Search results for "Endmember"

showing 3 items of 3 documents

Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over …

2009

Abstract: In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixe…

Endmember010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologies550 - Earth sciences02 engineering and technologyLand coverlcsh:Chemical technology01 natural sciencesBiochemistryNormalized Difference Vegetation IndexArticleCHRISAnalytical ChemistryRoot mean squareFractional Vegetation Cover; Vegetation Indices; Spectral Mixture Analysis; PROBA; CHRISPROBAlcsh:TP1-1185Electrical and Electronic EngineeringInstrumentation021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingFractional Vegetation CoverPixelVegetation15. Life on landAtomic and Molecular Physics and OpticsStandard errorSpectral Mixture AnalysisVegetation Indices
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Mapping a-priori defined plant associations using remotely sensed vegetation characteristics

2014

Abstract Incorporation of a priori defined plant associations into remote sensing products is a major challenge that has only recently been confronted by the remote sensing community. We present an approach to map the spatial distribution of such associations by using plant indicator values (IVs) for salinity, moisture and nutrients as an intermediate between spectral reflectance and association occurrences. For a 12 km 2 study site in the Netherlands, the relations between observed IVs at local vegetation plots and visible and near-infrared (VNIR) and short-wave infrared (SWIR) airborne reflectance data were modelled using Gaussian Process Regression (GPR) (R 2 0.73, 0.64 and 0.76 for sali…

endmember selectionCalibration (statistics)Vegetation classificationcontinuous floristic gradientsSoil Scienceimaging spectroscopy/dk/atira/pure/sustainabledevelopmentgoals/clean_water_and_sanitationLaboratory of Geo-information Science and Remote SensingKrigingmoistureLaboratorium voor Geo-informatiekunde en Remote SensingComputers in Earth SciencesRemote sensingtropical forestsHyperspectral imagingGeologyVegetationPE&RCRegressionVNIRhyperspectral imageryclassificationaviris dataellenberg indicator valuesEnvironmental scienceregressionIndicator valueSDG 6 - Clean Water and SanitationRemote Sensing of Environment
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Linear spectral mixture modelling to estimate vegetation amount from optical spectral data

1996

Abstract Spectral mixture modelling has developed in recent years as a suitable remote sensing tool for analysing the biophysical and compositional character of ground surfaces. In this paper the potentiality of the linear spectral mixture model to extract vegetation related parameters from 0·4-2·5 μm reflectance data has been tested. High spectral resolution reflectance measurements of soil-plant mixtures with different soil colour and plant densities were carried out in a laboratory experiment. The constrained least-squares and the factor analysis unmixing procedures were applied to generate endmember fractions of the components present in the mixtures and to test the validity of the mode…

EndmemberApplied physicsLinear modelGeneral Earth and Planetary SciencesEnvironmental scienceVegetationSpectral resolutionMixture modelMultispectral ScannerMultispectral pattern recognitionRemote sensingInternational Journal of Remote Sensing
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