Optimal segmentation of classification and prediction maps for monitoring forest condition with spectral and spatial information from hyperspectral data

Fusion of spectral and spatial information has good potential for building highly accurate classification model for land cover and prediction model for biomass estimation. In this study, a new method with spectral-spatial this fusion and object-based segmentation for monitoring peat swamp forest condition is proposed. Peatland is a major CO₂ emission source by peat burn, peat decomposition and forest fire. Remote sensing is effective tool for monitoring environmental condition of peatland and forest ecosystem. For the monitoring, forest type classification map and biomass distribution map are useful for understanding about the forest condition and to estimate mass volume of CO₂ storage. In order to have enough accurate maps without overfitting problem, sparse discrimination analysis (SDA) was applied to spectral and spatial information from hyperspectral data for the classification model, and LASSO regression was applied for the biomass prediction model. Furthermore, to obtain the well-segmented maps, mean shift clustering as object-based segmentation was applied to those maps for identifying suitable class and biomass with majority voting in each segmentation. These proposed scheme improved classification and prediction accuracies and provides accurate segmented maps.