Program statement for regional geologic studies in remote sensing, Yellowstone National Park
Multispectral remote aerial sensing for geological terrain analysis
Smedes, H. W.↗
SEARCH · Search NASA
Results for “multispectral sensing”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Multispectral data acquisition and classification - Computer modeling for smart sensor design
In this paper a model of the processes involved in multispectral remote sensing and data classification is developed as a tool for designing and evaluating smart sensors. The model has both stochastic and deterministic elements and accounts for solar radiation, atmospheric radiative transfer, surface reflectance, sensor spectral reponses, and classification algorithms. Preliminary results are presented which indicate the validity and usefulness of this approach. Future capabilities of smart sensors will ultimately be limited by the accuracy with which multispectral remote sensing processes and their error sources can be computationally modeled.
Park, S. K.↗
Remote sensing of benthic microalgal biomass with a tower-mounted multispectral scanner
A remote sensing instrument was mounted on a 50-ft tower overlooking North Inlet Estuary, South Carolina in order to conduct a remote sensing study of benthic microalgae. The instrument was programmed to take multispectral imagery data along a 90 deg horizontal frame in six spectral bands ranging from 400-1050 nm and had a ground resolution of about 3 cm. Imagery measurements were encoded in digital form on magnetic tape and were stored, decoded, and manipulated by computer. Correlation coefficients were calculated on imagery data and chlorophyll a concentrations derived from ground truth data. The most significant correlation occurred in the blue spectral band with numerical values ranging from -0.81 to -0.88 for three separate sampling periods. Mean values of chlorophyll a for a larger section of mudflat were estimated using regression equations. The scanner has provided encouraging results and promises to be a useful tool in sampling the biomass of intertidal benthic microalgae.
Jobson, D. J.↗
Earth resources sensing technology - 24-channel multispectral sensor system development.
Description of the technology applied to a 24-channel multispectral scanner developed as a tool to investigate the portion of the electromagnetic spectrum between 0.34 and 13.0 microns. Energy reflected or emitted from the terrain and system calibration sources is collected by the scan mirror, reflected into the collecting optics, and brought to focus in a plane containing a square aperture. A dichroic optical element splits the energy which passes through the aperture into two wavelength bands in which wavelengths are greater than and less than 2.0 microns, respectively. These two wavelength bands are dispersed spectrally by two grating spectrometers. The energy from 24 distinct spectral intervals is transformed into electrical signals by 24 detector/preamplifier combinations. These signals then become the inputs to a video processor in an airborne electronics console.
Kessel, J. E.↗
Snow Property Inversion from Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach with Examples from MODIS and Landsat 8 OLI
Spectral mixture analysis has a history in mappingsnow, especially where mixed pixels prevail. Using multiplespectral bands rather than band ratios or band indices, retrievalsof snow properties that affect its albedo lead to more accu-rate estimates than widely used age-based models of albedoevolution. Nevertheless, there is substantial room for improve-ment. We present the Snow Property Inversion from RemoteSensing (SPIReS) approach, offering the following improve-ments: 1) Solutions for grain size and concentrations of lightabsorbing particles are computed simultaneously; 2) Only snowand snow-free endmembers are employed; 3) Cloud-maskingand smoothing are integrated; 4) Similar spectra are groupedtogether and interpolants are used to reduce computation time.The source codes are available in an open repository. Com-putation is fast enough that users can process imagery ondemand. Validation of retrievals from Landsat 8 operational landimager (OLI) and moderate-resolution imaging spectroradiome-ter (MODIS) against WorldView-2/3 and the Airborne SnowObservatory shows accurate detection of snow and estimatesof fractional snow cover. Validation of albedo shows low errorsusing terrain-correctedin situmeasurements. We conclude bydiscussing the applicability of this approach to any airborne orspaceborne multispectral sensor and options to further improve retrievals.
Edward H Blair↗
Remote sensing with optical-mechanical line scanners to detect stress in forests
Airborne multispectral remote sensing of forests, describing previsual detection of damage from insect infestations, disease organisms and oxidant air pollution
Polcyn, F. C.↗
Applications of multispectral data collection and processing techniques applied to hydrobiological investigations in Everglades National Park, Florida
Remote aerial sensing and multispectral data processing for hydrobiological survey in Florida
Higer, A. L.↗
Improved image classification with neural networks by fusing multispectral signatures with topological data
Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans suceed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat mulitspectral signatures. A file of georeferenced ground truth classifications were used as the training criterion. The network was trained to classify urban, agriculture, range, and forest with an accuracy of 65.7 percent. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topological file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7 and 75.7 percent.
