Abstract:

Objective: UAV LiDAR technology has become an important technical means to obtain refined forest parameters. Individual tree segmentation using LiDAR point cloud data is the basis for extracting refined forest parameters, and the accuracy of forest parameters acquisition depends on whether the individual tree segmentation is accurate or not. To address the problems that existing individual tree segmentation algorithms is difficult to extract trees under high canopy cover stand conditions with low overall accuracy, this study proposes a layered segmentation method based on CRR (canopy relief ratio), combined with watershed algorithm and the local maximum clustering algorithm based on point cloud to provide a new strategy to improve the accuracy of individual tree segmentation. Method: The ULS(unmanned aerial vehicle laser scanning) data were used to segment the individual tree of high canopy cover Pinus kesiya var. langbianensis plantation using the watershed algorithm, the local maximum clustering algorithm and the stratified segmentation method based on CRR proposed in this paper, and the effects of the watershed algorithm on the individual tree segmentation result under 4 CHM spatial resolutions and 3 DSM interpolation method were analyzed separately for the watershed algorithm, and the result of the visual interpretation of individual tree crown from UAV high-resolution images were used as validation, with detection rate r, accuracy p and F-score as evaluation indexes. Result: In young forest, the CCR is higher, and the segmentation accuracy of the watershed algorithm is better than that of the local maximum clustering algorithm for single-layer forest. In middle-aged and near-mature forest, the CCR is lower, and the watershed algorithm can easily identify Simao Pine branches as canopy, and the segmentation effect of the local maximum clustering algorithm is better than that of the watershed algorithm. The segmentation method based on CCR combined with the watershed algorithm and the local maximum clustering algorithm has the highest accuracy (F=0.75). The layered-segmentation method based on CCR fully considers the differences in stand structure among different age groups, and the layered-segmentation method had the highest accuracy (F=0.75), which is better than watershed algorithm (F=0.71) and local maximum clustering algorithm based on point cloud (F=0.68). Secondly, in the watershed algorithm, when the resolution is 0.5 m × 0.5 m, the CHM individual tree segmentation accuracy obtained by interpolation using the inverse distance weighting (IDW) is the highest (r = 0.70, p = 0.94 and F = 0.81). Conclusion: The accuracy of using only one individual tree segmentation algorithm is often unsatisfactory due to the differences in stand structure. The combination of the watershed algorithm and the local maximum clustering algorithm can expand the applicability of a single segmentation method under different stand and thus improve the accuracy of individual tree segmentation by determining the respective applicability range through CCR.

Key words: LiDAR, individual tree segmentation, watershed algorithm, local maximum clustering algorithm based on point cloud, hierarchical segmentation