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At least 253 records · Page 14
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NASA NTRS1995

Measurement of the spectral absorption of liquid water in melting snow with an imaging spectrometer

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. 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 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.

Green, Robert O.↗
NASA NTRS1995

Vegetation species composition and canopy architecture information expressed in leaf water absorption measured in the 1000 nm and 2200 spectral region by an imaging spectrometer

Plant species composition and plant architectural attributes are critical parameters required for the measuring, monitoring, and modeling of terrestrial ecosystems. Remote sensing is commonly cited as an important tool for deriving vegetation properties at an appropriate scale for ecosystem studies, ranging from local to regional and even synoptic scales. Classical approaches rely on vegetation indices such as the normalized difference vegetation index (NDVI) to estimate biophysical parameters such as leaf area index or intercepted photosynthetically active radiation (IPAR). Another approach is to apply a variety of classification schemes to map vegetation and thus extrapolate fine-scale information about specific sites to larger areas of similar composition. Imaging spectrometry provides additional information that is not obtainable through broad-band sensors and that may provide improved inputs both to direct biophysical estimates as well as classification schemes. Some of this capability has been demonstrated through improved discrimination of vegetation, estimates of canopy biochemistry, and liquid water estimates from vegetation. We investigate further the potential of leaf water absorption estimated from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data as a means for discriminating vegetation types and deriving canopy architectural information. We expand our analysis to incorporate liquid water estimates from two spectral regions, the 1000-nm region and the 2200-nm region. The study was conducted in the vicinity of Jasper Ridge, California, which is located on the San Francisco peninsula to the west of the Stanford University campus. AVIRIS data were acquired over Jasper Ridge, CA, on June 2, 1992, at 19:31 UTC. Spectra from three sites in this image were analyzed. These data are from an area of healthy grass, oak woodland, and redwood forest, respectively. For these analyses, the AVIRIS-measured upwelling radiance spectra for the entire Jasper Ridge scene were transformed to apparent surface reflectance using a radiative transfer code-based inversion algorithm.

Green, Robert O.↗
NASA NTRS1995

Impact of differences in the solar irradiance spectrum on surface reflectance retrieval with different radiative transfer codes

Surface reflectance retrieval from imaging spectrometer data as acquired with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has become important for quantitative analysis. In order to calculate surface reflectance from remotely measured radiance, radiative transfer codes such as 5S and MODTRAN2 play an increasing role for removal of scattering and absorption effects of the atmosphere. Accurate knowledge of the exo-atmospheric solar irradiance (E(sub 0)) spectrum at the spectral resolution of the sensor is important for this purpose. The present study investigates the impact of differences in the solar irradiance function, as implemented in a modified version of 5S (M5S), 6S, and MODTRAN2, and as proposed by Green and Gao, on the surface reflectance retrieved from AVIRIS data. Reflectance measured in situ is used as a basis of comparison.

Staenz, K.↗
NASA NTRS1994

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

Hoffbeck, Joseph P.↗
NASA NTRS1996

Mapping Volcanic Gas Emissions in the Mammoth Mountain Area Using AVIRIS

The Long Valley Caldera located in the eastern Sierra Nevada (California) shows new signs of volcanic activity. This renewed activity is expressed by gas emissions, hydrothermal activity and frequent earthquakes. Analysis of the gas composition regarding the percentage biogenic carbon and the He-3/He-4 ratio revealed that the gas source is the magma body approximately 7 km beneath the Long Valley Caldera. The gas from the magma body surfaces not only via the fumaroles but also emerges along geological faults. Some of the spots where gas surfaces are marked by dead or stressed trees. Other spots may not yet be identified. It is only recently known, from research at 'Vulcano Island' in southern Italy, that volcanoes release abundant carbon dioxide from their flanks as diffuse soil emanations. Mammoth Mountain seems to behave in a similar manner. The research described in this paper is designed to determine whether AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) can be used to identify areas of volcanic gas emissions.

deJong, Steven M.↗
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.↗
NASA NTRS1999

