Classification of Transmission Line Corridor Tree Species Based on Drone Data and Machine Learning

Abstract

Tree growth in power line corridors poses a threat to power lines and requires regular inspections. In order to achieve sustainable and intelligent management of transmission line corridor forests, a transmission line corridor tree barrier management system is needed, and tree species classification is an important part of this. In order to accurately identify tree species in transmission line corridors, this study combines airborne LiDAR (light detection and ranging) point-cloud data and synchronously acquired high-resolution aerial image data to classify tree species. First, individual-tree segmentation and feature extraction are performed. Then, the random forest (RF) algorithm is used to sort and filter the feature importance. Finally, two non-parametric classification algorithms, RF and support vector machine (SVM), are selected, and 12 classification schemes are designed to perform tree species classification and accuracy evaluation research. The results show that after using RF for feature filtering, the classification results are better than those without feature filtering, and the overall accuracy can be improved by 3.655% on average. The highest classification accuracy is achieved when using SVM after combining a digital orthorectification map (DOM) and LiDAR for feature filtering, with an overall accuracy of 85.16% and a kappa coefficient of 0.79.