Junnan Xia

The Wildland–Human Interface (WHI), where human infrastructure and activities meet or intermingle with wildland vegetation, represents a critical zone of human–environmental conflict. Here, we present the first global map to explicitly differentiate the WHI into its Wildland–Urban Interface (WUI) and Wildland–Agriculture Interface (WAI) components between 2000 and 2020, using a scale-adaptive HEALpix Quadtree algorithm. We show that the global WHI covered 4.87% of terrestrial area in 2020, with the WAI (4.68%) being significantly more extensive than the WUI (0.19%). Over the two decades, the global WUI area expanded dramatically by 59.76%, whereas the WAI area experienced a net decrease of 3.26%. This marginal net change in WAI masks high spatial turnover. Major losses of original WAI due to agricultural expansion were largely compensated by gains in new WAI elsewhere. Notably, 5.03% of the world’s key protected areas were affected by WHI encroachment over this period. Our analysis reveals that the type of WHI has distinctly shaped both wildfire regimes and biodiversity gradients. These findings underscore the critical need for establishing a global WHI monitoring network to mitigate rising threats to ecosystems and human security.

Exploring the evolutionary dynamics of urban, agricultural, and ecological spaces is critical for regional sustainable development and spatial governance. However, traditional spatial simulation methods based on Cellular Automata often struggle to accommodate top-down spatial regulation, non-linear development patterns, and coordinated regional growth. The objective of this scientific research is to address these limitations by proposing a deep learning-based framework for simulating the future distribution of these three spaces. Utilizing the Unet++ model and integrating empirical data sources including multi-period remote sensing land-use mapping and prefecture-level socioeconomic statistical data, the framework predicts regional spatial patterns for the year 2030. Empirical results from the Yangtze River Economic Belt demonstrate that the model achieves high precision in large-scale spatial forecasting (with an average test accuracy of 99.32%) and effectively captures non-linear evolutionary characteristics. Predictions across various growth scenarios reveal that a moderate socioeconomic growth rate facilitates ecological preservation; controlling the expansion of urban space to approximately 20% by 2030 can prevent excessive resource depletion and regional imbalances. Consequently, it is recommended to implement the construction land increment targets outlined in current spatial planning to achieve a balance between economic growth and ecological protection.

The Yangtze River Economic Belt is a crucial driver of China’s economy, and its shoreline is a strategic, finite resource vital for ecological security, flood control, navigation, and socioeconomic development. However, intensive development has resulted in functional conflicts and ecological degradation, underscoring the need for accurate identification and suitability assessment of shoreline functions. Conventional methods, which predominantly rely on land use data and remote sensing imagery, are often limited in their ability to capture dynamic changes in large river systems. This study introduces an integrated framework combining macro-level “Three-Zone Space” (urban, agricultural, ecological) theory with micro-level Point of Interest (POI) data to rapidly identify shoreline functions along the Yichang section of the Yangtze River. We further developed a multi-criteria evaluation system incorporating ecological, production, developmental, and risk constraints, utilizing a combined AHP-Entropy weight method to assess suitability. The results reveal a clear upstream-downstream gradient: ecological functions dominate upstream, while agricultural and urban functions increase downstream. POI data enabled refined classification into five functional types, revealing that ecological conservation shorelines are extensively distributed upstream, port and urban development shorelines concentrate in downstream nodal zones, and agricultural production shorelines are widespread yet exhibit a spatial mismatch with suitability scores. The comprehensive evaluation identified high-suitability units, primarily in downstream urban cores with superior development conditions and lower risks, whereas low-suitability units are constrained by high geological hazards and poor infrastructure. These findings provide a scientific basis for differentiated shoreline management strategies. The proposed framework offers a transferable approach for the sustainable planning of major river corridors, offering insights applicable to similar contexts.

As the foundational carrier of socio-economic development and ecological security, territorial space reflects the degree of coordination between functional structure and efficiency output. However, most existing evaluation methods overlook the heterogeneous functional endowments of spatial units and therefore cannot reasonably assess the efficiency that each unit should achieve under comparable conditions. To address this limitation, this study proposes a function-oriented and interpretable framework for territorial spatial efficiency evaluation based on the Production–Living–Ecological (PLE) paradigm. An entropy-weighted indicator system is constructed to measure production, living, and ecological efficiency, and an XGBoost–SHAP model is developed to infer the nonlinear mapping between functional attributes and efficiency performance and to estimate the ideal efficiency of each spatial unit under Quanzhou’s prevailing macro-environment. By comparing ideal and observed efficiency, functional–efficiency deviations are identified and spatially diagnosed. The results show that territorial efficiency exhibits strong spatial heterogeneity: production and living efficiency concentrate in the southeastern coastal belt, whereas ecological efficiency dominates in the northwestern mountainous region. The mechanisms differ substantially across dimensions. Production efficiency is primarily driven by neighborhood living and productive conditions; living efficiency is dominated by structural inheritance and strengthened by service-related spillovers; and ecological efficiency depends overwhelmingly on local ecological endowments with additional neighborhood synergy. Approximately 45% of spatial units achieve functional–efficiency alignment, while peri-urban transition zones and hilly areas present significant negative deviations. This study advances territorial efficiency research by linking functional structure to efficiency generation through explainable machine learning, providing an interpretable analytical tool and actionable guidance for place-based spatial optimization and high-quality territorial governance.

