Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic

编号 030033905

推送时间 20220418

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic

来源期刊 EUROPEAN JOURNAL OF REMOTE SENSING

期 第339期

发表时间 20220327

关键词 Random forest;  landsat;  machine learning;  tropical forest;  environmental covariates;  Google Earth Engine; 

摘要 Mapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0-15 cm) in forest lands of the Dominican Republic. The methodology was developed using geospatial datasets available in the Google Earth Engine (GEE) platform combined with a set of 268 soil samples. Twenty environmental covariates were analyzed, including climate, topography, and vegetation. The results indicate that Model A (combining all 20 covariates) was only marginally better than Model B (combining topographic and climatic covariates), and Model C (only combining multispectral remote sensing data derived from Landsat 8 OLI images). Model A and Model B yielded SOC mean values of 110.35 and 110.87 Mg C ha(-1), respectively. Model A reported the lowest prediction error and uncertainty with an R-2 of 0.83, an RMSE of 35.02 Mg C ha(-1). There was a strong dependence of SOC stocks on multispectral remote sensing data. Therefore, multispectral remote sensing proved accurate to map SOC stocks in forest ecosystems in the region.

服务人员 付贺龙

服务院士 唐守正

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