Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine

Abstract

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 study proposes a novel framework that advances land cover classification to estimate biomass and carbon stocks using machine learning algorithms in Google Earth Engine. By integrating remote sensing data, machine learning algorithms, and allometric models, the framework automates above-ground biomass (ABG) and below-ground biomass (BGB) calculations, facilitating large-scale carbon stock assessments. The methodology leverages Landsat imagery, alongside derived Normalized Difference Vegetation Indices, to classify seven land cover types and estimate biomass. Equations are applied to derive AGB, with BGB calculated as a fraction of AGB. Carbon stock is estimated using a standard conversion factor of 0.47. Real-time processing capabilities of GEE ensure continuous monitoring and updates, enhancing accuracy and scalability. Findings demonstrate the potential for real-time biomass mapping and the identification of carbon-dense regions. The proposed approach 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 the Sustainable Development Goals 13 and 15.