Airborne Visible And Infrared Imaging Spectrometer Research Articles

Autonomous Coral Reef Survey in Support of Remote Sensing

An autonomous surface vehicle instrumented with optical and acoustical sensors was deployed in Kane'ohe Bay, HI, U.S.A., to provide high-resolution, in situ observations of coral reef reflectance with minimal human presence. The data represented a wide range in bottom type, water depth, and illumination and supported more thorough investigations of remote sensing methods for identifying and mapping shallow reef features. The in situ data were used to compute spectral bottom reflectance and remote sensing reflectance R_(rs,λ) as a function of water depth and benthic features. The signals were used to distinguish between live coral and uncolonized sediment within the depth range of the measurements (2.5 m to 5 m). In situ R_(rs,λ) were found to compare well with remotely sensed measurements from an imaging spectrometer, the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), deployed on an aircraft at high altitude. Cloud cover and in situ sensor orientation were found to have minimal impact on in situ R_(rs,λ), suggesting that valid reflectance data may be collected using autonomous surveys even when atmospheric conditions are not favorable for remote sensing operations. The use of reflectance in the red and near infrared portions of the spectrum, expressed as the red edge height 〖REH〗_λ, was investigated for detecting live aquatic vegetative biomass, including coral symbionts and turf algae. The 〖REH〗_λ signal from live coral was detected in Kane'ohe Bay to a depth of approximately 4 m with in situ measurements. A remote sensing algorithm based on the 〖REH〗_λ signal was defined and applied to AVIRIS imagery of the entire bay and was found to reveal areas of shallow, dense coral and algal cover. The peak wavelength of 〖REH〗_λ decreased with increasing water depth, indicating that a more complete examination of the red edge signal may potentially yield a remote sensing approach to simultaneously estimate vegetative biomass and bathymetry in shallow water.

Improvements in Land-Use Classification for Estimating Daytime Surface Temperatures and Sea-Breeze Flows in Southern California

Abstract Understanding the interactions between large-scale atmospheric and oceanic circulation patterns and changes in land cover and land use (LCLU) due to urbanization is a relevant subject in many coastal climates. Recent studies by Lebassi et al. found that the average maximum air temperatures during the summer in two populated California coastal areas decreased at low-elevation areas open to marine air penetration during the period of 1970–2005. This coastal cooling was attributed to an increase in sea-breeze activity. The aims of this work are to better understand the coastal flow patterns and sea–land thermal gradient by improving the land-cover classification scheme in the region using updated airborne remote sensing data and to assess the suitability of the updated regional atmospheric modeling system for representing maritime flows in this region. This study uses high-resolution airborne data from the NASA Hyperspectral Infrared Imager (HyspIRI) mission preparatory flight campaign over Southern California and surface ground stations to compare observations against model estimations. Five new urban land classes were created using broadband albedo derived from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor and then assimilated into the Weather Research and Forecasting (WRF) Model. The updated model captures the diurnal spatial and temporal sea-breeze patterns in the region. Results show notable improvements of simulated daytime surface temperature and coastal winds using the HyspIRI-derived products in the model against the default land classification, reaffirming the importance of accounting for heterogeneity of urban surface properties.

Radiometric Intercomparison between Suomi-NPP VIIRS and Aqua MODIS Reflective Solar Bands Using Simultaneous Nadir Overpass in the Low Latitudes

