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.

Forest biomass is a key index to comprehend the changes of ecosystem productivity and forest growth and development. Accurate acquisition of single tree scale biomass information is of great significance to the protection, management and monitoring of forest resources. LiDAR technology can penetrate the forest canopy and obtain information on the vertical structure of the forest. Aerial photography technology has the advantages of low cost and high speed, and can obtain information on the horizontal structure of the forest. Therefore, in this study, multispectral imagery and LiDAR data were integrated, and a part of the Zengcheng Forest Farm in Guangdong Province was selected as the study area. Large-scale and high-precision Eucalyptus biomass estimation research was gradually carried out by screening influencing factors and establishing models. This study compared and analysed the performance of multiple stepwise regression methods, random forest algorithms, support vector machine algorithms and decision tree algorithms for Eucalyptus biomass estimation to determine the best method for Eucalyptus biomass estimation. The results demonstrated that the accuracy of the model established by the machine learning method was higher than that of the linear regression model, and in the machine learning model, the random forest model had the best performance on both the training set (R² = 0.9346, RMSE = 8.8399) and the test set (R² = 0.8670, RMSE = 15.0377). RF was more suitable for the biomass estimation of Eucalyptus in this study. The spatial resolution of Eucalyptus biomass distribution was 0.05 m in this study, which had higher accuracy and was more accurate. It can provide data reference for the details about biomass distribution of Eucalyptus in the majority of provinces, and has certain practical reference significance.