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Dar A. Roberts - One of the best experts on this subject based on the ideXlab platform.
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multiple endmember Spectral Mixture Analysis mesma to map burn severity levels from landsat images in mediterranean countries
Remote Sensing of Environment, 2013Co-Authors: Carmen Quintano, Alfonso Fernandezmanso, Dar A. RobertsAbstract:Abstract Forest fires are a major hazard in Mediterranean countries, with an average of 45,000 fires per year. Discrimination of different degrees of burn severity is critical to improve management of fire-affected areas. In this work, an unmixing-based methodology was evaluated in three Mediterranean study areas to estimate burn severity from medium spatial resolution optical satellite data. Post-fire Landsat 5 Thematic Mapper (TM) images were unmixed into four fraction images: non-photosynthetic vegetation and ash (NPV–Ash), green vegetation (GV), soil and shade using Multiple Endmember Spectral Mixture Analysis (MESMA). MESMA decomposes each pixel using different combinations of potential endmembers, overcoming the Linear Spectral Mixture Analysis limitation of using the same number of endmembers to model all image pixels. Next, a decision tree was used to classify the shade normalized fraction images into four classes: unburned and low, moderate, and high levels of burn severity. Finally, the burn severity estimates were validated using error matrix, producer and user accuracies per class, and κ statistic. For reference data, we used 50 plots per class defined from a 50 cm post-fire orthophotography (proportion of dead tree 90%, high severity). MESMA-based burn severity estimates showed a high accuracy (0.80, 0.80, and 0.78) for the three test sites. We conclude that the proposed MESMA based methodology is valid to accurately map burn severity in Mediterranean countries from moderate resolution satellite data.
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hierarchical multiple endmember Spectral Mixture Analysis mesma of hyperSpectral imagery for urban environments
Remote Sensing of Environment, 2009Co-Authors: Jonas Franke, Dar A. Roberts, Kerry Q Halligan, Gunter MenzAbstract:Abstract 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.
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mapping tree and shrub leaf area indices in an ombrotrophic peatland through multiple endmember Spectral unmixing
Remote Sensing of Environment, 2007Co-Authors: Oliver Sonnentag, Dar A. Roberts, Kerry Q Halligan, Jing M Chen, Julie Talbot, Ajit GovindAbstract:Abstract Leaf area index (LAI) is an important parameter used by most process-oriented ecosystem models. LAI of forest ecosystems has routinely been mapped using Spectral vegetation indices (SVI) derived from remote sensing imagery. The application of SVI-based approaches to map LAI in peatlands presents a challenge, mainly due to peatlands characteristic multi-layer canopy comprising shrubs and open, discontinuous tree canopies underlain by a continuous ground cover of different moss species, which reduces the greenness contrast between the canopy and the background. Our goal is to develop a methodology to map tree and shrub LAI in peatlands and similar ecosystems based on multiple endmember Spectral Mixture Analysis (MESMA). This new mapping method is validated using LAI field measurements from a precipitation-fed (ombrotrophic) peatland near Ottawa, Ontario, Canada. We demonstrate first that three commonly applied SVI are not suitable for tree and shrub LAI mapping in ombrotrophic peatlands. Secondly, we demonstrate for a three-endmember model the limitations of traditional linear Spectral Mixture Analysis (SMA) due to the unique and widely varying Spectral characteristics of Sphagnum mosses, which are significantly different from vascular plants. Next, by using a geometric-optical radiative transfer model, we determine the nature of the equation describing the empirical relationship between shadow fraction and tree LAI using nonlinear ordinary least square (OLS) regression. We then apply this equation to describe the empirical relationships between shadow and shrub fractions obtained from Mixture decomposition with SMA and MESMA, respectively, and tree and shrub LAI, respectively. Less accurate fractions obtained from SMA result in weaker relationships between shadow fraction and tree LAI ( R 2 = 0.61) and shrub fraction and shrub LAI ( R 2 = 0.49) compared to the same relationships based on fractions obtained from MESMA with R 2 = 0.75 and R 2 = 0.68, respectively. Cross-validation of tree LAI ( R 2 = 0.74; RMSE = 0.48) and shrub LAI ( R 2 = 0.68; RMSE = 0.42) maps using fractions from MESMA shows the suitability of this approach for mapping tree and shrub LAI in ombrotrophic peatlands. The ability to account for a Spectrally varying, unique Sphagnum moss ground cover during Mixture decomposition and a two layer canopy is particularly important.
