Airborne Visible/Infrared Imaging Spectrometer Data Research Articles

Imaging spectrometer emulates Landsat: A case study with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Operational Land Imager (OLI) data

Remote sensing data are most useful if they are available with sufficient precision, accuracy, spatiotemporal and spectral sampling, as well as continuity across decades. The Landsat and Sentinel series, as well other satellites are currently covering significant parts of this observational trade space. It can be expected that growing demands and budget constraints will require new capabilities in orbit that can address as many observables as possible with a single instrument. Recent optical performance improvements of imaging spectrometers make them true alternatives to traditional multispectral imagers. However, they are much more adaptable to a wide range of Earth observation needs due to the combination of continuous high spectral sampling with spatial sampling consistent with previous sensors (e.g., Landsat). Unfortunately, there is a knowledge gap in demonstrating that imaging spectroscopy data can substitute for multi-spectral data while sustaining the long-term record. Thus, the objective of this analysis is to test the hypothesis that imaging spectroscopy data compare radiometrically with multi-spectral data to within 5%. Using a coincident Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flight with over-passing Operational Land Imager (OLI) data on Landsat 8, we document a procedure for simulating OLI multi-spectral bands from AVIRIS data, evaluate influencing factors on the observed radiance, and assess the difference in top-of-atmosphere radiance as compared to OLI. The procedure for simulating OLI data include spectral convolution, accounting for the minimal atmospheric effects between the two sensors, and spatial resampling. The remaining differences between the simulated and the real OLI data result mainly from differences in sensor calibration, surface bi-directional reflectance, and spatial sampling. The median relative radiometric difference for each band ranges from −8.3% to 0.6%. After bias-correction to minimize potential calibration discrepancies, we find no more than a 1.2% relative difference. This analysis therefore successfully demonstrates that imaging spectrometer data can contribute to Landsat-type or other multi-spectral data records. It also shows that cross-calibration from a spectrometer to a radiometer can be easily performed as a result of the imaging spectrometer high spectral sampling and its ability to recreate multi-spectral response functions.

On the Feasibility of Characterizing Soil Properties From AVIRIS Data

We evaluate the feasibility of quantifying surface soil properties over large areas and at a fine spatial resolution using high-resolution airborne imaging spectroscopy. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data collected by the National Aeronautics and Space Administration immediately after the large 2011 Mississippi River flood at the Birds Point New Madrid (BPNM, $\approx 700 \mathrm{km}^{2} $ ) floodway in Missouri, USA, was used in a data mining lasso framework for mapping of soil textural properties such as percentages of sand, silt, clay, soil-organic matter, and many other soil chemicals constituents. The modeling results show that the approach is feasible and provide insights in the accuracy and uncertainty of the approach for both soil textural properties and chemical constituents. These models were further used for a pixel-by-pixel prediction of each the soil constituent, resulting in high-resolution (7.6 m) quantitative spatial maps in the entire floodway. These maps reveal coherent spatial correlations with historical meander patterns of Mississippi River and fine-scale features such as erosional gullies, represented by difference in constituent concentration, e.g., low soil organic matter, with the underlying topography immediately disturbed by the large flooding event. Further, we have argued and established that the independent validation results are better represented as a probability density function as compared with a single calibration–validation set. It is also found that modeled soil constituents are sensitive to NDVI and the calibration sample sizes, and the results improve with stricter (lower) NDVI thresholds and larger calibration sets.

High spatial resolution mapping of elevated atmospheric carbon dioxide using airborne imaging spectroscopy: Radiative transfer modeling and power plant plume detection

