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

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 km 2 ), cropland (+218 km 2 ), bareland (+290.09 km 2 ), and built-up areas (+0.27 km 2 ) expanded, while closed forest (−141.55 km 2 ) and shrubland and grassland (−567.93 km 2 ) 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 km 2 ), built-up areas (+0.49 km 2 ), and open forest (+471.72 km 2 ), with declines in closed forest (−423.53 km 2 ) and shrubland and grassland (−357.79 km 2 ). 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. • Historical and future forest cover change patterns were, quantified, mapped and projected in the Mount Kenya Ecosystem. • Closed forest areas decreased while open forests increased between 2000 and 2023. • Projected analysis under the Business-as-Usual scenario showed similar trends are likely to persist by 2035. • Proactive management measures are necessary to mitigate further deforestation and forest degradation.

Authors

Institutions

Publication Details

Journal
Environmental and Sustainability Indicators
Published
2025-02-07
DOI
https://doi.org/10.1016/j.indic.2025.100628
Citations
6
Primary Topic
Conservation, Biodiversity, and Resource Management
Type
article
Field-Weighted Citation Impact
6.63
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Harison Kiplagat Kipkulei, Benjamin Mutuku Kinyili, Brian Rotich, Abdalrahman Ahmed
6 citations
Environmental and Sustainability Indicators
Conservation, Biodiversity, and Resource Management
6.63
article

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

Harison Kiplagat Kipkulei, Benjamin Mutuku Kinyili, Brian Rotich, Abdalrahman Ahmed
article en
6 citations

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 km 2 ), cropland (+218 km 2 ), bareland (+290.09 km 2 ), and built-up areas (+0.27 km 2 ) expanded, while closed forest (−141.55 km 2 ) and shrubland and grassland (−567.93 km 2 ) 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 km 2 ), built-up areas (+0.49 km 2 ), and open forest (+471.72 km 2 ), with declines in closed forest (−423.53 km 2 ) and shrubland and grassland (−357.79 km 2 ). 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. • Historical and future forest cover change patterns were, quantified, mapped and projected in the Mount Kenya Ecosystem. • Closed forest areas decreased while open forests increased between 2000 and 2023. • Projected analysis under the Business-as-Usual scenario showed similar trends are likely to persist by 2035. • Proactive management measures are necessary to mitigate further deforestation and forest degradation.

Environmental and Sustainability IndicatorsVol. 26
Magyar Agrár- és Élettudományi Egyetem (HU), University of Augsburg (DE), Jomo Kenyatta University of Agriculture and Technology (KE), Chuka University (KE), Leibniz Centre for Agricultural Landscape Research (DE), Kenya Forest Service (KE), University of Gezira (SD), University of Sopron (HU)
Life in Land
Openalex Percentile: Top 4%
Conservation, Biodiversity, and Resource Management
6.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.