Fractal Characterization of Hyperspectral Imagery

Two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral images selected from the Los Angeles area, one representing urban and the other, rural, were used to examine their spatial complexity across their entire spectrum of the remote sensing data. Using the ICAMS (Image Characterization And Modeling System) software, we computed the fractal dimension values via the isarithm and triangular prism methods for all 224 bands in the two AVIRIS scenes. The resultant fractal dimensions reflect changes in image complexity across the spectral range of the hyperspectral images. Both the isarithm and triangular prism methods detect unusually high D values on the spectral bands that fall within the atmospheric absorption and scattering zones where signature to noise ratios are low. Fractal dimensions for the urban area resulted in higher values than for the rural landscape, and the differences between the resulting D values are more distinct in the visible bands. The triangular prism method is sensitive to a few random speckles in the images, leading to a lower dimensionality. On the contrary, the isarithm method will ignore the speckles and focus on the major variation dominating the surface, thus resulting in a higher dimension. It is seen where the fractal curves plotted for the entire bandwidth range of the hyperspectral images could be used to distinguish landscape types as well as for screening noisy bands.

Qiu, Hon-Iie↗
NASA NTRS1999

Remote Sensing of smoke and its Direct Radiative Forcing in South America

Smoke aerosol, organic and black carbon particles suspended in the air, can absorb and reflect solar radiation to space, i.e., direct radiative forcing. Depending on the ratio of scattering to absorption, and the surface spectral properties, the net smoke effect can be warming or cooling of the climate system. Data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flown on the ER-2 at altitude of 20km, and of the AERONET ground based sun/sky radiometers are used to assess the smoke aerosol optical thickness over the land and the direct radiative forcing at the top of the atmosphere and at ground level, respectively. The data are collected during the Smoke, Clouds, and Radiation - Brazil (SCAR-B) experiment. In contrast to model results of the smoke effect, we show using the AVIRIS data, that even though the surface albedo is high (0.17 - average on the solar spectrum) and the smoke single scattering albedo is low (0.88), smoke does increase the instantaneous reflectance of sunlight to space by 30-40 W/m2 for an increase in the smoke optical thickness of 0.5. The AERONET data are used to derive, simultaneously, the effect of smoke on the downward radiation. Models using spectrally averaged smoke optical properties and averaged surface reflectance, predicted very small smoke forcing. The reason is that smoke is mainly opaque in short wavelengths (e.g.<0.7 micron) while the surface is mainly bright in longer wavelengths (>0.7 micron). Modeling the effect of smoke on climate forcing cannot be done using the spectrally average properties. It requires the knowledge of the smoke and surface spectral properties.

Kaufman, Yoram J.↗
NASA NTRS1994

Extraction of Dry Leaf Spectral Features from Reflectance Spectra of Green Vegetation

Reflectance spectra of green vegetation in the 1.4-2.5 micrometer region are dominated by liquid water absorptions. In this article, we describe the analysis of a set of reflectance spectra of green vegetation, dry vegetation, and liquid water measured in a laboratory using a nonlinear least squares spectral matching technique. We conclude that both the liquid water and the dry materials contribute to the reflectance spectra of green vegetation in the 1.0-2.5 micrometer region. Our matching of data acquired with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over two vegetated areas in Oregon Transect with a pure water spectrum also shows a residual absorption feature near 1.72 micrometers.

Gao, Bo-Cai↗
NASA NTRS2001

Sensing, Spectra and Scaling: What's in Store for Land Observations

Bill Pecora's 1960's vision of the future, using spacecraft-based sensors for mapping the environment and exploring for resources, is being implemented today. New technology has produced better sensors in space such as the Landsat Thematic Mapper (TM) and SPOT, and creative researchers are continuing to find new applications. However, with existing sensors, and those intended for launch in this century, the potential for extracting information from the land surface is far from being exploited. The most recent technology development is imaging spectrometry, the acquisition of images in hundreds of contiguous spectral bands, such that for any pixel a complete reflectance spectrum can be acquired. Experience with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has shown that, with proper attention paid to absolute calibration, it is possible to acquire apparent surface reflectance to 5% accuracy without any ground-based measurement. The data reduction incorporates in educated guess of the aerosol scattering, development of a precipitable water vapor map from the data and mapping of cirrus clouds in the 1.38 micrometer band. This is not possible with TM. The pixel size in images of the earth plays and important role in the type and quality of information that can be derived. Less understood is the coupling between spatial and spectral resolution in a sensor. Recent work has shown that in processing the data to derive the relative abundance of materials in a pixel, also known is unmixing, the pixel size is an important parameter. A variance in the relative abundance of materials among the pixels is necessary to be able to derive the endmembers or pure material constituent spectra. In most cases, the 1 km pixel size for the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is too large to meet the variance criterion. A pointable high spatial and spectral resolution imaging spectrometer in orbit will be necessary to make the major next step in our understanding of the solid earth surface and its changing face.

