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

为提高小光斑激光雷达估测针叶林郁闭度的精度,采用回归分析法建立多变量回归模型,通过对小光斑激光雷达点云数据进行处理,分别提取3个数量比值变量、3个能量比值变量,并建立郁闭度单变量反演模型,接着在单变量的基础上进行多元线性回归分析,建立郁闭度多变量反演模型,最后用剩余数据对所建立的反演模型进行精度评价。结果表明:在郁闭度单变量反演模型中I2反演模型最好,拟合相关性为R2=0.818,Adj R2=0.810,RMSE=0.016,模型精度为P=0.978;多变量反演模型中LPI'和I'3组合的模型最好,拟合相关性为R2=0.898,Adj R2=0.889,RMSE=0.012,模型精度为P=0.972。由最终所得模型可知,能量比值变量模型所得结果相对数量比值变量模型结果要好且稳定,多变量反演模型的拟合相关性及精度都比单变量模型要高。本研究所提取的参数相对较少,且能量比值变量的提取有一定的局限性,未来研究中应提取更多的高效参数且进一步加强能量比值变量的探究。

In order to improve the accuracy of measuring coniferous forest crown density by small-footprint LiD AR,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 smallfootprint LiD AR,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’3was 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.