Unlocking the potential of hyperspectral and LiDAR for above-ground biomass (AGB) and tree species classification in tropical forests

Abstract

Tree species classification using a combination of airborne hyperspectral and Light Detection and Ranging (LiDAR) can provide valuable and effective methods for forest management, such as planning and monitoring purposes. However, only a few studies have applied tree species classification using both combinations in the tropical forest. The research takes a comparative classification approach to examine several classifiers using airborne hyperspectral in the tropical forest. In addition, Object-Based Image Analysis (OBIA) method was applied on hyperspectral data to extract the crown of individual tree species for classification and estimation purposes. Minimum Noise Fraction Transform (MNF) was applied to reduce the data dimensionality and different training samples from the various species used in this study. The result shows that Support Vector Machine (SVM) and Random Forest (RF) achieved the highest overall accuracy above 50% compared to other classifiers in the tropical forest. Besides, LiDAR data was also used to estimate individual trees' height for all species in the study area. The multiple coefficients of determination (R2) test result between LiDAR and field observation data in eight years gaps is 0.754. Therefore, Above-Ground Biomass (AGB) and carbon stock will estimate using a combination of LiDAR, hyperspectral, and field observation data for individual tree species. This method has proven to provide the required information for forest planning and generation in a short time, especially in tree species identification, AGB, and carbon stock estimation in tropical forests.