Harston, Craig↗
Application of Remote Sensing Techniques for Appraising Changes in Wildlife Habitat
An attempt was made to investigate the potential of airborne, multispectral, line scanner data acquisition and computer-implemented automatic recognition techniques for providing useful information about waterfowl breeding habitat in North Dakota. The spectral characteristics of the components of a landscape containing waterfowl habitat can be detected with airborne scanners. By analyzing these spectral characteristics it is possible to identify and map the landscape components through analog and digital processing methods. At the present stage of development multispectral remote sensing techniques are not ready for operational application to surveys of migratory bird habitat and other such resources. Further developments are needed to: (1) increase accuracy; (2) decrease retrieval and processing time; and (3) reduce costs.
Nelson, H. K.↗
The large area crop inventory experiment - A major demonstration of space remote sensing
The NASA-U.S. Department of Agriculture Large Area Crop Inventory Experiment (LACIE), aimed at using multispectral remote sensing data from Landsat 1 and 2 to generate accurate annual global crop production forecasts, is discussed. The forecasts take into account meteorological conditions as well as yield and acreage, and may be used to increase the discrimination of U.S. harvest estimates down to regional levels and to provide more accurate early-season predictions. Sample problems involving the determination of wheat harvests and the monitoring of drought conditions are described. Difficulties related to misidentification of abnormally-developing plantations, the automatic classification of homogeneous spectral groups, the computerized generation of colored maps, and the estimation of yields during years when exceptional meteorological conditions prevail are also considered. Samples of Landsat-generated classification maps for Western U.S. and for the Saratov, U.S.S.R. crop regions are given.
Macdonald, R. B.↗
Estimation of sunlight penetration in the sea for remote sensing
There is a need for a simple theoretical approach to the calculation of sunlight penetration depths suitable for passive multispectral remote sensing of water resources. An earlier paper presented an approach which is readily adapted to this calculation and which provides reasonably good agreement with more accurate but time-consuming radiative transfer models. The needed modifications are described and the model is used to calculate the penetration of sunlight into clear ocean water at several wavelengths throughout the visible portion of the spectrum. Calculations for both clear and turbid water are carried out for the two visible channels of the multispectral scanner on NASA's ERTS-1 satellite. The effect of a reflective bottom on the upwelling light field is discussed. Measurement parameters needed for the passive remote determination of water depths are identified and the use of submerged reflective panels for surface truth measurements is discussed.
Mccluney, W. R.↗
Machine processing of remotely sensed data; Proceedings of the Fifth Annual Symposium, Purdue University, West Lafayette, Ind., June 27-29, 1979
Papers are presented on techniques and applications for the machine processing of remotely sensed data. Specific topics include the Landsat-D mission and thematic mapper, data preprocessing to account for atmospheric and solar illumination effects, sampling in crop area estimation, the LACIE program, the assessment of revegetation on surface mine land using color infrared aerial photography, the identification of surface-disturbed features through a nonparametric analysis of Landsat MSS data, the extraction of soil data in vegetated areas, and the transfer of remote sensing computer technology to developing nations. Attention is also given to the classification of multispectral remote sensing data using context, the use of guided clustering techniques for Landsat data analysis in forest land cover mapping, crop classification using an interactive color display, and future trends in image processing software and hardware.
Tendam, I. M.↗
Monitoring Geothermal Features in Yellowstone National Park with ATLAS Multispectral Imagery
The National Park Service (NPS) must produce an Environmental Impact Statement for each proposed development in the vicinity of known geothermal resource areas (KGRAs) in Yellowstone National Park. In addition, the NPS monitors indicator KGRAs for environmental quality and is still in the process of mapping many geothermal areas. The NPS currently maps geothermal features with field survey techniques. High resolution aerial multispectral remote sensing in the visible, NIR, SWIR, and thermal spectral regions could enable YNP geothermal features to be mapped more quickly and in greater detail In response, Yellowstone Ecosystems Studies, in partnership with NASA's Commercial Remote Sensing Program, is conducting a study on the use of Airborne Terrestrial Applications Sensor (ATLAS) multispectral data for monitoring geothermal features in the Upper Geyser Basin. ATLAS data were acquired at 2.5 meter resolution on August 17, 2000. These data were processed into land cover classifications and relative temperature maps. For sufficiently large features, the ATLAS data can map geothermal areas in terms of geyser pools and hot springs, plus multiple categories of geothermal runoff that are apparently indicative of temperature gradients and microbial matting communities. In addition, the ATLAS maps clearly identify geyserite areas. The thermal bands contributed to classification success and to the computation of relative temperature. With masking techniques, one can assess the influence of geothermal features on the Firehole River. Preliminary results appear to confirm ATLAS data utility for mapping and monitoring geothermal features. Future work will include classification refinement and additional validation.
