A Preliminary Random Forest Baseline for Above-Ground Carbon Stock Estimation in Abuja Municipal Area Council Using Free Multi-Sensor Satellite Data: A GeoAI Approach

Accurate quantification of above-ground carbon (AGC) stock is essential for climate mitigation planning, yet fine-scale carbon mapping remains scarce for rapidly urbanizing West African savanna cities. This study presents a preliminary geospatial artificial intelligence (GeoAI) baseline for AGC estimation in the Abuja Municipal Area Council (AMAC), Federal Capital Territory, Nigeria, using entirely free, cloud-hosted satellite data. A Random Forest regression model was trained on Google Earth Engine using a dry-season Sentinel-2 optical composite, a Sentinel-1 SAR composite, Copernicus DEM elevation, and NASA GEDI L4A above-ground biomass density (AGBD) footprints as the reference label. A total of 10,978 quality-filtered GEDI footprints were retrieved across AMAC's approximately 172,993-hectare extent, of which 3,309 were held out for testing. On the held-out set, the model achieved a root-mean-square error (RMSE) of 47.02 Mg/ha, a mean absolute error (MAE) of 16.52 Mg/ha, and a coefficient of determination (R²) of 0.345. Wall-to-wall prediction yielded a mean AGC density of 10.12 Mg C/ha and an estimated total AGC stock of approximately 1.75 million Mg C for AMAC. A small fraction (<1%) of anomalously high GEDI AGBD values was found to disproportionately influence RMSE relative to MAE, consistent with known GEDI performance limitations in structurally heterogeneous, non-forest-dominated landscapes. These results are presented explicitly as a reproducible starting baseline rather than a validated final product: the model uses GEDI as both the training label and the accuracy reference, and the train/test split was not spatially blocked. Field-inventory calibration, spatially blocked validation, and fine-tuning of multimodal geospatial foundation models are identified as concrete future research directions building directly on this baseline.

GeoAI; above-ground carbon; Google Earth Engine; GEDI; Random Forest

1. Abubakar, I. R. (2014). Abuja city profile. Cities, 41, 81-91. [Google Scholar] [Crossref]

2. Adewale, A., Olatunde, S., & Martins, O. (2019). Assessment of the relationship between urban growth and surface temperature in Abuja Municipal Area Council. FUDMA Journal of Sciences, 3(3), 309–327. [Google Scholar] [Crossref]

3. Dangulla, M., Manaf, L. A., Ramli, M. F., Yacob, M. R., & Namadi, S. (2021). Exploring urban tree diversity and carbon stocks in Zaria Metropolis, North-Western Nigeria. Applied Geography, 127, 102385. [Google Scholar] [Crossref]

4. Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., Crowther, T. W., Falkowski, M., Kellner, J. R., Labrière, N., Lucas, R., MacBean, N., McRoberts, R. E., Næsset, E., Nickeson, J., Réjou-Méchain, M., … Whitehurst, A. (2022). Aboveground biomass density models for the NASA GEDI mission. Remote Sensing of Environment, 270, 112845. [Google Scholar] [Crossref]

5. Duncanson, L., Leitold, V., Minor, D., Adu-Bredu, S., Armston, J., Dannunzio, R., Gamarra, J. G. P., Gutierrez, J. A., Hunka, N., Kellner, J. R., Ruiz Villar, M., Tavani, R., Duah-Gyamfi, A., Kusi, K. K., & Valbuena, R. (2026). West African footprint-level GEDI aboveground biomass estimates (Version 1) [Data set]. ORNL DAAC. https://doi.org/10.3334/ORNLDAAC/2475 [Google Scholar] [Crossref]

6. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. [Google Scholar] [Crossref]

7. Isioye, O. A., Ikwueze, H. U., & Akomolafe, E. A. (2020). Urban heat island effects and thermal comfort in Abuja Municipal Area Council of Nigeria. FUTY Journal of the Environment, 14(1). [Google Scholar] [Crossref]

8. Jakubik, J., Roy, S., Phillips, C. E., Fraccaro, P., Godwin, D., Zadrozny, B., Szwarcman, D., Gomes, C., Nyirjesy, G., Edwards, B., Kimura, D., Simumba, N., Chu, L., Mukkavilli, S. K., Lambhate, D., Das, K., Bangalore, R., Oliveira, D., Muszynski, M., … Ramachandran, R. (2024). Prithvi-EO-2.0: A versatile multi-temporal foundation model for Earth observation applications. arXiv:2412.02732. [Google Scholar] [Crossref]

9. Kanmegne Tamga, D., Latifi, H., Ullmann, T., Baumhauer, R., Bayala, J., & Thiel, M. (2023). Estimation of aboveground biomass in agroforestry systems over three climatic regions in West Africa using Sentinel-1, Sentinel-2, ALOS, and GEDI data. Sensors, 23(1), 349. [Google Scholar] [Crossref]

10. Kellner, J. R., Armston, J., & Duncanson, L. (2022). Algorithm theoretical basis document for GEDI footprint aboveground biomass density (GEDI04_A). NASA Goddard Space Flight Center. [Google Scholar] [Crossref]

11. Kouamé, Y. A. G., Millan, M., et al. (2022). Multispecies allometric equations for shrubs and trees biomass prediction in a Guinean savanna (West Africa). Silva Fennica, 56(1). [Google Scholar] [Crossref]

12. Li, X., Wessels, K., Armston, J., Duncanson, L., Urbazaev, M., Naidoo, L., Mathieu, R., & Main, R. (2024). Evaluation of GEDI footprint level biomass models in Southern African savannas using airborne LiDAR and field measurements. Science of Remote Sensing, 10. [Google Scholar] [Crossref]

13. McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. [Google Scholar] [Crossref]

14. Nero, B. F., & Callo-Concha, D. (2018). Structure, diversity, and carbon stocks of the tree community of Kumasi, Ghana. Forests, 9(9), 519. [Google Scholar] [Crossref]

15. [Author(s) verify]. (2016). Allometric models and aboveground biomass stocks of a West African Sudan Savannah watershed in Benin. Carbon Balance and Management, 11, 15. [Google Scholar] [Crossref]

16. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309. [Google Scholar] [Crossref]

17. Shang, Z., Das, S., & Eldawy, A. (2026). Benchmarking geospatial foundation models for agriculture applications. arXiv:2606.29664. [Google Scholar] [Crossref]

18. Wari, B. O., Zakari, S., Djaouga, M., Imorou, I. T., Yabi, I., Tente, B. A., & Djego, J. G. (2023). Urban flora structure and carbon storage potential of woody trees in different land use units of Cotonou (West Africa). Urban Science, 7(4), 106. [Google Scholar] [Crossref]

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