Extraction and analysis of tree canopy height information in high-voltage transmission-line corridors by using integrated optical remote sensing and LiDAR

Abstract

Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines. To solve these problems, in this study, we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR, and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region. The results showed that the relative error of tree height extraction using UAV optical images was less than 5%, and the lowest relative error was 0.11%. The determination coefficient R² between the optical image tree height extraction results and the measured tree height was 0.97, thus indicating a high correlation for both. In the field of tree barrier monitoring, the determination coefficient R² of tree height extracted using airborne LiDAR point cloud, and canopy height model (CHM) and of the measured tree height were 0.947 and 0.931, respectively. The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91% and 0.2%, respectively, with an extraction accuracy of over 95%. The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately, which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.