基于机载LiDAR多回波类型森林叶面积指数反演研究

摘要

森林叶面积指数(Laj)作为森林的重要结构参数,对于研究森林物质能量交换相关的生理活动具有重要意义.为提高森林Lai的反演精度,本研究充分利用激光雷达点云数据多回波类型之间所含信息的差异,通过对机载激光雷达点云数据预处理后,基于点云数据的多回波类型,共提取了6个激光穿透指数(Lpi),分别与野外样方实测Lai建立线性回归模型用于估测森林Lai.结果发现:单变量估测模型中,基于首次回波强度Lpi(iLPIfirst)模型最好(R2=0.836,Mad=0.091).多变量模型中,基于首次回波强度Lpi(iLPIfirst)、冠层回波数量Lpi(nLPIcan)及冠层回波能量Lpi(iLPIcan)的三变量模型估测精度最高(R2=0.883,Mad=0.076),相比于单变量估测模型而言,R2提高了0.047,Mad减少了0.015.结果表明,基于点云回波类型分类的Lpi能够较好的估测森林Lai,且多变量模型的估测精度要优于单变量模型的估测精度.%Leaf Area Index (Lai) is an important forest structure parameter,which is of great significance to the research of physiological activities relating the energy exchange in forest.To improve the accuracy of forest Lai estimation,the difference of the information contained in multi-echos type of LiDAR data were explored after the preprocess of airborne LiDAR data in this study.Six laser penetration indexes (Lpi) were extracted from LiDAR multi-echos types data.Then the LPIs were used to estimate the forest Lai by regression with field measured Lsi.It was shown that the model based on Lpi derived from first echo intensity (iLPIfirst) achieved the best result (R2 =0.836,Mad =0.091) among all univariate estimation models.For the multivariate models,the model involving Lpi derived from first echo intensity (iLPIfirst),Lpi derived from canopy echo number (nLPIcan) and Lpi derived from canopy echo intensity (iLPIcan) was the best (R2=0.883,Mad=0.076).By comparing the results,it was found that the R2 from multivariate model increased by 0.047 and Mad decreased by 0.015 than that from univariate model.It was concluded that the Lpi derived from LiDAR different echo types intensity data could estimate forest Lai.And the accuracy of multivariate model is better than that from the univariate model.