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影响因子：5.349

DOI码：10.3390/rs14174361

发表刊物：Remote Sensing

关键字：hierarchical Bayesian; UAV-LiDAR; TLS; AGB; mixed-effect model

摘要：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., <50). This methodology (i.e., multi-platform LiDAR data and the hierarchical Bayesian method) provides a potential solution for non-destructive individual-tree AGB modeling with small sample size and high accuracy in both forestry and remote sensing communities.

合写作者：Jungho Im

第一作者：Q1, Man Wang

论文类型：期刊论文

通讯作者：Zhen Zhen*,Yinghui Zhao*

卷号：14

期号：17

页面范围：4361

ISSN号：2072-4292

是否译文：否

发表时间：2022-01-01

收录刊物：SCI