CLUE: a conceptual model to study the Conversion of Land Use and its Effects

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

“…These technologies have proved their efficacy for updating and managing spatial data in developing countries by providing the advantage for rapid data acquisition to collect LULC information regularly at a much lower cost than traditional ground survey methods (Dong, Forster, & Ticehurst, 1997). The application of RS and GIS in urban and environmental planning has led to the formation of spatial modeling methods as a decision support tool, such as Markov chain (MC) model (Arsanjani, Kainz, & Mousivand, 2011), logistic regression (LR) model (Hu & Lo, 2007), artificial neural network (ANN) model (Maithani, Arora, & Jain, 2010;Pijanowski, Brown, Shellito, & Manik, 2002), cellular automata (CA) model (Clarke, Hoppen, & Gaydos, 1997;Kamusoko, Aniya, Adi, & Manjoro, 2009;Yuan, 2010), a modified cellular automata-based SLEUTH model (Clarke et al, 1997;Hua, Tang, Cui, & Yin, 2014) and conversion of land use and its effects (CLUE) model (Veldkamp & Fresco, 1996;Verburg et al, 2002). These models have proved their capability in providing a quantitative tool to facilitate the decision-making process for urban and environmental planning, and suitability assessment of lands for development, which is essential to the efficient management of a large metropolis (Yang, 2002).…”

Evolution of land use-change modeling: routes of different schools of knowledge

“…Other similar combinations of these models include DELTA, which integrates sub-models that pertain to human colonization and ecological interactions in order to estimate the amount of deforestation that occurs in various immigration and land management scenarios. Further examples that utilize different statistical techniques in combination with cellular and system models consist of larger-scale models, such as GEOMOD2 (Hall et al 1995) and the CLUE family (Veldkamp and Fresco 1996b). The latter is a cross-disciplinary approach, integrating both socioeconomic and biophysical aspects that can be described as an integrated, spatially explicit, multi-scale, dynamic, and economy-environment-society-land use model (Briassoulis 2000).…”

Design of Sustainable Biofuel Processes and Supply Chains: Challenges and Opportunities

“…For example, models such as FASOM have high resolution for the United States but lack information regarding international trade; while models such as GTAP can provide estimates for domestic and international land-use change but at a low level resolution. Synthesis and coupling these dynamic economic models with biophysical land-use models such as the Integrated Model to Assess the Greenhouse Effect, Conversion of Land Use and its Effects as well as energy models such as the PRIMES Energy System Model can help support informed decision making [135,136]. However, it is important to note that that economic systems exhibit structural inconstancy (i.e., change in individual behavior in response to a policy change), and coupling economic and biophysical models will increase overall model uncertainty [137].…”