Xuesong Zhang

Rural settlements serve as the core spatial carriers of rural living space, and their spatial evolution and functional transformation reflect the dynamic restructuring of human–land relationships. In karst mountainous areas, complex topography, fragmented land resources, and uneven distribution of public facilities significantly influence settlement patterns and residents’ living spaces. This study aims to quantify the relationship between settlement clustering characteristics and living-space demand and to construct a spatially explicit framework for rural settlement restructuring from a living-space perspective. Taking the Qixingguan District of Bijie City, Guizhou Province—a representative karst mountainous area in Southwest China—as a case study, we develop an integrated analytical framework encompassing spatial identification, demand measurement, and zoning optimization. Settlement clusters were identified using the Nearest Neighbor Index and Kernel Density Analysis, while accessibility to essential services—including education, healthcare, and shopping—was quantified via a Gaussian-based two-step floating catchment area method. Living-space demand was further assessed by integrating accessibility gradients with residential conditions, and restructuring types were classified based on the Living Space Index and the distance from settlements to town centers. The results indicate that (1) rural settlements in Qixingguan District exhibit significant clustering, with high-density zones concentrated around urban peripheries and along transportation corridors; (2) accessibility to living services follows a distance-decay pattern modulated by transportation networks, forming hotspots in suburban and town-center areas and cold spots in peripheral karst mountainous areas; and (3) based on the comprehensive assessment, settlements are categorized into four types—urbanizing villages, central villages, preserved villages, and relocation villages—with corresponding targeted spatial restructuring strategies proposed. This study advances the geographical understanding of rural settlement restructuring in karst mountainous areas and provides empirical evidence for optimizing human–land relationships and promoting more equitable and sustainable spatial development in mountainous regions.

Located in the middle reaches of the Yangtze River, Jianghan Plain is an important part of the middle and lower reaches of the Yangtze River Plain, and together with Dongting Lake Plain, it is known as the Two-Lake Plain. It is a well-developed agricultural area and is an important source of grain in China, as well as one of the major cotton-producing areas, and aquaculture is also an important local industry. With the rapid development of urbanization in China, the impact of human activities on the ecological environment of Jianghan Plain has become increasingly obvious in recent years, and how to timely and objectively assess spatial and temporal changes in ecological environmental quality is of great practical significance for the sustainable development of the region and the construction of an ecological civilization. The Google Earth Engine platform was used to optimize the reconstruction of Landsat TM/OLI images of Jianghan Plain from 1990 to 2021, coupled with four indicators of the natural ecological environment such as the Normalized Difference Vegetation Index, WET, Normalized Difference Soil Index, and Land Surface Temperature to construct the remote sensing ecological index (RSEI) and evaluate the spatial and temporal changes in ecological environmental quality on Jianghan Plain. The results showed that the mean RSEI values in 1990, 1998, 2006, 2014, and 2021 were 0.667, 0.636, 0.599, 0.621, and 0.648, respectively, indicating that the overall ecological environmental quality of Jianghan Plain showed a decreasing trend from 1990 to 2006 and an increasing trend from 2006 to 2021. Degradation was most serious from 1990 to 1998, accounting for 44.86% of the total area, and improvement was most obvious from 2006 to 2014, accounting for 26.64% of the total area. Moran’s I values from 1990 to 2021 were 0.531, 0.529, 0.525, 0.540, and 0.545, respectively, indicating that the spatial distribution of ecological environmental quality was positively correlated. The local spatial clustering of the RSEI local indicators of spatial association showed that H-H clustering areas on Jianghan Plain were mainly distributed in the northern and western regions, and L-L clustering areas were mainly distributed in the densely populated eastern regions with frequent human activities. The results can provide a theoretical basis for ecological environmental protection and improvement on Jianghan Plain.

In recent years, ecosystem service values (ESV) have attracted much attention. However, studies that use ecological sensitivity methods as a basis for predicting future urban expansion and thus analyzing spatial-temporal change of ESV are scarce in the region. In this study, we used the CA-Markov model to predict the 2030 urban expansion under ecological sensitivity in the Three Gorges Reservoir area based on multi-source data, estimations of ESV from 2000 to 2018 and predictions of ESV losses from 2018 to 2030. Research results: (i) In the concept of green development, the ecological sensitive zone has been identified in Three Gorges Reservoir area; it accounts for about 35.86% of the study area. (ii) It is predicted that the 2030 urban land will reach 211,412.51 ha by overlaying the ecological sensitive zone. (iii) The total ESV of Three Gorges Reservoir area showed an increasing trend from 2000 to 2018 with growth values of about USD 3644.26 million, but the ESVs of 16 districts were decreasing, with Dadukou and Jiangbei having the highest reductions. (iv) New urban land increases by 80,026.02 ha from 2018 to 2030. The overall ESV losses are about USD 268.75 million. Jiulongpo, Banan and Shapingba had the highest ESV losses.

