Continuous wavelets for the improved use of spectral libraries and hyperspectral data

Spectral libraries are commonly established as a means to archive representative signatures of natural materials. Such signatures can then be used to train feature extraction and classification algorithms applied to imagery, for comparison with unlabeled spectra. A number of spectral libraries are publicly available and widely used in the community. Disparities in viewing and illumination measurement configurations between libraries generally preclude the direct comparison of spectra for the same materials. Within libraries, measurements may be reported for varying sample properties, such as grain size in the case of powdered minerals or leaf or canopy structure in the case of vegetation. In such instances, use of the library and the selection of representative spectra to identify an unknown material may require a priori knowledge or an educated guess of the physical properties of the unknown material to conduct the comparison.This study demonstrates that continuous wavelet analysis can provide a new and useful representation of spectral libraries and minimize these disparities amongst libraries. In the context of spectral mixture analysis we suggest that the selection of representative endmember spectra from spectral libraries can be more readily defined in the wavelet domain than using reflectance data. In the context of sensing target compositional variability, for example changes in the chemistry of a given mineral, spectral differences due to distinct sample composition are more readily identified using wavelets. The examples provided in this paper are mainly for powdered mineral spectra because there are a number of widely known public spectral libraries of powdered minerals that have been in common use in the hyperspectral community but the principles apply to a range of natural materials including vegetation.     

Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery

Exotic plant invasion is a major environmental and ecological concern and is a particular issue for Mediterranean-type ecosystems. Early detection of invasive plants is crucial for effective weed management. Several studies have explored hyperspectral imagery for mapping invasive plants with promising results. However, only a few extensive or comparative studies about image processing techniques for invasive plant detection have been reported, and even fewer studies have involved very high spatial and spectral resolution imagery. The primary goal of this study was to investigate the utility of very high spatial (0.5 m) and spectral (4 nm) resolution imagery and several classification approaches for detecting tamarisk (Tamarix spp.) infestations, the most problematic invasive plant species in the riparian habitats of southern California.Hierarchical clustering was a particularly effective and efficient statistical method for identifying wavebands and spectral transforms having the greatest discriminatory power. Products resulting from the classification of airborne hyperspectral image data varied by scene, input data type, classifier, and minimum patch size. Overall accuracy of image classification accuracy of products co-varied with commission error rates, such that products having strong agreement with reference data also had a high number of false detections. Integrating the findings from qualitative map analysis, areal proportion statistics, and object-based accuracy assessment indicates that the parallelepiped classifier with several narrow wavebands selected through hierarchical clustering yielded the most accurate and reliable tamarisk classification products.     

Mapping nonnative plants using hyperspectral imagery

Nonnative plant species are causing enormous ecological and environmental impacts from local to global scale. Remote sensing images have had mixed success in providing spatial information on land cover characteristics to land managers that increase effective management of invasions into native habitats. However, there has been limited evaluation of the use of hyperspectral data and processing techniques for mapping specific invasive species based on their spectral characteristics. This research evaluated three different methods of processing hyperspectral imagery: minimum noise fraction (MNF), continuum removal, and band ratio indices for mapping iceplant (Carpobrotus edulis) and jubata grass (Cortaderia jubata) in California's coastal habitat. Validation with field sampling data showed high mapping accuracies for all methods for identifying presence or absence of iceplant (97%), with the MNF procedure producing the highest accuracy (55%) when the classes were divided into four different densities of iceplant.     

