Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images

作者：Yiran Li, Ruirui Wang, Wei Shi, Qiang Yu, Xiuting Li, Xingwang Chen · 发表于：Sustainability · 年份：2022 · DOI：10.3390/su141710576 · 被引用次数：11 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Forest ecology and management

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 th...