Compact Airborne Spectrographic Imager Images Research Articles

Assessment of Eutrophication and DOC Sources Tracing in the Sea Area around Dajin Island Using CASI and MODIS Images Coupled with CDOM Optical Properties

The sea area around Dajin Island in the Pearl River Estuary is the second-largest habitat in China for the Indo-Pacific humpback dolphin (Sousa Chinensis). However, the rapid economic development of this area brings potential threats to the aquatic ecology around Dajin Island. Real-time monitoring and evaluation of the ecological health of the sea area are urgent. In this study, band ratio and single-band inversion algorithms were performed to obtain Chlorophyll-a (Chl-a) and Suspended Sediment Concentration (SSC), relying on both Compact Airborne Spectrographic Imager (CASI) and Moderate resolution Imaging Spectrometer (MODIS) images. The CASI/Chl-a with high spatial resolution was adopted to assess the eutrophication status, while the dissolved organic carbon (DOC) concentration and chromophoric dissolved organic matter (CDOM) optical properties were used to derive the material composition and sources. The results suggest that the study area is under a low to medium eutrophication state with evenly distributed low Chl-a concentration. However, higher Chl-a is observed in the outer estuary with MODIS/Chl-a. The relatively high DOC concentration, especially in the north, where aquaculture is practiced, and near the estuary’s main axis, i.e., east Dajin Island, indicates that the eutrophication state might be underestimated using satellite chlorophyll alone. CDOM optical properties indicated that terrestrial materials are the DOC’s primary material sources, but the DOC derived from fishery aquaculture cannot be ignored. The low Chl-a concentration is likely due to the turbulent hydrodynamic regime caused by jet flow and reciprocating flow in this marine area. Comprehensive observation, including the assessment of different technological platforms, is suggested for the aquatic environment.

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network.

Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.

Anthropogenic aerosol emissions mapping and characterization by imaging spectroscopy – application to a metallurgical industry and a petrochemical complex

ABSTRACTThis paper is focused on the retrieval of industrial aerosol optical thickness (AOT) and microphysical properties by means of airborne imaging spectroscopy. Industrial emissions generally lead to optically thin plumes requiring an adapted detection method taking into account the weak proportion of particles sought in the atmosphere. To this end, a semi-analytical model combined with the Cluster-Tuned Matched Filter (CTMF) algorithm is presented to characterize those plumes, requiring the knowledge of the soil under the plume. The model allows the direct computation of the at-sensor radiance when a plume is included in the radiative transfer. When applied to industrial aerosol classes as defined in this paper, simulated spectral radiances can be compared to ‘real’ MODTRAN (Moderate Resolution Atmospheric Transmission) radiances using the Spectral Angle Mapper (SAM). On the range from 0.4 to 0.7 µm, for three grounds (water, vegetation, and bright one), SAM scores are lower than 0.043 in the worst case (a both absorbing and scattering particle over a bright ground), and usually lower than 0.025. The darker the ground reflectance is, the more accurate the results are (typically for reflectance lower than 0.3). Concerning AOT retrieval capabilities, with a pre-calculated model for a reference optical thickness of 0.25, we are able to retrieve plume AOT at 550 nm in the range 0.0 to 0.4 with an error usually ranging between 9% and 13%. The first test case is a CASI (Compact Airborne Spectrographic Imager) image acquired over the metallurgical industry of Fos-sur-Mer (France). First results of the use of the model coupled with CTMF algorithm reveal a scattering aerosol plume with particle sizes increasing with the distance from the stack (from detection score of 54% near the stack for particles with a diameter of 0.1 µm, to 69% away from it for 1.0 µm particles). A refinement is made then to estimate more precisely aerosol plume properties, using a multimodal distribution based on the previous results. It leads to find a mixture of sulfate and brown carbon particles with a plume AOT ranging between 0.2 and 0.5. The second test case is an AHS (Airborne Hyperspectral Scanner) image acquired over the petrochemical site of Antwerp (Belgium). The first CTMF application results in detecting a brown carbon aerosol of 0.1 µm mode (detection score is 51%). Refined results show the evolution of the AOT decreasing from 0.15 to 0.05 along the plume for a mixture of brown carbon fine mode and 0.3 µm radius of sulfate aerosol.

COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA

Abstract. In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

Determining Biophysical Parameters for Olive Trees Using CASI‐Airborne and Quickbird‐Satellite Imagery

This study reports on the development of remote sensing methods for estimation of biophysical parameters in olive orchards. Field and airborne campaigns were conducted in 2003 and 2004 in two orchards located in southern Spain. Ground measurements of crown transmittance and leaf area index (LAI) of individual olive trees (Olea europaea L.) were done using the LAI‐2000 Plant Canopy Analyzer. Hyperspectral images were acquired with a compact airborne spectrographic imager (CASI) at 1‐m spatial and with QuickBird satellite sensor at 2.5‐m spatial resolution. The panchromatic 0.6‐m spatial resolution image was also acquired with QuickBird. These images enabled the application of automatic algorithms for olive crown identification and delineation, to determine tree crown size and LAI. These methods proved successful for determining projected olive crown area, obtaining determination coefficients (r 2) in the range of 0.82 to 0.65, and root mean square errors (RMSE) of 4.8 and 6.2 m2 for CASI hyperspectral and QuickBird panchromatic images, respectively. The olive crown volume was estimated using image‐estimated projected crown area values, yielding r 2 ranging between 0.87 and 0.70, with RMSE of 8.4 and 11.3 m3 for CASI hyperspectral and QuickBird panchromatic images, respectively. Olive crown transmittance and LAI of individual olive trees were evaluated using spectral vegetation indices (normalized difference vegetation index [NDVI], renormalized difference vegetation index [RDVI], simple ratio index [SR], modified simple ratio [MSR]) yielding better correlations with CASI images, r 2 in the range 0.71 to 0.75 (P < 0.0001) and 0.57 to 0.62 (P < 0.0001) for crown transmittance and LAI, respectively. These methods enable obtaining maps of biophysical parameters in olive trees at farm scale in an operational way demonstrating the validity of the methodology used.

Invasive species change detection using artificial neural networks and CASI hyperspectral imagery

For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (salt cedar) change detection in the study area of Lovelock, Nevada. With multidate Compact Airborne Spectrographic Imager (CASI) hyperspectral data sets, two types of hyperspectral CASI input data and two classifiers have been examined and compared for mapping and monitoring the salt cedar change. The two types of input data are all two-date original CASI bands and 12 principal component images (PCs) derived from the two-date CASI images. The two classifiers are an artificial neural network (ANN) and linear discriminant analysis (LDA). The experimental results indicate that (1) the direct multitemporal image classification method applied in land cover change detection is feasible either with original CASI bands or PCs, but a better accuracy was obtained from the CASI PCA transformed data; (2) with the same inputs of 12 PCs, the ANN outperforms the LDA due to the ANN's non-linear property and ability of handling data without a prerequisite of a certain distribution of the analysis data.

Modelling geometric and misregistration error in airborne sensor data to enhance change detection

One of the major goals of remote sensing is to carry out monitoring programmes such as land‐cover change detection. However, the accuracy of such change detection activities can be limited by several factors. A key variable that can limit the accuracy of change detection is the misregistration error between the images used. Although the impacts of misregistration on change detection have been considered in various studies, a single global value for misregistration has typically been applied across the whole scene. The effect of misregistration, however, varies spatially and its effects on change detection could be more accurately predicted and ultimately removed if this spatial variation in error were modelled. The current study aimed to develop a model that described the spatial variation of misregistration for airborne image data. As misregistration is a function of geometric error, the geometric errors associated with the airborne data were modelled and this geometric error model was used to derive a model of misregistration. The impacts of various navigational variables on the accuracy of the automated geocorrection of compact airborne spectrographic imager (CASI) data were evaluated. A significant relationship was found between geometric error and angular acceleration (adjusted r 2 = 0.651; p = 0.017). The relationship between geometric error and angular acceleration together with a model of orthometric errors was used to derive an error model that described the spatial variation in geometric errors associated with the automated geocorrection of the CASI data. This model gave a probabilistic description of the spatial variation in geometric error. From the geometric error model, a model of misregistration between CASI images from two times was derived. This model was tested using data from an urban test site and a significant correlation, at 95% confidence, was found between predicted and measured misregistration. The models derived could be used in change detection, potentially reducing the impact of geometric errors and so misregistration in airborne sensor data, which is a major limitation in the use of remote sensing for environmental monitoring.

