The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.
REF developed a method to estimate tree metrics for urban forest inventory purposes by detecting treetops and using a region growing algorithm for segmentation.
Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory
Remote Sensing
Remote Sensing
Computer Science Geology