文献类型：期刊文献

英文题名：Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

作者：Jiang, Xiandie[1,2] Li, Guiying[3,4] Lu, Dengsheng[1,2] Chen, Erxue[5] Wei, Xinliang[1,2]

第一作者：Jiang, Xiandie

通信作者：Lu, DS[1];Wei, XL[1];Lu, DS[2];Wei, XL[2]

机构：[1]Zhejiang A&F Univ, State Key Lab Subtrop Silviculture, Hangzhou 311300, Peoples R China;[2]Zhejiang A&F Univ, Sch Environm & Resource Sci, Hangzhou 311300, Peoples R China;[3]Fujian Normal Univ, State Key Lab Subtrop Mt Ecol, Minist Sci & Technol & Fujian Prov, Fuzhou 350007, Peoples R China;[4]Fujian Normal Univ, Sch Geog Sci, Fuzhou 350007, Peoples R China;[5]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2020

卷号：12

期号：7

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20201908635518);Scopus(收录号：2-s2.0-85084251075);WOS:【SCI-EXPANDED(收录号:WOS:000537709600051)】；

基金：This study was financially supported by the National Key R&D Program of China project "Research of Key Technologies for Monitoring Forest Plantation Resources" (2017YFD0600900) and by the National Natural Science Foundation of China (grant #41571411).

语种：英文

外文关键词：lidar; aboveground biomass; forest types; stratification; linear regression; random forest; subtropical forest

摘要：Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.