Multi‐scenario simulation of land use change based on MCR‐SD‐FLUS model: A case study of Nanchang, China

Abstract

Modeling land use/cover change (LUCC) is a helpful approach to simulate land use changes, analyze land use driving mechanisms, and support territorial spatial planning. However, there is no single model that comprehensively represents spatiotemporal changes, impact mechanisms, and ecological conservation of land use. This article focuses on Nanchang City and how the minimum cumulative resistance (MCR) model, the system dynamics (SD) model, and the future land use simulation (FLUS) model can be combined and applied to simulate the trends of land utilization change in different future scenarios. This allows us to explore the environmental protection policies and constraints, as well as to assess the socioeconomic regulation mechanisms and dynamic spatial patterns of land use. The results proved that the integrated model is competent in monitoring and projecting the dynamics of LUCC. The areas suitable for ecological protection are mainly located in the southeast, northeast and Meiling Scenic Area of Nanchang. By 2030, the scale of cultivated land will decrease, while the scale of forestland, grassland, water area, and total construction land will increase. Spatially, some cultivated land will be occupied due to the expansion of urban land, while the rural settlement land will be converted into other construction land. The interconversion of ecological lands also encroaches on part of the cultivated land. This scenario simulation based on the coupled MCR‐SD‐FLUS model provides decision‐making options for land resource planning and ecological environment assessment not only in the study region but also in other similar cities.

Land-Use Assessment and Trend Simulation from a Resilient Urban Perspective: A Case Study of Changsha City

“…SD models are a type of model that studies the structure, function, and dynamic behavior of feedback systems on the basis of control theory, system theory, and information theory. Their outstanding feature is that they can reflect the interaction relationship between the structure, function, and dynamic behavior of complex systems, providing dynamic simulation experiments to examine the changing behavior and trends of complex systems under different scenarios, and providing decision support [37][38][39]. According to relevant studies, incorporating SD methods into urban land-use prediction processes can effectively analyze the coupling relationship between various factors that affect urban land use.…”

Projecting the response of carbon sink potential to land use/land cover change in ecologically fragile regions

“…Furthermore, land prediction models have been employed to forecast future LULC patterns and changes in carbon stocks at the regional level. In existing land prediction models, FLUS (Gu et al, 2022;Xiang et al, 2022), CLUE-S (Islam et al, 2021;Kiziridis et al, 2023), and CA-Markov (Alhameedi et al, 2022;Zhang et al, 2023) have been widely used. However, these models primarily focus on improving modeling techniques, model rules, and accuracy, while paying less attention to exploring the underlying driving forces behind land cover change (Sohl and Claggett, 2013).…”