Land Change Modeling: Status and Challenges

Abstract

Over the past years, land change science has emerged as a fundamental component of global environmental change and sustainability research, and modeling of land change has been recognized as a premier research area in land change science. Various land change modeling approaches have been developed to explore the functioning of land changes at aggregated and individual levels, across various spatiotemporal scales, as well as in human, natural, or the coupled systems. This chapter will review a collection of land change modeling approaches including statistical regression models, artificial neural networks, Markov chain models, cellular automata, economic models, and agent-based models. For each approach, the theoretical and methodological basics and major characteristics will be examined. Moreover, several important issues challenging the successful implementation of land change modeling will be discussed, which include coupling human and environmental systems, scale dependency and multilevel interactions, and temporal dynamics and complexity. Finally, a review on the progress of integrating land change models with other environmental modeling techniques for global environmental change research will be provided.