Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass

• Published in: Ecological informatics 2023-12, Vol.78, p.102348, Article 102348
• Main Authors: Guo, Qiyu, Du, Shouhang, Jiang, Jinbao, Guo, Wei, Zhao, Hengqian, Yan, Xuzhe, Zhao, Yinpeng, Xiao, Wanshan
• Format: Article
• Language: English
• Subjects: Feature filtering; Forest aboveground biomass; Forest canopy mean height; GEDI; Sentinel data
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2023.102348

Forest canopy mean height (CMH) and aboveground biomass (AGB) are key indicators for evaluating forest ecosystem productivity. In this study, we proposed a new approach to integrate field measurement data, GEDI LiDAR, sentinel, and terrain data to construct multi-source data-driven forest CMH and AGB models at a 30-m resolution. First, we employed the RFE-SVM (Recursive Feature Elimination- Support Vector Machine) method to determine the features sensitive to forest height and AGB. Second, we used three regression models to construct the CMH model to extend the GEDI point data to wall-to-wall CMH maps thereby providing sensitive features for AGB estimation. Third, we jointly selected the features and field measurement data to build a model to estimate AGB. The CMH and AGB models, evaluated within the study area, achieved R2 values of 0.64 and 0.89, respectively. Fourth, we performed transferability tests for the AGB model. The AGB model built based on data from study area was applied to three other test areas, resulting in R2 values of 0.66, 0.76, and 0.91, respectively. Overall, this study presented a method that utilizes extensive open data with great potential for mapping forest CMH and AGB over large areas. [Display omitted] •Using GEDI and Sentinel to estimate forest canopy mean height and aboveground biomass.•Feature filtering by RFE-SVM to improve the estimation accuracy.•Discussing the transferability of the AGB model.