Search Results (1,298)

Cultivated land fragmentation has become a critical constraint on regional agricultural sustainable development. Revealing its spatial patterns and driving mechanisms is of great significance for optimizing the utilization and management of cultivated land resources and enhancing regional agricultural productivity. This study focuses on the main grain-producing areas in the middle and lower reaches of the Yangtze River Basin. It constructs a Cultivated Land Fragmentation Index (CLFI) using an integrated method that combines landscape index analysis with an entropy-weighted approach, based on 2023 land-use data. The optimal analytical grain size and extent were determined before employing geographic detectors to identify dominant factors influencing cultivated land fragmentation. The key findings include the following: (1) The appropriate spatial resolution for fragmentation analysis was identified as 330 m, with an optimal analysis extent of 8910 m. (2) CLFI values ranged from 0.001 to 0.973, exhibiting significant spatial heterogeneity. The central plains and northeastern regions demonstrated low fragmentation levels and better contiguous cultivated land distribution, while the western and peripheral areas showed higher fragmentation. A provincial-scale comparison revealed that Jiangxi Province had the highest fragmentation level (0.255), whereas Jiangsu Province had the lowest (0.146). The topographic gradient analysis indicated a decreasing trend from the Guizhou Plateau (0.503) to the North China Plain (0.125), with plateaus and basins showing significantly higher fragmentation than hilly and plain regions. (3) Dominant controlling factors varied among provinces: In provinces with greater topographic relief (Anhui, Hubei, Hunan, Jiangxi), natural factors like elevation, slope gradient, and NDVI primarily controlled fragmentation patterns; in contrast, socioeconomic factors such as nighttime light intensity dominated in Jiangsu Province, characterized by flat terrain and high urbanization. Multi-factor interactions generally enhanced explanatory power regarding spatial patterns, confirming that cultivated land fragmentation is a result of comprehensive multi-factor interactions. This study reveals the spatial distribution characteristics of cultivated land fragmentation at the pixel scale in the study region, providing theoretical foundations and decision-making references for the efficient utilization of cultivated land resources and rural land system reforms. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

As modern urban systems advance, video surveillance has become indispensable for ensuring high-quality urban development. Nighttime images acquired in urban monitoring scenarios are often degraded by haze and non-uniform illumination, resulting in reduced visibility, color distortion, and blurred structural boundaries. To address these issues, this paper proposes a nighttime image dehazing framework that combines mixed-norm variational atmospheric-light estimation with adaptive boundary-constrained transmission refinement. Specifically, an  L₂ − L_p mixed-norm regularization model is introduced to improve atmospheric-light estimation under complex nighttime illumination and suppress halo diffusion and color distortion around strong light sources. In addition, an adaptive boundary-constrained transmission refinement strategy with weighted soft-threshold shrinkage is developed to reduce residual artifacts while preserving structural edges. Experimental results on synthetic and real nighttime haze datasets demonstrate that the proposed method consistently outperforms representative state-of-the-art methods in both visual quality and quantitative metrics, showing superior robustness and restoration performance for nighttime urban monitoring applications. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

Net primary productivity (NPP) is a critical indicator of carbon sequestration and biomass accumulation in terrestrial ecosystems, directly reflecting ecosystem carbon sink capacity. Existing NPP studies have primarily emphasized climate-driven interannual variability. Spatially explicit analyses that jointly quantify multi-factor driving mechanisms, thresholds, and land-use transition risks remain limited. Here, we develop an integrated multi-method analytical workflow (DRIBS) that integrates Distributional Response, Informative Boundary constraints, and Spatial Interpretability Optimization, and apply it to the Jiziwan region in the Yellow River Basin, one of China’s major ecological restoration hotspot regions. From 2000 to 2020, the annual increasing rate of NPP was 5.80 gC·m⁻²·yr⁻¹, and 78% of the area showed a significant increasing trend. Among them, grasslands and croplands in the eastern and western parts exhibited strong fluctuations and low long-term stability. Evapotranspiration (ET) and fractional vegetation cover (FVC) were the dominant drivers of NPP spatial heterogeneity, and precipitation around ~220 mm marked a critical water-stress threshold. Population density and nighttime lights showed a non-linear “ecological adaptation window”, implying both disturbance and management potential. Land-use transitions exhibited divergent risk signatures: grassland/cropland-to-forest transitions produced stable enhancement (priority restoration zones), whereas cropland/unused-to-urban transitions were associated with degradation risk (urgent management). Overall, DRIBS provides an interpretable “change-mechanism-threshold-risk” assessment to support carbon-sink regulation and restoration prioritization in arid and semi-arid regions. Full article

