Scientific and Technical Aerospace Reports Volume 38 July 28, 2000

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20000064552 Geological Survey, Denver, CO USA Spectroscopic Determination of Leaf Biochemistry: Use of Normalized Band-Depths and Laboratory Measurements and Possible Extension to Remote Sensing Measurements Kokaly, Raymond F., Geological Survey, USA; Clark, Roger N., Geological Survey, USA; Summaries of the Seventh JPL Airborne Earth Science Workshop January 12-16, 1998; Dec. 19, 1998; Volume 1, pp. 235-344; In English; See also 20000064520; No Copyright; Avail: CASI; A02, Hardcopy; A04, Microfiche Ecosystems play an important role in the exchange of water, energy and greenhouse gases between soil, vegetation, and the atmosphere. The ability to detect changes in ecosystem processes such as carbon fixation, nutrient cycling, net primary production and litter decomposition is an important part in defining global biogeochemical cycles and identifying changes in climate. These processes have been linked in models of forest ecosystems to canopy biochemical content, specifically to the nitrogen, lignin and cellulose concentrations in vegetation. However, measurements of canopy chemistry by traditional field sampling methods are difficult to perform for large regional and global studies. Therefore, remote sensing measurement of canopy biochemistry is crucial to studying changes in ecosystem functioning. Several studies have suggested that estimates of canopy chemistry based on remote spectroscopic measurements may be possible. These studies used stepwise multiple linear regression to predict canopy chemistry from derivative reflectance spectra. This methodology is based on laboratory techniques developed in the agriculture industry for rapid estimation of forage quality parameters, for example, crude protein content and digestibility. The original applications stressed the importance of controlled laboratory methods for reducing noise levels and the limited application of regression equations to samples of the same type used in calibration. Recently, Grossman et al. (1996) found the use of regression techniques with derivative reflectance spectra to give inconsistent results between dry leaf and needle data sets for forest vegetation. Under the NASA Accelerated Canopy Chemistry Program, analysis using derivatives had also given inconsistent results between test sites (ACCP 1994). Furthermore, although recent studies have correlated plant canopy chemistry to imaging spectrometer measurements, the results at leaf and canopy scales are inconsistent and the derived regression equations are applicable to only the study area and are not reliable predictors for other remotely-sensed data. Because of site-specific results in wavelength selection by derivative analysis and the sensitivity of the technique to noise, we employed a different approach. Before applying the stepwise multiple linear regression, we utilized continuum-removal and band ratios, traditional methods of spectral analysis used in remote sensing by the terrestrial geology and planetary science disciplines. Absorption band-depths were calculated from continuum-removed spectral features in reflectance data. Since all band-depths in an absorption feature were ratioed to the maximum depth at the center of the feature we termed the result ”normalized band-depths.” Subsequently, we used a multiple stepwise linear regression algorithm applied to normalized band-depths to select wavelengths highly correlated to laboratory measurements of chemical concentrations. Wavelength selection was based on only two of the seven data sets used in this paper. Subsequently, the selected wavelengths were tested for correlation with the chemical concentrations of the five other data sets. To test the robustness of this approach, regression equations developed from a subset of the data were used to predict the concentrations in the remaining samples. Finally, since a broadly applicable method for dry leaves serves only as a foundation for a remote sensing algorithm, the method was tested for its extension to remote sensing measurements of plant canopies. The influences of leaf water content, sensitivity to noise in the reflectance spectra, sensor bandwidths, incomplete vegetation coverage, and atmospheric influences were considered. Derived from text Spectrum Analysis; Regression Analysis; Continuums; Band Ratioing; Remote Sensing; Spectroscopy; Canopies (Vegetation) 20000064553 Geological Survey, Denver, CO USA Mapping the Biology and Mineralogy of Yellowstone National Park Using Imaging Spectroscopy Kokaly, Raymond F., Geological Survey, USA; Clark, Roger N., Geological Survey, USA; Livo, K. Eric, Geological Survey, USA; Summaries of the Seventh JPL Airborne Earth Science Workshop January 12-16, 1998; Dec. 19, 1998; Volume 1, pp. 245-254; In English; See also 20000064520; No Copyright; Avail: CASI; A02, Hardcopy; A04, Microfiche Yellowstone National Park preserves and protects unique geologic features and biologic systems. The area contains the world’s largest assemblage of hot springs, geysers, and mud pots. Every year millions of visitors are drawn to see the active geysers, such as Old Faithful, and the colorful eroded remains of ancient hydrothermal systems in the Grand Canyon of the Yellowstone. Within the park, visitors also observe the activities of many large mammals, including bison, elk, moose, grizzly bears, and recently, wolves. The populations and movements of these animals are directly and indirectly influenced by the vegetation covering the park. The large fires of 1988 in Yellowstone demonstrated how dramatically and rapidly the vegetation and the state of the ecosystem can change. Although sometimes overlooked by park visitors, smaller organisms, the biota of the hot springs, are receiving increasing attention. These microorganisms are being researched by the biotechnology field for their potential benefits for the health of humans and the environment (Brock, 1994). The U.S, Geological Survey (USGS), in cooperation with the National Park Service, is using imaging spectroscopy to advance the understanding of the geologic features and biologic systems 110