Carbon dioxide is emitted from the combustion of fossil fuels and is an important contributor to anthropogenic climate change. Multiple current and planned satellite missions are designed to quantify atmospheric carbon dioxide concentrations on a global scale, but most of these sensors do not have the spatial resolution necessary to resolve point sources such as fossil fuel power plants. Airborne imaging spectrometer data, such as those from the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS), can have multiple, contiguous bands covering shortwave infrared (SWIR) absorption features produced by carbon dioxide. Therefore, high spatial resolution data from AVIRIS-like sensors may offer a means for detecting plumes and retrieving carbon dioxide concentrations for point source emissions. The objectives of this study include modeling minimum carbon dioxide anomalies detectable in AVIRIS data under different conditions and applying a Cluster-Tuned Matched Filter for detection of carbon dioxide plumes in simulated data and in AVIRIS images acquired over power plants. Radiative transfer simulations were used to model the residual radiance produced by increased absorption by carbon dioxide as concentration was elevated above background levels within a 0–500m layer. Carbon dioxide anomalies, surface reflectance, water vapor concentration, solar zenith angle, sensor height, and aerosol scattering were varied in simulation sets and the resulting residual radiance spectra were compared against noise equivalent delta radiance (NEdL) for the “classic” and “next generation” AVIRIS instruments. Sensitivity to carbon dioxide anomalies improved with increased surface reflectance and declined with increased water vapor concentration, solar zenith angle, sensor height, and aerosol scattering. Zero to 500m concentration anomalies as low as 100 parts per milion by volume (ppm) for AVIRIS C and 25ppm for AVIRIS NG produced residual radiance values that exceeded SWIR NEdL. Carbon dioxide concentrations modeled for a generic power plant emissions scenario using a plume dispersion model were combined with randomly-generated reflectance spectra to create simulated images with varying surface reflectance and NEdL. For these simulated images, true positive and false positive detection rates improved as background reflectance increased and as NEdL decreased. Apparent plumes were detected in all four AVIRIS C images acquired over power plants, although the characteristics of the plumes varied according to solar-plume-sensor geometry. Improvements in modeling may allow retrieval of plume concentration, providing a valuable means for quantifying point source emissions and a basis for comparison with column concentrations retrieved from in situ measurements and coarse resolution satellite data.

Simultaneous retrieval of CO2 and aerosols in a plume from hyperspectral imagery: application to the characterization of forest fire smoke using AVIRIS data

Hyperspectral imagery is a widely used technique to study atmospheric composition. For several years, many methods have been developed to estimate the abundance of gases. However, existing methods do not simultaneously retrieve the properties of aerosols and often use standard aerosol models to describe the radiative impact of particles. This approach is not suited to the characterization of plumes, because plume particles may have a very different composition and size distribution from aerosols described by the standard models given by radiative transfer codes. This article presents a new method to simultaneously retrieve carbon dioxide (CO2) and aerosols inside a plume, combining an aerosol retrieval algorithm using visible and near-infrared (VNIR) wavelengths and a CO2 estimation algorithm using shortwave infrared (SWIR) wavelengths. The microphysical properties of the plume particles, obtained after aerosol retrieval, are used to calculate their optical properties in the SWIR. Then, a database of atmospheric terms is generated with the radiative transfer code, Moderate Resolution Atmospheric Transmission (MODTRAN). Finally, pixel radiances around the 2.0 μm absorption feature are used to retrieve the CO2 abundances. After conducting a signal sensitivity analysis, the method was applied to two airborne visible/infrared imaging spectrometer (AVIRIS) images acquired over areas of biomass burning. For the first image, in situ measurements were available. The results show that including the aerosol retrieval step before the CO2 estimation: (1) induces a better agreement between in situ measurements and retrieved CO2 abundances (the CO2 overestimation of about 15%, induced by neglecting aerosols has been corrected, especially for pixels where the plume is not very thick); (2) reduces the standard deviation of estimated CO2 abundance by a factor of four; and (3) causes the spatial distribution of retrieved concentrations to be coherent.

Mineral Mapping Using Simulated Worldview-3 Short-Wave-Infrared Imagery

Abstract: WorldView commercial imaging satellites comprise a constellation developed by DigitalGlobe Inc. (Longmont, CO, USA). Worldview-3 (WV-3), currently planned for launch in 2014, will have 8 spectral bands in the Visible and Near-Infrared (VNIR), and an additional 8 bands in the Short-Wave-Infrared (SWIR); the approximately 1.0–2.5 μm spectral range. WV-3 will be the first commercial system with both high spatial resolution and multispectral SWIR capability. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected at 3 m spatial resolution with 86 SWIR bands having 10 nm spectral resolution were used to simulate the new WV-3 SWIR data. AVIRIS data were converted to reflectance, geographically registered, and resized to the proposed 3.7 and 7.5 m spatial resolutions. WV-3 SWIR band pass functions were used to spectrally resample the data to the proposed 8 SWIR bands. Characteristic reflectance signatures extracted from the data for known mineral locations (endmembers) were used to map spatial locations of specific minerals. The WV-3 results, when compared to spectral mapping using the full AVIRIS SWIR dataset, illustrate that the WV-3 spectral bands should permit identification and mapping of some key minerals, however, minerals with similar spectral features may be confused and will not be mapped with the same detail as using hyperspectral systems. The high spatial resolution should provide detailed mapping of complex alteration mineral patterns not achievable by current multispectral systems. The WV-3 simulation results are promising and indicate that this sensor will be a significant tool for geologic remote sensing.

Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.

Discriminating urban vegetation from a metropolitan matrix through partial unmixing with hyperspectral AVIRIS data

In the semi-arid western United States, urbanization transforms landscapes from sparsely vegetated grasslands into matrices of asphalt, concrete, turf grass, and multistrata wooded stands. Such land-cover change affects ecological processes, such as carbon (C) storage. This research investigates quantifying the vegetated and anthropogenic components of urbanized landscapes, in order to contribute ultimately to further ecological process studies, through a case study of Boulder, Colorado. Rather than map the urban land-cover types, the primary motivation was to extract biophysical information from hyperspectral imagery to understand how urbanization shifts regional biomass. Using convex geometry and partial unmixing algorithms with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery, major landscape elements were identified, including five vegetation endmembers that comprised cultivated and natural vegetation (both herbaceous and woody), soil, water, and five impervious surfaces. Urban vegetation equalled or exceeded surrounding vegetation fractional abundance. Trees were detected in the city center, and trees and grass intermingled in neighborhoods. Thus, this study expands the multispectral unmixing of the vegetation - impervious surface - soil model and demonstrates a viable method for mapping the composition of urban areas. Shifts in vegetation due to urbanization are best detected through biophysical remote sensing of actual ground components.

Spectral calibration of imaging spectrometers by atmospheric absorption feature matching

A procedure has been developed to measure the band centres and bandwidths for imaging spectrometers using data acquired by the sensor in flight. This is done for each across-track pixel, thus allowing the measurement of the instrument's slit curvature or spectral "smile." The procedure uses spectral features present in the at-sensor radiance that are common to all pixels in the scene. These are principally atmospheric absorption lines. The band-centre and bandwidth determinations are made by correlating the sensor-measured radiance with a modelled radiance, the latter calculated using MODTRAN 4.2. Measurements have been made for a number of instruments, including the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS), shortwave infrared full spectrum imager (SFSI), Compact Airborne Spectrographic Imager (casi), compact high-resolution imaging spectrometer (CHRIS), and Hyperion. The measurements on AVIRIS data were performed as a test of the procedure; since AVIRIS is a whisk-broom scanner, it is expected to be free of spectral smile. SFSI and casi are airborne pushbroom instruments, and CHRIS and Hyperion are satellite pushbroom sensors all exhibiting, to varying degrees, spectral smile. Measurements of Hyperion were made using three different datasets to check for temporal variations.

Comparing and validating spectral unmixing algorithms with AVIRIS imagery

Spectral unmixing of hyperspectral imagery has been utilized in many application areas to identify and map targets at the subpixel level. Its applicability to forest applications, however, raises concerns because of scene heterogeneity and the multiple scattering between sun radiation and targets of illumination, which may introduce nonlinearity to hyperspectral data and lead to violating the assumption of linearity upon which many spectral unmixing algorithms are based. To investigate the applicability of spectral unmixing to forest spectral mixture analysis, we tested three commonly used spectral unmixing algorithms with hyperspectral imagery of coastal forests acquired by the National Aeronautics and Space Administration (NASA) Airborne Visible / Infrared Imaging Spectrometer (AVIRIS). The algorithms tested were separated based on whether the two constraints on end-member fractions, i.e., non-negativity and sum-to-one, were enforced when solving the unmixing problem. The first algorithm investigated was unconstrained, where both constraints were not imposed. The other two algorithms applied one or both of the constraints. The results of this study indicate that all three algorithms tested were applicable to calculating forest canopy fractions when forest density was high. With the 20 m AVIRIS data employed in this study, the fully constrained unmixing algorithm outperformed the unconstrained and partially constrained algorithms on unmixing accuracy. According to the results derived from the high forest density plots, the average deviations from the ground truth of the unmixed forest fractions were 19 ± 11% by the unconstrained unmixing, 21 ± 13% by the partially constrained unmixing, and 8 ± 7% by the fully constrained unmixing.

Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest

It has been suggested that attempts to use remote sensing to map the spatial and structural patterns of individual tree species abundances in heterogeneous forests, such as those found in northeastern North America, may benefit from the integration of hyperspectral or multi-spectral information with other active sensor data such as lidar. Towards this end, we describe the integrated and individual capabilities of waveform lidar and hyperspectral data to estimate three common forest measurements – basal area (BA), above-ground biomass (AGBM) and quadratic mean stem diameter (QMSD) – in a northern temperate mixed conifer and deciduous forest. The use of this data to discriminate distribution and abundance patterns of five common and often, dominant tree species was also explored. Waveform lidar imagery was acquired in July 2003 over the 1000 ha. Bartlett Experimental Forest (BEF) in central New Hampshire (USA) using NASA's airborne Laser Vegetation Imaging Sensor (LVIS). High spectral resolution imagery was likewise acquired in August 2003 using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data (2001–2003) from over 400 US Forest Service Northern Research Station (USFS NRS) plots were used to determine actual site conditions. Results suggest that the integrated data sets of hyperspectral and waveform lidar provide improved outcomes over use of either data set alone in evaluating common forest metrics. Across all forest conditions, 8–9% more of the variation in AGBM, BA, and QMSD was explained by use of the integrated sensor data in comparison to either AVIRIS or LVIS metrics applied singly, with estimated error 5–8% lower for these variables. Notably, in an analysis using integrated data limited to unmanaged forest tracts, AGBM coefficients of determination improved by 25% or more, while corresponding error levels decreased by over 25%. When data were restricted based on the presence of individual tree species within plots, AVIRIS data alone best predicted species-specific patterns of abundance as determined by species fraction of biomass. Nonetheless, use of LVIS and AVIRIS data – in tandem – produced complementary maps of estimated abundance and structure for individual tree species, providing a promising adjunct to traditional forest inventory and conservation biology planning efforts.

Parallel techniques for information extraction from hyperspectral imagery using heterogeneous networks of workstations

Recent advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. In particular, NASA is continuously gathering very high-dimensional imagery data from the surface of the Earth with hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible-infrared imaging spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) satellite platform. The development of efficient techniques for extracting scientific understanding from the massive amount of collected data is critical for space-based Earth science and planetary exploration. In particular, many hyperspectral imaging applications demand real time or near real-time performance. Examples include homeland security/defense, environmental modeling and assessment, wild-land fire tracking, biological threat detection, and monitoring of oil spills and other types of chemical contamination. Only a few parallel processing strategies for hyperspectral imagery are currently available, and most of them assume homogeneity in the underlying computing platform. In turn, heterogeneous networks of workstations (NOWs) have rapidly become a very promising computing solution which is expected to play a major role in the design of high-performance systems for many on-going and planned remote sensing missions. In order to address the need for cost-effective parallel solutions in this fast growing and emerging research area, this paper develops several highly innovative parallel algorithms for unsupervised information extraction and mining from hyperspectral image data sets, which have been specifically designed to be run in heterogeneous NOWs. The considered approaches fall into three highly representative categories: clustering, classification and spectral mixture analysis. Analytical and experimental results are presented in the context of realistic applications (based on hyperspectral data sets from the AVIRIS data repository) using several homogeneous and heterogeneous parallel computing facilities available at NASA's Goddard Space Flight Center and the University of Maryland.

Integrating visible, near‐infrared and short wave infrared hyperspectral and multispectral thermal imagery for geologic mapping: simulated data

Hyperspectral and thermal infrared (TIR) multispectral remote sensing have great potential for surface geological mapping. This paper investigates the potential impact of combining these data on the comparative accuracy of different classification methods. A series of simulated datasets based on the characteristics of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER) sensors was created from surface reflectance and emissivity data derived from library spectra of 16 common minerals and rocks occurring in Cuprite, Nevada. System noise, illumination effects, the presence of vegetation, and spectral mixing were added to create the simulated data. Five commonly used classification algorithms, minimum distance, maximum likelihood classification, binary encoding, spectral angle mapper (SAM) and spectral feature fitting (SFF), were applied to all datasets. All the classification methods, excluding binary encoding, achieved nominal to significant improvement in overall accuracy when applied to the combined datasets in comparison to using only the AVIRIS dataset. Furthermore, certain classification methods of the combined datasets show a marked increase in individual rock or mineral class accuracies. Limestone, silicified and muscovite, for instance, show an improvement of almost 30% or greater in either producer's or user's accuracy using the combined datasets with SAM. SFF provides a great improvement in accuracy for limestone, quartz and muscovite. In terms of overall comparative accuracy for the individual and the combined datasets, maximum likelihood classification shows the best performance. For the simulated AVIRIS data, SFF was generally superior to SAM, although the accuracy of SAM applied to the combined datasets was slightly better than that of SFF. SAM applied to the combined datasets increases classification accuracy for some minerals and rocks which do not exhibit distinct absorption feature in the TIR region, while for SFF, only the accuracy of minerals and rocks with characteristic absorption features in the TIR region is improved.

