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At least 253 records · Page 14
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NASA NTRS2001

Improvements in AVHRR Daytime Cloud Detection Over the ARM NSA Site

Clouds play an important role in the radiation budget over Arctic and Antarctic. Because of limited surface observing capabilities, it is necessary to detect clouds over large areas using satellite imagery. At low and mid-latitudes, satellite-observed visible (VIS; 0.65 micrometers) and infrared (IR; 11 micrometers) radiance data are used to derive cloud fraction, temperature, and optical depth. However, the extreme variability in the VIS surface albedo makes the detection of clouds from satellite a difficult process in polar regions. The IR data often show that the surface is nearly the same temperature or even colder than clouds, further complicating cloud detection. Also, the boundary layer can have large areas of haze, thin fog, or diamond dust that are not seen in standard satellite imagery. Other spectral radiances measured by satellite imagers provide additional information that can be used to more accurately discriminate clouds from snow and ice. Most techniques currently use a fixed reflectance or temperature threshold to decide between clouds and clear snow. Using a subjective approach, Minnis et al. (2001) found that the clear snow radiance signatures vary as a function of viewing and illumination conditions as well as snow condition. To routinely process satellite imagery over polar regions with an automated algorithm, it is necessary to account for this angular variability and the change in the background reflectance as snow melts, vegetation grows over land, and melt ponds form on pack ice. This paper documents the initial satellite-based cloud product over the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site at Barrow for use by the modeling community. Cloud amount and height are determined subjectively using an adaptation of the methodology of Minnis et al. (2001) and the radiation fields arc determined following the methods of Doelling et al. (2001) as applied to data taken during the Surface Heat and Energy Budget of the Arctic (SHEBA). The procedures and data produced in this empirically based analysis will also facilitate the development of the automated algorithm for future processing of satellite data over the ARM NSA domain. Results are presented for May, June, and July 1998. ARM surface data are use to partially validate the results taken directly over the ARM site.

Chakrapani, V.↗
NASA NTRS1995

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

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.

Green, Robert O.↗
NASA NTRS2012

Current Usage and Future Prospects of Multispectral (RGB) Satellite Imagery in Support of NWS Forecast Offices and National Centers

Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low-Earth orbits. The NASA Short-term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA s Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channel s available from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard METEOSAT-9. This broader suite includes products that discriminate between air mass types associated with synoptic-scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Similarly, researchers at NOAA/NESDIS and CIRA have developed air mass discrimination capabilities using channels available from the current GOES Sounders. Other applications of multispectral composites include combinations of high and low frequency, horizontal and vertically polarized passive microwave brightness temperatures to discriminate tropical cyclone structures and other synoptic-scale features. Many of these capabilities have been transitioned for evaluation and operational use at NWS Weather Forecast Offices and National Centers through collaborations with SPoRT and CIRA. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES-R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar-Orbiting Partnership (S-NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS), which have unrivaled spectral and spatial resolution, as precursors to the JPSS era (i.e., the next generation of polar orbiting satellites). At the same time, new image manipulation and display capabilities are available within AWIPS II, the next generation of the NWS forecaster decision support system. This presentation will present a review of SPoRT, CIRA, and NRL collaborations regarding multispectral satellite imagery and articulate an integrated and collaborative path forward with Raytheon AWIPS II development staff for integrating current and future capabilities that support new satellite instrumentation and the AWIPS II decision support system.

Molthan, Andrew L.↗
NASA NTRS1995

Machine learning for a Toolkit for Image Mining

A prototype user environment is described that enables a user with very limited computer skills to collaborate with a computer algorithm to develop search tools (agents) that can be used for image analysis, creating metadata for tagging images, searching for images in an image database on the basis of image content, or as a component of computer vision algorithms. Agents are learned in an ongoing, two-way dialogue between the user and the algorithm. The user points to mistakes made in classification. The algorithm, in response, attempts to discover which image attributes are discriminating between objects of interest and clutter. It then builds a candidate agent and applies it to an input image, producing an 'interest' image highlighting features that are consistent with the set of objects and clutter indicated by the user. The dialogue repeats until the user is satisfied. The prototype environment, called the Toolkit for Image Mining (TIM) is currently capable of learning spectral and textural patterns. Learning exhibits rapid convergence to reasonable levels of performance and, when thoroughly trained, Fo appears to be competitive in discrimination accuracy with other classification techniques.