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Hyperspectral imaging, which records a detailed spectrum of light arriving in each pixel, has many potential uses in remote sensing as well as other application areas. Practical applications will typically require real-time processing of large data volumes recorded by a hyperspectral imager. This paper investigates the use of graphics processing units (GPU) for such real-time processing. In particular, the paper studies a hyperspectral anomaly detection algorithm based on normal mixture modelling of the background spectral distribution, a computationally demanding task relevant to military target detection and numerous other applications. The algorithm parts are analysed with respect to complexity and potential for parallellization. The computationally dominating parts are implemented on an Nvidia GeForce 8800 GPU using the Compute Unified Device Architecture programming interface. GPU computing performance is compared to a multi-core central processing unit implementation. Overall, the GPU implementation runs significantly faster, particularly for highly data-parallelizable and arithmetically intensive algorithm parts. For the parts related to covariance computation, the speed gain is less pronounced, probably due to a smaller ratio of arithmetic to memory access. Detection results on an actual data set demonstrate that the total speedup provided by the GPU is sufficient to enable real-time anomaly detection with normal mixture models even for an airborne hyperspectral imager with high spatial and spectral resolution.     

Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data

Crop residues left on agricultural lands after harvest play an important role in controlling and protecting soil against water and wind erosion. One challenge of remote sensing is to differentiate crop residues from bare soil and crop cover, especially when the residues have been weathered and/or when the crop cover phenology is more advanced. Several techniques for mapping and estimating crop residues exist in the literature. However, these methods are time consuming and not suited for quantitative evaluation. They have the disadvantage of being less rigorous and accurate because they do not consider the spectral mixture of different materials in the same pixel. In this study, the potential of hyperspectral (Probe-1) and multispectral high spatial resolution (IKONOS) data were compared for estimating and mapping crop residues on agricultural lands using the constrained linear spectral mixture analysis approach. Image data were spectrally and radiometrically calibrated, atmospherically corrected, as well as geometrically rectified. Pure spectral signatures of residues, bare soil and crop cover were manually extracted from image data based on prior knowledge of the fields. Percent (fraction) cover for each sampling point was extracted using unmixing and validated against ground reference measurements. The best results were achieved for the crop cover (index of agreement (D) = 0.92 and root mean square error (RMSE) = 0.09) adjusted for the impurity of the endmembers canola, pea and wheat, followed by the wheat residues (D = 0.76 and RMSE = 0.12). Considering only the wheat residues in fields with a canola crop, D increases to 0.86. The soil fractions were generally underestimated with D = 0.72, and no significant improvements could be made after adjusting for the shadow effect. The estimations from the IKONOS data were poorer for the same cover types (residues: D = 0.40 and RMSE = 0.24; crop: D = 0.51 and RMSE = 0.38; soil: D = 0.58 and RMSE = 0.29). Relative to the IKONOS data, the better performance of the hyperspectral data is mainly due to the improved spectral band characteristics, especially in the SWIR, which is sensitive to the residues (lignin and cellulose absorption features), soil and crop cover.     

Estimation of urban vegetation abundance by spectral mixture analysis

The spatio-temporal distribution of vegetation is a fundamental component of the urban environment that can be quantified using multispectral imagery. However, spectral heterogeneity at scales comparable to sensor resolution limits the utility of conventional hard classification methods with multispectral reflectance data in urban areas. Spectral mixture models may provide a physically based solution to the problem of spectral heterogeneity. The objective of this study is to examine the applicability of linear spectral mixture models to the estimation of urban vegetation abundance using Landsat Thematic Mapper (TM) data. The inherent dimensionality of TM imagery of the New York City area suggests that urban reflectance measurements may be described by linear mixing between high albedo, low albedo and vegetative endmembers. A three-component linear mixing model provides stable, consistent estimates of vegetation fraction for both constrained and unconstrained inversions of three different endmember ensembles. Quantitative validation using vegetation abundance measurements derived from high-resolution (2 m) aerial photography shows agreement to within fractional abundances of 0.1 for vegetation fractions greater than 0.2. In contrast to the Normalised Difference Vegetation Index (NDVI), vegetation fraction estimates provide a physically based measure of areal vegetation abundance that may be more easily translated to constraints on physical quantities such as vegetative biomass and evapotranspiration.     