Rapid economic development has a significant negative impact on the rural ecological environment. Evaluating the ecological environmental effect of land use and its change trend at the village scale has important practical significance for maintaining ecological functions and ensuring ecological safety. Taking a typical village in Yudian Town as an example, we applied a land-use ecological environment effect evaluation and the CA-Markov change trend prediction model and constructed an index of ecological environmental effect status. Based on the land use, resource environment, and social economic data from 2014 and 2019, we evaluated the ecological environmental effects of land use in each village, simulated the land-use change in each village in two different scenarios, i.e., the developmental orientation (DO) and ecological orientation (EO), in 2030, and analyzed the corresponding change trend of the land-use effect. The ecological environmental effect of land use showed obvious characteristic differentiation in villages with different development levels. For example, villages with poor natural geographic background conditions and slower economic development had a good level of ecological environmental effect, whereas villages with better resource and environmental endowments but faster economic development had lower levels of ecological environmental effect. Village land-use management methods have had a certain effect on improving ecological security, but the effect has been slow. In conclusion, the research results portray the relationship between rural land use and ecological environmental effects in low hilly areas in northern Hubei at a small scale and have reference value for land resource allocation and spatial pattern optimization in similar regions.

(1) Background: Clarify the coordinated development level of production, living, and ecological spaces in the underdeveloped areas of China and their relationship with the intensity of human activity. Explore and address the problems that are likely to be faced when developing these areas and improve the quality of China’s new urbanization development. Promote the coordinated and sustainable development of the economy, society and ecology in underdeveloped areas. Guizhou Province is located in southwest China; the landform is broken and complex. Its economic development level is low. It is one of the representatives of underdeveloped areas in China. Therefore, Guizhou Province of China was selected as the study area. (2) Methods: This paper constructs the evaluation index system of the production–living–ecological space (PLES) functional system in China’s underdeveloped areas, and uses the coupling coordination degree model to measure the development coordination level of the study area. The human activity intensity model was used to calculate the human activity intensity in the study area. Response index is introduced to analyze the relationship between the spatial function coupling coordination degree and the intensity of human activities. (3) Results: Before 2015, the level of functional coupling coordination degree of production–living–ecological space (PLES) in the study area fluctuated, and after 2015, it showed a stable and coordinated development trend. The intensity of human activity continues to increase, and the interaction between human activity and local production–living–ecological space (PLES) function coupling coordination is intense. (4) Conclusions: human activity is a significant factor affecting regional, coordinated and sustainable development. In less developed areas, the impact of human activity is more obvious. Human activity, in combination with the theory of the human–land relationship and the moderate intensity of human activity, are important ways to improve the coordinated and sustainable development of underdeveloped regions.

The healthy development of the city has received widespread attention in the world, and urban resilience is an important issue in the study of urban development. In order to better provide a useful reference for urban resilience and urban health development, this paper takes 56 cities in China as the research object, and selects 29 indicators from urban infrastructure, economy, ecology and society. The combination weight method, exploratory spatial data analysis (ESDA) and spatial measurement model are used to explore the spatial distribution of urban resilience and its influencing factors. From 2006 to 2017, the urban resilience of prefecture-level cities in the four provinces showed a wave-like rise. During the study period, the urban resilience values, measured as Moran’s Is, were greater than 0.3300, showing a significantly positive correlation in regard to their spatial distribution. Regarding the local spatial correlation, the urban resilience of the study area had spatial agglomeration characteristics within the province, with a significant distribution of "cold hot spots" in the spatial distribution. From the perspective of the factors that affected urban resilience, the proportion of the actual use of foreign capital in GDP and carbon emissions per 10,000 CNY of GDP had a negative impact and GDP per square kilometer, the proportion of urban pension insurance coverage, the proportion of the population with higher education, and expenditure to maintain and build cities had a positive impact. The development strategy of urban resilience must be combined with the actual situation of the region, and the rational resilience performance evaluation system and the top-level design of urban resilience improvement should be formulated to comprehensively improve urban resilience.