Expansion of cities drives tree cover loss in rapidly urbanizing regions of West Africa
Published 28 Mar 2026 · Added to radar 5 Nov 2026
Source abstract
Urbanization and forest loss are both major elements of global environmental change, yet their connections are understudied in tropical West Africa, a hotspot for rapid urban sprawl and deforestation. We explored tree cover trends and drivers related to urban growth in the moist forest ecoregions of Ghana and Nigeria, extending methods from a previous regional study of urban expansion to generate comparable tree cover data. Landsat data from 2001 to 2020 were processed using machine learning and time series approaches to classify tree cover (> 30% canopy cover) with a producer accuracy of 82% and a user accuracy of 83%. Across the study area, tree cover decreased by 4.7%, from 162,466 km² in 2001 to 154,758 km² in 2020. City boundaries encompassing urban and peri-urban areas accounted for 42% of this change area despite occupying only 7.4% of the study area. Over this period, the average annual tree cover change rate in cities was -4.68% and was highest in medium-sized cities. We analyzed how urban expansion influenced tree cover change at multiple scales. Urban expansion rates were associated with high rates of tree cover loss within city boundaries, reflecting direct effects of development, and in adjacent buffers, reflecting spillover effects from land use displacement. Forest loss within city boundaries and buffers was also associated urban expansion in neighboring cities, reflecting broader regional telecoupling within the urban network. In West Africa, recognition of urbanization as an important regional driver of tree cover loss should catalyze more forest conservation and urban greening initiatives.
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- Study objective (from source abstract)
- We explored tree cover trends and drivers related to urban growth in the moist forest ecoregions of Ghana and Nigeria, extending methods from a previous regional study of urban expansion to generate comparable tree cover data.
- Methods
- machine learning