激光雷达生物量指数（LiDAR Biomass Index，LBI）能够基于机载激光雷达数据计算单株树木的地上生物量，并被证明在单木及样地尺度上具有较高的生物量计算精度，但其大范围生物量制图的能力尚未被充分应用。本文以我国北方广泛种植的落叶松树种为例，利用LBI计算了森林小班内每株单木的生物量并累加得到了对应小班的生物量，结合作业设计调查数据验证了计算精度。同时，结合其他林场现有的落叶松生物量AGB_LBI模型评估了不同区域相同树种模型的通用性，并与常用的变量回归法 (LiDAR metrics-based regression, LMR) 进行了对比。结果表明，LBI能够以较高精度实现小班尺度的森林生物量计算，采用选自不同区域的样本单木对AGB_LBI模型进行校准，计算的生物量与实测数据进行对比，R2在0.86到0.88之间，相对均方根偏差（relative Root Mean Square Difference，rRMSD）在33.51%到40.23%之间；LBI方法采用35株单木建模计算的生物量与LMR方法采用30+的样地（包含大于3000株单木）建模计算的生物量整体精度相当，且LBI在不同林场的相同树种之间的通用性更强。最终，本文利用AGB_LBI模型进行孟家岗林场西部区域每个小班内单木生物量的计算并实现了落叶松生物量制图，激光雷达计算的生物量分布与地面调查的生物量图具有相似的趋势，二者在20m×20m的尺度上获取了较高的一致性（R2=0.75, RMSD=1.55 t）。本研究在区域范围内验证了LBI方法估算小班尺度森林地上生物量的能力，表明其具有在大范围开展森林地上生物量估算的潜力。

Objective: The LiDAR biomass index can calculate the above ground biomass of individual trees based on airborne LiDAR data, and it has been verified to have high accuracy for biomass calculation at tree level and plot level. 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 aboveground biomass estimation at sub-compartment 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. Method: First, the existing tree species classification results based on hyperspectral data was used to select the point clouds of Larix olgensis species in Mengjiagang forest farm. Second, the NSC algorithm was used to complete the individual tree segmentation of the selected point clouds. And then, the LBI was used to calculate the forest biomass of each individual tree. Combining with the AGB_LBI biomass model of Larix olgensis species that has been constructed based on 35 individual sample trees, the biomass of each individual tree was calculated, and the biomass of the each sub-compartment was obtained through accumulating the biomass of individual trees within the sub-compartment. 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 for the same tree species of different regions at the sub-compartment level was evaluated based on the existing AGB_LBI models of other forest farms, and the results were compared with the commonly used LiDAR metrics-based regression (LMR) methods. Result: The results indicated that LBI can achieve forest biomass estimation at the sub-compartment level with high accuracy. When using individual trees samples selected from different regions 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 rRMSD (relative Root Mean Square Difference) 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 has a certain impact on the robustness and accuracy of biomass calculation. Overall, the accuracy of LBI-based method is comparable to the LMR method although the sample trees used to calibrate the AGB_LBI model is only accounts for 1% that used to calibrate the LMR model. Meanwhile, the LBI method has stronger universality among the same tree species in different forest farms. Finally, 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 has a similar trend to the existing biomass map and is consistent with the forest sub-compartment map, which achieved high consistency at the scale of 20 m×20 m (R2=0.75, RMSD=1.55 t). Conclusion: The high-precision estimation of biomass by LBI at the sub-compartment scale demonstrates its potential for conducting large-scale estimation of forest AGB. However, due to the difficulty in obtaining validation data, this research only verified its accuracy on the species of Larix olgensis, and did not conduct experiments on other tree species. But previous studies have shown that this method can theoretically be applied to more tree species and forest situations, which is worth further exploration. Overall, this research provides a theoretical basis for more precise, large-scale, and high-precision forest biomass estimation.