Abstract:
Developing towns and cities worldwide face high deforestation rates, yet accurate information on
its spatial extent, dynamics, and potential restoration sites remains limited. Forest ecosystems
play a pivotal role in carbon sequestration; however, their degradation disrupts ecological
functions, necessitating spatially explicit assessments of carbon stock changes over time. Understanding
the spatiotemporal patterns of forest loss is essential for assessing carbon dynamics
and guiding targeted restoration interventions across ecologically sensitive and socioeconomically
vulnerable landscapes. This study aimed to assess deforestation dynamics, quantify
forest cover, and identify potential restoration sites in Chinhoyi, Zimbabwe. Using
Geographical Information Systems (GIS), Remote Sensing (RS), and the Random Forest (RF)
machine learning algorithm, the study assessed forest cover loss and restoration suitability. Potential
restoration sites were identified by evaluating factors such as slope, proximity to roads and
settlements, and land use land cover (LULC) patterns using the Weighted Overlay Analysis (WOA)
and Analytical Hierarchy Process (AHP). Findings revealed a 54.58 % net reduction in forest
cover from 2014 to 2024, largely driven by agricultural expansion, urbanization, and land
degradation. However, transition analysis also indicated localized regeneration, with 4.57 %–
15.17 % ha of forest gains observed in different intervals, highlighting natural regrowth and
reforestation processes. Carbon stock analysis indicated significant losses, with 45,252 tons of
carbon emissions exceeding regional averages over the decade. The study recommends prioritizing
reforestation and forest restoration efforts in highly suitable areas to recover forest cover
and mitigate the impacts of continued deforestation.