Variability of seasonal CASI image data products and potential application for management zone delineation for precision agriculture

The delineation of management zones is an important step to implementing site-specific crop management practices. Remote sensing is a cost-effective way to acquire information needed for delineating management zones, since it has been successfully used for mapping soil properties and monitoring crop growth conditions. Remotely sensed hyperspectral data are particularly effective in deriving crop biophysical parameters in agricultural fields; therefore, the potential of hyperspectral data to contribute to management zone delineation needs to be assessed. In this study, the spatial variability of soil and crops in two agricultural fields was studied using seasonal compact airborne spectrographic imager (CASI) hyperspectral images. Different spectral features including soil brightness and colouration indices, principal components of soil reflectance data, and crop descriptors (leaf area index (LAI) and leaf chlorophyll content) were derived from CASI data and used to partition the fields into homogeneous zones using the fuzzy k means unsupervised classification method. The reduction of variances of soil electrical conductivity, LAI, leaf chlorophyll content, and yield was inspected to determine the appropriate number of zones for each field. The zones obtained were interpreted according to the soil survey map and field practices. Analysis of variance (ANOVA) was conducted to examine the effectiveness of the delineation. The study shows that the spatial patterns of the resulting soil zones faithfully represent the soil classes described by the soil survey maps, and the spatial patterns of the resulting crop classes discriminated the different crop growth conditions well. These results show that hyperspectral data provide important information on field variability for management zone delineation in precision agriculture.

Relationship between airborne multispectral image texture and aspen defoliation

A Compact Airborne Spectrographic Imager (CASI) multiresolution dataset, comprised of imagery with spatial resolutions of 60 cm, 1 m and 2 m, was used to asses the relationship between defoliation severity of aspen (Populus tremuloides Michx.) stands infested with the Bruce spanworm (Operophtera bruceata), the Leaf Area Index (LAI) of these stands and the CASI image components comprising of Normalized Difference Vegetation Index (NDVI) and image texture. The study area was located in the foothills of the Alberta Canadian Rockies. Defoliation severity, LAI, and crown closure of the stands were measured on the ground. Multiple stepwise regression methods were used to develop relationships between the field and imagery data. Image texture derived from the grey-level co-occurrence matrix of the first principle component, or ‘brightness' image, was incorporated into the discriminant analysis of defoliation severity classes. The highest spatial resolution imagery outperformed the coarser image resolutions. The characteristics of the defoliation changed the spectral response of the moderately and severely defoliated stands considerably when compared to healthy stands. The following paper demonstrates that aspen defoliation severity can be detected with CASI image data, specifically through the incorporation of imagery spatial component captured by image texture.

Using high spatial resolution hyperspectral imagery to map intertidal habitat structure in Hood Canal, Washington, U.S.A.

An understanding of the distribution and structure of discontinuous nearshore habitats is critical to effectively manage estuarine resources, especially migratory and mobile nekton that may depend on the integrity of shallow-water environments. We used 19-band compact airborne spectrographic imager (CASI) imagery, collected at 1.5 m spatial resolution, to map two cover classes of eelgrass (Zostera marina) and six other estuarine habitat classes along 64 km of the intertidal shoreline of Hood Canal in the Pacific Northwest, United States. We used control points derived from digital orthoquads (DOQ) and a differential global positioning system (GPS) to geometrically correct CASI flight lines to within 4.3–23.5 m root mean square error (RMSE). After unsupervised and supervised classification, we found good correspondence between classified eelgrass polygons and field training and ground-truthing data. Although this was not the focus of our habitat mapping, the same was true for most of the other habitat classes, except for oyster beds, which were difficult to separate from the wet sand–gravel–cobble class. We are encouraged by the ability of CASI to produce spatially accurate, high-resolution descriptions of intertidal habitat structure. Results from this study will become the foundation of a broader study to develop a quantitative index of essential habitat quality for migrating juvenile summer chum salmon (Oncorhynchus keta) in Hood Canal. When CASI image processing is complete, fisheries scientists–managers will be able to effectively evaluate estuarine landscape patterns at a spatial scale appropriate for migrating juvenile summer chum salmon.

