Engineering topics

Green Robert O.

Publications and source records attributed to Green Robert O..

8 records

NASA NTRS1996

Calibration of the Airborne Visible/Infrared Imaging Spectrometer in the Laboratory

Imaging spectrometry data must be spectrally, radiometrically and geometrically calibrated in order to: 1) derive physical parameters from measured spectral radiance, 2) compare data acquired from different regions and at different times, 3) compare and analyze the imaging spectrometry data with data acquired from other calibrated sensors, and 4) compare and analyze data with results from computer models. The calibration of AVIRIS data is the process by which laboratory characterization data are applied to raw instrument data (digitized number versus spectral channels) to produce quantitative spectra (radiance versus wavelength) for each image pixel in units of spectral radiance. The AVIRIS sensor and calibration process are described by Vane and the application of the calibration data to the raw digital data is described by Green. This calibration process is validated for in-flight performance of the sensor using a rigorous ground-truth campaign. This workshop paper reviews the laboratory characterization data set that is used in the AVIRIS calibration process. The laboratory measurements used to acquire the calibration data are divided into three classes: 1) spectral calibration, 2) radiometric calibration, and 3) spatial calibration.

Green Robert O.↗

NASA NTRS1996

Examination of Biomass Fire Temperature and Areal Extent from Calibrated AVIRIS Spectra

Biomass burning is an important process on the Earth at the local, regional and global scales. To investigate issues related to biomass burning, a range of remotely acquired data were measured as part of the NASA Smoke Cloud Aerosol and Radiation experiment in Brazil, 1995. As part of this experiment, images of calibrated spectral radiance from 400 to 2500 nm at 10 nm intervals were acquired by AVIRIS. To investigate the expression of biomass fires in AVIRIS spectra, a model of the upwelling radiance from a burning fire was developed. This spectral model accounts for four components in the 20 by 20 m AVIRIS spatial resolution element. These are: (1) the atmospheric path radiance, (2) the solar reflected radiance from unburnt vegetation and soil, (3) the apparent temperature and area of a primary fire, and (4) the apparent temperature and area of a secondary fire. A nonlinear least squares spectral fitting algorithm was developed to invert this model for the AVIRIS spectra. The derived biomass burning parameters from this algorithm are presented for AVIRIS spectral images acquired over Cuiaba, Brazil on 25 August 1995.

Green Robert O.↗

NASA NTRS1996

In-Flight Calibration and Validation of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Calibrated spectra acquired remotely as images allow determination of surface and atmospheric properties based on absorption and scattering expressed in the spectra. AVIRIS measures spectra as images in the solar reflected portion of the electromagnetic spectrum. To use these spectra for scientific research and applications, the calibration of the spectra must be known at the time of measurement in flight. To validate the calibration of AVIRIS in flight, an in-flight calibration experiment was performed for an over-flight on May 9, 1995 at Ivanpah Playa, California. In-situ measurements of the atmosphere and surface at a calibration target were measured. These measurements were used to constrain a radiative transfer code and predict the total upwelling spectral radiance incident at AVIRIS. This prediction was compared to the radiance measured by AVIRIS for the calibration target. An agreement of 96.5% was determined. The in-flight signal-to-noise was determined and shown to have more than doubled over the previous year.

Green Robert O.↗

NASA NTRS1996

Retrieval of Surface Snow Grain Size and Melt Water from AVIRIS Spectra

The Earth's energy balance and hydrology are affected by the distribution and characteristics of snow cover on the surface. Snow grain size and snow melt influence surface albedo and hydrology. A model of snow reflectance that depends on both grain size and surface melt water was developed to derive these parameters from remote spectral measurements. This reflectance model is based on a discrete ordinate radiative transfer approach that uses Mie calculations of snow optical properties, which are based on the complex refractive index of ice and water. This snow model was linked to an atmospheric radiative transfer code and a nonlinear least squares fitting algorithm. The resulting combined algorithm was applied to an AVIRIS snow data set acquired over Mammoth Mountain, California. Maps of grain size and surface snow melt were generated that are consistent with the expected ranges and distributions for conditions at the site.

