Abstract:

In order to improve the accuracy of identification of trees, based on the full waveform LiDAR data, firstly, the author focused on partitioning canopy height model by adopting the marker controlled watershed algorithm to identify the position of an individual tree. On the basis of the features of an individual tree, the next step was to carry out the point cloud segmentation in a three-dimensional space by utilizing the markov random fields. Lastly, by using the nine field plots data, the author validated the regression analysis of individual tree and plot parameters. The results showed that individual tree recognition rate was as high as 76%, position error mean and variance was 0.67 m and 0.19 m, respectively. In addition, RMSE of height, crown diameters and DBH (diameter at breast height) for individual tree was 1.03 m (4.57%), 0.56 m (10.48%) and 3.01 cm (11.01%) respectively and RMSE of basal area 2.42 m2/hm2 (8.11%), volume for sample plots 17.83 m3/hm2 (9.11%). This study can effectively improve the accuracy of the single tree point cloud, and meet the requirements of single tree and stands inversion parameters, improve the degree of automation forestry survey.