Engineering topics
Publications and source records attributed to Goetz, A. F. H..
Participants at the 12th Airborne Earth Science Workshop reported science research and applications results with spectral images measured by the NASA Airborne Invisible/Infrared Imaging Spectrometer (AVIRIS).
Approximately 100,000 sq. km of the High Plains of the central United States are covered by sand dunes and sand sheets deposited during the Holocene. Soil-dating evidence shows that there were at least four periods of dune reactivation during major droughts in the last 10,000 years. The dunes in this region are anchored by vegetation. We have undertaken a study of land-use change in the High Plains from 1985 to the present using Landsat 5 TM and Landsat 7 ETM+ images to map variation in vegetation cover during wet and dry years. Mapping vegetation cover of less than 20% is important in modeling potential surface reactivation since at this level the vegetation no longer sufficiently shields sandy surfaces from movement by wind. Landsat TM data have both the spatial resolution and temporal coverage to facilitate vegetation cover analysis for model development and verification. However, there is still the question of how accurate TM data are for the measurement of both growing and senescent vegetation in and and semi-arid regions. AVIRIS provides both high spectral resolution as well as high signal-to-noise ratio and can be used to test the accuracy of Landsat TM and ETM+ data. We have analyzed data from AVIRIS flown nearly concurrently with a Landsat 7 overpass. The comparison between an AVIRIS image swath of 11 km width subtending a 30 deg. angle and the same area covered by a 0.8 deg. angle from Landsat required accounting for the BRDF. A normalization technique using the ratio of the reflectances from registered AVIRIS and Landsat data proved superior to the techniques of column averaging on AVIRIS data alone published previously by Kennedy et al. This technique can be applied to aircraft data covering a wider swath angle than AVIRIS to develop BRDF responses for a wide variety of surfaces more efficiently than from ground measurements.
Recent studies (Charlock, et al.; Kato, et. al) have indicated a potential discrepancy between measured solar irradiance in the cloud-free atmosphere and model derived downwelling solar irradiance. These conclusions were based primarily on broadband integrated solar flux. Extinction (both absorption and scattering) phenomena, however, typically have spectral characteristics that would be present in moderate resolution (e.g., 10 nm) spectra, indicating the need for such measurements to thoroughly investigate the cause of any discrepancies. The 1996 Department of Energy Atmospheric Radiation Measurement Program (ARM) Intensive Observation Period (IOP), held simultaneously with the NASA Subsonic Aircraft: Contrail and Cloud Effects Special Study (SUCCESS) Program, provided an opportunity for two simultaneous but independent measurements of moderate resolution solar spectral downwelling irradiance at the surface. The instruments were the NASA Ames Solar Spectral Flux Radiometer and the Analytical Spectral Devices, Inc., FieldSpecT-FR. Spectral and band integrated quantities from both sets of measurements will be presented, along with estimates of the downwelling solar irradiance from band model and line by line calculations, in an effort to determine the compatibility between measured and calculated solar irradiance in the cloud-free atmosphere.
Remote sensing of troposheric water vapor profiles from current geostationary weather satellites is made using a few broadband infrared (IR) channels in the 6-13 micron region. Uncertainties greater than 20% exist in derived water vapor values just above the surface from the IR emission measurements. In this paper, we propose three near-IR channels, one within the 0.94-micron water vapor band absorption region, and the other two in nearby atmospheric windows, for remote sensing of precipitable water vapor over land areas, excluding lakes and rivers, during daytime from future geostationary satellite platforms. The physical principles are as follows. The reflectance of most surface targets varies approximately linearly with wavelength near 1 micron. The solar radiation on the sun-surface-sensor ray path is attenuated by atmospheric water vapor. The ratio of the radiance from the absorption channel with the radiances from the two window channels removes the surface reflectance effects and yields approximately the mean atmospheric water vapor transmittance of the absorption channel. The integrated water vapor amount from ground to space can be obtained with a precision of better than 5% from the mean transmittance. Because surface reflectances vary slowly with time, temporal variation of precipitable water vapor can be determined reliably. High spatial resolution, precipitable water vapor images are derived from spectral data collected by the Airborne Visable-Infrared Imaging Spectrometer, which measures solar radiation reflected by the surface in the 0.4-2.5 micron region in 10-nm channels and has a ground instantaneous field of view of 20 m from its platform on an ER-2 aircraft at 20 km. The proposed near-IR reflectance technique would complement the IR emission techniques for remote sensing of water vapor profiles from geostationary satellite platforms, especially in the boundary layer where most of the water vapor is located.
None given. From the paper: It appears that the best possible AVIRIS calibration would be a combination of a radiative transfer correction followed by a ground calibration.