Aerial remote‐sensing techniques used in the management of archaeological monuments on the British Army's Salisbury Plain Training Area, Wiltshire, UK

AbstractSalisbury Plain Training Area, covering 38 000 ha in Wiltshire, southern England, is the UK's largest military training area. Military ownership has protected, from modern agricultural practices, over 2300 archaeological monuments dating from the late prehistoric and Romano‐British periods. Consequently the majority of sites still survive as earthworks.The Defence Estates is tasked with managing this landscape, balancing military, agricultural, archaeological and nature conservation priorities within a national and international legislative framework.The Defence Estates is evaluating the use of airborne LIDAR (light detection and ranging) and CASI (compact airborne spectrographic imager) surveys to map ‘condition’, results being directly transferable to a geographical information system. It is hoped, by comparing successive surveys, that trends can be identified, allowing management decisions to be reached. The work, funded by Headquarters Army Training Estate, is being conducted in partnership with the Environment Agency's National Centre for Environmental Data and Surveillance.An initial trial, covering 40 km2, was held in 2001. The capacity of LIDAR and CASI to identify areas of, and trends in, formation of bare ground, grazing, disturbance (military and animal digging) and scrub infestation was evaluated.Results showed that CASI imagery, particularly when processed as a normalized difference vegetation index (NDVI) image, was ideal for showing bare ground, scrub and levels of grazing, whereas LIDAR provided a means of measuring the magnitude of change. The techniques were less successful at distinguishing disturbed ground such as back‐filled military trenches. Combined LIDAR and CASI images were shown to be ideal media for identifying archaeological earthworks. The potential for the development of automated trend analysis was proved but manual systems were shown to be more appropriate.Development work is continuing and it is hoped to have a remote‐sensing‐based monitoring system in place on Salisbury Plain Training Area in the foreseeable future. Copyright © Crown Copyright 2002. Recorded with the permission of the controller of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.

A multi-scale approach to mapping effective Leaf Area Index in Boreal Picea mariana stands using high spatial resolution CASI imagery

This study evaluates the performance of a simple geometric-optics reflectance model, used in combination with multi-spectral clustering, to map spatial patterns of effective Leaf Area Index ( L e ) within Boreal Picea mariana stands. Two metre resolution Compact Airborne Spectrographic Imager (CASI) images, acquired during the winter to minimize variability in understory reflectance, are used to map L e over BOREAS northern and southern Old Black Spruce tower flux sites. A combined multi-spectral clustering and ray-tracing approach is used to map open areas in each site at 2 m scale. A modified version of the Forest Light Interaction Model (FLIM) is then applied over canopy areas using 30 m scale red and near-infrared reflectance values derived from CASI images. Comparison of the combined FLIM and clustering approach (FLIMCLUS) with surface L e measurements in areas with overstory cover indicate a R 2 of 0.67 for the SSA-OBS site and 0.16 for the NSA-OBS site. The poor NSAOBS performance may be due to the low observed range of L e along the transect selected since additional measurements in a sparse canopy area closely match FLIM estimates. The relative standard error at both sites is under 10% and is close to the 5% precision error in surface L e estimates. Comparison of FLIMCLUS with FLIM and the simple ratio indicate substantial differences over open areas where the latter methods map-zero L e values. Further validation over other study sites, including surface data mapping edges between canopy and open areas, is proposed. The FLIM-CLUS L e maps may be useful for testing scale dependent assumptions within remote sensing algorithms and ecosystem flux models applied to the study sites and similar Picea mariana stands.

Stand Parameter Estimation of Artificial Evergreen Conifer Forests Using Airborne Images: An Evaluation of Seasonal Difference on Accuracy and Best Wavelength

