Analysing the Change of Aboveground Biomass Density Using Earth Observation and Machine Learning Technology: Alanya Case

Year 2025, Volume: 8 Issue: 5, 1429 - 1439, 15.09.2025

Ercument Aksoy

https://doi.org/10.34248/bsengineering.1695801

Abstract

Aboveground biomass (AGB) is a key parameter in assessing forest carbon stocks, ecosystem productivity, and the global carbon cycle. This study aims to model the annual AGB change between 2019 and 2024 in Alanya, Türkiye, using remote sensing (RS) technologies and open-source datasets. Sentinel-2 surface reflectance data, slope data derived from the Copernicus GLO-30 Digital Elevation Model (DEM), and GEDI L4A biomass data were utilized. As GEDI point data cannot be directly used for mapping, it was employed as a reference for model training. Spectral bands and vegetation indices from Sentinel-2 imagery were modeled using the Random Forest algorithm. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). The highest total AGB was observed during the 2022–2023 period, while the lowest occurred between 2019–2020. The findings indicate that biomass dynamics in the region are influenced not only by climatic conditions but also significantly by anthropogenic activities. The study presents a remote sensing-based approach to support carbon-neutral strategies through accurate biomass monitoring.

Keywords

Geographic information systems , Remote sensing , Earth observation , Biomass , Above ground biomass (AGB)

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

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