Early detection of tree encroachment in high voltage powerline corridor using growth model and UAV-borne LiDAR

Abstract

UAV-borne LiDAR is an innovative and effective technique for tree encroachment detection in high voltage powerline corridor. However, the periodical inspection of the whole powerline corridor is inefficient, as the powerline segments occurring the tree encroachment only account for a very small part. In this paper, taking one segment of the powerline corridor in Taining County, Fujian province, China as the test site, we acquired the point cloud data using a UAV-borne LiDAR, and then combined the tree growth model and two-phase encroachment detection algorithm to realize efficiently early detection of tree encroachment. First, the points of powerlines and trees were classified from the point cloud, and then the individual tree heights and the belonging tree species were extracted. Based on tree plot data, the relationships between tree heights and tree ages were established using Richards growth model. Secondly, the individual tree heights were predicted at given time points, and the tree encroachments were detected in advance according to the required safe distance between powerlines and trees. To tackle the huge amount of point cloud data and calculation, the two-phase tree encroachment detection algorithm based on bounding boxes was applied to replace the traditional point traversal algorithm. As a result, the exact locations of tree encroachment were early detected and the specific encroaching trees were also pre-identified. Lastly, the pre-detected tree encroachment should be verified through field survey, and treated accordingly. Thus, the inspection efficiency would be greatly improved. In accuracy assessment, the coefficient of determination (R2) and the root mean square error (RMSE) of fitted growth model for Masson pine were 0.812 and 2.308 m, and 0.861 and 2.556 m for Eucalyptus, respectively. Compared with point traversal algorithm, the calculation efficiency of the two-phase tree encroachment detection algorithm was improved by nearly 76 times on average.