| Summary: | Developing a robust point cloud segmentation algorithm for individual trees from an amount of point cloud data has great significance for tracking tree changes. This method can measure the size, growth, and mortality of individual trees to track and understand forest carbon storage and variation. Traditional measurement methods are not only slow but also tardy. In order to obtain forest information better and faster, this article focuses on two aspects: The first is using UAVs to obtain multiview remote sensing images of the forest, and then using the structure from motion algorithm to construct the forest sparse point cloud and patch-based MVS algorithm to construct the dense point cloud. The second is that a targeted point cloud deep learning method is proposed to extract the point cloud of a single tree. The research results show that the accuracy of single-tree point cloud segmentation of deep learning methods is more than 90%, and the accuracy is far better than traditional planar image segmentation and point cloud segmentation. The combination of point cloud data acquisition with UAV remote sensing and point cloud deep learning algorithms can meet the needs of forestry surveys. It is undeniable that this method, as a forestry survey tool, has a large space for promotion and possible future development.
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