Hyperspectral Data Transformation and Vegetation Index Performance Based on the Universal Pattern Decomposition Method

Traditional vegetation indices are based on only a few spectral bands. However, hyperspectral spectrometers, such as the airborne visible infrared imaging spectrometer (AVIRIS), collect data with 224 contiguous spectral bands. Traditional vegetation index extraction methods lose much of the information contained in hyperspectral data. The universal pattern decomposition method (UPDM) is tailored for hyperspectral data analysis. In this article, we consider the UPDM as a type of multivariate analysis; standard patterns are interpreted as an oblique coordinate system and coefficients are thought of as the coordinates of a pixel';s reflectance. This article describes UPDM hyperspectral data transformation of AVIRIS data, the performance of a vegetation index based on the universal pattern decomposition method (VIUPD), and the influences of a noise-to-vegetation index. The results demonstrate that the VIUPD is an effective vegetation information extraction approach for hyperspectral data. The VIUPD is more sensitive to vegetation conditions than the normalized difference vegetation index and enhanced vegetation index. Furthermore, noise influences can be neglected in VIUPD computations, with satisfactory accuracy. (c) 2007 Society for Imaging Science and Technology.

A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of field, AVIRIS and MODIS data

Using field data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery, and Moderate-Resolution Imaging Spectroradiometer (MODIS) data, a multi-scale analysis of ecosystem optical properties was performed for Sky Oaks, a Southern California chaparral ecosystem in the spectral network (SpecNet) and FLUXNET networks. The study covered a 4-year period (2000–2004), which included a severe drought in 2002 and a subsequent wildfire in July 2003, leading to extreme perturbation in ecosystem productivity and optical properties. Two vegetation greenness indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)), and a measure of the fraction of photosynthetically active radiation absorbed by vegetation (fPAR), were compared across sampling platforms, which ranged in pixel size from 1 m (tram system in the field) to 1000 m (MODIS satellite sensor). The three MODIS products closely followed the same seasonal trends as the tram and AVIRIS data, but tended to be higher than the tram and AVIRIS values, particularly for fPAR and NDVI. Following a wildfire that removed all green vegetation, the overestimation in MODIS fPAR values was particularly clear. The MODIS fPAR algorithm (version 4 vs. v.4.1) had a significant effect on the degree of overestimation, with v. 4.1 improving the agreement with the other sensors (AVIRIS and tram) for vegetated conditions, but not for low, post-fire values. The differences between MODIS products and the products from the other platform sensors could not be entirely attributed to differences in sensor spectral responses or sampling scale. These results are consistent with several other recently published studies that indicate that MODIS overestimates fPAR and thus net primary production (NPP) for many terrestrial ecosystems, and demonstrates the need for proper validation of MODIS terrestrial biospheric products by direct comparison against optical signals at other spatial scales, as is now possible at several SpecNet sites. The study also demonstrates the utility of in-situ field sampling (e.g. tram systems) and hyperspectral aircraft imagery for proper interpretation of satellite data taken at coarse spatial scales.

Retrieval of marine water constituents from AVIRIS data in the Hudson/Raritan Estuary