Spruce, Joseph↗
A stochastic atmospheric model for remote sensing applications
There are many factors which reduce the accuracy of classification of objects in the satellite remote sensing of Earth's surface. One important factor is the variability in the scattering and absorptive properties of the atmospheric components such as particulates and the variable gases. For multispectral remote sensing of the Earth's surface in the visible and infrared parts of the spectrum the atmospheric particulates are a major source of variability in the received signal. It is difficult to design a sensor which will determine the unknown atmospheric components by remote sensing methods, at least to the accuracy needed for multispectral classification. The problem of spatial and temporal variations in the atmospheric quantities which can affect the measured radiances are examined. A method based upon the stochastic nature of the atmospheric components was developed, and, using actual data the statistical parameters needed for inclusion into a radiometric model was generated. Methods are then described for an improved correction of radiances. These algorithms will then result in a more accurate and consistent classification procedure.
Turner, R. E.↗
Remote sensing technology - The 24-channel multispectral scanner.
The multispectral scanner system installed in the NASA C-130 aircraft for use in the Earth Observations Aircraft Program constitutes a 24-channel imaging spectrometer that senses electromagnetic energy in the spectral interval from .34 to 13 microns. Energy reflected or emitted from the terrain and system calibration sources is collected by a scan mirror, reflected into collecting optics, and brought to a focus in a plane containing a 0.09 inch square aperture. A dichroic optical element splits the energy which passes through the aperture into two wavelength bands (wavelengths above and below 2 microns). These wavelength bands are then dispersed spectrally into 24 distinct spectral bands by two grating spectrometers. The spectral intervals are transformed into electrical signals by separate detector-preamplifier combinations, and the signals are used as inputs to a video processor in an airborne electronics console. System operation and performance are described.
Hayre, H. S.↗
Remote sensing investigations of wetland biomass and productivity for global biosystems research
Monitoring biomass of wetlands ecosystems can provide information on net primary production and on the chemical and physical status of wetland soils relative to anaerobic microbial transformation of key elements. Multispectral remote sensing techniques successfully estimated macrophytic biomass in wetlands systems. Regression models developed from ground spectral data for predicting Spartina alterniflora biomass over an entire growing season include seasonal variations in biomass density and illumination intensity. An independent set of biomass and spectral data were collected and the standing crop biomass and net primary productivity were estimated. The improved spatial, radiometric and spectral resolution of th LANDSAT-4 Thematic Mapper over the LANDSAT MSS can greatly enhance multispectral techniques for estimating wetlands biomass over large areas. These techniques can provide the biomass data necessary for global ecology studies.
Harkisky, M.↗
Operational Use of VIIRS Multispectral Imagery and NUCAPS Soundings in Short-Term Weather Forecasting
No abstract available
Remote Sensing↗
Some considerations in low-cost image processing on a university main frame: The Penn State (ORSER) experience
The ORSER System as implemented on the Penn State University Computation Center's IBM main frame is an exceptionally useful mechanism for instructing the present and future remote sensing community in the theory and practice of automated analysis of digitized data. From its beginning, the system was used as the hands-on back-bone of a graduate level "Multispectral Remote Sensing" one-term course taught annually in the School of Forest Resources. It was also used at the end of a senior-level undergraduate "Introduction to Remote Sensing" course, and in teaching a variety of short courses, ranging in length from a few days for small groups of remote sensing consultants with specific application interests, to a 2-week course oganized by NASA for regional and urban planners who had little or no previous exposure to remote sensing. Because of its modular construction, the ORSER is suitable for all classes of remote sensing users. Although in a beginning course only 6 to 10 of the programs are generally used, the researcher has a set of about 35 programs available for his analyses.
Turner, B. J.↗