AbstractOn-orbit radiometric performance of the Suomi National Polar-Orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) is studied using the extended simultaneous nadir overpass (SNO-x) approach. Unlike the traditional SNO analysis of data in the high latitudes, this study extends the analysis to the low latitudes—in particular, over desert and ocean sites with relatively stable and homogeneous radiometric properties—for intersatellite comparisons. This approach utilizes a pixel-by-pixel match with an efficient geospatial matching algorithm to map VIIRS data into the Moderate Resolution Imaging Spectroradiometer (MODIS). VIIRS moderate-resolution bands M-1 through M-8 are compared with Aqua MODIS equivalent bands to quantify radiometric bias over the North African desert and over the ocean. Biases exist between VIIRS and MODIS in several bands, primarily because of spectral differences as well as possible calibration uncertainties, residual cloud contamination, and bidirectional reflectance distribution function (BRDF). The impact of spectral differences on bias is quantified by using the Moderate Resolution Atmospheric Transmission (MODTRAN) and hyperspectral measurements from the Earth Observing-1 (EO-1) Hyperion and the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). After accounting for spectral differences and bias uncertainties, the VIIRS radiometric bias over desert agrees with MODIS measurements within 2% except for the VIIRS shortwave infrared (SWIR) band M-8, which indicates a nearly 3% bias. Over ocean, VIIRS agrees with MODIS within 2% by the end of January 2013 with uncertainty less than 1%. Furthermore, VIIRS bias relative to MODIS is also computed at the Antarctica Dome C site for validation and the result agrees well within 1% with the bias estimated using SNO-x over desert.

Fusing optical and radar data to estimate sagebrush, herbaceous, and bare ground cover in Yellowstone

The arid and semi-arid sagebrush–grass ecosystem occupies a substantial portion of rangelands in the western United States. Using remote sensing techniques to map the percent of sagebrush, grass/forb, and bare ground components is necessary for forage production estimation and natural resource management over large areas. However optical data have significant deficiencies in these ecosystems because of exposed bright soil, spectrally-indeterminate vegetation, and a large dead vegetation component. Radar data also have deficiencies caused by factors such as antenna pattern calibration, local incidence angle (LIA), soil moisture, and surface roughness. With the complementary vegetation information gained from optical data and radar data, these two datasets were fused to estimate 10-m sagebrush, grass, and bare ground percent cover in the non-forested areas of Yellowstone National Park, which is a representative native western rangeland ecosystem of the US. The datasets were processed to resolve the issues of antenna pattern calibration and LIA effect. Peak green Landsat, late fall Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AirSAR) data were fused in this analysis. AVIRIS, Landsat, AirSAR and elevation data were used to segment the study area into two main subcategories of “pure grass” and “mixed sagebrush and grass”. Landsat Tasseled Cap Greenness (LTCG) was used to retrieve bare land and grass percentages in pure grass areas. In the areas with mixed grass and sagebrush, standardized LTCG and radar C vv were used to derive the vegetation cover percentage, and the ratio of standardized LTCG and radar L hv was further used to calculate the relative abundance of sagebrush and grass. Comparison between the field and remote sensing estimations shows the correlation coefficients were 0.838, 0.746, and 0.830 for bare land, grass, and sagebrush, respectively. When grouped into three discrete categories of “low”, “medium”, and “high”, the overall accuracies were 79.4%, 75.9%, and 77.6%, respectively. Our study shows the potential for application of global spaceborne C- and L-band radar and optical data fusion for large-area rangeland monitoring.

Spectral identification of ozone‐damaged pine needles

Needles were collected from ponderosa and Jeffrey pine trees at three sites in the Sierra Nevada, and were assembled into 504 samples and grouped according to five dominant live needle conditions – green, winter fleck, sucking insect damage, scale insect damage, and ozone damage – and a random mixture. Reflectance and transmittance measurements of abaxial and adaxial surfaces were obtained at ca 0.3 nm spectral resolution from 400–800 nm, and binned to simulate Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data. There were no significant differences in optical properties between the two surfaces. Ozone‐damaged needles were collected from Jeffrey pine trees at one site, and exhibited significantly different (family‐wise α = 0.01) reflectance and transmittance signatures – and significantly different signature slopes – at both spectral resolutions, from green and winter fleck needles from the same site. Ozone‐damaged needles had significantly different (family‐wise α = 0.01) abaxial surface reflectance and reflectance slope signatures from all other groups of needles, at both spectral resolutions. In comparison with three chlorophyll reflectance indices, a new red fall index (RFI) provides high classification accuracies for ozone‐damaged and non‐ozone‐damaged pine needles (overall acc. = 94%; κ = 59%). Thus, ozone‐damaged Jeffrey pine needles have a unique spectral signature in relation to dominant needle conditions of ponderosa and Jeffrey pine trees.

