• Special Issue •

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

Petar DONEV1(),Hong WANG1(),Shuhong QIN2,Pengyu MENG1,Jinbo LU1

1. 1. School of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
• Received:2020-09-15 Accepted:2021-01-15 Online:2021-09-20 Published:2021-10-09
• Contact: Hong WANG E-mail:petardonev@hhu.edu.cn;hongwang@hhu.edu.cn
• About author:Petar DONEV E-mail: petardonev@hhu.edu.cn

Abstract:

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: ① The 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; ② We 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)=3.61cm, later validated by the 2017 dataset; ③ Forest 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 forest AGB. This study shows that being cost-free is not the only advantage of free available software. In the performance of ITS and the LiDAR, metrics extraction proved to be as good as the commercially available software.