This study develops a framework to capture spatiotemporal population dynamics and assess future earthquake exposure risk, using Hubei Province as a case study. Future population changes at the county level were projected under different shared socioeconomic pathways (SSPs). These projections were then integrated with NPP-VIIRS nighttime light data and the normalized difference vegetation index (NDVI) to simulate the spatiotemporal dynamics of the population from 2020 to 2070 at a 500 m grid resolution. Combined with seismic hazard zoning, the evolution of population exposure risk under different pathways was assessed. The results indicate the following: 1. Different SSPs profoundly influence future population exposure patterns. Under the SSP3 (regional rivalry) pathway, population growth is the fastest with the strongest agglomeration effect and significantly elevated exposure levels. 2. The refined spatiotemporal population model can more realistically reveal the heterogeneity and evolutionary trajectory of population distribution, providing a high-precision data foundation for exposure analysis and effectively enhancing the scientific rigor of risk assessment. 3. Population exposure risk under various pathways exhibits distinct spatiotemporal dynamics, and monitoring its evolution under different scenarios helps identify high-risk counties that require priority attention. This study is expected to provide precise scientific evidence for implementing differentiated disaster prevention and mitigation strategies and territorial spatial resilience planning in Hubei Province, while it demonstrates the forward-looking value of combining long-term scenario simulations with refined exposure assessments. Full article

Utilizing MODIS LST data from 2003 to 2024, in conjunction with multi-source remote sensing data including DEM, land use, NDVI, and nighttime lights, this study conducts a remote sensing quantitative assessment and spatiotemporal characteristic analysis of the urban heat island (UHI) effect in Yinchuan City. An improved urban-rural dichotomy approach was adopted to select rural background areas, and elevation correction of land surface temperature was performed based on the zonal ordinary least squares (OLS) regression to eliminate systematic errors caused by topographic differences. The results show that: (1) From 2003 to 2024, the overall intensity of the UHI in Yinchuan City showed a slight downward trend, while the UHI area continued to expand, presenting the characteristics of “decreasing intensity and expanding scope”; (2) The UHI exhibited concentrated and contiguous distribution in summer, and the cold island phenomenon was significant in winter, reflecting the typical seasonal contrast between summer and winter; (3) The global Moran’s I value increased from 0.39 to 0.82, indicating a significant enhancement in the spatial agglomeration of the UHI; (4) The standard deviation ellipse analysis revealed that the centroid of the UHI migrated toward the westward as a whole, which was consistent with the main axis of urban construction. The research results reveal the long-term evolution law and spatial pattern characteristics of the UHI effect in Yinchuan City, and provide a scientific reference for ecological planning and thermal environment regulation of cities in arid regions. These findings enhance the understanding of long-term urban thermal environment dynamics and provide important scientific support for sustainable urban planning, climate adaptation, and ecological management in arid regions. The study contributes to the quantitative monitoring of urban environmental sustainability and supports sustainable development goals related to climate action and sustainable cities. Full article