This paper reports on the validation of bio‐optical models in estuarine and nearshore (case 2) waters of New Jersey–New York to retrieve accurate water leaving radiance spectra and chlorophyll concentration from the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data complemented with in situ measurements. The study area—Hudson/Raritan Estuary—is a complex estuarine system where tidal and wind‐driven currents are modified by freshwater discharges from the Hudson, Raritan, Hackensack, and Passaic rivers. Over the last century the estuarine water quality has degraded, in part due to eutrophication, which has disrupted the pre‐existing natural balance, resulting in phytoplankton blooms of both increased frequency and intensity, increasing oxygen demand and leading to episodes of hypoxia. During 1999–2001 data acquisitions by NASA AVIRIS field measurements were obtained to establish hydrological optical properties of the Hudson/Raritan Estuary: (1) concurrent above‐ and below‐surface spectral irradiance; (2) sampling for laboratory determination of inherent optical properties; and (3) concentrations of optically‐important water quality parameters. We used a bio‐optical model based on Gordon et al. to predict the sub‐surface irradiance reflectance from optically important water constituents. Modelling of reflectance is a prerequisite for processing remote sensing data to desired thematic maps for input into the geographical information system (GIS) for use as a management tool in water quality assessment. A Radiative Transfer Code—MODTRAN‐4—was applied to remove the effects of the atmosphere so as to infer the water leaving radiance from the AVIRS data. The results of this procedure were not satisfactory, therefore an alternative approach was tested to directly correct the AVIRIS image using modelled spectra based on measured optical characteristics. The atmospherically corrected AVIRIS ratio image was used to calculate a thematic map of water quality parameters (i.e. chlorophyll‐a) concentration, which subsequently were integrated into a GIS for management of water quality purposes.

Hyperspectral versus multispectral data for estimating leaf area index in four different biomes

Motivated by the increasing importance of hyperspectral remote sensing data, this study sought to determine whether current-generation narrow-band hyperspectral remote sensing data could better track vegetation leaf area index (LAI) than traditional broad-band multispectral data. The study takes advantage of a unique dataset, wherein field measurements of LAI were acquired at the same general time and grain size as both Landsat ETM+ and AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) imagery in four different biomes. Biome types sampled included row-crop agriculture, tallgrass prairie, mixed hardwood-conifer forest, and boreal conifer forest. The effects of bandwidth, band placement, and number of bands were isolated from radiometric quality by comparing regression models derived from individual AVIRIS channels with those derived from simulated ETM+ and MODIS channels using the AVIRIS data. Models with selected subsets of individual AVIRIS channels performed better to predict LAI than those based on the broadband datasets, although the potential to overfit models using the large number of available AVIRIS bands is a concern. Models based on actual ETM+ data were generally stronger than those based on simulated ETM+ data, suggesting that, for predicting LAI, ETM+ data suffer no penalty for having lower radiometric quality. NDVI was generally not sensitive to LAI at the four sites. Band placement of broad-band sensors (e.g., simulated ETM+ and MODIS) did not affect relationships with LAI, suggesting that there is no inherent advantage to MODIS spectral properties over those of ETM+ for estimating LAI. Spectral channels in the red-edge and shortwave-infrared regions were generally more important than those in the near-infrared for predicting LAI.

Imaging spectroscopy for desertification studies: comparing aviris and eo-1 hyperion in argentina drylands

Arid and semiarid ("dryland") regions are complex mosaics of vegetation cover, structure, and phenology. Few multispectral remote sensing approaches have quantitatively resolved the spatial complexity of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare soil in drylands. In combination, these surface properties provide insight to land degradation and desertification known to be occurring in many drylands worldwide. Given sufficient spatial resolution and sensor performance, imaging spectroscopy provides this information using reflectance measurements over the 0.4-2.5-/spl mu/m region. We tested and compared the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the spaceborne Earth Observing 1 Hyperion imaging spectrometer for measuring PV, NPV, and bare soil fractional cover in the Monte Desert biome of Central Argentina. A probabilistic spectral mixture model was used to decompose image pixels into subpixel surface constituents using a spectral endmember bundling approach. AVIRIS (4.5-m pixels) and Hyperion (30-m pixels) data were collected over a 763-km/sup 2/ region containing the Nacunan Man-and-Biosphere Reserve and surrounding unprotected areas. The AVIRIS data, combined with the mixture modeling, provided highly accurate estimates of PV, NPV, and bare soil in comparison with field measurements (0.83<r/sup 2/<0.86). Hyperion provided accurate estimates of PV (r/sup 2/=0.68) due to good red-edge (0.69-0.71 /spl mu/m) performance, but NPV and bare soil estimates were accurate only when cover values exceeded /spl sim/30% (r/sup 2/=0.39-0.40). Lower NPV and soil cover accuracies from Hyperion were due to lower S/N performance in the shortwave-infrared (2.0-2.4 /spl mu/m) region. Convolving the AVIRIS reflectance data to 30-m pixel size showed that apparent accuracies decrease by 20% due to field/remote sensing colocation errors. AVIRIS 30-m results for PV were statistically similar to Hyperion 30-m results.

Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals

Remote sensing estimates of vegetation nitrogen (N) and lignin concentration are central to assess ecosystem processes such as growth and decomposition. Although remote sensing techniques have been proven useful to assess N and lignin contents in continuous green canopies, more studies are needed to address their capabilities, particularly in low and sparsely vegetated ecosystems. We investigated the possibility of estimating canopy N and lignin concentrations in chaparral vegetation using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance acquired over an area around Point Dume in the Santa Monica Mountains (Los Angeles, CA, USA). Two approaches were tested: multiple stepwise regression based on first difference reflectance (FDR) and reflectance ( R) indices. Multiple stepwise regressions (of three or fewer wavelengths) accounted for a large variance in canopy biochemical concentration ( r 2∼0.9, P<0.01). Log transformed R indices [log (1/ R)] formulated on the basis of previously known N and lignin absorption wavelengths also showed significant correlations ( P<0.01) with canopy biochemical concentration ( r 2 ranging from 0.39 to 0.48). In addition, the contribution of structural and biochemical signals and background effects on the performance of these indices was evaluated. These indices accounted for a increased variance when adding information on canopy structural attributes (e.g., relative contribution of each species and biomass amount) to foliar biochemical concentration. The relative contributions of foliar biochemical concentration and canopy structure (biomass amount) on the spectral signal were further evaluated by analyzing the residuals from linear regressions: foliar N concentration accounted for 42% of the variance for a normalized difference index based on the 1510-nm N absorption feature, while the foliar lignin concentration accounted for 44% of the variance for a normalized difference index based on the 1754 nm lignin absorption feature. These percentages increased to 58% when stands with senescing vegetation were disregarded. We propose the two indices, Normalized Difference Nitrogen Index (NDNI=[log (1/ R 1510)−log (1/ R 1680)]/[log (1/ R 1510)+log (1/ R 1680)]) and Normalized Difference Lignin Index (NDLI=[log (1/ R 1754)−log (1/ R 1680)]/[log (1/ R 1754)+log (1/ R 1680)]) as indices to assess N and lignin in native shrub vegetation.

Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California

Ecosystem responses to interannual weather variability are large and superimposed over any long-term directional climatic responses making it difficult to assign causal relationships to vegetation change. Better understanding of ecosystem responses to interannual climatic variability is crucial to predicting long-term functioning and stability. Hyperspectral data have the potential to detect ecosystem responses that are undetected by broadband sensors and can be used to scale to coarser resolution global mapping sensors, e.g., advanced very high resolution radiometer (AVHRR) and MODIS. This research focused on detecting vegetation responses to interannual climate using the airborne visible-infrared imaging spectrometer (AVIRIS) data over a natural savanna in the Central Coast Range in California. Results of linear spectral mixture analysis and assessment of the model errors were compared for two AVIRIS images acquired in spring of a dry and a wet year. The results show that mean unmixed fractions for these vegetation types were not significantly different between years due to the high spatial variability within the landscape. However, significant community differences were found between years on a pixel basis, underlying the importance of site-specific analysis. Multitemporal hyperspectral coverage is necessary to understand vegetation dynamics.

On-orbit calibration of an ocean color sensor with an underflight of the airborne visible/infrared imaging spectrometer (AVIRIS)

Abstract Accurate calibration of ocean color sensors in the orbital environment is essential to achieve the objectives for which these sensors were proposed, developed, and launched. However, the trauma of launch and the orbital environment may cause changes in sensor calibration. An innovative approach to calibrating ocean color sensors uses the simultaneous underflight of the spaceborne ocean color sensor by a calibrated airborne imaging spectrometer. On May 20, 1997, the high-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) underflew the Ocean Color and Temperature Scanner (OCTS) onboard the Advanced Earth Observing System (ADEOS) satellite. AVIRIS measures the total upwelling spectral radiance from 400 to 2500 nm at 10-nm intervals at high radiometric and spatial resolution. This spectral range includes the eight ocean bands of OCTS. The spectral, radiometric, and spatial calibrations of AVIRIS are determined in the laboratory and validated in flight. The AVIRIS underflight was designed to match the observation geometry of OCTS. An ocean surface calibration target was determined based on common azimuth and zenith observation. The AVIRIS spectra of the calibration target were corrected to the top of the atmosphere radiance and convolved to the OCTS spectral, radiometric, and spatial characteristics. These AVIRIS data were then used in conjunction with the OCTS data to calculate an on-orbit calibration for OCTS with uncertainty analysis.