文献类型：期刊文献

中文题名：激光雷达生物量指数计算落叶松小班地上生物量

英文题名：LiDAR biomass index-based method for aboveground biomass calculation of larch subcompartments

作者：Du, Liming[1] Pang, Yong[1]

第一作者：杜黎明

机构：[1] 1. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China, 2. Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

年份：2025

卷号：29

期号：10

起止页码：2933-2943

外文期刊名：National Remote Sensing Bulletin

收录：EI(收录号：20254419440208)

语种：中文

外文关键词：Classification (of information) - Clouds - Data accuracy - Ecology - Forest ecology - Forestry - Mapping - Mean square error - Optical radar - Plants (botany) - Remote sensing - Timber

摘要：The LiDAR Biomass Index (LBI) can calculate the aboveground biomass (AGB) of individual trees on the basis of airborne LiDAR data, and it has been verified to have high accuracy for biomass calculation at tree and plot levels. However, its ability to complete large-scale forest biomass mapping has not been fully explored. The aim of this research is to verify the accuracy of LBI for AGB estimation on a subcompartment scale, taking the widely planted Larix olgensis tree species in north China as an example and laying a theoretical foundation for the widespread application of this index. First, the existing tree species classification results based on hyperspectral data were used to select the point clouds of L. olgensis species in Mengjiagang Forest Farm. Second, the NSC algorithm was employed to complete the individual tree segmentation of the selected point clouds. Third, the LBI was used to calculate the forest biomass of each individual tree. Finally, with reference to the AGB_LBI biomass model of L. olgensis species constructed on the basis of 35 individual sample trees, the biomass of each individual tree was calculated, and the biomass of each subcompartment was obtained through accumulating the biomass of individual trees within the subcompartment. In this research, the calculation accuracy was verified through the silviculture survey data obtained from the local forestry department, including over 70000 individual trees. Meanwhile, the universality of LBI in estimating the biomass of the same tree species across different regions at the subcompartment level was evaluated on the basis of the existing AGB_LBI models of other forest farms, and the results were compared with those of the commonly used LiDAR Metric-based Regression (LMR) methods. The results indicated that LBI can achieve forest biomass estimation at the subcompartment level with high accuracy. When individual tree samples selected from different regions were used to calibrate the AGB_ LBI model, the obtained biomass values were comparable with the measured data, with R2 ranging from 0.86 to 0.87 and relative root-mean-square error (RMSE) ranging from 34.20% to 40.23%. The biomass results calculated from each model did not have significant differences. However, the increase in the number of sample trees used for model calibration still exerted a certain effect on the robustness and accuracy of biomass calculation. Overall, the accuracy of the LBI-based method was comparable to that of the LMR method, although the sample trees used to calibrate the AGB_LBI model only accounted for 1% of that used to calibrate the LMR model. Meanwhile, the LBI method exhibited stronger universality among the same tree species in different forest farms. The AGB_LBI model was used to calculate the biomass of each individual tree in the western region of Mengjiagang Forest Farm and complete the biomass mapping. The obtained biomass distribution presented a similar trend to the existing biomass map and was consistent with the forest subcompartment map, achieving high consistency at the scale of 20 m×20 m (R2=0.75, RMSE=1.55 t). The high-precision estimation of biomass by LBI at the subcompartment scale demonstrates its potential for conducting large-scale estimation of forest AGB. Because of the difficulty in obtaining validation data, this research only verified its accuracy on the species of L. olgensis and did not conduct experiments on other tree species. Nevertheless, previous studies have shown that this method can theoretically be applied to other tree species and forest situations, which is worth further exploration. This research provides a theoretical basis for precise, large-scale, and high-precision forest biomass estimation. ? 2025 Science Press. All rights reserved.