Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.
TopIntroduction
Changing patterns of land-use development have significant implications for future changes in climate, land function, global food demand, biodiversity, and associated ecosystem services, and in return, these have implications on subsequent land-use change (de Groot et al., 2002; de Groot et al., 2010; FAO, 2016). During the past three centuries, almost 12 million km2 of forest and woodlands were cleared, and 5.6 km2 of grassland and pasture have been diminished on a global scale (Bryant et al., 1997; Ramankutty and Foley, 1999; Davidson, 2017). During the same period, cropland has increased by 12 million km2 (Ramankutty and Foley, 1999; Davidson, 2017). These land-use changes have significant implications for future changes of global climate, and consequently, have implications for subsequent land-use change. Rapid changes in land-use development patterns increase uncertainties related to possible future trends and the effectiveness of policies. This is of importance for the studies of land-change science (Turner et al., 2007) and land-system science (Rounsevell et al., 2012) considering that land is a scarce resource where competing claims interact. This is also important to governments, planners and policy-makers to understand the interactions between human activities and natural resources. The long-term impacts of policy decisions on land-use change may cover many diverse issues such as transportation, zoning, water supply, air quality, natural resources, recreation and ecosystem conservation. In this respect, there is a growing need for forward-looking information and scenario-based policy analysis that addresses these issues through ensuring consistency among past, present, and future assessments.
Considering that land-use change is the outcome of human decisions, land-use patterns reflect the decision-making processes of those who control scarce land resources (Verburg et al., 2006a). Land-use policies such as tax regulations and subsidies, urban growth restrictions, setting aside of land in return for monetary compensation, schemes to encourage urban regeneration and brownfield development, and land-zoning policies on different land uses have caused significant changes in urban landscapes (Randolph, 2004). As such policies and restrictions are influential on spatial development, it is important for policy-makers to develop appropriate policies and take necessary actions to keep pace with continuous spatial development. In this regard, land-use modeling approach can be considered as a tool to examine changes of land-use and pattern and to support planners and policy-makers in developing more informed policies and decisions (Verburg et al., 2004). Models are useful for resolving the complexities stemming from socio-economic and biophysical factors that influence the spatial pattern of land-use change (Figure 1), and for estimating the impacts of changes in land-use (Veldkamp and Fresco, 1996a; Verburg et al., 2004; Meyfroidt et al., 2013).
Figure 1. Source: Reproduced from Geist and Lambin (2002)