Engineering topics
Publications and source records attributed to Green, Robert O..
The Earth's energy balance and hydrology are effected by the distribution and characteristics of snow cover on the surface. Snow grainsize and snow melt, influence surface albedo and hydrology.
This paper describes an approach to derive a range of biomass fire parameters from spectra measured by NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS measures images of the upwelling spectral radiance from 400 to 2500 nm at 10nm intervals. Images are measured of 11 by up to 100 km with 20 by 20 m spatial resolution.
Snow-covered-area (SCA) and snow grain size are crucial inputs to hydrologic and climatologic modeling of alpine and other seasonally snow-covered regions. SCA is necessary to parameterize energy budget calculations in climate models, to determine in which regions point snowmelt models are to be run for distributed snowmelt modeling efforts and to provide a basis from which estimates of snow water equivalent (SWE) may be made.
California chaparral ecosystems are exceptionally fire adapted and typically are subject to wildfire at decadal to century frequencies. The hot dry Mediterranean climate summers and the chaparral communities of the Santa Monica Mountains make wildfire one of the most serious economic and life-threatening natural disasters faced by the region.
This paper compares the performance and operational parameters of the Hyperspectral Imager (HSI), scheduled for launch aboard the Lewis spacecraft in July 1996, with those of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)
Calibrated radiance spectra measured remotely, record the integrated effects of the solar source, the atmosphere, and the surface. To pursue scientific research and applications, based on the molecular absorptions and constituent scattering properties of the surface, the solar source and atmosphere must be characterized and compensated in the spectra. This paper describes a set of radiative transfer spectral fitting algorithms to characterize the absorbing and scattering constituents of the atmosphere from calibrated AVIRIS spectra.
Imaging spectrometry data must be spectrally, radiometrically and geometrically calibrated. This calibration process is validated for in-flight performance of the sensor using a rigorous ground truth campaign.
AVIRIS data represent a new and important approach for the retrieval of atmospheric and surface parameters from optical remote sensing data. Not only as a test for future space systems, but also as an operational airborne remote sensing system, the development of algorithms to retrieve information from AVIRIS data is an important step to these new approaches and capabilities.
This publication contains the summaries for the Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996. The main workshop is divided into two smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on March 4-6. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on March 6-8. The summaries for this workshop appear in Volume 2.
This paper compares the performance and operational parameters of the Hyperspectral Imager (HSI), scheduled for launch aboard the Lewis spacecraft in July 1996, with those of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The HSI is a pushbroom, imaging spectrometer with 30 meter spatial resolution, generating 384 spectral channels over the range 400 to 2500 nm at 5.0 to 6.4 nm resolution for each of its 256 crosstrack pixels.
California chaparral ecosystems are exceptionally fire adapted and typically are subject to wildfire at decadal to century frequencies. The hot dry Mediterranean climate summers and the chaparral communities of the Santa Monica Mountains make wildfire one of the most serious economic and life-threatening natural disasters faced by the region. Additionally, the steep fire-burned hillsides are subject to erosion, slumpage, and mud slides during the winter rains. The Santa Monica Mountain Zone (SMMZ) is a 104,000 ha eastwest trending range with 607 m of vertical relief and located in the center of the greater Los Angeles region. A series of fires in the fall of 1993 burned from Simi Valley to Santa Monica within a few hours. Developing techniques to monitor fire hazard and predict the spread of fire is of major concern to the region. One key factor in the susceptibility to fire is the water content of the vegetation canopy. The development of imaging spectrometry and remote sensing techniques may constitute a tool to provide this information.
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Melting of the snowpack is a critical parameter that drives aspects of the hydrology in regions of the Earth where snow accumulates seasonally. New techniques for measurement of snow melt over regional scales offer the potential to improve monitoring and modeling of snow-driven hydrological processes. In this paper we present the results of measuring the spectral absorption of liquid water in a melting snowpack with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data were acquired over Mammoth Mountain, in east central California on 21 May 1994 at 18:35 UTC. The air temperature at 2926 m on Mammoth Mountain at site A was measured at 15-minute intervals during the day preceding the AVIRIS data acquisition. At this elevation. the air temperature did not drop below freezing the night of the May 20 and had risen to 6 degrees Celsius by the time of the overflight on May 21. These temperature conditions support the presence of melting snow at the surface as the AVIRIS data were acquired.
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Explore the source record for details and available documents.
This publication is the first of three containing summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on January 23-24. The summaries for this workshop appear in this volume; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on January 25-26. The summaries for this workshop appear in Volume 3; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop, on January 26. The summaries for this workshop appear in Volume 2.
A complete spectral unmixing of a complicated AVIRIS scene may not always be possible or even desired. High quality data of spectrally complex areas are very high dimensional and are consequently difficult to fully unravel. Partial unmixing provides a method of solving only that fraction of the data inversion problem that directly relates to the specific goals of the investigation. Many applications of imaging spectrometry can be cast in the form of the following question: 'Are my target signatures present in the scene, and if so, how much of each target material is present in each pixel?' This is a partial unmixing problem. The number of unmixing endmembers is one greater than the number of spectrally defined target materials. The one additional endmember can be thought of as the composite of all the other scene materials, or 'everything else'. Several workers have proposed partial unmixing schemes for imaging spectrometry data, but each has significant limitations for operational application. The low probability detection methods described by Farrand and Harsanyi and the foreground-background method of Smith et al are both examples of such partial unmixing strategies. The new method presented here builds on these innovative analysis concepts, combining their different positive attributes while attempting to circumvent their limitations. This new method partially unmixes AVIRIS data, mapping apparent target abundances, in the presence of an arbitrary and unknown spectrally mixed background. It permits the target materials to be present in abundances that drive significant portions of the scene covariance. Furthermore it does not require a priori knowledge of the background material spectral signatures. The challenge is to find the proper projection of the data that hides the background variance while simultaneously maximizing the variance amongst the targets.
The AVIRIS instrument uses an onboard calibration system to provide auxiliary calibration data. The system consist of a tungsten halogen cycle lamp imaged onto a fiber bundle through an eight position filter wheel. The fiber bundle illuminates the back side of the foreoptics shutter during a pre-run and post-run calibration sequence. The filter wheel contains two neutral density filters, five spectral filters and one blocked position. This paper reviews the general workings of the onboard calibrator system and discusses recent modifications.