Bayesian classification of hyperspectral images using spatially-varying Gaussian mixture model

We propose a spatially-varying Gaussian mixture model for joint spectral and spatial classification of hyperspectral images. The model provides a robust estimation framework for small sample size training sets. Defining prior distributions for the mean vector and the covariance matrix enables us to regularize the parameter estimation problem. More specifically, we can obtain invertible positive definite covariance matrices by the help of this regularization. Moreover, the proposed model also takes into account the spatial alignments of the pixels by using spatially-varying mixture proportions. The spatially-varying mixture model is based on spatial multinomial logistic regression. The classification results obtained on Indian Pines, Pavia Centre, Pavia University, and Salinas data sets show that the proposed methods perform better especially for small-sized training sets compared to the state-of-the-art classifiers.     

Real-time constrained linear discriminant analysis to target detection and classification in hyperspectral imagery

In this paper, we present a constrained linear discriminant analysis (CLDA) approach to hyperspectral image detection and classification as well as its real-time implementation. The basic idea of CLDA is to design an optimal transformation matrix which can maximize the ratio of inter-class distance to intra-class distance while imposing the constraint that different class centers after transformation are along different directions such that different classes can be better separated. The solution turns out to be a constrained version of orthogonal subspace projection (OSP) implemented with a data whitening process. The CLDA approach can be applied to solve both detection and classification problems. In particular, by introducing color for display the classification is achieved with a single classified image where a pre-assigned color is used to display a specified class. The real-time implementation is also developed to meet the requirement of on-line image analysis when the immediate data assessment is critical. The experiments using HYDICE data demonstrate the strength of CLDA approach in discriminating the targets with subtle spectral difference.     

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.     

Mapping of continuous floristic gradients in grasslands using hyperspectral imagery

Transitions between plant species assemblages are often continuous with the form of the transition dependent on the ‘slope’ of environmental gradients and on the style of self-organization in vegetation. Image segmentation can present misleading or even erroneous results if applied to continuous spatial changes in vegetation. Even methods that allow for multiple-class memberships of pixels presuppose the existence of ideal types of species assemblages that constitute mixtures—an assumption that does not fit the case of continua where any section of a gradient is as ‘pure’ as any other section like in modulations of grassland species composition.Thus, we attempted to spatially model floristic gradients in Bavarian meadows by extrapolating axes of an unconstrained ordination of species data. The models were based on high-resolution hyperspectral airborne imagery. We further modelled the distribution of plant functional response types (Ellenberg indicator values) and the cover values of selected species. The models were made with partial least squares (PLS) regression analyses. The realistic utility of the regression models was evaluated by full leave-one-out cross-validation.The modelled floristic gradients showed a considerable agreement with ground-based observations of floristic gradients (R²=0.71 and 0.66 for the first two axes of ordination). Apart from mapping the most important continuous floristic differences, we mapped gradients in the appearance of plant functional response groups as represented by averaged Ellenberg indicator values for soil pH (R²=0.76), water supply (R²=0.66) and nutrient supply (R²=0.75), while models for the cover of single species were weak.Compared to many other vegetation attributes, plant species composition is difficult to detect with remote sensing techniques. This is partly caused by a lack of compatibility between methods of vegetation ecology and remote sensing. We believe that the present study has the potential to increase compatibility as neither spectral nor vegetation information gets lost by a classifying step.     

Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor

Mapping tools are needed to document the location and extent of Phragmites australis, a tall grass that invades coastal marshes throughout North America, displacing native plant species and degrading wetland habitat. Mapping Phragmites is particularly challenging in the freshwater Great Lakes coastal wetlands due to dynamic lake levels and vegetation diversity. We tested the applicability of Hyperion hyperspectral satellite imagery for mapping Phragmites in wetlands of the west coast of Green Bay in Wisconsin, U.S.A. A reference spectrum created using Hyperion data from several pure Phragmites stands within the image was used with a Spectral Correlation Mapper (SCM) algorithm to create a raster map with values ranging from 0 to 1, where 0 represented the greatest similarity between the reference spectrum and the image spectrum and 1 the least similarity. The final two-class thematic classification predicted monodominant Phragmites covering 3.4% of the study area. Most of this was concentrated in long linear features parallel to the Green Bay shoreline, particularly in areas that had been under water only six years earlier when lake levels were 66 cm higher. An error matrix using spring 2005 field validation points (n = 129) showed good overall accuracy—81.4%. The small size and linear arrangement of Phragmites stands was less than optimal relative to the sensor resolution, and Hyperion's 30 m resolution captured few if any pure pixels. Contemporary Phragmites maps prepared with Hyperion imagery would provide wetland managers with a tool that they currently lack, which could aid attempts to stem the spread of this invasive species.     

Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

Both moderate and high spatial resolution imagery can be used to quantify abundance and distribution of urban vegetation for urban landscape management and to provide inputs to physical process models. Estimation of vegetation fraction from Landsat ETM+ and Quickbird allows for operational monitoring and reconnaissance at moderate resolution with calibration and vicarious validation at higher resolution. Establishing a linear correspondence between ETM-derived vegetation fraction and Quickbird-derived vegetation fraction facilitates the validation task by extending the spatial scale from 30 × 30 m to a more manageable 2.8 × 2.8 m. A comparative analysis indicates that urban reflectance can be accurately represented with a three component linear mixture model for both Landsat ETM+ and Quickbird imagery in the New York metro area. The strong linearity of the Substrate Vegetation Dark surface (SVD) mixture model provides consistent estimates of illuminated vegetation fraction that can be used to constrain physical process models that require biophysical inputs related to vegetation abundance. When Quickbird-derived 2.8 m estimates of vegetation fraction are integrated to 30 m scales and coregistered to Landsat-derived 30 m estimates, median estimates agree with the integrated fractions to within 5% for fractions > 0.2. The resulting Quickbird-ETM+ scatter distribution cannot be explained with estimate error alone but is consistent with a 3% to 6% estimation error combined with a 17 m subpixel registration ambiguity. The 3D endmember fraction space obtained from ETM+ imagery forms a ternary distribution of reflectance properties corresponding to distinct biophysical surface types. The SVD model is a reflectance analog to Ridd's V–I–S land cover model but acknowledges the fact that permeable and impermeable surfaces cannot generally be distinguished on the basis of broadband reflectance alone. We therefore propose that vegetation fraction be used as a proxy for permeable surface distribution to avoid the common erroneous assumption that all nonvegetated surfaces along the gray axis are completely impermeable. Comparison of mean vegetation fractions to street tree counts in New York City shows a consistent relationship between minimum fraction and tree count. However, moderate and high resolution areal estimates of vegetation fraction provide complementary information because they image all illuminated vegetation, including that not counted by the in situ street tree inventory.     

Spectral mixture analyses of hyperspectral data acquired using a tethered balloon

