Portal de Periódicos da CAPES

Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects

2019; Elsevier BV; Volume: 190; Linguagem: Inglês

10.1016/j.earscirev.2019.01.001

1872-6828

Yanjiao Ren, Yihe Lü, Alexis Comber, Bojie Fu, Paul Harris, Lianhai Wu,

Conservation, Biodiversity, and Resource Management

Land use/land cover (LULC) change models are powerful tools used to understand and explain the causes and effects of LULC dynamics, and scenario-based analyses with these models can support land management and decision-making better. This paper provides a synoptic and selective review of current LULC change models and the novel frameworks that are being used to investigate LULC dynamics. Existing LULC models that explore the interactions between human and the environment can be pattern- or process-based, inductive or deductive, dynamic or static, spatial or non-spatial, and regional or global. This review focuses on the spectrum from pattern- to process-based approaches and compares their strengths, weaknesses, applications, and broad differences. We draw insights from the recent land use change literature and make five suggestions that can support a deeper understanding of land system science by: (1) overcoming the difficulties in comparing and scaling Agent Based Models; (2) capturing interactions of human-environment systems; (3) enhancing the credibility of LULC change modeling; (4) constructing common modeling platforms by coupling data and models, and (5) bridging the associations between LULC change modeling and policy-making. Although considerable progress has been made, theoretical and empirical efforts are still needed to improve our understanding of LULC dynamics and their implications for policy-oriented research. It is crucial to integrate the key elements of research involved in this study (e.g., use of common protocols and online portals, integration of top-down and bottom-up approaches, effective quantification and communication of modeling uncertainties, generalization and simplification of models, increased focus on the theoretical and empirical bases of models, and open comparative research) to bridge the gaps between small-scale process exploration and large-scale representation of LULC patterns, and to use LULC change modeling to inform decision-making.