Monitoring and Forecasting Land Cover Dynamics Using Remote Sensing and Geospatial Technology

In recent years, the continuous increase in world population has led to a consequent rise in demand for natural resources and living spaces, deteriorating essential environmental services. Therefore, understanding spatiotemporal dynamics in land cover changes has become an essential area of research for scientists to deal with the challenges of making sustainable cities. This study aims to present an innovative methodology based on remote sensing techniques and Geographic Information Systems (GIS) to monitor and predict urban growth and greening transformation patterns. A Cellular Automata model (CA-ANN) is developed to evaluate the driving factors influencing land cover dynamics. The results showed a process of urbanization oriented towards excessive housing dispersion and how a land cover evolution may be more sustainable with scientific spatial plans that consider facilities for people and ecological protection. The proposed method can represent a valid Decision Support System quantifying the land cover dynamics in changing environmental settings and can serve as a helpful tool for sustainable urban development.