Modeling Spatial and Temporal Dynamics of Land use and Land cover in the Songwe Sub-basin, Tanzania, using Cellular Automata Markov Model

Using the Cellular Automata (CA) Markov Model in combination with Geographic Information System (GIS) and Remote Sensing (RS) technologies, this study examines the spatial and temporal dynamics of land use and land cover (LULC) in the Songwe sub-basin, Tanzania. By combining historical data spanning from 1990 to 2020 at a spatial resolution of 30 meters, the study aims to forecast future LULC changes up to the year 2100. GIS technologies made it easier to spatially analyze and visualize these changes, and LULC data was used to calibrate the CA-Markov model with transition probabilities taken from the historical era. The results highlight significant land use transitions, with significant transitions from woodland and natural vegetation to agricultural land and urban areas. These changes are largely driven by population growth and the rising demand for food production. Forest cover and woodland areas have notably decreased by 54.01% and 59.10%, respectively, while agricultural land has increased by 216.97%. Projections indicate that by 2100, forests and woodlands will largely disappear, being replaced by agricultural land. Additionally, significant reductions in wetlands and water bodies are expected. Rapid urbanization is anticipated to intensify the degradation of natural ecosystems, leading to further strain on hydrological systems. The study recommends the implementation of stronger conservation policies, including the expansion of protected areas for vital habitats, and the promotion of sustainable agricultural practices such as agroforestry and conservation tillage. To enhance long-term ecological resilience, the study also emphasizes the need for ecosystem-based management approaches that prioritize water resource preservation and biodiversity. Additionally, involving local communities in land management decisions is crucial for ensuring the sustainability of natural resources and ecosystem services in the Songwe sub-basin.