Historical and projected forest cover changes in the Mount Kenya Ecosystem: Implications for sustainable forest management

Abstract

Understanding historical patterns of forest cover change (FCC) is critical for predicting future trends and informing sustainable management strategies. This study quantified and analyzed historical and projected FCC in the Mount Kenya Ecosystem (MKE), central Kenya. Land Use Land Cover (LULC) maps for 2000, 2014, and 2023 were classified using Random Forest (RF) in Google Earth Engine (GEE). Explanatory factors of LULC change (slope, aspect, population density, proximity to rivers, roads, and towns) were used to project LULC for 2035 using Cellular Automata and Markov Chain Analysis (CA-MCA).

Six LULC types (open forest, closed forest, cropland, bareland, built-up, shrubland and grassland) were successfully classified with accuracies exceeding 82.5% and Kappa coefficients above 0.77. Between 2000 and 2023, open forest (+201.12 km²), cropland (+218 km²), bareland (+290.09 km²), and built-up areas (+0.27 km²) expanded, while closed forest (−141.55 km²) and shrubland and grassland (−567.93 km²) declined. An overall Kappa coefficient value of 0.78 and an accuracy of 82% indicated good results for LULC statistics and projected map for 2035. LULC projections for the year 2035 under the Business as Usual (BAU) scenario suggest continued expansion of cropland (+174.70 km²), built-up areas (+0.49 km²), and open forest (+471.72 km²), with declines in closed forest (−423.53 km²) and shrubland and grassland (−357.79 km²).

These results highlight the ongoing pressures on the MKE's biodiversity and ecosystem services. The study's methods offer a replicable framework for assessing FCC in similar ecosystems to inform evidence-based conservation and land management policies.