in Yellowstone National Park. We are applying recent advances in remote sensing methods to detect and map the mineralogy of active and ancient hydrothermal systems, vegetation cover types, and hot springs microorganisms. These maps will be used to increase the understanding of the hydrothermal systems and to examine the links between the distribution of plant species and large mammal populations (specifically, the use of whitebark pine by grizzly bears). This paper presents the initial results of our efforts in mapping biology and mineralogy in Yellowstone National Park using imaging spectroscopy. Author Yellowstone National Park (ID-MT-WY); Thematic Mapping; Mineral Deposits; Remote Sensing; Imaging Spectrometers; Spectroscopy 20000064554 Analytical Imaging and Geophysics, LLC, Boulder, CO USA Mapping Active Hot Springs Using AVIRIS and TIMS Kruse, F. A., Analytical Imaging and Geophysics, LLC, USA; Summaries of the Seventh JPL Airborne Earth Science Workshop January 12-16, 1998; Dec. 19, 1998; Volume 1, pp. 255; In English; See also 20000064520; No Copyright; Abstract Only; Available from CASI only as part of the entire parent document This research is studying the occurrence and characteristics of both active and fossil hot springs systems using Thermal Infrared Multispectral Scanner (TIMS) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data to provide insights into the origins of hydrothermal systems and precious metal ore deposits. At Steamboat Springs, Nevada, TIMS delineates the silica sinter associated with active alkaline-type hot springs, while AVIRIS data map alteration minerals including alunite, kaolinite, and hydrothermal silica associated with inactive acid-sulfate hot springs. Sites studied at Yellowstone National Park, Wyoming, include Mammoth Hot Springs, consisting of several ages of travertine terraces as well as glacially-transported travertine material. Hot springs in the Firehole River basin, include active hydrothermal areas associated with the Upper, Midway, and Lower geyser basins, with mineralogy characteristic of the both the alkaline and acid-sulfate type hot springs, including hydrothermal silica, alunite, and kaolinite. Surface exposures in Hayden Valley and in the Norris and Gibbon Geyser Basins indicate predominantly acid-sulfate alteration, along with smaller occurrences of silica sinter. Study of the nature and spatial distribution of specific hydrothermal-alteration minerals at active hot springs using hyperspectral remote sensing provides insight into the geochemistry of these systems, and to the occurrence and characteristics of hot springs systems in the fossil record. These findings potentially could lead to new and/or improved exploration methods for epithermal ore deposits. Author Hydrothermal Systems; Infrared Imagery; Mineral Deposits; Remote Sensing; Thematic Mapping; Springs (Water) 20000064555 Analytical Imaging and Geophysics, LLC, Boulder, CO USA Spectral Identification of Image Endmembers Determined from AVIRIS Data Kruse, F. A., Analytical Imaging and Geophysics, LLC, USA; Summaries of the Seventh JPL Airborne Earth Science Workshop January 12-16, 1998; Dec. 19, 1998; Volume 1, pp. 257; In English; See also 20000064520; No Copyright; Abstract Only; Available from CASI only as part of the entire parent document Experienced scientists can easily identify materials based on their visible/infrared reflectance spectra using field or laboratory instruments. Imaging spectrometry (hyperspectral data) also allow identification of materials using spectroscopy; however, these data typically consist of hundreds-of-thousands of spectra, so which spectra do you identify? Some researchers have taken the approach of matching every spectrum in an image to a spectral library. This works well when pure materials are present on the ground and all of the materials are contained in the library. In real-world situations, however, where materials are spatially or intimately mixed, only the strongest features are matched. In this case, it’s also not possible to have all of the possible mixtures in the library, and thus the above approach will only ”Identify” the predominant material, if any material at all. The research described here concentrates on identifying only the purest spectra extracted from the hyperspectral data. After applying data reduction and endmember extraction methodologies, the endmember spectra are used in an automated identification procedure based on analysis of spectral features. This approach has an improved likelihood of success because 1) the best endmember spectra have already been extracted for analysis, 2) the extracted endmembers are mean spectra and thus have improved signal-to-noise over single spectra, 3) these spectra are typically one material (no mixing), 4) not every spectrum in the image needs to be analyzed. Once the individual endmembers have been identified, then a variety of mapping methods can be used to map their spatial distributions, associations, and abundances. Author Spectroscopy; Spectrum Analysis; Remote Sensing; Spectral Correlation; Imaging Spectrometers; Image Classification 111
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Scientific and Technical Aerospace
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Introduction Scientific and Technic
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Subject Divisions Table of Contents
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ground equipment and systems. For a
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Subject Categories of the Division
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48 Oceanography 139 Includes the ph
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72 Atomic and Molecular Physics 191
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Document Availability Information T
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Avail: NASA Public Document Rooms.
