Land Utilization, Landscape Pattern, and Ecological Efficiency: An Empirical Analysis of Discrimination and Overlap from Suining, China

The rapid urbanization in recent decades has highlighted the impact of rural land utilization, which further affects the spatial structure and efficiency of rural ecosystems. Optimizing the structure of urban green infrastructure is an effective way to alleviate the fragmentation of rural landscapes, coordinate the relationship between rural development and ecosystem services, and ensure sustainable rural development. The purpose of this paper is to provide a clear direction for the optimization of construction for the sustainable development of rural green infrastructure (RGI). This study provides a new method for RGI identification and analysis by taking Suining County, a typical rural area on the North China Plain, as an example. Morphological spatial pattern analysis (MSPA) was used to distinguish different space scenery elements in RGI and combine them with land utilization elements, so as to obtain two types of overlapping degree data in each village and town. We further combined the overlapping degree data with ecological efficiency indicators to evaluate the spatial structure construction priorities of different land use components in the RGI system. The results show that the MSPA and ecological efficiency analysis method proposed in this paper are conducive to qualitative and quantitative analysis of the relationship between land use type, spatial structure, and ecological efficiency in the RGI system and are suitable for the construction of a green infrastructure network. This method can be used to better understand the spatial distribution and priority of green infrastructure networks to achieve sustainable rural development on the North China Plain

Construction and Optimization of Green Infrastructure Network Based on Space Syntax: A Case Study of Suining County, Jiangsu Province

The construction of green infrastructure (GI) plays an important role in improving the rural ecological functions and building a green livable environment. In this paper, the methods of morpho spatial pattern analysis (MSPA) and space syntax analysis are used to study the GI network construction in Suining County, Jiangsu Province. The results show that: (1) In 2018, the area of ecological patches increased by 110% compared with 1998, and the utilization rate of the GI network was significantly improved. (2) A total of 66 ecological corridors were analyzed in the county, and the main corridors were distributed in the central and western regions. The correlation analysis of core ecological patches in 1998, 2008, and 2018 proved that location factors had the greatest impact on the results of function and connectivity. (3) According to the optimization results, ecological benefits can be improved through engineering measures to realize the revitalization and development of regional rural areas

Construction and Optimization of Green Infrastructure Network Based on Space Syntax: A Case Study of Suining County, Jiangsu Province

The construction of green infrastructure (GI) plays an important role in improving the rural ecological functions and building a green livable environment. In this paper, the methods of morpho spatial pattern analysis (MSPA) and space syntax analysis are used to study the GI network construction in Suining County, Jiangsu Province. The results show that: (1) In 2018, the area of ecological patches increased by 110% compared with 1998, and the utilization rate of the GI network was significantly improved. (2) A total of 66 ecological corridors were analyzed in the county, and the main corridors were distributed in the central and western regions. The correlation analysis of core ecological patches in 1998, 2008, and 2018 proved that location factors had the greatest impact on the results of function and connectivity. (3) According to the optimization results, ecological benefits can be improved through engineering measures to realize the revitalization and development of regional rural areas