Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data. Issue 27 (13th December 2022)

• Record Type: Journal Article
• Authors: Huang, Zhuomei; Tian, Yichao; Zhang, Qiang; Huang, Youju; Liu, Rundong; Huang, Hu; Zhou, Guoqing; Wang, Jingzhen; Tao, Jin; Yang, Yongwei; Zhang, Yali; Lin, Junliang; Tan, Yuxin; Deng, Jingwen; Liu, Hongxiu
• Is Part Of: Geocarto international. Volume 37:Issue 27(2023)
• Page Start: 15778
• Page End: 15805
• Publication Date: 2022-12-13
• Subjects: Multisource satellite data -- mangrove -- aboveground biomass -- machine learning algorithms -- Mawei Sea of Beibu Gulf; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2102226
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient ( R 2 ) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results ( R 2 = 0.7807, RMSE = 24.6864 Mg·ha −1 ), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha −1 . Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves.