi-source approach. We present this concept of combining numerous optical remote sensing data and methods to generate inputs for ecosystem process models.

Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado

USGS Publications Warehouse

Rockwell, Barnaby W.

2012-01-01

The efficacy of airborne spectroscopic, or "hyperspectral," remote sensing for geoenvironmental watershed evaluations and deposit-scale mapping of exposed mineral deposits has been demonstrated. However, the acquisition, processing, and analysis of such airborne data at regional and national scales can be time and cost prohibitive. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor carried by the NASA Earth Observing System Terra satellite was designed for mineral mapping and the acquired data can be efficiently used to generate uniform mineral maps over very large areas. Multispectral remote sensing data acquired by the ASTER sensor were analyzed to identify and map minerals, mineral groups, hydrothermal alteration types, and vegetation groups in the western San Juan Mountains, Colorado, including the Silverton and Lake City calderas. This mapping was performed in support of multidisciplinary studies involving the predictive modeling of surface water geochemistry at watershed and regional scales. Detailed maps of minerals, vegetation groups, and water were produced from an ASTER scene using spectroscopic, expert system-based analysis techniques which have been previously described. New methodologies are presented for the modeling of hydrothermal alteration type based on the Boolean combination of the detailed mineral maps, and for the entirely automated mapping of alteration types, mineral groups, and green vegetation. Results of these methodologies are compared with the more detailed maps and with previously published mineral mapping results derived from analysis of high-resolution spectroscopic data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Such comparisons are also presented for other mineralized and (or) altered areas including the Goldfield and Cuprite mining districts, Nevada and the central Marysvale volcanic field, Wah Wah Mountains, and San Francisco Mountains, Utah. The automated mineral group mapping products described in this study are ideal for application to mineral resource and mineral-environmental assessments at regional and national scales.

In-depth Analysis of Land Surface Emissivity using Microwave Polarization Difference Index to Improve Satellite QPE

NASA Astrophysics Data System (ADS)

Zheng, Y.; Kirstetter, P. E.; Hong, Y.; Wen, Y.; Turk, J.; Gourley, J. J.

2015-12-01

One of primary uncertainties in satellite overland quantitative precipitation estimates (QPE) from passive sensors such as radiometers is the impact on the brightness temperatures by the surface land emissivity. The complexity of surface land emissivity is linked to its temporal variations (diurnal and seasonal) and spatial variations (subsurface vertical profiles of soil moisture, vegetation structure and surface temperature) translating into sub-pixel heterogeneity within the satellite field of view (FOV). To better extract the useful signal from hydrometeors, surface land emissivity needs to be determined and filtered from the satellite-measured brightness temperatures. Based on the dielectric properties of surface land cover constitutes, Microwave Polarization Differential index (MPDI) is expected to carry the composite effect of surface land properties on land surface emissivity, with a higher MPDI indicating a lower emissivity. This study analyses the dependence of MPDI to soil moisture, vegetation and surface skin temperature over 9 different land surface types. Such analysis is performed using the normalized difference vegetation index (NDVI) from MODIS, the near surface air temperature from the RAP model and ante-precedent precipitation accumulation from the Multi-Radar Multi-Sensor as surrogates for the vegetation, surface skin temperature and shallow layer soil moisture, respectively. This paper provides 1) evaluations of brightness temperature-based MPDI from the TRMM and GPM Microwave Imagers in both raining and non-raining conditions to test the dependence of MPDI to precipitation; 2) comparisons of MPDI categorized into instantly before, during and immediately after selected precipitation events to examine the impact of modest-to-heavy precipitation on the spatial pattern of MPDI; 3) inspections of relationship between MPDI versus rain fraction and rain rate within the satellite sensors FOV to investigate the behaviors of MPDI in varying precipitation conditions; 4) analysis of discrepancies of MPDI over 10.65, 19.35, 37 and 85.8 GHz to identify the sensitivity of MPDS to microwave wavelengths.

Application of Deep Learning of Multi-Temporal SENTINEL-1 Images for the Classification of Coastal Vegetation Zone of the Danube Delta

NASA Astrophysics Data System (ADS)

Niculescu, S.; Ienco, D.; Hanganu, J.

2018-04-01

Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Spectral Reconstruction for Obtaining Virtual Hyperspectral Images

NASA Astrophysics Data System (ADS)

Perez, G. J. P.; Castro, E. C.

2016-12-01

Hyperspectral sensors demonstrated its capabalities in identifying materials and detecting processes in a satellite scene. However, availability of hyperspectral images are limited due to the high development cost of these sensors. Currently, most of the readily available data are from multi-spectral instruments. Spectral reconstruction is an alternative method to address the need for hyperspectral information. The spectral reconstruction technique has been shown to provide a quick and accurate detection of defects in an integrated circuit, recovers damaged parts of frescoes, and it also aids in converting a microscope into an imaging spectrometer. By using several spectral bands together with a spectral library, a spectrum acquired by a sensor can be expressed as a linear superposition of elementary signals. In this study, spectral reconstruction is used to estimate the spectra of different surfaces imaged by Landsat 8. Four atmospherically corrected surface reflectance from three visible bands (499 nm, 585 nm, 670 nm) and one near-infrared band (872 nm) of Landsat 8, and a spectral library of ground elements acquired from the United States Geological Survey (USGS) are used. The spectral library is limited to 420-1020 nm spectral range, and is interpolated at one nanometer resolution. Singular Value Decomposition (SVD) is used to calculate the basis spectra, which are then applied to reconstruct the spectrum. The spectral reconstruction is applied for test cases within the library consisting of vegetation communities. This technique was successful in reconstructing a hyperspectral signal with error of less than 12% for most of the test cases. Hence, this study demonstrated the potential of simulating information at any desired wavelength, creating a virtual hyperspectral sensor without the need for additional satellite bands.

Effects of Spectral Band Differences between Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI)

NASA Astrophysics Data System (ADS)

Micijevic, E.; Haque, M. O.

2015-12-01

In satellite remote sensing, Landsat sensors are recognized for providing well calibrated satellite images for over four decades. This image data set provides an important contribution to detection and temporal analysis of land changes. Landsat 8 (L8), the latest satellite of the Landsat series, was designed to continue its legacy as well as to embrace advanced technology and satisfy the demand of the broader scientific community. Sentinel 2A (S2A), a European satellite launched in June 2015, is designed to keep data continuity of Landsat and SPOT like satellites. The S2A MSI sensor is equipped with spectral bands similar to L8 OLI and includes some additional ones. Compared to L8 OLI, green and near infrared MSI bands have narrower bandwidths, whereas coastal-aerosol (CA) and cirrus have larger bandwidths. The blue and red MSI bands cover higher wavelengths than the matching OLI bands. Although the spectral band differences are not large, their combination with the spectral signature of a studied target can largely affect the Top Of Atmosphere (TOA) reflectance seen by the sensors. This study investigates the effect of spectral band differences between S2A MSI and L8 OLI sensors. The differences in spectral bands between sensors can be assessed by calculating Spectral Band Adjustment Factors (SBAF). For radiometric calibration purposes, the SBAFs for the calibration test site are used to bring the two sensors to the same radiometric scale. However, the SBAFs are target dependent and different sensors calibrated to the same radiometric scale will (correctly!) measure different reflectance for the same target. Thus, when multiple sensors are used to study a given target, the sensor responses need to be adjusted using SBAFs specific to that target. Comparison of the SBAFs for S2A MSI and L8 OLI based on various vegetation spectral profiles revealed variations in radiometric responses as high as 15%. Depending on target under study, these differences could be even higher.

Change Detection Processing Chain Dedicated to Sentinel Data Time Series. Application to Forest and Water Bodies Monitoring

NASA Astrophysics Data System (ADS)

Perez Saavedra, L.-M.; Mercier, G.; Yesou, H.; Liege, F.; Pasero, G.

2016-08-01

The Copernicus program of ESA and European commission (6 Sentinels Missions, among them Sentinel-1 with Synthetic Aperture Radar sensor and Sentinel-2 with 13-band 10 to 60 meter resolution optical sensors), offers a new opportunity to Earth Observation with high temporal acquisition capability ( 12 days repetitiveness and 5 days in some geographic areas of the world) with high spatial resolution.Due to these high temporal and spatial resolutions, it opens new challenges in several fields such as image processing, new algorithms for Time Series and big data analysis. In addition, these missions will be able to analyze several topics of earth temporal evolution such as crop vegetation, water bodies, Land use and Land Cover (LULC), sea and ice information, etc. This is particularly useful for end users and policy makers to detect early signs of damages, vegetation illness, flooding areas, etc.From the state of the art, one can find algorithms and methods that use a bi-date comparison for change detection [1-3] or time series analysis. Actually, these methods are essentially used for target detection or for abrupt change detection that requires 2 observations only.A Hölder means-based change detection technique has been proposed in [2,3] for high resolution radar images. This so-called MIMOSA technique has been mainly dedicated to man-made change detection in urban areas and CARABAS - II project by using a couple of SAR images. An extension to multitemporal change detection technique has been investigated but its application to land use and cover changes still has to be validated.The Hölder Hp is a Time Series pixel by pixel feature extraction and is defined by:H𝑝[X]=[1/n∑ⁿᵢ₌1 Xᴾᵢ]1/p p∈R Hp[X] : N images * S Bandes * t datesn is the number of images in the time series. N > 2Hp (X) is continuous and monotonic increasing in p for - ∞ < p < ∞

Multifrequency remote sensing of soil moisture. [Guymon, Oklahoma and Dalhart, Texas

NASA Technical Reports Server (NTRS)

Theis, S. W.; Mcfarland, M. J.; Rosenthal, W. D.; Jones, C. L. (Principal Investigator)

1982-01-01

Multifrequency sensor data collected at Guymon, Oklahoma and Dalhart, Texas using NASA's C-130 aircraft were used to determine which of the all-weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. In comparison to other active and passive microwave sensors the L-band radiometer (1) was influenced least by ranges in surface roughness; (2) demonstrated the most sensitivity to soil moisture differences in terms of the range of return from the full range of soil moisture; and (3) was less sensitive to errors in measurement in relation to the range of sensor response. L-band emissivity related more strongly to soil moisture when moisture was expressed as percent of field capacity. The perpendicular vegetation index as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture.

The EO-1 hyperion and advanced land imager sensors for use in tundra classification studies within the Upper Kuparuk River Basin, Alaska

NASA Astrophysics Data System (ADS)

Hall-Brown, Mary

The heterogeneity of Arctic vegetation can make land cover classification vey difficult when using medium to small resolution imagery (Schneider et al., 2009; Muller et al., 1999). Using high radiometric and spatial resolution imagery, such as the SPOT 5 and IKONOS satellites, have helped arctic land cover classification accuracies rise into the 80 and 90 percentiles (Allard, 2003; Stine et al., 2010; Muller et al., 1999). However, those increases usually come at a high price. High resolution imagery is very expensive and can often add tens of thousands of dollars onto the cost of the research. The EO-1 satellite launched in 2002 carries two sensors that have high specral and/or high spatial resolutions and can be an acceptable compromise between the resolution versus cost issues. The Hyperion is a hyperspectral sensor with the capability of collecting 242 spectral bands of information. The Advanced Land Imager (ALI) is an advanced multispectral sensor whose spatial resolution can be sharpened to 10 meters. This dissertation compares the accuracies of arctic land cover classifications produced by the Hyperion and ALI sensors to the classification accuracies produced by the Systeme Pour l' Observation de le Terre (SPOT), the Landsat Thematic Mapper (TM) and the Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. Hyperion and ALI images from August 2004 were collected over the Upper Kuparuk River Basin, Alaska. Image processing included the stepwise discriminant analysis of pixels that were positively classified from coinciding ground control points, geometric and radiometric correction, and principle component analysis. Finally, stratified random sampling was used to perform accuracy assessments on satellite derived land cover classifications. Accuracy was estimated from an error matrix (confusion matrix) that provided the overall, producer's and user's accuracies. This research found that while the Hyperion sensor produced classfication accuracies that were equivalent to the TM and ETM+ sensor (approximately 78%), the Hyperion could not obtain the accuracy of the SPOT 5 HRV sensor. However, the land cover classifications derived from the ALI sensor exceeded most classification accuracies derived from the TM and ETM+ senors and were even comparable to most SPOT 5 HRV classifications (87%). With the deactivation of the Landsat series satellites, the monitoring of remote locations such as in the Arctic on an uninterupted basis thoughout the world is in jeopardy. The utilization of the Hyperion and ALI sensors are a way to keep that endeavor operational. By keeping the ALI sensor active at all times, uninterupted observation of the entire Earth can be accomplished. Keeping the Hyperion sensor as a "tasked" sensor can provide scientists with additional imagery and options for their studies without overburdening storage issues.

Initial Validation of NDVI time seriesfrom AVHRR, VEGETATION, and MODIS

NASA Technical Reports Server (NTRS)

Morisette, Jeffrey T.; Pinzon, Jorge E.; Brown, Molly E.; Tucker, Jim; Justice, Christopher O.

2004-01-01

The paper will address Theme 7: Multi-sensor opportunities for VEGETATION. We present analysis of a long-term vegetation record derived from three moderate resolution sensors: AVHRR, VEGETATION, and MODIS. While empirically based manipulation can ensure agreement between the three data sets, there is a need to validate the series. This paper uses atmospherically corrected ETM+ data available over the EOS Land Validation Core Sites as an independent data set with which to compare the time series. We use ETM+ data from 15 globally distributed sites, 7 of which contain repeat coverage in time. These high-resolution data are compared to the values of each sensor by spatially aggregating the ETM+ to each specific sensors' spatial coverage. The aggregated ETM+ value provides a point estimate for a specific site on a specific date. The standard deviation of that point estimate is used to construct a confidence interval for that point estimate. The values from each moderate resolution sensor are then evaluated with respect to that confident interval. Result show that AVHRR, VEGETATION, and MODIS data can be combined to assess temporal uncertainties and address data continuity issues and that the atmospherically corrected ETM+ data provide an independent source with which to compare that record. The final product is a consistent time series climate record that links historical observations to current and future measurements.

Machine processing of remotely sensed data - quantifying global process: Models, sensor systems, and analytical methods; Proceedings of the Eleventh International Symposium, Purdue University, West Lafayette, IN, June 25-27, 1985

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

Mengel, S.K.; Morrison, D.B.

1985-01-01

Consideration is given to global biogeochemical issues, image processing, remote sensing of tropical environments, global processes, geology, landcover hydrology, and ecosystems modeling. Topics discussed include multisensor remote sensing strategies, geographic information systems, radars, and agricultural remote sensing. Papers are presented on fast feature extraction; a computational approach for adjusting TM imagery terrain distortions; the segmentation of a textured image by a maximum likelihood classifier; analysis of MSS Landsat data; sun angle and background effects on spectral response of simulated forest canopies; an integrated approach for vegetation/landcover mapping with digital Landsat images; geological and geomorphological studies using an image processing technique;more » and wavelength intensity indices in relation to tree conditions and leaf-nutrient content.« less

A decadal observation of vegetation dynamics using multi-resolution satellite images

NASA Astrophysics Data System (ADS)

Chiang, Yang-Sheng; Chen, Kun-Shan; Chu, Chang-Jen

2012-10-01

Vegetation cover not just affects the habitability of the earth, but also provides potential terrestrial mechanism for mitigation of greenhouse gases. This study aims at quantifying such green resources by incorporating multi-resolution satellite images from different platforms, including Formosat-2(RSI), SPOT(HRV/HRG), and Terra(MODIS), to investigate vegetation fractional cover (VFC) and its inter-/intra-annual variation in Taiwan. Given different sensor capabilities in terms of their spatial coverage and resolution, infusion of NDVIs at different scales was used to determine fraction of vegetation cover based on NDVI. Field campaign has been constantly conducted on a monthly basis for 6 years to calibrate the critical NDVI threshold for the presence of vegetation cover, with test sites covering IPCC-defined land cover types of Taiwan. Based on the proposed method, we analyzed spatio- temporal changes of VFC for the entire Taiwan Island. A bimodal sequence of VFC was observed for intra-annual variation based on MODIS data, with level around 5% and two peaks in spring and autumn marking the principal dual-cropping agriculture pattern in southwestern Taiwan. Compared to anthropogenic-prone variation, the inter-annual VFC (Aug.-Oct.) derived from HRV/HRG/RSI reveals that the moderate variations (3%) and the oscillations were strongly linked with regional climate pattern and major disturbances resulting from extreme weather events. Two distinct cycles (2002-2005 and 2005-2009) were identified in the decadal observations, with VFC peaks at 87.60% and 88.12% in 2003 and 2006, respectively. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring.

PubMed

Gillan, Jeffrey K; Karl, Jason W; Duniway, Michael; Elaksher, Ahmed

2014-11-01

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring

USGS Publications Warehouse

Gillan, Jeffrey K.; Karl, Jason W.; Duniway, Michael; Elaksher, Ahmed

2014-01-01

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs.

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.

Analysis of the role of urban vegetation in local climate of Budapest using satellite measurements

NASA Astrophysics Data System (ADS)

Pongracz, Rita; Bartholy, Judit; Dezso, Zsuzsanna; Fricke, Cathy

2016-08-01

Urban areas significantly modify the natural environment due to the concentrated presence of humans and the associated anthropogenic activities. In order to assess this effect, it is essential to evaluate the relationship between urban and vegetated surface covers. In our study we focused on the Hungarian capital, Budapest, in which about 1.7 million inhabitants are living nowadays. The entire city is divided by the river Danube into the hilly, greener Buda side on the west, and the flat, more densely built-up Pest side on the east. Most of the extended urban vegetation, i.e., forests are located in the western Buda side. The effects of the past changing of these green areas are analyzed using surface temperature data calculated from satellite measurements in the infrared channels, and NDVI (Normalized Difference Vegetation Index) derived from visible and near-infrared satellite measurements. For this purpose, data available from sensor MODIS (Moderate Resolution Imaging Spectroradiometer) of NASA satellites (i.e., Terra and Aqua) are used. First, the climatological effects of forests on the urban heat island intensity are evaluated. Then, we also aim to evaluate the relationship of surface temperature and NDVI in this urban environment with special focus on vegetation-related sections of the city where the vegetation cover either increased or decreased remarkably.

Toward daily monitoring of vegetation conditions at field scale through fusing data from multiple sensors

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires remote sensing data at fine spatial and temporal resolution. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for crop and rangeland monitoring. The Landsat satellite s...

Vulnerable land ecosystems classification using spatial context and spectral indices

NASA Astrophysics Data System (ADS)

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

2017-10-01

Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat

PubMed Central

Casanova, Joaquin J.; O'Shaughnessy, Susan A.; Evett, Steven R.; Rush, Charles M.

2014-01-01

Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress independent of vegetation cover. This paper presents a technique using computer vision to detect disease stress in wheat. Digital images of differentially stressed wheat were segmented into soil and vegetation pixels using expectation maximization (EM). In the first season, the algorithm to segment vegetation from soil and distinguish between healthy and stressed wheat was developed and tested using digital images taken in the field and later processed on a desktop computer. In the second season, a wireless camera with near real-time computer vision capabilities was tested in conjunction with the conventional camera and desktop computer. For wheat irrigated at different levels and inoculated with wheat streak mosaic virus (WSMV), vegetation hue determined by the EM algorithm showed significant effects from irrigation level and infection. Unstressed wheat had a higher hue (118.32) than stressed wheat (111.34). In the second season, the hue and cover measured by the wireless computer vision sensor showed significant effects from infection (p = 0.0014), as did the conventional camera (p < 0.0001). Vegetation hue obtained through a wireless computer vision system in this study is a viable option for determining biotic crop stress in irrigation scheduling. Such a low-cost system could be suitable for use in the field in automated irrigation scheduling applications. PMID:25251410

Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters.

PubMed

Bousbih, Safa; Zribi, Mehrez; Lili-Chabaane, Zohra; Baghdadi, Nicolas; El Hajj, Mohammad; Gao, Qi; Mougenot, Bernard

2017-11-14

The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects.

Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters

PubMed Central

Bousbih, Safa; Lili-Chabaane, Zohra; El Hajj, Mohammad; Gao, Qi

2017-01-01

The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects. PMID:29135929

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.

Hyperspectral and multispectral satellite sensors for mapping chlorophyll content in a Mediterranean Pinus sylvestris L. plantation

NASA Astrophysics Data System (ADS)

Navarro-Cerrillo, Rafael Mª; Trujillo, Jesus; de la Orden, Manuel Sánchez; Hernández-Clemente, Rocío

2014-02-01

A new generation of narrow-band hyperspectral remote sensing data offers an alternative to broad-band multispectral data for the estimation of vegetation chlorophyll content. This paper examines the potential of some of these sensors comparing red-edge and simple ratio indices to develop a rapid and cost-effective system for monitoring Mediterranean pine plantations in Spain. Chlorophyll content retrieval was analyzed with the red-edge R750/R710 index and the simple ratio R800/R560 index using the PROSPECT-5 leaf model and the Discrete Anisotropic Radiative Transfer (DART) and experimental approach. Five sensors were used: AHS, CHRIS/Proba, Hyperion, Landsat and QuickBird. The model simulation results obtained with synthetic spectra demonstrated the feasibility of estimating Ca + b content in conifers using the simple ratio R800/R560 index formulated with different full widths at half maximum (FWHM) at the leaf level. This index yielded a r2 = 0.69 for a FWHM of 30 nm and r2 = 0.55 for a FWHM of 70 nm. Experimental results compared the regression coefficients obtained with various multispectral and hyperspectral images with different spatial resolutions at the stand level. The strongest relationships where obtained using high-resolution hyperspectral images acquired with the AHS sensor (r2 = 0.65) while coarser spatial and spectral resolution images yielded a lower root mean square error (QuickBird r2 = 0.42; Landsat r2 = 0.48; Hyperion r2 = 0.56; CHRIS/Proba r2 = 0.57). This study shows the need to estimate chlorophyll content in forest plantations at the stand level with high spatial and spectral resolution sensors. Nevertheless, these results also show the accuracy obtained with medium-resolution sensors when monitoring physiological processes. Generating biochemical maps at the stand level could play a critical rule in the early detection of forest decline processes enabling their use in precision forestry.

Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

NASA Astrophysics Data System (ADS)

Pan, Xin; Cao, Chen; Yang, Yingbao; Li, Xiaolong; Shan, Liangliang; Zhu, Xi

2018-04-01

The land surface temperature (LST) derived from thermal infrared satellite images is a meaningful variable in many remote sensing applications. However, at present, the spatial resolution of the satellite thermal infrared remote sensing sensor is coarser, which cannot meet the needs. In this study, LST image was downscaled by a random forest model between LST and multiple predictors in an arid region with an oasis-desert ecotone. The proposed downscaling approach was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The primary result of LST downscaling has been shown that the distribution of downscaled LST matched with that of the ecosystem of oasis and desert. By the way of sensitivity analysis, the most sensitive factors to LST downscaling were modified normalized difference water index (MNDWI)/normalized multi-band drought index (NMDI), soil adjusted vegetation index (SAVI)/ shortwave infrared reflectance (SWIR)/normalized difference vegetation index (NDVI), normalized difference building index (NDBI)/SAVI and SWIR/NDBI/MNDWI/NDWI for the region of water, vegetation, building and desert, with LST variation (at most) of 0.20/-0.22 K, 0.92/0.62/0.46 K, 0.28/-0.29 K and 3.87/-1.53/-0.64/-0.25 K in the situation of +/-0.02 predictor perturbances, respectively.

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.

Mapping of submerged vegetation using remote sensing technology

NASA Technical Reports Server (NTRS)

Savastano, K. J.; Faller, K. H.; Mcfadin, L. W.; Holley, H.

1981-01-01

Techniques for mapping submerged sea grasses using aircraft supported remote sensors are described. The 21 channel solid state array spectroradiometer was successfully used as a remote sensor in the experiment in that the system operated without problem and obtained data. The environmental conditions of clear water, bright sandy bottom and monospecific vegetation (Thalassia) were ideal.

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

Ground Field-Based Hyperspectral Imaging: A Preliminary Study to Assess the Potential of Established Vegetation Indices to Infer Variation in Water-Use Efficiency.

NASA Astrophysics Data System (ADS)

Pelech, E. A.; McGrath, J.; Pederson, T.; Bernacchi, C.

2017-12-01

Increases in the global average temperature will consequently induce a higher occurrence of severe environmental conditions such as drought on arable land. To mitigate these threats, crops for fuel and food must be bred for higher water-use efficiencies (WUE). Defining genomic variation through high-throughput phenotypic analysis in field conditions has the potential to relieve the major bottleneck in linking desirable genetic traits to the associated phenotypic response. This can subsequently enable breeders to create new agricultural germplasm that supports the need for higher water-use efficient crops. From satellites to field-based aerial and ground sensors, the reflectance properties of vegetation measured by hyperspectral imaging is becoming a rapid high-throughput phenotyping technique. A variety of physiological traits can be inferred by regression analysis with leaf reflectance which is controlled by the properties and abundance of water, carbon, nitrogen and pigments. Although, given that the current established vegetation indices are designed to accentuate these properties from spectral reflectance, it becomes a challenge to infer relative measurements of WUE at a crop canopy scale without ground-truth data collection. This study aims to correlate established biomass and canopy-water-content indices with ground-truth data. Five bioenergy sorghum genotypes (Sorghum bicolor L. Moench) that have differences in WUE and wild-type Tobacco (Nicotiana tabacum var. Samsun) under irrigated and rainfed field conditions were examined. A linear regression analysis was conducted to determine if variation in canopy water content and biomass, driven by natural genotypic and artificial treatment influences, can be inferred using established vegetation indices. The results from this study will elucidate the ability of ground field-based hyperspectral imaging to assess variation in water content, biomass and water-use efficiency. This can lead to improved opportunities to select ideal genotypes for an increasing water-limited environment and to help parameterize and validate terrestrial vegetation models that require a better representation of genetic variation within crop species.

Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation

USGS Publications Warehouse

Byrd, Kristin B.; O'Connell, Jessica L.; Di Tommaso, Stefania; Kelly, Maggi

2014-01-01

There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral reflectance. To address these challenges, we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species, Typha spp. and Schoenoplectus acutus, at two restored marshes in the Sacramento–San Joaquin River Delta, California, USA. We used field spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data [R2 = 0.46 and percent normalized RMSE (%RMSE) = 16% for narrowband models]. However hyperspectral first derivative reflectance spectra best predicted biomass for plots where water levels were less than 15 cm (R2 = 0.69, %RMSE = 12.6%). In species-specific models, error rates differed by species (Typha spp.: %RMSE = 18.5%; S. acutus: %RMSE = 24.9%), likely due to the more vertical structure and deeper water habitat of S. acutus. The Landsat 7 dataset (7 images) predicted biomass slightly better than the WV-2 dataset (6 images) (R2 = 0.56, %RMSE = 20.9%, compared to R2 = 0.45, RMSE = 21.5%). The Hyperion dataset (one image) was least successful in predicting biomass (R2 = 0.27, %RMSE = 33.5%). Shortwave infrared bands on 30 m-resolution Hyperion and Landsat 7 sensors aided biomass estimation; however managers need to weigh tradeoffs between cost, additional spectral information, and high spatial resolution that will identify variability in small, fragmented marshes common to the Sacramento–San Joaquin River Delta and elsewhere in the Western U.S.

Determination of soil degradation in argentine semi-arid environments from remote sensors: case department of patagones, province of buenos aires

NASA Astrophysics Data System (ADS)

Pezzola, Alejandro; Cacella, Alejandra; Enrique, Mario; Winschel, Cristina

2017-04-01

The continental territory of the Argentine Republic owns 75% of its surface under arid and semiarid conditions to the west of the meridian of 64°. Wind erosion is the main physical cause of desertification. In the Pampena area, studies showed that the sandy loam soils were more pronounced than the sandy loam with significant losses of organic matter, decreases in the cation exchange capacity and modification of the mineral composition of the very fine sand fraction (From 73 to 100 μm), with increases in the proportion of heavy minerals (magnetite) relative to light (quartz). In the Patagones department, Buenos Aires province, the soils with a sandy-loamy texture, which are transported by wind and deposited on calcium carbonate (tosca), with little moisture retention and susceptible to wind erosion. In the 1980s and 1990s, increases in rainfall above the historical average led to a shift of the isohytes towards the southwest, leading to agricultural intensification that caused greater pressure on the soil and native vegetation. This advance on the native vegetation within the Patagones produced a reduction between 1975 and 2009 of 432,280 ha, leaving only 31% of the area covered by native forest - shrub xerophyte today. Between 2005-2009, the call "agricultural drought" caused losses in crops, wheat - oats and natural pastures associated with the native forest, causing a significant deterioration of the soil, exposing them to wind erosion. Remote sensors represent a very valuable technology for the mapping and evaluation of soil erosion. The availability of multispectral images allows the mapping and monitoring of changes in the dynamics of the erosion process. The objective of this work was to make an expeditious diagnosis of the surface affected by wind erosion and to evaluate the degree to which the soils destined for agriculture and livestock were affected. For this purpose, Terra's MODIS (Moderate-Resolution Imaging Spectroradiometer) sensor information was used with a temporal resolution of 1 to 2 days, 36 spectral bands, spatial resolution of 250m and Improved Vegetation Index (EVI). The period was covered from July 2007 to July 2009 by analyzing 47 images of the EVI product. The phenological curves of the soil cover were obtained. Of 1,360,717 ha it was estimated that there are a total of 393,511 hectares of eroded soils: 47,337 ha from mild to moderate, 219,204 ha moderate to severe and 126,970 ha severe to severe

Application and Comparison of the MODIS-Derived Enhanced Vegetation Index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A Case Study in the Arid Colorado River Delta, Mexico.

PubMed

Jarchow, Christopher J; Didan, Kamel; Barreto-Muñoz, Armando; Nagler, Pamela L; Glenn, Edward P

2018-05-13

The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000⁻2011), 8 (2013⁻2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013⁻2016) to MODIS EVI (2000⁻2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R² = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R² = 0.27) and riparian vegetation (R² = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R² = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area.

Multi-sensor data processing method for improved satellite retrievals

NASA Astrophysics Data System (ADS)

Fan, Xingwang

2017-04-01

Satellite remote sensing has provided massive data that improve the overall accuracy and extend the time series of environmental studies. In reflective solar bands, satellite data are related to land surface properties via radiative transfer (RT) equations. These equations generally include sensor-related (calibration coefficients), atmosphere-related (aerosol optical thickness) and surface-related (surface reflectance) parameters. It is an ill-posed problem to solve three parameters with only one RT equation. Even if there are two RT equations (dual-sensor data), the problem is still unsolvable. However, a robust solution can be obtained when any two parameters are known. If surface and atmosphere are known, sensor intercalibration can be performed. For example, the Advanced Very High Resolution Radiometer (AVHRR) was calibrated to the MODerate-resolution Imaging Spectroradiometer (MODIS) in Fan and Liu (2014) [Fan, X., and Liu, Y. (2014). Quantifying the relationship between intersensor images in solar reflective bands: Implications for intercalibration. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7727-7737.]. If sensor and surface are known, atmospheric data can be retrieved. For example, aerosol data were retrieved using tandem TERRA and AQUA MODIS images in Fan and Liu (2016a) [Fan, X., and Liu, Y. (2016a). Exploiting TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. Remote Sensing, 8(12), 996.]. If sensor and atmosphere are known, data consistency can be obtained. For example, Normalized Difference Vegetation Index (NDVI) data were intercalibrated among coarse-resolution sensors in Fan and Liu (2016b) [Fan, X., and Liu, Y. (2016b). A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 177-191.], and among fine-resolution sensors in Fan and Liu (2017) [Fan, X., and Liu, Y. (2017). A generalized model for intersensor NDVI calibration and its comparison with regression approaches. IEEE Transactions on Geoscience and Remote Sensing, 55(3), doi: 10.1109/TGRS.2016.2635802.]. These studies demonstrate the success of multi-sensor data and novel methods in the research domain of geoscience. These data will benefit remote sensing of terrestrial parameters in decadal timescales, such as soil salinity content in Fan et al. (2016) [Fan, X., Weng, Y., and Tao, J. (2016). Towards decadal soil salinity mapping using Landsat time series data. International Journal of Applied Earth Observation and Geoinformation, 52, 32-41.].

Development of a Near-Real Time Hail Damage Swath Identification Algorithm for Vegetation

NASA Technical Reports Server (NTRS)

Bell, Jordan R.; Molthan, Andrew L.; Schultz, Lori A.; McGrath, Kevin M.; Burks, Jason E.

2015-01-01

The Midwest is home to one of the world's largest agricultural growing regions. Between the time period of late May through early September, and with irrigation and seasonal rainfall these crops are able to reach their full maturity. Using moderate to high resolution remote sensors, the monitoring of the vegetation can be achieved using the red and near-infrared wavelengths. These wavelengths allow for the calculation of vegetation indices, such as Normalized Difference Vegetation Index (NDVI). The vegetation growth and greenness, in this region, grows and evolves uniformly as the growing season progresses. However one of the biggest threats to Midwest vegetation during the time period is thunderstorms that bring large hail and damaging winds. Hail and wind damage to crops can be very expensive to crop growers and, damage can be spread over long swaths associated with the tracks of the damaging storms. Damage to the vegetation can be apparent in remotely sensed imagery and is visible from space after storms slightly damage the crops, allowing for changes to occur slowly over time as the crops wilt or more readily apparent if the storms strip material from the crops or destroy them completely. Previous work on identifying these hail damage swaths used manual interpretation by the way of moderate and higher resolution satellite imagery. With the development of an automated and near-real time hail swath damage identification algorithm, detection can be improved, and more damage indicators be created in a faster and more efficient way. The automated detection of hail damage swaths will examine short-term, large changes in the vegetation by differencing near-real time eight day NDVI composites and comparing them to post storm imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi NPP. In addition land surface temperatures from these instruments will be examined as for hail damage swath identification. Initial validation of the automated algorithm is based upon Storm Prediction Center storm reports but also the National Severe Storm Laboratory (NSSL) Maximum Estimated Size Hail (MESH) product. Opportunities for future work are also shown, with focus on expansion of this algorithm with pixel-based image classification techniques for tracking surface changes as a result of severe weather.

Normalization of time-series satellite reflectance data to a standard sun-target-sensor geometry using a semi-empirical model

NASA Astrophysics Data System (ADS)

Zhao, Yongguang; Li, Chuanrong; Ma, Lingling; Tang, Lingli; Wang, Ning; Zhou, Chuncheng; Qian, Yonggang

2017-10-01

Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.

Got Point Clouds: Characterizing Canopy Structure With Active and Passive Sensors

NASA Astrophysics Data System (ADS)

Popescu, S. C.; Malambo, L.; Sheridan, R.; Putman, E.; Murray, S.; Rooney, W.; Rajan, N.

2016-12-01

Unmanned Aerial Systems (UAS) provide the means to acquire highly customized aerial data at local scale with a multitude of sensors. UAS allow us to obtain affordably repeated observations of canopy structure for agricultural and natural resources applications by using passive optical sensors, such as cameras and photogrammetric techniques, and active sensors, such as lidar (Light Detection and Ranging). The objectives of this presentation are to: (1) offer a brief overview of UAS used for agriculture and natural resources studies, (2) describe experiences in conducting agriculture phenotyping and forest vegetation measurements, and (3) give details on the methodology developed for image and lidar data processing for characterizing the three dimensional structure of plant canopies. The UAS types used for this purpose included rotary platforms, such as quadcopters, hexacopters, and octocopters, with a payload capacity of up to 19 lbs. The sensors that collected data over two crop seasons include multispectral cameras in the visible color spectrum and near infrared, and UAS-lidar. For ground reference data we used terrestrial lidar scanners and field measurements. Results comparing UAS and terrestrial measurements show high correlation and open new areas of scientific investigation of crop canopies previously not possible with affordable techniques.

Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images

USGS Publications Warehouse

Chen, Xuexia; Vogelmann, James E.; Chander, Gyanesh; Ji, Lei; Tolk, Brian; Huang, Chengquan; Rollins, Matthew

2013-01-01

Routine acquisition of Landsat 5 Thematic Mapper (TM) data was discontinued recently and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) has an ongoing problem with the scan line corrector (SLC), thereby creating spatial gaps when covering images obtained during the process. Since temporal and spatial discontinuities of Landsat data are now imminent, it is therefore important to investigate other potential satellite data that can be used to replace Landsat data. We thus cross-compared two near-simultaneous images obtained from Landsat 5 TM and the Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS), both captured on 29 May 2007 over Los Angeles, CA. TM and AWiFS reflectances were compared for the green, red, near-infrared (NIR), and shortwave infrared (SWIR) bands, as well as the normalized difference vegetation index (NDVI) based on manually selected polygons in homogeneous areas. All R2 values of linear regressions were found to be higher than 0.99. The temporally invariant cluster (TIC) method was used to calculate the NDVI correlation between the TM and AWiFS images. The NDVI regression line derived from selected polygons passed through several invariant cluster centres of the TIC density maps and demonstrated that both the scene-dependent polygon regression method and TIC method can generate accurate radiometric normalization. A scene-independent normalization method was also used to normalize the AWiFS data. Image agreement assessment demonstrated that the scene-dependent normalization using homogeneous polygons provided slightly higher accuracy values than those obtained by the scene-independent method. Finally, the non-normalized and relatively normalized ‘Landsat-like’ AWiFS 2007 images were integrated into 1984 to 2010 Landsat time-series stacks (LTSS) for disturbance detection using the Vegetation Change Tracker (VCT) model. Both scene-dependent and scene-independent normalized AWiFS data sets could generate disturbance maps similar to what were generated using the LTSS data set, and their kappa coefficients were higher than 0.97. These results indicate that AWiFS can be used instead of Landsat data to detect multitemporal disturbance in the event of Landsat data discontinuity.

Advancing reference emission levels in subnational and national REDD+ initiatives: a CLASlite approach

PubMed Central

Asner, Gregory P; Joseph, Shijo

2015-01-01

Conservation and monitoring of tropical forests requires accurate information on their extent and change dynamics. Cloud cover, sensor errors and technical barriers associated with satellite remote sensing data continue to prevent many national and sub-national REDD+ initiatives from developing their reference deforestation and forest degradation emission levels. Here we present a framework for large-scale historical forest cover change analysis using free multispectral satellite imagery in an extremely cloudy tropical forest region. The CLASlite approach provided highly automated mapping of tropical forest cover, deforestation and degradation from Landsat satellite imagery. Critically, the fractional cover of forest photosynthetic vegetation, non-photosynthetic vegetation, and bare substrates calculated by CLASlite provided scene-invariant quantities for forest cover, allowing for systematic mosaicking of incomplete satellite data coverage. A synthesized satellite-based data set of forest cover was thereby created, reducing image incompleteness caused by clouds, shadows or sensor errors. This approach can readily be implemented by single operators with highly constrained budgets. We test this framework on tropical forests of the Colombian Pacific Coast (Chocó) – one of the cloudiest regions on Earth, with successful comparison to the Colombian government’s deforestation map and a global deforestation map. PMID:25678933

The role of space borne imaging radars in environmental monitoring: Some shuttle imaging radar results in Asia

NASA Technical Reports Server (NTRS)

Imhoff, M.; Vermillion, C.

1986-01-01

The synoptic view afforded by orbiting Earth sensors can be extremely valuable for resource evaluation, environmental monitoring and development planning. For many regions of the world, however, cloud cover has prevented the acquisition of remotely sensed data during the most environmentally stressful periods of the year. This paper discusses how synthetic aperture imaging radar can be used to provide valuable data about the condition of the Earth's surface during periods of bad weather. Examples are given of applications using data from the Shuttle Imaging Radars (SIR) A and B for agriculture land use and crop condition assessment, monsoon flood boundary and flood damage assessment, water resource monitoring and terrain modeling, coastal forest mapping and vegetation penetration, and coastal development monitoring. Recent SIR-B results in Bangladesh are emphasized, radar system basics are reviewed and future SAR systems discussed.

The role of space borne imaging radars in environmental monitoring: Some shuttle imaging radar results in Asia

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Vermillion, C. H.