Remote monitoring of hypersaline environments in San Francisco Bay, CA, USA

As part of a historic remediation project, approximately 61 km2 of salt evaporation ponds in the southern portion of San Francisco Bay, CA (USA) are scheduled for restoration to natural tidal marsh habitat over the next several decades. We have investigated the correlation of remotely sensed infrared spectral information with in situ field measurements and sampling, and evaluated the usefulness of a remote sensing approach to monitor salinity and population distributions of microbial communities in the hypersaline ponds. The Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument operated by the Jet Propulsion Laboratory has created a ten‐year archive of spectral information concerning these ponds. We utilized spectral signatures of microbial populations that are sensitive to salinity, and trained a supervised classification algorithm to identify physical parameters from an AVIRIS scene based upon microbe spectra gathered in the field using a portable visible to near‐infrared (VNIR) spectrometer. Our results indicate that automated analyses of hyperspectral observations are capable of detecting variations in microbial populations and discriminating corresponding salinity levels.

A combined methodology to produce highly accurate classification for AVIRIS hyperspectral data

The primary objective of this paper was to produce highly accurate supervised classification for airborne visible and infrared imaging spectrometer (AVIRIS) hyperspectral data for Chequamegon National Forest vegetation species in northern Wisconsin. This was done using a combined methodology of an atmospheric correction scheme and neural network together with data reduction and an increase in feature separation through three types of transformations, namely the fast Fourier transform (FFT), principal component (PC), and canonical transformation (CT). The atmospheric correction scheme was performed by removing the contaminating effects of aerosol particle scattering that are contained within the hyperspectral dataset due to the significant variation of the solar zenith angle and aerosol optical properties corresponding to cloud-free Earth targets. An algorithm in C++ has been developed to correct for the influence of solar zenith angle and the atmospheric components on scattering. The accuracy assessment criteria including overall accuracy, Ǩ (Kappa or KHAT statistics), and the statistical separability between classes were used. Using the correction of aerosol scattering, transformations, and the neural network classification, the classification accuracy of the vegetation species was shown by the overall accuracy of 97.09%, Ǩ = 0.96, and the average statistical separability of 1.99. The classification accuracy of the vegetation species for the uncorrected image was shown by the overall accuracy of 84.40%, Ǩ = 0.80, and the average statistical separability of 1.91. Therefore, the combined methodology including the atmospheric correction scheme, transformations, and the neural network classification yielded relatively high classification accuracy.

Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR

Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests.The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated <6.8% and <18.6% error rates in the detection of invasive tree species at ∼7 m2 and ∼2 m2 minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them.

Remote sensing of native and invasive species in Hawaiian forests

Detection and mapping of invasive species is an important component of conservation and management efforts in Hawai'i, but the spectral separability of native, introduced, and invasive species has not been established. We used high spatial resolution airborne imaging spectroscopy to analyze the canopy hyperspectral reflectance properties of 37 distinct species or phenotypes, 7 common native and 24 introduced tree species, the latter group containing 12 highly invasive species. Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) reflectance and derivative-reflectance signatures of Hawaiian native trees were generically unique from those of introduced trees. Nitrogen-fixing trees were also spectrally unique from other groups of non-fixing trees. There were subtle but significant differences in the spectral properties of highly invasive tree species in comparison to introduced species that do not proliferate across Hawaiian ecosystems. The observed differences in canopy spectral signatures were linked to relative differences in measured leaf pigment (chlorophyll, carotenoids), nutrient (N, P), and structural (specific leaf area; SLA) properties, as well as to canopy leaf area index. These leaf and canopy properties contributed variably to the spectral separability of the trees, with wavelength-specific reflectance and absorption features that overlapped, but which were unique from one another. A combination of canopy reflectance from 1125–2500 nm associated with leaf and canopy water content, along with pigment-related absorption features (reflectance derivatives) in the 400–700 nm range, was best for delineating native, introduced, and invasive species. There was no single spectral region that always defined the separability of the species groups, and thus the full-range (400–2500 nm) spectrum was highly advantageous in differentiating these groups. These results provide a basis for more detailed studies of invasive species in Hawai'i, along with more explicit treatment of the biochemical properties of the canopies and their prediction using imaging spectroscopy.

