Reasonable identification of the ecosystem security space and pattern restoration for the Yellow River Basin will be significant for facilitating future ecological protection and restoration projects. This study employs ecosystem services and landscape connectivity to discern ecological source areas and conducts an optimization evaluation method of resistance surfaces and the minimum cumulative resistance (MCR) model to identify the ecosystem security pattern of urban agglomeration. Then, restoration measures are proposed. The results indicate that: (1) There are 46 ecological source areas in the ecosystem security patterns of the Lanxi urban agglomeration, with a total area of 8199.249 km². Moreover, in terms of spatial distribution, ecological source areas are sparse in the east while crowded in the west of the Lanxi urban agglomeration and are mainly composed of natural reserves, forest parks, and farmlands. (2) The ecosystem security patterns contain 914 ecological corridors with a total length of 62,970.181 km, most of which are short-distance corridors, being dense in the part of the northwest with a webbed feature. (3) The study adds 10 ecological source areas to restore the ecosystem security pattern and ecological corridors to improve the rationality of the ecosystem security pattern. Meanwhile, this study proposes restoration measures to protect the ecological environment by defining the levels of ecological security in order to provide a reference for the government to formulate policies and regulations.

It is of great significance to study urban carbon metabolism and explore the low-carbon land use management framework from the perspective of “ecological-production-living” space, an important means for the government to strengthen spatial regulation. In the study, first of all, a carbon metabolism network model was established based on the evolution of the “ecological-production-living” space. Secondly, an ecological network analysis (ENA) method was used to identify the ecological relationships between land use types under the effect of carbon metabolism. In addition, ArcGIS software was used to visualize the spatial distribution of carbon flow and ecological relationships. Finally, a low-carbon oriented land use management framework was proposed based on the above research. Yulin, a typical coal resource-based city in China, was taken as a case study for verification. The results showed that Yulin had net carbon emissions from 2010 to 2020, indicating that the evolution of “ecological-production-living” space had a negative impact on the carbon metabolism. Industrial, mining and transportation land dominated carbon emissions, while forestland played an important role in carbon sequestration. Under the effect of carbon metabolism, a controlling and exploitative relationship was the main ecological relationship, and a mutualism relationship accounted for the smallest proportion, indicating that the urban ecological conflict was obvious in the evolution of the “ecological-production-living” space. Based on the above research, a land use management framework was proposed, which divided urban space into six types of control units. In conclusion, the results provided experience for other coal resource-based cities to promote low-carbon and sustainable land use.

Urban heat islands (UHIs) endanger the health of urban residents. Different urban growth patterns (UGPs) have different effects on heat islands. However, the dynamic relationship between UGP landscape information and urban surface heat island intensity (SUHII) remains unclear. This study explored the dynamic relationship between SUHII and UGP landscape information through spatial regression and landscape pattern analysis using Landsat imagery and urban construction land data from five cities in the Beijing–Tianjin–Hebei urban agglomeration from 2010 to 2018. The results show that SUHII increase areas overlap with expansion patches, and the edge expansion and outlying areas show a warming effect. The influence of the edge expansion landscape area and pattern on SUHII changes is greater than the other two growth patterns. The relationship between UGPs’ landscape information and SUHII changes varies among cities. The larger the city size, the stronger the influence of landscape information. Among the landscape patterns, the influence of the landscape area and pattern on SUHII change is large and the influence of landscape fragmentation is smaller. Exploring the dynamic relationship between UGP landscape information and SUHII is conducive to optimizing the spatial layout and pattern selection of urban development and providing a scientific reference for sustainable and livable urban development planning.