Document Type
Article
Publication Date
4-2026
Keywords
Above ground biomass, Data fusion, GEDI, Google earth engine, Machine learning
Abstract
Forests are essential for biodiversity conservation, climate change, natural education, scientific research, and carbon sequestration. This study uses machine learning-based Random Forest (RF) regression to estimate the Above Ground Biomass (AGB) of the Ozark and Ouachita forests at a 10-meter resolution by combining data from Sentinel-2, Sentinel-1, and GEDI (Global Ecosystem Dynamics Investigation) on Google Earth Engine. The RF model included 34 out of 154 variables representing topographical, spectral, and textural factors demonstrating strong correlations with measured biomass. The RF model showed strong performance with R-squared and RMSE values of 0.95 and 18.46 for the training dataset and 0.75 and 34.52 for the validation dataset, respectively. The primary predictors were elevation, vegetation indices such as Leaf Area Index (LAI), Normalized Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI), as well as forest height measurements like RH100, RH98, and RH95. The study also extrapolates the historical biomass from 2015 to 2023 using Landsat 8 data using the image normalization technique. The model effectively identified spatial patterns in biomass, with mean extrapolated values ranging from approximately 100 Mg/ha to 200 Mg/ha from 2015 to 2023, underscoring the value of open-source cloud platforms and integrating topographical, spectral, and textural data for accurate regional AGB estimation. The results support precise estimation of fire-related emissions and strategic planning to improve forest health and sustainability, contributing significantly to biodiversity conservation and carbon sequestration efforts.
Citation
Saim, A.A., Aly, M.H. Machine Learning and Multisensor Data Fusion for Forest above Ground Biomass Estimation in Arkansas. Earth Syst Environ 10, 1783–1800 (2026). https://doi.org/10.1007/s41748-025-00713-z
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
Web of Science
Clarivate