1986-01-01

The synoptic view afforded by orbiting Earth sensors can be extremely valuable for resource evaluation, environmental monitoring and development planning. For many regions of the world, however, cloud cover has prevented the acquisition of remotely sensed data during the most environmentally stressful periods of the year. How synthetic aperture imaging radar can be used to provide valuable data about the condition of the Earth's surface during periods of bad weather is discussed. Examples are given of applications using data from the Shuttle Imaging Radars (SIR) A and B for agricultural land use and crop condition assessment, monsoon flood boundary and flood damage assessment, water resource monitoring and terrain modeling, coastal forest mapping and vegetation penetration, and coastal development monitoring. Recent SIR-B results in Bangladesh are emphasized, radar system basics are reviewed and future SAR systems are discussed.

Modeling Coniferous Canopy Structure over Extensive Areas for Ray Tracing Simulations: Scaling from the Leaf to the Stand Level

NASA Astrophysics Data System (ADS)

van Aardt, J. A.; van Leeuwen, M.; Kelbe, D.; Kampe, T.; Krause, K.

2015-12-01

Remote sensing is widely accepted as a useful technology for characterizing the Earth surface in an objective, reproducible, and economically feasible manner. To date, the calibration and validation of remote sensing data sets and biophysical parameter estimates remain challenging due to the requirements to sample large areas for ground-truth data collection, and restrictions to sample these data within narrow temporal windows centered around flight campaigns or satellite overpasses. The computer graphics community have taken significant steps to ameliorate some of these challenges by providing an ability to generate synthetic images based on geometrically and optically realistic representations of complex targets and imaging instruments. These synthetic data can be used for conceptual and diagnostic tests of instrumentation prior to sensor deployment or to examine linkages between biophysical characteristics of the Earth surface and at-sensor radiance. In the last two decades, the use of image generation techniques for remote sensing of the vegetated environment has evolved from the simulation of simple homogeneous, hypothetical vegetation canopies, to advanced scenes and renderings with a high degree of photo-realism. Reported virtual scenes comprise up to 100M surface facets; however, due to the tighter coupling between hardware and software development, the full potential of image generation techniques for forestry applications yet remains to be fully explored. In this presentation, we examine the potential computer graphics techniques have for the analysis of forest structure-function relationships and demonstrate techniques that provide for the modeling of extremely high-faceted virtual forest canopies, comprising billions of scene elements. We demonstrate the use of ray tracing simulations for the analysis of gap size distributions and characterization of foliage clumping within spatial footprints that allow for a tight matching between characteristics derived from these virtual scenes and typical pixel resolutions of remote sensing imagery.

Acquisition, calibration, and performance of airborne high-resolution ADS40 SH52 sensor data for monitoring the Colorado River below Glen Canyon Dam

NASA Astrophysics Data System (ADS)

Davis, P. A.; Cagney, L. E.; Kohl, K. A.; Gushue, T. M.; Fritzinger, C.; Bennett, G. E.; Hamill, J. F.; Melis, T. S.

2010-12-01

Periodically, the Grand Canyon Monitoring and Research Center of the U.S. Geological Survey collects and interprets high-resolution (20-cm), airborne multispectral imagery and digital surface models (DSMs) to monitor the effects of Glen Canyon Dam operations on natural and cultural resources of the Colorado River in Grand Canyon. We previously employed the first generation of the ADS40 in 2000 and the Zeiss-Imaging Digital Mapping Camera (DMC) in 2005. Data from both sensors displayed band-image misregistration owing to multiple sensor optics and image smearing along abrupt scarps due to errors in image rectification software, both of which increased post-processing time, cost, and errors from image classification. Also, the near-infrared gain on the early, 8-bit ADS40 was not properly set and its signal was saturated for the more chlorophyll-rich vegetation, which limited our vegetation mapping. Both sensors had stereo panchromatic capability for generating a DSM. The ADS40 performed to specifications; the DMC failed. In 2009, we employed the new ADS40 SH52 to acquire 11-bit multispectral data with a single lens (20-cm positional accuracy), as well as stereo panchromatic data that provided a 1-m cell DSM (40-cm root-mean-square vertical error at one sigma). Analyses of the multispectral data showed near-perfect registration of its four band images at our 20-cm resolution, a linear response to ground reflectance, and a large dynamic range and good sensitivity (except for the blue band). Data were acquired over a 10-day period for the 450-km-long river corridor in which acquisition time and atmospheric conditions varied considerably during inclement weather. We received 266 orthorectified flightlines for the corridor, choosing to calibrate and mosaic the data ourselves to ensure a flawless mosaic with consistent, realistic spectral information. A linear least-squares cross-calibration of overlapping flightlines for the corridor showed that the dominate factors in inter-flightline variability were solar zenith angle and atmospheric scattering, which respectively affect the slope and intercept of the calibration. The inter-flightline calibration slopes were consistently close to the square of the ratio of the cosines of the zenith angles of each pair of overlapping flightlines. Our results corroborate previous observations that the cosine of solar zenith angle is a good approximation for atmospheric transmission and the use of its square in radiometric calibrations may compensate for that effect and the effect of non-nadir sun angle on surface reflectance. It was more expedient to acquire imagery for each sub-linear river segment by collecting 5-6 parallel flightlines; river sinuosity caused us to use 2-3 flightlines for each segment. Surfaces near flightline edges were often smeared and replaced with adjacent, more nadir-viewed flightline data. Eliminating surface smearing was the most time consuming aspect of creating a flawless image mosaic for the river corridor, but its removal will increase the efficiency and accuracy of image analyses of monitoring parameters of interest to river managers.

Autonomous agricultural remote sensing systems with high spatial and temporal resolutions

NASA Astrophysics Data System (ADS)

Xiang, Haitao

In this research, two novel agricultural remote sensing (RS) systems, a Stand-alone Infield Crop Monitor RS System (SICMRS) and an autonomous Unmanned Aerial Vehicles (UAV) based RS system have been studied. A high-resolution digital color and multi-spectral camera was used as the image sensor for the SICMRS system. An artificially intelligent (AI) controller based on artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) was developed. Morrow Plots corn field RS images in the 2004 and 2006 growing seasons were collected by the SICMRS system. The field site contained 8 subplots (9.14 m x 9.14 m) that were planted with corn and three different fertilizer treatments were used among those subplots. The raw RS images were geometrically corrected, resampled to 10cm resolution, removed soil background and calibrated to real reflectance. The RS images from two growing seasons were studied and 10 different vegetation indices were derived from each day's image. The result from the image processing demonstrated that the vegetation indices have temporal effects. To achieve high quality RS data, one has to utilize the right indices and capture the images at the right time in the growing season. Maximum variations among the image data set are within the V6-V10 stages, which indicated that these stages are the best period to identify the spatial variability caused by the nutrient stress in the corn field. The derived vegetation indices were also used to build yield prediction models via the linear regression method. At that point, all of the yield prediction models were evaluated by comparing the R2-value and the best index model from each day's image was picked based on the highest R 2-value. It was shown that the green normalized difference vegetation (GNDVI) based model is more sensitive to yield prediction than other indices-based models. During the VT-R4 stages, the GNDVI based models were able to explain more than 95% potential corn yield consistently for both seasons. The VT-R4 stages are the best period of time to estimate the corn yield. The SICMS system is only suitable for the RS research at a fixed location. In order to provide more flexibility of the RS image collection, a novel UAV based system has been studied. The UAV based agricultural RS system used a light helicopter platform equipped with a multi-spectral camera. The UAV control system consisted of an on-board and a ground station subsystem. For the on-board subsystem, an Extended Kalman Filter (EKF) based UAV navigation system was designed and implemented. The navigation system, using low cost inertial sensors, magnetometer, GPS and a single board computer, was capable of providing continuous estimates of UAV position and attitude at 50 Hz using sensor fusion techniques. The ground station subsystem was designed to be an interface between a human operator and the UAV to implement mission planning, flight command activation, and real-time flight monitoring. The navigation system is controlled by the ground station, and able to navigate the UAV in the air to reach the predefined waypoints and trigger the multi-spectral camera. By so doing, the aerial images at each point could be captured automatically. The developed UAV RS system can provide a maximum flexibility in crop field RS image collection. It is essential to perform the geometric correction and the geocoding before an aerial image can be used for precision farming. An automatic (no Ground Control Point (GCP) needed) UAV image georeferencing algorithm was developed. This algorithm can do the automatic image correction and georeferencing based on the real-time navigation data and a camera lens distortion model. The accuracy of the georeferencing algorithm was better than 90 cm according to a series test. The accuracy that has been achieved indicates that, not only is the position solution good, but the attitude error is extremely small. The waypoints planning for UAV flight was investigated. It suggested that a 16.5% forward overlap and a 15% lateral overlap were required to avoiding missing desired mapping area when the UAV flies above 45 m high with 4.5 mm lens. A whole field mosaic image can be generated according to the individual image georeferencing information. A 0.569 m mosaic error has been achieved and this accuracy is sufficient for many of the intended precision agricultural applications. With careful interpretation, the UAV images are an excellent source of high spatial and temporal resolution data for precision agricultural applications. (Abstract shortened by UMI.)

Interdisciplinary study of atmospheric processes and constituents of the mid-Atlantic coastal region.. [air pollution control studies in Virginia

NASA Technical Reports Server (NTRS)

Kindle, E. C.; Bandy, E. C.; Copeland, G.; Blais, R.; Levy, G.; Sonenshine, D.

1975-01-01

Past research projects for the year 1974-1975 are listed along with future research programs in the area of air pollution control, remote sensor analysis of smoke plumes, the biosphere component, and field experiments. A detailed budget analysis is presented. Attachments are included on the following topics: mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques, and use of LARS system for the quantitative determination of smoke plume lateral diffusion coefficients from ERTS images of Virginia.

Validation of Vegetation Index Time Series from Suomi NPP Visible Infrared Imaging Radiometer Suite Using Tower Radiation Flux Measurements

NASA Astrophysics Data System (ADS)

Miura, T.; Kato, A.; Wang, J.; Vargas, M.; Lindquist, M.

2015-12-01

Satellite vegetation index (VI) time series data serve as an important means to monitor and characterize seasonal changes of terrestrial vegetation and their interannual variability. It is, therefore, critical to ensure quality of such VI products and one method of validating VI product quality is cross-comparison with in situ flux tower measurements. In this study, we evaluated the quality of VI time series derived from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) spacecraft by cross-comparison with in situ radiation flux measurements at select flux tower sites over North America and Europe. VIIRS is a new polar-orbiting satellite sensor series, slated to replace National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer in the afternoon overpass and to continue the highly-calibrated data streams initiated with Moderate Resolution Imaging Spectrometer of National Aeronautics and Space Administration's Earth Observing System. The selected sites covered a wide range of biomes, including croplands, grasslands, evergreen needle forest, woody savanna, and open shrublands. The two VIIRS indices of the Top-of-Atmosphere (TOA) Normalized Difference Vegetation Index (NDVI) and the atmospherically-corrected, Top-of-Canopy (TOC) Enhanced Vegetation Index (EVI) (daily, 375 m spatial resolution) were compared against the TOC NDVI and a two-band version of EVI (EVI2) calculated from tower radiation flux measurements, respectively. VIIRS and Tower VI time series showed comparable seasonal profiles across biomes with statistically significant correlations (> 0.60; p-value < 0.01). "Start-of-season (SOS)" phenological metric values extracted from VIIRS and Tower VI time series were also highly compatible (R2 > 0.95), with mean differences of 2.3 days and 5.0 days for the NDVI and the EVI, respectively. These results indicate that VIIRS VI time series can capture seasonal evolution of vegetated land surface as good as in situ radiometric measurements. Future studies that address biophysical or physiological interpretations of Tower VI time series derived from radiation flux measurements are desirable.

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay - A comparison of Landsat MSS to TM imagery

NASA Technical Reports Server (NTRS)

Ackleson, S. G.; Klemas, V.

1987-01-01

Landsat MSS and TM imagery, obtained simultaneously over Guinea Marsh, VA, as analyzed and compares for its ability to detect submerged aquatic vegetation (SAV). An unsupervised clustering algorithm was applied to each image, where the input classification parameters are defined as functions of apparent sensor noise. Class confidence and accuracy were computed for all water areas by comparing the classified images, pixel-by-pixel, to rasterized SAV distributions derived from color aerial photography. To illustrate the effect of water depth on classification error, areas of depth greater than 1.9 m were masked, and class confidence and accuracy recalculated. A single-scattering radiative-transfer model is used to illustrate how percent canopy cover and water depth affect the volume reflectance from a water column containing SAV. For a submerged canopy that is morphologically and optically similar to Zostera marina inhabiting Lower Chesapeake Bay, dense canopies may be isolated by masking optically deep water. For less dense canopies, the effect of increasing water depth is to increase the apparent percent crown cover, which may result in classification error.

Estimating the relationship between urban 3D morphology and land surface temperature using airborne LiDAR and Landsat-8 Thermal Infrared Sensor data

NASA Astrophysics Data System (ADS)

Lee, J. H.

2015-12-01

Urban forests are known for mitigating the urban heat island effect and heat-related health issues by reducing air and surface temperature. Beyond the amount of the canopy area, however, little is known what kind of spatial patterns and structures of urban forests best contributes to reducing temperatures and mitigating the urban heat effects. Previous studies attempted to find the relationship between the land surface temperature and various indicators of vegetation abundance using remote sensed data but the majority of those studies relied on two dimensional area based metrics, such as tree canopy cover, impervious surface area, and Normalized Differential Vegetation Index, etc. This study investigates the relationship between the three-dimensional spatial structure of urban forests and urban surface temperature focusing on vertical variance. We use a Landsat-8 Thermal Infrared Sensor image (acquired on July 24, 2014) to estimate the land surface temperature of the City of Sacramento, CA. We extract the height and volume of urban features (both vegetation and non-vegetation) using airborne LiDAR (Light Detection and Ranging) and high spatial resolution aerial imagery. Using regression analysis, we apply empirical approach to find the relationship between the land surface temperature and different sets of variables, which describe spatial patterns and structures of various urban features including trees. Our analysis demonstrates that incorporating vertical variance parameters improve the accuracy of the model. The results of the study suggest urban tree planting is an effective and viable solution to mitigate urban heat by increasing the variance of urban surface as well as evaporative cooling effect.

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

NASA Astrophysics Data System (ADS)

Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

2015-10-01

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

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.

Calibration of UAS imagery inside and outside of shadows for improved vegetation index computation

NASA Astrophysics Data System (ADS)

Bondi, Elizabeth; Salvaggio, Carl; Montanaro, Matthew; Gerace, Aaron D.

2016-05-01

Vegetation health and vigor can be assessed with data from multi- and hyperspectral airborne and satellite- borne sensors using index products such as the normalized difference vegetation index (NDVI). Recent advances in unmanned aerial systems (UAS) technology have created the opportunity to access these same image data sets in a more cost effective manner with higher temporal and spatial resolution. Another advantage of these systems includes the ability to gather data in almost any weather condition, including complete cloud cover, when data has not been available before from traditional platforms. The ability to collect in these varied conditions, meteorological and temporal, will present researchers and producers with many new challenges. Particularly, cloud shadows and self-shadowing by vegetation must be taken into consideration in imagery collected from UAS platforms to avoid variation in NDVI due to changes in illumination within a single scene, and between collection flights. A workflow is presented to compensate for variations in vegetation indices due to shadows and variation in illumination levels in high resolution imagery collected from UAS platforms. Other calibration methods that producers may currently be utilizing produce NDVI products that still contain shadow boundaries and variations due to illumination, whereas the final NDVI mosaic from this workflow does not.

Synegies Between Visible/Near-Infrared Imaging Spectrometry and the Thermal Infrared in an Urban Environment: An Evaluation of the Hyperspectral Infrared Imager (HYSPIRI) Mission

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Quattrochi, Dale A.; Hulley, Glynn C.; Hook, Simon J.; Green, Robert O.

2012-01-01

A majority of the human population lives in urban areas and as such, the quality of urban environments is becoming increasingly important to the human population. Furthermore, these areas are major sources of environmental contaminants and sinks of energy and materials. Remote sensing provides an improved understanding of urban areas and their impacts by mapping urban extent, urban composition (vegetation and impervious cover fractions), and urban radiation balance through measures of albedo, emissivity and land surface temperature (LST). Recently, the National Research Council (NRC) completed an assessment of remote sensing needs for the next decade (NRC, 2007), proposing several missions suitable for urban studies, including a visible, near-infrared and shortwave infrared (VSWIR) imaging spectrometer and a multispectral thermal infrared (TIR) instrument called the Hyperspectral Infrared Imagery (HyspIRI). In this talk, we introduce the HyspIRI mission, focusing on potential synergies between VSWIR and TIR data in an urban area. We evaluate potential synergies using an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS-ASTER (MASTER) image pair acquired over Santa Barbara, United States. AVIRIS data were analyzed at their native spatial resolutions (7.5m VSWIR and 15m TIR), and aggregated 60 m spatial resolution similar to HyspIRI. Surface reflectance was calculated using ACORN and a ground reflectance target to remove atmospheric and sensor artifacts. MASTER data were processed to generate estimates of spectral emissivity and LST using Modtran radiative transfer code and the ASTER Temperature Emissivity Separation algorithm. A spectral library of common urban materials, including urban vegetation, roofs and roads was assembled from combined AVIRIS and field-measured reflectance spectra. LST and emissivity were also retrieved from MASTER and reflectance/emissivity spectra for a subset of urban materials were retrieved from co-located MASTER and AVIRIS pixels. Fractions of Impervious, Soil, Green Vegetation (GV) and Non-photosynthetic Vegetation (NPV), were estimated using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to AVIRIS data at 7.5, 15 and 60 m spatial scales. Surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and LST were also determined for urban and natural land-cover classes in the region. Fractions were validated using 1m digital photography.

Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species

NASA Astrophysics Data System (ADS)

Shoko, C.; Mutanga, O.

2017-07-01

C3 and C4 grass species discrimination has increasingly become relevant in understanding their response to environmental changes and to monitor their integrity in providing goods and services. While remotely-sensed data provide robust, cost-effective and repeatable monitoring tools for C3 and C4 grasses, this has been largely limited by the scarcity of sensors with better earth imaging characteristics. The recent launch of the advanced Sentinel 2 MultiSpectral Instrument (MSI) presents a new prospect for discriminating C3 and C4 grasses. The present study tested the potential of Sentinel 2, characterized by refined spatial resolution and more unique spectral bands in discriminating between Festuca (C3) and Themeda (C4) grasses. To evaluate the performance of Sentinel 2 MSI; spectral bands, vegetation indices and spectral bands plus indices were used. Findings from Sentinel 2 were compared with those derived from the widely-used Worldview 2 commercial sensor and the Landsat 8 Operational Land Imager (OLI). Overall classification accuracies have shown that Sentinel 2 bands have potential (90.36%), than indices (85.54%) and combined variables (88.61%). The results were comparable to Worldview 2 sensor, which produced slightly higher accuracies using spectral bands (95.69%), indices (86.02%) and combined variables (87.09%), and better than Landsat 8 OLI spectral bands (75.26%), indices (82.79%) and combined variables (86.02%). Sentinel 2 bands produced lower errors of commission and omission (between 4.76 and 14.63%), comparable to Worldview 2 (between 1.96 and 7.14%), than Landsat 8 (between 18.18 and 30.61%), when classifying the two species. The classification accuracy from Sentinel 2 also did not differ significantly (z = 1.34) from Worldview 2, using standard bands; it was significantly (z > 1.96) different using indices and combined variables, whereas when compared to Landsat 8, Sentinel 2 accuracies were significantly different (z > 1.96) using all variables. These results demonstrated that key vegetation species discrimination could be improved by the use of the freely and improved Sentinel 2 MSI data.

Developing Methods for Fraction Cover Estimation Toward Global Mapping of Ecosystem Composition

NASA Astrophysics Data System (ADS)

Roberts, D. A.; Thompson, D. R.; Dennison, P. E.; Green, R. O.; Kokaly, R. F.; Pavlick, R.; Schimel, D.; Stavros, E. N.

2016-12-01

Terrestrial vegetation seldom covers an entire pixel due to spatial mixing at many scales. Estimating the fractional contributions of photosynthetic green vegetation (GV), non-photosynthetic vegetation (NPV), and substrate (soil, rock, etc.) to mixed spectra can significantly improve quantitative remote measurement of terrestrial ecosystems. Traditional methods for estimating fractional vegetation cover rely on vegetation indices that are sensitive to variable substrate brightness, NPV and sun-sensor geometry. Spectral mixture analysis (SMA) is an alternate framework that provides estimates of fractional cover. However, simple SMA, in which the same set of endmembers is used for an entire image, fails to account for natural spectral variability within a cover class. Multiple Endmember Spectral Mixture Analysis (MESMA) is a variant of SMA that allows the number and types of pure spectra to vary on a per-pixel basis, thereby accounting for endmember variability and generating more accurate cover estimates, but at a higher computational cost. Routine generation and delivery of GV, NPV, and substrate (S) fractions using MESMA is currently in development for large, diverse datasets acquired by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We present initial results, including our methodology for ensuring consistency and generalizability of fractional cover estimates across a wide range of regions, seasons, and biomes. We also assess uncertainty and provide a strategy for validation. GV, NPV, and S fractions are an important precursor for deriving consistent measurements of ecosystem parameters such as plant stress and mortality, functional trait assessment, disturbance susceptibility and recovery, and biomass and carbon stock assessment. Copyright 2016 California Institute of Technology. All Rights Reserved. We acknowledge support of the US Government, NASA, the Earth Science Division and Terrestrial Ecology program.

San Jose, Costa Rica

NASA Technical Reports Server (NTRS)

2007-01-01

San Jose, capital city of Costa Rica, fills the valley between two steep mountain ranges. In this image made from data collected by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on NASA's Terra satellite, visible, shortwave, and near-infrared wavelengths of light that the sensor observed have been combined to produce a false-color version of the scene in which vegetation is red, urban areas are silvery gray, water is dark blue, and clouds are white. The image was captured on February 8, 2007. San Jose is in the center of the image. The Rio Torres winds through downtown San Jose. Cartago, the much smaller colonial capital, sits in the lower right corner, while the city of Alajuela appears across the river, northwest of San Jose. The cities' manmade surfaces contrast sharply with the lushly vegetated landscape surrounding the city. Greenhouses are common in the region, and their glass roofs may be the brilliant white spots around the outer edges the cities. The long, straight runway of the Tobias Bolanos International Airport is visible as a dark line southeast of Alajuela. The landscape around the two cities shown here is rugged. Steep mountain peaks cast dark shadows across their leeward slopes. Patches of dark red vegetation on the mountains north of San Jose may be rainforest. Coffee plantations also cover the slopes of the mountains around the city. February is the dry season in Costa Rica. During the rainy season, from about April to November, clouds usually block the satellite's view of this tropical location. NASA image created by Jesse Allen, using data provided courtesy of Asaf Ullah and Tim Gubbels, SERVIR project.

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

PubMed Central

Shafian, Sanaz; Schnell, Ronnie; Bagavathiannan, Muthukumar; Valasek, John; Shi, Yeyin; Olsenholler, Jeff

2018-01-01

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April–October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for within-season data collection of agricultural crops such as sorghum. PMID:29715311

Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

2010-12-01

High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

Seasonal and Inter-annual Phenological Varibility is Greatest in Low-Arctic and Wet Sites Across the North Slope of Alaska as Observed from Multiple Remote Sensing Platforms

NASA Astrophysics Data System (ADS)

Vargas, S. A., Jr.; Andresen, C. G.; May, J. L.; Oberbauer, S. F.; Hollister, R. D.; Tweedie, C. E.

2017-12-01

The Arctic is experiencing among the most dramatic impacts from climate variability on the planet. Arctic plant phenology has been identified as an ideal indicator of climate change impacts and provides great insight into seasonal and inter-annual vegetative trends and their responses to such changes. Traditionally, phenology has been quantified using satellite-based systems and plot-level observations but each approach presents limitations especially in high latitude regions. Mid-scale systems (e.g. automated sensor platforms and trams) have shown to provide alternative, and in most cases, cheaper solutions with comparable results to those acquired traditionally. This study contributes to the US Arctic Observing Network (AON) and assesses the effectiveness of using digital images acquired from pheno-cams, a kite aerial photography (KAP) system, and plot-level images (PLI) in their capacity to assess phenological variability (e.g. snow melt, greening and end-of-season) for dominant vegetation communities present at two sites in both Utqiagvik and Atqasuk, Alaska, namely the Mobile Instrumented Sensor Platform (MISP) and the Circum-arctic Active Layer Monitoring (CALM) grids. RGB indices (e.g. GEI and %G) acquired from these methods were compared to the normalized difference vegetation index (NDVI) calculated from multispectral ground-based reflectance measurements, which has been identified and used as a proxy of primary productivity across multiple ecosystems including the Arctic. The 5 years of growing season data collected generally resulted with stronger Pearson's correlations between indices located in plots containing higher soil moisture versus those that were drier. Future studies will extend platform inter-comparison to the satellite level by scaling trends to MODIS land surface products. Trends documented thus far, however, suggest that the long-term changes in satellite NDVI for these study areas, could be a direct response from wet tundra landscapes.

Imaging Radar in the Mojave Desert-Death Valley Region

NASA Technical Reports Server (NTRS)

Farr, Tom G.

2001-01-01

The Mojave Desert-Death Valley region has had a long history as a test bed for remote sensing techniques. Along with visible-near infrared and thermal IR sensors, imaging radars have flown and orbited over the area since the 1970's, yielding new insights into the geologic applications of these technologies. More recently, radar interferometry has been used to derive digital topographic maps of the area, supplementing the USGS 7.5' digital quadrangles currently available for nearly the entire area. As for their shorter-wavelength brethren, imaging radars were tested early in their civilian history in the Mojave Desert-Death Valley region because it contains a variety of surface types in a small area without the confounding effects of vegetation. The earliest imaging radars to be flown over the region included military tests of short-wavelength (3 cm) X-band sensors. Later, the Jet Propulsion Laboratory began its development of imaging radars with an airborne sensor, followed by the Seasat orbital radar in 1978. These systems were L-band (25 cm). Following Seasat, JPL embarked upon a series of Space Shuttle Imaging Radars: SIRA (1981), SIR-B (1984), and SIR-C (1994). The most recent in the series was the most capable radar sensor flown in space and acquired large numbers of data swaths in a variety of test areas around the world. The Mojave Desert-Death Valley region was one of those test areas, and was covered very well with 3 wavelengths, multiple polarizations, and at multiple angles. At the same time, the JPL aircraft radar program continued improving and collecting data over the Mojave Desert Death Valley region. Now called AIRSAR, the system includes 3 bands (P-band, 67 cm; L-band, 25 cm; C-band, 5 cm). Each band can collect all possible polarizations in a mode called polarimetry. In addition, AIRSAR can be operated in the TOPSAR mode wherein 2 antennas collect data interferometrically, yielding a digital elevation model (DEM). Both L-band and C-band can be operated in this way, with horizontal resolution of about 5 m and vertical errors less than 2 m. The findings and developments of these earlier investigations are discussed.

Los Alamos Fires From Landsat 7

NASA Technical Reports Server (NTRS)

2002-01-01

On May 9, 2000, the Landsat 7 satellite acquired an image of the area around Los Alamos, New Mexico. The Landsat 7 satellite acquired this image from 427 miles in space through its sensor called the Enhanced Thematic Mapper Plus (ETM+). Evident within the imagery is a view of the ongoing Cerro Grande fire near the town of Los Alamos and the Los Alamos National Laboratory. Combining the high-resolution (30 meters per pixel in this scene) imaging capacity of ETM+ with its multi-spectral capabilities allows scientists to penetrate the smoke plume and see the structure of the fire on the surface. Notice the high-level of detail in the infrared image (bottom), in which burn scars are clearly distinguished from the hotter smoldering and flaming parts of the fire. Within this image pair several features are clearly visible, including the Cerro Grande fire and smoke plume, the town of Los Alamos, the Los Alamos National Laboratory and associated property, and Cerro Grande peak. Combining ETM+ channels 7, 4, and 2 (one visible and two infrared channels) results in a false color image where vegetation appears as bright to dark green (bottom image). Forested areas are generally dark green while herbaceous vegetation is light green. Rangeland or more open areas appear pink to light purple. Areas with extensive pavement or urban development appear light blue or white to purple. Less densely-developed residential areas appear light green and golf courses are very bright green. The areas recently burned appear black. Dark red to bright red patches, or linear features within the burned area, are the hottest and possibly actively burning areas of the fire. The fire is spreading downslope and the front of the fire is readily detectable about 2 kilometers to the west and south of Los Alamos. Combining ETM+ channels 3, 2, and 1 provides a true-color image of the greater Los Alamos region (top image). Vegetation is generally dark to medium green. Forested areas are very dark green while herbaceous vegetation is medium green. Rangeland or more open areas appear as tan or light brown. Areas with extensive pavement or urban development appear white to light green. Less densely-developed residential areas appear medium green and golf courses are medium green. The fires and areas recently burned are obscured by smoke plumes which are white to light blue. Landsat 7 data are archived and available from EDC. Image by Rob Simmon, Earth Observatory, NASA Goddard Space Flight Center. Data courtesy Randy McKinley, EROS Data Center (EDC)

Los Alamos Before and After the Fire

NASA Technical Reports Server (NTRS)

2002-01-01

On May 4, 2000, a prescribed fire was set at Bandelier National Monument, New Mexico, to clear brush and dead and dying undergrowth to prevent a larger, subsequent wildfire. Unfortunately, due to high winds and extremely dry conditions in the surrounding area, the prescribed fire quickly raged out of control and, by May 10, the blaze had spread into the nearby town of Los Alamos. In all, more than 20,000 people were evacuated from their homes and more than 200 houses were destroyed as the flames consumed about 48,000 acres in and around the Los Alamos area. The pair of images above were acquired by the Enhanced Thematic Mapper Plus (ETM+) sensor, flying aboard NASA's Landsat 7 satellite, shortly before the Los Alamos fire (top image, acquired April 14) and shortly after the fire was extinguished (lower image, June 17). The images reveal the extent of the damage caused by the fire. Combining ETM+ channels 7, 4, and 2 (one visible and two infrared channels) results in a false-color image where vegetation appears as bright to dark green. Forested areas are generally dark green while herbaceous vegetation is light green. Rangeland or more open areas appear pink to light purple. Areas with extensive pavement or urban development appear light blue or white to purple. Less densely-developed residential areas appear light green and golf courses are very bright green. In the lower image, the areas recently burned appear bright red. Landsat 7 data courtesy United States Geological Survey EROS DataCenter. Images by Robert Simmon, NASA GSFC.

Integration of Multi-sensor Data for Desertification Monitoring

NASA Astrophysics Data System (ADS)

Lin, S.; Kim, J.

2010-12-01

The desert area has been rapidly expanding globally due to reasons such as climate change, uninhibited human activities, etc. The continuous desertification has seriously affected in (and near) desert area all over the world. As sand dune activity has been recognised as an essential indicator of desertification (it is the signature and the consequence of desertification), an accurate monitoring of desert dune movement hence becomes crucial for understanding and modelling the progress of desertification. In order to determine dune’s moving speed and tendency, also to understand the propagation occurring in transition region between desert and soil rich area, a monitoring system applying multi-temporal and multi-sensor remote sensed data are proposed and implemented. Remote sensed data involved in the monitoring scheme include space-borne optical image, Synthetic Aperture Radar (SAR) data, multi- and hyper-spectral image, and terrestrial close range image. In order to determine the movement of dunes, a reference terrain surface is required. To this end, a digital terrain model (DTM) covering the test site is firstly produced using high resolution optical stereo satellite images. Subsequently, ERS-1/2 SAR imagery are employed as another resource for dune field observation. Through the interferometric SAR (InSAR) technique combining with image-based stereo DTM, the surface displacements are obtained. From which the movement and speed of the dunes can be determined. To understand the effect of desertification combating activities, the correlation between dune activities and the landcover change is also an important issue to be covered in the monitoring scheme. The task is accomplished by tracing soil and vegetation canopy variation with the multi and hyper spectral image analysis using Hyperion and Ali imagery derived from Earth Observation Mission 1 (EO-1). As a result, the correlation between the soil restorations, expanding of vegetation canopy and the ceasing of dune activities can be clearly revealed. For the very detailed measurement, a terrestrial system applying close range photogrammetry will be set up in the test sites to acquire sequential images and used to generate 4D model of the dunes in future. Finally, all the outputs from the multi-sensor data will be crossly verified and compiled to model the desertification process and the consequences. A desertification combating activity which is performed by Korea-China NGO alliance has been conducted in Qubuqi desert in Nei Mongol, China. The method and system proposed above will be established and applied to monitor the dune mobility occurring in this area. The results are expected to be of great value to demonstrate the first case of remote sensing monitoring over the combat desertification activities.

Depth Estimation of Submerged Aquatic Vegetation in Clear Water Streams Using Low-Altitude Optical Remote Sensing

PubMed Central

Visser, Fleur; Buis, Kerst; Verschoren, Veerle; Meire, Patrick

2015-01-01

UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R2-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R2-values up to 0.77) corresponded with the OBRA findings. A 10% error was achieved under sub-optimal data collection conditions, which indicates that the method could be suitable for many SAV mapping applications. PMID:26437410

Depth Estimation of Submerged Aquatic Vegetation in Clear Water Streams Using Low-Altitude Optical Remote Sensing.

PubMed

Visser, Fleur; Buis, Kerst; Verschoren, Veerle; Meire, Patrick

2015-09-30

UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R²-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R²-values up to 0.77) corresponded with the OBRA findings. A 10% error was achieved under sub-optimal data collection conditions, which indicates that the method could be suitable for many SAV mapping applications.

East Africa

NASA Technical Reports Server (NTRS)

2002-01-01

This image shows the East African nations of Ethiopia, Eritrea, and Somalia, as well as portions of Kenya, Sudan, Yemen, and Saudi Arabia. Dominating the scene are the green Ethiopian Highlands. With altitudes as high as 4,620 meters (15,157 feet), the highlands pull moisture from the arid air, resulting in relatively lush vegetation. In fact, coffee-one of the world's most prized crops-originated here. To the north (above) the highlands is Eritrea, which became independent in 1993. East (right) of Ethiopia is Somalia, jutting out into the Indian Ocean. The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) captured this true-color image on November 29, 2000. Provided by the SeaWiFS Project, NASA/Goddard Space Flight Center, and ORBIMAGE

Quantification of seasonal biomass effects on cosmic-ray soil water content determination

NASA Astrophysics Data System (ADS)

Baatz, R.; Bogena, H. R.; Hendricks Franssen, H.; Huisman, J. A.; Qu, W.; Montzka, C.; Korres, W.; Vereecken, H.

2013-12-01

The novel cosmic-ray soil moisture probes (CRPs) measure neutron flux density close to the earth surface. High energy cosmic-rays penetrate the Earth's atmosphere from the cosmos and become moderated by terrestrial nuclei. Hydrogen is the most effective neutron moderator out of all chemical elements. Therefore, neutron flux density measured with a CRP at the earth surface correlates inversely with the hydrogen content in the CRP's footprint. A major contributor to the amount of hydrogen in the sensor's footprint is soil water content. The ability to measure changes in soil water content within the CRP footprint at a larger-than-point scale (~30 ha) and at high temporal resolution (hourly) make these sensors an appealing measurement instrument for hydrologic modeling purposes. Recent developments focus on the identification and quantification of major uncertainties inherent in CRP soil moisture measurements. In this study, a cosmic-ray soil moisture network for the Rur catchment in Western Germany is presented. It is proposed to correct the measured neutron flux density for above ground biomass yielding vegetation corrected soil water content from cosmic-ray measurements. The correction for above ground water equivalents aims to remove biases in soil water content measurements on sites with high seasonal vegetation dynamics such as agricultural fields. Above ground biomass is estimated as function of indices like NDVI and NDWI using regression equations. The regression equations were obtained with help of literature information, ground-based control measurements, a crop growth model and globally available data from the Moderate Resolution Imaging Spectrometer (MODIS). The results show that above ground biomass could be well estimated during the first half of the year. Seasonal changes in vegetation water content yielded biases in soil water content of ~0.05 cm3/cm3 that could be corrected for with the vegetation correction. The vegetation correction has particularly high potential when applied at long term cosmic-ray monitoring sites and the cosmic-ray rover.

Land and atmosphere interactions using satellite remote sensing and a coupled mesoscale/land surface model

NASA Astrophysics Data System (ADS)

Hong, Seungbum

Land and atmosphere interactions have long been recognized for playing a key role in climate and weather modeling. However their quantification has been challenging due to the complex nature of the land surface amongst various other reasons. One of the difficult parts in the quantification is the effect of vegetation which are related to land surface processes such soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examined the effects of vegetation and its relationship with soil moisture on the simulated land-atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Then, this study evaluates the model improvements for each simulation method.

A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system

PubMed Central

Li, Xin; Liu, Shaomin; Xiao, Qin; Ma, Mingguo; Jin, Rui; Che, Tao; Wang, Weizhen; Hu, Xiaoli; Xu, Ziwei; Wen, Jianguang; Wang, Liangxu

2017-01-01

We introduce a multiscale dataset obtained from Heihe Watershed Allied Telemetry Experimental Research (HiWATER) in an oasis-desert area in 2012. Upscaling of eco-hydrological processes on a heterogeneous surface is a grand challenge. Progress in this field is hindered by the poor availability of multiscale observations. HiWATER is an experiment designed to address this challenge through instrumentation on hierarchically nested scales to obtain multiscale and multidisciplinary data. The HiWATER observation system consists of a flux observation matrix of eddy covariance towers, large aperture scintillometers, and automatic meteorological stations; an eco-hydrological sensor network of soil moisture and leaf area index; hyper-resolution airborne remote sensing using LiDAR, imaging spectrometer, multi-angle thermal imager, and L-band microwave radiometer; and synchronical ground measurements of vegetation dynamics, and photosynthesis processes. All observational data were carefully quality controlled throughout sensor calibration, data collection, data processing, and datasets generation. The data are freely available at figshare and the Cold and Arid Regions Science Data Centre. The data should be useful for elucidating multiscale eco-hydrological processes and developing upscaling methods. PMID:28654086

Early Validation of Sentinel-2 L2A Processor and Products

NASA Astrophysics Data System (ADS)

Pflug, Bringfried; Main-Knorn, Magdalena; Bieniarz, Jakub; Debaecker, Vincent; Louis, Jerome

2016-08-01

Sentinel-2 is a constellation of two polar orbiting satellite units each one equipped with an optical imaging sensor MSI (Multi-Spectral Instrument). Sentinel-2A was launched on June 23, 2015 and Sentinel-2B will follow in 2017.The Level-2A (L2A) processor Sen2Cor implemented for Sentinel-2 data provides a scene classification image, aerosol optical thickness (AOT) and water vapour (WV) maps and the Bottom-Of-Atmosphere (BOA) corrected reflectance product. First validation results of Sen2Cor scene classification showed an overall accuracy of 81%. AOT at 550 nm is estimated by Sen2Cor with uncertainty of 0.035 for cloudless images and locations with dense dark vegetation (DDV) pixels present in the image. Aerosol estimation fails if the image contains no DDV-pixels. Mean difference between Sen2Cor WV and ground-truth is 0.29 cm. Uncertainty of up to 0.04 was found for the BOA- reflectance product.

Comparison of C-band and Ku-band scatterometry for medium-resolution tropical forest inventory

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1993-08-01

Since 1978, AVHRR imagery from NOAA polar orbiters has provided coverage of tropical regions at this desirable resolution, but much of the imagery is plagued with heavy cloud cover typical of equatorial regions. Clearly a medium resolution radar sensor would be a useful addition to AVHRR, but none are planned to fly in the future. In contrast, scatterometers are an important radar component of many future earth remote sensing systems, but the inherent resolution of these instruments is too low (approximately equals 50 km) for monitoring earth's land surfaces. However, a recently developed image reconstruction technique can increase the spatial resolution of scatterometer data to levels (approximately equals 4 to 14 km) approaching AVHRR global area coverage (approximately equals 4 km). When reconstructed, scatterometer data may prove to be an important asset in evaluating equatorial land cover. In this paper, the authors compare the utility of reconstructed Seasat scatterometer (SASS), Ku-band microwave data to reconstructed ERS-1 C-band scatterometer imagery for discrimination and monitoring of tropical vegetation formations. In comparative classification experiments conducted on reconstructed images of Brasil, the ERS-1 C-band imagery was slightly superior to its reconstructed SASS Ku-band counterpart for discriminating between several equatorial land cover classes. A classification accuracy approaching .90 was achieved when the two scatterometer images were combined with an AVHRR normalized difference vegetation index (NDVI) image. The success of these experiments indicates that further research into reconstructed image applications to tropical forest monitoring is warranted.