Delanoy, Richard L.↗
NASA NTRS1974

Inventories of Delaware's coastal vegetation and land-use utilizing digital processing of ERTS-1 imagery

The author has identified the following significant results. Analysis of ERTS-1 color composite images using analogy processing equipment confirmed that all the major wetlands plant species were distinguishable at ERTS-1 scale. Furthermore, human alterations of the coastal zone were easily recognized since such alterations typically involve removal of vegetative cover resulting in a change of spectral signature. The superior spectral resolution of the CCTs as compared with single band or composite imagery has indeed provided good discrimination through digital analysis of the CCTs with the added advantage of rapid production of thematic maps and data.

Klemas, V.↗
NASA NTRS1984

Characterization of LANDSAT-4 TM and MSS Image Quality for Interpretation of Agricultural and Forest Resources

Systematic analysis of both image and numeric data shows that the overall spectral, spatial, and radiometric quality of the TM data are excellent. Spectral variations in fallow fields are due to the vaiability in soil moisture and surface roughness resulting from the various stages of field preparation for small grains production. Spectrally, the addition of the first TM short wave infrared band (Band 5) significantly enhanced ability to discriminate different crop types. Bands 1, 5, and 6 contain saturated pixels due to high albedo effects, low moisture conditions, and high radiant temperatures of granite and dry, bare soil on south facing slopes, respectively. Spatially, the two fold decrease in interpixel distance and four fold decrease in area per pixel between the TM and MSS allow for improved discrimination of small fields, boundary conditions, road and stream networks in rough terrain, and small forest clearings resulting from various forest management practices.

Degloria, S. D.↗
NASA NTRS2012

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

Rampe, E. B.↗
NASA NTRS1986

Department of Geology/NASA-GSFC geobotanical investigation

Geobotanical mapping in the southern Appalachians and soil metal associated changes in vegatation spectral responses in the reflective and emissive portions of the electromagnetic spectrum (EMS) is studied. It is determined whether deciduous forest vegetation reflection and emission characteristics show detectable changes that can be related to residual soil characteristics. Analyses of such related characteristics enhances the ability to discriminate underlying lithologies as well as variances within lithologies associated with natural heavy metal occurences. An understanding is developed of the factors which contribute to the differing spectral signatures of various lithology/soil types. A combination of field data and remote spectral analysis is studied.

Source record↗
NASA NTRS1990

Remote Detection and Mapping of Supergene Iron Oxides in the Cripple Creek Mining District, Colorado

The Geophysical and Environmental Research Imaging Spectrometer (GERIS) was flown over the Cripple Creek mining district in south-central Colorado to improve the geological understanding of the district. As part of the study, an airborne mapping technique was developed for the discrimination of the ferric iron minerals hematite, goethite, and jarosite, minerals often important indicators of the distribution of economic mineralization. A software technique was developed which uses the binary encoding of spectral slopes to identify the mineral hematite from the group goethite/jarosite. Mixtures of hematite and goethite can also be detected with GERIS data. The study included district-wide field mapping and spectral measurements to evaluate the accuracy of the image classifications. The ARC/INFO geographic information system (GIS) was a useful tool which allowed quantitative comparison of the field mapping and GERIS image data sets. The study results demonstrate the ability to discriminate individual iron minerals using imaging spectroscopy, and the development of a rapid mapping technique useful in the reconnaissance stage of minerals exploration.

