The urban expansion process involves multiple stakeholders whose interactions and decision-making behaviors have a complex impact on urban land conversion. In this study, we established an urban expansion simulation model that couples two sub-models: the residential location selection model and the land acquisition bargaining model. Those sub-models include four types of agents: resident agent (RA), real estate developer agent (DA), government agent (GA), and farmer agent (FA). The residential location selection model is composed of three agents, RA, DA, and GA, and is first used to select residential locations, while an artificial neural network (ANN) is used to define the behavior rules of RA and RA selects pixels as candidate locations according to the joint decision probability. Then the land acquisition bargaining model is used, which is composed of GA and FA. If the land acquisition is successful, a pixel is converted into urban land, which is occupied by the corresponding RA; otherwise, the RA selects the next pixel and enters the bargaining process again, and so on, until the RA successfully selects a residential location. Each iteration represents the selection process of an agent. We used this model to simulate urban expansion within the Wuhan Urban Development Zone (WHUDZ) of central China from 2009 to 2019. The overall accuracy and Kappa coefficient of the simulation results were 92.78% and 55.24%, respectively, which were higher than the results using logistic regression cellular automata. Moreover, we obtained the relative contributions of various influencing factors in the ANN on the residential location selection, revealing the influence of the land acquisition process on land expansion. In addition, the coupled model predicted that the WHUDZ’s urban land area will reach 1415.82 km² in 2029, mainly through extensional expansion, and the southeast and northwest will be expansion hot spots.

As a key food production base, land use changes in the Jianghan Plain (JHP) significantly affect the surface landscape structure and ecological risks, posing challenges to food security. Assessing the ecological risk of the JHP, identifying its drivers, and predicting the risk trends under different scenarios can provide strategic support for ecological risk management and safeguarding food security in the JHP. In this study, the landscape ecological risk (LER) index was constructed by integrating landscape indices from 2000 to 2020, firstly analyzing its spatiotemporal characteristics, subsequently identifying the key influencing factors by using the GeoDetector model, and finally, simulating the risk changes under the four scenarios by using the Markov-PLUS model. The results showed that: (1) Cropland was the dominant land use, and the most significant decreases and increases occurred in cropland and built-up land, respectively. The primary land use conversion was cropland to built-up land and the interconversion of cropland and water body. (2) LER exhibited a trend of initially increasing and subsequently decreasing, and the risk levels were predominantly medium and higher. The spatial pattern was high in the southeast and low in the central and northern areas. (3) The spatiotemporal patterns of LER resulted from the combined effect of multiple factors and were mainly influenced by the natural environment, of which the NDVI was the first dominant factor. (4) The land use intensity was higher in the natural and economic development scenarios than in the cropland and ecological protection scenarios, and the predicted LER in 2030 was higher in the former than in the latter two. These findings are important for formulating scientific and reasonable land use planning and ecological risk management strategies to balance economic growth and ecological preservation and maintain food security and ecological sustainability in the JHP.

The study of ecological security patterns is of great significance to the balance between regional economic development and environmental protection. By optimizing the regional ecological security pattern through reasonable land-use planning and resource management strategies, the purpose of maintaining ecosystem stability and improving ecosystem service capacity can be achieved, and ultimately regional ecological security can be achieved. As a typical ecological civilization city in the middle reaches of the Yangtze River, Yichang City is also facing the dual challenges of urban expansion and environmental pressure. The construction and optimization of its ecological security pattern is the key to achieving the harmonious coexistence of economic development and environmental protection and ensuring regional sustainable development. Based on the ecological environment characteristics and land-use data of Yichang City, this paper uses morphological spatial pattern analysis and landscape connectivity analysis to identify core ecological sources, constructs a comprehensive ecological resistance surface based on the sensitivity–pressure–resilience (SPR) model, and combines circuit theory and Linkage Mapper tools to extract ecological corridors, ecological pinch points, and ecological barrier points and construct the ecological security pattern of Yichang City with ecological elements of points, lines, and surfaces. Finally, the community mining method was introduced and combined with habitat quality to analyze the spatial topological structure of the ecological network in Yichang City and conduct ecological security zoning management. The following conclusions were drawn: Yichang City has a good ecological background value. A total of 64 core ecological sources were screened out with a total area of 3239.5 km². In total, 157 ecological corridors in Yichang City were identified. These corridors were divided into 104 general corridors, 42 important corridors, and 11 key corridors according to the flow centrality score. In addition, 49 key ecological pinch points and 36 ecological barrier points were identified. The combination of these points, lines, and surfaces formed the ecological security pattern of Yichang City. Based on the community mining algorithm in complex networks and the principle of Thiessen polygons, Yichang City was divided into five ecological functional zones. Among them, Community No. 2 has the highest ecological security level, high vegetation coverage, close distribution of ecological sources, a large number of corridors, and high connectivity. Community No. 5 has the largest area, but it contains most of the human activity space and construction and development zones, with low habitat quality and severely squeezed ecological space. In this regard, large-scale ecological restoration projects should be implemented, such as artificial wetland construction and ecological island establishment, to supplement ecological activity space and mobility and enhance ecosystem service functions. This study aims to construct a multi-scale ecological security pattern in Yichang City, propose a dynamic zoning management strategy based on complex network analysis, and provide a scientific basis for ecological protection and restoration in rapidly urbanizing areas.

The spatiotemporal differentiation of net primary productivity (NPP) is non-linearly regulated by multiple factors, with the identification of driving factor thresholds and ecological zoning remaining a key challenge in ecosystem research. This study examined the spatiotemporal evolution of NPP using the Sen+MK test and the Hurst index. It then identified thresholds and interactions among NPP driving factors through an XGBoost–SHAP–Restricted Cubic Splines (RCS) model. Finally, it developed ecological zoning based on the identified thresholds. The main conclusions are as follows: (1) Over the past 21 years, NPP in the study area exhibited a fluctuating upward trend with significant spatial heterogeneity (higher in the west, lower in the east), and a potential decline is projected for the future. (2) Driving factors were ranked by importance, and their critical thresholds identified: precipitation (1103.77 mm) > soil organic matter content (4%, 8.31%) > vegetation index (0.44) > elevation (129.99 m) > temperature (13.71 °C) > distance to roads (1.11 km) > distance to water bodies (1.65 km, 8.91 km) > slope (7.24°) > distance to residential areas (4.1 km) > nighttime light intensity (1.55 NW/cm ² /sr). Significant non-linear effects and interactions among the driving factors were identified. (3) Ecological zoning was implemented based on the identified critical thresholds. The results indicate that most areas of Yichang City were classified as key protection zones and key prevention zones, primarily influenced by natural factors; while a small portion was designated as key treatment zones, primarily influenced by anthropogenic factors. This zoning provides a clear scientific basis for implementing differentiated ecosystem management strategies.