Classification of Forest Stratification and Evaluation of Forest Stratification Changes over Two Periods Using UAV-LiDAR

The demand for spatially explicit and comprehensive forest attribute data has continued to increase. Light detection and ranging (LiDAR) remote sensing, which can measure three-dimensional (3D) forest attributes, plays a significant role. However, only a few studies have used uncrewed aerial vehicle (UAV)-LiDAR to extract the characteristics of the 3D structure of the forest understory. Therefore, this study proposes a method for classifying and mapping forest stratification and evaluating forest stratification changes using multitemporal UAV-LiDAR data. The study area is a forest of approximately 25 ha on the west side of the Expo Commemorative Park (Suita City, Osaka Prefecture, Japan). Three-dimensional point cloud models from two measurement periods during the leaf-fall season were used. Forest stratification was classified using time-series clustering of 2024 data. The classification of forest stratification and its spatial distribution effectively reflected the actual site conditions. By applying time-series clustering, the forest stratification was successfully classified using only UAV-LiDAR data. Changes in forest stratification were evaluated using data from 2022 to 2024. In areas where changes in forest stratification were evaluated as significant, evidence of tree felling was confirmed. In addition, changes in forest stratification were quantitatively evaluated. The proposed method uses only UAV-LiDAR, which is highly versatile; thus, it is expected to apply to various forests. The results of this study are expected to deepen our ecological understanding of forests and contribute to forest monitoring and management.