Application of hyperspectral data for assessing peatland forest condition with spectral and texture classification

Peatland in tropical region is a major CO₂ emission source because of peat decomposition and forest fire by human induced activities. Remote sensing is effective tool to monitor environmental condition of peatland and forest ecosystem in peatland. A pixel-based approach is one of the most attractive choices for forest type classification or biomass prediction. The traditional method, however, is not sufficient for using spatial information. The spatial information, such as image texture, is an important factor for identifying objects or types, because a pixel is not independent of its neighbors and its dependence can be useful for classification and biomass prediction in forest regions. In this paper, we used combined data of spectral and spatial information from hyperspectral data (Hymap) to develop a more accurate classification or biomass prediction model. The spatial information was texture data by using Grey Level Co-occurrence Matrix (GLCM) texture measures. Sparse discrimination analysis (SDA) was applied for the classification model, and LASSO regression was applied for the biomass prediction model. The results were compared to find out how the spatial information enhances the classification and biomass prediction. According to the accuracy assessment, both classification and biomass prediction model derived from the combined data performed high accuracy.