The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Land Use Optimization for Coastal Urban Agglomerations Based on Economic and Ecological Gravitational Linkages and Accessibility

Urban agglomerations (UA) are attracting increasing research attention as a global emergent phenomenon, whereby regional collaborative linkages between cities attracts and agglomerates development. However, these studies also acknowledge that ecological values may be negatively impacted by re-development, ecological fragmentation, and proximity or downstream impacts. Sustainable development, therefore, requires balancing forces from economic attraction and ecological repulsion. Forces similar to economic ones may also operate in attracting ecological enhancement towards higher-valued ecological regions; however, research regarding the role of the self-collaborative gravity-like forces shaping UA is limited in land use optimization. To assist planners, this study developed a new multi-objective land use optimization of UA that explored the intensity of economic ties and ecological gradients using the multi-objective NSGA-II algorithm. In this model, economic linkage intensity (ELI) and accessibility were used to calculate a modified GDP (gross domestic product), while the NDVI (normalized difference vegetation index) was used for the modified ESV (ecosystem services value). Spatial allocation with implicit economic accessibility relationships was enhanced through a two-step mutation operator, including a “gravity flip” spatial orientation factor. Compared to the standard NSGA-II algorithm, models of future land use of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in 2030 have shown that the modified GDP value in our model increased by 7.41%, while the conversion rate of high-density vegetation reduced by 7.92%. The results highlighted the importance of linkage and accessibility factors in enhancing the clustering of cities. In tandem, the modified ESV also enhances ecosystem services contributions of higher value vegetated land through decentralized built-up developments. The proposed model provides managers with a comprehensive and efficient land use solution model that accounts for intrinsic linkage factors shaping the development of compact urban agglomerations.

Optimizing the Production-Living-Ecological Space for Reducing the Ecosystem Services Deficit

With rapid urbanization and industrialization, China’s metropolises have undergone a huge shift in land use, which has had a profound impact on the ecological environment. Accordingly, the contradictions between regional production, living, and ecological spaces have intensified. The study of the optimization of production-living-ecological space (PLES) is crucial for the sustainable use of land resources and regional socio-economic development. However, research on the optimization of land patterns based on PLES is still being explored, and a unified technical framework for integrated optimization has yet to be developed. Ecosystem services (ES), as a bridge between people and nature, provide a vehicle for the interlinking of elements of the human-land system coupling. The integration of ES supply and demand into ecosystem assessments can enhance the policy relevance and practical application of the ES concept in land management and is also conducive to achieving ecological security and safeguarding human well-being. In this study, an integrated framework comprising four core steps was developed to optimize the PLES in such a way that all ecosystem services are in surplus as far as possible. It was also applied to a case study in the middle and lower reaches of the Yellow River Basin. A regression analysis between ES and PLES was used to derive equilibrium thresholds for the supply and demand of ES. The ternary phase diagram method was used to determine the direction and magnitude of the optimization of the PLES, and finally, the corresponding optimization recommendations were made at different scales.

Spatial Pattern Simulation of Land Use Based on FLUS Model under Ecological Protection: A Case Study of Hengyang City

With rapid economic development in China, the excessive expansion of cities has led to the imbalance of land use structure, and then the ecological regulation function of the land ecosystem experiences problems, which has become an obstacle to sustainable development. Therefore, in order to protect the ecological environment, regulate urban development and pursue the maximization of ecological benefits, it is necessary to analyze, simulate and predict land use change. In this study, Hengyang City was taken as the study area, and based on the current land use data of Hengyang City in 2010, 2015, and 2018, the land use type transfer during 2010–2015 and 2015–2018 was analyzed. Then, starting from 2010, the FLUS model was used to simulate the spatial distribution of land use in 2015 and 2018, and then the spatial distribution of land use in Hengyang City in 2025 was predicted with the Markov prediction method under the premise of ecological protection priority. The results show that the change in ecological land in Hengyang City is mainly distributed in the surrounding and marginal areas, because the topography of Hengyang City is a basin. Changes in land type in Hengyang City in 2015 were subtle and difficult to observe. However, in 2018, the transformation of non-ecological land into ecological land was obvious, and the distribution area of ecological land expanded significantly. The Kappa index of the results simulated by the FLUS model based on neural network is above 0.72, and overall accuracy is above 0.9, which is highly consistent with the actual situation. It is reasonable and convincing to predict the spatial distribution of land use in the context of ecological protection. The predicted results can be useful for urban planning and land use distribution and provide a reference for relevant decision-makers.