Tree Morphology and Structure Analysis Based on 3D Point Cloud Density with Backpack and Drone LiDAR Integration

Monitoring and mapping tree structure accurately is a critical challenge in natural resource management and ecosystem restoration. Traditional methods are often limited by time and difficulty in accessing hard-To-reach areas, as well as low measurement accuracy. Therefore, LiDAR-based 3D mapping technology (Light Detection and Ranging) becomes an effective solution for obtaining more detailed and accurate tree morphology data. In this study, Backpack LiDAR and Drone LiDAR are used to explore the capabilities of each technology in mapping the vertical parts of the tree, such as the trunk and branches, as well as the horizontal parts of the tree, such as the canopy. Backpack LiDAR is more effective for detecting the lower part of the tree (Z < 38 m), while Drone LiDAR excels in mapping the upper part of the tree, including the canopy (Z > 38 m). The data generated from both technologies are combined to produce a more comprehensive representation of the tree. The integration of both LiDAR systems shows a significant improvement in point cloud density, with an RMSE value of 0.144 m, indicating high accuracy in mapping. The combination of Backpack LiDAR and Drone LiDAR allows for the creation of more detailed 3D tree models, improving the efficiency of forest monitoring and enhancing natural resource management and ecosystem restoration.