Estimating plant area density of individual trees from discrete airborne laser scanning data using intensity information and path length distribution

作者：Ge Gao, Jianbo Qi, Simei Lin, Ronghai Hu, Huaguo Huang · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2023 · DOI：10.1016/j.jag.2023.103281 · 被引用次数：19 · 研究领域：Remote Sensing and LiDAR Applications、Forest ecology and management、Remote Sensing in Agriculture

Plant area density (PAD) of individual trees is an important structural indicator related to tree growth status, stress levels due to pests and diseases, photosynthesis potential, and evapotranspiration. Airborne laser scanning (ALS) provides unprecedented 3D information for mapping forest canopy parameters. Previous studies mainly focused on mapping stand-level and 2D leaf area index. This study proposes a method to estimate PAD from discrete and multiple return ALS data at individual tree scales. The proposed method uses path length distribution to eliminate crown-shape-induced clumping, as well as intensity information to estimate crown transmittance from relative low-density points. The path length distribution is derived from the 3D crown boundary contours created by an alpha shape algorithm, which explicitly considers the non-uniform LiDAR pulse penetration distances. Pulse intensity is calibrated with the nearest pure-ground pulse to mitigate the need for prior leaf and ground reflectance information, which can be used in areas with a heterogeneous background. The proposed method was evaluated both in virtual experiments as well as with terrestrial laser scanning (TLS) data. The virtual experiments used the large-scale remote sensing data and image simulation model (LESS) to simulate virtual ALS scanning data based on abstract and realistic canopies. Results showed that the ALS-derived PAD is highly accurate, with RMSE less than 0.02 and R2 > 0.99 for the abstract sphe...