Optimizing land use spatial patterns to balance urban development and resource-environmental constraints: A case...

The unprecedented urbanization of the Central Plains Urban Agglomeration (CPUA) has significantly improved human well-being but has also led to severe land degradation and environmental challenges. Spatial zoning is a crucial tool for advancing sustainable land management in urban agglomerations. However, spatial zoning has become increasingly challenging due to the diversity associated with future land-use changes and the vulnerability of the ecological environment. This study applies a Self-Organizing Map (SOM) neural network model to emphasize the dual pivotal role of future land-use scenarios and resource-environment carrying capacity in optimizing sustainable land management zoning. Focusing on the CPUA in China, the study reveals several key findings: (1) Under the natural evolution scenario, the proportion of cultivated land decreases from 58.76 % to 55.58 %, resulting in a reduction of 9146 km², while construction land expands from 14.72 % to 21.96 %, with an increase of 23,100 km² and an average annual growth rate of 1.17 %. (2) The resource-environment carrying capacity across the CPUA is generally low to medium, with an average index value of 35.28. Spatially, the surrounding areas concentrate higher carrying capacity, while the central regions exhibit lower values. The western, northern, and southern edge regions show relatively higher capacities. (3) Based on comprehensive assessments of land-use patterns, ecological quality, and resource-environment carrying capacity, the CPUA is divided into nine distinct sustainable land management zones. Each zone requires tailored strategies that consider its specific resource endowments, ecological conditions, agricultural productivity, and urban development potential. Coordinated infrastructure development and resource-sharing initiatives are essential for promoting sustainable land management throughout the urban agglomeration. The proposed zoning optimization strategy strikes a balance between urban development demands and resource-environment constraints, offering a practical framework for refining land management policies and advancing sustainable development goals in large urban agglomerations.