Hyperspectral Remote Sensing of Canopy Biodiversity in Hawaiian Lowland Rainforests

Mapping biological diversity is a high priority for conservation research, management and policy development, but few studies have provided diversity data at high spatial resolution from remote sensing. We used airborne imaging spectroscopy to map woody vascular plant species richness in lowland tropical forest ecosystems in Hawai’i. Hyperspectral signatures spanning the 400–2,500 nm wavelength range acquired by the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) were analyzed at 17 forest sites with species richness values ranging from 1 to 17 species per 0.1–0.3 ha. Spatial variation (range) in the shape of the AVIRIS spectra (derivative reflectance) in wavelength regions associated with upper-canopy pigments, water, and nitrogen content were well correlated with species richness across field sites. An analysis of leaf chlorophyll, water, and nitrogen content within and across species suggested that increasing spectral diversity was linked to increasing species richness by way of increasing biochemical diversity. A linear regression analysis showed that species richness was predicted by a combination of four biochemically-distinct wavelength observations centered at 530, 720, 1,201, and 1,523 nm (r2 = 0.85, p < 0.01). This relationship was used to map species richness at approximately 0.1 ha resolution in lowland forest reserves throughout the study region. Future remote sensing studies of biodiversity will benefit from explicitly connecting chemical and physical properties of the organisms to remotely sensed data.

Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing

Forest fires leave behind a changed ecosystem with a patchwork of surface cover that includes ash, charred organic matter, soils and soil minerals, and dead, damaged, and living vegetation. The distributions of these materials affect post-fire processes of erosion, nutrient cycling, and vegetation regrowth. We analyzed high spatial resolution (2.4 m pixel size) Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data collected over the Cerro Grande fire, to map post-fire surface cover into 10 classes, including ash, soil minerals, scorched conifer trees, and green vegetation. The Cerro Grande fire occurred near Los Alamos, New Mexico, in May 2000. The AVIRIS data were collected September 3, 2000. The surface cover map revealed complex patterns of ash, iron oxide minerals, and clay minerals in areas of complete combustion. Scorched conifer trees, which retained dry needles heated by the fire but not fully combusted by the flames, were found to cover much of the post-fire landscape. These scorched trees were found in narrow zones at the edges of completely burned areas. A surface cover map was also made using Landsat Enhanced Thematic Mapper plus (ETM+) data, collected September 5, 2000, and a maximum likelihood, supervised classification. When compared to AVIRIS, the Landsat classification grossly overestimated cover by dry conifer and ash classes and severely underestimated soil and green vegetation cover. In a comparison of AVIRIS surface cover to the Burned Area Emergency Rehabilitation (BAER) map of burn severity, the BAER high burn severity areas did not capture the variable patterns of post-fire surface cover by ash, soil, and scorched conifer trees seen in the AVIRIS map. The BAER map, derived from air photos, also did not capture the distribution of scorched trees that were observed in the AVIRIS map. Similarly, the moderate severity class of Landsat-derived burn severity maps generated from the differenced Normalized Burn Ratio (dNBR) calculation had low agreement with the AVIRIS classes of scorched conifer trees. Burn severity and surface cover images were found to contain complementary information, with the dNBR map presenting an image of degree of change caused by fire and the AVIRIS-derived map showing specific surface cover resulting from fire.

