文献类型：期刊文献

英文题名：Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach

作者：Wang, Mengxi[1,2] Liu, Qingwang[1] Fu, Liyong[1] Wang, Guangxing[3] Zhang, Xiongqing[2]

第一作者：Wang, Mengxi

通信作者：Zhang, XQ[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forestry, State Forestry Adm, Key Lab Tree Breeding & Cultivat, Beijing 100091, Peoples R China;[3]Southern Illinois Univ Carbondale, Dept Geog & Environm Resources, Carbondale, IL 62901 USA

年份：2019

卷号：11

期号：9

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20192106946438);Scopus(收录号：2-s2.0-85065707910);WOS:【SCI-EXPANDED(收录号:WOS:000469763600058)】；

基金：This work was funded by the Central Public Interest Scientific Institution, Basal Research Fund (No. CAFYBB2019QD003), the Young Elite Scientists Sponsorship Program by CAST (No. 2017QNRC001), and the Central Public-Interest Scientific Institution Basal Research Fund of China under (Grant NO. CAFYBB2018GC005).

语种：英文

外文关键词：hierarchical Bayesian; classical method; airborne LIDAR data; diameter at breast height; aboveground biomass

摘要：Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.