Assessment of aboveground biomass and carbon stock of rubber plantation using random forest regression with satellite imagery data from Planet NICFI and GEDI data

Assessing the aboveground biomass and carbon stock of rubber plantation areas accurately and precisely is crucial for utilizing them as tools to drive low-carbon societies in line with the Sustainable Development Goals (SDGs). Goal 13 specifically targets reducing greenhouse gas emissions. This study classified rubber plantation areas using satellite image data from Planet NICFI through Random Forest (RF) classification. Additionally, it evaluated the above-ground biomass and soil carbon assessment of rubber plantation areas in Bueng Kan Province using GEDI data combined with random forest regression (RFR). The study found that the overall accuracy and kappa of rubber plantation area classification in Bueng Kan Province exceeded 85%. Meanwhile, for above-ground biomass assessment and soil carbon sequestration in rubber plantation areas, the model’s correlation coefficient (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) was 0.77, indicating a relatively high level of accuracy with a root mean square error (RMSE) of 65.98 megagrams per hectare. The spatial distribution of carbon sequestration ranged from 15.16 to 212.28 megagrams per hectare. These results can support biomass and carbon stock education technology for use in formulating spatial management policies and plans from regional to local levels and serve as tools to drive low-carbon societies alongside sustainable environmental management.