Mapping marshland vegetation of San Francisco Bay, California, using hyperspectral data

Sustainable management of wetland ecosystems requires monitoring of vegetation dynamics, which can be achieved through remote sensing. This paper assesses the use of hyperspectral imagery to study the structure of wetlands of San Francisco Bay, California, USA. Spectral mixture analysis (SMA) and multiple endmember spectral mixture analysis (MESMA) were applied on an AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) image to investigate their appropriateness to characterize marshes, with emphasis on the Spartina species complex. The role of rms. error as a measure of model adequacy and different methods for image endmember extraction were also evaluated. Results indicate that both SMA and MESMA are suitable for mapping the main components of the marsh, although MESMA seems more appropriate since it can incorporate more than one endmember per class. rms. error was shown not to be a measure of SMA model adequacy, but it can be used to help to assess model adequacy within groups of related models.

Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: a case study in China Camp, CA, USA

Multiple endmember spectral mixture analysis (MESMA) was applied to the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) imagery of a salt marsh in China Camp at San Pablo Bay, California. A nine‐endmember set representing materials within the scene was used including: two Salicornia and two soils, and Grindelia, Spartina, dry grass, water and shade. The resultant abundance maps were used to investigate the spatial distribution of the marsh vegetation species, Salicornia virginica, Grindelia Stricta and Spartina foliosa. The Spartina abundance map exhibited a well‐defined zone bordering the water and the lower marsh, which is in good agreement with the field observations made in 2002. Comparison of the Salicornia map with all six field global positional system (GPS) polygons indicates Salicornia was classified with high accuracy. The proposed approach did a good job in classifying Spartina and Salicornia which cover 93.81% of the total marsh. The Grindelia fraction image underestimates in some areas, while in other areas it shows false detection. This misclassification is attributed to the spectral similarity between Grindelia and Salicornia and to the small patch size of Grindelia. Further work is required to solve this confusion.

Indicators of plant species richness in AVIRIS spectra of a mesic grassland

Hyperspectral imagery of the Konza Prairie Biological Station in northeastern Kansas was used to evaluate upwelling spectral radiance, prairie spectral reflectance and band ratios of each as potential indicators of vascular plant species richness in a mesic grassland. The extent to which spatial variability in these parameters related to plant species richness also was investigated. A 224 channel hyperspectral data cube acquired in June 2000 by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) provided complete coverage of the 400–2500 nm range at approximately 10 nm per channel. After band deletions accounted for detector overlap and strong atmospheric attenuation features, 176 bands were retained for analysis and spanned the 404–2400 nm range. Prairie reflectance was estimated via radiative transfer modeling and scaling to a library spectrum of highway construction material. Data were sampled from pixels having a 19 m ground sample distance (GSD) to represent each of 93 vegetation sampling transects. Reflectance and radiance at mid-infrared wavelengths (e.g., 1553 nm), and band ratios that were based on atmospheric windows in the red, near-infrared and mid-infrared spectra estimated species richness to within 6 to 7 species per transect. The 856 to 780 nm radiance or reflectance ratio yielded maximum adjusted coefficients of determination ( r 2) of approximately 0.4 in regressions with richness when data from bison-grazed and ungrazed areas were combined. These regressions remained significant ( p ≤ 0.001) when only ungrazed areas were assessed although r 2 reduced to approximately 0.2. Richness was related significantly also to the 433 to 674 nm reflectance ratio for grazed-plus-ungrazed and ungrazed-only areas. In contrast, the effectiveness of the 433 to 674 nm radiance ratio was reduced by atmospheric backscatter. Species richness did not correlate strongly or consistently with transect spatial variability (coefficient of variation or range) in radiance, reflectance or band ratio value, apparently as a consequence of the relatively small area sampled for each transect (approximately 0.5 ha). Relationships between richness and prairie spectral features were explained by the influence of soil exposure on both parameters. Richness and estimated soil exposure tended to increase from ungrazed lowlands, to ungrazed slopes, to ungrazed uplands to grazed areas. Remotely sensed estimates of soil exposure may be particularly useful in addressing plant species richness on grazed grasslands owing to an overall similarity in spectral reflectance among dominant plant species.

Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity

Our study compares data on burn severity collected from multi-temporal Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) with similar data from the Enhanced Thematic Mapper Plus (ETM+) using the differenced Normalized Burn Ratio (dNBR). Two AVIRIS and ETM+ data acquisitions recorded surface conditions immediately before the Hoover Fire began to spread rapidly and again the following year. Data were validated with 63 field plots using the Composite Burn Index (CBI). The relationship between spectral channels and burn severity was examined by comparing pre- and post-fire datasets. Based on the high burn severity comparison, AVIRIS channels 47 and 60 at wavelengths of 788 and 913 nm showed the greatest negative response to fire. Post-fire reflectance values decreased the most on average at those wavelengths, while channel 210 at 2370 nm showed the greatest positive response on average. Fire increased reflectance the most at that wavelength over the entire measured spectral range. Furthermore, channel 210 at 2370 nm exhibited the greatest variation in spectral response, suggesting potentially high information content for fire severity. Based on general remote sensing principles and the logic of variable spectral responses to fire, dNBR from both sensors should produce useful results in quantifying burn severity. The results verify the band–response relationships to burn severity as seen with ETM+ data and confirm the relationships by way of a distinctly different sensor system.

A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantially different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied.

Identification of spectrally similar materials using the USGS Tetracorder algorithm: the calcite–epidote–chlorite problem

A scheme to discriminate and identify materials having overlapping spectral absorption features has been developed and tested based on the U.S. Geological Survey (USGS) Tetracorder system. The scheme has been applied to remotely sensed imaging spectroscopy data acquired by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument. This approach was used to identify the minerals calcite, epidote, and chlorite in the upper Animas River watershed, Colorado. The study was motivated by the need to characterize the distribution of calcite in the watershed and assess its acid-neutralizing potential with regard to acidic mine drainage. Identification of these three minerals is difficult because their diagnostic spectral features are all centered at 2.3 μm, and have similar shapes and widths. Previous studies overestimated calcite abundance as a result of these spectral overlaps. The use of a reference library containing synthetic mixtures of the three minerals in varying proportions was found to simplify the task of identifying these minerals when used in conjunction with a rule-based expert system. Some inaccuracies in the mineral distribution maps remain, however, due to the influence of a fourth spectral component, sericite, which exhibits spectral absorption features at 2.2 and 2.4 μm that overlap the 2.3-μm absorption features of the other three minerals. Whereas the endmember minerals calcite, epidote, chlorite, and sericite can be identified by the method presented here, discrepancies occur in areas where all four occur together as intimate mixtures. It is expected that future work will be able to reduce these discrepancies by including reference mixtures containing sericite.

Relationships among vegetation, climatic zonation, soil, and bedrock in the central White-Inyo Range, eastern California: A ground-based and remote-sensing study

To assess the effects of regional climate change using remote-sensing methods, we conducted integrated geologic, botanical, and field-radiometer baseline studies on the ground in the central White-Inyo Mountains. Vegetation on contrasting geologic substrates in 58, 50 × 50 m sites was studied annually in early July and in late September 1997–2001. We quantified areal abundances of plant species by using field inventories and ground-based values of the normalized-difference vegetation index (NDVI). Distributions and abundances of species are complex functions of microclimate (elevation, slope, and facing direction), seasonality, soil development, bedrock lithologies, and albedo. X-ray diffraction studies demonstrate that the soils consist chiefly of in situ weathering components such as illite, montmorillonite, chlorite, quartz, carbonates, and feldspars. Soils along higher ridges contain modest amounts of wind-blown grains derived from glaciated granodioritic plutons and volcanic roof pendants of the Sierra Nevada. Light-colored limestone and dolomite support only sparse amounts of Sagebrush at any altitude, whereas this drought-tolerant species is widespread on dark argillite, quartzite, phyllite, and granitic rocks. Grasses grow luxuriantly on dark granite and quartzite, but poorly on carbonate bedrock. Trees are numerous on carbonate rocks, but are uncommon on dark granite and quartzite. Higher-albedo rocks and soils retain moisture better than darker substrates and in general support greater vegetative cover and biomass. These studies provided ground control for hyperspectral data collected by ER-2 aircraft along three Airborne Visible and InfraRed Imaging Spectrometer (AVIRIS) flight lines. Because of the ∼16 m spatial resolution of the remotely sensed data, only plant communities could be mapped from AVIRIS imagery. On the basis of the dominant species, we identified the following communities: Shadscale/desert scrub; Pinyon-Juniper woodlands; Sagebrush meadows; Mountain Mahogany woodlands; Aspen woodlands; Bristlecone Pine–Limber Pine woodlands; and Alpine fell-fields. When judged by correlation with the 50 × 50 m ground-based inventoried sites, botanical community assignments for the June 2000 flight lines achieved an accuracy of 81%; in contrast, October 1996 assignments were only 65% correct, reflecting low sun angle and senesced vegetation.

