Year: 2025 | Month: January | Volume: 12 | Issue: 1 | Pages: 240-246
DOI: https://doi.org/10.52403/ijrr.20250130
Evaluation and Monitoring of the Aerial Woody Plant Biomass of the Classified Forests of Forest Guinea on the Basis of Satellite Imagery Data
Amadou Lamarana BAH1, Tidiane Alpha CAMARA1, Yacouba CAMARA2
1Université Gamal Abdel Nasser de Conakry, Laboratoire d’Enseignement et de Recherche en Energétique Appliquée, Conakry, Guinée
2Institut Supérieur de Technologie de Mamou, Département Energétique, Mamou, Guinée
Corresponding Author: Yacouba CAMARA
ABSTRACT
Guinea has a large block of tropical rainforests that reflect a high carbon dioxide (CO2) sequestration potential. Accurately determining the quantity and distribution of carbon in these forests will make it possible to assess CO2 emissions associated with deforestation and degradation. The objective of this research is to explore the contribution of remote sensing to the estimation of plant biomass in the classified forests of Forest Guinea (Guinea). The “Random Forest” method allowed us to predict the aboveground biomass by integrating the biomass samples from the study area. The results of the carbon stock estimates (in tonnes) for the three (3) Classified Forests and two (2) Biosphere Reserves of Forest Guinea were compared to the studies of Nasi et al. (2011) and Gibbs et al. (2014). Generally speaking, our methodology made it possible to arrive at estimates higher than those of Gibbs et al. (2014) and lower than those of Nasi et al. (2011). These results obtained from MODIS data could be considered as average values of the biomass gradient and carbon stock estimates for these different Forests and Reserves in the said area. However, we note the existence of two (2) Areas where we obtained higher values compared to the aforementioned studies, namely the Ziama classified forest (17.58 - 544.12 t/ha and 60.53 teCO2/ha) and the Mount Nimba Biosphere Reserve (25.39 - 300.44 t/ha and 42.18 teCO2/ha).
Keywords: Assessment; Monitoring; Biomass; Woody Plant; MODIS; REDD; Forest
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