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

Dan Abudu, Lucy Bastin, Katie Chong, Mirjam Röder

2025

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.

Paper Citation

in Harvard Style

Abudu D., Bastin L., Chong K. and Röder M. (2025). Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine. In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM; ISBN 978-989-758-741-2, SciTePress, pages 203-210. DOI: 10.5220/0013434200003935

in Bibtex Style

@conference{gistam25,
author={Dan Abudu and Lucy Bastin and Katie Chong and Mirjam Röder},
title={Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine},
booktitle={Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM},
year={2025},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013434200003935},
isbn={978-989-758-741-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM
TI - Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine
SN - 978-989-758-741-2
AU - Abudu D.
AU - Bastin L.
AU - Chong K.
AU - Röder M.
PY - 2025
SP - 203
EP - 210
DO - 10.5220/0013434200003935
PB - SciTePress