Goetz, Alexander F. H.↗
NASA NTRS2001

High Altitude Measurements of Radiance at High Spectral and Spatial Resolution for SIMBIOS Sensor Calibration, Validation, and Intercomparisons

The successful combination of data from different ocean color sensors depends on the correct interpretation of signal from each of these sensors. Ideally, the sensor measured signals are calibrated to geophysical units of spectral radiance, and sensor artifacts are removed and corrected. The calibration process resamples the signal into a common radiometric data space so that subsequent ocean color algorithms that are applied to the data are based on physical processes and are inherently sensor independent. The objective of this project is to calibrate and validate the on-orbit radiometric characteristics of Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) with underflights of NASA's calibrated Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This objective is feasible because AVIRIS measures the same spectral range as SeaWIFS at higher spectral resolution. In addition to satellite sensor underflights, the AVIRIS project has supported comparison and analysis of the radiometric calibration standards used for AVIRIS and SeaWIFS. To date, both the OCTS and SeaWIFS satellite sensors have been underflown by AVIRIS with matching spectral, spatial, geometric, radiometric, and temporal domains. The calibration and validation objective of this project is pursued for the following reasons: (1) Calibration is essential for the quantitative use of SeaWIFS and other SIMBIOS (Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies) sensor data; (2) Calibration in the laboratory of spaceborne sensors is challenging; (3) Satellite sensors are subjected aging on the ground and to trauma during launch; (4) The Earth orbit environment is significantly different than the laboratory calibration environment; (5) Through years of effort AVIRIS has been demonstrated to be well calibrated; and (6) AVIRIS can match the spectral and spatial observation characteristics near the top of the atmosphere at the time of SeaWIFS measurements.

Green, Robert O.↗
NASA NTRS2001

Exploring a Black Body Source as an Absolute Radiometric Calibration Standard and Comparison with a NIST Traced Lamp Standard

Radiometric calibration of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is required for the scientific research and application objectives pursued with the spectroscopic measurements. Specifically calibration is required for: inter-comparison of AVIRIS data measured at different locations and at different times; analysis of AVIRIS data with data measured by other instruments; and analysis of AVIRIS data in conjunction with computer models. The primary effect of radiometric calibration is conversion of AVIRIS instrument response values (digitized numbers, or DN) to units of absolute radiance. For example, a figure shows the instrument response spectrum measured by AVIRIS over a portion of Rogers Dry Lake, California, and another figure shows the same spectrum calibrated to radiance. Only the calibrated spectrum may be quantitatively analyzed for science research and application objectives. Since the initial development of the AVIRIS instrument-radiometric calibration has been based upon a 1000-W irradiance lamp with a calibration traced to the National Institute of Standards and Technology (NIST). There are several advantages to this irradiance-lamp calibration approach. First, the considerable effort of NIST backs up the calibration. Second, by changing the distance to the lamp, the output can closely span the radiance levels measured by AVIRIS. Third, this type of standard is widely used. Fourth, these calibrated lamps are comparatively inexpensive. Conversely, there are several disadvantages to this approach as well. First, the lamp is not a primary standard. Second, the lamp output characteristics may change in an unknown manner through time. Third, it is difficult to assess, constrain, or improve the calibration uncertainty delivered with the lamp. In an attempt to explore the effect and potentially address some of these disadvantages a set of analyses and measurements comparing an irradiance lamp with a black-body source have been completed. This research is ongoing, and the current set of measurements, analyses, and results are presented in this paper.

