LiDAR-Guided Vegetation Vertical Structure Classification Using PolInSAR Data

Understanding the vertical structure of vegetation is crucial for applications such as tree height inversion, biomass estimation, and terrain detection in forests. A novel approach for the classification of vegetation vertical structure is presented, utilizing multi-source data integration. The analysis begins with lidar waveform characteristics, defining vegetation layers based on peak count. Employing machine learning, a correlation is established between polarimetric features, polarimetric interferometric features, and vertical vegetation structure. The study explores the potential of spatial information extraction through combined polarimetric and polarimetric interferometric SAR techniques. This innovative method offers a fresh perspective on vertical vegetation classification.