摘要
单木生物量是遥感反演大尺度森林生物量的基础,为提高森林单木生物量估测精度和效率,利用无人机LiDAR点云精确估算桉树、马尾松的单木生物量。首先通过优化算法,提取树高和冠幅,然后采用改进的凸包算法计算树冠面积与体积,把单木结构参数引入CAR模型,构建单木生物量估测模型,并与线性模型进行比较。结果表明:桉树样地树高、冠幅相关性系数R 2分别为0.92、0.72;马尾松样地相关性系数R 2分别为0.94、0.78,算法提取的树木参数与实测数据相关性较好。改进的CAR模型的精度优于线性模型,桉树和马尾松样地R 2分别为0.821、0.830,RMSE分别为17.731、19.149 kg/株。CAR模型引入冠幅面积、体积等树冠因子的生物量模型拟合度更好、精度更高,其中桉树、马尾松样地R 2提高了0.102、0.115,RMSE下降了4.484、5.683 kg/株。利用无人机LiDAR数据提取单木结构参数进行生物量估测可取得很好拟合优度和精度。
Individual tree biomass is the basis of retrieving large-scale forest biomass by remote sensing.In order to improve the accuracy and efficiency of forest individual tree biomass estimation,the individual tree biomass of Eucalyptus and Masson pine was accurately estimated by UAV LiDAR point cloud.Firstly,the tree height and crown width were extracted by the optimization algorithm,then the crown area and volume were calculated by the improved convex hull algorithm,and the parameters of individual tree structure are introduced into CAR model to build individual tree biomass estimation model,which was compared with the linear model.The results show that:The correlation coefficients R 2 of height and crown width of Eucalyptus plots were 0.92 and 0.72,respectively.The correlation coefficient R 2 of Masson pine plot is 0.94 and 0.78,respectively.The tree parameters extracted by the algorithm have a good correlation with the measured data.The accuracy of the improved CAR model is better than that of the linear model,with R 2 of Eucalyptus and Masson pine plots being 0.821 and 0.830,RMSE being 17.731 and 19.149 kg/tree,respectively.The CAR model introduces canopy factors such as canopy area and volume,and the biomass model has better fitting degree and higher accuracy,among which R 2 of Eucalyptus and Masson pine plots increased by 0.102 and 0.115,and RMSE decreased by 4.484 and 5.683 kg/tree.Using UAV LiDAR data to extract parameters of individual tree structure for biomass estimation can obtain good goodness of fit and accuracy.
作者
武晓康
王浩宇
冯宝坤
王成
张高腾
WU Xiao-kang;WANG Hao-yu;FENG Bao-kun;WANG Cheng;ZHANG Gao-teng(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541004,China;Key Lab of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Science,Beijing 100094,China;Faculty of Geography,Yunnan Normal University,Kunming 650000,China)
出处
《科学技术与工程》
北大核心
2022年第34期15028-15035,共8页
Science Technology and Engineering
基金
广西自然科学基金创新团队项目(2019GXNSFGA245001)
中国科学院战略性先导科技专项(A类)(XDA19090130)
广西高校中青年教师基础能力提升项目(2020KY06031)。