Skylab

NASA Image and Video Library

1973-09-01

This Earth Resource Experiment Package (EREP) photograph of the Uncompahgre area of Colorado was electronically acquired in September of 1973 by the Multi-spectral Scarner, Skylab Experiment S192. EREP images were used to analyze the vegetation conditions and landscape characteristic of this area. Skylab's Earth sensors played the dual roles of gathering information about the planet and perfecting instruments and techniques for future satellites and manned stations. An array of six fixed cameras, another for high resolution, and the astronauts' handheld cameras photographed surface features. Other instruments, recording on magnetic tape, measured the reflectivity of plants, soils, and water. Radar measured the altitude of land and water surfaces. The sensors' objectives were to survey croplands and forests, identify soils and rock types, map natural features and urban developments, detect sediments and the spread of pollutants, study clouds and the sea, and determine the extent of snow and ice cover.

Exploitation of Digital Surface Models Generated from WORLDVIEW-2 Data for SAR Simulation Techniques

NASA Astrophysics Data System (ADS)

Ilehag, R.; Auer, S.; d'Angelo, P.

2017-05-01

GeoRaySAR, an automated SAR simulator developed at DLR, identifies buildings in high resolution SAR data by utilizing geometric knowledge extracted from digital surface models (DSMs). Hitherto, the simulator has utilized DSMs generated from LiDAR data from airborne sensors with pre-filtered vegetation. Discarding the need for pre-optimized model input, DSMs generated from high resolution optical data (acquired with WorldView-2) are used for the extraction of building-related SAR image parts in this work. An automatic preprocessing of the DSMs has been developed for separating buildings from elevated vegetation (trees, bushes) and reducing the noise level. Based on that, automated simulations are triggered considering the properties of real SAR images. Locations in three cities, Munich, London and Istanbul, were chosen as study areas to determine advantages and limitations related to WorldView-2 DSMs as input for GeoRaySAR. Beyond, the impact of the quality of the DSM in terms of building extraction is evaluated as well as evaluation of building DSM, a DSM only containing buildings. The results indicate that building extents can be detected with DSMs from optical satellite data with various success, dependent on the quality of the DSM as well as on the SAR imaging perspective.

Deforestation in Sumatra

NASA Technical Reports Server (NTRS)

2002-01-01

Indonesia is rapidly losing its lowland forests to logging, much of it illegal. At present, logging is claiming the forests at a rate of nearly two million hectares (slightly less than 5 million acres: roughly the same area as the state of Massachusetts) each year. At this rate, the island of Sumatra will have no more lowland forests by 2005, a fate already befallen the island of Sulawesi. Indonesia's lowland forests are home to a wide variety of wildlife and are considered among the richest ecosystems in the world. Among the unique life forms in these forests are the Orangutan and the Sumatra Tiger. Sixteen percent of the entire world's bird species, eleven percent of its plants, and ten percent of all mammals on Earth call these forests home. Many are found nowhere else. In the two Landsat scenes shown above, the pattern of deforestation can be clearly discerned. Deep green in these images shows lush vegetation in the forest cover. In both scenes, deep and pale red shows areas where there is little or no vegetation, often bare ground from where forest has been completely stripped. The latter Landsat scene from 2001 not only shows extensive clear cut areas, but also new logging roads built into the remaining forest to facilitate future cutting. This lowland forest region is located on Indonesia's largest island, Sumatra, roughly 100 km southwest of the provincial capital of Jambi. The first image was acquired by Landsat 5's Thematic Mapper (TM) sensor on June 22, 1992, the second by Landsat 7's Enhanced Thematic Mapper plus (ETM+) sensor on January 14, 2001. Both are false-color composite images made using shortwave infrared, infrared, and green wavelengths. The area shown above is roughly 30 km x 22 km (19 miles x 14 miles). The large versions of these images show the same general area covering 60 km x 60 km. Images provided by the Tropical Rain Forest Information Center (TRFIC) through the Basic Science and Remote Sensing Initiative (BSRSI) based at Michigan State University, and the Landsat 7 Project Science Office at NASA Goddard Space Flight Center

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).

Using normalized difference vegetation index to estimate carbon fluxes from small rotationally grazed pastures

USGS Publications Warehouse

Skinner, R.H.; Wylie, B.K.; Gilmanov, T.G.

2011-01-01

Satellite-based normalized difference vegetation index (NDVI) data have been extensively used for estimating gross primary productivity (GPP) and yield of grazing lands throughout the world. However, the usefulness of satellite-based images for monitoring rotationally-grazed pastures in the northeastern United States might be limited because paddock size is often smaller than the resolution limits of the satellite image. This research compared NDVI data from satellites with data obtained using a ground-based system capable of fine-scale (submeter) NDVI measurements. Gross primary productivity was measured by eddy covariance on two pastures in central Pennsylvania from 2003 to 2008. Weekly 250-m resolution satellite NDVI estimates were also obtained for each pasture from the moderate resolution imaging spectroradiometer (MODIS) sensor. Ground-based NDVI data were periodically collected in 2006, 2007, and 2008 from one of the two pastures. Multiple-regression and regression-tree estimates of GPP, based primarily on MODIS 7-d NDVI and on-site measurements of photosynthetically active radiation (PAR), were generally able to predict growing-season GPP to within an average of 3% of measured values. The exception was drought years when estimated and measured GPP differed from each other by 11 to 13%. Ground-based measurements improved the ability of vegetation indices to capture short-term grazing management effects on GPP. However, the eMODIS product appeared to be adequate for regional GPP estimates where total growing-season GPP across a wide area would be of greater interest than short-term management-induced changes in GPP at individual sites.

Integration of Landsat TM and SPOT HRG Images for Vegetation Change Detection in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Moran, Emilio

2009-01-01

Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal component analysis was used to integrate TM and HRG panchromatic data. Vegetation change/non-change was detected with the image differencing approach based on the TM and HRG fused image and the corresponding TM image. A rule-based approach was used to classify the TM and HRG multispectral images into thematic maps with three coarse land-cover classes: forest, non-forest vegetation, and non-vegetation lands. A hybrid approach combining image differencing and post-classification comparison was used to detect vegetation change trajectories. This research indicates promising vegetation change techniques, especially for vegetation gain and loss, even if very limited reference data are available. PMID:19789721

Cross-calibration of MODIS with ETM+ and ALI sensors for long-term monitoring of land surface processes

USGS Publications Warehouse

Meyer, D.; Chander, G.

2006-01-01

Increasingly, data from multiple sensors are used to gain a more complete understanding of land surface processes at a variety of scales. Although higher-level products (e.g., vegetation cover, albedo, surface temperature) derived from different sensors can be validated independently, the degree to which these sensors and their products can be compared to one another is vastly improved if their relative spectroradiometric responses are known. Most often, sensors are directly calibrated to diffuse solar irradiation or vicariously to ground targets. However, space-based targets are not traceable to metrological standards, and vicarious calibrations are expensive and provide a poor sampling of a sensor's full dynamic range. Crosscalibration of two sensors can augment these methods if certain conditions can be met: (1) the spectral responses are similar, (2) the observations are reasonably concurrent (similar atmospheric & solar illumination conditions), (3) errors due to misregistrations of inhomogeneous surfaces can be minimized (including scale differences), and (4) the viewing geometry is similar (or, some reasonable knowledge of surface bi-directional reflectance distribution functions is available). This study explores the impacts of cross-calibrating sensors when such conditions are met to some degree but not perfectly. In order to constrain the range of conditions at some level, the analysis is limited to sensors where cross-calibration studies have been conducted (Enhanced Thematic Mapper Plus (ETM+) on Landsat-7 (L7), Advance Land Imager (ALI) and Hyperion on Earth Observer-1 (EO-1)) and including systems having somewhat dissimilar geometry, spatial resolution & spectral response characteristics but are still part of the so-called "A.M. constellation" (Moderate Resolution Imaging Spectrometer (MODIS) aboard the Terra platform). Measures for spectral response differences and methods for cross calibrating such sensors are provided in this study. These instruments are cross calibrated using the Railroad Valley playa in Nevada. Best fit linear coefficients (slope and offset) are provided for ALI-to-MODIS and ETM+-to-MODIS cross calibrations, and root-mean-squared errors (RMSEs) and correlation coefficients are provided to quantify the uncertainty in these relationships. In theory, the linear fits and uncertainties can be used to compare radiance and reflectance products derived from each instrument.

Rapid detection of Bacillus anthracis using monoclonal antibody functionalized QCM sensor.

PubMed

Hao, Rongzhang; Wang, Dianbing; Zhang, Xian'en; Zuo, Guomin; Wei, Hongping; Yang, Ruifu; Zhang, Zhiping; Cheng, Zhenxing; Guo, Yongchao; Cui, Zongqiang; Zhou, Yafeng

2009-01-01

Since the anthrax spore bioterrorism attacks in America in 2001, the early detection of Bacillus anthracis spores and vegetative cells has gained significant interest. At present, many polyclonal antibody-based quartz crystal microbalance (QCM) sensors have been developed to detect B. anthracis simulates. To achieve a simultaneous rapid detection of B. anthracis spores and vegetative cells, this paper presents a biosensor that utilizes an anti-B. anthracis monoclonal antibody designated to 8G3 (mAb 8G3, IgG) functionalized QCM sensor. Having compared four kinds of antibody immobilizations on Au surface, an optimized mAb 8G3 was immobilized onto the Au electrode with protein A on a mixed self-assembled monolayer (SAM) of 11-mercaptoundecanoic acid (11-MUA) and 6-mercaptohexan-1-ol (6-MHO) as adhesive layer. The detection of B. anthracis was investigated under three conditions: dip-and-dry, static addition and flow through procedure. The results indicated that the sensor yielded a distinct response to B. anthracis spores or vegetative cells but had no significant response to Bacillus thuringiensis species. The functionalized sensor recognized B. anthracis spores and vegetative cells specifically from its homophylic ones, and the limit of detection (LOD) reached 10(3)CFU or spores/ml of B. anthracis in less than 30 min. Cyclic voltammogram (CV) and scanning electronic microscopy (SEM) were performed to characterize the surface of the sensor in variable steps during the modification and after the detection. The mAb functionalized QCM biosensor will be helpful in the fabrication of a similar biosensor that may be available in anti-bioterrorism in the future.

Application and Comparison of the MODIS-Derived Enhanced Vegetation Index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A Case Study in the Arid Colorado River Delta, Mexico

PubMed Central

Jarchow, Christopher J.; Didan, Kamel; Barreto-Muñoz, Armando; Glenn, Edward P.

2018-01-01

The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R2 = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R2 = 0.27) and riparian vegetation (R2 = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R2 = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area. PMID:29757265

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

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.

Spectral and spatial resolution analysis of multi sensor satellite data for coral reef mapping: Tioman Island, Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet; Kabiri, Keivan

2012-07-01

This paper describes an assessment of coral reef mapping using multi sensor satellite images such as Landsat ETM, SPOT and IKONOS images for Tioman Island, Malaysia. The study area is known to be one of the best Islands in South East Asia for its unique collection of diversified coral reefs and serves host to thousands of tourists every year. For the coral reef identification, classification and analysis, Landsat ETM, SPOT and IKONOS images were collected processed and classified using hierarchical classification schemes. At first, Decision tree classification method was implemented to separate three main land cover classes i.e. water, rural and vegetation and then maximum likelihood supervised classification method was used to classify these main classes. The accuracy of the classification result is evaluated by a separated test sample set, which is selected based on the fieldwork survey and view interpretation from IKONOS image. Few types of ancillary data in used are: (a) DGPS ground control points; (b) Water quality parameters measured by Hydrolab DS4a; (c) Sea-bed substrates spectrum measured by Unispec and; (d) Landcover observation photos along Tioman island coastal area. The overall accuracy of the final classification result obtained was 92.25% with the kappa coefficient is 0.8940. Key words: Coral reef, Multi-spectral Segmentation, Pixel-Based Classification, Decision Tree, Tioman Island

Analysis of radio wave propagation for ISM 2.4 GHz Wireless Sensor Networks in inhomogeneous vegetation environments.

PubMed

Azpilicueta, Leire; López-Iturri, Peio; Aguirre, Erik; Mateo, Ignacio; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

2014-12-10

The use of wireless networks has experienced exponential growth due to the improvements in terms of battery life and low consumption of the devices. However, it is compulsory to conduct previous radio propagation analysis when deploying a wireless sensor network. These studies are necessary to perform an estimation of the range coverage, in order to optimize the distance between devices in an actual network deployment. In this work, the radio channel characterization for ISM 2.4 GHz Wireless Sensor Networks (WSNs) in an inhomogeneous vegetation environment has been analyzed. This analysis allows designing environment monitoring tools based on ZigBee and WiFi where WSN and smartphones cooperate, providing rich and customized monitoring information to users in a friendly manner. The impact of topology as well as morphology of the environment is assessed by means of an in-house developed 3D Ray Launching code, to emulate the realistic operation in the framework of the scenario. Experimental results gathered from a measurement campaign conducted by deploying a ZigBee Wireless Sensor Network, are analyzed and compared with simulations in this paper. The scenario where this network is intended to operate is a combination of buildings and diverse vegetation species. To gain insight in the effects of radio propagation, a simplified vegetation model has been developed, considering the material parameters and simplified geometry embedded in the simulation scenario. An initial location-based application has been implemented in a real scenario, to test the functionality within a context aware scenario. The use of deterministic tools can aid to know the impact of the topological influence in the deployment of the optimal Wireless Sensor Network in terms of capacity, coverage and energy consumption, making the use of these systems attractive for multiple applications in inhomogeneous vegetation environments.

Remote sensing technologies applied to the irrigation water management on a golf course

NASA Astrophysics Data System (ADS)

Pedras, Celestina; Lança, Rui; Martins, Fernando; Soares, Cristina; Guerrero, Carlos; Paixão, Helena

2015-04-01

An adequate irrigation water management in a golf course is a complex task that depends upon climate (multiple microclimates) and land cover (where crops differ in morphology, physiology, plant density, sensitivity to water stress, etc.). These factors change both in time and space on a landscape. A direct measurement provides localized values of the evapotranspiration and climate conditions. Therefore this is not a practical or economical methodology for large-scale use due to spatial and temporal variability of vegetation, soils, and irrigation management strategies. Remote sensing technology combines large scale with ground measurement of vegetation indexes. These indexes are mathematical combinations of different spectral bands mostly in the visible and near infrared regions of the electromagnetic spectrum. They represent the measures of vegetation activity that vary not only with the seasonal variability of green foliage, but also across space, thus they are suitable for detecting spatial landscape variability. The spectral vegetation indexes may enhance irrigation management through the information contained in spectral reflectance data. This study was carried out on the 18th fairway of the Royal Golf Course, Vale do Lobo, Portugal, and it aims to establish the relationship between direct measurements and vegetation indexes. For that it is required (1) to characterize the soil and climatic conditions, (2) to assessment of the irrigation system, (3) to estimate the evapotranspiration (4) and to calculate the vegetation indices. The vegetation indices were determined with basis on spectral bands red, green and blue, RGB, and near Infrared, NIR, obtained from the analysis of images acquired from a unpiloted aerial vehicle, UAV, platform. The measurements of reference evapotranspiration (ETo) were obtained from two meteorological stations located in the study area. The landscape evapotranspiration, ETL, was determined in the fairway with multiple microclimates and managed stress. The ETL was obtained thru the use of mobile reference ET stations and also by the development of the surface renewal (SR) measurement technique. The sprinkler irrigation system installed was evaluated according to the methodology described by ASAE. The Normalized Difference Vegetation Index, NDVI, and Visible atmospherically Resistant Index, VARI, are confronted with the direct localized measurements. The NDVI is the most used indicator to assess the vigor status of the vegetation. However, this index depends of the use of NIR bands which demands quite expensive sensors. The use vegetation indexes obtained by sensors that collect data in the visible wavelength, such as VARI is less expensive and allow the vegetative vigor evaluation with a similar rigor. The information of vegetation indices is crossed with edafoclimatic data obtained in situ, in order to improve the irrigation water management based on aerial imagery.

Terrestrial vegetation dynamics and global climate controls

NASA Astrophysics Data System (ADS)

Potter, Christopher; Boriah, Shyam; Steinbach, Michael; Kumar, Vipin; Klooster, Steven

2008-07-01

Monthly data from the moderate resolution imaging spectroradiometer (MODIS) and its predecessor satellite sensors was used to reconstruct vegetation dynamics in response to climate patterns over the period 1983 2005. Results suggest that plant growth over extensive land areas of southern Africa and Central Asia were the most closely coupled of any major land area to El Niño southern oscillation (ENSO) effects on regional climate. Others land areas strongly tied to recent ENSO climate effects were in northern Canada, Alaska, western US, northern Mexico, northern Argentina, and Australia. Localized variations in precipitation were the most common controllers of monthly values for the fraction absorbed of photosynthetically active radiation (FPAR) over these regions. In addition to the areas cited above, seasonal FPAR values from MODIS were closely coupled to rainfall patterns in grassland and cropland areas of the northern and central US. Historical associations between global vegetation FPAR and atmospheric carbon dioxide (CO2) anomalies suggest that the terrestrial biosphere can contribute major fluxes of CO2 during major drought events, such as those triggered by 1997 1998 El Niño event.

Effects of Emergent Vegetation on Sediment Dynamics within a Retreating Coastal Marshland

NASA Astrophysics Data System (ADS)

Stellern, C.; Grossman, E.; Fuller, R.; Wallin, D.; Linneman, S. R.

2015-12-01

Coastal emergent vegetation in estuaries physically interrupts flow within the water column, reduces wave energy and increases sediment deposition. Previous workers conclude that wave attenuation rates decrease exponentially with distance from the marsh edge and are dependent on site and species-specific plant characteristics (Yang et al., 2011). Sediment deposition may exhibit similar patterns; however, sediment, geomorphic and habitat models seldom integrate site-specific biophysical plant parameters into change analyses. We paired vegetation and sediment dynamic studies to: (1) characterize vegetation structure, (2) estimate sediment available for deposition, (3) estimate rate, distribution and composition of sediment deposits, (4) determine sediment accumulation on vegetation, (5) compare sediment deposition within dense tidal wetland relative to non-vegetated tidal flat. These studies integrate a variety of monitoring methods, including the use of sediment traps, turbidity sensors, side-on photographs of vegetation and remote sensing image analysis. We compared sedimentation data with vegetation characteristics and spatial distribution data to examine the relative role of vegetation morphologic traits (species, stem density, biomass, distribution, tidal channels, etc.) on sediment dynamics. Our study is focused on Port Susan Bay of Washington State; a protected delta that has experienced up to 1 kilometer of marsh retreat (loss) over the past fifty years. Preliminary results show that the highest winter deposition occurred in the high marsh/mid-marsh boundary, up to 300m inland of the marsh edge, where bulrush species are most dense. These results will inform restoration efforts aimed at reestablishing sediment supply to the retreating marshland. This research is necessary to understand the vulnerability and adaptability of coastal marshlands to climate change related stressors such as, increased water levels (sea-level rise) and wave energy.

Detection of potato beetle damage using remote sensing from small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

Hunt, E. Raymond; Rondon, Silvia I.

2017-04-01

Colorado potato beetle (CPB) adults and larvae devour leaves of potato and other solanaceous crops and weeds, and may quickly develop resistance to pesticides. With early detection of CPB damage, more options are available for precision integrated pest management, which reduces the amount of pesticides applied in a field. Remote sensing with small unmanned aircraft systems (sUAS) has potential for CPB detection because low flight altitudes allow image acquisition at very high spatial resolution. A five-band multispectral sensor and up-looking incident light sensor were mounted on a six-rotor sUAS, which was flown at altitudes of 60 and 30 m in June 2014. Plants went from visibly undamaged to having some damage in just 1 day. Whole-plot normalized difference vegetation index (NDVI) and the number of pixels classified as damaged (0.70≤NDVI≤0.80) were not correlated with visible CPB damage ranked from least to most. Area of CPB damage estimated using object-based image analysis was highly correlated to the visual ranking of damage. Furthermore, plant height calculated using structure-from-motion point clouds was related to CPB damage, but this method required extensive operator intervention for success. Object-based image analysis has potential for early detection based on high spatial resolution sUAS remote sensing.

The Ring-Barking Experiment: Analysis of Forest Vitality Using Multi-Temporal Hyperspectral Data

NASA Astrophysics Data System (ADS)

Reichmuth, Anne; Bachmann, Martin; Heiden, Uta; Pinnel, Nicole; Holzwarth, Stefanie; Muller, Andreas; Henning, Lea; Einzmann, Kathrin; Immitzer, Markus; Seitz, Rudolf

2016-08-01

Through new operational optical spaceborne sensors (En- MAP and Sentinel-2) the impact analysis of climate change on forest ecosystems will be fostered. This analysis examines the potential of high spectral, spatial and temporal resolution data for detecting forest vegetation parameters, in particular Chlorophyll and Canopy Water content. The study site is a temperate spruce forest in Germany where in 2013 several trees were Ring-barked for a controlled die-off. During this experiment Ring- barked and Control trees were observed. Twelve airborne hyperspectral HySpex VNIR (Visible/Near Infrared) and SWIR (Shortwave Infrared) data with 1m spatial and 416 bands spectral resolution were acquired during the vegetation periods of 2013 and 2014. Additional laboratory spectral measurements of collected needle samples from Ring-barked and Control trees are available for needle level analysis. Index analysis of the laboratory measurements and image data are presented in this study.

Munsell color analysis of Landsat color-ratio-composite images of limonitic areas in southwest New Mexico

NASA Technical Reports Server (NTRS)

Kruse, F. A.

1985-01-01

The causes of color variations in the green areas on Landsat 4/5-4/6-6/7 (red-blue-green) color-ratio-composite (CRC) images, defined as limonitic areas, were investigated by analyzing the CRC images of the Lordsburg, New Mexico area. The red-blue-green additive color system was mathematically transformed into the cylindrical Munsell color coordinates (hue, saturation, and value), and selected areas were digitally analyzed for color variation. The obtained precise color characteristics were then correlated with properties of surface material. The amount of limonite (L) visible to the sensor was found to be the primary cause of the observed color differences. The visible L is, is turn, affected by the amount of L on the material's surface and by within-pixel mixing of limonitic and nonlimonitic materials. The secondary cause of variation was vegetation density, which shifted CRC hues towards yellow-green, decreased saturation, and increased value.

Preliminary assessment of several parameters to measure and compare usefulness of the CEOS reference pseudo-invariant calibration sites

USGS Publications Warehouse

Chander, Gyanesh; Angal, Amit; Xiong, Xiaoxiong; Helder, Dennis L.; Mishra, Nischal; Choi, Taeyoung; Wu, Aisheng

2010-01-01

Test sites are central to any future quality assurance and quality control (QA/QC) strategy. The Committee on Earth Observation Satellites (CEOS) Working Group for Calibration and Validation (WGCV) Infrared Visible Optical Sensors (IVOS) worked with collaborators around the world to establish a core set of CEOS-endorsed, globally distributed, reference standard test sites (both instrumented and pseudo-invariant) for the post-launch calibration of space-based optical imaging sensors. The pseudo-invariant calibration sites (PICS) have high reflectance and are usually made up of sand dunes with low aerosol loading and practically no vegetation. The goal of this paper is to provide preliminary assessment of "several parameters" than can be used on an operational basis to compare and measure usefulness of reference sites all over the world. The data from Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Earth Observing-1 (EO-1) Hyperion sensors over the CEOS PICS were used to perform a preliminary assessment of several parameters, such as usable area, data availability, top-of-atmosphere (TOA) reflectance, at-sensor brightness temperature, spatial uniformity, temporal stability, spectral stability, and typical spectrum observed over the sites.

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index

PubMed Central

Pu, Ruiliang; Gong, Peng; Yu, Qian

2008-01-01

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data. PMID:27879906

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index.

PubMed

Pu, Ruiliang; Gong, Peng; Yu, Qian

2008-06-06

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data.

Remote sensing science for the Nineties; Proceedings of IGARSS '90 - 10th Annual International Geoscience and Remote Sensing Symposium, University of Maryland, College Park, May 20-24, 1990. Vols. 1, 2, & 3

NASA Technical Reports Server (NTRS)

1990-01-01

Various papers on remote sensing (RS) for the nineties are presented. The general topics addressed include: subsurface methods, radar scattering, oceanography, microwave models, atmospheric correction, passive microwave systems, RS in tropical forests, moderate resolution land analysis, SAR geometry and SNR improvement, image analysis, inversion and signal processing for geoscience, surface scattering, rain measurements, sensor calibration, wind measurements, terrestrial ecology, agriculture, geometric registration, subsurface sediment geology, radar modulation mechanisms, radar ocean scattering, SAR calibration, airborne radar systems, water vapor retrieval, forest ecosystem dynamics, land analysis, multisensor data fusion. Also considered are: geologic RS, RS sensor optical measurements, RS of snow, temperature retrieval, vegetation structure, global change, artificial intelligence, SAR processing techniques, geologic RS field experiment, stochastic modeling, topography and Digital Elevation model, SAR ocean waves, spaceborne lidar and optical, sea ice field measurements, millimeter waves, advanced spectroscopy, spatial analysis and data compression, SAR polarimetry techniques. Also discussed are: plant canopy modeling, optical RS techniques, optical and IR oceanography, soil moisture, sea ice back scattering, lightning cloud measurements, spatial textural analysis, SAR systems and techniques, active microwave sensing, lidar and optical, radar scatterometry, RS of estuaries, vegetation modeling, RS systems, EOS/SAR Alaska, applications for developing countries, SAR speckle and texture.

Cropland measurement using Thematic Mapper data and radiometric model

NASA Technical Reports Server (NTRS)

Lyon, John G.; Khuwaiter, I. H. S.

1989-01-01

To halt erosion and desertification, it is necessary to quantify resources that are affected. Necessary information includes inventory of croplands and desert areas as they change over time. Several studies indicate the value of remote sensor data as input to inventories. In this study, the radiometric modeling of spectral characteristics of soil and vegetation provides the theoretical basis for the remote sensing approach. Use of Landsat Thematic Mapper images allows measurement of croplands in Saudi Arabia, demonstrating the capability of the approach. The inventory techniques and remote sensing approach presented are potentially useful in developing countries.

NOAA-AVHRR image mosaics applied to vegetation identification

NASA Astrophysics Data System (ADS)

de Almeida, Maria d. G.; Ruddorff, Bernardo F.; Shimabukuro, Yosio E.

2001-06-01

In this paper, the maximum-value composite of images procedure from Normalized Difference Vegetation Index is used to get a cloud free image mosaic. The image mosaic is used to identify vegetation targets such as tropical forest, savanna and caatinga as well to make the vegetation cover mapping of Minas Gerais state, Brazil.

Scientific impact of MODIS C5 calibration degradation and C6+ improvements

NASA Astrophysics Data System (ADS)

Lyapustin, A.; Wang, Y.; Xiong, X.; Meister, G.; Platnick, S.; Levy, R.; Franz, B.; Korkin, S.; Hilker, T.; Tucker, J.; Hall, F.; Sellers, P.; Wu, A.; Angal, A.

2014-12-01

The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra-Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1-B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

On the estimate of the transpiration in Mediterranean heterogeneous ecosystems with the coupled use of eddy covariance and sap flow techniques.

NASA Astrophysics Data System (ADS)

Corona, Roberto; Curreli, Matteo; Montaldo, Nicola; Oren, Ram

2013-04-01

Mediterranean ecosystems are commonly heterogeneous savanna-like ecosystems, with contrasting plant functional types (PFT) competing for the water use. Mediterranean regions suffer water scarcity due to the dry climate conditions. In semi-arid regions evapotranspiration (ET) is the leading loss term of the root-zone water budget with a yearly magnitude that may be roughly equal to the precipitation. Despite the attention these ecosystems are receiving, a general lack of knowledge persists about the estimate of ET and the relationship between ET and the plant survival strategies for the different PFTs under water stress. During the dry summers these water-limited heterogeneous ecosystems are mainly characterized by a simple dual PFT-landscapes with strong-resistant woody vegetation and bare soil since grass died. In these conditions due to the low signal of the land surface fluxes captured by the sonic anemometer and gas analyzer the widely used eddy covariance may fail and its ET estimate is not robust enough. In these conditions the use of the sap flow technique may have a key role, because theoretically it provides a direct estimate of the woody vegetation transpiration. Through the coupled use of the sap flow sensor observations, a 2D foot print model of the eddy covariance tower and high resolution satellite images for the estimate of the foot print land cover map, the eddy covariance measurements can be correctly interpreted, and ET components (bare soil evaporation and woody vegetation transpiration) can be separated. The case study is at the Orroli site in Sardinia (Italy). The site landscape is a mixture of Mediterranean patchy vegetation types: trees, including wild olives and cork oaks, different shrubs and herbaceous species. An extensive field campaign started in 2004. Land-surface fluxes and CO2 fluxes are estimated by an eddy covariance technique based micrometeorological tower. Soil moisture profiles were also continuously estimated using water content reflectometers and gravimetric method, and periodically leaf area index (LAI) PFTs are estimated. From 2012 sap flow sensors based on the thermal Dissipation Method are installed on numerous trees around the tower. Preliminary results show first the need of careful use sap flow sensors outputs which are affected by errors in the estimates of their main parameters, mainly allometric relationships between, for instance, sapwood area, diameter, canopy cover area, which affect the upscale of the local tree measurements to the site plot larger scale. Finally we demonstrate that the sap flow sensors are essential for the estimate of ET in such dry conditions, typical of Mediterranean ecosystems.

Scientific Impact of MODIS C5 Calibration Degradation and C6+ Improvements

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Wang, Y.; Xiong, X.; Meister, G.; Platnick, S.; Levy, R.; Franz, B.; Korkin, S.; Hilker, T.; Tucker, J.;

Geometric processing workflow for vertical and oblique hyperspectral frame images collected using UAV

NASA Astrophysics Data System (ADS)

Markelin, L.; Honkavaara, E.; Näsi, R.; Nurminen, K.; Hakala, T.

2014-08-01

Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology. UAVs enable accurate, flexible, low-cost and multiangular measurements of 3D geometric, radiometric, and temporal properties of land and vegetation using various sensors. In this paper we present a geometric processing chain for multiangular measurement system that is designed for measuring object directional reflectance characteristics in a wavelength range of 400-900 nm. The technique is based on a novel, lightweight spectral camera designed for UAV use. The multiangular measurement is conducted by collecting vertical and oblique area-format spectral images. End products of the geometric processing are image exterior orientations, 3D point clouds and digital surface models (DSM). This data is needed for the radiometric processing chain that produces reflectance image mosaics and multiangular bidirectional reflectance factor (BRF) observations. The geometric processing workflow consists of the following three steps: (1) determining approximate image orientations using Visual Structure from Motion (VisualSFM) software, (2) calculating improved orientations and sensor calibration using a method based on self-calibrating bundle block adjustment (standard photogrammetric software) (this step is optional), and finally (3) creating dense 3D point clouds and DSMs using Photogrammetric Surface Reconstruction from Imagery (SURE) software that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the image. We have tested the geometric processing workflow over various targets, including test fields, agricultural fields, lakes and complex 3D structures like forests.

Hyperspectral remote sensing for monitoring species-specific drought impacts in southern California

NASA Astrophysics Data System (ADS)

Coates, Austin Reece

A drought persisting since the winter of 2011-2012 has resulted in severe impacts on shrublands and forests in southern California, USA. Effects of drought on vegetation include leaf wilting, leaf abscission, and potential plant mortality. These impacts vary across plant species, depending on differences in species' adaptations to drought, rooting depth, and edaphic factors. During 2013 and 2014, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data were acquired seasonally over the Santa Ynez Mountains and Santa Ynez Valley north of Santa Barbara, California. To determine the impacts of drought on individual plant species, spectral mixture analysis was used to model a relative green vegetation fraction (RGVF) for each image date in 2013 and 2014. A July 2011 AVIRIS image acquired during the last nondrought year was used to determine a reference green vegetation (GV) endmember for each pixel. For each image date in 2013 and 2014, a three-endmember model using the 2011 pixel spectrum as GV, a lab nonphotosynthetic vegetation (NPV) spectrum, and a photometric shade spectrum was applied. The resulting RGVF provided a change in green vegetation cover relative to 2011. Reference polygons collected for 14 plant species and land cover classes were used to extract the RGVF values from each date. The deeply rooted tree species and tree species found in mesic areas appeared to be the least affected by the drought, whereas the evergreen chaparral showed the most extreme signs of distress. Coastal sage scrub had large seasonal variability; however, each year, it returned to an RGVF value only slightly below the previous year. By binning all the RGVF values together, a general decreasing trend was observed from the spring of 2013 to the fall of 2014. This study intends to lay the groundwork for future research in the area of multitemporal, hyperspectral remote sensing. With proposed plans for a hyperspectral sensor in space (HyspIRI), this type of research will prove to be invaluable in the years to come. This study also intends to be used as a benchmark to show how specific species of plants are being affected by a prolonged drought. The research performed in this study will provide a reference point for analysis of future droughts.

Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program

NASA Astrophysics Data System (ADS)

Hojas-Gascon, L.; Belward, A.; Eva, H.; Ceccherini, G.; Hagolle, O.; Garcia, J.; Cerutti, P.

2015-04-01

The forthcoming European Space Agency's Sentinel-2 mission promises to provide high (10 m) resolution optical data at higher temporal frequencies (5 day revisit with two operational satellites) than previously available. CNES, the French national space agency, launched a program in 2013, 'SPOT4 take 5', to simulate such a dataflow using the SPOT HRV sensor, which has similar spectral characteristics to the Sentinel sensor, but lower (20m) spatial resolution. Such data flow enables the analysis of the satellite images using temporal analysis, an approach previously restricted to lower spatial resolution sensors. We acquired 23 such images over Tanzania for the period from February to June 2013. The data were analysed with aim of discriminating between different forest cover percentages for landscape units of 0.5 ha over a site characterised by deciduous intact and degraded forests. The SPOT data were processed by one extracting temporal vegetation indices. We assessed the impact of the high acquisition rate with respect to the current rate of one image every 16 days. Validation data, giving the percentage of forest canopy cover in each land unit were provided by very high resolution satellite data. Results show that using the full temporal series it is possible to discriminate between forest units with differences of more than 40% tree cover or more. Classification errors fell exclusively into the adjacent forest canopy cover class of 20% or less. The analyses show that forestation mapping and degradation monitoring will be substantially improved with the Sentinel-2 program.

Application of High-Resolution Thermal Infrared Remote Sensing and GIS to Assess the Urban Heat Island Effect

NASA Technical Reports Server (NTRS)

Lo, C. P.; Quattrochi, D. A.; Luvall, J. C.

1997-01-01

Day and night airborne thermal infrared image data at 5 m spatial resolution acquired with the 15-channel (0.45 micron - 12.2 micron) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signatures of urban land cover types between day and night. Thermal channel number 13 (9.6 micron - 10.2 micron) data with the best noise-equivalent temperature change (NEAT) of 0.25 C after atmospheric corrections and temperature calibration were selected for use in this analysis. This research also examined the relation between land cover irradiance and vegetation amount, using the Normalized Difference Vegetation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0.60-0.63 micron) and reflected infrared (channel number 6: 0.76-0.90 micron) ATLAS data. Based on the mean radiance values, standard deviations, and NDVI extracted from 351 pairs of polygons of day and night channel number 13 images for the city of Huntsville, a spatial model of warming and cooling characteristics of commercial, residential, agricultural, vegetation, and water features was developed using a GIS approach. There is a strong negative correlation between NDVI and irradiance of residential, agricultural, and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential uses associated with varying degrees of tree cover showed great contrasts with commercial and services land cover types in the center of the city, and favors the development of urban heat islands. The high-resolution thermal infrared images match the complexity of the urban environment, and are capable of characterizing accurately the urban land cover types for the spatial modeling of the urban heat island effect using a GIS approach.

Using NASA Earth Observing Satellites and Statistical Model Analysis to Monitor Vegetation and Habitat Rehabilitation in Southwest Virginia's Reclaimed Mine Lands

NASA Astrophysics Data System (ADS)

Tate, Z.; Dusenge, D.; Elliot, T. S.; Hafashimana, P.; Medley, S.; Porter, R. P.; Rajappan, R.; Rodriguez, P.; Spangler, J.; Swaminathan, R. S.; VanGundy, R. D.

2014-12-01

The majority of the population in southwest Virginia depends economically on coal mining. In 2011, coal mining generated $2,000,000 in tax revenue to Wise County alone. However, surface mining completely removes land cover and leaves the land exposed to erosion. The destruction of the forest cover directly impacts local species, as some are displaced and others perish in the mining process. Even though surface mining has a negative impact on the environment, land reclamation efforts are in place to either restore mined areas to their natural vegetated state or to transform these areas for economic purposes. This project aimed to monitor the progress of land reclamation and the effect on the return of local species. By incorporating NASA Earth observations, such as Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM), re-vegetation process in reclamation sites was estimated through a Time series analysis using the Normalized Difference Vegetation Index (NDVI). A continuous source of cloud free images was accomplished by utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STAR-FM). This model developed synthetic Landsat imagery by integrating the high-frequency temporal information from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution spatial information from Landsat sensors In addition, the Maximum Entropy Modeling (MaxENT), an eco-niche model was used to estimate the adaptation of animal species to the newly formed habitats. By combining factors such as land type, precipitation from Tropical Rainfall Measuring Mission (TRMM), and slope from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the MaxENT model produced a statistical analysis on the probability of species habitat. Altogether, the project compiled the ecological information which can be used to identify suitable habitats for local species in reclaimed mined areas.

Research support of the WETNET Program

NASA Technical Reports Server (NTRS)

Estes, John E.; Mcgwire, Kenneth C.; Scepan, Joseph; Henderson, SY; Lawless, Michael

1995-01-01

This study examines various aspects of the Microwave Vegetation Index (MVI). MVI is a derived signal created by differencing the spectral response of the 37 GHz horizontally and vertically polarized passive microwave signals. The microwave signal employed to derive this index is thought to be primarily influenced by vegetation structure, vegetation growth, standing water, and precipitation. The state of California is the study site for this research. Imagery from the Special Sensor Microwave/Imager (SSM/I) is used for the creation of MVI datasets analyzed in this research. The object of this research is to determine whether MVI corresponds with some quantifiable vegetation parameter (such as vegetation density) or whether the index is more affected by known biogeophysical parameters such antecedent precipitation. A secondary question associated with the above is whether the vegetation attributes that MVI is employed to determine can be more easily and accurately evaluated by other remote sensing means. An important associated question to be addressed in the study is the effect of different multi-temporal composting techniques on the derived MVI dataset. This work advances our understanding of the fundamental nature of MVI by studying vegetation as a mixture of structural types, such as forest and grassland. The study further advances our understanding by creating multitemporal precipitation datasets to compare the affects of precipitation upon MVI. This work will help to lay the groundwork for the use of passive microwave spectral information either as an adjunct to visible and near infrared imagery in areas where that is feasible or for the use of passive microwave alone in areas of moderate cloud coverage. In this research, an MVI dataset, spanning the period February 15, 1989 through April 25, 1990, has been created using National Aeronautic and Space Administration (NASA) supplied brightness temperature data. Information from the DMSP satellite 37 GHz wavelength SSM/I sensor in both horizontal and vertical polarization has been processed using the MVI algorithm. In conjunction with the MVI algorithm a multitemporal compositing technique was used to create datasets that correspond to 14 day periods. In this technical report, Section Two contains background information on the State of California and the three MVI study sites. Section Three describes the methods used to create the MVI and independent variables datasets. Section Four presents the results of the experiment. Section Five summarizes and concludes the work.

Procedures for the Systematic Evaluation of Remote Sensor Performance and Quantitative Mission Planning

DTIC Science & Technology

1976-08-01

bare soil and grass areas, Vicksburg, Mississippi . ....... .. 101 25 Schematic of typical thermal IR scanner system . . . . 103 26 Sensor spatial...following categories: a. Soils b. Vegetation S. Topography d. Bedrock It is the knowledge of these characteristics and their distribution within the...necessary to know the changes in soil , vegetation, topography, and bedrock characteristics as a function of time as well as their spa- tial distribution at

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.

Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China

NASA Astrophysics Data System (ADS)

Wu, Xiaodan; Wen, Jianguang; Xiao, Qing; Yu, Yunyue; You, Dongqin; Hueni, Andreas

2017-12-01

In this paper, the accuracy of Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) land surface albedo, which is derived from the direct estimation algorithm, was assessed using ground-based albedo observations from a wireless sensor network over a heterogeneous cropland in the Huailai station, northern China. Data from six nodes spanning 2013-2014 over vegetation, bare soil, and mixed terrain surfaces were utilized to provide ground reference at VIIRS pixel scale. The performance of VIIRS albedo was also compared with Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) albedos (Collection 5 and 6). The results indicate that the current granular VIIRS albedo has a high accuracy with a root-mean-square error of 0.02 for typical land covers. They are significantly correlated with ground references indicated by a correlation coefficient (R) of 0.73. The VIIRS albedo shows distinct advantages to GLASS and MODIS albedos over bare soil and mixed-cover surfaces, while it is inferior to the other two products over vegetated surfaces. Furthermore, its time continuity and the ability to capture the abrupt change of surface albedo are better than that of GLASS and MODIS albedo.

Identifying Severe Weather Impacts and Damage with Google Earth Engine

NASA Astrophysics Data System (ADS)

Molthan, A.; Burks, J. E.; Bell, J. R.

2015-12-01

Hazards associated with severe convective storms can lead to rapid changes in land surface vegetation. Depending upon the type of vegetation that has been impacted, their impacts can be relatively short lived, such as damage to seasonal crops that are eventually removed by harvest, or longer-lived, such as damage to a stand of trees or expanse of forest that require several years to recover. Since many remote sensing imagers provide their highest spatial resolution bands in the red and near-infrared to support monitoring of vegetation, these impacts can be readily identified as short-term and marked decreases in common vegetation indices such as NDVI, along with increases in land surface temperature that are observed at a reduced spatial resolution. The ability to identify an area of vegetation change is improved by understanding the conditions that are normal for a given time of year and location, along with a typical range of variability in a given parameter. This analysis requires a period of record well beyond the availability of near real-time data. These activities would typically require an analyst to download large volumes of data from sensors such as NASA's MODIS (aboard Terra and Aqua) or higher resolution imagers from the Landsat series of satellites. Google's Earth Engine offers a "big data" solution to these challenges, by providing a streamlined API and option to process the period of record of NASA MODIS and Landsat products through relatively simple Javascript coding. This presentation will highlight efforts to date in using Earth Engine holdings to produce vegetation and land surface temperature anomalies that are associated with damage to agricultural and other vegetation caused by severe thunderstorms across the Central and Southeastern United States. Earth Engine applications will show how large data holdings can be used to map severe weather damage, ascertain longer-term impacts, and share best practices learned and challenges with applying Earth Engine holdings to the analysis of severe weather damage. Other applications are also demonstrated, such as use of Earth Engine to prepare pre-event composites that can be used to subjectively identify other severe weather impacts. Future extension to flooding and wildfires is also proposed.

A Simple Downscaling Algorithm for Remotely Sensed Land Surface Temperature

NASA Astrophysics Data System (ADS)

Sandholt, I.; Nielsen, C.; Stisen, S.

2009-05-01

The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of land surfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of Land Surface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution Land Surface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic downscaling methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.

Assessing the Performance of LVIS Waveform Lidar Topography and Canopy Structure Measurements in Gabon

NASA Astrophysics Data System (ADS)

Hofton, M. A.; Blair, J. B.; Rabine, D.; Brooks, C.; Cornejo, H.; Story, S.

2016-12-01

In February-March 2016, NASA's Land, Vegetation and Ice Sensor (LVIS) was used to image sub-canopy topography, canopy topography and structure at several sites in Gabon. Data were collected as part of the NASA and ESA Afrisar Campaign, a joint remote sensing mission involving multiple airborne and ground-based data collection activities that support the calibration and validation of future spaceborne missions, particularly GEDI, NISAR and BIOMASS, as well as other investigations. LVIS is a wide-swath, medium-footprint, waveform recording laser altimeter (lidar) sensor that can collect contiguous data within a 2 km-wide swath using 20m wide footprints from 10km altitude. For the Gabon deployment, the sensor was mounted in the NASA Langley King Air aircraft and flown at 8 km altitude over five, 70x15km-wide areas and along multiple country-wide transects. Data products include footprint-level canopy height, ground topography and canopy metrics, as well as vertically and horizontally-geolocated lidar return waveforms that enable end users to produce additional georeferenced data products as needed. We present a summary of the data products from the campaign, as well as a performance assessment of the ground and canopy structure data using available airborne and ground based data. Uses of the data include the simulation of GEDI-like data and the derivation of canopy height and profile metric algorithms for implementation in GEDI level2 products, as well as to improve our understanding of ground-finding errors in dense vegetation environments from waveform lidar.

A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2004-01-01

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.

[Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

PubMed

Jing, Ran; Deng, Lei; Zhao, Wen Ji; Gong, Zhao Ning

2016-05-01

Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

Construction of a small and lightweight hyperspectral imaging system

NASA Astrophysics Data System (ADS)

Vogel, Britta; Hünniger, Dirk; Bastian, Georg

2014-05-01

The analysis of the reflected sunlight offers great opportunity to gain information about the environment, including vegetation and soil. In the case of plants the wavelength ratio of the reflected light usually undergoes a change if the state of growth or state of health changes. So the measurement of the reflected light allows drawing conclusions about the state of, amongst others, vegetation. Using a hyperspectral imaging system for data acquisition leads to a large dataset, which can be evaluated with respect to several different questions to obtain various information by one measurement. Based on commercially available plain optical components we developed a small and lightweight hyperspectral imaging system within the INTERREG IV A-Project SMART INSPECTORS. The project SMART INSPECTORS [Smart Aerial Test Rigs with Infrared Spectrometers and Radar] deals with the fusion of airborne visible and infrared imaging remote sensing instruments and wireless sensor networks for precision agriculture and environmental research. A high performance camera was required in terms of good signal, good wavelength resolution and good spatial resolution, while severe constraints of size, proportions and mass had to be met due to the intended use on small unmanned aerial vehicles. The detector was chosen to operate without additional cooling. The refractive and focusing optical components were identified by supporting works with an optical raytracing software and a self-developed program. We present details of design and construction of our camera system, test results to confirm the optical simulation predictions as well as our first measurements.

An optical sensor network for vegetation phenology monitoring and satellite data calibration.

PubMed

Eklundh, Lars; Jin, Hongxiao; Schubert, Per; Guzinski, Radoslaw; Heliasz, Michal

2011-01-01

We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located in coniferous forests, one in a deciduous forest, and two on peatland. The instrumentation consists of dual-beam sensors measuring incoming and reflected red, green, NIR, and PAR fluxes at 10-min intervals, year-round. The sensors are mounted on separate masts or in flux towers in order to capture radiation reflected from within the flux footprint of current eddy covariance measurements. Our computations and model simulations demonstrate the validity of using off-nadir sampling, and we show the results from the first year of measurement. NDVI is computed and compared to that of the MODIS instrument on-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity.

An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration

PubMed Central

Eklundh, Lars; Jin, Hongxiao; Schubert, Per; Guzinski, Radoslaw; Heliasz, Michal

2011-01-01

We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located in coniferous forests, one in a deciduous forest, and two on peatland. The instrumentation consists of dual-beam sensors measuring incoming and reflected red, green, NIR, and PAR fluxes at 10-min intervals, year-round. The sensors are mounted on separate masts or in flux towers in order to capture radiation reflected from within the flux footprint of current eddy covariance measurements. Our computations and model simulations demonstrate the validity of using off-nadir sampling, and we show the results from the first year of measurement. NDVI is computed and compared to that of the MODIS instrument on-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity. PMID:22164039

Air-coupled ultrasonic sensing of grass-covered vibrating surfaces; qualitative comparisons with laser Doppler vibrometry

NASA Astrophysics Data System (ADS)

Petculescu, Andi G.; Sabatier, James M.

2004-04-01

The paper addresses several sensitive issues concerning the use of air-coupled ultrasound to probe small vibrations of surfaces covered with low-lying vegetation such as grass. The operation of the ultrasonic sensor is compared to that of a laser Doppler vibrometer, in various contexts. It is shown that ambient air motion affects either system, albeit differently. As air speed increases, the acoustic sensor detects a progressively richer turbulent spectrum, which reduces its sensitivity. In turn, optical sensors are prone to tremendous signal losses when probing moving vegetation, due to randomly varying speckle patterns. The work was supported by the Office of Naval Research.

Performance of different vegetation indices in assessing degradation of community grazing lands in Indian arid zone

NASA Astrophysics Data System (ADS)

Kumar, Suresh; Bastin, Gary; Friedel, Margaret; Narain, Pratap; Saha, D. K.; Ahuja, U. R.; Mathur, B. K.

2006-12-01

Vegetation in arid community grazinglands shows monsoonal growth. Its matching phenology with crops makes its detection difficult during July to September. While crops are harvested during September-October, using satellite data thereafter for the natural vegetation seems most appropriate but by then it turns dry. An index capable of sensing dry vegetation was needed since conventional NDVI is sensitive to greenness of vegetation. Performance of NDVI vis-à-vis another index, PD54, based on cover was therefore compared in assessing degradation of grazinglands. The PD54 was used to isolate anthropogenic impacts from environmental induced degradation by analyzing satellite images from dry and wet seasons. Substantial absence of appreciable vegetation response indicated poor resilience and severe degradation. Five grazinglands in Shergarh tehsil of Jodhpur district in Rajasthan were studied following above approach. Ground radiometric observations were recorded. Satellite data of IRS 1C/1D/P6 with LISS 3 sensor for both pre and post monsoon season were acquired for three contrasting wet-dry season events. These were geometrically registered and radiometrically calibrated to calculate an index of vegetation cover PD54 as well as NDVI. PD54 is a perpendicular vegetation index based on the green and red spectral band width. The PD54 and NDVI calculated from spectro-radiometer were related to vegetation cover measured on ground in permanent plots. This confirmed that PD54 was superior index for estimating cover in arid dry grasslands. These ground vegetation trends in a good rainfall year (2001) with drought year (2002) were related with satellite data for a protected and four unprotected grazinglands. NDVI failed to detect any vegetation in protected areas supporting excellent grass cover which was succinctly brought out by PD54. Successful validation of PD54 in detecting degradation of 13 additional sites confirmed its efficacy. These findings have implication in forage availability assessments, forage forecasting, drought preparedness, pastoralism and transhumance.

Fire Impacts on Mixed Pine-oak Forests Assessed with High Spatial Resolution Imagery, Imaging Spectroscopy, and LiDAR

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Kathy, S. L.; Dennison, P. E.; Cook, B.; Hanavan, R. P.; Serbin, S.

2016-12-01

As a primary disturbance agent, fire significantly influences forest ecosystems, including the modification or resetting of vegetation composition and structure, which can then significantly impact landscape-scale plant function and carbon stocks. Most ecological processes associated with fire effects (e.g. tree damage, mortality, and vegetation recovery) display fine-scale, species specific responses but can also vary spatially within the boundary of the perturbation. For example, both oak and pine species are fire-adapted, but fire can still induce changes in composition, structure, and dominance in a mixed pine-oak forest, mainly because of their varying degrees of fire adaption. Evidence of post-fire shifts in dominance between oak and pine species has been documented in mixed pine-oak forests, but these processes have been poorly investigated in a spatially explicit manner. In addition, traditional field-based means of quantifying the response of partially damaged trees across space and time is logistically challenging. Here we show how combining high resolution satellite imagery (i.e. Worldview-2,WV-2) and airborne imaging spectroscopy and LiDAR (i.e. NASA Goddard's Lidar, Hyperspectral and Thermal airborne imager, G-LiHT) can be effectively used to remotely quantify spatial and temporal patterns of vegetation recovery following a top-killing fire that occurred in 2012 within mixed pine-oak forests in the Long Island Central Pine Barrens Region, New York. We explore the following questions: 1) what are the impacts of fire on species composition, dominance, plant health, and vertical structure; 2) what are the recovery trajectories of forest biomass, structure, and spectral properties for three years following the fire; and 3) to what extent can fire impacts be captured and characterized by multi-sensor remote sensing techniques from active and passive optical remote sensing.

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems.

PubMed

Muller-Karger, Frank E; Hestir, Erin; Ade, Christiana; Turpie, Kevin; Roberts, Dar A; Siegel, David; Miller, Robert J; Humm, David; Izenberg, Noam; Keller, Mary; Morgan, Frank; Frouin, Robert; Dekker, Arnold G; Gardner, Royal; Goodman, James; Schaeffer, Blake; Franz, Bryan A; Pahlevan, Nima; Mannino, Antonio G; Concha, Javier A; Ackleson, Steven G; Cavanaugh, Kyle C; Romanou, Anastasia; Tzortziou, Maria; Boss, Emmanuel S; Pavlick, Ryan; Freeman, Anthony; Rousseaux, Cecile S; Dunne, John; Long, Matthew C; Klein, Eduardo; McKinley, Galen A; Goes, Joachim; Letelier, Ricardo; Kavanaugh, Maria; Roffer, Mitchell; Bracher, Astrid; Arrigo, Kevin R; Dierssen, Heidi; Zhang, Xiaodong; Davis, Frank W; Best, Ben; Guralnick, Robert; Moisan, John; Sosik, Heidi M; Kudela, Raphael; Mouw, Colleen B; Barnard, Andrew H; Palacios, Sherry; Roesler, Collin; Drakou, Evangelia G; Appeltans, Ward; Jetz, Walter

2018-04-01

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications. © 2018 The Authors Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.

Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems

NASA Technical Reports Server (NTRS)

Muller-Karger, Frank E.; Hestir, Erin; Ade, Christiana; Turpie, Kevin; Roberts, Dar A.; Siegel, David; Miller, Robert J.; Humm, David; Izenberg, Noam; Keller, Mary;

Intelligent Network-Centric Sensors Development Program

DTIC Science & Technology

2012-07-31

Image sensor Configuration: ; Cone 360 degree LWIR PFx Sensor: •■. Image sensor . Configuration: Image MWIR Configuration; Cone 360 degree... LWIR PFx Sensor: Video Configuration: Cone 360 degree SW1R, 2. Reasoning Process to Match Sensor Systems to Algorithms The ontological...effects of coherent imaging because of aberrations. Another reason is the specular nature of active imaging. Both contribute to the nonuniformity

Spectroscopic remote sensing for material identification, vegetation characterization, and mapping

USGS Publications Warehouse

Kokaly, Raymond F.; Lewis, Paul E.; Shen, Sylvia S.

2012-01-01

Identifying materials by measuring and analyzing their reflectance spectra has been an important procedure in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow materials to be mapped across the landscape. With many existing airborne sensors and new satellite-borne sensors planned for the future, robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral feature analyses of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described. MICA is a module of the PRISM (Processing Routines in IDL for Spectroscopic Measurements) software, available to the public from the U.S. Geological Survey (USGS) at http://pubs.usgs.gov/of/2011/1155/. The core concepts of MICA include continuum removal and linear regression to compare key diagnostic absorption features in reference laboratory/field spectra and the spectra being analyzed. The reference spectra, diagnostic features, and threshold constraints are defined within a user-developed MICA command file (MCF). Building on several decades of experience in mineral mapping, a broadly-applicable MCF was developed to detect a set of minerals frequently occurring on the Earth's surface and applied to map minerals in the country-wide coverage of the 2007 Afghanistan HyMap data set. MICA has also been applied to detect sub-pixel oil contamination in marshes impacted by the Deepwater Horizon incident by discriminating the C-H absorption features in oil residues from background vegetation. These two recent examples demonstrate the utility of a spectroscopic approach to remote sensing for identifying and mapping the distributions of materials in imaging spectrometer data.

A 128 x 128 CMOS Active Pixel Image Sensor for Highly Integrated Imaging Systems

NASA Technical Reports Server (NTRS)

Mendis, Sunetra K.; Kemeny, Sabrina E.; Fossum, Eric R.

1993-01-01

A new CMOS-based image sensor that is intrinsically compatible with on-chip CMOS circuitry is reported. The new CMOS active pixel image sensor achieves low noise, high sensitivity, X-Y addressability, and has simple timing requirements. The image sensor was fabricated using a 2 micrometer p-well CMOS process, and consists of a 128 x 128 array of 40 micrometer x 40 micrometer pixels. The CMOS image sensor technology enables highly integrated smart image sensors, and makes the design, incorporation and fabrication of such sensors widely accessible to the integrated circuit community.

Nanophotonic Image Sensors

PubMed Central

Hu, Xin; Wen, Long; Yu, Yan; Cumming, David R. S.

2016-01-01

The increasing miniaturization and resolution of image sensors bring challenges to conventional optical elements such as spectral filters and polarizers, the properties of which are determined mainly by the materials used, including dye polymers. Recent developments in spectral filtering and optical manipulating techniques based on nanophotonics have opened up the possibility of an alternative method to control light spectrally and spatially. By integrating these technologies into image sensors, it will become possible to achieve high compactness, improved process compatibility, robust stability and tunable functionality. In this Review, recent representative achievements on nanophotonic image sensors are presented and analyzed including image sensors with nanophotonic color filters and polarizers, metamaterial‐based THz image sensors, filter‐free nanowire image sensors and nanostructured‐based multispectral image sensors. This novel combination of cutting edge photonics research and well‐developed commercial products may not only lead to an important application of nanophotonics but also offer great potential for next generation image sensors beyond Moore's Law expectations. PMID:27239941

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

PubMed Central

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2016-01-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting. PMID:27667901

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data.

PubMed

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2015-08-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.

Image quality evaluation of eight complementary metal-oxide semiconductor intraoral digital X-ray sensors.

PubMed

Teich, Sorin; Al-Rawi, Wisam; Heima, Masahiro; Faddoul, Fady F; Goldzweig, Gil; Gutmacher, Zvi; Aizenbud, Dror

2016-10-01

To evaluate the image quality generated by eight commercially available intraoral sensors. Eighteen clinicians ranked the quality of a bitewing acquired from one subject using eight different intraoral sensors. Analytical methods used to evaluate clinical image quality included the Visual Grading Characteristics method, which helps to quantify subjective opinions to make them suitable for analysis. The Dexis sensor was ranked significantly better than Sirona and Carestream-Kodak sensors; and the image captured using the Carestream-Kodak sensor was ranked significantly worse than those captured using Dexis, Schick and Cyber Medical Imaging sensors. The Image Works sensor image was rated the lowest by all clinicians. Other comparisons resulted in non-significant results. None of the sensors was considered to generate images of significantly better quality than the other sensors tested. Further research should be directed towards determining the clinical significance of the differences in image quality reported in this study. © 2016 FDI World Dental Federation.

Comparison of different cutaneous carotenoid sensors and influence of age, skin type, and kinetic changes subsequent to intake of a vegetable extract

NASA Astrophysics Data System (ADS)

Meinke, Martina C.; Schanzer, Sabine; Lohan, Silke B.; Shchatsinin, Ihar; Darvin, Maxim E.; Vollert, Henning; Magnussen, Björn; Köcher, Wolfang; Helfmann, Jürgen; Lademann, Jürgen

2016-10-01

In the last decade, cutaneous carotenoid measurements have become increasingly popular, as carotenoids were found to be a biomarker of nutrition rich in fruits and vegetables, permitting monitoring of the influence of various stress factors. For such measurements, in addition to the specific and selective resonance Raman spectroscopy (RRS), newly developed low expensive small and mobile sensors that are based on spatially resolved reflectance spectroscopy (SRRS) are used for cutaneous carotenoid measurements. Human volunteers of different age exhibiting skin types I to III were investigated using RRS and two SRRS-based sensors to determine the influence of these parameters on the measuring results. In two studies on volunteers of either the same age or skin type, however, the respective other parameter being varied and no significant influences of age or skin type could be detected. Furthermore, the kinetic changes resulting from the intake and discontinued intake of a vegetable extract rich in carotenoids showed a good correlation among the three sensors and with the detected blood carotenoids. This illustrates that the SRRS-based sensors and RRS device provide reliable cutaneous carotenoid values independent of age and skin types I to III of the volunteers.

Comparison of different cutaneous carotenoid sensors and influence of age, skin type, and kinetic changes subsequent to intake of a vegetable extract.

PubMed

Meinke, Martina C; Schanzer, Sabine; Lohan, Silke B; Shchatsinin, Ihar; Darvin, Maxim E; Vollert, Henning; Magnussen, Björn; Köcher, Wolfang; Helfmann, Jürgen; Lademann, Jürgen

2016-10-01

In the last decade, cutaneous carotenoid measurements have become increasingly popular, as carotenoids were found to be a biomarker of nutrition rich in fruits and vegetables, permitting monitoring of the influence of various stress factors. For such measurements, in addition to the specific and selective resonance Raman spectroscopy (RRS), newly developed low expensive small and mobile sensors that are based on spatially resolved reflectance spectroscopy (SRRS) are used for cutaneous carotenoid measurements. Human volunteers of different age exhibiting skin types I to III were investigated using RRS and two SRRS-based sensors to determine the influence of these parameters on the measuring results. In two studies on volunteers of either the same age or skin type, however, the respective other parameter being varied and no significant influences of age or skin type could be detected. Furthermore, the kinetic changes resulting from the intake and discontinued intake of a vegetable extract rich in carotenoids showed a good correlation among the three sensors and with the detected blood carotenoids. This illustrates that the SRRS-based sensors and RRS device provide reliable cutaneous carotenoid values independent of age and skin types I to III of the volunteers.

Tsunami Damage in Northwest Sumatra

NASA Technical Reports Server (NTRS)

2005-01-01

The island of Sumatra suffered from both the rumblings of the submarine earthquake and the tsunamis that were generated on December 26, 2004. Within minutes of the quake, the sea surged ashore, bringing destruction to the coasts of the northern Sumatra. This pair of images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite shows the Aceh province of northern Sumatra, Indonesia, on December 17, 2004, before the quake (bottom), and on December 29, 2004 (top), three days after the catastrophe. Although MODIS was not specifically designed to make the very detailed observations that are usually necessary for mapping coastline changes, the sensor nevertheless observed obvious differences in the Sumatran coastline. On December 17, the green vegetation along the west coast appears to reach all the way to the sea, with only an occasional thin stretch of white that is likely sand. After the earthquake and tsunamis, the entire western coast is lined with a noticeable purplish-brown border. The brownish border could be deposited sand, or perhaps exposed soil that was stripped bare of vegetation when the large waves rushed ashore and then raced away. Another possibility is that parts of the coastline may have sunk as the sea floor near the plate boundary rose. On a moderate-resolution image such as this, the affected area may seem small, but each pixel in the full resolution image is 250 by 250 meters. In places the brown strip reaches inland roughly 13 pixels, equal to a distance of 3.25 kilometers, or about 2 miles. On the northern tip of the island (shown in the large image), the incursion is even larger. NASA images created by Jesse Allen, Earth Observatory, using data obtained from the MODIS Rapid Response team and the Goddard Earth Sciences DAAC.

Validation of Spaceborne Radar Surface Water Mapping with Optical sUAS Images

NASA Astrophysics Data System (ADS)

Li-Chee-Ming, J.; Murnaghan, K.; Sherman, D.; Poncos, V.; Brisco, B.; Armenakis, C.

2015-08-01

The Canada Centre for Remote Sensing (CCRS) has over 40 years of experience with airborne and spaceborne sensors and is now starting to use small Unmanned Aerial Systems (sUAS) to validate products from large coverage area sensors and create new methodologies for very high resolution products. Wetlands have several functions including water storage and retention which can reduce flooding and provide continuous flow for hydroelectric generation and irrigation for agriculture. Synthetic Aperture Radar is well suited as a tool for monitoring surface water by supplying acquisitions irrespective of cloud cover or time of day. Wetlands can be subdivided into three classes: open water, flooded vegetation and upland which can vary seasonally with time and water level changes. RADARSAT-2 data from the Wide-Ultra Fine, Spotlight and Fine Quad-Pol modes has been used to map the open water in the Peace-Athabasca Delta, Alberta using intensity thresholding. We also use spotlight modes for higher resolution and the fully polarimetric mode (FQ) for polarimetric decomposition. Validation of these products will be done using a low altitude flying sUAS to generate optical georeferenced images. This project provides methodologies which could be used for flood mapping as well as ecological monitoring.

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

NASA Astrophysics Data System (ADS)

Rußwurm, Marc; Körner, Marco

2018-03-01

Earth observation (EO) sensors deliver data with daily or weekly temporal resolution. Most land use and land cover (LULC) approaches, however, expect cloud-free and mono-temporal observations. The increasing temporal capabilities of today's sensors enables the use of temporal, along with spectral and spatial features. Domains, such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells, which reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, we achieved in our experiments state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing compared to other classification approaches.

Observation of a Large Landslide on La Reunion Island Using Differential Sar Interferometry (JERS and Radarsat) and Correlation of Optical (Spot5 and Aerial) Images

PubMed Central

Delacourt, Christophe; Raucoules, Daniel; Le Mouélic, Stéphane; Carnec, Claudie; Feurer, Denis; Allemand, Pascal; Cruchet, Marc

2009-01-01

Slope instabilities are one of the most important geo-hazards in terms of socio-economic costs. The island of La Réunion (Indian Ocean) is affected by constant slope movements and huge landslides due to a combination of rough topography, wet tropical climate and its specific geological context. We show that remote sensing techniques (Differential SAR Interferometry and correlation of optical images) provide complementary means to characterize landslides on a regional scale. The vegetation cover generally hampers the analysis of C–band interferograms. We used JERS-1 images to show that the L-band can be used to overcome the loss of coherence observed in Radarsat C-band interferograms. Image correlation was applied to optical airborne and SPOT 5 sensors images. The two techniques were applied to a landslide near the town of Hellbourg in order to assess their performance for detecting and quantifying the ground motion associated to this landslide. They allowed the mapping of the unstable areas. Ground displacement of about 0.5 m yr-1 was measured. PMID:22389620

Observation of a Large Landslide on La Reunion Island Using Differential Sar Interferometry (JERS and Radarsat) and Correlation of Optical (Spot5 and Aerial) Images.

PubMed

Delacourt, Christophe; Raucoules, Daniel; Le Mouélic, Stéphane; Carnec, Claudie; Feurer, Denis; Allemand, Pascal; Cruchet, Marc

2009-01-01

Slope instabilities are one of the most important geo-hazards in terms of socio-economic costs. The island of La Réunion (Indian Ocean) is affected by constant slope movements and huge landslides due to a combination of rough topography, wet tropical climate and its specific geological context. We show that remote sensing techniques (Differential SAR Interferometry and correlation of optical images) provide complementary means to characterize landslides on a regional scale. The vegetation cover generally hampers the analysis of C-band interferograms. We used JERS-1 images to show that the L-band can be used to overcome the loss of coherence observed in Radarsat C-band interferograms. Image correlation was applied to optical airborne and SPOT 5 sensors images. The two techniques were applied to a landslide near the town of Hellbourg in order to assess their performance for detecting and quantifying the ground motion associated to this landslide. They allowed the mapping of the unstable areas. Ground displacement of about 0.5 m yr(-1) was measured.

Structural geologic interpretations from radar imagery

USGS Publications Warehouse

Reeves, Robert G.

1969-01-01

Certain structural geologic features may be more readily recognized on sidelooking airborne radar (SLAR) images than on conventional aerial photographs, other remote sensor imagery, or by ground observations. SLAR systems look obliquely to one or both sides and their images resemble aerial photographs taken at low sun angle with the sun directly behind the camera. They differ from air photos in geometry, resolution, and information content. Radar operates at much lower frequencies than the human eye, camera, or infrared sensors, and thus "sees" differently. The lower frequency enables it to penetrate most clouds and some precipitation, haze, dust, and some vegetation. Radar provides its own illumination, which can be closely controlled in intensity and frequency. It is narrow band, or essentially monochromatic. Low relief and subdued features are accentuated when viewed from the proper direction. Runs over the same area in significantly different directions (more than 45° from each other), show that images taken in one direction may emphasize features that are not emphasized on those taken in the other direction; optimum direction is determined by those features which need to be emphasized for study purposes. Lineaments interpreted as faults stand out on radar imagery of central and western Nevada; folded sedimentary rocks cut by faults can be clearly seen on radar imagery of northern Alabama. In these areas, certain structural and stratigraphic features are more pronounced on radar images than on conventional photographs; thus radar imagery materially aids structural interpretation.

Quantification of LiDAR measurement uncertainty through propagation of errors due to sensor sub-systems and terrain morphology

NASA Astrophysics Data System (ADS)

Goulden, T.; Hopkinson, C.

2013-12-01

The quantification of LiDAR sensor measurement uncertainty is important for evaluating the quality of derived DEM products, compiling risk assessment of management decisions based from LiDAR information, and enhancing LiDAR mission planning capabilities. Current quality assurance estimates of LiDAR measurement uncertainty are limited to post-survey empirical assessments or vendor estimates from commercial literature. Empirical evidence can provide valuable information for the performance of the sensor in validated areas; however, it cannot characterize the spatial distribution of measurement uncertainty throughout the extensive coverage of typical LiDAR surveys. Vendor advertised error estimates are often restricted to strict and optimal survey conditions, resulting in idealized values. Numerical modeling of individual pulse uncertainty provides an alternative method for estimating LiDAR measurement uncertainty. LiDAR measurement uncertainty is theoretically assumed to fall into three distinct categories, 1) sensor sub-system errors, 2) terrain influences, and 3) vegetative influences. This research details the procedures for numerical modeling of measurement uncertainty from the sensor sub-system (GPS, IMU, laser scanner, laser ranger) and terrain influences. Results show that errors tend to increase as the laser scan angle, altitude or laser beam incidence angle increase. An experimental survey over a flat and paved runway site, performed with an Optech ALTM 3100 sensor, showed an increase in modeled vertical errors of 5 cm, at a nadir scan orientation, to 8 cm at scan edges; for an aircraft altitude of 1200 m and half scan angle of 15°. In a survey with the same sensor, at a highly sloped glacial basin site absent of vegetation, modeled vertical errors reached over 2 m. Validation of error models within the glacial environment, over three separate flight lines, respectively showed 100%, 85%, and 75% of elevation residuals fell below error predictions. Future work in LiDAR sensor measurement uncertainty must focus on the development of vegetative error models to create more robust error prediction algorithms. To achieve this objective, comprehensive empirical exploratory analysis is recommended to relate vegetative parameters to observed errors.

A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms

PubMed Central

Hassanein, Mohamed; El-Sheimy, Naser

2018-01-01

Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%. PMID:29670055

Mapping Daily Evapotranspiration based on Spatiotemporal Fusion of ASTER and MODIS Images over Irrigated Agricultural Areas in the Heihe River Basin, Northwest China

NASA Astrophysics Data System (ADS)

Huang, C.; LI, Y.

2017-12-01

Continuous monitoring of daily evapotranspiration (ET) is crucial for allocating and managing water resources in irrigated agricultural areas in arid regions. In this study, continuous daily ET at a 90-m spatial resolution was estimated using the Surface Energy Balance System (SEBS) by fusing Moderate Resolution Imaging Spectroradiometer (MODIS) images with high temporal resolution and Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER) images with high spatial resolution. The spatiotemporal characteristics of these sensors were obtained using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The performance of this approach was validated over a heterogeneous oasis-desert region covered by cropland, residential, woodland, water, Gobi desert, sandy desert, desert steppe, and wetland areas using in situ observations from automatic meteorological systems (AMS) and eddy covariance (EC) systems in the middle reaches of the Heihe River Basin in Northwest China. The error introduced during the data fusion process based on STARFM is within an acceptable range for predicted LST at a 90-m spatial resolution. The surface energy fluxes estimated using SEBS based on predicted remotely sensed data that combined the spatiotemporal characteristics of MODIS and ASTER agree well with the surface energy fluxes observed using EC systems for all land cover types, especially for vegetated area with MAP values range from 9% to 15%, which are less than the uncertainty (18%) of the observed in this study area. Time series of daily ET modelled from SEBS were compared to that modelled from PT-JPL (one of Satellite-based Priestley-Taylor ET model) and observations from EC systems. SEBS performed generally better than PT-JPL for vegetated area, especially irrigated cropland with bias, RMSE, and MAP values of 0.29 mm/d, 0.75 mm/d, 13% at maize site, -0.33 mm/d, 0.81 mm/d, and 14% at vegetable sites.

Critical Zone Co-dynamics: Quantifying Interactions between Subsurface, Land Surface, and Vegetation Properties Using UAV and Geophysical Approaches

NASA Astrophysics Data System (ADS)

Dafflon, B.; Leger, E.; Peterson, J.; Falco, N.; Wainwright, H. M.; Wu, Y.; Tran, A. P.; Brodie, E.; Williams, K. H.; Versteeg, R.; Hubbard, S. S.

2017-12-01

Improving understanding and modelling of terrestrial systems requires advances in measuring and quantifying interactions among subsurface, land surface and vegetation processes over relevant spatiotemporal scales. Such advances are important to quantify natural and managed ecosystem behaviors, as well as to predict how watershed systems respond to increasingly frequent hydrological perturbations, such as droughts, floods and early snowmelt. Our study focuses on the joint use of UAV-based multi-spectral aerial imaging, ground-based geophysical tomographic monitoring (incl., electrical and electromagnetic imaging) and point-scale sensing (soil moisture sensors and soil sampling) to quantify interactions between above and below ground compartments of the East River Watershed in the Upper Colorado River Basin. We evaluate linkages between physical properties (incl. soil composition, soil electrical conductivity, soil water content), metrics extracted from digital surface and terrain elevation models (incl., slope, wetness index) and vegetation properties (incl., greenness, plant type) in a 500 x 500 m hillslope-floodplain subsystem of the watershed. Data integration and analysis is supported by numerical approaches that simulate the control of soil and geomorphic characteristic on hydrological processes. Results provide an unprecedented window into critical zone interactions, revealing significant below- and above-ground co-dynamics. Baseline geophysical datasets provide lithological structure along the hillslope, which includes a surface soil horizon, underlain by a saprolite layer and the fractured Mancos shale. Time-lapse geophysical data show very different moisture dynamics in various compartments and locations during the winter and growing season. Integration with aerial imaging reveals a significant linkage between plant growth and the subsurface wetness, soil characteristics and the topographic gradient. The obtained information about the organization and connectivity of the landscape is being transferred to larger regions using aerial imaging and will be used to constrain multi-scale, multi-physics hydro-biogeochemical simulations of the East River watershed response to hydrological perturbations.

Satellite assessment of early-season forecasts for vegetation conditions of grazing allotments in Nevada, United States

USDA-ARS?s Scientific Manuscript database

Fifteen years of enhanced vegetation index data from the MODIS sensor are examined in conjunction with precipitation and the Palmer drought severity index to assess how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year. ...

Response of alpine vegetation growth dynamics to snow cover phenology on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wang, X.; Wu, C.

2017-12-01

Alpine vegetation plays a crucial role in global energy cycles with snow cover, an essential component of alpine land cover showing high sensitivity to climate change. The Tibetan Plateau (TP) has a typical alpine vegetation ecosystem and is rich of snow resources. With global warming, the snow of the TP has undergone significant changes that will inevitably affect the growth of alpine vegetation, but observed evidence of such interaction is limited. In particular, a comprehensive understanding of the responses of alpine vegetation growth to snow cover variability is still not well characterized on TP region. To investigate this, we calculated three indicators, the start (SOS) and length (LOS) of growing season, and the maximum of normalized difference vegetation index (NDVImax) as proxies of vegetation growth dynamics from the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000-2015. Snow cover duration (SCD) and melt (SCM) dates were also extracted during the same time frame from the combination of MODIS and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data. We found that the snow cover phenology had a strong control on alpine vegetation growth dynamics. Furthermore, the responses of SOS, LOS and NDVImax to snow cover phenology varied among plant functional types, eco-geographical zones, and temperature and precipitation gradients. The alpine steppes showed a much stronger negative correlation between SOS and SCD, and also a more evidently positive relationship between LOS and SCD than other types, indicating a longer SCD would lead to an earlier SOS and longer LOS. Most areas showed positive correlation between SOS and SCM, while a contrary response was also found in the warm but drier areas. Both SCD and SCM showed positive correlations with NDVImax, but the relationship became weaker with the increase of precipitation. Our findings provided strong evidences between vegetation growth and snow cover phenology, and changes in snow cover should be also considered when analyzing alpine vegetation growth dynamics in future.

Nanophotonic Image Sensors.

PubMed

Chen, Qin; Hu, Xin; Wen, Long; Yu, Yan; Cumming, David R S

2016-09-01

The increasing miniaturization and resolution of image sensors bring challenges to conventional optical elements such as spectral filters and polarizers, the properties of which are determined mainly by the materials used, including dye polymers. Recent developments in spectral filtering and optical manipulating techniques based on nanophotonics have opened up the possibility of an alternative method to control light spectrally and spatially. By integrating these technologies into image sensors, it will become possible to achieve high compactness, improved process compatibility, robust stability and tunable functionality. In this Review, recent representative achievements on nanophotonic image sensors are presented and analyzed including image sensors with nanophotonic color filters and polarizers, metamaterial-based THz image sensors, filter-free nanowire image sensors and nanostructured-based multispectral image sensors. This novel combination of cutting edge photonics research and well-developed commercial products may not only lead to an important application of nanophotonics but also offer great potential for next generation image sensors beyond Moore's Law expectations. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An evaluation of remote sensing technologies for the detection of fugitive contamination at selected Superfund hazardous waste sites in Pennsylvania

USGS Publications Warehouse

Slonecker, E. Terrence; Fisher, Gary B.

2014-01-01

This evaluation was conducted to assess the potential for using both traditional remote sensing, such as aerial imagery, and emerging remote sensing technology, such as hyperspectral imaging, as tools for postclosure monitoring of selected hazardous waste sites. Sixteen deleted Superfund (SF) National Priorities List (NPL) sites in Pennsylvania were imaged with a Civil Air Patrol (CAP) Airborne Real-Time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor between 2009 and 2012. Deleted sites are those sites that have been remediated and removed from the NPL. The imagery was processed to radiance and atmospherically corrected to relative reflectance with standard software routines using the Environment for Visualizing Imagery (ENVI, ITT–VIS, Boulder, Colorado) software. Standard routines for anomaly detection, endmember collection, vegetation stress, and spectral analysis were applied.

PREDICTION OF PEROMYSCUS MANICULATUS (DEER MOUSE) POPULATION DYNAMICS IN MONTANA, USA, USING SATELLITE-DRIVEN VEGETATION PRODUCTIVITY AND WEATHER DATA

PubMed Central

Loehman, Rachel A.; Elias, Joran; Douglass, Richard J.; Kuenzi, Amy J.; Mills, James N.; Wagoner, Kent

2013-01-01

Deer mice (Peromyscus maniculatus) are the main reservoir host for Sin Nombre virus, the primary etiologic agent of hantavirus pulmonary syndrome in North America. Sequential changes in weather and plant productivity (trophic cascades) have been noted as likely catalysts of deer mouse population irruptions, and monitoring and modeling of these phenomena may allow for development of early-warning systems for disease risk. Relationships among weather variables, satellite-derived vegetation productivity, and deer mouse populations were examined for a grassland site east of the Continental Divide and a sage-steppe site west of the Continental Divide in Montana, USA. We acquired monthly deer mouse population data for mid-1994 through 2007 from long-term study sites maintained for monitoring changes in hantavirus reservoir populations, and we compared these with monthly bioclimatology data from the same period and gross primary productivity data from the Moderate Resolution Imaging Spectroradiometer sensor for 2000–06. We used the Random Forests statistical learning technique to fit a series of predictive models based on temperature, precipitation, and vegetation productivity variables. Although we attempted several iterations of models, including incorporating lag effects and classifying rodent density by seasonal thresholds, our results showed no ability to predict rodent populations using vegetation productivity or weather data. We concluded that trophic cascade connections to rodent population levels may be weaker than originally supposed, may be specific to only certain climatic regions, or may not be detectable using remotely sensed vegetation productivity measures, although weather patterns and vegetation dynamics were positively correlated. PMID:22493110

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.

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.

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

PubMed

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

2014-10-01

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

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

PubMed Central

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

2014-01-01

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

Study of vegetation cover distribution using DVI, PVI, WDVI indices with 2D-space plot

NASA Astrophysics Data System (ADS)

Naji, Taghreed A. H.