Green Robert O.↗

NASA NTRS1996

Characterization and Compensation of the Atmosphere for the Inversion of AVIRIS Calibrated Radiance to Apparent Surface Reflectance

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 that characterize the absorbing and scattering constituents of the atmosphere from calibrated AVIRIS spectra. These atmospheric characteristics were used in conjunction with the illumination and observation geometries to invert the AVIRIS calibrated radiance spectra to apparent surface reflectance. A validation of the algorithm was performed with in-situ reflectance spectra acquired at the time of the AVIRIS overflight over Pasadena, California, in 1994.

Green Robert O.↗

NASA NTRS1996

Surface and Atmospheric Parameter Retrieval From AVIRIS Data: The Importance of Non-Linear Effects

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. Many things have been learned since AVIRIS became operational, and the successive technical improvements in the hardware and the more sophisticated calibration techniques employed have increased the quality of the data to the point of almost meeting optimum user requirements. However, the potential capabilities of imaging spectrometry over the standard multispectral techniques have still not been fully demonstrated. Reasons for this are the technical difficulties in handling the data, the critical aspect of calibration for advanced retrieval methods, and the lack of proper models with which to invert the measured AVIRIS radiances in all the spectral channels. To achieve the potential of imaging spectrometry, these issues must be addressed. In this paper, an algorithm to retrieve information about both atmospheric and surface parameters from AVIRIS data, by using model inversion techniques, is described. Emphasis is put on the derivation of the model itself as well as proper inversion techniques, robust to noise in the data and an inadequate ability of the model to describe natural variability in the data. The problem of non-linear effects is addressed, as it has been demonstrated to be a major source of error in the numerical values retrieved by more simple, linear-based approaches. Non-linear effects are especially critical for the retrieval of surface parameters where both scattering and absorption effects are coupled, as well as in the cases of significant multiple-scattering contributions. However, sophisticated modeling approaches can handle such non-linear effects, which are especially important over vegetated surfaces. All the data used in this study were acquired during the 1991 Multisensor Airborne Campaign (MAC-Europe), as part of the European Field Experiment on a Desertification-threatened Area (EFEDA), carried out in Spain in June-July 1991.

Green Robert O.↗

NASA NTRS1996

Subpixel Snow-Covered-Area and Snow Grain Size From Mixture Analysis with AVIRIS Data

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. Snow grain size, SWE and snow impurities determine the spectral albedo of snow, which controls the net solar flux at the snowpack surface. Snow albedo is of the utmost importance in snowmelt modeling, yet the difficulty with which grain size, SWE, and impurities are mapped has left the spatial distribution of snow albedo in alpine catchments poorly understood. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has been used to estimate sub-pixel snow-covered-area and snow grain size independently. In this paper we present a technique which improves estimates of both snow parameters by treating their mapping simultaneously.

Green Robert O.↗

NASA NTRS1996

Mapping Chaparral in the Santa Monica Mountains Using Multiple Spectral Mixture Models

California chaparral is one of the most important natural vegetation communities in Southern California, representing a significant source of species diversity and, through a high susceptibility to fire, playing a major role in ecosystem dynamics. Due to steep topographic gradients, harsh edaphic conditions and variable fire histories, chaparral typically forms a complex mosaic of different species dominants and age classes, each with unique successional responses to fire and canopy characteristics (e.g. moisture content, biomass, fuel load) that modify fire susceptibility. The high human cost of fire and intimate mixing along the urban interface combine to modify the natural fire regime as well as provide additional impetus for a better understanding of how to predict fire and its management. Management problems have been further magnified by nearly seventy years of fire suppression and drought related die-back over the last few years resulting in a large accumulation of highly combustible fuels. Chaparral communities in the Santa Monica Mountains exemplify many of the management challenges associated with fire and biodiversity. A study was initiated in the Santa Monica Mountains to investigate the use of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for providing improved maps of chaparral coupled with direct estimates of canopy attributes (e.g. biomass, leaf area, fuel load). The Santa Monica Mountains are an east-west trending range located approximately 75 kilometers north of Los Angeles extending westward into Ventura County. Within the Santa Monica Mountains a diverse number of ecosystems are located, including four distinct types of chaparral, wetlands, riparian habitats, woodlands, and coastal sage scrub. In this study we focus on mapping three types of chaparral, oak woodlands and grasslands. Chaparral mapped included coastal sage scrub, chamise chaparral and mixed chaparral that consisted predominantly of two species of Ceanothus.

Green Robert O.↗