Document Type
Article
Publication Date
4-2020
Keywords
UAV-LiDAR, Backpack-LiDAR, Individual tree segmentation, Forest AGB, The Yellow River Delta
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.jag.2019.102014
Abstract
Forest plantations are an important source of terrestrial carbon sequestration. The forest of Robinia pseudoacacia in the Yellow River Delta (YRD) is the largest artificial ecological protection forest in China. However, more than half of the forest has appeared different degrees of dieback and even death since the 1990s. Timely and accurate estimation of the forest aboveground biomass (AGB) is a basis for studying the carbon cycle of forests. Light Detecting and Ranging (LiDAR) has been proved to be one of the most powerful methods for forest biomass estimation. However, because of an irregular and overlapping shape of the broadleaved forest canopy in a growing season, it is difficult to segment individual trees and estimate the tree biomass from airborne LiDAR data. In this study, a new method was proposed to solve this problem of individual tree detection in the Robinia pseudoacacia forest based on a combination of the Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) with the Backpack-LiDAR. The proposed method mainly consists of following steps: (i) at a plot level, trees in the UAV-LiDAR data were detected by seed points obtained by an individual tree segmentation (ITS) method from the Backpack-LiDAR data; (ii) height and diameter at breast height (DBH) of an individual tree would be extracted from UAV and Backpack LiDAR data, respectively; (iii) the individual tree AGB would be calculated through an allometric equation and the forest AGB at the plot level was accumulated; and (iv) the plot-level forest AGB was taken as a dependent variable, and various metrics extracted from UAV-LiDAR point cloud data as independent variables to estimate forest AGB distribution in the study area by using both multiple linear regression (MLR) and random forest (RF) models. The results demonstrate that: (1) the seed points extracted from Backpack-LiDAR could significantly improve the overall accuracy of individual tree detection (F = 0.99), and thus increase the forest AGB estimation accuracy; (2) compared with MLR model, the RF model led to a higher estimation accuracy (p < 0.05); and (3) LiDAR intensity information selected by both MLR and RF models and laser penetration rate (LP) played an important role in estimating healthy forest AGB.
Rights Information
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
International Journal of Applied Earth Observation and Geoinformation, v. 86, art. 102014
Scholar Commons Citation
Lu, Jinbo; Wang, Hong; Qin, Shuhong; Cao, Lin; Pu, Ruiliang; Li, Guilin; and Sun, Jing, "Estimation of Aboveground Biomass of Robinia pseudoacacia Forest in the Yellow River Delta based on UAV and Backpack LiDAR Point Clouds" (2020). School of Geosciences Faculty and Staff Publications. 2263.
https://digitalcommons.usf.edu/geo_facpub/2263
