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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

The data facility of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

AVIRIS operations at the Jet Propulsion Laboratory include a significant data task. The AVIRIS data facility is responsible for data archiving, data calibration, quality monitoring and distribution. Since 1987, the data facility has archived over one terabyte of AVIRIS data and distributed these data to science investigators as requested. In this paper we describe recent improvements in the AVIRIS data facility.

Nielsen, Pia J.↗
NASA NTRS1995

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) onboard calibration system

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.

Chrien, Thomas G.↗
NASA NTRS1995

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Sensor improvements for 1994 and 1995

AVIRIS is a NASA-sponsored Earth-remote-sensing imaging spectrometer designed, built and operated by the Jet Propulsion Laboratory (JPL). While AVIRIS has been operational since 1989, major improvements have been completed in most of the sensor subsystems during the winter maintenance cycles. As a consequence of these efforts, the capabilities of AVIRIS to reliably acquire and deliver consistently high quality, calibrated imaging spectrometer data continue to improve annually, significantly over those in 1989. Improvements to AVIRIS prior to 1994 have been described previously. This paper details recent and planned improvements to AVIRIS in the sensor task.

Sarture, C. M.↗
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 NTRS1993

Validation of Atmospheric Correction of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Radiance Data Based on Radiative Transfer Modeling

An evaluation of atmospheric correction of AVIRIS data using radiative transfer codes LOWTRAN7 and MODTRAN is presented. The algorithm employed is based on a simple model of radiance L at each wavelength at the sensor that can be written approximately LAV=Lp+Tp, where subscript AV referes to AVIRIS, Lp is the path radiance and Tp is the diffuse + direct transmitted radiance of the atmosphere at AVIRIS.

Infrared Mapping↗
NASA NTRS1993

Application of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to Determination of Atmospheric Aerosol Optical Depth and Precipitable Water Content

This paper focuses on determination of the temporal and spatial gas absorption and aerosol scattering properties of the atmosphere itself including the radiative properties of clouds, specifically derivation of estimates of aerosol optical depth, atmospheric water vapor, and oxygen pressure surface altitude form upwelling near-TOA spectral radiance measurements obtained with the AVIRIS.

Remote SensingAVIRIS↗
NASA NTRS1995

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Onboard Calibration System

The AVIRIS instrument uses an onboard calibration system to provide auxiliary calibration data. The system consists of 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 2 neutral density filters, 5 spectral filters and blocked position.

spectrometer↗
NASA NTRS1994

Remote Sensing of Seasonal Leaf Area Index Across the Oregon Transect

Remotely sensed data acquired from four remote-sensing instruments on three different aircraft platforms over a transect of coniferous forest stands in Oregon were analyzed with respect to seasonal leaf area index (LAI). Data from the four instruments were corrected for the varying seasonal and geographic atmospheric conditions present along the transect. Strong logarithmic relationships were observed between seasonal maximum and minimum LAI and the simple ratio (SR) (near infrared/red reflectance) calculated from the broad-spectral-band Thematic Mapper Simulator (TMS), as well as from the narrow-spectral-band Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the Compact Airborne Spectrographic Imager (CASI), and a Spectron SE590 spectro-radiometer (R(exp 2) = 0.82-0.97). The TMS SR reached an asymptote at an LAI of approx. 7-8. However, the SE590 and the CASI SR continued to increase up to the maximum LAI of 10.6. The variability of the relationship between the AVIRIS SR and LAI increased at stands with LAIs greater than 7, making a trend in the AVIRIS SR-LAI relationship at LAIs greater than 7 difficult to discern. The SRs of the coniferous forest stands measured by the narrow-spectral-band instruments were higher than they were from the broad-spectral-band TMS. This is attributed partially to the integration of the TMS over a broad wavelength region in the red and more strongly to calibration differences between the sensors. Seasonal TMS SR trends for four time periods for some of the stands deviated from the expected seasonal LAI trends, possibly because of smoke and very low sun angles during some of the acquisition periods. However, the expected SR differences for the seasonal minimum and maximum LAI were observed for all of the sensors for nearly all of the forest stands. This study, demonstrates that remotely, sensed data from both broad- and narrow spectral band instruments can provide estimates of LAI for use in forest ecosystems simulation models to estimate evapotranspiration, photosynthesis, canopy turnover, and net primary production over large areas.

Spanner, Michael↗
NASA NTRS1991

Approaches to vegetation mapping and ecophysiological hypothesis testing using combined information from TIMS, AVIRIS, and AIRSAR

The Tropical Rainforest Ecology Experiment (TREE) had two primary objectives: (1) to design a method for mapping vegetation in tropical regions using remote sensing and determine whether the result improves on available vegetation maps; and (2) to test a specific hypothesis on plant/water relations. Both objectives were thought achievable with the combined information from the Thermal Infrared Multispectral Scanner (TIMS), Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AIRSAR). Implicitly, two additional objectives were: (1) to ascertain that the range within each variable potentially measurable with the three instruments is large enough in the site, relative to the sensitivity of the instruments, so that differences between ecological groups may be detectable; and (2) to determine the ability of the three systems to quantify different variables and sensitivities. We found that the ranges in values of foliar nitrogen concentration, water availability, stand structure and species composition, and plant/water relations were large, even within the upland broadleaf vegetation type. The range was larger when other vegetation types were considered. Unfortunately, cloud cover and navigation errors compromised the utility of the TIMS and AVIRIS data. Nevertheless, the AIRSAR data alone appear to have improved on the available vegetation map for the study area. An example from an area converted to a farm is given to demonstrate how the combined information from AIRSAR, TIMS, and AVIRIS can uniquely identify distinct classes of land use. The example alludes to the potential utility of the three instruments for identifying vegetation at an ecological scale finer than vegetation types.

Oren, R.↗
NASA NTRS2001

Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.

Roberts, Dar A.↗
NASA NTRS2001

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Reith, Ernest↗