Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method.

TL;DR: In this article , the authors analyzed the spatial characteristics of LULC and urban expansion in the years 2000-2019 in Estonia, and used the revealed spatiotemporal transitions of land use/land cover (LULC) to predict future LULC in Estonia.

TL;DR: In this article , the impact of land use change on ecosystem services (ESs) was analyzed in the context of local land use changes and the trade-offs among the ESs were explored.

TL;DR: In this article , the authors used Markov and multi-objective optimization models to predict the demands for land use in Nanjing in 2030 and 2060 under the natural growth (NG) and minimum carbon emission (MCE) scenarios to coordinate the needs of economic development, ecological protection and food security as well as the target of carbon emissions reduction in the future.

TL;DR: A future land use simulation (FLUS) model that explicitly simulates the long-term spatial trajectories of multiple LUCCs, and the simulation accuracy is higher than other well-accepted models, such as CLUE-S and CA models.

TL;DR: A cellular automaton model, that was calibrated by using historical digital maps of urban areas and can be used to predict the future extent of an urban area, is applied to two rapidly growing, but remarkably different urban areas.

TL;DR: A three-layer neural network with multiple output neurons is designed to calculate conversion probabilities for competing multiple land uses and has been successfully applied to the simulation of multiple land use changes in a fast growing area in southern China.

TL;DR: In this paper, the authors introduce a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes to simulate the evolution of natural vegetation on abandoned farmland in Europe.