The relationship between the stand parameters (top layer height (H1) and volume/ha (Vha)) and digital number (DN) were evaluated for evergreen conifer stands using three airborne images with 4-m spatial resolution, which were taken in June 1995, September 1993, and October 1994 using the Compact Airborne Spectrographic Imager (CASI). Estimation accuracy of the stand parameters, their seasonal changes, and suitable wavelength were analyzed using correlation coefficients and a regression analysis. The minimum DN of stands, which showed the darkness of a canopy shadow, had a higher correlation with H1 than the average and maximum DN while the average DN had a higher correlation with Vha. The green channels gave the highest correlation coefficients with H1 and Vha, which exceeded—0.9 for the September and October images. However, the red channels had a consistently high correlation with the stand parameters for the three images. The near infrared channels gave poor correlations with H1 and Vha for the June image. Spectral variations among trees may affect the relationship between DN and the stand parameters in the leaf maturation period in June. Consequently, the late growing season was better at giving consistent results for the stand parameter studies. There was a linear relationship between the measured and the estimated stand parameters for the validation plots especially for the H1 case of September with sufficient accuracy. Nadir viewing images, which had high spatial resolution and a wide dynamic range such as the CASI images, were necessary to estimate the stand parameters accurately.

Retrieval of Leaf Area Index and Canopy Closure from CASI Data over the BOREAS Flux Tower Sites

This study is to investigate the retrievals of the canopy leaf area index (LAI) and canopy closure using a canopy model. The data used were obtained by the Compact Airborne Spectrographic Imager (CASI) with a spatial resolution of 2 m over the flux tower sites in the BOREAS study area in the winter of 1994 and in the summer of 1996. The FLIM (Forest–Light Interaction Model) was adapted to describe the reflectance properties of the boreal forest canopies investigated by considering the effect of the ellipticity of the conifer crowns on shadows and crown transmittance. The LAI and canopy closure images were produced for the flux tower sites investigated from the CASI winter images using the modified FLIM. Visual evaluation shows that the spatial patterns shown in the canopy LAI and canopy closure images are consistent with those exhibited by the color composite CASI images for the same sites. The canopy LAI images were further validated by field-measured LAI along specific transects. The R 2 between the retrieved canopy LAI and the field-measured LAI ranges from 0.51 to 0.86 for the sites investigated. Furthermore, the CASI summer image over the Old Black Spruce site in the BOREAS souther study area (SOBS) was used to investigate the effect of the understory reflectance on the retrieval of the canopy LAI. The R 2 between the field-measured LAI and the retrieved LAI is 0.27, under the assumption that the reflectance is uniform for each understory vegetation in the SOBS site. The R 2 increases to 0.78 when the understory reflectance is allowed to vary within the range of variability as determined by field measurements. This result conforms to the sensitivity analysis result in which the at-canopy reflectance is expressed as a function of the understory reflectance at the CASI viewing geometry. The results imply that it is questionable to assume a uniform understory reflectance in canopy model inversions in the boreal forest summer scenes.

Reducing boundary effects in a kernel-based classifier

Abstract In this letter, a method is presented to reduce misclassification at the boundaries between different types of land covers. Errors are caused by use of spatial features extracted from pixel neighbourhoods. Simple thresholding and region-growing techniques are used to reduce such errors. The method is included as an extension to a frequency-based contextual classifier (FBC). Some experiments have been carried out to evaluate both the original classifier, FBC, and the extended version using an image acquired with a Compact Airborne Spectrographic Imager (CASI). Experimental results show that overall classification accuracies with the CASI image are 84-80 per cent and 89-37 per cent obtained using the FBC and the extended FBC, respectively.

Forest canopy closure from classification and spectral unmixing of scene components-multisensor evaluation of an open canopy

Three types of remote sensing data, color infrared aerial photography (CIR), compact airborne spectrographic imager (CASI) imagery, and airborne visible/infrared imaging spectrometer (AVIRIS) imagery, have been used to estimate forest canopy closure for an open-canopy forest environment. The high-spatial-resolution CIR and CASI images were classified to generate forest canopy closure estimates. These estimates were used to validate the forest canopy closure estimation accuracy obtained using the AVIRIS image. Reflectance spectra extracted from the spectral-mode CASI image were used to normalize the raw AVIRIS image to a reflectance image. Classification and spectral unmixing methods have been applied to the AVIRIS image. Results indicate that both the classification and the spectral unmixing methods can produce reasonably accurate estimates of forest canopy closure (within 3 percent agreement) when related statistics are extracted from the AVIRIS image and relatively pure reflectance spectra are extracted from the CASI image. However, it is more challenging to use the spectral unmixing technique to derive subpixel-scale components whose reflectance spectra cannot be directly extracted from the AVIRIS image.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>