Spatiotemporal Analysis of Land use and Land Cover Changes in Tokwe Subcatchment, Zimbabwe: Implications for Agriculture

Abstract

The study integrated geospatial mapping and modeling techniques to detect, simulate, and predict land use land cover (LULC) dynamics in the Tokwe Sub Catchment, providing a comprehensive framework for understanding and managing land use changes. To simulate spatiotemporal dynamic transition potential LULC changes, a multi-temporal remotely sensed big data acquired from Landsat TM/OLI for the period 1998 to 2023 with a 5-year interval was utilized. The study further predicted LULC changes for the period 2023 to 2043. Dependent variables (classified LULC maps), independent variables (DEM, slope, and population density) and an integrated CA-ANN algorithm within the MOLUSCE plugin of QGIS were deployed. Support Vector Machine (SVM) classifiers in Arcmap 10.5 were also utilized. Robust ground truthing was conducted for accuracy assessment of LULC for 2023, historical Google Earth Pro images were utilized for 1998, 2003, 2008, 2013 and 2018, through the use of overall accuracy and kappa coefficient indices. It was observed that between 1998 and 2023, the area (%) under water body, mining, wetland, cultivation and bareland increased by 67.8%, 97,0%, 21.5%, 16.7% and 42.6% respectively, while commercial forest and natural forest decreased by -8.6% and -82.0% respectively. The kappa coefficients for 1998, 2003, 2008, 2013 and 2018 were 0.79, 0.75, 0.87 and 0.85 respectively. LULC changes were attributed to anthropogenic factors that include increase in population and agricultural activities. The projection for 2043 indicate that water bodies, wetland, cultivation and natural forest would increase by 63.4%, 71.2%, 74.7% and 21.0 % respectively, while commercial forest, bare land and mining are projected to drop by -13.8%, -43.8% and -10.9%, respectively. The study provides a continuous methodological framework for stakeholders, including Environmental Management Agency, policymakers, legislators, catchment councils, miners, and farmers, thereby enabling the integration of geospatial technologies into environmental management practices for sustainable resource use and catchment resilience.