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Rignot, Eric

Publications and source records attributed to Rignot, Eric.

68 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Direction angle sensitivity of agricultural field backscatter with AIRSAR data

This paper presents a study of the direction angle sensitivity of radar backscatter from agricultural fields. The direction angle is defined as the angle between the incident plane and the perpendicular to the field row direction. Previous studies have concentrated on comparing the backscatter of agricultural fields imaged with 0, 45, and 90 degree direction angles. In contrast, this study concentrates on the backscatter changes occurring when the direction angle is modified by a few degrees or even by fractions of a degree. This is possible by using the output of the NASA/JPL AIRSAR processor, in which sixteen independent frames are formed, each one corresponding to the same radar scene imaged with a slightly different squint angle. The studied data set is an agricultural area in La Mancha, Spain acquired in June and July 1991 during the EFEDA experiment. This paper describes the observed backscatter variations of the agricultural fields with direction angle measured at P, L, and C bands. As expected, the backscatter is maximum for a 0 degree direction angle. For several fields, the backscatter at P and L bands drops by more than 10 dB for a 5 degree change in direction angle. Furthermore, the sensitivity to the direction angle decreases with increasing vegetation. The variations in backscatter are compared with model predictions. One model, which agrees with scatterometer data, underestimates the observed backscatter variations with direction angle by more than 10 dB. It does not take into account the possible coherent component of the radar signal. We believe the strong direction sensitivity of agricultural field backscatter obtained with SAR data is due to a Bragg resonant effect, resulting in a strong coherent return when the direction angle is zero. The observations are then projected to the case of spaceborne SAR data.

Dubois, Pascale C.↗
NASA NTRS1993

Radar backscattering from snow facies of the Greenland ice sheet: Results from the AIRSAR 1991 campaign

In June 1991, the NASA/JPL airborne SAR (AIRSAR) acquired C- (lambda = 5.6cm), L- (lambda = 24cm), and P- (lambda = 68m) band polarimetric SAR data over the Greenland ice sheet. These data are processed using version 3.55 of the AIRSAR processor which provides radiometrically and polarimetrically calibrated images. The internal calibration of the AIRSAR data is cross-checked using the radar response from corner reflectors deployed prior to flight in one of the scenes. In addition, a quantitative assessment of the noise power level at various frequencies and polarizations is made in all the scenes. Synoptic SAR data corresponding to a swath width of about 12 by 50 km in length (compared to the standard 12 x 12 km size of high-resolution scenes) are also processed and calibrated to study transitions in radar backscatter as a function of snow facies at selected frequencies and polarizations. The snow facies on the Greenland ice sheet are traditionally categorized based on differences in melting regime during the summer months. The interior of Greenland corresponds to the dry snow zone where terrain elevation is the highest and no snow melt occurs. The lowest elevation boundary of the dry snow zone is known traditionally as the dry snow line. Beneath it is the percolation zone where melting occurs in the summer and water percolates through the snow freezing at depth to form massive ice lenses and ice pipes. At the downslope margin of this zone is the wet snow line. Below it, the wet snow zone corresponds to the lowest elevations where snow remains at the end of the summer. Ablation produces enough meltwater to create areas of snow saturated with water, together with ponds and lakes. The lowest altitude zone of ablation sees enough summer melt to remove all traces of seasonal snow accumulation, such that the surface comprises bare glacier ice.

