Spatial Simulation of Land-Use Development of Feixi County, China, Based on Optimized Productive–Living–Ecological Functions

Abstract

Rural revitalization places higher demands on the productive–living–ecological (P-L-E) spaces of towns and cities. It is necessary, therefore, to identify, evaluate, and optimize P-L-E spaces to better guide spatial planning. Existing studies typically evaluate a single space, lacking a comprehensive consideration of whole-area integration. This study, therefore, developed a coupled spatial/developmental suitability evaluation system for Feixi County, Anhui Province, China, combining spatial quality evaluation, a coupled coordination model, and future land-use simulation (FLUS) model. The spatial quality of Feixi County in 2010, 2015, and 2020 was obtained by applying the evaluation system to the spatial development pattern. The results were analyzed and verified using the landscape pattern index and development suitability evaluation. The results showed the following: (1) The coupling coordination degree of the region increased from 0.131 to 0.372, changing from low to moderate coordination. (2) Based on the FLUS model to better capture the uncertainty and stochastic basis of the development in the study area. The kappa coefficient and Figure of Merit (FoM) index of the land-use simulation accuracy verification index were 0.7647 and 0.0508, respectively, and the logistic regression ROC values were above 0.75, thus meeting accuracy requirements. This demonstrated that the simulation model—based on a factor library of the evaluation of resource and environmental carrying capacity and suitability for development and construction—could better reflect future land-use changes. (3) The simulation showed that under the baseline development scenario, the area’s spatial layout is too concentrated in terms of construction land, ignoring P-L-E coordination. Under the ecological optimization scenario, high-quality ecological space is ensured, but other types of spaces are lacking. Under the comprehensive guidance scenario, lagging ecological space is optimized and P-L-E spatial development is enhanced through aggregation, clustering, concentration and integration. This way, the spatial quantity structure and distribution form can meet P-L-E spatial development needs in Feixi County. In this study, on the basis of scientific assessment of the current P-L-E space, the FLUS model was applied to carry out a scenario simulation according to different objectives. Moreover, based on the construction of the coupling system of human–nature system, the driving factors were improved to enhance the prediction accuracy of the FLUS model. This study’s findings can help improve the scientificity, flexibility and management efficiency of Feixi County’s P-L-E spatial layout, thereby supporting its sustainable development.

Spatial Simulation and Prediction of Land Use/Land Cover in the Transnational Ili-Balkhash Basin

“…To model various land use processes and their interactions, several widely used CA aggregation models have been developed, including Logistic-CA, artificial neural networks-CA (ANN-CA), CA-Markov, and Future Land Use Simulation (FLUS) [15][16][17][18][19]. The FLUS model incorporates the influence of natural factors and is a top-down quantitative estimation method [20][21][22]. However, the previous models still have limitations in simulating different land class processes and capturing complex spatial interactions and internal mechanisms between individual land classes.…”

Simulated Validation and Prediction of Land Use under Multiple Scenarios in Daxing District, Beijing, China, Based on GeoSOS-FLUS Model

“…The coupling of the GeoSOS-FLUS model and Markov chain model as well as the simulation process is shown in Figure 1. The combination of the two models makes the FLUS model able to handle complex spatial change while the Markov chain model predicts land quantity, thus realizing the full exploitation of spatial and quantitative information on the dynamic evolution of land use [22][23][24].…”

Simulation and Prediction of Urban Land Use Change Considering Multiple Classes and Transitions by Means of Random Change Allocation Algorithms

“…As stated by [4], the success of the majority of mankind s undertakings depends on a sound governance of their financial, institutional, and especially physical layer perceptron neural network considering population density, topographic features, and proximity variables. ANN-based CA models also include works at regional scales relying on the FLUS platform, which operates with the concept of adaptive inertia mechanism and employs a roulette wheel selection [27][28][29].…”