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sub pixel mapping of urban land cover using multiple endmember Spectral Mixture Analysis manaus brazil
Remote Sensing of Environment, 2007Co-Authors: Rebecca L. Powell, Philip E Dennison, Dar A. Roberts, Laura L. HessAbstract:Abstract The spatial and Spectral variability of urban environments present fundamental challenges to deriving accurate remote sensing products for urban areas. Multiple endmember Spectral Mixture Analysis (MESMA) is a technique that potentially addresses both challenges. MESMA models spectra as the linear sum of Spectrally pure endmembers that vary on a per-pixel basis. Spatial variability is addressed by mapping sub-pixel components of land cover as a combination of endmembers. Spectral variability is addressed by allowing the number and type of endmembers to vary from pixel to pixel. This paper presents an application of MESMA to map the physical components of urban land cover for the city of Manaus, Brazil, using Landsat Enhanced Thematic Mapper (ETM+) imagery. We present a methodology to build a regionally specific Spectral library of urban materials based on generalized categories of urban land-cover components: vegetation, impervious surfaces, soil, and water. Using this library, we applied MESMA to generate a total of 1137 two-, three-, and four-endmember models for each pixel; the model with the lowest root-mean-squared (RMS) error and lowest complexity was selected on a per-pixel basis. Almost 97% of the pixels within the image were modeled within the 2.5% RMS error constraint. The modeled fractions were used to generate continuous maps of the per-pixel abundance of each generalized land-cover component. We provide an example to demonstrate that land-cover components have the potential to characterize trajectories of physical landscape change as urban neighborhoods develop through time. Accuracy of land-cover fractions was assessed using high-resolution, geocoded images mosaicked from digital aerial videography. Modeled vegetation and impervious fractions corresponded well with the reference fractions. Modeled soil fractions did not correspond as closely with the reference fractions, in part due to limitations of the reference data. This work demonstrates the potential of moderate-resolution, multiSpectral imagery to map and monitor the evolution of the physical urban environment.
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large area mapping of land cover change in rondonia using multitemporal Spectral Mixture Analysis and decision tree classifiers
Journal of Geophysical Research, 2002Co-Authors: Dar A. Roberts, Izaya Numata, Karen Holmes, Getulio Teixeira Batista, T Krug, A Monteiro, B Powell, Oliver A ChadwickAbstract:[1] We describe spatiotemporal variation in land cover over 80,000 km2 in central Rondonia. We use a multistage process to map primary forest, pasture, second growth, urban, rock/savanna, and water using 33 Landsat scenes acquired over three contiguous areas between 1975 and 1999. Accuracy of the 1999 classified maps was assessed as exceeding 85% based on digital airborne videography. Rondonia is highly fragmented, in which forests outside of restricted areas consist of numerous, small irregular patches. Pastures in Rondonia persist over many years and are not typically abandoned to second growth, which when present rarely remains unchanged longer than 8 years. Within the state, annual deforestation rates, pasture area, and ratio of second growth to cleared area varied spatially. Highest initial deforestation rates occurred in the southeast (Luiza), at over 2%, increasing to 3% by the late 1990s. In this area, the percentage of cleared land in second growth averaged 18% and few pastures were abandoned. In central Rondonia (Ji-Parana), deforestation rates rose from 1.2% between 1978 and 1986 to a high of 4.2% in 1999. In the northwest (Ariquemes), initial deforestation rates were lowest at 0.5% but rose substantially in the late 1990s, peaking at 3% in 1998. The ratio of second growth to cleared area was more than double the ratio in Luiza and few pastures remained unchanged beyond 8 years. Land clearing was most intense close to the major highway, BR364, except in Ariquemes. Intense forest clearing extended at least 50 km along the margins of BR364 in Ji-Parana and Luiza. Spatial differences in land use are hypothesized to result from a combination of economic factors and soil fertility.
Ben Somers - One of the best experts on this subject based on the ideXlab platform.