Tethered balloon remote sensing platforms can be used to study radiometric issues in terrestrial ecosystems by effectively bridging the spatial gap between measurements made on the ground and those acquired via airplane or satellite. In this study, the Short Wave Aerostat-Mounted Imager (SWAMI) tethered balloon-mounted platform was utilized to evaluate linear and nonlinear spectral mixture analysis (SMA) for a grassland-conifer forest ecotone during the summer of 2003. Hyperspectral measurement of a 74-m diameter ground instantaneous field of view (GIFOV) attained by the SWAMI was studied. Hyperspectral spectra of four common endmembers, bare soil, grass, tree, and shadow, were collected in situ, and images captured via video camera were interpreted into accurate areal ground cover fractions for evaluating the mixture models. The comparison between the SWAMI spectrum and the spectrum derived by combining in situ spectral data with video-derived areal fractions indicated that nonlinear effects occurred in the near infrared (NIR) region, while nonlinear influences were minimal in the visible region. The evaluation of hyperspectral and multispectral mixture models indicated that nonlinear mixture model-derived areal fractions were sensitive to the model input data, while the linear mixture model performed more stably. Areal fractions of bare soil were overestimated in all models due to the increased radiance of bare soil resulting from side scattering of NIR radiation by adjacent grass and trees. Unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. In addition, though an apparent endmember assemblage could be derived using linear approaches to yield low residual error, the tree and shade endmember fractions calculated using this technique were erroneous and therefore separate treatment of endmembers subject to high amounts of multiple scattering (i.e. shadows and trees) must be done with caution. Including the short wave infrared (SWIR) region in the hyperspectral and multispectral endmember data significantly reduced the Pearson correlation coefficient values among endmember spectra. Therefore, combination of visible, NIR, and SWIR information is likely to further improve the utility of SMA in understanding ecosystem structure and function and may help narrow uncertainties when utilizing remotely sensed data to extrapolate trace glas flux measurements from the canopy scale to the landscape scale.     

Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)

Invasive nonindigenous plants are threatening the biological integrity of North American rangelands, as well as the economies that are supported by those ecosystems. Spatial information is critical to fulfilling invasive plant management strategies. Traditional invasive plant mapping has utilized ground-based hand or GPS mapping. The shortfalls of ground-based methods include the limited spatial extent covered and the associated time and cost. Mapping vegetation with remote sensing covers large spatial areas and maps can be updated at an interval determined by management needs. The objective of the study was to map leafy spurge (Euphorbia esula L.) and spotted knapweed (Centaurea maculosa Lam.) using 128-band hyperspectral (5-m and 3-m resolution) imagery and assess the accuracy of the resulting maps. Beiman Cutler classifications (BCC) were used to classify the imagery using the randomForest package in the R statistical program. BCC builds multiple classification trees by repeatedly taking random subsets of the observational data and using random subsets of the spectral bands to determine each split in the classification trees. The resulting classification trees vote on the correct classification. Overall accuracy was 84% for the spotted knapweed classification, with class accuracies ranging from 60% to 93%; overall accuracy was 86% for the leafy spurge classification, with class accuracies ranging from 66% to 93%. Our results indicate that (1) BCC can achieve substantial improvements in accuracy over single classification trees with these data and (2) it might be unnecessary to have separate accuracy assessment data when using BCC, as the algorithm provides a reliable internal estimate of accuracy.     

Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data

A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and endmember spectra. The land-cover fraction map is applied in the unmixing procedure to identify the number and types of endmembers for every pixel, but also to set constraints on the area fractions of the forest endmembers. SnowFrac was applied on two Terra Moderate Resolution Imaging Spectroradiometer (MODIS) images with different snow conditions covering a forested area in southern Norway. Six experiments were carried out, each with different endmember constraints. Estimated snow-cover fractions were compared with snow-cover fraction reference maps derived from two Landsat Enhanced Thematic Mapper Plus (ETM+) images acquired the same days as the MODIS images. Results are presented for non-forested areas, deciduous forests, coniferous forests and mixed deciduous/coniferous forests. The snow-cover fraction estimates are enhanced by increasing constraints introduced to the unmixing procedure. The classification accuracy shows that 96% of the pixels are classified with less than 20% error (absolute units) on 7 May 2001 when all forested and non-forested areas are included. The corresponding figure for 4 May 2000 is 88%.     

Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models

Future remote sensing satellite missions exploring the earth will feature advanced hyperspectral and directional optical imaging instruments. Given the complex nature of the data to be expected from these missions, a thorough preparation for them is essential and this can be accomplished by realistic simulation of the imagery data, years before the actual launch. Based on given spectral and directional capabilities of the instrument, and in combination with biophysical land surface properties obtained from existing imagery, the spectral and directional responses of several types of vegetation and bare soil have been simulated pixel by pixel using the radiative transfer models PROSPECT (for hyperspectral leaf reflectance and transmittance), GeoSAIL (for two-layer canopy bidirectional spectral reflectance), and MODTRAN4 (for atmospheric hyperspectral and directional effects). In this way, one obtains realistically simulated hyperspectral and directional top-of-atmosphere spectral radiance images, with all major effects included, such as heterogeneity of the landscape, non-Lambertian reflectance of the land surface, the atmospheric adjacency effect, and the limited spatial resolution of the instrument. The output of the image simulations can be used to demonstrate the capabilities of future earth observation missions. In addition, instrument specifications and image acquisition strategies might be optimized on the basis of simulated image analysis results, and new advanced data assimilation procedures could be validated with realistic inputs under controlled circumstances. This paper describes the applied methodology, the study area with the input images, the set-up of the actual image simulations, and discusses the final results obtained.     

Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments

Remote sensing has considerable potential for providing accurate, up-to-date information in urban areas. Urban remote sensing is complicated, however, by very high spectral and spatial complexity. In this paper, Multiple Endmember Spectral Mixture Analysis (MESMA) was applied to map urban land cover using HyMap data acquired over the city of Bonn, Germany. MESMA is well suited for urban environments because it allows the number and types of endmembers to vary on a per-pixel basis, which allows controlling the large spectral variability in these environments. We employed a hierarchical approach, in which MESMA was applied to map four levels of complexity ranging from the simplest level consisting of only two classes, impervious and pervious, to 20 classes that differentiated material composition and plant species. Lower levels of complexity, mapped at the highest accuracies, were used to constrain spatially models at higher levels of complexity, reducing spectral confusion between materials. A spectral library containing 1521 endmembers was created from the HyMap data. Three endmember selection procedures, Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA) and Count Based Endmember Selection (COB), were used to identify the most representative endmembers for each level of complexity. Combined two-, three- or four-endmember models - depending on the hierarchical level - were applied, and the highest endmember fractions were used to assign a land cover class. Classification accuracies of 97.2% were achieved for the two lowest complexity levels, consisting of impervious and pervious classes, and a four class map consisting of vegetation, bare soil, water and built-up. At the next level of complexity, consisting of seven classes including trees, grass, bare soil, river, lakes/basins, road and roof/building, classification accuracies remained high at 81.7% with most classes mapped above 85% accuracy. At the highest level, consisting of 20 land cover classes, a 75.9% classification accuracy was achieved. The ability of MESMA to incorporate within-class spectral variability, combined with a hierarchical approach that uses spatial information from one level to constrain model selection at a higher level of complexity was shown to be particularly well suited for urban environments.     

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.     

Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements

This paper examines how reflectance spectrometry used in the laboratory to estimate clay and calcium carbonate (CaCO₃) soil contents can be applied to field and airborne measurements for soil property mapping. A continuum removal (CR) technique quantifying specific absorption features of clay (2206 nm) and CaCO₃ (2341 nm) was applied to laboratory, field and airborne HYMAP reflectance measurements collected in 2003 (33 sites) and 2005 (19 sites) over bare soil sites of a few meters within the La Peyne Valley area, southern France. Nine intermediate stages from the laboratory up to HYMAP sensor measurements were considered for separately evaluating the possible degradation of estimation performances when going across scales and sensors, e.g. radiometric calibration, spectral resolution, spatial variability, illumination conditions, and surface status including roughness, soil moisture and presence and nature of pebbles.Significant relationships were observed between clay and CaCO₃ contents and CR values computed respectively at 2206 nm and 2341 nm from reflectance measurements at the laboratory level with an ASD spectrophotometer up to the HYMAP spectro-imaging sensor. Performances of clay and CaCO₃ estimations decreased from the laboratory to airborne scales. The main factors inducing uncertainties in the estimates were radiometric and wavelength calibration uncertainties of the HYMAP sensor as well as possible residual atmospheric effects.