The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides 'point and click' operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
Remote sensing of tropospheric water vapor profiles from current geostationary weather satellites are made using a few broadband infrared (IR) channels in the 6 to 13- micrometers region. Uncertainties greater than 20 percent exist in derived water vapor values just above the surface from the IR emission measurements. In this paper, we propose three near-IR channels, one within the 0.94-micrometer water vapor band absorption region, and the other two in nearby atmospheric windows, for remote sensing of precipitable water vapor over land areas, excluding lakes and rivers, during daytime from future geostationary satellite platforms. The physical principles are as follows. The reflectance of most surface targets varies approximately linearly with wavelength near 1 micrometer. The solar radiation on the sun-surface-sensor ray path is attenuated by atmospheric water vapor...
The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below.
High spatial resolution column atmospheric water vapor amounts and equivalent liquid water thicknesses of surface targets are retrieved from spectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The retrievals are made using a nonlinear least squares curve fitting technique. Two case studies from AVIRIS data acquired over Denver-Platteville area, Colorado and over Death Valley, California are presented. The column water vapor values derived from AVIRIS data over the Denver-Platteville area are compared with those obtained from radiosondes, ground level upward-looking microwave radiometers, and geostationary satellite measurements. The column water vapor image shows spatial variation patterns related to the passage of a weather front system. The column water vapor amounts derived from AVIRIS data over Death Valley decrease with increasing surface elevation. The derived liquid water image clearly shows surface drainage patterns.
Estimation of biochemical constituents in vegetation such as lignin, cellulose, starch, sugar and protein by remote sensing methods is an important goal in ecological research. The spectral reflectances of dried leaves exhibit diagnostic absorption features which can be used to estimate the abundance of important constituents. Lignin and nitrogen concentrations have been obtained from canopies by use of imaging spectrometry and multiple linear regression techniques. The difficulty in identifying individual spectra of leaf constituents in the region beyond 1 micrometer is that liquid water contained in the leaf dominates the spectral reflectance of leaves in this region. By use of spectrum matching techniques, originally used to quantify whole column water abundance in the atmosphere and equivalent liquid water thickness in leaves, we have been able to remove the liquid water contribution to the spectrum. The residual spectra resemble spectra for cellulose in the 1.1 micrometer region, lignin in the 1.7 micrometer region, and starch in the 2.0-2.3 micrometer region. In the entire 1.0-2.3 micrometer region each of the major constituents contributes to the spectrum. Quantitative estimates will require using unmixing techniques on the residual spectra.
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.
Proposed portable spectrometer for analyzing minerals in field generates spectral images like camera and process spectral data for real-time identification of materials. To identify unknown mineral, user locates significant peaks in displayed spectrum and matches them to spectrum in portfolio of reference spectra. Alternatively, user calls up display of prerecorded spectra in same wavelength region for comparison or allows spectrometer to determine best match automatically. New instrument makes it unnecessary to return data to laboratory or equipment trailer for processing.
Images at 128 wavelengths allow direct identification of many earth surface materials. Two reports describe advanced airborne spectrometer that creates images of terrain at many wavelengths. Airborne imaging spectrometer (AIS) produces two-dimensional images in 128 spectral bands in 1.2-to-2.4-micrometer wavelength region. Images created by 32-by-32 array of mercury cadmium telluride detector elements. Array views swath of Earth below moving aircraft. Used for agricultural, geological, and other surveys.
Imaging spectrometer for use in crop and mineral resource-mapping experiments is scaled-down version. Compact imaging spectrometer uses Schmidt/Littrow optics with single spherical primary mirror. Radiation from scene strikes reflecting Schmidt corrector plate located near center of curvature of primary spherical mirror.
Imaging spectrometry for the remote sensing of the Earth from aircraft and satellites is discussed. Results with an aircraft instrument show that remote, direct identification of surface materials is possible. The airborne and spaceborne sensors can acquire images in 100 to 200 spectral bands. The next generation aircraft scanner (AVIRIS) is expected to be operational in 1987. Plans are underway for a Shuttle instrument (SISEX) to fly in 1991.
The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase.
The airborne imaging spectrometer (AIS) has provided for the first time, the possibility to map mineralogical constituents in the Earth's surface and thus has enormously increased the value of remote-sensing data as a tool in the solution of geologic problems. The question addressed with AIS at Cuprite was how well could the mineral components at the surface of a hydrothermal alteration zone be detected, identified and mapped? The question was answered positively and is discussed. A relatively rare mineral, buddingtonie, that could not have been detected by conventional means, was discovered and mapped by the use of AIS.