文献类型：期刊文献

英文题名：Tropical forest AGB estimation based on structure parameters extracted by TomoSAR

作者：Li, Wenmei[1] Zhang, Yu[1] Zhang, Jiadong[1] Chen, Huaihuai[1] Chen, Erxue[2] Zhao, Lei[2] Zhao, Dan[3]

第一作者：Li, Wenmei

通信作者：Li, WM[1];Zhao, L[2]

机构：[1]Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, 9 Wenyuan Rd, Nanjing 210023, Jiangsu, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, 1 Dongxiaofu, Beijing 100091, Peoples R China;[3]Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, 52 Sanlihe Rd, Beijing 100864, Peoples R China

年份：2023

卷号：121

外文期刊名：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

收录：;Scopus(收录号：2-s2.0-85161012906);WOS:【SCI-EXPANDED(收录号:WOS:001265811600001)】；

基金：This work was funded by Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China (Grant No. KLSMNR-K202201, 202305), the National Natural Science Foundation of China under Grant 42071414, the Natural Science Foundation of Jiangsu Province under Grant BK20191384, Open Fund of State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS202202), and the China Postdoctoral Science Foundation under Grant 2019M661896.

语种：英文

外文关键词：Tomographic SAR; Tropical forest; Forest aboveground biomass; Forest height; Canopy height; Structure parameters

摘要：Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of practical value. As we know, forest AGB relates with height, density and diameter at breast height, and how to relate the ecophysical parameters with remote sensing images is vital for forest AGB estimation. In this paper, we aim to explore structure parameters about forest density and height, extracted by tomographic SAR (TomoSAR) techniques, for further improving the precision of AGB estimation models. Firstly, vertical structure profiles are constructed via TomoSAR, and the structure features are extracted. Secondly, the correlation between these features and the in-situ forest maximum height, tree density, and average AGB in plot scale is analyzed. Thirdly, the 8-fold cross-validation and step-wise regression methods were utilized to construct the tropical forest AGB models. Finally, the results of these models have been presented and analyzed. Based on the analysis, it indicates that ''Model 7" is the most effective model, and its performance at both plot and pixel scales indicates a high level of accuracy for predicting forest AGB. These findings suggest that the proposed method can be effectively applied to tropical forested areas and has good scalability.