Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers

Abstract

Abstract:Fractional green vegetation cover (FVC) is a useful parameter for many environmental and climate-related applications. A common approach for estimating FVC involves the linear unmixing of two spectral endmembers in a remote sensing image; bare soil and green vegetation. The spectral properties of these two endmembers are typically determined based on field measurements, estimated using additional data sources (e.g., soil databases or land cover maps), or extracted directly from the imagery. Most FVC estimation approaches do not consider that the spectral properties of endmembers may vary across space. However, due to local differences in climate, soil type, vegetation species, etc., the spectral characteristics of soil and green vegetation may exhibit positive spatial autocorrelation. When this is the case, it may be useful to take these local variations into account for estimating FVC. In this study, spatial interpolation (Inverse Distance Weighting and Ordinary Kriging) was used to predict variations in the spectral characteristics of bare soil and green vegetation across space. When the spatially-interpolated values were used in place of scene-invariant endmember values to estimate FVC in an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image, the accuracy of FVC estimates increased, providing evidence that it may be useful to consider the effects of spatial autocorrelation for spectral mixture analysis.

Effects of Pansharpening on Vegetation Indices

“…VIs have been used extensively for remote estimation of above-ground biomass [1], leaf area index [2], fraction of photosynthetically active radiation [3], net primary productivity [4], crop yields [5], fractional green vegetation cover [6,7], and many other important vegetation parameters for agricultural, ecological, and climate models. High resolution VI data is particularly important for monitoring small agricultural fields/small vegetation patches (to reduce the number of mixed pixels along field or patch boundaries), for monitoring sub-field/sub-patch variability in vegetation (e.g., to identify areas of vegetation stress, disease, or physical damage), and for detecting fine-scale changes in vegetation over time.…”

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

“…Scanlon et al [53] used seasonally averaged NDVI and interannual variations in wet-season NDVI as state-space variables in a linear unmixing model to estimate fractional cover of trees, grass and bare soil along an aridity gradient in southern Africa. Johnson et al [54] achieved improved accuracy in estimating fractional green vegetation cover with spectral mixture modeling by using spatial interpolation to account for spatial variation in the values of spectral end-members. Gessner et al [55] applied decision tree regression to a set of satellite-derived phenology metrics for the wet and dry season to estimate fractional cover of woody, herbaceous and bare soil in savanna regions of southern Africa.…”

An Improved Estimation of Regional Fractional Woody/Herbaceous Cover Using Combined Satellite Data and High-Quality Training Samples

“…A suite of MODIS-derived data including specific composites (e.g., annual maximum Normalized Difference vegetation Index NDVI) and some temporal metrics (e.g., the range of NDVI during the growing season) are preferred as model inputs for mapping vegetation fractional cover [19,20]. They represent an advance in describing vegetation cover due to their capability of depicting phenology for different vegetation cover types.…”