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 Petar DONEV;Hong WANG;Shuhong QIN;Pengyu MENG;Jinbo LU

作者机构：School of Hydrology and Water ResourcesHohai UniversityNanjing 210098China School of Earth Sciences and Engi-neeringHohai UniversityNanjing 210098China 

出 版 物：《Journal of Geodesy and Geoinformation Science》 (测绘学报（英文版）)

年 卷 期：2021年第4卷第3期

页      面：13-24页

学科分类：090704[农学-森林经理学] 0907[农学-林学] 09[农学] 

主　　题：forest AGB DBH estimation UAV LiDAR Backpack LiDAR 

摘      要：Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest *** sensing technology,especially the Light Detection and Ranging(LiDAR)technology,has been proven to estimate important tree variables *** study proposes predicting DBH and AGB from tree height and other LiDAR data extracted *** the suggested DBH prediction,we developed a nonlinear estimation equation using the total tree *** 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),*** 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 *** 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 *** AGB at plot level using the 2017 LiDAR data was used to validate both models’*** the UAV LiDAR data and the Backpack LiDAR significantly improved the overall *** UAV LiDAR ability to provide high accuracy tree height abstraction,the DBH of the regression equation and other extracted LiDAR metrics sh