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7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

J-ERS-1↗
NASA NTRS1993

Calibration Results for J-ERS-1 SAR Data Produced by the Alaska SAR Facility

The Alaska SAR Facility has been receiving and processing SAR data from the J-ERS-1 satellite since Spring 1992. Corner reflectors have been set up for J-ERS-1 SAR calibration at a site near Delta Junction, in central Alaska. Image quality and calibration analysis results from the Delta Junction site and others will be presented in this paper. The impact of the 3-bit Analog-to-Digital Converter and the automatic stepping of the gain as a function of range in the J-ERS-1 radar receiver on calibration performance has been assessed. Preliminary observations on J-ERS-1 SAR data are that the average Signal-to-Noise ratio is generally fairly low, in the range 5-6dB. Azimuth ambiguity levels are higher than preflight analysis indicated. Over land, the dynamic range in the backscatter at L-band for approximately 36 degree incidence angle is often fairly high.

Freeman, A.↗
NASA NTRS1993

Calibration Results for J-ERS-1 SAR Data Produced by the Alaska SAR Facility

The Alaska SAR Facility has been receiving and processing SAR data from the J-ERS-1 satellite since Spring 1992. Corner reflectors have been set up for J-ERS-1 SAR calibration at a site near Delta Junction, in central Alaska. Image quality and calibration analysis results from the Delta Junction site and others will be presented in this paper. The impact of the 3-bit Analog-to-Digital Converter and the automatic stepping of the gain as a function of range in the J-ERS-1 radar receiver on calibration performance has been assessed. Preliminary observations on J-ERS-1 SAR data are that the average Signal-to-Noise ratio is generally fairly low, in the range 5-6 dB. Azimuth ambiguity levels are higher than preflight analysis indicated. Over land, the dynamic range in the backscatter at L-band for approximately 36 degree incidence angle is often fairly high...

Freeman, A.↗
NASA NTRS1988

Plans for the development of EOS SAR systems using the Alaska SAR facility

The Alaska SAR Facility (ASF) program for the acquisition and processing of data from the ESA ERS-1, the NASDA ERS-1, and Radarsat and to carry out a program of science investigations using the data is introduced. Agreements for data acquisition and analysis are in place except for the agreement between NASA and Radarsat which is in negotiation. The ASF baseline system, consisting of the Receiving Ground System, the SAR Processor System and the Archive and Operations System, passed critical design review and is fully in implementation phase. Augments to the baseline system for systems to perform geophysical processing and for processing of J-ERS-1 optical data are in the design and implementation phase. The ASF provides a very effective vehicle with which to prepare for the Earth Observing System (EOS) in that it will aid the development of systems and technologies for handling the data volumes produced by the systems of the next decades, and it will also supply some of the data types that will be produced by EOS.

Carsey, F. D.↗
NASA NTRS1988

The Alaska SAR processor

The Alaska SAR processor was designed to process over 200 100 km x 100 km (Seasat like) frames per day from the raw SAR data, at a ground resolution of 30 m x 30 m from ERS-1, J-ERS-1, and Radarsat. The near real time processor is a set of custom hardware modules operating in a pipelined architecture, controlled by a general purpose computer. Input to the processor is provided from a high density digital cassette recording of the raw data stream as received by the ground station. A two pass processing is performed. During the first pass clutter-lock and auto-focus measurements are made. The second pass uses the results to accomplish final image formation which is recorded on a high density digital cassette. The processing algorithm uses fast correlation techniques for range and azimuth compression. Radiometric compensation, interpolation and deskewing is also performed by the processor. The standard product of the ASP is a high resolution four-look image, with a low resolution (100 to 200 m) many look image provided simultaneously.

Carande, R. E.↗
NASA NTRS1989

The Alaska SAR processor - Operations and control

The Alaska SAR (synthetic-aperture radar) Facility (ASF) will be capable of receiving, processing, archiving, and producing a variety of SAR image products from three satellite-borne SARs: E-ERS-1 (ESA), J-ERS-1 (NASDA) and Radarsat (Canada). Crucial to the success of the ASF is the Alaska SAR processor (ASP), which will be capable of processing over 200 100-km x 100-km (Seasat-like) frames per day from the raw SAR data, at a ground resolution of about 30 m x 30 m. The processed imagery is of high geometric and radiometric accuracy, and is geolocated to within 500 m. Special-purpose hardware has been designed to execute a SAR processing algorithm to achieve this performance. This hardware is currently undergoing acceptance testing for delivery to the University of Alaska. Particular attention has been devoted to making the operations semi-automated and to providing a friendly operator interface via a computer workstation. The operations and control of the Alaska SAR processor are described.

Carande, Richard E.↗
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↗