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invasive species mapping in hawaiian rainforests using multi temporal hyperion spaceborne imaging spectroscopy
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013Co-Authors: Ben Somers, Gregory P. AsnerAbstract:We evaluated the potential of multi-temporal Multiple Endmember Spectral Mixture Analysis (MESMA) of Earth Observing-1 Hyperion data for detection of invasive tree species in the montane rainforest area of the Hawaii Volcanoes National Park, Island of Hawaii. We observed a clear seasonal trend in invasive species detection success when unmixing results were cross-referenced to ground observations; with Kappa coefficients (indicating detection success, 0-1) ranging between 0.66 (summer) and 0.69 (winter) and 0.51-0.53 during seasonal transition periods. An increase of Kappa to 0.80 was observed when Spectral features extracted from September, August and January were integrated into MESMA. Multi-temporal unmixing improved the detection success of invasive species because Spectral information acquired over different portions of the growing season allowed us to capture species-specific phenology, thereby reducing Spectral similarity among species.
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Spectral Mixture Analysis to assess post fire vegetation regeneration using landsat thematic mapper imagery accounting for soil brightness variation
International Journal of Applied Earth Observation and Geoinformation, 2012Co-Authors: Ben Somers, Sander Veraverbeke, Ioannis Z Gitas, Thomas Katagis, Anastasia Polychronaki, Rudi GoossensAbstract:Abstract Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large 2007 Peloponnese (Greece) wildfires. Post-fire recovery landscapes typically are mixed vegetation–substrate environments which makes Spectral Mixture Analysis (SMA) a very effective tool to derive fractional vegetation cover maps. Using a combination of field and simulation techniques this study aimed to account for the impact of background brightness variability on SMA model performance. The field data consisted out of a Spectral library of in situ measured reflectance signals of vegetation and substrate and 78 line transect plots. In addition, a Landsat Thematic Mapper (TM) scene was employed in the study. A simple SMA, in which each constituting terrain feature is represented by its mean Spectral signature, a multiple endmember SMA (MESMA) and a segmented SMA, which accounts for soil brightness variations by forcing the substrate endmember choice based on ancillary data (lithological map), were applied. In the study area two main Spectrally different lithological units were present: relatively bright limestone and relatively dark flysch (sand-siltstone). Although the simple SMA model resulted in reasonable regression fits for the flysch and limestones subsets separately (coefficient of determination R2 of respectively 0.67 and 0.72 between field and TM data), the performance of the regression model on the pooled dataset was considerably weaker (R2 = 0.65). Moreover, the regression lines significantly diverged among the different subsets leading to systematic over-or underestimations of the vegetative fraction depending on the substrate type. MESMA did not solve the endmember variability issue. The MESMA model did not manage to select the proper substrate spectrum on a reliable basis due to the lack of shape differences between the flysch and limestone spectra,. The segmented SMA model which accounts for soil brightness variations minimized the variability problems. Compared to the simple SMA and MESMA models, the segmented SMA resulted in a higher overall correlation (R2 = 0.70), its regression slope and intercept were more similar among the different substrate types and its resulting regression lines more closely resembled the expected one-one line. This paper demonstrates the improvement of a segmented approach in accounting for soil brightness variations in estimating vegetative cover using SMA. However, further research is required to evaluate the model's performance for other soil types, with other image data and at different post-fire timings.
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Endmember variability in Spectral Mixture Analysis: A review
Remote Sensing of Environment, 2011Co-Authors: Ben Somers, Gregory P. Asner, Laurent Tits, Pol CoppinAbstract:The composite nature of remotely sensed Spectral information often masks diagnostic Spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this Mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent Spectral components or Spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear Mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative Mixture Analysis cycle, (ii) the selection of a subset of stable Spectral features, (iii) the Spectral weighting of bands, (iv) Spectral signal transformations and (v) the use of radiative transfer models in a Mixture Analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA.