Taranik, D. L.↗
NASA NTRS1973

Iron-absorption band analysis for the discrimination of iron-rich zones

The author has identified the following significant results. Study has concentrated on the two primary aspects of the project, structural analysis through evaluation of lineaments and circular features and spectral analyses through digital computer-processing techniques. Several previously unrecognized lineaments are mapped which may be the surface manifestations of major fault or fracture zones. Two of these, the Walker Lane and the Midas Trench lineament system, transect the predominantly NNE-NNW-trending moutain ranges for more than 500 km. Correlation of major lineaments with productive mining districts implies a genetic relationship, the 50 circular or elliptical features delineated suggest a related role for Tertiary volcanism. Color-ratio composites have been used to identify limonitic zones and to discriminate mafic and felsic rock by combing diazo color transparencies of three different ratios. EROS Data Center scene identification number for color composite in this report is ER 1 CC 500. Refinement of enhancement procedures for the ratio images is progressing. Fieldwork in coordination with both spectral and structural analyses is underway.

Rowan, L. C.↗
NASA NTRS2006

Chlorophyll Fluorescence Emissions of Vegetation Canopies From High Resolution Field Reflectance Spectra

A two-year experiment was performed on corn (Zea mays L.) crops under nitrogen (N) fertilization regimes to examine the use of hyperspectral canopy reflectance information for estimating chlorophyll fluorescence (ChlF) and vegetation production. Fluorescence of foliage in the laboratory has proven more rigorous than reflectance for correlation to plant physiology. Especially useful are emissions produced from two stable red and far-red chlorophyll ChlF peaks centered at 685V10 nm and 735V5 nm. Methods have been developed elsewhere to extract steady state solar induced fluorescence (SF) from apparent reflectance of vegetation canopies/landscapes using the Fraunhofer Line Depth (FLD) principal. Our study utilized these methods in conjunction with field-acquired high spectral resolution canopy reflectance spectra obtained in 2004 and 2005 over corn crops, as part of an ongoing multi-year experiment at the USDA/Agriculture Research Service in Beltsville, MD. A spectroradiometer (ASD-FR Fieldspec Pro, Analytical Spectral Devices, Inc., Boulder, CO) was used to measure canopy radiances 1 m above plant canopies with a 22deg field of view and a 0deg nadir view zenith angle. Canopy and plant measurements were made at the R3 grain fill reproductive stage on 3-4 replicate N application plots provided seasonal inputs of 280, 140, 70, and 28 kg N/ha. Leaf level measurements were also made which included ChlF, photosynthesis, and leaf constituents (photosynthetic pigment, carbon (C), and N contents). Crop yields were determined at harvest. SIF intensities for ChlF were derived directly from canopy reflectance spectra in specific narrowband regions associated with atmospheric oxygen absorption features centered at 688 and 760 nm. The red/far-red S F ratio derived from these field reflectance spectra successfully discriminated foliar pigment levels (e.g., total chlorophyll, Chl) associated with N application rates in both corn crops. This canopy-level spectral ratio was also positively correlated to the foliar C/N ratio (r = 0.89, n = go), as was a leaf-level steady state fluorescence ratio (Fs/Chl, r = 0.92). The latter ratio was inversely correlated with crop grain yield (Kg 1 ha) (r = 0.9). This study has relevance to future passive satellite remote sensing approaches to monitoring C dynamics from space.

Middleton, E. M.↗
NASA NTRS1972

Effect of radiometric errors on accuracy of temperature-profile measurement by spectral scanning using absorption-emission pyrometry

The spectral-scanning method may be used to determine the temperature profile of a jet- or rocket-engine exhaust stream by measurements of gas radiation and transmittance, at two or more wavelengths. A single, fixed line of sight is used, using immobile radiators outside of the gas stream, and there is no interference with the flow. At least two sets of measurements are made, each set consisting of the conventional three radiometric measurements of absorption-emission pyrometry, but each set is taken over a different spectral interval that gives different weight to the radiation from a different portion of the optical path. Thereby, discrimination is obtained with respect to location along the path. A given radiometric error causes an error in computed temperatures. The ratio between temperature error and radiometric error depends on profile shape, path length, temperature level, and strength of line absorption, and the absorption coefficient and its temperature dependency. These influence the choice of wavelengths, for any given gas. Conditions for minimum temperature error are derived. Numerical results are presented for a two-wavelength measurement on a family of profiles that may be expected in a practical case of hydrogen-oxygen combustion. Under favorable conditions, the fractional error in temperature approximates the fractional error in radiant-flux measurement.

