Abstract
Estimating above-ground biomass (AGB) using machine learning (ML) algorithms and multi-sensor satellite data is a promising approach for monitoring and managing forest resources. This research integrated synthetic aperture radar (SAR) and multispectral imagery alongside in-field observations to accurately estimate above-ground biomass (AGB) in the Purna regional landscape of northern Western Ghats, India. The satellite data employed in the study included dual-polarization (VV + VH) imagery from Sentinel-1 and multi-spectral bands from Sentinel-2, processed and analysed using advanced ML algorithms. The ML algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGB), and Boosted Regression Trees (BRT), were strategically applied across different model scenarios to determine their effectiveness in AGB prediction. The XGB model displayed the highest accuracy with an R2 value of 0.61 and the lowest RMSE of 37.85 t/ha. The spatial distribution of AGB was successfully mapped, showing varied biomass concentrations throughout the study area. The study’s findings demonstrate the potential of integrating SAR and multispectral data for enhanced AGB estimation and suggest that ML models, specifically algorithms like RF, XGB, and BRT can address the complex relationships between AGB and satellite-derived variables more effectively than traditional methods.
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References
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Acknowledgements
The authors express their gratitude for the financial support provided to Faseela V. Sainuddin by the Department of Environment and Climate Change (DoECC), Kerala, as part of the Paristhithi Poshini Fellowship scheme. Gratitude is extended to the Director of NRSC, the Principal of All Saints’ College, Thiruvananthapuram, and the Head of the Department of Botany at The Maharaja Sayajirao University, Vadodara, for providing facilities and encouragement.
Funding
This work has been carried out as part of a project on ‘Biodiversity Characterization at Community level in India using Earth Observation Data’ through the Department of Biotechnology and the Department of Space, Government of India.
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FVS: Conceptualization, Methodology, Data curation, Software, Writing—original draft, Visualization. GM: Data curation, Writing—review & editing, AR: Data curation, Writing—review & editing. PSN: Data curation, Validation, Writing—review & editing, Supervision. SVA: Writing—review & editing, Supervision. CSR: Data curation, Writing—review & editing, Supervision, Project administration, funding acquisition.
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Sainuddin, F.V., Malek, G., Rajwadi, A. et al. Estimating Above-Ground Biomass of the Regional Forest Landscape of Northern Western Ghats Using Machine Learning Algorithms and Multi-sensor Remote Sensing Data. J Indian Soc Remote Sens 52, 885–902 (2024). https://doi.org/10.1007/s12524-024-01836-y
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DOI: https://doi.org/10.1007/s12524-024-01836-y