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Spectral Mixture Analysis to monitor defoliation in mixed aged eucalyptus globulus labill plantations in southern australia using landsat 5 tm and eo 1 hyperion data
International Journal of Applied Earth Observation and Geoinformation, 2010Co-Authors: Ben Somers, Willem W Verstraeten, Jan Verbesselt, Eva M Ampe, Neil Sims, Pol CoppinAbstract:Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel Spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative Mixture Analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral Mixture Analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-Spectral (R2no age correction = 0.55 vs R2age correction = 0.73 for Landsat) and hyperSpectral (R2no age correction = 0.12 vs R2age correction = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for Analysis of multi-Spectral datasets such as MODIS and SPOT-VEGETATION.
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nonlinear hyperSpectral Mixture Analysis for tree cover estimates in orchards
Remote Sensing of Environment, 2009Co-Authors: Ben Somers, Stephanie Delalieux, Jan Stuckens, Willem W Verstraeten, Kenneth Cools, Dimitry Van Der Zande, Pol CoppinAbstract:Abstract Accurate monitoring of spatial and temporal variation in tree cover provides essential information for steering management practices in orchards. In this light, the present study investigates the potential of HyperSpectral Mixture Analysis. Specific focus lies on a thorough study of non-linear mixing effects caused by multiple photon scattering. In a series of experiments the importance of multiple scattering is demonstrated while a novel conceptual Nonlinear Spectral Mixture Analysis approach is presented and successfully tested on in situ measured mixed pixels in Citrus sinensis L. orchards. The rationale behind the approach is the redistribution of nonlinear fractions (i.e., virtual fractions) among the actual physical ground cover entities (e.g., tree, soil). These ‘virtual’ fractions, which account for the extent and nature of multiple photon scattering only have a physical meaning at the Spectral level but cannot be interpreted as an actual physical part of the ground cover. Results illustrate that the effect of multiple scattering on Spectral Mixture Analysis is significant as the linear approach provides a mean relative root mean square error (RMSE) for tree cover fraction estimates of 27%. While traditional nonlinear approaches only slightly reduce this error (RMSE = 23%), important improvements are obtained for the novel Nonlinear Spectral Mixture Analysis approach (RMSE = 12%).
Christopher Small - One of the best experts on this subject based on the ideXlab platform.
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estimation and vicarious validation of urban vegetation abundance by Spectral Mixture Analysis
Remote Sensing of Environment, 2006Co-Authors: Christopher SmallAbstract:Abstract 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.
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estimation and vicarious validation of urban vegetation abundance by Spectral Mixture Analysis
Remote Sensing of Environment, 2006Co-Authors: Christopher Small, Jacqueline W T LuAbstract:Abstract 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.
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high spatial resolution Spectral Mixture Analysis of urban reflectance
Remote Sensing of Environment, 2003Co-Authors: Christopher SmallAbstract:This study uses IKONOS imagery to quantify the combined spatial and Spectral characteristics of urban reflectance in 14 urban areas worldwide. IKONOS 1-m panchromatic imagery provides a detailed measure of spatial variations in albedo while IKONOS 4-m multiSpectral imagery allows the relative contributions of different materials to the Spectrally heterogeneous radiance field to be determined and their abundance to be mapped. Spatial autocorrelation analyses indicate that the characteristic scale of urban reflectance is consistently between 10 and 20 m for the cities in this study. Spectral Mixture Analysis quantifies the relative contributions of the dominant Spectral endmembers to the overall reflectance of the urban mosaic. Spectral mixing spaces defined by the two low-order principal components account for 96% to 99% of image variance and have a consistent triangular structure spanned by high albedo, low albedo and vegetation endmembers. Spectral mixing among these endmembers is predominantly linear although some nonlinear mixing is observed along the gray axis spanning the high and low albedo endmembers. Inversion of a constrained three-component linear mixing model produces stable, consistent estimates of endmember abundance. RMS errors based on the misfit between observed radiance vectors and modeled radiance vectors (derived from fraction estimates and image endmembers) are generally less than 3% of the mean of the observed radiance. Agreement between observed radiance and fraction estimates does not guarantee the accuracy of the areal fraction estimates, but it does indicate that the three-component linear model provides a consistent and widely applicable physical characterization of urban reflectance. Field validated fraction estimates have applications in urban vegetation monitoring and pervious surface mapping.
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estimation of urban vegetation abundance by Spectral Mixture Analysis
International Journal of Remote Sensing, 2001Co-Authors: Christopher SmallAbstract: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 ensembl...
