Modeling the dynamics of land-use and land-cover change: A Complex systems and computational intelligence approach

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Land-use and land-cover (LULC) change is a dynamic and complex spatial process involving human-environment interactions with direct impact on the quality and distribution of our biophysical system. In the next decade, the United Nations Environment Program estimates more than 60% of the world's population will be concentrated in urban areas. Uncontrolled urbanization and development will increase pressures on natural habitats, forests, agricultural lands, and available water resources thus affecting climate change. There is a vital need to monitor the LULC change processes, understand the changes from environmental and social perspectives, and model the spatial dynamics of land change for forecasting patterns and assessing their ecological consequences. The goal of this research is to continue developing novel simulation modeling approaches that would improve scientific understanding and knowledge about the underlying interactions and mechanisms driving LULC change processes. The work is theoretically situated within the geographic information science and systems (GIS), computational intelligence, complexity and land use sciences disciplines. The specific objectives are to: (1) develop a new generation of computationally intelligent complex system models capable of incorporating human-environment interactions and change dynamics at several spatial scales; (2) test and compare the effectiveness of the developed models and advance them to address model reliability and usability for spatial decision-making; (3) extend the developed complex system models from common three dimensional representation to better depict the change process in a four dimensional land-air-water continuum. Collectively, this research program will incrementally develop more realistic LULC models capable of generating future outcome scenarios of land change dynamics. With the development of enhanced LULC models scientists and planners would be able to deepen their understanding of the complexity of land change processes and be better equipped to examine outcome scenarios towards effective planning, decision-making and policy assessment for environmental mitigation at local, national and global levels.