Rignot, Eric↗
NASA NTRS1993

Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

A maximum a posteriori Bayesian classifier for multifrequency polarimetric SAR data is used to perform a supervised classification of forest types in the floodplains of Alaska. The image classes include white spruce, balsam poplar, black spruce, alder, non-forests, and open water. The authors investigate the effect on classification accuracy of changing environmental conditions, and of frequency and polarization of the signal. The highest classification accuracy (86 percent correctly classified forest pixels, and 91 percent overall) is obtained combining L- and C-band frequencies fully polarimetric on a date where the forest is just recovering from flooding. The forest map compares favorably with a vegetation map assembled from digitized aerial photos which took five years for completion, and address the state of the forest in 1978, ignoring subsequent fires, changes in the course of the river, clear-cutting of trees, and tree growth. HV-polarization is the most useful polarization at L- and C-band for classification. C-band VV (ERS-1 mode) and L-band HH (J-ERS-1 mode) alone or combined yield unsatisfactory classification accuracies. Additional data acquired in the winter season during thawed and frozen days yield classification accuracies respectively 20 percent and 30 percent lower due to a greater confusion between conifers and deciduous trees. Data acquired at the peak of flooding in May 1991 also yield classification accuracies 10 percent lower because of dominant trunk-ground interactions which mask out finer differences in radar backscatter between tree species. Combination of several of these dates does not improve classification accuracy. For comparison, panchromatic optical data acquired by SPOT in the summer season of 1991 are used to classify the same area. The classification accuracy (78 percent for the forest types and 90 percent if open water is included) is lower than that obtained with AIRSAR although conifers and deciduous trees are better separated due to the presence of leaves on the deciduous trees. Optical data do not separate black spruce and white spruce as well as SAR data, cannot separate alder from balsam poplar, and are of course limited by the frequent cloud cover in the polar regions. Yet, combining SPOT and AIRSAR offers better chances to identify vegetation types independent of ground truth information using a combination of NDVI indexes from SPOT, biomass numbers from AIRSAR, and a segmentation map from either one.

Rignot, Eric↗
NASA NTRS1992

Polarimetric Radar Signatures Of Frozen And Thawed Forests

Report summarizes study of radiometrically and polarimetrically calibrated data from airborne synthetic-aperture-radar images of single-species forest stands in frozen and thawed conditions. Copolarization radar cross sections found as much as 6 dB less in frozen than in thawed condition. Effect attributed to fact that dielectric constants are greater in thawed condition. More-subtle differences determined by computing two features of statistics of Strokes matrices over resolution elements of each forest stand.

Kwok, Ronald↗
NASA NTRS1992

Monitoring environmental state of Alaskan forests with AIRSAR

During March 1988 and May 1991, the JPL airborne synthetic aperture radar, AIRSAR, collected sets of multi-temporal imagery of the Bonanza Creek Experimental Forest near Fairbanks, Alaska. These data sets consist of series of multi-polarized images collected at P-, L-, and C-bands each over a period of a few days. The AIRSAR campaigns were complemented with extensive ground measurements that included observations of both static canopy characteristics such as forest architecture as well as properties that vary on short term time scales such as canopy dielectric conditions. Observations exist for several stands of deciduous and coniferous species including white spruce (Picea glauca), black spruce (Picea mariana), and balsam poplar (Populus balsamifera). Although the duration of each campaign was fairly short, significant changes in environmental conditions caused notable variations in the physiological state of the canopies. During the 1988 campaign, environmental conditions ranged from unseasonably warm to more normal subfreezing temperatures. This permitted AIRSAR observations of frozen and thawed canopy states. During May 1991, ice jams that occurred along the river caused many stands to flood while the subsequent clearing of the river then allowed the waters to recede, leaving a snow covered ground surface. This allowed observations of several stands during both flooded and nonflooded conditions. Furthermore, the local weather varied from clear sunny days to heavy overcast days with some occurrence of rain. Measurements of leaf water potential indicated that this caused significant variations in canopy water status, allowing SAR observations of water stressed and unstressed trees. Mean backscatter from several stands is examined for the various canopy physiological states. The changes in canopy backscatter that occur as a function of environmental and physiological state are analyzed. Preliminary results of a backscatter signature modeling analysis are presented. The implications of using SAR to monitor canopy phenological state are addressed.

Mcdonald, Kyle C.↗
NASA NTRS1992

Identification of sea ice types in spaceborne synthetic aperture radar data

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

Kwok, Ronald↗
NASA NTRS1992

Analysis of scattering behavior and radar penetration in AIRSAR data

A technique is presented to physically characterize changes in radar backscatter with frequency in multifrequency single polarization radar images that can be used as a first step in the analysis of the data and the retrieval of geophysical parameters. The technique is automatic, relatively independent of the incidence angle, and only requires a good calibration accuracy between the different frequencies. The technique reveals large areas where scattering changes significantly with frequency and whether the surface has the characteristics of a smooth, slightly rough, rough, or very rough surface.

Rignot, Eric↗
NASA NTRS1992

Monitoring of environmental conditions in the Alaskan forests using ERS-1 SAR data

Preliminary results from an analysis of the multitemporal radar backscatter signatures of tree species acquired by European Remote Sensing Satellite (ERS-1) synthetic aperture radar (SAR) data are presented. Significant changes in radar backscatter are detected. Correlation of these differences with ground truth observations indicate that these are due to changes in soil and liquid water content as a result of freeze/thaw events. C-band observations acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (JPL AIRSAR) instrument demonstrate the potential of a C-band radar instrument to monitor drought/flood events. The potential of ERS-1 for monitoring phenologic changes in the forest and for classifying tree species is less promising.

