基于BLS数据的人工红松冠幅预测模型构建  

Constructing a crown width prediction model for Pinus koraiensi using BLS data

作　　者：康义龙 贾炜玮[1,2] 王帆 刘奇峰 朱万才 KANG Yilong;JIA Weiwei;WANG Fan;LIU Qifeng;ZHU Wancai(School of Forestry,Northeast Forestry University,Harbin 150040,China;Key Laboratory of Sustainable Forest Ecosystem Management,Ministry of Education,Northeast Forestry University,Harbin 150040,China;Heilongjiang Forestry Science Research Institute,Harbin 150081,China)

机构地区：[1]东北林业大学林学院,哈尔滨150040 [2]东北林业大学森林生态系统可持续经营教育部重点实验室,哈尔滨150040 [3]黑龙江省林业科学研究所,哈尔滨150081

出　　处：《农业工程学报》2025年第3期179-186,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(U21A20244)。

摘　　要：为预测人工红松冠幅,该研究利用背包式激光雷达(backpack laser scanning,BLS)提取各单木因子并构建人工红松冠幅预测模型。结果表明:BLS点云数据与实测数据平均匹配率达到98.5%,显示出较高的准确度。点云数据与实测胸径的RMSE均值为0.924,R^(2)均在0.9以上,表现出较强的相关性;与实测树高的RMSE均值为2.067,R^(2)均在0.650以上,相关性较弱;与实测树冠的RMSE均值为0.376,R^(2)均在0.8以上,相关性较强。在模型拟合方面,二次型函数的基础模型拟合和预估效果最好(R_(a)^(2)=0.528,RMSE=0.718,MAE=0.580,MAPE=0.157),引入林分平均胸径、高径比和每公顷大于对象木断面积之和能够显著提升模型拟合检验精度。考虑样地作为随机效应的混合效应模型预测效果最好(R_(a)^(2)=0.655,RMSE=0.620,MAE=0.484,MAPE=0.130)。综上所述,结合BLS点云数据与测量数据构建冠幅预测模型具有一定的可行性,可以利用BLS辅助林业调查。Backpack Laser Scanning (BLS),as a portable new laser radar technology,has been rarely applied in forestry surveys.This study aims to extract the individual tree factors and crown width of Pinus koraiensis using BLS.A crown width prediction model was then established using BLS data.12 sample plots of Pinus koraiensis plantations were selected in Mengjiagang Forest Farm,Jiamusi City,Heilongjiang Province.Backpack laser scanning was utilized to acquire the poin cloud data from these plots.The preprocessing steps included denoising,ground point normalization,and single tree segmentation.The processed point cloud data was then registered with the actual field measurements for the individual tree matching.Parameters of each single tree were extracted to calculate their extraction accuracies.A prediction model was then constructed for the crown width of Korean pine using extracted parameters.Eight commonly used crown width prediction models were evaluated to determine the best-performing as the basic model.Furthermore,a generalized model was obtained to incorporate the stand and single tree factors.Additionally,a nonlinear mixed prediction model was constructed for the crown width to consider the random effects at the plot level,particularly for the artificially cultivated Korean pine.The point cloud data from the backpack laser scanning was matched well with the actual field data,with an average matching rate of 98.5%.The accuracies of extraction were 0.964,0.871,and 0.928 for the breast height diameter,tree height,and crown width respectively.The extracted parameters of the point cloud showed significant correlations with the measured ones.The correlation coefficient (R~2) between point cloud-extracted and field-measured breast height diameter was above 0.9(0.904~0.973).The R~2 between extracted and measured tree height was above 0.650 (0.650~0.740).The R~2 between extracted and measured crown width was above 0.8 (0.817~0.888).A quadratic function-based model was provided for the best fitting and prediction (R_a~2=0.

关 键 词：激光雷达 点云 红松 冠幅 非线性混合效应模型 

分 类 号：S757[农业科学—森林经理学]