2018-05-01

The present work aims to study the effect of using vegetation indices technique on image segmentation for subdividing an image into the homogeneous regions. Three of these vegetation indices technique has been adopted (i.e. Difference Vegetation-Index (DVI), Perpendicular Vegetation Index (PVI) and Weighted Difference Vegetation Index (WDVI)) for detecting and monitoring vegetation distribution and healthiness. Image binarization method being followed the implementation of the indices to isolating the vegetation areas from the image background. The separated agriculture regions from other land use regions and their percentages are presented for two years (2001 and 2002) of the (ETM+) scenes. The counted areas resulted from 2D-space plot technique and the separated vegetated areas resulted from the using of the vegetation indices are also presented. The separated agriculture regions from the implementation of the DVI-index have proved better than other used indices. Because it showed better coincident approximately with 2D-space plot segmentation.

Interpretation of high-resolution imagery for detecting vegetation cover composition change after fuels reduction treatments in woodlands

USGS Publications Warehouse

Karl, Jason W.; Gillan, Jeffrey K.; Barger, Nichole N.; Herrick, Jeffrey E.; Duniway, Michael C.

2014-01-01

The use of very high resolution (VHR; ground sampling distances < ∼5 cm) aerial imagery to estimate site vegetation cover and to detect changes from management has been well documented. However, as the purpose of monitoring is to document change over time, the ability to detect changes from imagery at the same or better level of accuracy and precision as those measured in situ must be assessed for image-based techniques to become reliable tools for ecosystem monitoring. Our objective with this study was to quantify the relationship between field-measured and image-interpreted changes in vegetation and ground cover measured one year apart in a Piñon and Juniper (P–J) woodland in southern Utah, USA. The study area was subject to a variety of fuel removal treatments between 2009 and 2010. We measured changes in plant community composition and ground cover along transects in a control area and three different treatments prior to and following P–J removal. We compared these measurements to vegetation composition and change based on photo-interpretation of ∼4 cm ground sampling distance imagery along similar transects. Estimates of cover were similar between field-based and image-interpreted methods in 2009 and 2010 for woody vegetation, no vegetation, herbaceous vegetation, and litter (including woody litter). Image-interpretation slightly overestimated cover for woody vegetation and no-vegetation classes (average difference between methods of 1.34% and 5.85%) and tended to underestimate cover for herbaceous vegetation and litter (average difference of −5.18% and 0.27%), but the differences were significant only for litter cover in 2009. Level of agreement between the field-measurements and image-interpretation was good for woody vegetation and no-vegetation classes (r between 0.47 and 0.89), but generally poorer for herbaceous vegetation and litter (r between 0.18 and 0.81) likely due to differences in image quality by year and the difficulty in discriminating fine vegetation and litter in imagery. Our results show that image interpretation to detect vegetation changes has utility for monitoring fuels reduction treatments in terms of woody vegetation and no-vegetation classes. The benefits of this technique are that it provides objective and repeatable measurements of site conditions that could be implemented relatively inexpensively and easily without the need for highly specialized software or technical expertise. Perhaps the biggest limitations of image interpretation to monitoring fuels treatments are challenges in estimating litter and herbaceous vegetation cover and the sensitivity of herbaceous cover estimates to image quality and shadowing.

Point Cloud Generation from sUAS-Mounted iPhone Imagery: Performance Analysis

NASA Astrophysics Data System (ADS)

Ladai, A. D.; Miller, J.

2014-11-01

The rapidly growing use of sUAS technology and fast sensor developments continuously inspire mapping professionals to experiment with low-cost airborne systems. Smartphones has all the sensors used in modern airborne surveying systems, including GPS, IMU, camera, etc. Of course, the performance level of the sensors differs by orders, yet it is intriguing to assess the potential of using inexpensive sensors installed on sUAS systems for topographic applications. This paper focuses on the quality analysis of point clouds generated based on overlapping images acquired by an iPhone 5s mounted on a sUAS platform. To support the investigation, test data was acquired over an area with complex topography and varying vegetation. In addition, extensive ground control, including GCPs and transects were collected with GSP and traditional geodetic surveying methods. The statistical and visual analysis is based on a comparison of the UAS data and reference dataset. The results with the evaluation provide a realistic measure of data acquisition system performance. The paper also gives a recommendation for data processing workflow to achieve the best quality of the final products: the digital terrain model and orthophoto mosaic. After a successful data collection the main question is always the reliability and the accuracy of the georeferenced data.

Photoacoustic imaging with planoconcave optical microresonator sensors: feasibility studies based on phantom imaging

NASA Astrophysics Data System (ADS)

Guggenheim, James A.; Zhang, Edward Z.; Beard, Paul C.

2017-03-01

The planar Fabry-Pérot (FP) sensor provides high quality photoacoustic (PA) images but beam walk-off limits sensitivity and thus penetration depth to ≍1 cm. Planoconcave microresonator sensors eliminate beam walk-off enabling sensitivity to be increased by an order-of-magnitude whilst retaining the highly favourable frequency response and directional characteristics of the FP sensor. The first tomographic PA images obtained in a tissue-realistic phantom using the new sensors are described. These show that the microresonator sensors provide near identical image quality as the planar FP sensor but with significantly greater penetration depth (e.g. 2-3cm) due to their higher sensitivity. This offers the prospect of whole body small animal imaging and clinical imaging to depths previously unattainable using the FP planar sensor.

Peat drainage conditions assessment in Scotland

NASA Astrophysics Data System (ADS)

Poggio, Laura; Artz, Rebekka; Donaldson-Selby, Gillian; Aitkenhead, Matt; Donnelly, David; Gimona, Alessandro

2017-04-01

Large areas of Scotland are covered in peat, providing an important sink of carbon but also a notable source of emission where peatlands are not in good condition. However, despite data from designated sites that peat degradation is common, a detailed spatial assessment of the condition of most peatlands across the whole of Scotland is missing. An assessment of peatland drainage was carried out at >600 random sampling locations with an expert-based estimation of presence or absence of drainage ditches within a 500 metre block using 25 cm resolution aerial imagery. The resulting dataset was modelled using a scorpan-kriging approach, in particular using Generalised Additive Models for the description of the trend. Remote sensing images from different sensors (i.e. MODIS, Landsat and Sentinel 1 and 2) were used. In particular we used indices describing vegetation greenness (Enhanced Vegetation Index), water availability (Normalised Water Difference index), Land Surface Temperature and vegetation productivity. When considering MODIS indices we used time series and phenological summaries. The model provides also uncertainty of the estimations. The derived dataset can then be used in the decision making process for the selection of sites for restoration, emissions estimation and accounting.

Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Dube, Timothy; Mutanga, Onisimo

2015-03-01

Aboveground biomass estimation is critical in understanding forest contribution to regional carbon cycles. Despite the successful application of high spatial and spectral resolution sensors in aboveground biomass (AGB) estimation, there are challenges related to high acquisition costs, small area coverage, multicollinearity and limited availability. These challenges hamper the successful regional scale AGB quantification. The aim of this study was to assess the utility of the newly-launched medium-resolution multispectral Landsat 8 Operational Land Imager (OLI) dataset with a large swath width, in quantifying AGB in a forest plantation. We applied different sets of spectral analysis (test I: spectral bands; test II: spectral vegetation indices and test III: spectral bands + spectral vegetation indices) in testing the utility of Landsat 8 OLI using two non-parametric algorithms: stochastic gradient boosting and the random forest ensembles. The results of the study show that the medium-resolution multispectral Landsat 8 OLI dataset provides better AGB estimates for Eucalyptus dunii, Eucalyptus grandis and Pinus taeda especially when using the extracted spectral information together with the derived spectral vegetation indices. We also noted that incorporating the optimal subset of the most important selected medium-resolution multispectral Landsat 8 OLI bands improved AGB accuracies. We compared medium-resolution multispectral Landsat 8 OLI AGB estimates with Landsat 7 ETM + estimates and the latter yielded lower estimation accuracies. Overall, this study demonstrates the invaluable potential and strength of applying the relatively affordable and readily available newly-launched medium-resolution Landsat 8 OLI dataset, with a large swath width (185-km) in precisely estimating AGB. This strength of the Landsat OLI dataset is crucial especially in sub-Saharan Africa where high-resolution remote sensing data availability remains a challenge.

Comparison of Landsat-8 and Sentinel-2A reflectance and normalized difference vegetation index

NASA Astrophysics Data System (ADS)

Zhang, H.; Roy, D. P.; Yan, L.; Li, Z.; Huang, H.

2017-12-01

The moderate spatial resolution satellite data from the polar-orbiting Landsat-8 (launched 2013) and Sentinel-2A (launched 2015) sensors provide 10 m to 30 m multi-spectral global coverage with a better than 5-day revisit. Although a national laboratory traceable cross-calibration comparison of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2A MultiSpectral Instrument (MSI) was undertaken pre-launch, there are a number of other sensor differences, notably due to spectral, spatial and angular differences. To examine these in a comprehensive way, Landsat-8 and Sentinel-2A data for approximately 20° × 10° of southern Africa acquired in the summer (January to March) and winter (July to September) of 2016 were compared. Only Landsat-8 and Sentinel-2A observations acquired within one-day apart were considered. The sensor data were registered and then each orbit projected into 30 m fixed global Web Enabled Landsat Data (GWELD) tiles defined in the MODIS sinusoidal equal area projection. Only corresponding sensor observations of each 30 m tile pixel that were flagged as cloud and snow-free, unsaturated, and that had no significant change in their one day separation, were compared. Both the Landsat-8 and Sentinel-2A data were atmospherically corrected using the Landsat Surface Reflectance Code (LaSRC) and were also corrected to nadir BRDF adjusted reflectance (NBAR). Top of atmosphere and surface reflectance for the spectrally corresponding visible, near infrared and shortwave infrared OLI and MSI bands, and derived normalized difference vegetation index (NDVI), were compared and their differences quantified using regression analyses. The resulting statistical transformations may be used to improve the consistency between the Landsat-8 OLI and Sentinel-2A MSI data. The importance and sensitivity of the results to correct filtering, atmospheric correction and adjustment to NBAR is demonstrated.

Spectral methods to detect surface mines

NASA Astrophysics Data System (ADS)

Winter, Edwin M.; Schatten Silvious, Miranda

2008-04-01

Over the past five years, advances have been made in the spectral detection of surface mines under minefield detection programs at the U. S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD). The problem of detecting surface land mines ranges from the relatively simple, the detection of large anti-vehicle mines on bare soil, to the very difficult, the detection of anti-personnel mines in thick vegetation. While spatial and spectral approaches can be applied to the detection of surface mines, spatial-only detection requires many pixels-on-target such that the mine is actually imaged and shape-based features can be exploited. This method is unreliable in vegetated areas because only part of the mine may be exposed, while spectral detection is possible without the mine being resolved. At NVESD, hyperspectral and multi-spectral sensors throughout the reflection and thermal spectral regimes have been applied to the mine detection problem. Data has been collected on mines in forest and desert regions and algorithms have been developed both to detect the mines as anomalies and to detect the mines based on their spectral signature. In addition to the detection of individual mines, algorithms have been developed to exploit the similarities of mines in a minefield to improve their detection probability. In this paper, the types of spectral data collected over the past five years will be summarized along with the advances in algorithm development.

Vegetation dynamics in response to water inflow rates and fire in a brackish Typha domingensis Pers. marsh in the delta of the Colorado River, Mexico

USGS Publications Warehouse

Mexicano, Lourdes; Nagler, Pamela L.; Zamora-Arroyo, Francisco; Glenn, Edward P.

2012-01-01

The Cienega de Santa Clara is a 5600 ha, anthropogenic wetland in the delta of the Colorado River in Mexico. It is the inadvertent creation of the disposal of brackish agricultural waste water from the U.S. into the intertidal zone of the river delta in Mexico, but has become an internationally important wetland for resident and migratory water birds. We used high resolution Quickbird and WorldView-2 images to produce seasonal vegetation maps of the Cienega before, during and after a test run of the Yuma Desalting Plant, which will remove water from the inflow stream and replace it with brine. We also used moderate resolution, 16-day composite NDVI imagery from the Moderate Resolution Imaging Spectrometer (MODIS) sensors on the Terra satellite to determine the main factors controlling green vegetation density over the years 2000–2011. The marsh is dominated by Typha domingensis Pers. with Phragmites australis (Cav.) Trin. Ex Steud. as a sub-dominant species in shallower marsh areas. The most important factor controlling vegetation density was fire. Spring fires in 2006 and 2011 were followed by much more rapid green-up of T. domingensis in late spring and 30% higher peak summer NDVI values compared to non-fire years (P < 0.001). Fires removed thatch and returned nutrients to the water, resulting in more vigorous vegetation growth compared to non-fire years. The second significant (P < 0.01) factor controlling NDVI was flow rate of agricultural drain water from the U.S. into the marsh. Reduced summer flows in 2001 due to canal repairs, and in 2010 during the YDP test run, produced the two lowest NDVI values of the time series from 2000 to 2011 (P < 0.05). Salinity is a further determinant of vegetation dynamics as determined by greenhouse experiments, but was nearly constant over the period 2000–2011, so it was not a significant variable in regression analyses. It is concluded that any reduction in inflow volumes will result in a linear decrease in green foliage density in the marsh.

An approach for combining airborne LiDAR and high-resolution aerial color imagery using Gaussian processes

NASA Astrophysics Data System (ADS)

Liu, Yansong; Monteiro, Sildomar T.; Saber, Eli

2015-10-01

Changes in vegetation cover, building construction, road network and traffic conditions caused by urban expansion affect the human habitat as well as the natural environment in rapidly developing cities. It is crucial to assess these changes and respond accordingly by identifying man-made and natural structures with accurate classification algorithms. With the increase in use of multi-sensor remote sensing systems, researchers are able to obtain a more complete description of the scene of interest. By utilizing multi-sensor data, the accuracy of classification algorithms can be improved. In this paper, we propose a method for combining 3D LiDAR point clouds and high-resolution color images to classify urban areas using Gaussian processes (GP). GP classification is a powerful non-parametric classification method that yields probabilistic classification results. It makes predictions in a way that addresses the uncertainty of real world. In this paper, we attempt to identify man-made and natural objects in urban areas including buildings, roads, trees, grass, water and vehicles. LiDAR features are derived from the 3D point clouds and the spatial and color features are extracted from RGB images. For classification, we use the Laplacian approximation for GP binary classification on the new combined feature space. The multiclass classification has been implemented by using one-vs-all binary classification strategy. The result of applying support vector machines (SVMs) and logistic regression (LR) classifier is also provided for comparison. Our experiments show a clear improvement of classification results by using the two sensors combined instead of each sensor separately. Also we found the advantage of applying GP approach to handle the uncertainty in classification result without compromising accuracy compared to SVM, which is considered as the state-of-the-art classification method.

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production

PubMed Central

Domingues Franceschini, Marston Héracles; Bartholomeus, Harm; van Apeldoorn, Dirk; Suomalainen, Juha; Kooistra, Lammert

2017-01-01

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm−2), leaf area index (RMSE = 0.67 m2·m−2), canopy chlorophyll (RMSE = 0.24 g·m−2) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm−2, 0.85 m2·m−2, 0.28 g·m−2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system. PMID:28629159

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production.

PubMed

Domingues Franceschini, Marston Héracles; Bartholomeus, Harm; van Apeldoorn, Dirk; Suomalainen, Juha; Kooistra, Lammert

2017-06-18

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm -2 ), leaf area index (RMSE = 0.67 m²·m -2 ), canopy chlorophyll (RMSE = 0.24 g·m -2 ) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm -2 , 0.85 m²·m -2 , 0.28 g·m -2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CI g provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system.

EO-1 Prototyping for Environmental Applications

NASA Astrophysics Data System (ADS)

Campbell, P. K.; Middleton, E.; Ungar, S.; Zhang, Q.; Ong, L.; Huemmrich, K. F.

2009-12-01

The Earth Observing One (EO-1) Mission, launched in November, 2000 as part of NASA’s New Millennium Program, is in it’s eight year of operation. From the start it was recognized that a key criteria for evaluating the EO-1 technology and outlining future Earth science mission needs is the ability of the technology to characterize terrestrial surface state and processes. EO-1 is participating in a broad range of investigations, demonstrating the utility of imaging spectroscopy in applications relating to forestry, agriculture, species discrimination, invasive species, desertification, land-use, vulcanization, fire management, homeland security, natural and anthropogenic hazards and disaster assessments and has provided characterization for a variety of instruments on EOS platforms. By generating a high spectral and spatial resolution data set for the corral reefs and islands, it is contributing for realizing the goals of the National Decadal survey and providing an excellent platform for testing strategies to be employed in the HyspIRI mission. The EO1 Mission Science Office (MSO) is developing tools and prototypes for new science products, addressing the HyspIRI goals to assess vegetation status and health and provide vegetation spectral bio-indicators and biophysical parameters such as LAI and fAPAR at <100 m spatial resolution. These are being used to resolve variability in heterogeneous areas (e.g. agriculture, narrow shapes, urban and developed lands) and for managed ecosystems less than 10 km2. A set of invariable reference targets (e.g. sun, moon, deserts, Antarctica) are being characterised to allow cross-calibration of current and future EO sensors, comparison of land products generated by multiple sensors and retroactive processing of time series data. Such products are needed to develop Science Requirements for the next generation of hyperspectral satellite sensors and to address global societal needs.

On the soil moisture estimate at basin scale in Mediterranean basins with the ASAR sensor: the Mulargia basin case study

NASA Astrophysics Data System (ADS)

Fois, Laura; Montaldo, Nicola

2017-04-01

Soil moisture plays a key role in water and energy exchanges between soil, vegetation and atmosphere. For water resources planning and managementthesoil moistureneeds to be accurately and spatially monitored, specially where the risk of desertification is high, such as Mediterranean basins. In this sense active remote sensors are very attractive for soil moisture monitoring. But Mediterranean basinsaretypicallycharacterized by strong topography and high spatial variability of physiographic properties, and only high spatial resolution sensorsare potentially able to monitor the strong soil moisture spatial variability.In this regard the Envisat ASAR (Advanced Synthetic Aperture Radar) sensor offers the attractive opportunity ofsoil moisture mapping at fine spatial and temporal resolutions(up to 30 m, every 30 days). We test the ASAR sensor for soil moisture estimate in an interesting Sardinian case study, the Mulargia basin withan area of about 70 sq.km. The position of the Sardinia island in the center of the western Mediterranean Sea basin, its low urbanization and human activity make Sardinia a perfect reference laboratory for Mediterranean hydrologic studies. The Mulargia basin is a typical Mediterranean basinin water-limited conditions, and is an experimental basin from 2003. For soil moisture mapping23 satellite ASAR imagery at single and dual polarization were acquired for the 2003-2004period.Satellite observationsmay bevalidated through spatially distributed soil moisture ground-truth data, collected over the whole basin using the TDR technique and the gravimetric method, in days with available radar images. The results show that ASAR sensor observations can be successfully used for soil moisture mapping at different seasons, both wet and dry, but an accurate calibration with field data is necessary. We detect a strong relationship between the soil moisture spatial variability and the physiographic properties of the basin, such as soil water storage capacity, deep and texture of soils, type and density of vegetation, and topographic parameters. Finally we demonstrate that the high resolution ASAR imagery are an attractive tool for estimating surface soil moisture at basin scale, offering a unique opportunity for monitoring the soil moisture spatial variability in typical Mediterranean basins.

Using endmembers in AVIRIS images to estimate changes in vegetative biomass

NASA Technical Reports Server (NTRS)

Smith, Milton O.; Adams, John B.; Ustin, Susan L.; Roberts, Dar A.

1992-01-01

Field techniques for estimating vegetative biomass are labor intensive, and rarely are used to monitor changes in biomass over time. Remote-sensing offers an attractive alternative to field measurements; however, because there is no simple correspondence between encoded radiance in multispectral images and biomass, it is not possible to measure vegetative biomass directly from AVIRIS images. Ways to estimate vegetative biomass by identifying community types and then applying biomass scalars derived from field measurements are investigated. Field measurements of community-scale vegetative biomass can be made, at least for local areas, but it is not always possible to identify vegetation communities unambiguously using remote measurements and conventional image-processing techniques. Furthermore, even when communities are well characterized in a single image, it typically is difficult to assess the extent and nature of changes in a time series of images, owing to uncertainties introduced by variations in illumination geometry, atmospheric attenuation, and instrumental responses. Our objective is to develop an improved method based on spectral mixture analysis to characterize and identify vegetative communities, that can be applied to multi-temporal AVIRIS and other types of images. In previous studies, multi-temporal data sets (AVIRIS and TM) of Owens Valley, CA were analyzed and vegetation communities were defined in terms of fractions of reference (laboratory and field) endmember spectra. An advantage of converting an image to fractions of reference endmembers is that, although fractions in a given pixel may vary from image to image in a time series, the endmembers themselves typically are constant, thus providing a consistent frame of reference.

Multispectral imaging of plant stress for detection of CO2 leaking from underground

NASA Astrophysics Data System (ADS)

Rouse, J.; Shaw, J. A.; Repasky, K. S.; Lawrence, R. L.

2008-12-01

Multispectral imaging of plant stress is a potentially useful method of detecting CO2 leaking from underground. During the summers of 2007 and 2008, we deployed a multispectral imager for vegetation sensing as part of an underground CO2 release experiment conducted at the Zero Emission Research and Technology (ZERT) field site near the Montana State University campus in Bozeman, Montana. The imager was mounted on a low tower and observed the vegetation in a region near an underground pipe during a multi-week CO2 release. The imager was calibrated to measure absolute reflectance, from which vegetation indices were calculated as a measure of vegetation health. The temporal evolution of these indices over the course of the experiment show that the vegetation nearest the pipe exhibited more stress than the vegetation located further from the pipe. The imager observed notably increased stress in vegetation at locations exhibiting particularly high flux of CO2 from the ground into the atmosphere. These data from the 2007 and 2008 experiments will be used to demonstrate the utility of a tower-mounted multispectral imaging system for detecting CO2 leakage from below ground with the ability to operate continuously during clear and cloudy conditions.

Mapping Fire Severity Using Imaging Spectroscopy and Kernel Based Image Analysis

NASA Astrophysics Data System (ADS)

Prasad, S.; Cui, M.; Zhang, Y.; Veraverbeke, S.

2014-12-01

Improved spatial representation of within-burn heterogeneity after wildfires is paramount to effective land management decisions and more accurate fire emissions estimates. In this work, we demonstrate feasibility and efficacy of airborne imaging spectroscopy (hyperspectral imagery) for quantifying wildfire burn severity, using kernel based image analysis techniques. Two different airborne hyperspectral datasets, acquired over the 2011 Canyon and 2013 Rim fire in California using the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) sensor, were used in this study. The Rim Fire, covering parts of the Yosemite National Park started on August 17, 2013, and was the third largest fire in California's history. Canyon Fire occurred in the Tehachapi mountains, and started on September 4, 2011. In addition to post-fire data for both fires, half of the Rim fire was also covered with pre-fire images. Fire severity was measured in the field using Geo Composite Burn Index (GeoCBI). The field data was utilized to train and validate our models, wherein the trained models, in conjunction with imaging spectroscopy data were used for GeoCBI estimation wide geographical regions. This work presents an approach for using remotely sensed imagery combined with GeoCBI field data to map fire scars based on a non-linear (kernel based) epsilon-Support Vector Regression (e-SVR), which was used to learn the relationship between spectra and GeoCBI in a kernel-induced feature space. Classification of healthy vegetation versus fire-affected areas based on morphological multi-attribute profiles was also studied. The availability of pre- and post-fire imaging spectroscopy data over the Rim Fire provided a unique opportunity to evaluate the performance of bi-temporal imaging spectroscopy for assessing post-fire effects. This type of data is currently constrained because of limited airborne acquisitions before a fire, but will become widespread with future spaceborne sensors such as those on the planned NASA HyspIRI mission.

Arizona land use experiment

NASA Technical Reports Server (NTRS)

Winikka, C. C.; Schumann, H. H.

1975-01-01

Utilization of new sources of statewide remote sensing data, taken from high-altitude aircraft and from spacecraft is discussed along with incorporation of information extracted from these sources into on-going land and resources management programs in Arizona. Statewide cartographic applications of remote sensor data taken by NASA high-altitude aircraft include the development of a statewide semi-analytic control network, the production of nearly 1900 orthophotoquads (image maps) that are coincident in scale and area with the U.S. Geological Survey (USGS) 7. 5 minute topographic quadrangle map series, and satellite image maps of Arizona produced from LANDSAt multispectral scanner imagery. These cartographic products are utilized for a wide variety of experimental and operational earth resources applications. Applications of the imagery, image maps, and derived information discussed include: soils and geologic mapping projects, water resources investigations, land use inventories, environmental impact studies, highway route locations and mapping, vegetation cover mapping, wildlife habitat studies, power plant siting studies, statewide delineation of irrigation cropland, position determination of drilling sites, pictorial geographic bases for thematic mapping, and court exhibits.

Robotic Vehicle Communications Interoperability

DTIC Science & Technology

1988-08-01

starter (cold start) X X Fire suppression X Fording control X Fuel control X Fuel tank selector X Garage toggle X Gear selector X X X X Hazard warning...optic Sensors Sensor switch Video Radar IR Thermal imaging system Image intensifier Laser ranger Video camera selector Forward Stereo Rear Sensor control...optic sensors Sensor switch Video Radar IR Thermal imaging system Image intensifier Laser ranger Video camera selector Forward Stereo Rear Sensor

Evaluation of spatial, radiometric and spectral thematic mapper performance for coastal studies

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator)

1983-01-01

An area along the southeastern shore of the Chesapeake Bay was subsetted from TM imagery. The subsetted image was then enhanced and classified using an ERDAS 400 system. Results obtained were compared with a chart showing the distribution of both Zolsters marina and Rupplia martime in the Vaucluse Shores and which supports a large community of SAV. Radiative transfer models describing the irradiance reflectance of a water column containing SAV are being refined. Radiative transfer theory was used to model upwelling radiance for an orbiting sensor viewing an estuarine environment. Upwelling radiance was calculated for a clear maritime atmosphere, an optically shallow estuary of either clear or turbid water, and one of three bottom types: vegetation, sand, or mud using TM bands 1, 2, and 3 and MSS bands 4 and 5. A spectral quality index was defined similar to the equation for apparent contrast and used to evaluate the relative effectiveness of TM and MSS bands in detecting submerged vegetation.

Evaluation of spatial, radiometric and spectral thematic mapper performance for coastal studies

NASA Technical Reports Server (NTRS)

Klemas, V.

1983-01-01

Radiative transfer theory was used to model upwelling radiance for an orbiting sensor viewing an estuarine environment. Radiance was calculated in Tm bands 3,4, and 5 and MSS bands 4 and 5 for an optically shallow estuary of either clear or turbid water, and of three bottom types: vegetation, sand, or mud. A portion of a TM image of Chesapeake Bay was enhanced to obtain a quick look at what submerged features could be detected. The enhancements were compared with low altitude color aerial photography. The TM bands 1,2, and 3 were found to contain water and submerged features information. Band 1 contained a significant amount of noise and low contrast. Band 2 appeared to contain the most amount of bottom information. Band 3, while having the least amount of noise and best constrast, contained a lesser amount of bottom information because of increase water absorption. Several water signatures were identified which correlated with submerged vegetation shown in the aerial photography.

On the coupled use of sapflow and eddy covariance measurements: environmental impacts on the evapotranspiration of an heterogeneous - wild olives based - Sardinian ecosystem.

NASA Astrophysics Data System (ADS)

Curreli, Matteo; Corona, Roberto; Montaldo, Nicola; Oren, Ram

2015-04-01

Sapflow and eddy covariance techniques are attractive methods for evapotranspiration (ET) estimates. We demonstrated that in Mediterranean ecosystems, characterized by an heterogeneous spatial distribution of different plant functional types (PFT) such as grass and trees, the combined use of these techniques becomes essential for the actual ET estimates. Indeed, during the dry summers these water-limited heterogeneous ecosystems are typically characterized by a simple dual PFT system with strong-resistant woody vegetation and bare soil, since grass died. An eddy covariance - micrometeorological tower has been installed over an heterogeneous ecosystem at the Orroli site in Sardinia (Italy) from 2003. The site landscape is a mixture of Mediterranean patchy vegetation types: wild olives, different shrubs and herbaceous species, which died during the summer. Where patchy land cover leads and the surface fluxes from different cover are largely different, ET evaluation may be not robust enough and eddy covariance method hypothesis are not anymore preserved. In these conditions the sapflow measurements, performed by thermodissipation probes, provide robust estimates of the transpiration from woody vegetation. Through the coupled use of the sapflow sensor observations, a 2D footprint model of the eddy covariance tower and high resolution satellite images for the estimate of the foot print land cover map, the eddy covariance measurements can be correctly interpreted, and ET components (bare soil evaporation and woody vegetation transpiration) can be separated. Based on the Granier technique, 33 thermo-dissipation probes have been built and 6 power regulators have been assembled to provide a constant current of 3V to the sensors. The sensors have been installed at the Orroli site into 15 wild olives clumps with different characteristics in terms of tree size, exposition to wind and solar radiation and soil depth. The sap flow sensors outputs are analyzed to estimate innovative allometric relationships between sapwood area, diameter, canopy cover area, which are needed for the correct upscale of the local tree measurements to the site plot larger scale. Results show the response of wild olives stomatal conductance to vapor pressure deficit that follow an exponential decrease. Interestingly the tree exposure impacts transpiration significantly, showing double rates for the trees in the south part of the wild olive clumps. The soil depth also affects ET dynamics due to the influence on water absorption of the root tree system. Finally using an innovative scaling procedure, the sap-flow transpiration at field scale have been compared to the eddy covariance ET, showing the impact of climate dynamics on the ET estimates with the two tecniques.

UAV hyperspectral and lidar data analysis for vegetation applications

NASA Astrophysics Data System (ADS)

Sankey, Temuulen; Sankey, Joel; Donager, Jonathon

2017-04-01

High spatial and spectral resolution remote sensing data are critically needed to classify forest vegetation and measure their structure at the level of individual species and canopies. Here we test high-resolution lidar and hyperspectral data from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone with a gradient of vegetation and topography in northern Arizona, USA. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signature, but different canopy sizes. The lidar data provides estimates of individual tree height (R2=0.90; RMSE=2.3m) and crown diameter (R2=0.72; RMSE=0.71m) as well as total tree canopy cover (R2=0.87; RMSE=9.5%) and tree density (R2=0.77; RMSE=0.69 trees/cell) in 10 m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22-50% and 1-3.5 trees/cell, respectively. The lidar data also produces high accuracy DEM (R2=0.95; RMSE=0.43m). The lidar and hyperspectral sensors and methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring ecosystem changes.

Primary and Secondary Controls on Measurements of Forest Height Using Large-Footprint Lidar at the Hubbard Brook LTER

NASA Technical Reports Server (NTRS)

Knox, Robert G.; Blair, J. Bryan; Schwarz, Paul A.; Hofton, Michelle A.; Dubayah, Ralph; Smith, David E. (Technical Monitor)

2000-01-01

On September 26, 1999, we mapped canopy structure over 90% of the Hubbard Brook Experimental Forest in White Mountain National Forest, New Hampshire, using the Laser Vegetation Imaging Sensor (LVIS). This airborne instrument was configured to emulate data expected from the Vegetation Canopy Lidar (VCL) space mission. We compared above ground heights of the tallest surfaces detected by lidar with average forest canopy heights estimated from tree-based measurements in or near 346 0.05 ha plots (made in autumn of 1997 and 1998). Vegetation heights had by far the predominant influence on lidar top heights, but with this large data set we were able to measure two significant secondary effects: those of steepness or slope of the underlying terrain and of tree crown form. The size of the slope effect was intermediate between that expected from models of homogeneous canopy layers and for solitary tree crowns. The first detected surfaces were also proportionately taller for plots with more basal area in broad leaved northern hardwoods than for mostly coniferous plots. We expected this because of the contrast between the shapes of cumulative distributions of surface area for elliptical or hemi-elliptical tree crowns and those for conical crowns. Correcting for these secondary effects, when appropriate data are available for calibration, may improve vegetation structure estimates in regional studies using VCL or similar lidar data sources.

iPot: Improved potato monitoring in Belgium using remote sensing and crop growth modelling

NASA Astrophysics Data System (ADS)

Piccard, Isabelle; Gobin, Anne; Curnel, Yannick; Goffart, Jean-Pierre; Planchon, Viviane; Wellens, Joost; Tychon, Bernard; Cattoor, Nele; Cools, Romain

2016-04-01

Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these. The combination of geo-information and crop modelling might strengthen the competitiveness of the Belgian potato chain in a global market. The iPot project, financed by the Belgian Science Policy Office (Belspo), aims at providing the Belgian potato processing sector, represented by Belgapom, with near real time information on field condition (weather-soil), crop development and yield estimates, derived from a combination of satellite images and crop growth models. During the cropping season regular UAV flights (RGB, 3x3 cm) and high resolution satellite images (DMC/Deimos, 22m pixel size) were combined to elucidate crop phenology and performance at variety trials. UAV images were processed using a K-means clustering algorithm to classify the crop according to its greenness at 5m resolution. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) on the DMC images. Both DMC and UAV-based cover maps showed similar patterns, and helped detect different crop stages during the season. A wide spread field monitoring campaign with crop observations and measurements allowed for further calibration of the satellite image derived vegetation indices. Curve fitting techniques and phenological models were developed and compared with the vegetation indices during the season, both at trials and farmers' fields. Understanding and predicting crop phenology and canopy development is important for timely crop management and ultimately for yield estimates. An intuitive web-based geo-information platform is developed to allow both the industry and the research centres to access, analyse and combine the data with their own field observations for improved decision-making.

Employing UAVs to Acquire Detailed Vegetation and Bare Ground Data for Assessing Rangeland Health

NASA Astrophysics Data System (ADS)

Rango, A.; Laliberte, A.; Herrick, J. E.; Winters, C.

2007-12-01

Because of its value as a historical record (extending back to the mid 1930s), aerial photography is an important tool used in many rangeland studies. However, these historical photos are not very useful for detailed analysis of rangeland health because of inadequate spatial resolution and scheduling limitations. These issues are now being resolved by using Unmanned Aerial Vehicles (UAVs) over rangeland study areas. Spatial resolution improvements have been rapid in the last 10 years from the QuickBird satellite through improved aerial photography to the new UAV coverage and have utilized improved sensors and the more simplistic approach of low altitude flights. Our rangeland health experiments have shown that the low altitude UAV digital photography is preferred by rangeland scientists because it allows, for the first time, their identification of vegetation and land surface patterns and patches, gap sizes, bare soil percentages, and vegetation type. This hyperspatial imagery (imagery with a resolution finer than the object of interest) is obtained at about 5cm resolution by flying at an altitude of 150m above the surface of the Jornada Experimental Range in southern New Mexico. Additionally, the UAV provides improved temporal flexibility, such as flights immediately following fires, floods, and other catastrophic disturbances, because the flight capability is located near the study area and the vehicles are under the direct control of the users, eliminating the additional steps associated with budgets and contracts. There are significant challenges to improve the data to make them useful for operational agencies, namely, image distortion with inexpensive, consumer grade digital cameras, difficulty in detecting sufficient ground control points in small scenes (152m by 114m), accuracy of exterior UAV information on X,Y, Z, roll, pitch, and heading, the sheer number of images collected, and developing reliable relationships with ground-based data across a broad range of topographies and plant communities. Our efforts are currently focused on developing a complete and efficient workflow for UAV operational missions consisting of flight planning, image acquisition, image rectification and mosaicking, and image classification. The remote sensing capability is being incorporated into existing rangeland health assessment and monitoring protocols.

Interpretation of surface flux measurements in heterogeneous terrain during the Monsoon '90 experiment

USGS Publications Warehouse

Stannard, D.I.; Blanford, J.H.; Kustas, William P.; Nichols, W.D.; Amer, S.A.; Schmugge, T.J.; Weltz, M.A.

1994-01-01

A network of 9-m-tall surface flux measurement stations were deployed at eight sparsely vegetated sites during the Monsoon '90 experiment to measure net radiation, Q, soil heat flux, G, sensible heat flux, H (using eddy correlation), and latent heat flux, λE (using the energy balance equation). At four of these sites, 2-m-tall eddy correlation systems were used to measure all four fluxes directly. Also a 2-m-tall Bowen ratio system was deployed at one site. Magnitudes of the energy balance closure (Q + G + H + λE) increased as the complexity of terrain increased. The daytime Bowen ratio decreased from about 10 before the monsoon season to about 0.3 during the monsoons. Source areas of the measurements are developed and compared to scales of heterogeneity arising from the sparse vegetation and the topography. There was very good agreement among simultaneous measurements of Q with the same model sensor at different heights (representing different source areas), but poor agreement among different brands of sensors. Comparisons of simultaneous measurements of G suggest that because of the extremely small source area, extreme care in sensor deployment is necessary for accurate measurement in sparse canopies. A recently published model to estimate fetch is used to interpret measurements of H at the 2 m and 9 m heights. Three sites were characterized by undulating topography, with ridgetops separated by about 200–600 m. At these sites, sensors were located on ridgetops, and the 9-m fetch included the adjacent valley, whereas the 2-m fetch was limited to the immediate ridgetop and hillside. Before the monsoons began, vegetation was mostly dormant, the watershed was uniformly hot and dry, and the two measurements of H were in close agreement. After the monsoons began and vegetation fully matured, the 2-m measurements of H were significantly greater than the 9-m measurements, presumably because the vegetation in the valleys was denser and cooler than on the ridgetops and hillsides. At one lowland site with little topographic relief, the vegetation was more uniform, and the two measurements of H were in close agreement during peak vegetation. Values of λE could only be compared at two sites, but the 9-m values were greater than the 2-m values, suggesting λE from the dense vegetation in the valleys was greater than elsewhere.

Usability of multiangular imaging spectroscopy data for analysis of vegetation canopy shadow fraction in boreal forest

NASA Astrophysics Data System (ADS)

Markiet, Vincent; Perheentupa, Viljami; Mõttus, Matti; Hernández-Clemente, Rocío

2016-04-01

Imaging spectroscopy is a remote sensing technology which records continuous spectral data at a very high (better than 10 nm) resolution. Such spectral images can be used to monitor, for example, the photosynthetic activity of vegetation. Photosynthetic activity is dependent on varying light conditions and varies within the canopy. To measure this variation we need very high spatial resolution data with resolution better than the dominating canopy element size (e.g., tree crown in a forest canopy). This is useful, e.g., for detecting photosynthetic downregulation and thus plant stress. Canopy illumination conditions are often quantified using the shadow fraction: the fraction of visible foliage which is not sunlit. Shadow fraction is known to depend on view angle (e.g., hot spot images have very low shadow fraction). Hence, multiple observation angles potentially increase the range of shadow fraction in the imagery in high spatial resolution imaging spectroscopy data. To investigate the potential of multi-angle imaging spectroscopy in investigating canopy processes which vary with shadow fraction, we obtained a unique multiangular airborne imaging spectroscopy data for the Hyytiälä forest research station located in Finland (61° 50'N, 24° 17'E) in July 2015. The main tree species are Norway spruce (Picea abies L. karst), Scots pine (Pinus sylvestris L.) and birch (Betula pubescens Ehrh., Betula pendula Roth). We used an airborne hyperspectral sensor AISA Eagle II (Specim - Spectral Imaging Ltd., Finland) mounted on a tilting platform. The tilting platform allowed us to measure at nadir and approximately 35 degrees off-nadir. The hyperspectral sensor has a 37.5 degrees field of view (FOV), 0.6m pixel size, 128 spectral bands with an average spectral bandwidth of 4.6nm and is sensitive in the 400-1000 nm spectral region. The airborne data was radiometrically, atmospherically and geometrically processed using the Parge and Atcor software (Re Se applications Schläpfer, Switzerland). However, even after meticulous geolocation, the canopy elements (needles) seen from the three view angles were different: at each overpass, different parts of the same crowns were observed. To overcome this, we used a 200m x 200m test site covered with pure pine stands. We assumed that for sunlit, shaded and understory spectral signatures are independent of viewing direction to the accuracy of a constant BRDF factor. Thus, we compared the spectral signatures for sunlit and shaded canopy and understory obtained for each view direction. We selected visually six hundred of the brightest and darkest canopy pixels. Next, we performed a minimum noise fraction (MNF) transformation, created a pixel purity index (PPI) and used Envi's n-D scatterplot to determine pure spectral signatures for the two classes. The pure endmembers for different view angles were compared to determine the BRDF factor and to analyze its spectral invariance. We demonstrate the compatibility of multi-angle data with high spatial resolution data. In principle, both carry similar information on structured (non-flat) targets thus as a vegetation canopy. Nevertheless, multiple view angles helped us to extend the range of shadow fraction in the images. Also, correct separation of shaded crown and shaded understory pixels remains a challenge.

