北京林业大学学报Issue(6)：30-35,6.DOI:10.13332/j.cnki.jbfu.2014.06.009
基于LiDAR点云能量信息的樟子松郁闭度反演方法
Inversion method for the crown density of Mongolian scotch pine from point cloud data of small-footprint LiDAR
摘要
Abstract
In order to improve the accuracy of measuring coniferous forest crown density by small-footprint LiDAR, linear regression analysis was used to establish multi-variable inversion models. Three number ratio variables and three energy ratio variables were extracted by processing the point cloud data of small-footprint LiDAR, and then a series of single-variable inversion models of crown density were set up. Afterwards the multi-variable inversion models were built with multiple linear regression analysis on the basis of single variables. Finally the remaining data were used to evaluate the accuracy of inversion models. The results revealed that I2 inversion model was the best one among all single-variable crown density inversion models with fitting correlations R2 =0. 818, Adj R2 =0. 810,RMSE =0. 016 and the accuracy P=0. 978. The combination model of LPI’ and I’3 was the best among multi-variable inversion models with fitting correlations R2 =0. 898,Adj R2 =0. 889,RMSE=0. 012 and the accuracy P=0. 972. The final model showed that the energy ratio variable model was better and more stable than the number ratio variable model, and the multi-variable inversion model was better than the single-variable model with higher fitting correlations and accuracy. In the future we should extract more efficient variables and further explore the potential of energy ratio variables, because the extracted parameters are relatively less and have limitations on the extraction of energy ratio variables.关键词
小光斑激光雷达/针叶林/郁闭度/能量比值变量/反演模型/多元线性回归Key words
small-footprint LiDAR/coniferous forest/crown density/energy ratio variables/inversion model/multivariate linear regression分类
农业科技引用本文复制引用
尤号田,邢艳秋,冉慧,王蕊,霍达..基于LiDAR点云能量信息的樟子松郁闭度反演方法[J].北京林业大学学报,2014,(6):30-35,6.基金项目
中央高校基本科研业务费专项(DL12EB07)、国家自然科学基金项目(41171274)。 （DL12EB07）
