Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach

Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-09, Vol.14 (17), p.4361
Main Authors: Wang, Man, Im, Jungho, Zhao, Yinghui, Zhen, Zhen
Format: Article
Language:English
Subjects:
Accuracy
AGB
Algorithms
Bayesian analysis
Biomass
Carbon
Feasibility studies
Forest farming
Forestry
Forests
Height
hierarchical Bayesian
Larix olgensis
Lasers
Lidar
Mathematical models
mixed-effect model
Parameter estimation
Random variables
Remote sensing
Sample size
Statistical methods
TLS
UAV-LiDAR
Unmanned aerial vehicles
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Summary:Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree AGB estimation of Changbai larch (Larix olgensis Henry) of eight plots from three different regions in Maoershan Forest Farm of Heilongjiang, China, using nonlinear mixed effect model with hierarchical Bayesian approach. Results showed that the fused LiDAR data estimated the individual tree parameters (i.e., diameter at breast height (DBH), tree height (TH), and crown projection area (CPA)) with high accuracies (all R2 > 0.9 and relatively low RMSE and rRMSE) using region-based hierarchical cross-section analysis (RHCSA) algorithm. Considering regions as random variables, the nonlinear mixed-effects AGB model with three predictor variables (i.e., DBH, TH, and CPA) performed better than its corresponding nonlinear model. In addition, the hierarchical Bayesian method provided better model-fitting performances and more stable parameter estimates than the classical method (i.e., nonlinear mixed-effect model), especially for small sample sizes (e.g.,
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14174361