Buchele, D. R.↗
NASA NTRS1973

An ERTS multispectral scanner experiment for mapping iron compounds.

An experiment is described which is designed to produce visible-reflective infrared ratio images from ERTS satellite data. The ERTS-A multispectral scanner has two channels in the visible-reflective infrared wavelength region which should be capable of duplicating aircraft scanner results. The average reflectances in the four ERTS-A MSS spectral bands for a few minerals and some vegetation are tabulated. The mineral laboratory samples were granular, with a particle diameter range of 250 to 1200 micrometer. If there were no spectral atmospheric or illumination variation effects, the ratio of energies in band 1 to band 4 would permit discrimination of pyroxenes (found in basic and ultrabasic rocks), quartz (found in acidic rocks), limonite and hematite (iron oxides), and green vegetation, on the basis of their respective ratio magnitudes.

Vincent, R. K.↗
NASA NTRS1973

Latest results from the earth resources program

The characteristics of optical measurements taken by the return-beam vidicon and multispectral scanner systems on the ERTS-1 satellite are described together with the interpretation of these observations for purposes of mapping and surveying earth resources over large areas. Each spectral interval covered by the sensors is discussed in terms of the type of features which can be discriminated and related to particular earth resources.

Nordberg, W.↗
NASA NTRS1975

Advances in automatic extraction of information from multispectral scanner data

The state-of-the-art of automatic multispectral scanner data analysis and interpretation is reviewed. Sources of system variability which tend to obscure the spectral characteristics of the classes under consideration are discussed, and examples of the application of spatial and temporal discrimination bases are given. Automatic processing functions, techniques and methods, and equipment are described with particular attention to those that are applicable to large land surveys using satellite data. The development and characteristics of the Multivariate Interactive Digital Analysis System (MIDAS) for processing aircraft or satellite multispectral scanning data are discussed in detail. The MIDAS system combines the parallel digital implementation capabilities of a low-cost processor with a general purpose PDP-11/45 minicomputer to provide near-real-time data processing. The preprocessing functions are user-selectable. The input subsystem accepts data stored on high density digital tape, computer compatible tape, and analog tape.

Erickson, J. D.↗
NASA NTRS1984

Remote sensing research for agricultural applications

Materials and methods used to characterize selected soil properties and agricultural crops in San Joaquin County, California are described. Results show that: (1) the location and widths of TM bands are suitable for detecting differences in selected soil properties; (2) the number of TM spectral bands allows the quantification of soil spectral curve form and magnitude; and (3) the spatial and geometric quality of TM data allows for the discrimination and quantification of within field variability of soil properties. The design of the LANDSAT based multiple crop acreage estimation experiment for the Idaho Department of Water Resources is described including the use of U.C. Berkeley's Survey Modeling Planning Model. Progress made on Peditor software development on MIDAS, and cooperative computing using local and remote systems is reported as well as development of MIDAS microcomputer systems.

Colwell, R. N.↗
NASA NTRS1985

Quick Look Analysis of TM Data of the Washington, District of Columbia, Area

Classification capabilities with TM data result from the interactive effects of all of the sensor's attributes which complicates a more quantitative evaluation of the effects of individual sensor improvements. An experiment conducted to quantify the effect of individual sensor parameters (e.g., spectral, spatial, and radiometric resolution) on classification accuracy is described on classification accuracy. Preliminary results obtained using TM data acquired over the Washington, D.C., area indicate that the additional number of spectral bands and quantization levels of the TM relative to the MSS increase capabilities for the recognition and discrimination of land cover/use categories by per-pixel maximum likelihood classification. The refinement of spatial resolution, however, seems to hinder classification.

Williams, D. L.↗
NASA NTRS1996

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

Penaloza, Mauel A.↗