Tree parameter extraction in plantation based on airborne LiDAR data.

Accurate and efficient extraction of tree parameters from plantations lay foundation for estimating individual wood volume and stand stocking. In this study, we proposed a method of extracting high-precision tree parameters based on airborne LiDAR data. The main process included data pre-processing, ground filtering, individual tree segmentation, and parameter extraction. We collected high-density airborne point cloud data from the large-diameter timber of plantation in Guanzhuang State Forestry Farm, Shaxian County, Fujian Province, and pre-processed the point cloud data by denoising, resampling and normalization. The vegetation point clouds and ground point clouds were separated by the Cloth Simulation Filter (CSF). The former data were interpolated using the Delaunay triangulation mesh method to generate a digital surface model (DSM), while the latter data were interpolated using the Inverse Distance Weighted to generate a digital elevation model (DEM). After that, we obtained the canopy height model (CHM) through the difference operation between the two, and analyzed the CHM with varying resolutions by the watershed algorithm on the accuracy of individual tree segmentation and parameter extraction. We used the point cloud distance clustering algorithm to segment the normalized vegetation point cloud into individual trees, and analyzed the effects of different distance thresholds on the accuracy of indivi-dual tree segmentation and parameter extraction. The results showed that the watershed algorithm for extracting tree height of 0.3 m resolution CHM had highest comprehensive evaluation index of 91.1% for individual tree segmentation and superior accuracy with of 0.967 and RMSE of 0.890 m. When the spacing threshold of the point cloud segmentation algorithm was the average crown diameter, the highest comprehensive evaluation index of 91.3% for individual tree segmentation, the extraction accuracy of the crown diameter was superior, with of 0.937 and RMSE of 0.418 m. Tree height, crown diameter, tree density, and spatial distribution of trees were estimated. There were 5994 , with an average tree height of 16.63 m and crown diameter of 3.98 m. Trees with height of 15-20 m were the most numerous (a total of 2661), followed by those between 10-15 m. This method of forest parameter extraction was useful for monitoring and managing plantations.