GEE-BioCarbonStock: Advancing real-time LULC for biomass density and carbon stocks estimation in GEE

Addressing climate change requires timely and accurate biomass and carbon stocks information. Traditional biomass estimation techniques rely on infrequent ground surveys and manual processing, limiting their scalability. This project advances landcover classification to estimate biomass and carbon stocks using machine learning algorithms in Google Earth Engine in real-time. The proposed framework integrates remote sensing data, learning algorithms, and allometric models, to automates biomass density calculations, facilitating large-scale carbon stock assessments. This is vital for sustainable land practices, carbon accounting, and forest conservation initiatives, to provide policymakers with accurate, real-time data, that supports climate mitigation efforts and contribute to realizing sustainable development goals such as SDG13 (climate action).