Assessment of Tree Species Classification by Decision Tree Algorithm Using Multiwavelength Airborne Polarimetric LiDAR Data

Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrument offers the full waveform data and polarimetric information simultaneously. Current studies have primarily used commercial non-polarimetric LiDAR for tree species classification, either at the dominant species level or at the individual tree level. Many classification approaches combine multiple features, such as tree height, stand width, and crown shape, without utilizing polarimetric information. In this work, a customized Multiwavelength Airborne Polarimetric LiDAR (MAPL) system was developed for field tree measurements. The MAPL is a unique system with unparalleled capabilities in vegetation remote sensing. It features four receiving channels at dual wavelengths and dual polarization: near infrared (NIR) co-polarization, NIR cross-polarization, green (GN) co-polarization, and GN cross-polarization, respectively. Data were collected from several tree species, including coniferous trees (blue spruce, ponderosa pine, and Austrian pine) and deciduous trees (ash and maple). The goal was to improve the target identification ability and detection accuracy. A machine learning (ML) approach, specifically a decision tree, was developed to classify tree species based on the peak reflectance values of the MAPL waveforms. The results indicate a re-substitution error of 3.23% and a k-fold loss error of 5.03% for the 2106 tree samples used in this study. The decision tree method proved to be both accurate and effective, and the classification of new observation data can be performed using the previously trained decision tree, as suggested by both error values. Future research will focus on incorporating additional LiDAR data features, exploring more advanced ML methods, and expanding to other vegetation classification applications. Furthermore, the MAPL data can be fused with data from other sensors to provide augmented reality applications, such as Simultaneous Localization and Mapping (SLAM) and Bird’s Eye View (BEV). Its polarimetric capability will enable target characterization beyond shape and distance.