Spatio-Temporal Dynamics of the Mayombe Forest (2000–2025) in Mvouti (Kouilou, Republic of the Congo): A Remote Sensing and CA-Markov Modeling Analysis Through 2050
DOI:
https://doi.org/10.59228/rcst.026.v5.i4.361Keywords:
Mayombe, remote sensing, modeling, Random Forest, deforestation, CA-Markov, land tenure, REDD+, forest fragmentationAbstract
The Mvouti district, located in the Kouilou department (Republic of the Congo), harbors a critical portion of the Mayombe forest massif, which encompasses the Dimonika Biosphere Reserve (136,000 ha, UNESCO-MAB 1988). This reserve was selected as it represents a biodiversity hotspot under severe anthropogenic pressures from artisanal gold mining and logging. Using six Landsat image composites (2000–2025, 30 m spatial resolution) classified into a five-class land-use/land-cover nomenclature via the Random Forest algorithm on Google Earth Engine, this article quantifies the spatiotemporal dynamics of forest cover, analyzes the fragmentation of the massif, and simulates three prospective trajectories through 2050 using CA-Markov modeling. With an overall classification accuracy of 90.6% (κ mean = 0.883), dense forest lost 32,106 ha (9.1% of the initial cover) between 2000 and 2025. The mean deforestation rate is estimated at 0.37%/year, three to four times higher than the Congolese national average. Disaggregation by land tenure status reveals contrasting dynamics: areas outside logging concessions peaked at 2.27%/year between 2015 and 2020, while managed concessions (FMUs) within the reserve buffer zone maintained significantly lower rates. Fragmentation intensified sharply (patch count rose from 442 to 6,189; MESH: −21.5%). Under the integrated governance scenario (SC3, consistent with Law No. 33-2020 and the Nationally Determined Contribution [NDC]), loss by 2050 would be limited to 14,255 ha, versus 45,634 ha under a governance failure scenario (SC2) a divergence of 31,379 ha, equivalent to approximately 9.8% of the 2025 forest cover. These results provide a directly applicable quantitative baseline for revising the Kouilou Land Use Plan and updating the Congolese NDC reports. The main methodological limitations are the 30 m resolution of the Landsat imagery, which constrains detection of sub-pixel degradation, and the stationarity assumption of the Markov transition probabilities, which does not explicitly incorporate future climatedriven shifts in land-use pressure
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