Rignot, Eric↗
NASA NTRS1992

On the application of multifrequency polarimetric radar observations for sea-ice classification

The use of multifrequency polarimetric radar imagery to enhance the ability to separate different sea-ice types using single-frequency, single-polarization synthetic aperture radar (SAR) data is investigated. Backscatter characteristics of six radiometrically and polarimetrically distinct sea-ice types are selected in an unsupervised range-dependent analysis of multifrequency polarimetric SAR data using the maximum a posteriori (MAP) polarimetric classifier. Maximum ice discrimination is achieved with combined C- and L-band full polarimetry, and collocated passive microwave imagery suggests greater than 90 percent classification accuracy. C-band VV-pol alone achieves only 68 percent relative accuracy because it confuses multiyear and rough compressed first year ice. L-band, relative classification accuracy is 75 percent, 83 percent, and 85 percent, using HH-pol, HH- and VV-combined, or the full polarimetry, respectively. P-band is less accurate. Combinations of two frequencies at a single polarization show the greatest improvement over a single channel.

Rignot, Eric↗
NASA NTRS1992

Direction angle sensitivity of agricultural field backscatter with Airsar data

The direction angle sensitivity of agricultural field backscatter is studied. The direction angle is defined as the angle between the incident plane and the perpendicular to the row direction. Maximum backscatter power from an angricultural field is expected to occur when the furrow induced slopes are oriented towards the radar, i.e., for a 0 deg direction angle. This effect is known as the cardinal effect. Because of the way the looks are formed in the NASA/JPL airborne synthetic aperture radar (AIRSAR) processor, each look corresponds to a slightly different squint angle. This provides a unique data set to analyze the cardinal effect, as it allows simultaneous observations of the backscatter of a field for sixteen different direction angles. The backscatter variations of the agricultural fields with direction angle at P-, L-, and C-bands is described. The observed variations in backscatter are compared with model predictions. The model predicts that the maximum backscatter occurs for a 0 deg direction angle, but underestimates the backscatter variations with direction angle by more than 10 dB.

Dubois, Pascale C.↗
NASA NTRS1991

Classification of multifrequency multilook synthetic aperture radar data

A technique for segmentation of multifrequency multilook intensity synthetic aperture radar (SAR) data into regions of homogeneous and similar backscatter characteristics is presented. Two statistical models, one for the multifrequency multilook SAR intensities and the other for the distribution of the region labels, are combined to obtain the a posteriori probability distribution function of the region labels given the multifrequency speckled intensities. As the maximization of the posterior distribution is computationally intensive, a suboptimal technique for region labeling is proposed. Several examples using both simulated and real multifrequency multilook imagery are given to illustrate the performance of the algorithm.

Rignot, Eric↗
NASA NTRS1991

Unsupervised segmentation of polarimetric SAR data using the covariance matrix

An unsupervised selection of polarimetric features useful for the segmentation and analysis of polarimetric synthetic aperture radar (SAR) data is presented. The technique is based on multidimensional clustering of the parameters composing the polarimetric covariance matrix of the data. Clustering is performed on the logarithm of these quantities. Once the polarimetric cluster centers have been determined, segmentation of the polarimetric data into regions is performed using a maximum likelihood polarimetric classifier. Segmentation maps are further improved using a Markov random field to describe the statistics of the regions and computing the maximum of the product of the local conditional densities. Examples with real polarimetric SAR imagery are given to illustrate the potential of this method.

Rignot, Eric↗
NASA NTRS1989

Ice classification algorithm development and verification for the Alaska SAR Facility using aircraft imagery

The Alaska SAR Facility (ASF) at the University of Alaska, Fairbanks is a NASA program designed to receive, process, and archive SAR data from ERS-1 and to support investigations that will use this regional data. As part of ASF, specialized subsystems and algorithms to produce certain geophysical products from the SAR data are under development. Of particular interest are ice motion, ice classification, and ice concentration. This work focuses on the algorithm under development for ice classification, and the verification of the algorithm using C-band aircraft SAR imagery recently acquired over the Alaskan arctic.

Holt, Benjamin↗