Green, Robert O.↗
NASA NTRS2001

Hyperspectral Image Classification using a Self-Organizing Map

The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging sensors are capable of generating unprecedented volumes of radiometric data. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), for example, routinely produces image cubes with 224 spectral bands. This undoubtedly opens a wide range of new possibilities, but the analysis of such a massive amount of information is not an easy task. In fact, most of the existing algorithms devoted to analyzing multispectral images are not applicable in the hyperspectral domain, because of the size and high dimensionality of the images. The application of neural networks to perform unsupervised classification of hyperspectral data has been tested by several authors and also by us in some previous work. We have also focused on analyzing the intrinsic capability of neural networks to parallelize the whole hyperspectral unmixing process. The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. The present work discusses the possibility of using a Self Organizing neural network to perform unsupervised classification of hyperspectral images. In sections 3 and 4, the topology of the proposed neural network and the training algorithm are respectively described. Section 5 provides the results we have obtained after applying the proposed methodology to real hyperspectral data, described in section 2. Different parameters in the learning stage have been modified in order to obtain a detailed description of their influence on the final results. Finally, in section 6 we provide the conclusions at which we have arrived.

Martinez, P.↗
NASA NTRS2001

Spectral Dimensionality and Scale of Urban Radiance

Characterization of urban radiance and reflectance is important for understanding the effects of solar energy flux on the urban environment as well as for satellite mapping of urban settlement patterns. Spectral mixture analyses of Landsat and Ikonos imagery suggest that the urban radiance field can very often be described with combinations of three or four spectral endmembers. Dimensionality estimates of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) radiance measurements of urban areas reveal the existence of 30 to 60 spectral dimensions. The extent to which broadband imagery collected by operational satellites can represent the higher dimensional mixing space is a function of both the spatial and spectral resolution of the sensor. AVIRIS imagery offers the spatial and spectral resolution necessary to investigate the scale dependence of the spectral dimensionality. Dimensionality estimates derived from Minimum Noise Fraction (MNF) eigenvalue distributions show a distinct scale dependence for AVIRIS radiance measurements of Milpitas, California. Apparent dimensionality diminishes from almost 40 to less than 10 spectral dimensions between scales of 8000 m and 300 m. The 10 to 30 m scale of most features in urban mosaics results in substantial spectral mixing at the 20 m scale of high altitude AVIRIS pixels. Much of the variance at pixel scales is therefore likely to result from actual differences in surface reflectance at pixel scales. Spatial smoothing and spectral subsampling of AVIRIS spectra both result in substantial loss of information and reduction of apparent dimensionality, but the primary spectral endmembers in all cases are analogous to those found in global analyses of Landsat and Ikonos imagery of other urban areas.

Small, Christopher↗
NASA NTRS2001

Spectral Band Characterization for Hyperspectral Monitoring of Water Quality

A method for selecting the set of spectral characteristics that provides the smallest increase in prediction error is of interest to those using hyperspectral imaging (HSI) to monitor water quality. The spectral characteristics of interest to these applications are spectral bandwidth and location. Three water quality constituents of interest that are detectable via remote sensing are chlorophyll (CHL), total suspended solids (TSS), and colored dissolved organic matter (CDOM). Hyperspectral data provides a rich source of information regarding the content and composition of these materials, but often provides more data than an analyst can manage. This study addresses the spectral characteristics need for water quality monitoring for two reasons. First, determination of the greatest contribution of these spectral characteristics would greatly improve computational ease and efficiency. Second, understanding the spectral capabilities of different spectral resolutions and specific regions is an essential part of future system development and characterization. As new systems are developed and tested, water quality managers will be asked to determine sensor specifications that provide the most accurate and efficient water quality measurements. We address these issues using data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and a set of models to predict constituent concentrations.

Vermillion, Stephanie C.↗
NASA NTRS1995

Measurement of the Spectral Absorption of Liquid Water in Melting Snow With an Imaging Spectrometer

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.

Green, Robert O.↗
NASA NTRS2004

Proceedings of the 12th JPL Airborne Earth Science Workshop

Participants at the 12th Airborne Earth Science Workshop reported science research and applications results with spectral images measured by the NASA Airborne Invisible/lnfrared Imaging Spectrometer (AVIRIS). The workshop was held in Pasadena, California, from February, 2003.

Green, Robert O.↗
NASA NTRS1996

Mapping Chaparral in the Santa Monica Mountatins Using Multiple Endmember Spectral Mixture Models

From Intro: 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 (eg. biomass, leaf area, fuel load)...Analysis focused on AVIRIS data collected on October 19, 1994.

AVIRIS chaparral California chaparral California w↗