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NASA CASI Price Tables — Effectiv
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ALABAMA AUBURN UNIV. AT MONTGOMERY
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��
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20000061433 Pisa Univ., Dipartiment
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English; 34th; 34th Thermophysics C
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ciency of the generated derivative
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20000063509 NASA Glenn Research Cen
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05 AIRCRAFT DESIGN, TESTING AND PER
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A long tradition of aerodynamic des
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sonic and transonic cases will be p
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20000061449 British Aerospace Publi
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20000064593 NASA Langley Research C
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performance required for a proposed
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ment cost and create better product
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tion (NPSS), is focused on the inte
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surface measurement techniques used
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with emphasis on the search for lin
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20000063520 NASA Kennedy Space Cent
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primary issues for it’s adaptatio
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Footage shows the night vertical ta
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necessary for ignition modeling, or
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engine lifetime, and high power ope
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24 COMPOSITE MATERIALS ��
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ments. For the screening of liner m
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formation of a neutral, fluorescent
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20000065655 Ames Lab., IA USA Funda
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This is a second Triennial Conferen
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of completely charged PSSNa solutio
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20000064100 NASA Langley Research C
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Spacecraft in low Earth orbit (LEO)
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85 dB amplifier design evolved by o
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20000064594 New Mexico Univ., Elect
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20000064615 NASA Ames Research Cent
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declarations with a pattern recogni
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containing point where interpolatio
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has his own responsibility, while t
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the people, documents and projects
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and experimental technology, many o
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tems. In particular this applicatio
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Over the past decade, high performa
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expensive than a single analysis. I
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20000064726 Carnegie-Mellon Univ.,
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20000064099 Teledyne Brown Engineer
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20000066597 Geophysical Observatory
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20000065633 Institute TNO of Applie
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important conclusion is that for su
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est frame of the string. The modifi
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isotopes. The method and program we
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A detailed study is undertaken, usi
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20000063497 Kaiser Electro-Optics,
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delivers 60% of the x-ray flux from
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76 SOLID-STATE PHYSICS ��
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20000064660 Abdus Salam Internation
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20000067648 NASA Marshall Space Fli
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20000064703 Institute of Atomic Ene
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The BRST approach is applied to the
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20000067671 Hampton Univ., VA USA A
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82 DOCUMENTATION AND INFORMATION SC
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ecoming almost too cumbersome to be
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physical world. These are the two p
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with WFPC2 data. Tests of HSTphot
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A brief description is given of the
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gas, show variations that have rema
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egy, called Power In that would all
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20000064089 Smithsonian Astrophysic
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A ABERRATION, 143, 198 ABRASION, 22
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COMPRESSIBLE FLOW, 83 COMPRESSION L
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FATIGUE (MATERIALS), 50, 93, 96 FAT
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LASING, 90 LATE STARS, 224 LATITUDE
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PATCH ANTENNAS, 74 PATHOLOGY, 98 PA
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SPACE PLATFORMS, 38 SPACE PROBES, 2
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WIND PROFILES, 137 WIND TUNNEL TEST
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Brown, Andy, 38 Brown, April S., 20
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Grosvenor, C. A., 70 Grosvenor, J.
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Lu, Gui-Ying, 50 Lugg, Desmond J.,
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Robertson, Tony, 39 Robinson, Jeffr
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Warner, J. D., 63 Warren, James, 16
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Scientific and Technical Aerospace Reports Volume 38 July 28, 2000

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