An infrared/video fusion system for military robotics

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

Davis, A.W.; Roberts, R.S.

1997-08-05

Sensory information is critical to the telerobotic operation of mobile robots. In particular, visual sensors are a key component of the sensor package on a robot engaged in urban military operations. Visual sensors provide the robot operator with a wealth of information including robot navigation and threat assessment. However, simple countermeasures such as darkness, smoke, or blinding by a laser, can easily neutralize visual sensors. In order to provide a robust visual sensing system, an infrared sensor is required to augment the primary visual sensor. An infrared sensor can acquire useful imagery in conditions that incapacitate a visual sensor. Amore » simple approach to incorporating an infrared sensor into the visual sensing system is to display two images to the operator: side-by-side visual and infrared images. However, dual images might overwhelm the operator with information, and result in degraded robot performance. A better solution is to combine the visual and infrared images into a single image that maximizes scene information. Fusing visual and infrared images into a single image demands balancing the mixture of visual and infrared information. Humans are accustom to viewing and interpreting visual images. They are not accustom to viewing or interpreting infrared images. Hence, the infrared image must be used to enhance the visual image, not obfuscate it.« less

Method and apparatus for measuring solar radiation in a vegetative canopy

DOEpatents

Gutschick, V.P.; Barron, M.H.; Waechter, D.A.; Wolf, M.A.

1985-04-30

An apparatus and method for measuring solar radiation received in a vegetative canopy. A multiplicity of sensors selectively generates electrical signals in response to impinging photosynthetically active radiation in sunlight. Each sensor is attached to a plant within the canopy and is electrically connected to a separate port in a junction box having a multiplicity of ports. Each port is connected to an operational amplifier. Each amplifier amplifies the signals generated by the sensors. Each amplifier is connected to an analog-to-digital convertor which digitizes each signal. A computer is connected to the convertors and accumulates and stores solar radiation data. A data output device such as a printer is connected to the computer and displays the data.

Method and apparatus for measuring solar radiation in a vegetative canopy

DOEpatents

Gutschick, Vincent P.; Barron, Michael H.; Waechter, David A.; Wolf, Michael A.

1987-01-01

An apparatus and method for measuring solar radiation received in a vegetative canopy. A multiplicity of sensors selectively generates electrical signals in response to impinging photosynthetically active radiation in sunlight. Each sensor is attached to a plant within the canopy and is electrically connected to a separate port in a junction box having a multiplicity of ports. Each port is connected to an operational amplifier. Each amplifier amplifies the signals generated by the sensors. Each amplifier is connected to an analog-to-digital convertor which digitizes each signal. A computer is connected to the convertors and accumulates and stores solar radiation data. A data output device such as a printer is connected to the computer and displays the data.

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

USGS Publications Warehouse

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

2004-01-01

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

CMOS Image Sensors: Electronic Camera On A Chip

NASA Technical Reports Server (NTRS)

Fossum, E. R.

1995-01-01

Recent advancements in CMOS image sensor technology are reviewed, including both passive pixel sensors and active pixel sensors. On- chip analog to digital converters and on-chip timing and control circuits permit realization of an electronic camera-on-a-chip. Highly miniaturized imaging systems based on CMOS image sensor technology are emerging as a competitor to charge-coupled devices for low cost uses.

The influence of vegetation, mesoclimate and meteorology on urban atmospheric microclimates across a coastal to desert climate gradient.

PubMed

Crum, Steven M; Shiflett, Sheri A; Jenerette, G Darrel

2017-09-15

Many cities are increasing vegetation in part due to the potential for microclimate cooling. However, the magnitude of vegetation cooling and sensitivity to mesoclimate and meteorology are uncertain. To improve understanding of the variation in vegetation's influence on urban microclimates we asked: how do meso- and regional-scale drivers influence the magnitude and timing of vegetation-based moderation on summertime air temperature (T a ), relative humidity (RH) and heat index (HI) across dryland cities? To answer this question we deployed a network of 180 temperature sensors in summer 2015 over 30 high- and 30 low-vegetated plots in three cities across a coastal to inland to desert climate gradient in southern California, USA. In a followup study, we deployed a network of temperature and humidity sensors in the inland city. We found negative T a and HI and positive RH correlations with vegetation intensity. Furthermore, vegetation effects were highest in evening hours, increasing across the climate gradient, with reductions in T a and increases in RH in low-vegetated plots. Vegetation increased temporal variability of T a , which corresponds with increased nighttime cooling. Increasing mean T a was associated with higher spatial variation in T a in coastal cities and lower variation in inland and desert cities, suggesting a climate dependent switch in vegetation sensitivity. These results show that urban vegetation increases spatiotemporal patterns of microclimate with greater cooling in warmer environments and during nighttime hours. Understanding urban microclimate variation will help city planners identify potential risk reductions associated with vegetation and develop effective strategies ameliorating urban microclimate. Published by Elsevier Ltd.

Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index Composites

USGS Publications Warehouse

,

2005-01-01

The Advanced Very High Resolution Radiometer (AVHRR) is a broad-band scanner with four to six bands, depending on the model. The AVHRR senses in the visible, near-, middle-, and thermal- infrared portions of the electromagnetic spectrum. This sensor is carried on a series of National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POES), beginning with the Television InfraRed Observation Satellite (TIROS-N) in 1978. Since 1989, the United States Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) has been mapping the vegetation condition of the United States and Alaska using satellite information from the AVHRR sensor. The vegetation condition composites, more commonly called greenness maps, are produced every week using the latest information on the growth and condition of the vegetation. One of the most important aspects of USGS greenness mapping is the historical archive of information dating back to 1989. This historical stretch of information has allowed the USGS to determine a 'normal' vegetation condition. As a result, it is possible to compare the current week's vegetation condition with normal vegetation conditions. An above normal condition could indicate wetter or warmer than normal conditions, while a below normal condition could indicate colder or dryer than normal conditions. The interpretation of departure from normal will depend on the season and geography of a region.

The Circumpolar Arctic Vegetation Map: AVHRR-derived base maps, environmental controls, and integrated mapping procedures

Treesearch

D. A. WALKER; W. A. GOULD; MAIERH. A.; M. K. RAYNOLDS

2002-01-01

A new false-colour-infrared image derived from biweekly 1993 and 1995 Advanced Very High Resolution Radiometer (AVHRR) data provides a snow-free and cloud-free base image for the interpretation of vegetation as part of a 1:7.5M-scale Circumpolar Arctic Vegetation Map (CAVM). A maximum-NDVI (Normalized DiVerence Vegetation Index) image prepared from the same data...

Climate and anthropogenic impacts on forest vegetation derived from satellite data

NASA Astrophysics Data System (ADS)

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

2010-09-01

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

Pseudo-spectral methodology for a quantitative assessment of the cover of in-stream vegetation in small streams

NASA Astrophysics Data System (ADS)

Hershkovitz, Yaron; Anker, Yaakov; Ben-Dor, Eyal; Schwartz, Guy; Gasith, Avital

2010-05-01

In-stream vegetation is a key ecosystem component in many fluvial ecosystems, having cascading effects on stream conditions and biotic structure. Traditionally, ground-level surveys (e.g. grid and transect analyses) are commonly used for estimating cover of aquatic macrophytes. Nonetheless, this methodological approach is highly time consuming and usually yields information which is practically limited to habitat and sub-reach scales. In contrast, remote-sensing techniques (e.g. satellite imagery and airborne photography), enable collection of large datasets over section, stream and basin scales, in relatively short time and reasonable cost. However, the commonly used spatial high resolution (1m) is often inadequate for examining aquatic vegetation on habitat or sub-reach scales. We examined the utility of a pseudo-spectral methodology, using RGB digital photography for estimating the cover of in-stream vegetation in a small Mediterranean-climate stream. We compared this methodology with that obtained by traditional ground-level grid methodology and with an airborne hyper-spectral remote sensing survey (AISA-ES). The study was conducted along a 2 km section of an intermittent stream (Taninim stream, Israel). When studied, the stream was dominated by patches of watercress (Nasturtium officinale) and mats of filamentous algae (Cladophora glomerata). The extent of vegetation cover at the habitat and section scales (100 and 104 m, respectively) were estimated by the pseudo-spectral methodology, using an airborne Roli camera with a Phase-One P 45 (39 MP) CCD image acquisition unit. The swaths were taken in elevation of about 460 m having a spatial resolution of about 4 cm (NADIR). For measuring vegetation cover at the section scale (104 m) we also used a 'push-broom' AISA-ES hyper-spectral swath having a sensor configuration of 182 bands (350-2500 nm) at elevation of ca. 1,200 m (i.e. spatial resolution of ca. 1 m). Simultaneously, with every swath we used an Analytical Spectral Device (ASD) to measure hyper-spectral signatures (2150 bands configuration; 350-2500 nm) of selected ground-level targets (located by GPS) of soil, water; vegetation (common reed, watercress, filamentous algae) and standard EVA foam colored sheets (red, green, blue, black and white). Processing and analysis of the data were performed over an ITT ENVI platform. The hyper-spectral image underwent radiometric calibration according to the flight and sensor calibration parameters on CALIGEO platform and the raw DN scale was converted into radiance scale. Ground level visual survey of vegetation cover and height was applied at the habitat scale (100 m) by placing a 1m2 netted grids (10x10cm cells) along 'bank-to-bank' transect (in triplicates). Estimates of plant cover obtained by the pseudo-spectral methodology at the habitat scale were 35-61% for the watercress, 0.4-25% for the filamentous algae and 27-51% for plant-free patches. The respective estimates by ground level visual survey were 26-50, 14-43% and 36-50%. The pseudo-spectral methodology also yielded estimates for the section scale (104 m) of ca. 39% for the watercress, ca. 32% for the filamentous algae and 6% for plant-free patches. The respective estimates obtained by hyper-spectral swath were 38, 26 and 8%. Validation against ground-level measurements proved that pseudo-spectral methodology gives reasonably good estimates of in-stream plant cover. Therefore, this methodology can serve as a substitute for ground level estimates at small stream scales and for the low resolution hyper-spectral methodology at larger scales.

Comparing Broad-Band and Red Edge-Based Spectral Vegetation Indices to Estimate Nitrogen Concentration of Crops Using Casi Data

NASA Astrophysics Data System (ADS)

Wang, Yanjie; Liao, Qinhong; Yang, Guijun; Feng, Haikuan; Yang, Xiaodong; Yue, Jibo

2016-06-01

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn't show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

77 FR 26787 - Certain CMOS Image Sensors and Products Containing Same; Notice of Receipt of Complaint...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-07

... INTERNATIONAL TRADE COMMISSION [Docket No. 2895] Certain CMOS Image Sensors and Products.... International Trade Commission has received a complaint entitled Certain CMOS Image Sensors and Products... importation, and the sale within the United States after importation of certain CMOS image sensors and...

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

USGS Publications Warehouse

Running, S. W.; Justice, C.O.; Salomonson, V.V.; Hall, D.; Barker, J.; Kaufmann, Y. J.; Strahler, Alan H.; Huete, A.R.; Muller, Jan-Peter; Vanderbilt, V.; Wan, Z.; Teillet, P.; Carneggie, David M. Geological Survey (U.S.) Ohlen

1994-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.

Spectral Characterization of Suspected Acid Deposition Damage in Red Spruce (picea Rubens) Stands from Vermont

NASA Technical Reports Server (NTRS)

Vogelmann, J. E.; Rock, B. N.

1985-01-01

In an attempt to demonstrate the utility of remote sensing systems to monitor sites of suspected acid rain deposition damage, intensive field activities, coupled with aircraft overflights, were centered on red spruce stands in Vermont during August and September of 1984. Remote sensing data were acquired using the Airborne Imaging Spectrometer, Thematic Mapper Simulator, Barnes Model 12 to 1000 Modular Multiband Radiometer and Spectron Engineering Spectrometer (the former two flown on the NASA C-130; the latter two on A Bell UH-1B Iroquois Helicopter). Field spectral data were acquired during the week of the August overflights using a high spectral resolution spectrometer and two broad-band radiometers. Preliminary analyses of these data indicate a number of spectral differences in vegetation between high and low damage sites. Some of these differences are subtle, and are observable only with high spectral resolution sensors; others are less subtle and are observable using broad-band sensors.

Development of a UAV system for VNIR-TIR acquisitions in precision agriculture

NASA Astrophysics Data System (ADS)

Misopolinos, L.; Zalidis, Ch.; Liakopoulos, V.; Stavridou, D.; Katsigiannis, P.; Alexandridis, T. K.; Zalidis, G.

2015-06-01

Adoption of precision agriculture techniques requires the development of specialized tools that provide spatially distributed information. Both flying platforms and airborne sensors are being continuously evolved to cover the needs of plant and soil sensing at affordable costs. Due to restrictions in payload, flying platforms are usually limited to carry a single sensor on board. The aim of this work is to present the development of a vertical take-off and landing autonomous unmanned aerial vehicle (VTOL UAV) system for the simultaneous acquisition of high resolution vertical images at the visible, near infrared (VNIR) and thermal infrared (TIR) wavelengths. A system was developed that has the ability to trigger two cameras simultaneously with a fully automated process and no pilot intervention. A commercial unmanned hexacopter UAV platform was optimized to increase reliability, ease of operation and automation. The designed systems communication platform is based on a reduced instruction set computing (RISC) processor running Linux OS with custom developed drivers in an efficient way, while keeping the cost and weight to a minimum. Special software was also developed for the automated image capture, data processing and on board data and metadata storage. The system was tested over a kiwifruit field in northern Greece, at flying heights of 70 and 100m above the ground. The acquired images were mosaicked and geo-corrected. Images from both flying heights were of good quality and revealed unprecedented detail within the field. The normalized difference vegetation index (NDVI) was calculated along with the thermal image in order to provide information on the accurate location of stressors and other parameters related to the crop productivity. Compared to other available sources of data, this system can provide low cost, high resolution and easily repeatable information to cover the requirements of precision agriculture.

The Earth's Biosphere

NASA Technical Reports Server (NTRS)

2002-01-01

In the last five years, scientists have been able to monitor our changing planet in ways never before possible. The Sea-viewing Wide Field-of-View Sensor (SeaWiFS), aboard the OrbView-2 satellite, has given researchers an unprecedented view of the biological engine that drives life on Earth-the countless forms of plants that cover the land and fill the oceans. 'There is no question the Earth is changing. SeaWiFS has enabled us, for the first time, to monitor the biological consequences of that change-to see how the things we do, as well as natural variability, affect the Earth's ability to support life,' said Gene Carl Feldman, SeaWiFS project manager at NASA's Goddard Space Flight Center, Greenbelt, Md. SeaWiFS data, based on continuous daily global observations, have helped scientists make a more accurate assessment of the oceans' role in the global carbon cycle. The data provide a key parameter in a number of ecological and environmental studies as well as global climate-change modeling. The images of the Earth's changing land, ocean and atmosphere from SeaWiFS have documented many previously unrecognized phenomena. The image above shows the global biosphere from June 2002 measured by SeaWiFS. Data in the oceans is chlorophyll concentration, a measure of the amount of phytoplankton (microscopic plants) living in the ocean. On land SeaWiFS measures Normalized Difference Vegetation Index, an indication of the density of plant growth. For more information and images, read: SeaWiFS Sensor Marks Five Years Documenting Earth'S Dynamic Biosphere Image courtesy SeaWiFS project and copyright Orbimage.

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.

Synergy between Sentinel-1 radar time series and Sentinel-2 optical for the mapping of restored areas in Danube delta

NASA Astrophysics Data System (ADS)

Niculescu, Simona; Lardeux, Cédric; Hanganu, Jenica

2018-05-01

Wetlands are important and valuable ecosystems, yet, since 1900, more than 50 % of wetlands have been lost worldwide. An example of altered and partially restored coastal wetlands is the Danube Delta in Romania. Over time, human intervention has manifested itself in more than a quarter of the entire Danube surface. This intervention was brutal and has rendered ecosystem restoration very difficult. Studies for the rehabilitation / re-vegetation were started immediately after the Danube Delta was declared as a Biosphere Reservation in 1990. Remote sensing offers accurate methods for detecting and mapping change in restored wetlands. Vegetation change detection is a powerful indicator of restoration success. The restoration projects use vegetative cover as an important indicator of restoration success. To follow the evolution of the vegetation cover of the restored areas, satellite images radar and optical of last generation have been used, such as Sentinel-1 and Sentinel-2. Indeed the sensor sensitivity to the landscape depends on the wavelength what- ever radar or optical data and their polarization for radar data. Combining this kind of data is particularly relevant for the classification of wetland vegetation, which are associated with the density and size of the vegetation. In addition, the high temporal acquisition frequency of Sentinel-1 which are not sensitive to cloud cover al- low to use temporal signature of the different land cover. Thus we analyse the polarimetric and temporal signature of Sentinel-1 data in order to better understand the signature of the different study classes. In a second phase, we performed classifications based on the Random Forest supervised classification algorithm involving the entire Sentinel-1 time series, then starting from a Sentinel-2 collection and finally involving combinations of Sentinel-1 and -2 data.

Characterization of a Field Spectroradiometer for Unattended Vegetation Monitoring. Key Sensor Models and Impacts on Reflectance

PubMed Central

Pacheco-Labrador, Javier; Martín, M. Pilar

2015-01-01

Field spectroradiometers integrated in automated systems at Eddy Covariance (EC) sites are a powerful tool for monitoring and upscaling vegetation physiology and carbon and water fluxes. However, exposure to varying environmental conditions can affect the functioning of these sensors, especially if these cannot be completely insulated and stabilized. This can cause inaccuracy in the spectral measurements and hinder the comparison between data acquired at different sites. This paper describes the characterization of key sensor models in a double beam spectroradiometer necessary to calculate the Hemispherical-Conical Reflectance Factor (HCRF). Dark current, temperature dependence, non-linearity, spectral calibration and cosine receptor directional responses are modeled in the laboratory as a function of temperature, instrument settings, radiation measured or illumination angle. These models are used to correct the spectral measurements acquired continuously by the same instrument integrated outdoors in an automated system (AMSPEC-MED). Results suggest that part of the instrumental issues cancel out mutually or can be controlled by the instrument configuration, so that changes induced in HCFR reached about 0.05 at maximum. However, these corrections are necessary to ensure the inter-comparison of data with other ground or remote sensors and to discriminate instrumentally induced changes in HCRF from those related with vegetation physiology and directional effects. PMID:25679315

Photon Counting Imaging with an Electron-Bombarded Pixel Image Sensor

PubMed Central

Hirvonen, Liisa M.; Suhling, Klaus

2016-01-01

Electron-bombarded pixel image sensors, where a single photoelectron is accelerated directly into a CCD or CMOS sensor, allow wide-field imaging at extremely low light levels as they are sensitive enough to detect single photons. This technology allows the detection of up to hundreds or thousands of photon events per frame, depending on the sensor size, and photon event centroiding can be employed to recover resolution lost in the detection process. Unlike photon events from electron-multiplying sensors, the photon events from electron-bombarded sensors have a narrow, acceleration-voltage-dependent pulse height distribution. Thus a gain voltage sweep during exposure in an electron-bombarded sensor could allow photon arrival time determination from the pulse height with sub-frame exposure time resolution. We give a brief overview of our work with electron-bombarded pixel image sensor technology and recent developments in this field for single photon counting imaging, and examples of some applications. PMID:27136556

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

NASA Astrophysics Data System (ADS)

Tucker, Compton

2016-04-01

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

Evaluation and comparison of the IRS-P6 and the landsat sensors

USGS Publications Warehouse

Chander, G.; Coan, M.J.; Scaramuzza, P.L.

2008-01-01

The Indian Remote Sensing Satellite (IRS-P6), also called ResourceSat-1, was launched in a polar sun-synchronous orbit on October 17, 2003. It carries three sensors: the highresolution Linear Imaging Self-Scanner (LISS-IV), the mediumresolution Linear Imaging Self-Scanner (LISS-III), and the Advanced Wide-Field Sensor (AWiFS). These three sensors provide images of different resolutions and coverage. To understand the absolute radiometric calibration accuracy of IRS-P6 AWiFS and LISS-III sensors, image pairs from these sensors were compared to images from the Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced TM Plus (ETM+) sensors. The approach involves calibration of surface observations based on image statistics from areas observed nearly simultaneously by the two sensors. This paper also evaluated the viability of data from these nextgeneration imagers for use in creating three National Land Cover Dataset (NLCD) products: land cover, percent tree canopy, and percent impervious surface. Individual products were consistent with previous studies but had slightly lower overall accuracies as compared to data from the Landsat sensors.

Imaging Systems Provide Maps for U.S. Soldiers

NASA Technical Reports Server (NTRS)

2012-01-01

Spanning nearly four decades, the remarkable Landsat program has continuously provided data about the Earth s surface, including detailed maps of vegetation, land use, forest extent and health, surface water, population distribution, as well as how these features have changed over time. Managed by NASA and the U.S. Geological Survey, Landsat s series of satellites obtain data through passive remote sensing, or the use of sensors to read the energy reflected or emitted from the Earth s surface. After the data from the sensors is processed and analyzed, it can be applied to create information-rich images of the planet. While the Landsat program has launched seven satellites since 1972, only Landsat 5 and 7 are currently operating. The next spacecraft in line to ensure continuity of data for years to come is the Landsat Data Continuity Mission (LDCM). Planned for launch in 2012, LDCM will take measurements of the Earth in visible, nearinfrared, shortwave infrared, and thermal infrared bands. In addition to widespread use for land use planning and monitoring on local to regional scales, support for disaster response and evaluations, as well as water use monitoring, LDCM measurements will directly serve NASA s research in the areas of climate, the carbon cycle, ecosystems, the water cycle, biogeochemistry, and Earth s surface and interior.

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data

PubMed Central

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-01-01

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97. PMID:26393607

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data.

PubMed

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-09-18

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97.

Performance test and image correction of CMOS image sensor in radiation environment

NASA Astrophysics Data System (ADS)

Wang, Congzheng; Hu, Song; Gao, Chunming; Feng, Chang

2016-09-01

CMOS image sensors rival CCDs in domains that include strong radiation resistance as well as simple drive signals, so it is widely applied in the high-energy radiation environment, such as space optical imaging application and video monitoring of nuclear power equipment. However, the silicon material of CMOS image sensors has the ionizing dose effect in the high-energy rays, and then the indicators of image sensors, such as signal noise ratio (SNR), non-uniformity (NU) and bad point (BP) are degraded because of the radiation. The radiation environment of test experiments was generated by the 60Co γ-rays source. The camera module based on image sensor CMV2000 from CMOSIS Inc. was chosen as the research object. The ray dose used for the experiments was with a dose rate of 20krad/h. In the test experiences, the output signals of the pixels of image sensor were measured on the different total dose. The results of data analysis showed that with the accumulation of irradiation dose, SNR of image sensors decreased, NU of sensors was enhanced, and the number of BP increased. The indicators correction of image sensors was necessary, as it was the main factors to image quality. The image processing arithmetic was adopt to the data from the experiences in the work, which combined local threshold method with NU correction based on non-local means (NLM) method. The results from image processing showed that image correction can effectively inhibit the BP, improve the SNR, and reduce the NU.

High speed three-dimensional laser scanner with real time processing

NASA Technical Reports Server (NTRS)

Lavelle, Joseph P. (Inventor); Schuet, Stefan R. (Inventor)

2008-01-01

A laser scanner computes a range from a laser line to an imaging sensor. The laser line illuminates a detail within an area covered by the imaging sensor, the area having a first dimension and a second dimension. The detail has a dimension perpendicular to the area. A traverse moves a laser emitter coupled to the imaging sensor, at a height above the area. The laser emitter is positioned at an offset along the scan direction with respect to the imaging sensor, and is oriented at a depression angle with respect to the area. The laser emitter projects the laser line along the second dimension of the area at a position where a image frame is acquired. The imaging sensor is sensitive to laser reflections from the detail produced by the laser line. The imaging sensor images the laser reflections from the detail to generate the image frame. A computer having a pipeline structure is connected to the imaging sensor for reception of the image frame, and for computing the range to the detail using height, depression angle and/or offset. The computer displays the range to the area and detail thereon covered by the image frame.

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.

CMOS Active-Pixel Image Sensor With Intensity-Driven Readout

NASA Technical Reports Server (NTRS)

Langenbacher, Harry T.; Fossum, Eric R.; Kemeny, Sabrina

1996-01-01

Proposed complementary metal oxide/semiconductor (CMOS) integrated-circuit image sensor automatically provides readouts from pixels in order of decreasing illumination intensity. Sensor operated in integration mode. Particularly useful in number of image-sensing tasks, including diffractive laser range-finding, three-dimensional imaging, event-driven readout of sparse sensor arrays, and star tracking.

Resolving uncertainties in the urban air quality, climate, and vegetation nexus through citizen science, satellite imagery, and atmospheric modeling

NASA Astrophysics Data System (ADS)

Jenerette, D.; Wang, J.; Chandler, M.; Ripplinger, J.; Koutzoukis, S.; Ge, C.; Castro Garcia, L.; Kucera, D.; Liu, X.

2017-12-01

Large uncertainties remain in identifying the distribution of urban air quality and temperature risks across neighborhood to regional scales. Nevertheless, many cities are actively expanding vegetation with an expectation to moderate both climate and air quality risks. We address these uncertainties through an integrated analysis of satellite data, atmospheric modeling, and in-situ environmental sensor networks maintained by citizen scientists. During the summer of 2017 we deployed neighborhood-scale networks of air temperature and ozone sensors through three campaigns across urbanized southern California. During each five-week campaign we deployed six sensor nodes that included an EPA federal equivalent method ozone sensor and a suite of meteorological sensors. Each node was further embedded in a network of 100 air temperature sensors that combined a randomized design developed by the research team and a design co-created by citizen scientists. Between 20 and 60 citizen scientists were recruited for each campaign, with local partners supporting outreach and training to ensure consistent deployment and data gathering. We observed substantial variation in both temperature and ozone concentrations at scales less than 4km, whole city, and the broader southern California region. At the whole city scale the average spatial variation with our ozone sensor network just for city of Long Beach was 26% of the mean, while corresponding variation in air temperature was only 7% of the mean. These findings contrast with atmospheric model estimates of variation at the regional scale of 11% and 1%. Our results show the magnitude of fine-scale variation underestimated by current models and may also suggest scaling functions that can connect neighborhood and regional variation in both ozone and temperature risks in southern California. By engaging citizen science with high quality sensors, satellite data, and real-time forecasting, our results help identify magnitudes of climate and air quality risk variation across scales and can guide individual decisions and urban policies surrounding vegetation to moderate these risks.

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.

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

USGS Publications Warehouse

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

2010-01-01

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

Inexpensive Tools To Quantify And Map Vegetative Cover For Large-Scale Research Or Management Decisions.

USDA-ARS?s Scientific Manuscript database

Vegetative cover can be quantified quickly and consistently and often at lower cost with image analysis of color digital images than with visual assessments. Image-based mapping of vegetative cover for large-scale research and management decisions can now be considered with the accuracy of these met...

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

Evaluation of Sun Glint Correction Algorithms for High-Spatial Resolution Hyperspectral Imagery

DTIC Science & Technology

2012-09-01

ACRONYMS AND ABBREVIATIONS AISA Airborne Imaging Spectrometer for Applications AVIRIS Airborne Visible/Infrared Imaging Spectrometer BIL Band...sensor bracket mount combining Airborne Imaging Spectrometer for Applications ( AISA ) Eagle and Hawk sensors into a single imaging system (SpecTIR 2011...The AISA Eagle is a VNIR sensor with a wavelength range of approximately 400–970 nm and the AISA Hawk sensor is a SWIR sensor with a wavelength

Linear high-boost fusion of Stokes vector imagery for effective discrimination and recognition of real targets in the presence of multiple identical decoys

NASA Astrophysics Data System (ADS)

El-Saba, Aed; Sakla, Wesam A.

2010-04-01

Recently, the use of imaging polarimetry has received considerable attention for use in automatic target recognition (ATR) applications. In military remote sensing applications, there is a great demand for sensors that are capable of discriminating between real targets and decoys. Accurate discrimination of decoys from real targets is a challenging task and often requires the fusion of various sensor modalities that operate simultaneously. In this paper, we use a simple linear fusion technique known as the high-boost fusion method for effective discrimination of real targets in the presence of multiple decoys. The HBF assigns more weight to the polarization-based imagery in forming the final fused image that is used for detection. We have captured both intensity and polarization-based imagery from an experimental laboratory arrangement containing a mixture of sand/dirt, rocks, vegetation, and other objects for the purpose of simulating scenery that would be acquired in a remote sensing military application. A target object and three decoys that are identical in physical appearance (shape, surface structure and color) and different in material composition have also been placed in the scene. We use the wavelet-filter joint transform correlation (WFJTC) technique to perform detection between input scenery and the target object. Our results show that use of the HBF method increases the correlation performance metrics associated with the WFJTC-based detection process when compared to using either the traditional intensity or polarization-based images.

Web camera as low cost multispectral sensor for quantification of chlorophyll in soybean leaves

NASA Astrophysics Data System (ADS)

Adhiwibawa, Marcelinus A.; Setiawan, Yonathan E.; Prilianti, Kestrilia R.; Brotosudarmo, Tatas H. P.

2015-01-01

Soybeans is one of main crops in Indonesia but the demand for soybeans is not followed by an increase in soybeans national production. One of the production limitation factor is the availability of lush cultivation area for soybeans plantation. Indonesian farners are usually grow soybeans in marginal cultivation area that requires soybeans varieties which tolerant with environmental stress such as drought, nutrition limitation, pest, disease and many others. Chlorophyll content in leaf is one of plant health indicator that can be used to determine environmental stress tolerant soybean varieties. However, there are difficulties in soybeans breeding research due to the manual acquisition of data that are time consume and labour extensive. In this paper authors proposed automatic system of soybeans leaves area and chlorophyll quantification based on low cost multispectral sensor using web camera as an indicator of soybean plant tollerance to environmental stress particularlly drought stress. The system acquires the image of the plant that is placed in the acquisition box from the top of the plant. The image is segmented using NDVI (Normalized Difference Vegetation Index) from image and quantified to yield an average value of NDVI and leaf area. The proposed system showed that acquired NDVI value has a strong relationship with SPAD value with r-square value 0.70, while the leaf area prediction has error of 18.41%. Thus the automation system can quantify plant data with good result.

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

NASA Astrophysics Data System (ADS)

Zoran, Maria

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

Smart sensors II; Proceedings of the Seminar, San Diego, CA, July 31, August 1, 1980

NASA Astrophysics Data System (ADS)

Barbe, D. F.

1980-01-01

Topics discussed include technology for smart sensors, smart sensors for tracking and surveillance, and techniques and algorithms for smart sensors. Papers are presented on the application of very large scale integrated circuits to smart sensors, imaging charge-coupled devices for deep-space surveillance, ultra-precise star tracking using charge coupled devices, and automatic target identification of blurred images with super-resolution features. Attention is also given to smart sensors for terminal homing, algorithms for estimating image position, and the computational efficiency of multiple image registration algorithms.

CMOS image sensor-based implantable glucose sensor using glucose-responsive fluorescent hydrogel.

PubMed

Tokuda, Takashi; Takahashi, Masayuki; Uejima, Kazuhiro; Masuda, Keita; Kawamura, Toshikazu; Ohta, Yasumi; Motoyama, Mayumi; Noda, Toshihiko; Sasagawa, Kiyotaka; Okitsu, Teru; Takeuchi, Shoji; Ohta, Jun

2014-11-01

A CMOS image sensor-based implantable glucose sensor based on an optical-sensing scheme is proposed and experimentally verified. A glucose-responsive fluorescent hydrogel is used as the mediator in the measurement scheme. The wired implantable glucose sensor was realized by integrating a CMOS image sensor, hydrogel, UV light emitting diodes, and an optical filter on a flexible polyimide substrate. Feasibility of the glucose sensor was verified by both in vitro and in vivo experiments.

Radiometric Normalization of Large Airborne Image Data Sets Acquired by Different Sensor Types

NASA Astrophysics Data System (ADS)

Gehrke, S.; Beshah, B. T.

2016-06-01

Generating seamless mosaics of aerial images is a particularly challenging task when the mosaic comprises a large number of im-ages, collected over longer periods of time and with different sensors under varying imaging conditions. Such large mosaics typically consist of very heterogeneous image data, both spatially (different terrain types and atmosphere) and temporally (unstable atmo-spheric properties and even changes in land coverage). We present a new radiometric normalization or, respectively, radiometric aerial triangulation approach that takes advantage of our knowledge about each sensor's properties. The current implementation supports medium and large format airborne imaging sensors of the Leica Geosystems family, namely the ADS line-scanner as well as DMC and RCD frame sensors. A hierarchical modelling - with parameters for the overall mosaic, the sensor type, different flight sessions, strips and individual images - allows for adaptation to each sensor's geometric and radiometric properties. Additional parameters at different hierarchy levels can compensate radiome-tric differences of various origins to compensate for shortcomings of the preceding radiometric sensor calibration as well as BRDF and atmospheric corrections. The final, relative normalization is based on radiometric tie points in overlapping images, absolute radiometric control points and image statistics. It is computed in a global least squares adjustment for the entire mosaic by altering each image's histogram using a location-dependent mathematical model. This model involves contrast and brightness corrections at radiometric fix points with bilinear interpolation for corrections in-between. The distribution of the radiometry fixes is adaptive to each image and generally increases with image size, hence enabling optimal local adaptation even for very long image strips as typi-cally captured by a line-scanner sensor. The normalization approach is implemented in HxMap software. It has been successfully applied to large sets of heterogeneous imagery, including the adjustment of original sensor images prior to quality control and further processing as well as radiometric adjustment for ortho-image mosaic generation.

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

NASA Astrophysics Data System (ADS)

Brown, I.; Wennbom, M.

2013-12-01

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

Railroad Valley, Nevada

NASA Image and Video Library

2002-02-01

Information from images of Railroad Valley, Nevada captured on August 17, 2001 by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) may provide a powerful tool for monitoring crop health and maintenance procedures. These images cover an area of north central Nevada. The top image shows irrigated fields, with healthy vegetation in red. The middle image highlights the amount of vegetation. The color code shows highest vegetation content in red, orange, yellow, green, blue, and purple and the lowest in black. The final image is a thermal infrared channel, with warmer temperatures in white and colder in black. In the thermal image, the northernmost and westernmost fields are markedly colder on their northwest areas, even though no differences are seen in the visible image or the second, Vegetation Index image. This can be attributed to the presence of excess water, which can lead to crop damage. http://photojournal.jpl.nasa.gov/catalog/PIA03463

Remote sensing of land degradation: experiences from Latin America and the Caribbean.

PubMed

Metternicht, G; Zinck, J A; Blanco, P D; del Valle, H F

2010-01-01

Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

Fine Resolution Air Quality Monitoring from a Small Satellite: CHRIS/PROBA.

PubMed

Nichol, Janet E; Wong, Man Sing; Chan, Yuk Ying

2008-11-27

Current remote sensing techniques fail to address the task of air quality monitoring over complex regions where multiple pollution sources produce high spatial variability. This is due to a lack of suitable satellite-sensor combinations and appropriate aerosol optical thickness (AOT) retrieval algorithms. The new generation of small satellites, with their lower costs and greater flexibility has the potential to address this problem, with customised platform-sensor combinations dedicated to monitoring single complex regions or mega-cities. This paper demonstrates the ability of the European Space Agency's small satellite sensor CHRIS/PROBA to provide reliable AOT estimates at a spatially detailed level over Hong Kong, using a modified version of the dense dark vegetation (DDV) algorithm devised for MODIS. Since CHRIS has no middle-IR band such as the MODIS 2,100 nm band which is transparent to fine aerosols, the longest waveband of CHRIS, the 1,019 nm band was used to approximate surface reflectance, by the subtraction of an offset derived from synchronous field reflectance spectra. Aerosol reflectance in the blue and red bands was then obtained from the strong empirical relationship observed between the CHRIS 1,019 nm, and the blue and red bands respectively. AOT retrievals for three different dates were shown to be reliable, when compared with AERONET and Microtops II sunphotometers, and a Lidar, as well as air quality data at ground stations. The AOT images exhibited considerable spatial variability over the 11 x 11km image area and were able to indicate both local and long distance sources.

Fluorescence lidar multi-color imaging of vegetation

NASA Technical Reports Server (NTRS)

Johansson, J.; Wallinder, E.; Edner, H.; Svanberg, S.

1992-01-01

Multi-color imaging of vegetation fluorescence following laser excitation is reported for distances of 50 m. A mobile laser radar system equipped with a Nd:YAG laser transmitter and a 40 cm diameter telescope was used. Image processing allows extraction of information related to the physiological status of the vegetation and might prove useful in forest decline research.

Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors.

PubMed

Dutton, Neale A W; Gyongy, Istvan; Parmesan, Luca; Henderson, Robert K

2016-07-20

SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors by evaluating the peak separation and width (PSW) of single photon peaks in a photon counting histogram (PCH). The technique is used to identify and analyse cumulative noise in analogue integrating SPC SPAD-based pixels. The DSERN of our SPAD image sensor is exploited to confirm recent multi-photon threshold quanta image sensor (QIS) theory. Finally, various single and multiple photon spatio-temporal oversampling techniques are reviewed.

Discrimination And Biophysical Characterization Of Brazilian Cerrado Physiognomies With Eo-1 Hyperspectral Hyperion

NASA Technical Reports Server (NTRS)

Miura, Tomoaki; Huete, Alfredo R.; Ferreira, Laerte G.; Sano, Edson E.

2004-01-01

The savanna, typically found in the sub-tropics and seasonal tropics, are the dominant vegetation biome type in the southern hemisphere, covering approximately 45% of the South America. In Brazil, the savanna, locally known as "cerrado," is the most intensely stressed biome with both natural environmental pressures (e.g., the strong seasonality in weather, extreme soil nutrient impoverishment, and widespread fire occurrences) and rapid/aggressive land conversions (Skole et al., 1994; Ratter et al., 1997). Better characterization and discrimination of cerrado physiognomies are needed in order to improve understanding of cerrado dynamics and its impact on carbon storage, nutrient dynamics, and the prospect for sustainable land use in the Brazilian cerrado biome. Satellite remote sensing have been known to be a useful tool for land cover and land use mapping (Rougharden et al., 1991; Hansen et al., 2000). However, attempts to discriminate and classify Brazilian cerrado using multi-spectral sensors (e.g., Landsat TM) and/or moderate resolution sensors (e.g., NOAA AVHRR NDVI) have often resulted in a limited success due partly to small contrasts depicted in their multiband, spectral reflectance or vegetation index values among cerrado classes (Seyler et al., 2002; Fran a and Setzer, 1998). In this study, we aimed to improve discrimination as well as biophysical characterization of the Brazilian cerrado physiognomies with hyperspectral remote sensing. We used Hyperion, the first satellite-based hyperspectral imager, onboard the Earth Observing-1 (EO-1) platform.

Microwave Sensors for Breast Cancer Detection

PubMed Central

2018-01-01

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript. PMID:29473867

Microwave Sensors for Breast Cancer Detection.

PubMed

Wang, Lulu

2018-02-23

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.

Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Houborg, Rasmus; McCabe, Matthew F.; Angel, Yoseline; Middleton, Elizabeth M.

2016-01-01

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense time series of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.; Angel, Yoseline; Middleton, Elizabeth M.

2016-10-01

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from the Terra MODIS Sensor: 2000-2005

NASA Technical Reports Server (NTRS)

Yang, Wenze; Huang, Dong; Tan, Bin; Stroeve, Julienne C.; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2006-01-01

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product.

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

NASA Astrophysics Data System (ADS)

Nitze, Ingmar; Barrett, Brian; Cawkwell, Fiona

2015-02-01

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8-10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database.