Atmospheric effects on the classification of surface minerals in an arid region using Short-Wave Infrared (SWIR) hyperspectral imagery and a spectral unmixing technique

This study focuses on the comparison of spectral unmixing results from at-sensor radiance data and atmospherically corrected data, i.e., surface reflectance. The airborne Short-Wave Infrared (SWIR) Full Spectrum Imager (SFSI) and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) were used to acquire data over Cuprite, Nevada, in June 1995 and 1996, respectively. This is an arid region that is well known for its exposed bedrock and alteration zones. For this project, a portion of the SWIR-2 spectral range covering the atmospheric window between 2050 and 2350 nm and a linear spectral unmixing technique were used to map surface minerals, including products of hydrothermal alteration. In total, eight end-members were extracted from the imagery using a new automatic procedure called Iterative Error Analysis (IEA). The atmospheric corrections were applied using a look-up-table procedure implemented in the Imaging Spectrometer Data Analysis System (ISDAS) and created with the radiative transfer model MODTRAN3. The fraction, or abundance, maps derived from the two types of data were compared using the coefficient of determination (R2) and the Average Euclidean Distance Coefficient (AEDC). Very good unmixing agreement was found between the results from the at-sensor radiance data and those from the surface reflectance data. For the SFSI data, the R2 values range from 0.72 to 0.95 and the AEDC values range from 0.098 to 0.023, whereas for the AVIRIS data, the R2 values range from 0.92 to 0.99 and the AEDC values range from 0.021 to 0.008. This suggests that, for the spectral range considered, atmospheric corrections are not necessary for mineral mapping in an arid region and for desert atmospheric conditions.

Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data

The leaf area density (LAD) of a forest is an important indicator of forest biomass and is therefore pertinent to monitoring carbon sequestration and change. Quantitative physical models were used to estimate forest LAD from radar and hyperspectral airborne remote sensing observations. A parameter‐estimation technique based on physical models minimizes the need for in situ observations and thereby facilitates global remote sensing of forest structure. Using data from the NASA Airborne Synthetic Aperture Radar (AIRSAR) and the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) over three forest plots in Central Oregon, parameters were estimated separately from the radar and hyperspectral data and then combined to form LAD. Gaussian relative LAD profiles were estimated from multialtitude interferometric and polarimetric AIRSAR data. Leaf area indices (LAI) were estimated from AVIRIS data and used to normalize the relative density profiles to produce LAD as a function of height. LAD was also determined from field measurements of geometric tree properties and LAI. LADs in the three forest plots were in the 0.02–0.18 m2 m−3 range, with LAIs in the range 0.8–2.4 m2 m−2. The agreement between the remotely sensed and field‐measured LAD was typically 0.02 m2 m−3 but occasionally as high as 0.06 m2 m−3, which was within a 1–2 standard error range. More complex models for the remotely sensed relative density, along with more diverse radar observation strategies, will improve LAD accuracy in the future. LAD estimation will also improve when radar, hyperspectral, and other relevant remote sensing data sets are combined in a single parameter‐estimation process, as opposed to the separate estimations performed in this first LAD demonstration.