Landscape ecological risk assessment can reflect the impact of landscape pattern on ecological processes and functions, and has become an important method for predicting and measuring the quality and dynamic evolution of the ecological environment. Studying the trend of landscape ecological risk evolution is important for optimizing the regional landscape pattern and maintaining the sustainable development of the ecological environment in ecologically fragile areas. Ningxia Hui Autonomous Region is a typical agro-pastoral region in northern China, which is facing prominent ecological and environmental problems such as soil erosion and land desertification. With the environmental problems becoming more and more serious, the ecological risk also poses increasing potential danger, which becomes one of the bottlenecks restricting regional economic development. This paper selects Ningxia Hui Autonomous Region as the study area. Based on land use data in 2000, 2005, 2010, and 2015, the landscape ecological risk assessment model is built from the perspective of landscape ecology, and combines the spatial statistical analysis method and the gravity shift model to explore the spatial–temporal evolution of the spatial distribution characteristics of landscape ecological risk. The results showed that: (1) During the study period, the area of farmland and grasslands has decreased, and the area of forest, water, and settlement has increased; (2) The transformation of land-use types is mainly distributed in the central northern plain and the south valley, where human activities are intensive. The transfer relationship among different land-use types is mainly between grasslands and farmland; (3) The hotspots and aggregation area of landscape ecological risk are mainly distributed along the city belt and irrigated agricultural areas along the Yellow River in the northern Ningxia Plain; (4) From 2000 to 2005, the landscape ecological risk level decreased, and from 2005 to 2015, the landscape ecological risk index continued to rise. We conclude that the natural environment of Ningxia determines the characteristics of the spatial distribution of landscape ecological risk, while the use and modification of land through human activities are the main driving factors of landscape ecological risk change. The results of landscape ecological risk assessment at different scales based on the landscape index will provide support for regional environmental management.

Habitat quality reflects the level of biodiversity, and habitat maintenance functions are related to human well-being and ecosystem stability. Ningxia Hui Autonomous Region is a typical ecologically fragile region in Western China with complex human-nature relationships. Maintaining good habitat is not only a fundamental requirement for biodiversity conservation but also a necessary path for sustainable regional development. In this study, we assessed and analysed the spatial and temporal patterns and changes in habitat quality in Ningxia from 2000 to 2020, and explored the driving factors of habitat quality using a geographically weighted regression (GWR) model. The results indicated: (1) The overall habitat quality level in Ningxia was low to intermediate, with an upwards and then downwards trend during the past 20 years, showing a small change in overall magnitude. (2) The high- and higher-level habitat quality patches in Ningxia were mainly distributed in areas with high vegetation cover, such as the Helan Mountain and Liupan Mountain. The patches of moderate-level habitat quality mainly included cultivated land, while the low- and lower-level patches were mainly distributed in areas subjected to more frequent human activities, such as cultivated land and construction land. (3) The spatial and temporal distribution patterns and changes in habitat quality in Ningxia from 2000 to 2020 were mainly influenced by fractional vegetation cover (FVC), soil moisture content (SMC), proportion of construction land area (PCL), and proportion of cultivated land area (CLP). Among them, FVC and SMC were positive driving factors, and PCL and CLP were negative driving factors. The results support that increasing vegetation cover and reducing anthropogenic disturbance to natural habitats are important measures to maintain fragile habitats and that key ecological function areas such as nature reserves are crucial for habitat quality protection in ecologically fragile areas.

The economic, social and ecological elements in the region constitute a complex ecosystem. The development trend, internal coordination and interactive effects of the economic–social–ecological (ESE) system have consistently constituted pivotal scientific propositions in the context of the social development process. The Yellow River Basin holds strategic importance, acting as both an ecological barrier and a center for economic development within China. Based on these considerations, this study focuses on the Yellow River Basin and innovatively establishes a theoretical framework and measurement model for the development of the ESE system. Quantitative methods, including the coupled coordination model and augmented regression tree model, are employed to evaluate the development, coordination, spatial patterns and influencing factors of the ESE system in the study area. The findings reveal that the economic and social subsystems are rapidly developing. Over the study period, the focus of ESE system development shifted eastward. Furthermore, there were noticeable disparities in the factors influencing coordinated ESE system development across the upper, middle and lower reaches of the Yellow River Basin. Thus, sustainable development policies for the region must be tailored to local conditions. This study also offers insights into the potential development paths for the Yellow River Basin and other river basins in China, contributing practical value to the promotion of sustainable development and the construction of an ESE system that reflects the unique characteristics of the Yellow River Basin.

Human-oriented smart cities and landscape systems increasingly depend on reusable, high-resolution geospatial observations, yet unmanned aerial vehicle (UAV) imagery is commonly acquired for a single task and retained by separate organizations. This study diagnoses the resulting reuse bottlenecks using an improved combinatorial weighting multi-criteria decision method (ICW-MCDM). Seven experts assessed four dimensions and 17 criteria. Data Rights (0.0912), Data Security (0.0823), Share Policy (0.0819), Data Description (0.0804), and Incentives (0.0706) received the highest integrated weights. A transparent raw-score comparator recovered the same five-item set, while bootstrap and leave-one-out checks supported a governance-oriented leading set with panel dependence for some criteria. After C1–C6 were excluded, Data Description, Reliable Data, Access Permissions, Service Facilities, Search and Discovery, and Data Citation and Provenance became the leading post-entry requirements. By 11 August 2026, a national directory platform had recorded 336,753 visits, fewer than ten formal applications, and two completed university research deliveries. The cases demonstrate small-scale matching, cross-institutional aggregation, and controlled delivery, but not general platform effectiveness or downstream GeoAI outcomes. The study separates governance entry from technical readiness and identifies governance and technical prerequisites for GeoAI-ready UAV data infrastructure.