Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model

The spatiotemporal variation of any landscape patterns is as a result of complex interactions of social, economic, demographic, technological, political, biophysical and cultural factors. Modelling land use and land cover (LULC) changes is essential for natural resource scientists, decision-makers and planners in developing comprehensive medium and long-term plans for tackling environmental or other related sustainable development issues. The current study used an integrated approach that combines remote sensing and GIS to simulate and predict plausible LULC changes for Dedza district in Malawi for the years 2025 and 2035 based on Cellular Automata (CA)-Markov Chain model embedded in IDRISI Software. The model was validated using a simulated and actual LULC of 2015. The overall agreement between the two maps was 0.98 (98%) with a simulation error of 0.03 (3.0%). The more detailed analysis of validation results based on the kappa variations showed a satisfactory level of accuracy with a K-no, K-standard and K-location of 0.97, 0.95 and 0.97, respectively. The future projections indicate that water bodies, barren land and built-up areas will increase while agricultural land, wetlands and forest land will substantially decrease by 2025 and 2035 respectively. According to the transition probability matrix, almost 94.8%, 97.6% and 95.7% of water bodies, agricultural land and barren land will more likely remain stable by 2025. In contrast, forest land exhibits the highest probability of change of 64.8% and 85.9% by 2025 and 2035 respectively. Results also indicate that the majority of the forest areas will be converted to barren land with a probability of 60.8% and 79.6% by 2025 and 2035, respectively. These findings serve as an important benchmark for planners, natural resource managers and policy-makers in the studied landscape to consider in pursuit of holistic sustainable development policies/strategies/guidelines for sustainable natural resource management.