Xuehong Chen - One of the best experts on this subject based on the ideXlab platform.
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assessing the impact of endmember variability on linear Spectral Mixture Analysis lsma a theoretical and simulation Analysis
Remote Sensing of Environment, 2019Co-Authors: Chishan Zhang, Jin Chen, Yuan Zhou, Yuhan Rao, Xuehong ChenAbstract:Abstract: The extensive existence of mixed pixels in satellite imagery is challenging for accurate land surface interpretation and parametric inversion. Spectral Mixture Analysis (SMA) addresses the issue by providing valuable sub-pixel information. However, it is affected by endmember variability exhibiting diverse Spectral characteristics for the same endmember, inducing substantial uncertainties in model estimations. A number of approaches have been proposed to reduce the uncertainties, but theoretical explanations of how endmember variability affects model estimation and how these approaches perform successfully have not yet been derived. In this study, error propagation caused by endmember variability in linear SMA (LSMA) was investigated using theoretical Analysis and simulation experiments. The major findings are as follows: (1) the impact of endmember variability on LSMA unmixing error depends on the interactions between deviation signal and gain vectors in a multiplicative way. (2) Unmixing error originates from the deviation signal that is governed jointly by Spectral variability within an endmember class (i.e., intra-class variability) and endmember abundances, while spectra magnitude and Spectral similarity among different endmember classes (i.e., inter-class variability) can amplify or reduce the deviation signal. (3) The typical approaches for mitigating endmember variability could not only change the deviation signal but also affect gain vectors, resulting in improved performance of LSMA to some extent. Based on these results, we recommend Multiple Endmember Spectral Mixture Analysis (MESMA) to be used in most applications considering its robustness in mitigating endmember variability. The results of this study will be of benefit to the application of LSMA in practice.
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two step constrained nonlinear Spectral Mixture Analysis method for mitigating the collinearity effect
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Jin Chen, Yuan Zhou, Xuehong ChenAbstract:Spectral Mixture Analysis (SMA) is widely used to quantify the fraction of each component (endmember) of mixed pixels that contain Spectral signals from more than one land surface type. Generally, nonlinear SMA (NSMA) outperforms linear SMA (LSMA) in the vegetation (tree, shrub, crop, and grass) and soil Mixture case because NSMA considers the significant multiple scattering that exists for these Mixtures. However, compared to LSMA, the bilinear NSMA method, which is a typical physical-based NSMA method, is undermined by its susceptibility to the collinearity effect. In this paper, a two-step constrained NSMA method (referred to as TsC-NSMA) is proposed to mitigate the collinearity effect in the bilinear NSMA method. The theoretical maximum likelihood range is mathematically derived for each endmember fraction, and the ranges are used as additional constraints for the bilinear NSMA method to optimize the unmixing results. Three different data sets, including simulated Spectral data, an in situ ground plot Spectral measurement, and a Landsat8 Operational Land Imager image, were used to assess the performance of the TsC-NSMA method. The results indicated that TsC-NSMA achieved the highest estimation accuracy for all mixed scenarios which either contain severe endmember collinearity or high noise levels, thereby suggesting its ability to mitigate the collinearity effect in the bilinear NSMA method with the potential to improve the estimation of endmember fractions in practical applications.
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estimation of fractional vegetation cover in semiarid areas by integrating endmember reflectance purification into nonlinear Spectral Mixture Analysis
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Yuan Zhou, Jin Chen, Xin Cao, Xuehong ChenAbstract:Fractional vegetation cover (FVC) is one of the fundamental parameters for characterizing terrestrial ecosystems, with wide uses in various environmental and climate-related modeling applications. The remote sensing technique provides a unique opportunity for estimating FVC over large geographical areas by employing Spectral Mixture Analysis (SMA). The effectiveness of SMA depends largely on the accurate extraction of representative and pure endmembers. However, in arid and semiarid environments that have sparse vegetation distributions, most current SMA models may produce large biases due to difficulties in obtaining pure vegetation spectra from the satellite images. This letter developed a new approach to estimate FVC from satellite observations by integrating an endmember spectrum purification procedure into a nonlinear SMA model. The proposed method is capable of extracting pure endmember spectra even though pure vegetation endmember is not present in target images in arid and semiarid environments, which improves the accuracy of FVC retrievals. Validation experiments conducted in the Xilingol grassland, Inner Mongolia, China, demonstrate that the proposed method produces more accurate FVC estimates $(\mathbf{RMSE} than do current algorithms. The better performance of the proposed method can be attributed to the purified vegetation spectra that more closely resemble the real pure vegetation spectra.
