BIOMASS AND HEALTH BASED FOREST COVER DELINEATION USING SPECTRAL UN-MIXING

Remote sensing is a well-suited source of information on various forest characteristics such as forest cover type, leaf area, biomass, and health. The use of appropriate layers helps to quantify the variables of interest. For example, normalized difference vegetation index (NDVI) and greenness help explain variability in biomass as well as health of forests. By delineating the forest into various predominant cover types, biomass and health pertaining to each forest cover type can be quantified. The relative size of the features as compared to sensor resolution can create mixtures of the component features within pixels and thereby decrease the accuracy of parameter estimations. Unmixing techniques unravel the mixed spectral components into fractional abundance estimates of selected end-members. The present study investigates the use of a spectral unmixing technique to portray forest cover types using minimum noise fraction (MNF) component 2, NDVI, and greenness derived from Landsat ETM+ imagery. The assumed purest pixels were identified for the four predominant forest covers of softwood, woody wetlands, hardwoods, and mixed forest using MNF transform followed by decorrelation stretch (DCS) and independent component analysis (ICA) algorithms. The pure pixels were used as region of interest (ROI) to derive fractional end-members using the mixture tuned match filtering (MTMF) subpixel approach of spectral unmixing. The unmixing technique was influential in deriving the component endmembers that need to be validated