Estimating the Forest Above-ground Biomass Based on Extracted LiDAR Metrics and Predicted Diameter at Breast Height  

作　　者：Petar DONEV Hong WANG Shuhong QIN Pengyu MENG Jinbo LU 

机构地区：[1]School of Hydrology and Water Resources,Hohai University,Nanjing 210098,China [2]School of Earth Sciences and Engi-neering,Hohai University,Nanjing 210098,China

出　　处：《Journal of Geodesy and Geoinformation Science》2021年第3期13-24,共12页测绘学报（英文版）

摘　　要：Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Ranging(LiDAR)technology,has been proven to estimate important tree variables effectively.This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics.In the suggested DBH prediction,we developed a nonlinear estimation equation using the total tree height.As for the AGB prediction approach,we used regression methods such as multiple linear regression(MLR),random forest(RF)and support vector machine for regression(SVR).We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees,located in the Yellow River Delta(YRD),China.For our developed approaches,we used Unmanned Aerial Vehicle(UAV)and Backpack LiDAR point cloud datasets obtained in June 2017,and three field data measurements gathered in June 2017 and 2019 and October 2019,all from the same study area.The results demonstrate that:①The LiDAR data individual tree segmentation(ITS)from which we extracted individual tree information like tree location and tree height,was carried out with an overall accuracy F=0.91;②We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error(RMSE)=3.61 cm,later validated by the 2017 dataset;③Forest AGB at stand level was estimated with the MLR,RF and also SVR regression methods,and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR.Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’accuracy.Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS.The UAV LiDAR ability to provide high accuracy tree height abstraction,the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the for

关 键 词：forest AGB DBH estimation UAV LiDAR Backpack LiDAR 

分 类 号：S758.5[农业科学—森林经理学]