Pol Coppin - One of the best experts on this subject based on the ideXlab platform.
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Endmember variability in Spectral Mixture Analysis: A review
Remote Sensing of Environment, 2011Co-Authors: Ben Somers, Gregory P. Asner, Laurent Tits, Pol CoppinAbstract:The composite nature of remotely sensed Spectral information often masks diagnostic Spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this Mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent Spectral components or Spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear Mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative Mixture Analysis cycle, (ii) the selection of a subset of stable Spectral features, (iii) the Spectral weighting of bands, (iv) Spectral signal transformations and (v) the use of radiative transfer models in a Mixture Analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA.
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Spectral Mixture Analysis to monitor defoliation in mixed aged eucalyptus globulus labill plantations in southern australia using landsat 5 tm and eo 1 hyperion data
International Journal of Applied Earth Observation and Geoinformation, 2010Co-Authors: Ben Somers, Willem W Verstraeten, Jan Verbesselt, Eva M Ampe, Neil Sims, Pol CoppinAbstract:Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel Spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative Mixture Analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral Mixture Analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-Spectral (R2no age correction = 0.55 vs R2age correction = 0.73 for Landsat) and hyperSpectral (R2no age correction = 0.12 vs R2age correction = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for Analysis of multi-Spectral datasets such as MODIS and SPOT-VEGETATION.
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nonlinear hyperSpectral Mixture Analysis for tree cover estimates in orchards
Remote Sensing of Environment, 2009Co-Authors: Ben Somers, Stephanie Delalieux, Jan Stuckens, Willem W Verstraeten, Kenneth Cools, Dimitry Van Der Zande, Pol CoppinAbstract:Abstract Accurate monitoring of spatial and temporal variation in tree cover provides essential information for steering management practices in orchards. In this light, the present study investigates the potential of HyperSpectral Mixture Analysis. Specific focus lies on a thorough study of non-linear mixing effects caused by multiple photon scattering. In a series of experiments the importance of multiple scattering is demonstrated while a novel conceptual Nonlinear Spectral Mixture Analysis approach is presented and successfully tested on in situ measured mixed pixels in Citrus sinensis L. orchards. The rationale behind the approach is the redistribution of nonlinear fractions (i.e., virtual fractions) among the actual physical ground cover entities (e.g., tree, soil). These ‘virtual’ fractions, which account for the extent and nature of multiple photon scattering only have a physical meaning at the Spectral level but cannot be interpreted as an actual physical part of the ground cover. Results illustrate that the effect of multiple scattering on Spectral Mixture Analysis is significant as the linear approach provides a mean relative root mean square error (RMSE) for tree cover fraction estimates of 27%. While traditional nonlinear approaches only slightly reduce this error (RMSE = 23%), important improvements are obtained for the novel Nonlinear Spectral Mixture Analysis approach (RMSE = 12%).
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a weighted linear Spectral Mixture Analysis approach to address endmember variability in agricultural production systems
Journal of remote sensing, 2009Co-Authors: Ben Somers, Stephanie Delalieux, Jan Stuckens, Willem W Verstraeten, Pol CoppinAbstract:The least squares error (LSE) technique is frequently used to estimate abundance fractions in linear Spectral Mixture Analysis (LSMA). The LSE is typically equally weighted for all wavebands, assuming equally important effects. This is, however, not always the case and therefore traditional LSMA often results in suboptimal fraction estimates. This study presents a weighted LSMA approach that prioritises wavebands with minor or no negative effects on fraction estimates. Synthetic mixed pixel spectra compiled from in situ measured spectra of bare soil, citrus tree and weed canopies were used for validation. The results show markedly improved fraction estimates obtained for the weighted approach, with a mean absolute gain of 0.24 in R 2 and a mean absolute reduction in fraction abundance error of 0.06.
