Individual Tree Height Extraction from Airborne LiDAR Data by Combining with DSM

The retrieval of tree height is very important for growth status evaluation and biomass estimation. The Canopy Height Models (CHMs) are commonly used to extract the heights of individual trees. However, airborne LiDAR-derived CHMs are prone to distortion in areas with complex terrain, which significantly limits the extraction accuracy of individual tree height. Therefore, this study aimed to propose a new method, which simultaneously utilized the CHM and Digital Surface Model (DSM) to extract the heights of individual trees. Firstly, the CHM was generated from the preprocessed point clouds using Inverse Distance Weighted (IDW) interpolation algorithm. Secondly, the local maximum algorithm and Mark-Controlled Watershed Segmentation (MCWS) algorithm were adopted to segment the CHM, and thereafter obtain the individual tree crown contour polygon. Thirdly, the local maximum algorithm with a fixed window was applied to the DSM to detect the tree vertices and extract its elevation. Lastly, the tree height was obtained by subtracting the ground elevation obtained by Delaunay triangulation interpolation algorithm. Taking the coniferous forest near Fujiang Village, Xing'an County, Guangxi Province as the test area, this study analyzed the accuracy of tree heights obtained by CHM and our proposed method. For trees located at different test sites with the average terrain slopes of 32°, 25°, and 15°, the coefficients of determination (R2) values of the estimated tree heights based on CHMs are 0.84, 0.85, and 0.87, respectively, while the Root Mean Square Error (RMSE) values are 1.48, 1.41, and 1.58m, respectively. In contrast, the R2 values of the tree height extracted from our method and the measured tree height are 0.92, 0.91, and 0.93, respectively, while the RMSE values are 0.93, 1.12, and 1.16 m, respectively. Compared with the CHM-based tree height extraction method, the R2 of our method increased by 0.08, 0.06, and 0.06, respectively, while the RMSE values decreased by 0.55, 0.29, and 0.42m, respectively. The results indicated that, compared with the traditional method, our proposed method can significantly improve the estimation accuracy of individual tree height in areas with large terrain slopes. © 2021, Science Press. All right reserved.