Forest aboveground biomass mapping in the Greater Mekong Subregion using multi-source remote sensing data fusion

doi:10.13474/j.cnki.11-2246.2025.0807

Abstract

Abstract: Accurate estimation of forest aboveground biomass density is crucial for advancing sustainable forest management.This study focuses on the Greater Mekong Subregion (GMS)and utilizes spaceborne global ecosystem dynamics investigation(GEDI),Sentinel-1,Sentinel-2,and auxiliary datasets to extract 52 feature variables.By applying the LightGBM machine learning model,a 1 km resolution forest aboveground biomass density map of the GMS is generated.The results indicate that the LightGBM model achieved R²=0.65,RMSE=38.11 Mg/hm²,and EA=72.03%.Across the study area,biomass density ranged from 15.16 to 423.87 Mg/hm².The derived biomass product demonstrated strong correlation with the GEDI L4B product (R²=0.52,RMSE=61.91 Mg/hm²).In conclusion,open-access earth observation (EO)data exhibits significant potential for estimating forest aboveground biomass.

Key words: aboveground biomass density, GEDI, remote sensing data, machine learning, greater mekong subregion

CLC Number:

P237
P208

YUAN Lili, YANG Xinwei, LI Menghua, CHEN Yuquan, TANG Bohui. Forest aboveground biomass mapping in the Greater Mekong Subregion using multi-source remote sensing data fusion[J]. Bulletin of Surveying and Mapping, 2025, 0(8): 43-47.

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