PubMed

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-28

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

PubMed Central

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-01

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m. PMID:26828496

Topographic Controls on Southern California Ecosystem Function and Post-fire Recovery: a Satellite and Near-surface Remote Sensing Approach

NASA Astrophysics Data System (ADS)

Azzari, George

Southern Californian wildfires can influence climate in a variety of ways, including changes in surface albedo, emission of greenhouse gases and aerosols, and the production of tropospheric ozone. Ecosystem post-fire recovery plays a key role in determining the strength, duration, and relative importance of these climate forcing agents. Southern California's ecosystems vary markedly with topography, creating sharp transitions with elevation, aspect, and slope. Little is known about the ways topography influences ecosystem properties and function, particularly in the context of post-fire recovery. We combined images from the USGS satellite Landsat 5 with flux tower measurements to analyze pre- and post-fire albedo and carbon exchanged by Southern California's ecosystems in the Santa Ana Mountains. We reduced the sources of external variability in Landsat images using several correction methods for topographic and bidirectional effects. We used time series of corrected images to infer the Net Ecosystem Exchange and surface albedo, and calculated the radiative forcing due to CO2 emissions and albedo changes. We analyzed the patterns of recovery and radiative forcing on north- and south-facing slopes, stratified by vegetation classes including grassland, coastal sage scrub, chaparral, and evergreen oak forest. We found that topography strongly influenced post-fire recovery and radiative forcing. Field observations are often limited by the difficulty of collecting ground validation data. Current instrumentation networks do not provide adequate spatial resolution for landscape-level analysis. The deployment of consumer-market technology could reduce the cost of near-surface measurements, allowing the installation of finer-scale instrument networks. We tested the performance of the Microsoft Kinect sensor for measuring vegetation structure. We used Kinect to acquire 3D vegetation point clouds in the field, and used these data to compute plant height, crown diameter, and volume. We found good agreement between Kinect-derived and manual measurements.

High-content analysis of single cells directly assembled on CMOS sensor based on color imaging.

PubMed

Tanaka, Tsuyoshi; Saeki, Tatsuya; Sunaga, Yoshihiko; Matsunaga, Tadashi

2010-12-15

A complementary metal oxide semiconductor (CMOS) image sensor was applied to high-content analysis of single cells which were assembled closely or directly onto the CMOS sensor surface. The direct assembling of cell groups on CMOS sensor surface allows large-field (6.66 mm×5.32 mm in entire active area of CMOS sensor) imaging within a second. Trypan blue-stained and non-stained cells in the same field area on the CMOS sensor were successfully distinguished as white- and blue-colored images under white LED light irradiation. Furthermore, the chemiluminescent signals of each cell were successfully visualized as blue-colored images on CMOS sensor only when HeLa cells were placed directly on the micro-lens array of the CMOS sensor. Our proposed approach will be a promising technique for real-time and high-content analysis of single cells in a large-field area based on color imaging. Copyright © 2010 Elsevier B.V. All rights reserved.

CMOS image sensor-based implantable glucose sensor using glucose-responsive fluorescent hydrogel

PubMed Central

Tokuda, Takashi; Takahashi, Masayuki; Uejima, Kazuhiro; Masuda, Keita; Kawamura, Toshikazu; Ohta, Yasumi; Motoyama, Mayumi; Noda, Toshihiko; Sasagawa, Kiyotaka; Okitsu, Teru; Takeuchi, Shoji; Ohta, Jun

2014-01-01

A CMOS image sensor-based implantable glucose sensor based on an optical-sensing scheme is proposed and experimentally verified. A glucose-responsive fluorescent hydrogel is used as the mediator in the measurement scheme. The wired implantable glucose sensor was realized by integrating a CMOS image sensor, hydrogel, UV light emitting diodes, and an optical filter on a flexible polyimide substrate. Feasibility of the glucose sensor was verified by both in vitro and in vivo experiments. PMID:25426316

The First Four Year's of Orthoimages from NEON's Airborne Observation Platform

NASA Astrophysics Data System (ADS)

Adler, J.; Gallery, W. O.

2016-12-01

The National Ecological Observatory Network (NEON), funded by the National Science Foundation (NSF), has been collecting orthorectified images in conjunction with lidar and spectrometer data for the past four years. The NEON project breaks up the United States into 20 regional areas from Puerto Rico to the North Slope of Alaska, with each region (Domain) typically having three specific sites of interest. Each site spans between 100km2 - 720km2 in area. Currently there are over 125,000 orthorectified images online from 6 Domains for the public and scientific community to freely download. These images are expected to assist researchers in many areas including vegetation cover, dominant vegetation type, and environmental change detection. In 2016 the NEON Airborne Observation Platform (AOP) group has collected digital imagery at 8.5 cm resolution over approximately 30 sites, for a total of 60,000 orthoimages. This presentation details the current status of the surveys conducted to date, and describes the scientific details of how NEON publishes Level 1 and Level 3 products. In particular, the onboard lidar system's contribution to the orthorectification process is outlined, in addition to the routines utilized for correcting white balance and exposure. Additionally, key flight parameters needed to produce NEON's complementary data of multi-sensor (camera/lidar/spectrometer) instruments are discussed. Problems with validating the orthoimages with the coarser resolution lidar system are addressed, to include the utilization of ground-truth locations. Lastly, methods to access NEON's publically available 10cm resolution orthoimages (in both individual image format, and in 1km by 1km tiles) are presented. A brief overview of the 2017 field season's nine new sites completes the presentation.

Beam imaging sensor and method for using same

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

McAninch, Michael D.; Root, Jeffrey J.

The present invention relates generally to the field of sensors for beam imaging and, in particular, to a new and useful beam imaging sensor for use in determining, for example, the power density distribution of a beam including, but not limited to, an electron beam or an ion beam. In one embodiment, the beam imaging sensor of the present invention comprises, among other items, a circumferential slit that is either circular, elliptical or polygonal in nature. In another embodiment, the beam imaging sensor of the present invention comprises, among other things, a discontinuous partially circumferential slit. Also disclosed is amore » method for using the various beams sensor embodiments of the present invention.« less

Analysis on the Effect of Sensor Views in Image Reconstruction Produced by Optical Tomography System Using Charge-Coupled Device.

PubMed

Jamaludin, Juliza; Rahim, Ruzairi Abdul; Fazul Rahiman, Mohd Hafiz; Mohd Rohani, Jemmy

2018-04-01

Optical tomography (OPT) is a method to capture a cross-sectional image based on the data obtained by sensors, distributed around the periphery of the analyzed system. This system is based on the measurement of the final light attenuation or absorption of radiation after crossing the measured objects. The number of sensor views will affect the results of image reconstruction, where the high number of sensor views per projection will give a high image quality. This research presents an application of charge-coupled device linear sensor and laser diode in an OPT system. Experiments in detecting solid and transparent objects in crystal clear water were conducted. Two numbers of sensors views, 160 and 320 views are evaluated in this research in reconstructing the images. The image reconstruction algorithms used were filtered images of linear back projection algorithms. Analysis on comparing the simulation and experiments image results shows that, with 320 image views giving less area error than 160 views. This suggests that high image view resulted in the high resolution of image reconstruction.

Integration of piezo-capacitive and piezo-electric nanoweb based pressure sensors for imaging of static and dynamic pressure distribution.

PubMed

Jeong, Y J; Oh, T I; Woo, E J; Kim, K J

2017-07-01

Recently, highly flexible and soft pressure distribution imaging sensor is in great demand for tactile sensing, gait analysis, ubiquitous life-care based on activity recognition, and therapeutics. In this study, we integrate the piezo-capacitive and piezo-electric nanowebs with the conductive fabric sheets for detecting static and dynamic pressure distributions on a large sensing area. Electrical impedance tomography (EIT) and electric source imaging are applied for reconstructing pressure distribution images from measured current-voltage data on the boundary of the hybrid fabric sensor. We evaluated the piezo-capacitive nanoweb sensor, piezo-electric nanoweb sensor, and hybrid fabric sensor. The results show the feasibility of static and dynamic pressure distribution imaging from the boundary measurements of the fabric sensors.

Extraction of urban vegetation with Pleiades multiangular images

NASA Astrophysics Data System (ADS)

Lefebvre, Antoine; Nabucet, Jean; Corpetti, Thomas; Courty, Nicolas; Hubert-Moy, Laurence

2016-10-01

Vegetation is essential in urban environments since it provides significant services in terms of health, heat, property value, ecology ... As part of the European Union Biodiversity Strategy Plan for 2020, the protection and development of green-infrastructures is strengthened in urban areas. In order to evaluate and monitor the quality of the green infra-structures, this article investigates contributions of Pléiades multi-angular images to extract and characterize low and high urban vegetation. From such images one can extract both spectral and elevation information from optical images. Our method is composed of 3 main steps : (1) the computation of a normalized Digital Surface Model from the multi-angular images ; (2) Extraction of spectral and contextual features ; (3) a classification of vegetation classes (tree and grass) performed with a random forest classifier. Results performed in the city of Rennes in France show the ability of multi-angular images to extract DEM in urban area despite building height. It also highlights its importance and its complementarity with contextual information to extract urban vegetation.

Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise.

PubMed

Zhang, Jiachao; Hirakawa, Keigo

2017-04-01

This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. A quantile analysis in pixel, wavelet transform, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed to correct the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we propose a mixture of Poisson denoising method to remove the denoising artifacts without affecting image details, such as edge and textures. Experiments with real sensor data verify that denoising for real image sensor data is indeed improved by this new technique.

Assessment of seasonal features based on Landsat time series for tree crown cover mapping in Burkina Faso

NASA Astrophysics Data System (ADS)

Liu, Jinxiu; Heiskanen, Janne; Aynekuly, Ermias; Pellikka, Petri

2016-04-01

Tree crown cover (CC) is an important vegetation attribute for land cover characterization, and for mapping and monitoring forest cover. Free data from Landsat and Sentinel-2 allow construction of fine resolution satellite image time series and extraction of seasonal features for predicting vegetation attributes. In the savannas, surface reflectance vary distinctively according to the rainy and dry seasons, and seasonal features are useful information for CC mapping. However, it is unclear if it is better to use spectral bands or vegetation indices (VI) for computation of seasonal features, and how feasible different VI are for CC prediction in the savanna woodlands and agroforestry parklands of West Africa. In this study, the objective was to compare seasonal features based on spectral bands and VI for CC mapping in southern Burkina Faso. A total of 35 Landsat images from November 2013 to October 2014 were processed. Seasonal features were computed using a harmonic model with three parameters (mean, amplitude and phase), and spectral bands, normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), normalized difference water index (NDWI), tasseled cap (TC) indices (brightness, greenness, wetness) as input data. The seasonal features were employed to predict field estimated CC (n = 160) using Random Forest algorithm. The most accurate results were achieved when using seasonal features based on TC indices (R2: 0.65; RMSE: 10.7%) and spectral bands (R2: 0.64; RMSE: 10.8%). GNDVI performed better than NDVI or NDWI, and NDWI resulted in the poorest results (R2: 0.56; RMSE: 11.9%). The results indicate that spectral features should be carefully selected for CC prediction as shown by relatively poor performance of commonly used NDVI. The seasonal features based on three TC indices and all the spectral bands provided superior accuracy in comparison to single VI. The method presented in this study provides a feasible method to map CC based on seasonal features with possibility to integrate medium resolution satellite observation from several sensors (e.g. Landsat and Sentinel-2) in the future.

A monitoring system for vegetable greenhouses based on a wireless sensor network.

PubMed

Li, Xiu-hong; Cheng, Xiao; Yan, Ke; Gong, Peng

2010-01-01

A wireless sensor network-based automatic monitoring system is designed for monitoring the life conditions of greenhouse vegetables. The complete system architecture includes a group of sensor nodes, a base station, and an internet data center. For the design of wireless sensor node, the JN5139 micro-processor is adopted as the core component and the Zigbee protocol is used for wireless communication between nodes. With an ARM7 microprocessor and embedded ZKOS operating system, a proprietary gateway node is developed to achieve data influx, screen display, system configuration and GPRS based remote data forwarding. Through a Client/Server mode the management software for remote data center achieves real-time data distribution and time-series analysis. Besides, a GSM-short-message-based interface is developed for sending real-time environmental measurements, and for alarming when a measurement is beyond some pre-defined threshold. The whole system has been tested for over one year and satisfactory results have been observed, which indicate that this system is very useful for greenhouse environment monitoring.

Apparatus and method for a light direction sensor

NASA Technical Reports Server (NTRS)

Leviton, Douglas B. (Inventor)

2011-01-01

The present invention provides a light direction sensor for determining the direction of a light source. The system includes an image sensor; a spacer attached to the image sensor, and a pattern mask attached to said spacer. The pattern mask has a slit pattern that as light passes through the slit pattern it casts a diffraction pattern onto the image sensor. The method operates by receiving a beam of light onto a patterned mask, wherein the patterned mask as a plurality of a slit segments. Then, diffusing the beam of light onto an image sensor and determining the direction of the light source.

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.

Development of a global LAI estimation algorithm for JAXA's new earth observation satellite sensor, GCOM-C/SGLI

NASA Astrophysics Data System (ADS)

Ono, Y.; Murakami, H.; Kobayashi, H.; Nasahara, K. N.; Kajiwara, K.; Honda, Y.

2014-12-01

Leaf Area Index (LAI) is defined as the one-side green leaf area per unit ground surface area. Global LAI products, such as MOD15 (Terra&Aqua/MODIS) and CYCLOPES (SPOT/VEGETATION) are used for many global terrestrial carbon models. Japan Aerospace eXploration Agency (JAXA) is planning to launch GCOM-C (Global Change Observation Mission-Climate) which carries SGLI (Second-generation GLobal Imager) in the Japanese Fiscal Year 2017. SGLI has the features, such as 17-channel from near ultraviolet to thermal infrared, 250-m spatial resolution, polarization, and multi-angle (nadir and ±45-deg. along-track slant) observation. In the GCOM-C/SGLI land science team, LAI is scheduled to be generated from GCOM-C/SGLI observation data as a standard product (daily 250-m). In extisting algorithms, LAI is estimated by the reverse analysis of vegetation radiative transfer models (RTMs) using multi-spectral and mono-angle observation data. Here, understory layer in vegetation RTMs is assumed by plane parallel (green leaves + soil) which set up arbitrary understroy LAI. However, actual understory consists of various elements, such as green leaves, dead leaves, branches, soil, and snow. Therefore, if understory in vegetation RTMs differs from reality, it will cause an error of LAI to estimate. This report describes an algorithm which estimates LAI in consideration of the influence of understory using GCOM-C/SGLI multi-spectral and multi-angle observation data.

A Modified Kriging Method to Interpolate the Soil Moisture Measured by Wireless Sensor Network with the Aid of Remote Sensing Images

NASA Astrophysics Data System (ADS)

Zhang, J.; Liu, Q.; Li, X.; Niu, H.; Cai, E.

2015-12-01

In recent years, wireless sensor network (WSN) emerges to collect Earth observation data at relatively low cost and light labor load, while its observations are still point-data. To learn the spatial distribution of a land surface parameter, interpolating the point data is necessary. Taking soil moisture (SM) for example, its spatial distribution is critical information for agriculture management, hydrological and ecological researches. This study developed a method to interpolate the WSN-measured SM to acquire the spatial distribution in a 5km*5km study area, located in the middle reaches of HEIHE River, western China. As SM is related to many factors such as topology, soil type, vegetation and etc., even the WSN observation grid is not dense enough to reflect the SM distribution pattern. Our idea is to revise the traditional Kriging algorithm, introducing spectral variables, i.e., vegetation index (VI) and abledo, from satellite imagery as supplementary information to aid the interpolation. Thus, the new Extended-Kriging algorithm operates on the spatial & spectral combined space. To run the algorithm, first we need to estimate the SM variance function, which is also extended to the combined space. As the number of WSN samples in the study area is not enough to gather robust statistics, we have to assume that the SM variance function is invariant over time. So, the variance function is estimated from a SM map, derived from the airborne CASI/TASI images acquired in July 10, 2012, and then applied to interpolate WSN data in that season. Data analysis indicates that the new algorithm can provide more details to the variation of land SM. Then, the Leave-one-out cross-validation is adopted to estimate the interpolation accuracy. Although a reasonable accuracy can be achieved, the result is not yet satisfactory. Besides improving the algorithm, the uncertainties in WSN measurements may also need to be controlled in our further work.

Near-surface Thermal Infrared Imaging of a Mixed Forest

NASA Astrophysics Data System (ADS)

Aubrecht, D. M.; Helliker, B. R.; Richardson, A. D.

2014-12-01

Measurement of an organism's temperature is of basic physiological importance and therefore necessary for ecosystem modeling, yet most models derive leaf temperature from energy balance arguments or assume it is equal to air temperature. This is because continuous, direct measurement of leaf temperature outside of a controlled environment is difficult and rarely done. Of even greater challenge is measuring leaf temperature with the resolution required to understand the underlying energy balance and regulation of plant processes. To measure leaf temperature through the year, we have mounted a high-resolution, thermal infrared camera overlooking the canopy of a temperate deciduous forest. The camera is co-located with an eddy covariance system and a suite of radiometric sensors. Our camera measures longwave thermal infrared (λ = 7.5-14 microns) using a microbolometer array. Suspended in the canopy within the camera FOV is a matte black copper plate instrumented with fine wire thermocouples that acts as a thermal reference for each image. In this presentation, I will discuss the challenges of continuous, long-term field operation of the camera, as well as measurement sensitivity to physical and environmental parameters. Based on this analysis, I will show that the uncertainties in converting radiometric signal to leaf temperature are well constrained. The key parameter for minimizing uncertainty is the emissivity of the objects being imaged: measuring the emissivity to within 0.01 enables leaf temperature to be calculated to within 0.5°C. Finally, I will present differences in leaf temperature observed amongst species. From our two-year record, we characterize high frequency, daily, and seasonal thermal signatures of leaves and crowns, in relation to environmental conditions. Our images are taken with sufficient spatial and temporal resolution to quantify the preferential heating of sunlit portions of the canopy and the cooling effect of wind gusts. Future work will be focused on correlations between hyperspectral vegetation indices, fluxes, and thermal signatures to characterize vegetation stress. As water stress increases, causing photosynthesis and transpiration to shutdown, heat fluxes, leaf temperature, and narrow band vegetation indices should report signatures of the affected processes.

Study the performance of star sensor influenced by space radiation damage of image sensor

NASA Astrophysics Data System (ADS)

Feng, Jie; Li, Yudong; Wen, Lin; Guo, Qi; Zhang, Xingyao

2018-03-01

Star sensor is an essential component of spacecraft attitude control system. Spatial radiation can cause star sensor performance degradation, abnormal work, attitude measurement accuracy and reliability reduction. Many studies have already been dedicated to the radiation effect on Charge-Coupled Device(CCD) image sensor, but fewer studies focus on the radiation effect of star sensor. The innovation of this paper is to study the radiation effects from the device level to the system level. The influence of the degradation of CCD image sensor radiation sensitive parameters on the performance parameters of star sensor is studied in this paper. The correlation among the radiation effect of proton, the non-uniformity noise of CCD image sensor and the performance parameter of star sensor is analyzed. This paper establishes a foundation for the study of error prediction and correction technology of star sensor on-orbit attitude measurement, and provides some theoretical basis for the design of high performance star sensor.

Water Catchment and Storage Monitoring

NASA Astrophysics Data System (ADS)

Bruenig, Michael; Dunbabin, Matt; Moore, Darren

2010-05-01

Sensors and Sensor Networks technologies provide the means for comprehensive understanding of natural processes in the environment by radically increasing the availability of empirical data about the natural world. This step change is achieved through a dramatic reduction in the cost of data acquisition and many orders of magnitude increase in the spatial and temporal granularity of measurements. Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO) is undertaking a strategic research program developing wireless sensor network technology for environmental monitoring. As part of this research initiative, we are engaging with government agencies to densely monitor water catchments and storages, thereby enhancing understanding of the environmental processes that affect water quality. In the Gold Coast hinterland in Queensland, Australia, we are building sensor networks to monitor restoration of rainforest within the catchment, and to monitor methane flux release and water quality in the water storages. This poster will present our ongoing work in this region of eastern Australia. The Springbrook plateau in the Gold Coast hinterland lies within a World Heritage listed area, has uniquely high rainfall, hosts a wide range of environmental gradients, and forms part of the catchment for Gold Coast's water storages. Parts of the plateau are being restored from agricultural grassland to native rainforest vegetation. Since April 2008, we have had a 10-node, multi-hop sensor network deployed there to monitor microclimate variables. This network will be expanded to 50-nodes in February 2010, and to around 200-nodes and 1000 sensors by mid-2011, spread over an area of approximately 0.8 square kilometers. The extremely dense microclimate sensing will enhance knowledge of the environmental factors that enhance or inhibit the regeneration of native rainforest. The final network will also include nodes with acoustic and image sensing capability for monitoring higher level parameters such as fauna diversity. The regenerating rainforest environment presents a number of interesting challenges for wireless sensor networks related to energy harvesting and to reliable low-power wireless communications through dense and wet vegetation. Located downstream from the Springbrook plateau, the Little Nerang and Hinze dams are the two major water supply storages for the Gold Coast region. In September 2009 we fitted methane, light, wind, and sonar sensors to our autonomous electric boat platform and successfully demonstrated autonomous collection of methane flux release data on Little Nerang Dam. Sensor and boat status data were relayed back to a human operator on the shore of the dam via a small network of our Fleck™ nodes. The network also included 4 floating nodes each fitted with a string of 6 temperature sensors for profiling temperature at different water depths. We plan to expand the network further during 2010 to incorporate floating methane nodes, additional temperature sensing nodes, as well as land-based microclimate nodes. The overall monitoring system will provide significant data to understand the connected catchment-to-storage system and will provide continuous data to monitor and understand change trends within this world heritage area.

The remote characterization of vegetation using Unmanned Aerial Vehicle photography

USDA-ARS?s Scientific Manuscript database

Unmanned Aerial Vehicles (UAVs) can fly in place of piloted aircraft to gather remote sensing information on vegetation characteristics. The type of sensors flown depends on the instrument payload capacity available, so that, depending on the specific UAV, it is possible to obtain video, aerial phot...

Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors

PubMed Central

Dutton, Neale A. W.; Gyongy, Istvan; Parmesan, Luca; Henderson, Robert K.

2016-01-01

SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors by evaluating the peak separation and width (PSW) of single photon peaks in a photon counting histogram (PCH). The technique is used to identify and analyse cumulative noise in analogue integrating SPC SPAD-based pixels. The DSERN of our SPAD image sensor is exploited to confirm recent multi-photon threshold quanta image sensor (QIS) theory. Finally, various single and multiple photon spatio-temporal oversampling techniques are reviewed. PMID:27447643

Variations in global land surface phenology: a comparison of satellite optical and passive microwave data

NASA Astrophysics Data System (ADS)

Tong, X.; Tian, F.; Brandt, M.; Zhang, W.; Liu, Y.; Fensholt, R.

2017-12-01

Changes in vegetation phenological events are among the most sensitive biological responses to climate change. In last decades, facilitating by satellite remote sensing techniques, land surface phenology (LSP) have been monitored at global scale using proxy approaches as tracking the temporal change of a satellite-derived vegetation index. However, the existing global assessments of changes in LSP are all established on the basis of leaf phenology using NDVI derived from optical sensors, being responsive to vegetation canopy cover and greenness. Instead, the vegetation optical depth (VOD) parameter from passive microwave sensors, which is sensitive to the aboveground vegetation water content by including as well the woody components in the observations, provides an alternative, independent and comprehensive means for global vegetation phenology monitoring. We used the unique long-term global VOD record available for the period 1992-2012 to monitoring the dynamics of LSP metrics (length of season, start of season and end of season) in comparison with the dynamics of LSP metrics derived from the latest GIMMS NDVI3G V1. We evaluated the differences in the linear trends of LSP metrics between two datasets. Currently, our results suggest that the level of seasonality variation of vegetation water content is less than the vegetation greenness. We found significant phenological changes in vegetation water content in African woodlands, where has been reported with little leaf phenological change regardless of the delays in rainfall onset. Therefore, VOD might allow us to detect temporal shifts in the timing difference of vegetation water storage vs. leaf emergence and to see if some ecophysiological thresholds seem to be reached, that could cause species turnover as climate change-driven alterations to the African monsoon proceed.

SAR studies in the Yuma Desert, Arizona: Sand penetration, geology, and the detection of military ordnance debris

USGS Publications Warehouse

Schaber, G.G.

1999-01-01

Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also Compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).

Land surface phenology

USGS Publications Warehouse

Hanes, Jonathan M.; Liang, Liang; Morisette, Jeffrey T.

2013-01-01

Certain vegetation types (e.g., deciduous shrubs, deciduous trees, grasslands) have distinct life cycles marked by the growth and senescence of leaves and periods of enhanced photosynthetic activity. Where these types exist, recurring changes in foliage alter the reflectance of electromagnetic radiation from the land surface, which can be measured using remote sensors. The timing of these recurring changes in reflectance is called land surface phenology (LSP). During recent decades, a variety of methods have been used to derive LSP metrics from time series of reflectance measurements acquired by satellite-borne sensors. In contrast to conventional phenology observations, LSP metrics represent the timing of reflectance changes that are driven by the aggregate activity of vegetation within the areal unit measured by the satellite sensor and do not directly provide information about the phenology of individual plants, species, or their phenophases. Despite the generalized nature of satellite sensor-derived measurements, they have proven useful for studying changes in LSP associated with various phenomena. This chapter provides a detailed overview of the use of satellite remote sensing to monitor LSP. First, the theoretical basis for the application of satellite remote sensing to the study of vegetation phenology is presented. After establishing a theoretical foundation for LSP, methods of deriving and validating LSP metrics are discussed. This chapter concludes with a discussion of major research findings and current and future research directions.

Evaluation of a HDR image sensor with logarithmic response for mobile video-based applications

NASA Astrophysics Data System (ADS)

Tektonidis, Marco; Pietrzak, Mateusz; Monnin, David

2017-10-01

The performance of mobile video-based applications using conventional LDR (Low Dynamic Range) image sensors highly depends on the illumination conditions. As an alternative, HDR (High Dynamic Range) image sensors with logarithmic response are capable to acquire illumination-invariant HDR images in a single shot. We have implemented a complete image processing framework for a HDR sensor, including preprocessing methods (nonuniformity correction (NUC), cross-talk correction (CTC), and demosaicing) as well as tone mapping (TM). We have evaluated the HDR sensor for video-based applications w.r.t. the display of images and w.r.t. image analysis techniques. Regarding the display we have investigated the image intensity statistics over time, and regarding image analysis we assessed the number of feature correspondences between consecutive frames of temporal image sequences. For the evaluation we used HDR image data recorded from a vehicle on outdoor or combined outdoor/indoor itineraries, and we performed a comparison with corresponding conventional LDR image data.

The lucky image-motion prediction for simple scene observation based soft-sensor technology

NASA Astrophysics Data System (ADS)

Li, Yan; Su, Yun; Hu, Bin

2015-08-01

High resolution is important to earth remote sensors, while the vibration of the platforms of the remote sensors is a major factor restricting high resolution imaging. The image-motion prediction and real-time compensation are key technologies to solve this problem. For the reason that the traditional autocorrelation image algorithm cannot meet the demand for the simple scene image stabilization, this paper proposes to utilize soft-sensor technology in image-motion prediction, and focus on the research of algorithm optimization in imaging image-motion prediction. Simulations results indicate that the improving lucky image-motion stabilization algorithm combining the Back Propagation Network (BP NN) and support vector machine (SVM) is the most suitable for the simple scene image stabilization. The relative error of the image-motion prediction based the soft-sensor technology is below 5%, the training computing speed of the mathematical predication model is as fast as the real-time image stabilization in aerial photography.

Fusion: ultra-high-speed and IR image sensors

NASA Astrophysics Data System (ADS)

Etoh, T. Goji; Dao, V. T. S.; Nguyen, Quang A.; Kimata, M.

2015-08-01

Most targets of ultra-high-speed video cameras operating at more than 1 Mfps, such as combustion, crack propagation, collision, plasma, spark discharge, an air bag at a car accident and a tire under a sudden brake, generate sudden heat. Researchers in these fields require tools to measure the high-speed motion and heat simultaneously. Ultra-high frame rate imaging is achieved by an in-situ storage image sensor. Each pixel of the sensor is equipped with multiple memory elements to record a series of image signals simultaneously at all pixels. Image signals stored in each pixel are read out after an image capturing operation. In 2002, we developed an in-situ storage image sensor operating at 1 Mfps 1). However, the fill factor of the sensor was only 15% due to a light shield covering the wide in-situ storage area. Therefore, in 2011, we developed a backside illuminated (BSI) in-situ storage image sensor to increase the sensitivity with 100% fill factor and a very high quantum efficiency 2). The sensor also achieved a much higher frame rate,16.7 Mfps, thanks to the wiring on the front side with more freedom 3). The BSI structure has another advantage that it has less difficulties in attaching an additional layer on the backside, such as scintillators. This paper proposes development of an ultra-high-speed IR image sensor in combination of advanced nano-technologies for IR imaging and the in-situ storage technology for ultra-highspeed imaging with discussion on issues in the integration.

ASTER Mexicali

NASA Technical Reports Server (NTRS)

2000-01-01

Dramatic differences in land use patterns are highlighted in this image of the U.S.-Mexico border. Lush, regularly gridded agricultural fields on the U.S. side contrast with the more barren fields of Mexico This June 12, 2000, sub-scene combines visible and near infrared bands, displaying vegetation in red. The town of Mexicali-Calexico spans the border in the middle of the image; El Centro, California, is in the upper left. Watered by canals fed from the Colorado River, California's Imperial Valley is one of the country's major fruit and vegetable producers. This image covers an area 24 kilometers (15 miles) wide and 30 kilometers (19 miles) long in three bands of the reflected visible and infrared wavelength region.

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of five Earth-observing instruments launched December 18, 1999, on NASA's Terra satellite. The instrument was built by Japan's Ministry of International Trade and Industry. A joint U.S./Japan science team is responsible for validation and calibration of the instrument and the data products. Dr. Anne Kahle at NASA's Jet Propulsion Laboratory, Pasadena, California, is the U.S. science team leader; Moshe Pniel of JPL is the project manager. ASTER is the only high-resolution imaging sensor on Terra. The primary goal of the ASTER mission is to obtain high-resolution image data in 14 channels over the entire land surface, as well as black and white stereo images. With revisit time of between 4 and 16 days, ASTER will provide the capability for repeat coverage of changing areas on Earth's surface. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of five Earth-observing instruments launched December 18, 1999, on NASA's Terra satellite. The instrument was built by Japan's Ministry of International Trade and Industry. A joint U.S./Japan science team is responsible for validation and calibration of the instrument and the data products. Dr. Anne Kahle at NASA's Jet Propulsion Laboratory, Pasadena, California, is the U.S. science team leader; Moshe Pniel of JPL is the project manager. ASTER is the only high-resolution imaging sensor on Terra. The primary goal of the ASTER mission is to obtain high-resolution image data in 14 channels over the entire land surface, as well as black and white stereo images. With revisit time of between 4 and 16 days, ASTER will provide the capability for repeat coverage of changing areas on Earth's surface.

The broad spectral coverage and high spectral resolution of ASTER will provide scientists in numerous disciplines with critical information for surface mapping and monitoring dynamic conditions and temporal change. Examples of applications include monitoring glacial advances and retreats, potentially active volcanoes, thermal pollution, and coral reef degradation; identifying crop stress; determining cloud morphology and physical properties; evaluating wetlands; mapping surface temperature of soils and geology; and measuring surface heat balance.

Advanced sensor-simulation capability

NASA Astrophysics Data System (ADS)

Cota, Stephen A.; Kalman, Linda S.; Keller, Robert A.

1990-09-01

This paper provides an overview of an advanced simulation capability currently in use for analyzing visible and infrared sensor systems. The software system, called VISTAS (VISIBLE/INFRARED SENSOR TRADES, ANALYSES, AND SIMULATIONS) combines classical image processing techniques with detailed sensor models to produce static and time dependent simulations of a variety of sensor systems including imaging, tracking, and point target detection systems. Systems modelled to date include space-based scanning line-array sensors as well as staring 2-dimensional array sensors which can be used for either imaging or point source detection.

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

Treesearch

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

2012-01-01

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

Development of an unmanned agricultural robotics system for measuring crop conditions for precision aerial application

USDA-ARS?s Scientific Manuscript database

An Unmanned Agricultural Robotics System (UARS) is acquired, rebuilt with desired hardware, and operated in both classrooms and field. The UARS includes crop height sensor, crop canopy analyzer, normalized difference vegetative index (NDVI) sensor, multispectral camera, and hyperspectral radiometer...

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.

Flexible phosphor sensors: a digital supplement or option to rigid sensors.

PubMed

Glazer, Howard S

2014-01-01

An increasing number of dental practices are upgrading from film radiography to digital radiography, for reasons that include faster image processing, easier image access, better patient education, enhanced data storage, and improved office productivity. Most practices that have converted to digital technology use rigid, or direct, sensors. Another digital option is flexible phosphor sensors, also called indirect sensors or phosphor storage plates (PSPs). Flexible phosphor sensors can be advantageous for use with certain patients who may be averse to direct sensors, and they can deliver a larger image area. Additionally, sensor cost for replacement PSPs is considerably lower than for hard sensors. As such, flexible phosphor sensors appear to be a viable supplement or option to direct sensors.

Using high-resolution radar images to determine vegetation cover for soil erosion assessments.

PubMed

Bargiel, D; Herrmann, S; Jadczyszyn, J

2013-07-30

Healthy soils are crucial for human well-being. Because soils are threatened worldwide, politicians recognize the need for soil protection. For example, the European Commission has launched the Thematic Strategy for Soil Protection, which requests the European member states to identify high risk areas for soil degradation. Most states use the Universal Soil Loss Equation (USLE) to assess soil erosion risk at the national scale. The USLE includes different factors, one of them is the vegetation cover and management factor (C factor). Modern satellite-based radar sensors now provide highly accurate vegetation cover data, enabling opportunities to improve the accuracy of the C factor. The presented study proves the suitability for C factor determination based on a multi-temporal classification of high-resolution radar images. Further USLE factors were derived from existing data sources (meteorological data, soil maps, digital elevation model) to conduct an USLE-based soil erosion assessment. The resulting map illustrates a qualitative assessment for soil erosion risk within a plot of about 7*12 km in an agricultural region in Poland that is very susceptible to soil erosion processes. A high erosion risk of more than 10 tonnes per ha and year was assessed to occur on 13.6% (646 ha) of the agricultural areas within the investigated plot. Further 7.8% (372 ha) of agricultural land is threaten by a medium risk of 5-10 tonnes per ha and year. Such a spatial information about areas of high or medium soil erosion risk are crucial for the development of strategies for the protection of soils. Copyright © 2013 Elsevier Ltd. All rights reserved.

Spaceborne imaging radar research in the 90's

NASA Technical Reports Server (NTRS)

Elachi, Charles

1986-01-01

The imaging radar experiments on SEASAT and on the space shuttle (SIR-A and SIR-B) have led to a wide interest in the use of spaceborne imaging radars in Earth and planetary sciences. The radar sensors provide unique and complimentary information to what is acquired with visible and infrared imagers. This includes subsurface imaging in arid regions, all weather observation of ocean surface dynamic phenomena, structural mapping, soil moisture mapping, stereo imaging and resulting topographic mapping. However, experiments up to now have exploited only a very limited range of the generic capability of radar sensors. With planned sensor developments in the late 80's and early 90's, a quantum jump will be made in our ability to fully exploit the potential of these sensors. These developments include: multiparameter research sensors such as SIR-C and X-SAR, long-term and global monitoring sensors such as ERS-1, JERS-1, EOS, Radarsat, GLORI and the spaceborne sounder, planetary mapping sensors such as the Magellan and Cassini/Titan mappers, topographic three-dimensional imagers such as the scanning radar altimeter and three-dimensional rain mapping. These sensors and their associated research are briefly described.

Star centroiding error compensation for intensified star sensors.

PubMed

Jiang, Jie; Xiong, Kun; Yu, Wenbo; Yan, Jinyun; Zhang, Guangjun

2016-12-26

A star sensor provides high-precision attitude information by capturing a stellar image; however, the traditional star sensor has poor dynamic performance, which is attributed to its low sensitivity. Regarding the intensified star sensor, the image intensifier is utilized to improve the sensitivity, thereby further improving the dynamic performance of the star sensor. However, the introduction of image intensifier results in star centroiding accuracy decrease, further influencing the attitude measurement precision of the star sensor. A star centroiding error compensation method for intensified star sensors is proposed in this paper to reduce the influences. First, the imaging model of the intensified detector, which includes the deformation parameter of the optical fiber panel, is established based on the orthographic projection through the analysis of errors introduced by the image intensifier. Thereafter, the position errors at the target points based on the model are obtained by using the Levenberg-Marquardt (LM) optimization method. Last, the nearest trigonometric interpolation method is presented to compensate for the arbitrary centroiding error of the image plane. Laboratory calibration result and night sky experiment result show that the compensation method effectively eliminates the error introduced by the image intensifier, thus remarkably improving the precision of the intensified star sensors.

Experimental application of simulation tools for evaluating UAV video change detection

NASA Astrophysics Data System (ADS)

Saur, Günter; Bartelsen, Jan

2015-10-01

Change detection is one of the most important tasks when unmanned aerial vehicles (UAV) are used for video reconnaissance and surveillance. In this paper, we address changes on short time scale, i.e. the observations are taken within time distances of a few hours. Each observation is a short video sequence corresponding to the near-nadir overflight of the UAV above the interesting area and the relevant changes are e.g. recently added or removed objects. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are versatile objects like trees and compression or transmission artifacts. To enable the usage of an automatic change detection within an interactive workflow of an UAV video exploitation system, an evaluation and assessment procedure has to be performed. Large video data sets which contain many relevant objects with varying scene background and altering influence parameters (e.g. image quality, sensor and flight parameters) including image metadata and ground truth data are necessary for a comprehensive evaluation. Since the acquisition of real video data is limited by cost and time constraints, from our point of view, the generation of synthetic data by simulation tools has to be considered. In this paper the processing chain of Saur et al. (2014) [1] and the interactive workflow for video change detection is described. We have selected the commercial simulation environment Virtual Battle Space 3 (VBS3) to generate synthetic data. For an experimental setup, an example scenario "road monitoring" has been defined and several video clips have been produced with varying flight and sensor parameters and varying objects in the scene. Image registration and change mask extraction, both components of the processing chain, are applied to corresponding frames of different video clips. For the selected examples, the images could be registered, the modelled changes could be extracted and the artifacts of the image rendering considered as noise (slight differences of heading angles, disparity of vegetation, 3D parallax) could be suppressed. We conclude that these image data could be considered to be realistic enough to serve as evaluation data for the selected processing components. Future work will extend the evaluation to other influence parameters and may include the human operator for mission planning and sensor control.

Beam imaging sensor

DOEpatents

McAninch, Michael D.; Root, Jeffrey J.

2016-07-05

The present invention relates generally to the field of sensors for beam imaging and, in particular, to a new and useful beam imaging sensor for use in determining, for example, the power density distribution of a beam including, but not limited to, an electron beam or an ion beam. In one embodiment, the beam imaging sensor of the present invention comprises, among other items, a circumferential slit that is either circular, elliptical or polygonal in nature.

Characterization of ASTER GDEM Elevation Data over Vegetated Area Compared with Lidar Data

NASA Technical Reports Server (NTRS)

Ni, Wenjian; Sun, Guoqing; Ranson, Kenneth J.

2013-01-01

Current researches based on areal or spaceborne stereo images with very high resolutions (less than 1 meter) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is a state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 meters) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that accurate co-registration between ASTER GDEM and the National Elevation Dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-seconds and further improved to 0.6 if only homogenous vegetated areas were considered.

Use of a digital camera to monitor the growth and nitrogen status of cotton.

PubMed

Jia, Biao; He, Haibing; Ma, Fuyu; Diao, Ming; Jiang, Guiying; Zheng, Zhong; Cui, Jin; Fan, Hua

2014-01-01

The main objective of this study was to develop a nondestructive method for monitoring cotton growth and N status using a digital camera. Digital images were taken of the cotton canopies between emergence and full bloom. The green and red values were extracted from the digital images and then used to calculate canopy cover. The values of canopy cover were closely correlated with the normalized difference vegetation index and the ratio vegetation index and were measured using a GreenSeeker handheld sensor. Models were calibrated to describe the relationship between canopy cover and three growth properties of the cotton crop (i.e., aboveground total N content, LAI, and aboveground biomass). There were close, exponential relationships between canopy cover and three growth properties. And the relationships for estimating cotton aboveground total N content were most precise, the coefficients of determination (R(2)) value was 0.978, and the root mean square error (RMSE) value was 1.479 g m(-2). Moreover, the models were validated in three fields of high-yield cotton. The result indicated that the best relationship between canopy cover and aboveground total N content had an R(2) value of 0.926 and an RMSE value of 1.631 g m(-2). In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring cotton growth and N status.