The land use dynamics of urban, agricultural, and ecological spaces are critical factors influencing land surface temperature (LST); however, the existing methods for describing the spatial carriers of land surface temperature evolution face issues such as granularity effects and projection sensitivity, which hinder effective comparisons across different regions and categories, thus limiting the progress of current research. This study introduces a quadtree-based spatial framework to achieve unified measurements of scale and fragmentation across Urban–Agricultural–Ecological spaces, with an empirical analysis of the Middle Yangtze River Region. Results show that between 2000 and 2020, urban and agricultural spaces expanded while ecological spaces declined, with all three types becoming increasingly fragmented. Urban agglomeration and expansion significantly elevated LST; agricultural spaces exerted relatively limited effects; and ecological fragmentation generated localized cooling but weakened core regulatory functions, ultimately leading to warming within ecological spaces themselves. This study proposes a robust method for spatial identification and fragmentation quantification, revealing the dual role of scale and morphology in regulating regional thermal environments and underscoring the importance of balanced Urban–Agricultural–Ecological configurations for climate-adaptive land use planning.

As the world’s third-longest river supporting 40% of China’s population, the Yangtze River Basin exemplifies the critical challenges of balancing riparian development and ecological resilience for major fluvial systems globally. This study analyzed the spatiotemporal evolution, proximity dynamics to the Yangtze River, and driving mechanisms of the “three types of spaces” (urban, agricultural, and ecological) in 130 counties along the Yangtze River mainstem from 2000 to 2020, utilizing an integrated approach incorporating land use transfer matrices, centroid-based distance metrics and GeoDetector models. Key findings reveal: (1) Urban space exhibited significant irreversible expansion while agricultural space continued to shrink, with ecological space maintaining overall stability but showing high-frequency bidirectional conversion with agricultural areas in localized zones. (2) Spatial proximity analysis demonstrated contrasting patterns—eastern riparian counties showed urban spatial agglomeration towards the river, whereas most mid-western regions experienced urban expansion away from the watercourse, with marked regional disparities in agricultural and ecological spatial changes. (3) Driving mechanism analysis identified topography as the dominant natural factor influencing ecological space evolution, while socioeconomic factors exerted stronger impacts on proximity variations of agricultural and urban spaces, with natural–socioeconomic interactive effects showing the most significant explanatory power. These spatial dynamics reflect universal trade-offs between economic development and ecosystem conservation in large river basins worldwide. We advocate differentiated spatial governance strategies, including rigorous riparian ecological redlines, eco-agricultural models in agricultural retreat zones, and proximity-based real-time monitoring for ecological early warning. The integrated methodology and spatial governance framework offer transferable solutions for sustainable management of major fluvial systems under rapid urbanization pressure. These findings provide scientific evidence and implementable pathways for coordinating socioeconomic development with ecosystem resilience in the Yangtze River Economic Belt.

Previous research has shown that residents’ perceptions significantly influence their residential satisfaction. However, the internal relationships within these perceptions and their impact on satisfaction across different demographic groups remain underexplored. This study, guided by Maslow’s hierarchy and Homo-urbanicus, categorizes residents’ perceptions into four dimensions: safety, convenience, comfort, and aesthetics. Using multi-group structural equation modeling, an empirical investigation was conducted in Wuhan, China. The findings indicate that perceptions of safety, convenience, comfort, and aesthetics exert a chained mediating effect on satisfaction, with the impact pathway being safety → convenience → comfort → aesthetics → satisfaction. Both convenience and comfort also directly influence satisfaction. Notably, perception pathways vary among different demographic groups: women are more influenced by perceptions, younger individuals prioritize convenience and aesthetics, and older adults emphasize safety and comfort. This study highlights demographic differences in perception and satisfaction levels, offering insights into the hierarchical relationships among resident perceptions and their impact pathways on satisfaction. This research suggests enhancing residential satisfaction by optimizing these perceptions, particularly for vulnerable groups. Policy implications include improving human settlement elements at the residential area level, incorporating cultural and innovative elements, and expanding public participation in communities.

Optimizing the urban–agricultural–ecological space in the Yangtze River Economic Belt is integral to China’s sustainable land development and protection. Based on land use data from 2000 to 2020, this study identified the urban-agricultural-ecological space in the Yangtze River Economic Belt. It analyzed its changes and driving forces using the land use transfer matrix, the Dagum Gini coefficient, and GeoDetector. The results show that urban space has increased significantly over the past 20 years, agricultural space has decreased dramatically, and ecological space has remained stable. The transformation of agricultural space into urban space was the dominant type of space transformation, followed by a mutual transformation between agricultural and ecological spaces. Each transformation type exhibited significant spatial inequality within and between regions. Socioeconomic and natural conditions significantly impacted the spatial transformation, and all factors have an apparently interactive reinforcing effect. The research has enhanced the identification accuracy of urban–agricultural–ecological spaces, precisely illustrating the changes and driving forces of the land spatial pattern in the Yangtze River Economic Belt over the last two decades. It holds vital theoretical and practical implications for the optimization of China’s land spatial pattern.