Use of MEMs and optical sensors for closed loop heliostat control

NASA Astrophysics Data System (ADS)

Harper, Paul Julian; Dreijer, Janto; Malan, Karel; Larmuth, James; Gauche, Paul

2016-05-01

The Helio 100 project at STERG (Stellenbosch Solar Thermal Research Group) aims to help reduce the cost of Concentrated Solar Thermal plants by deploying large numbers of small (1x2 m) low cost heliostats. One of the methods employed to reduce the cost of the heliostat field is to have a field that requires no site preparation (grading, leveling, vegetation clearance) and no expensive foundations or concrete pouring for each individual heliostat base. This implies that the heliostat pod frames and vertical mounts might be slightly out of vertical, and the normal method of dead reckoning using accurately surveyed and aligned heliostat bases cannot be used. This paper describes a combination of MEMs and optical sensors on the back of the heliostat, that together with a simple machine learning approach, give accurate and reproducible azimuth and elevation information for the heliostat plane. Initial experiments were done with an android phone mounted on the back of a heliostat as it was a readily available platform combining accelerometers' and camera into one programmable package. It was found quite easy to determine the pointing angle of the heliostat to within 1 milliradian using the rear facing camera and correlating known heliostat angles with target image features on the ground. We also tested the accuracy at various image resolutions by halving the image size successively till the feature detection failed. This showed that even a VGA (640x480) resolution image could give mean errors of 1.5 milliradian. The optical technique is exceedingly simple and does not use any camera calibration, angular reconstruction or knowledge of heliostat drive geometry. We also tested the ability of the 3d accelerometers to determine angle, but this was coarser than the camera and only accurate to around 10 milliradians.

Large Area Scene Selection Interface (LASSI). Methodology of Selecting Landsat Imagery for the Global Land Survey 2005

NASA Technical Reports Server (NTRS)

Franks, Shannon; Masek, Jeffrey G.; Headley, Rachel M.; Gasch, John; Arvidson, Terry

2009-01-01

The Global Land Survey (GLS) 2005 is a cloud-free, orthorectified collection of Landsat imagery acquired during the 2004-2007 epoch intended to support global land-cover and ecological monitoring. Due to the numerous complexities in selecting imagery for the GLS2005, NASA and the U.S. Geological Survey (USGS) sponsored the development of an automated scene selection tool, the Large Area Scene Selection Interface (LASSI), to aid in the selection of imagery for this data set. This innovative approach to scene selection applied a user-defined weighting system to various scene parameters: image cloud cover, image vegetation greenness, choice of sensor, and the ability of the Landsat 7 Scan Line Corrector (SLC)-off pair to completely fill image gaps, among others. The parameters considered in scene selection were weighted according to their relative importance to the data set, along with the algorithm's sensitivity to that weight. This paper describes the methodology and analysis that established the parameter weighting strategy, as well as the post-screening processes used in selecting the optimal data set for GLS2005.

Optical and Electric Multifunctional CMOS Image Sensors for On-Chip Biosensing Applications.

PubMed

Tokuda, Takashi; Noda, Toshihiko; Sasagawa, Kiyotaka; Ohta, Jun

2010-12-29

In this review, the concept, design, performance, and a functional demonstration of multifunctional complementary metal-oxide-semiconductor (CMOS) image sensors dedicated to on-chip biosensing applications are described. We developed a sensor architecture that allows flexible configuration of a sensing pixel array consisting of optical and electric sensing pixels, and designed multifunctional CMOS image sensors that can sense light intensity and electric potential or apply a voltage to an on-chip measurement target. We describe the sensors' architecture on the basis of the type of electric measurement or imaging functionalities.

A 100 Mfps image sensor for biological applications

NASA Astrophysics Data System (ADS)

Etoh, T. Goji; Shimonomura, Kazuhiro; Nguyen, Anh Quang; Takehara, Kosei; Kamakura, Yoshinari; Goetschalckx, Paul; Haspeslagh, Luc; De Moor, Piet; Dao, Vu Truong Son; Nguyen, Hoang Dung; Hayashi, Naoki; Mitsui, Yo; Inumaru, Hideo

2018-02-01

Two ultrahigh-speed CCD image sensors with different characteristics were fabricated for applications to advanced scientific measurement apparatuses. The sensors are BSI MCG (Backside-illuminated Multi-Collection-Gate) image sensors with multiple collection gates around the center of the front side of each pixel, placed like petals of a flower. One has five collection gates and one drain gate at the center, which can capture consecutive five frames at 100 Mfps with the pixel count of about 600 kpixels (512 x 576 x 2 pixels). In-pixel signal accumulation is possible for repetitive image capture of reproducible events. The target application is FLIM. The other is equipped with four collection gates each connected to an in-situ CCD memory with 305 elements, which enables capture of 1,220 (4 x 305) consecutive images at 50 Mfps. The CCD memory is folded and looped with the first element connected to the last element, which also makes possible the in-pixel signal accumulation. The sensor is a small test sensor with 32 x 32 pixels. The target applications are imaging TOF MS, pulse neutron tomography and dynamic PSP. The paper also briefly explains an expression of the temporal resolution of silicon image sensors theoretically derived by the authors in 2017. It is shown that the image sensor designed based on the theoretical analysis achieves imaging of consecutive frames at the frame interval of 50 ps.

Smart image sensors: an emerging key technology for advanced optical measurement and microsystems

NASA Astrophysics Data System (ADS)

Seitz, Peter

1996-08-01

Optical microsystems typically include photosensitive devices, analog preprocessing circuitry and digital signal processing electronics. The advances in semiconductor technology have made it possible today to integrate all photosensitive and electronical devices on one 'smart image sensor' or photo-ASIC (application-specific integrated circuits containing photosensitive elements). It is even possible to provide each 'smart pixel' with additional photoelectronic functionality, without compromising the fill factor substantially. This technological capability is the basis for advanced cameras and optical microsystems showing novel on-chip functionality: Single-chip cameras with on- chip analog-to-digital converters for less than $10 are advertised; image sensors have been developed including novel functionality such as real-time selectable pixel size and shape, the capability of performing arbitrary convolutions simultaneously with the exposure, as well as variable, programmable offset and sensitivity of the pixels leading to image sensors with a dynamic range exceeding 150 dB. Smart image sensors have been demonstrated offering synchronous detection and demodulation capabilities in each pixel (lock-in CCD), and conventional image sensors are combined with an on-chip digital processor for complete, single-chip image acquisition and processing systems. Technological problems of the monolithic integration of smart image sensors include offset non-uniformities, temperature variations of electronic properties, imperfect matching of circuit parameters, etc. These problems can often be overcome either by designing additional compensation circuitry or by providing digital correction routines. Where necessary for technological or economic reasons, smart image sensors can also be combined with or realized as hybrids, making use of commercially available electronic components. It is concluded that the possibilities offered by custom smart image sensors will influence the design and the performance of future electronic imaging systems in many disciplines, reaching from optical metrology to machine vision on the factory floor and in robotics applications.

Using Calibrated RGB Imagery from Low-Cost Uavs for Grassland Monitoring: Case Study at the Rengen Grassland Experiment (rge), Germany

NASA Astrophysics Data System (ADS)

Lussem, U.; Hollberg, J.; Menne, J.; Schellberg, J.; Bareth, G.

2017-08-01

Monitoring the spectral response of intensively managed grassland throughout the growing season allows optimizing fertilizer inputs by monitoring plant growth. For example, site-specific fertilizer application as part of precision agriculture (PA) management requires information within short time. But, this requires field-based measurements with hyper- or multispectral sensors, which may not be feasible on a day to day farming practice. Exploiting the information of RGB images from consumer grade cameras mounted on unmanned aerial vehicles (UAV) can offer cost-efficient as well as near-real time analysis of grasslands with high temporal and spatial resolution. The potential of RGB imagery-based vegetation indices (VI) from consumer grade cameras mounted on UAVs has been explored recently in several. However, for multitemporal analyses it is desirable to calibrate the digital numbers (DN) of RGB-images to physical units. In this study, we explored the comparability of the RGBVI from a consumer grade camera mounted on a low-cost UAV to well established vegetation indices from hyperspectral field measurements for applications in grassland. The study was conducted in 2014 on the Rengen Grassland Experiment (RGE) in Germany. Image DN values were calibrated into reflectance by using the Empirical Line Method (Smith & Milton 1999). Depending on sampling date and VI the correlation between the UAV-based RGBVI and VIs such as the NDVI resulted in varying R2 values from no correlation to up to 0.9. These results indicate, that calibrated RGB-based VIs have the potential to support or substitute hyperspectral field measurements to facilitate management decisions on grasslands.

Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images

NASA Astrophysics Data System (ADS)

Fernández-Manso, O.; Fernández-Manso, A.; Quintano, C.

2014-09-01

Aboveground biomass (AGB) estimation from optical satellite data is usually based on regression models of original or synthetic bands. To overcome the poor relation between AGB and spectral bands due to mixed-pixels when a medium spatial resolution sensor is considered, we propose to base the AGB estimation on fraction images from Linear Spectral Mixture Analysis (LSMA). Our study area is a managed Mediterranean pine woodland (Pinus pinaster Ait.) in central Spain. A total of 1033 circular field plots were used to estimate AGB from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) optical data. We applied Pearson correlation statistics and stepwise multiple regression to identify suitable predictors from the set of variables of original bands, fraction imagery, Normalized Difference Vegetation Index and Tasselled Cap components. Four linear models and one nonlinear model were tested. A linear combination of ASTER band 2 (red, 0.630-0.690 μm), band 8 (short wave infrared 5, 2.295-2.365 μm) and green vegetation fraction (from LSMA) was the best AGB predictor (Radj2=0.632, the root-mean-squared error of estimated AGB was 13.3 Mg ha-1 (or 37.7%), resulting from cross-validation), rather than other combinations of the above cited independent variables. Results indicated that using ASTER fraction images in regression models improves the AGB estimation in Mediterranean pine forests. The spatial distribution of the estimated AGB, based on a multiple linear regression model, may be used as baseline information for forest managers in future studies, such as quantifying the regional carbon budget, fuel accumulation or monitoring of management practices.

Testing and evaluation of tactical electro-optical sensors

NASA Astrophysics Data System (ADS)

Middlebrook, Christopher T.; Smith, John G.

2002-07-01

As integrated electro-optical sensor payloads (multi- sensors) comprised of infrared imagers, visible imagers, and lasers advance in performance, the tests and testing methods must also advance in order to fully evaluate them. Future operational requirements will require integrated sensor payloads to perform missions at further ranges and with increased targeting accuracy. In order to meet these requirements sensors will require advanced imaging algorithms, advanced tracking capability, high-powered lasers, and high-resolution imagers. To meet the U.S. Navy's testing requirements of such multi-sensors, the test and evaluation group in the Night Vision and Chemical Biological Warfare Department at NAVSEA Crane is developing automated testing methods, and improved tests to evaluate imaging algorithms, and procuring advanced testing hardware to measure high resolution imagers and line of sight stabilization of targeting systems. This paper addresses: descriptions of the multi-sensor payloads tested, testing methods used and under development, and the different types of testing hardware and specific payload tests that are being developed and used at NAVSEA Crane.

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronald; Russell, Jeffrey A.; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the basis for many inter-sensor interoperability or change detection techniques. Satellite inter-comparisons and accurate vegetation indices such as the Normalized Difference Vegetation Index, which is used to describe or to imply a wide variety of biophysical parameters and is defined in terms of near-infrared and redband reflectance, require the generation of accurate reflectance maps. This generation relies upon the removal of solar illumination, satellite geometry, and atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance, however, has been widely applied to only a few systems. In this study, we atmospherically corrected commercially available, high spatial resolution IKONOS and QuickBird imagery using several methods to determine the accuracy of the resulting reflectance maps. We used extensive ground measurement datasets for nine IKONOS and QuickBird scenes acquired over a two-year period to establish reflectance map accuracies. A correction approach using atmospheric products derived from Moderate Resolution Imaging Spectrometer data created excellent reflectance maps and demonstrated a reliable, effective method for reflectance map generation.

Imaging the atmosphere using volcanic infrasound recorded on a dense local sensor network

NASA Astrophysics Data System (ADS)

Marcillo, O. E.; Johnson, J. B.; Johnson, R.

2010-12-01

We deployed a 47-node infrasound sensor network around Kilauea’s Halemaumau Vent to image the atmospheric conditions of the near-surface. This active vent is a persistent radiator of energetic infrasound enabling us to probe atmospheric winds and temperatures. This research builds upon a previous experiment that recorded infrasound on a three-node network, to determine relative phase delay and invert for atmospheric wind. The technique developed for this previous analysis assumed the intrinsic sound speed and was able to track the evolution of the average wind field in a large area (around 10 km2) and was largely insensitive to local meteorological effects, caused by topography and vegetation. The results of this previous experiment showed the potential of this technique for atmospheric studies and called for a following experiment with a denser sensor network over a larger area. During the summer 2010, we returned to Kilauea and deployed a 47-sensor network in three different configurations around Kilauea summit and down the volcano’s flanks. Persistent infrasonic tremor was ‘loud’ with excess pressures up to 10 Pa (when scaled to 1 km) and periods of high acoustic emissions that lasted from hours to days. The instrumentation for this experiment was composed of single-channel RefTek RT125A Texan digitizers and InfraNMT infrasound sensors. The Texan digitizers provide high-resolution 24-bit analog to digital conversion and can operate continuously for approximately five days with two D-cell batteries. The InfraNMT sensor is based on a piezo-electric transducer and was developed at the Infrasound Laboratory at New Mexico Tech. This sensor features low power (< 3 mA at 9 V) and flat response between 0.02 to 50 Hz. Three different network topologies were tested during this two-week experiment. For the first and second topologies, the sensors were deployed along established roads on two almost perpendicular sensor lines centered at the Halema’uma’u crater. The furthest sensors were located at ~24 km and ~10 km from the vent respectively. Numerical analysis indicates that these two configurations will be able to probe the atmospheric conditions up to 2 km above the ground. The third topology featured most of the sensors on the summit crater at similar radial distances (2-4 km) and different azimuths. The data collected with the third topology is expected to provide detailed information of the very-local infrasonic field. Each configuration was on the ground and operational for around 84 hours. This full dataset will provide an opportunity to investigate source phenomenology and/or propagation effects of the infrasonic field. Tomographic studies of the atmosphere are expected to provide meteorological data that will be of value for ash and gas propagation models.

Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data

NASA Technical Reports Server (NTRS)

Roberts, D. A.; Smith, M. O.; Adams, J. B.

1993-01-01

The problem of distinguishing between green vegetation, nonphotosynthetic vegetation (NPV, such as dry grass, leaf litter, and woody material), and soils in imaging-spectrometer data is addressed by analyzing an image taken by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over the Jasper Ridge Biological Preserve (California) on September 20, 1989, using spectral mixture analysis. Over 98 percent of the spectral variation could be explained by linear mixtures of three endmembers, green vegetation, shade, and soil. NPV, which could not be distinguished from soil when included as an endmember, was discriminated by residual spectra that contained cellulose and lignin absorptions. Distinct communities of green vegetation were distinguished by (1) nonlinear mixing effect caused by transmission and scattering by green leaves, (2) variations in a derived canopy-shade spectrum, and (3) the fraction of NPV.

On the Relationship Between Hyperspectral Data and Foliar Nitrogen Content in Closed Canopy Forests

NASA Astrophysics Data System (ADS)

Knyazikhin, Y.; Schull, M.; Lepine, L. C.; Stenberg, P.; Mõttus, M.; Rautiainen, M.; Latorre, P.; Myneni, R.; Kaufmann, R.

2011-12-01

The importance of nitrogen for terrestrial ecosystem carbon dynamics and its climate feedback has been well recognized by the ecological community. Interaction between carbon and nitrogen at leaf level is among the fundamental mechanisms that directly control the dynamics of terrestrial vegetation carbon. This process influences absorption and scattering of solar radiation by foliage, which in turn impacts radiation reflected by the vegetation and measured by satellite sensors. NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and ground based data on canopy structure and foliage nitrogen concentration acquired over six sites in Maine, New England, Florida, North Carolina and Washington were analyzed to assess the role of canopy structure, leaf optics and its biochemical constituents in the spectral variation of radiation reflected by the forest. The study sites represent closed canopy forests (LAI~5). Our results suggest: 1. Impact of canopy structure is so strong that it can significantly suppress the sensitivity of hyperspectral data to leaf optics. 2. Forest reflectance spectra in the interval [710, 790 nm] are required to obtain the fraction of the total leaf area that a "sensor sees" in a given direction. For closed canopy forests its retrieval does not require canopy reflectance models, suggesting that canopy reflectance spectra in this interval provide a direct estimate of the leaf area fraction. 3. The leaf area fraction fully explains variation in measured reflectance spectra due to variation in canopy structure. This variable is used to estimate the mean leaf scattering over foliage that the "sensor sees." For example the nadir-viewing AVIRIS sensor accumulates foliage optical properties over 25% of the total foliage area in needle leaf forest and about 50% in broadleaf forest. 4. Leaf surface properties have an impact on forest reflectivity, lowering its sensitivity to leaf absorbing pigments. 5. Variation in foliar nitrogen concentration can explain up to 55% of variation in AVIRIS spectra in the interval between 400 and 900 nm. The remaining factors could be due to (a) impact of leaf surface properties and/or (b) under-sampling of leaf optical properties due to the single view of the AVIRIS sensor. The theory of canopy spectral invariants underlies the separation of leaf scattering from the total canopy reflectance spectrum.

On vegetation mapping in Alaska using LANDSAT imagery with primary concerns for method and purpose in satellite image-based vegetation and land-use mapping and the visual interpretation of imagery in photographic format

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A simulated color infrared LANDSAT image covering the western Seward Peninsula was used for identifying and mapping vegetation by direct visual examination. The 1:1,083,400 scale print used was prepared by a color additive process using positive transparencies from MSS bands 4, 5, and 7. Seven color classes were recognized. A vegetation map of 3200 sq km area just west of Fairbanks, Alaska was made. Five colors were recognized on the image and identified to vegetation types roughly equivalent to formations in the UNESCO classification: orange - broadleaf deciduous forest; gray - needleleaf evergreen forest; light violet - subarctic alpine tundra vegetation; violet - broadleaf deciduous shrub thicket; and dull violet - bog vegetation.

Crop sensors for automation of in-season nitrogen application

USDA-ARS?s Scientific Manuscript database

Crop canopy reflectance sensing can be used to assess in-season crop nitrogen (N) health for automatic control of N fertilization. Typically, sensor data are processed to an established index, such as the Normalized Difference Vegetative Index (NDVI) and differences in that index from a well-fertili...

Wireless image-data transmission from an implanted image sensor through a living mouse brain by intra body communication

NASA Astrophysics Data System (ADS)

Hayami, Hajime; Takehara, Hiroaki; Nagata, Kengo; Haruta, Makito; Noda, Toshihiko; Sasagawa, Kiyotaka; Tokuda, Takashi; Ohta, Jun

2016-04-01

Intra body communication technology allows the fabrication of compact implantable biomedical sensors compared with RF wireless technology. In this paper, we report the fabrication of an implantable image sensor of 625 µm width and 830 µm length and the demonstration of wireless image-data transmission through a brain tissue of a living mouse. The sensor was designed to transmit output signals of pixel values by pulse width modulation (PWM). The PWM signals from the sensor transmitted through a brain tissue were detected by a receiver electrode. Wireless data transmission of a two-dimensional image was successfully demonstrated in a living mouse brain. The technique reported here is expected to provide useful methods of data transmission using micro sized implantable biomedical sensors.

Cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM with the ResourceSat-1 (IRS-P6) AWiFS and LISS-III sensors

USGS Publications Warehouse

Chander, G.; Scaramuzza, P.L.

2006-01-01

Increasingly, data from multiple sensors are used to gain a more complete understanding of land surface processes at a variety of scales. The Landsat suite of satellites has collected the longest continuous archive of multispectral data. The ResourceSat-1 Satellite (also called as IRS-P6) was launched into the polar sunsynchronous orbit on Oct 17, 2003. It carries three remote sensing sensors: the High Resolution Linear Imaging Self-Scanner (LISS-IV), Medium Resolution Linear Imaging Self-Scanner (LISS-III), and the Advanced Wide Field Sensor (AWiFS). These three sensors are used together to provide images with different resolution and coverage. To understand the absolute radiometric calibration accuracy of IRS-P6 AWiFS and LISS-III sensors, image pairs from these sensors were compared to the Landsat-5 TM and Landsat-7 ETM+ sensors. The approach involved the calibration of nearly simultaneous surface observations based on image statistics from areas observed simultaneously by the two sensors.

Thermally assisted sensor for conformity assessment of biodiesel production

NASA Astrophysics Data System (ADS)

Kawano, M. S.; Kamikawachi, R. C.; Fabris, J. L.; Muller, M.

2015-02-01

Although biodiesel can be intentionally tampered with, impairing its quality, ineffective production processes may also result in a nonconforming final fuel. For an incomplete transesterification reaction, traces of alcohol (ethanol or methanol) or remaining raw material (vegetable oil or animal fats) may be harmful to consumers, the environment or to engines. Traditional methods for biodiesel assessment are complex, time consuming and expensive, leading to the need for the development of new and more versatile processes for quality control. This work describes a refractometric fibre optic based sensor that is thermally assisted, developed to quantify the remaining methanol or vegetable oil in biodiesel blends. The sensing relies on a long period grating to configure an in-fibre interferometer. A complete analytical routine is demonstrated for the sensor allowing the evaluation of the biodiesel blends without segregation of the components. The results show the sensor can determine the presence of oil or methanol in biodiesel with a concentration ranging from 0% to 10% v/v. The sensor presented a resolution and standard combined uncertainty of 0.013% v/v and 0.62% v/v for biodiesel-oil samples, and 0.007% v/v and 0.22% v/v for biodiesel-methanol samples, respectively.

Fusing Unmanned Aerial Vehicle Imagery with High Resolution Hydrologic Modeling (Invited)

NASA Astrophysics Data System (ADS)

Vivoni, E. R.; Pierini, N.; Schreiner-McGraw, A.; Anderson, C.; Saripalli, S.; Rango, A.

2013-12-01

After decades of development and applications, high resolution hydrologic models are now common tools in research and increasingly used in practice. More recently, high resolution imagery from unmanned aerial vehicles (UAVs) that provide information on land surface properties have become available for civilian applications. Fusing the two approaches promises to significantly advance the state-of-the-art in terms of hydrologic modeling capabilities. This combination will also challenge assumptions on model processes, parameterizations and scale as land surface characteristics (~0.1 to 1 m) may now surpass traditional model resolutions (~10 to 100 m). Ultimately, predictions from high resolution hydrologic models need to be consistent with the observational data that can be collected from UAVs. This talk will describe our efforts to develop, utilize and test the impact of UAV-derived topographic and vegetation fields on the simulation of two small watersheds in the Sonoran and Chihuahuan Deserts at the Santa Rita Experimental Range (Green Valley, AZ) and the Jornada Experimental Range (Las Cruces, NM). High resolution digital terrain models, image orthomosaics and vegetation species classification were obtained from a fixed wing airplane and a rotary wing helicopter, and compared to coarser analyses and products, including Light Detection and Ranging (LiDAR). We focus the discussion on the relative improvements achieved with UAV-derived fields in terms of terrain-hydrologic-vegetation analyses and summer season simulations using the TIN-based Real-time Integrated Basin Simulator (tRIBS) model. Model simulations are evaluated at each site with respect to a high-resolution sensor network consisting of six rain gauges, forty soil moisture and temperature profiles, four channel runoff flumes, a cosmic-ray soil moisture sensor and an eddy covariance tower over multiple summer periods. We also discuss prospects for the fusion of high resolution models with novel observations from UAVs, including synthetic aperture radar and multispectral imagery.

VIP Data Explorer: A Tool for Exploring 30 years of Vegetation Index and Phenology Observations

NASA Astrophysics Data System (ADS)

Barreto-munoz, A.; Didan, K.; Rivera-Camacho, J.; Yitayew, M.; Miura, T.; Tsend-Ayush, J.

2011-12-01

Continuous acquisition of global satellite imagery over the years has contributed to the creation of long term data records from AVHRR, MODIS, TM, SPOT-VGT and other sensors. These records account for 30+ years, as these archives grow, they become invaluable tools for environmental, resources management, and climate studies dealing with trends and changes from local, regional to global scale. In this project, the Vegetation Index and Phenology Lab (VIPLab) is processing 30 years of daily global surface reflectance data into an Earth Science Data Record of Vegetation Index and Phenology metrics. Data from AVHRR (N07,N09,N11 and N14) and MODIS (AQUA and TERRA collection 5) for the periods 1981-1999 and 2000-2010, at CMG resolution were processed into one seamless and sensor independent data record using various filtering, continuity and gap filling techniques (Tsend-Ayush et al., AGU 2011, Rivera-Camacho et al, AGU 2011). An interactive online tool (VIP Data Explorer) was developed to support the visualization, qualitative and quantitative exploration, distribution, and documentation of these records using a simple web 2.0 interface. The VIP Data explorer (http://vip.arizona.edu/viplab_data_explorer) can display any combination of multi temporal and multi source data, enable the quickly exploration and cross comparison of the various levels of processing of this data. It uses the Google Earth (GE) model and was developed using the GE API for images rendering, manipulation and geolocation. These ESDRs records can be quickly animated in this environment and explored for visual trends and anomalies detection. Additionally the tool enables extracting and visualizing any land pixel time series while showing the different levels of processing it went through. User can explore this ESDR database within this data explorer GUI environment, and any desired data can be placed into a dynamic "cart" to be ordered and downloaded later. More functionalities are planned and will be added to this data explorer tool as the project progresses.

Proceedings of the Augmented VIsual Display (AVID) Research Workshop

NASA Technical Reports Server (NTRS)

Kaiser, Mary K. (Editor); Sweet, Barbara T. (Editor)

1993-01-01

The papers, abstracts, and presentations were presented at a three day workshop focused on sensor modeling and simulation, and image enhancement, processing, and fusion. The technical sessions emphasized how sensor technology can be used to create visual imagery adequate for aircraft control and operations. Participants from industry, government, and academic laboratories contributed to panels on Sensor Systems, Sensor Modeling, Sensor Fusion, Image Processing (Computer and Human Vision), and Image Evaluation and Metrics.

Integrated Spectral Low Noise Image Sensor with Nanowire Polarization Filters for Low Contrast Imaging

DTIC Science & Technology

2015-11-05

AFRL-AFOSR-VA-TR-2015-0359 Integrated Spectral Low Noise Image Sensor with Nanowire Polarization Filters for Low Contrast Imaging Viktor Gruev...To) 02/15/2011 - 08/15/2015 4. TITLE AND SUBTITLE Integrated Spectral Low Noise Image Sensor with Nanowire Polarization Filters for Low Contrast...investigate alternative spectral imaging architectures based on my previous experience in this research area. I will develop nanowire polarization

Development of analysis techniques for remote sensing of vegetation resources

NASA Technical Reports Server (NTRS)

Draeger, W. C.

1972-01-01

Various data handling and analysis techniques are summarized for evaluation of ERTS-A and supporting high flight imagery. These evaluations are concerned with remote sensors applied to wildland and agricultural vegetation resource inventory problems. Monitoring California's annual grassland, automatic texture analysis, agricultural ground data collection techniques, and spectral measurements are included.

Application of passive imaging polarimetry in the discrimination and detection of different color targets of identical shapes using color-blind imaging sensors

NASA Astrophysics Data System (ADS)

El-Saba, A. M.; Alam, M. S.; Surpanani, A.

2006-05-01

Important aspects of automatic pattern recognition systems are their ability to efficiently discriminate and detect proper targets with low false alarms. In this paper we extend the applications of passive imaging polarimetry to effectively discriminate and detect different color targets of identical shapes using color-blind imaging sensor. For this case of study we demonstrate that traditional color-blind polarization-insensitive imaging sensors that rely only on the spatial distribution of targets suffer from high false detection rates, especially in scenarios where multiple identical shape targets are present. On the other hand we show that color-blind polarization-sensitive imaging sensors can successfully and efficiently discriminate and detect true targets based on their color only. We highlight the main advantages of using our proposed polarization-encoded imaging sensor.

Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery

PubMed Central

Scottá, Fernando C.; da Fonseca, Eliana L.

2015-01-01

This study aimed to evaluate changes in the aboveground net primary productivity (ANPP) of grasslands in the Pampa biome by using experimental plots and changes in the spectral responses of similar vegetation communities obtained by remote sensing and to compare both datasets with meteorological variations to validate the transition scales of the datasets. Two different geographic scales were considered in this study. At the local scale, an analysis of the climate and its direct influences on grassland ANPP was performed using data from a long-term experiment. At the regional scale, the influences of climate on the grassland reflectance patterns were determined using vegetation sensor imagery data. Overall, the monthly variations of vegetation canopy growth analysed using environmental changes (air temperature, total rainfall and total evapotranspiration) were similar. The results from the ANPP data and the NDVI data showed the that variations in grassland growth were similar and independent of the analysis scale, which indicated that local data and the relationships of local data with climate can be considered at the regional scale in the Pampa biome by using remote sensing. PMID:26197320

CMOS image sensor with organic photoconductive layer having narrow absorption band and proposal of stack type solid-state image sensors

NASA Astrophysics Data System (ADS)

Takada, Shunji; Ihama, Mikio; Inuiya, Masafumi

2006-02-01

Digital still cameras overtook film cameras in Japanese market in 2000 in terms of sales volume owing to their versatile functions. However, the image-capturing capabilities such as sensitivity and latitude of color films are still superior to those of digital image sensors. In this paper, we attribute the cause for the high performance of color films to their multi-layered structure, and propose the solid-state image sensors with stacked organic photoconductive layers having narrow absorption bands on CMOS read-out circuits.

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.

Phenopix: a R package to process digital images of a vegetation cover

NASA Astrophysics Data System (ADS)

Filippa, Gianluca; Cremonese, Edoardo; Migliavacca, Mirco; Galvagno, Marta; Morra di Cella, Umberto; Richardson, Andrew

2015-04-01

Plant phenology is a globally recognized indicator of the effects of climate change on the terrestrial biosphere. Accordingly, new tools to automatically track the seasonal development of a vegetation cover are becoming available and more and more deployed. Among them, near-continuous digital images are being collected in several networks in the US, Europe, Asia and Australia in a range of different ecosystems, including agricultural lands, deciduous and evergreen forests, and grasslands. The growing scientific interest in vegetation image analysis highlights the need of easy to use, flexible and standardized processing techniques. In this contribution we illustrate a new open source package called "phenopix" written in R language that allows to process images of a vegetation cover. The main features include: (i) define of one or more areas of interest on an image and process pixel information within them, (ii) compute vegetation indexes based on red green and blue channels, (iii) fit a curve to the seasonal trajectory of vegetation indexes and extract relevant dates (aka thresholds) on the seasonal trajectory; (iv) analyze image pixels separately to extract spatially explicit phenological information. The utilities of the package will be illustrated in detail for two subalpine sites, a grassland and a larch stand at about 2000 m in the Italian Western Alps. The phenopix package is a cost free and easy-to-use tool that allows to process digital images of a vegetation cover in a standardized, flexible and reproducible way. The software is available for download at the R forge web site (r-forge.r-project.org/projects/phenopix/).

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements

PubMed Central

Fontana, Fabio; Rixen, Christian; Jonas, Tobias; Aberegg, Gabriel; Wunderle, Stefan

2008-01-01

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG). We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand large-scale vegetation growth dynamics above the tree line in the European Alps. PMID:27879852

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements.

PubMed

Fontana, Fabio; Rixen, Christian; Jonas, Tobias; Aberegg, Gabriel; Wunderle, Stefan

2008-04-23

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps.

Simulation of Satellite, Airborne and Terrestrial LiDAR with DART (I):Waveform Simulation with Quasi-Monte Carlo Ray Tracing

NASA Technical Reports Server (NTRS)

Gastellu-Etchegorry, Jean-Philippe; Yin, Tiangang; Lauret, Nicolas; Grau, Eloi; Rubio, Jeremy; Cook, Bruce D.; Morton, Douglas C.; Sun, Guoqing

2016-01-01

Light Detection And Ranging (LiDAR) provides unique data on the 3-D structure of atmosphere constituents and the Earth's surface. Simulating LiDAR returns for different laser technologies and Earth scenes is fundamental for evaluating and interpreting signal and noise in LiDAR data. Different types of models are capable of simulating LiDAR waveforms of Earth surfaces. Semi-empirical and geometric models can be imprecise because they rely on simplified simulations of Earth surfaces and light interaction mechanisms. On the other hand, Monte Carlo ray tracing (MCRT) models are potentially accurate but require long computational time. Here, we present a new LiDAR waveform simulation tool that is based on the introduction of a quasi-Monte Carlo ray tracing approach in the Discrete Anisotropic Radiative Transfer (DART) model. Two new approaches, the so-called "box method" and "Ray Carlo method", are implemented to provide robust and accurate simulations of LiDAR waveforms for any landscape, atmosphere and LiDAR sensor configuration (view direction, footprint size, pulse characteristics, etc.). The box method accelerates the selection of the scattering direction of a photon in the presence of scatterers with non-invertible phase function. The Ray Carlo method brings traditional ray-tracking into MCRT simulation, which makes computational time independent of LiDAR field of view (FOV) and reception solid angle. Both methods are fast enough for simulating multi-pulse acquisition. Sensitivity studies with various landscapes and atmosphere constituents are presented, and the simulated LiDAR signals compare favorably with their associated reflectance images and Laser Vegetation Imaging Sensor (LVIS) waveforms. The LiDAR module is fully integrated into DART, enabling more detailed simulations of LiDAR sensitivity to specific scene elements (e.g., atmospheric aerosols, leaf area, branches, or topography) and sensor configuration for airborne or satellite LiDAR sensors.

Blur spot limitations in distal endoscope sensors

NASA Astrophysics Data System (ADS)

Yaron, Avi; Shechterman, Mark; Horesh, Nadav

2006-02-01

In years past, the picture quality of electronic video systems was limited by the image sensor. In the present, the resolution of miniature image sensors, as in medical endoscopy, is typically superior to the resolution of the optical system. This "excess resolution" is utilized by Visionsense to create stereoscopic vision. Visionsense has developed a single chip stereoscopic camera that multiplexes the horizontal dimension of the image sensor into two (left and right) images, compensates the blur phenomena, and provides additional depth resolution without sacrificing planar resolution. The camera is based on a dual-pupil imaging objective and an image sensor coated by an array of microlenses (a plenoptic camera). The camera has the advantage of being compact, providing simultaneous acquisition of left and right images, and offering resolution comparable to a dual chip stereoscopic camera with low to medium resolution imaging lenses. A stereoscopic vision system provides an improved 3-dimensional perspective of intra-operative sites that is crucial for advanced minimally invasive surgery and contributes to surgeon performance. An additional advantage of single chip stereo sensors is improvement of tolerance to electronic signal noise.

Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)

NASA Astrophysics Data System (ADS)

AlShamsi, Meera R.

2016-10-01

Over the past years, there has been various urban development all over the UAE. Dubai is one of the cities that experienced rapid growth in both development and population. That growth can have a negative effect on the surrounding environment. Hence, there has been a necessity to protect the environment from these fast pace changes. One of the major impacts this growth can have is on vegetation. As technology is evolving day by day, there is a possibility to monitor changes that are happening on different areas in the world using satellite imagery. The data from these imageries can be utilized to identify vegetation in different areas of an image through a process called vegetation detection. Being able to detect and monitor vegetation is very beneficial for municipal planning and management, and environment authorities. Through this, analysts can monitor vegetation growth in various areas and analyze these changes. By utilizing satellite imagery with the necessary data, different types of vegetation can be studied and analyzed, such as parks, farms, and artificial grass in sports fields. In this paper, vegetation features are detected and extracted through SAFIY system (i.e. the Smart Application for Feature extraction and 3D modeling using high resolution satellite ImagerY) by using high-resolution satellite imagery from DubaiSat-2 and DEIMOS-2 satellites, which provide panchromatic images of 1m resolution and spectral bands (red, green, blue and near infrared) of 4m resolution. SAFIY system is a joint collaboration between MBRSC and DEIMOS Space UK. It uses image-processing algorithms to extract different features (roads, water, vegetation, and buildings) to generate vector maps data. The process to extract green areas (vegetation) utilize spectral information (such as, the red and near infrared bands) from the satellite images. These detected vegetation features will be extracted as vector data in SAFIY system and can be updated and edited by end-users, such as governmental entities and municipalities.

Image Sensors Enhance Camera Technologies

NASA Technical Reports Server (NTRS)

2010-01-01

In the 1990s, a Jet Propulsion Laboratory team led by Eric Fossum researched ways of improving complementary metal-oxide semiconductor (CMOS) image sensors in order to miniaturize cameras on spacecraft while maintaining scientific image quality. Fossum s team founded a company to commercialize the resulting CMOS active pixel sensor. Now called the Aptina Imaging Corporation, based in San Jose, California, the company has shipped over 1 billion sensors for use in applications such as digital cameras, camera phones, Web cameras, and automotive cameras. Today, one of every three cell phone cameras on the planet feature Aptina s sensor technology.

A design of driving circuit for star sensor imaging camera

NASA Astrophysics Data System (ADS)

Li, Da-wei; Yang, Xiao-xu; Han, Jun-feng; Liu, Zhao-hui

2016-01-01

The star sensor is a high-precision attitude sensitive measuring instruments, which determine spacecraft attitude by detecting different positions on the celestial sphere. Imaging camera is an important portion of star sensor. The purpose of this study is to design a driving circuit based on Kodak CCD sensor. The design of driving circuit based on Kodak KAI-04022 is discussed, and the timing of this CCD sensor is analyzed. By the driving circuit testing laboratory and imaging experiments, it is found that the driving circuits can meet the requirements of Kodak CCD sensor.

Application of Hymap image in the environmental survey in Shenzhen, China

NASA Astrophysics Data System (ADS)

Pan, Wei; Yang, Xiaomao; Chen, Xuejiao; Feng, Ping

2017-10-01

Hyperspectral HyMap image with synchronous in-situ spectral data were used to survey the environmental condition in Shenzhen of South China. HyMap image was measured with 3.5m spatial resolution and 15nm spectral resolution from 0.44μm-2.5μm and corrected with Modtran5 model and synchronous solar illuminance and atmospheric visibility to the ground. The spectra of rocks, soils, water and vegetation were obtained by ASD spectrometer in reflectance. Both the fresh granite and eroded sandy soil was found with absorption at 2200nm+/-in-situ spectra, but the weathered granite and sandy soil have another absorption at 880nm 940 nm. Polluted water with high ammonia nitrogen and phosphorous and BOD5 get the strongest reflectance at 550 570nm, while polluted water of high CODcr and heavy metal ions content get the peak reflectance at 450 490nm. The in-situ spectra was resampled in wavelength range and spectral resolution to that of Hymap sensor for image classification with SAM algorithm, the unpaved granite among cement the paved mine pits , the newly excavated land surface and the eroded soil was mapped out with the accuracy over 95%. We also discriminate the artificial forest from the natural with the spectral endmember extracted from the image.

Design of electric control system for automatic vegetable bundling machine

NASA Astrophysics Data System (ADS)

Bao, Yan

2017-06-01

A design can meet the requirements of automatic bale food structure and has the advantages of simple circuit, and the volume is easy to enhance the electric control system of machine carrying bunch of dishes and low cost. The bundle of vegetable machine should meet the sensor to detect and control, in order to meet the control requirements; binding force can be adjusted by the button to achieve; strapping speed also can be adjusted, by the keys to set; sensors and mechanical line connection, convenient operation; can be directly connected with the plug, the 220V power supply can be connected to a power source; if, can work, by the transmission signal sensor, MCU to control the motor, drive and control procedures for small motor. The working principle of LED control circuit and temperature control circuit is described. The design of electric control system of automatic dish machine.