Chronic exposure to artificial light at night (ALAN) is increasingly recognized as an environmental risk factor that disrupts circadian regulation of endocrine and metabolic systems. In this study, we investigated the effects of extreme ALAN on oxidative stress and gut microbiota composition in mice using two complementary experiments. In Experiment 1, adult female mice were maintained under either as a standard 12 h light/12 h dark cycle (12 h group) or continuous 24 h light exposure (24 h group) throughout pregnancy and lactation. In Experiment 2, the offspring from the 12 h group were maintained under the same photoperiod, whereas offspring from the 24 h group were divided into a 12 h light/12 h dark group or a continuous 24 h light group, with treatments initiated on postnatal day 19 and continued until 2 months of age. For all 12 h groups, light exposure occurred from 8:00 to 20:00. Compared with dams in the 12 h group, dams exposed to continuous light exhibited significantly increased catalase activity, while their offspring maintained under the 12 h photoperiod showed elevated glutathione levels. No significant changes were detected in immune organ indices. These results suggest that extreme ALAN modulates antioxidant defenses, potentially reflecting adaptive responses to oxidative stress. Moreover, offspring exposed early to extreme ALAN showed significantly reduced gut microbial α-diversity, accompanied by decreased abundances of Firmicutes, Bacteroidota, Campylobacterota, and Desulfobacterota, and an increase in Proteobacteria. Notably, Verrucomicrobiota and Akkermansia failed to recover following photoperiod normalization, indicating persistent microbiota dysbiosis. Overall, these findings demonstrate that extreme ALAN induces oxidative stress and long-lasting alterations in gut microbiota composition, highlighting potential health risks associated with night-time light pollution. Full article

China’s rapidly aging population poses a significant challenge to the equitable allocation of primary healthcare resources. Conventional accessibility assessments often rely solely on economic indicators, overlooking the ecological constraints that shape human settlement and service provision. To address this problem, this study proposes a socio-ecological framework integrating remote sensing data with spatial accessibility modeling. This study employs the Vegetation Nighttime Condition Index (VNCI)—a fusion of VIIRS nighttime lights and MODIS NDVI—as a proxy for human activity intensity under ecological constraints. The spatial accessibility of primary healthcare for the elderly (aged 65+) is evaluated across 31 provinces in mainland China using the Enhanced Two-Step Floating Catchment Area (2SFCA) method. Furthermore, a coupling coordination model and the Relative Development Index (RDI) are applied to examine the relative alignment between healthcare accessibility and the socio-ecological development context represented by VNCI. Empirical results reveal a distinct East–West gradient. Eastern coastal regions exhibit high accessibility; however, the coupling analysis identifies that healthcare accessibility lags behind high socio-ecological development intensity (low RDI). Conversely, western and rural regions generally suffer from a “low-level trap,” characterized by both low accessibility and weak socio-ecological coordination. The findings demonstrate that satellite-derived indices like VNCI effectively capture fine-scale human-environment interactions, offering a basis for spatially differentiated healthcare planning. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

Reliable real-time detection and counting of tuna during distant-water deck operations is critical for automated catch monitoring but remains challenging due to strong illumination variation, background clutter, and frequent occlusion. This study proposes YOLOv10n-EMCNet, an improved lightweight detector based on YOLOv10n, integrating an ESC-based C2f enhancement in the backbone, a Multi-Branch and Scale Modulation-Fusion Feature Pyramid Network (SMFPN) in the neck, and a Convolutional Attention Fusion Module (CAFM) in the head for fine-grained representation and multi-scale feature fusion. An end-to-end detection–tracking–counting pipeline is further constructed by combining the detector with DeepSORT and an ROI-based de-duplication strategy. On the tuna dataset, YOLOv10n-EMCNet achieved 94.84% mAP@0.5, 65.29% mAP@0.5:0.95, and 91.77% recall with 6.5 GFLOPs. In addition, a controlled comparison among DeepSORT, ByteTrack, and OC-SORT on challenging videos showed that DeepSORT provided the best overall balance between counting accuracy, identity stability, and runtime efficiency. In shipboard video validation on four representative videos covering daytime high glare, nighttime low light, dense occlusion, and dense multi-target, the proposed pipeline achieved an average counting accuracy of 91.4%, with an average relative error of 8.62% and an average absolute error of 1.25 fish per video, while operating at approximately 30 FPS on an RTX 4090D platform. These results provide encouraging preliminary evidence that the proposed method can support automated tuna monitoring under representative shipboard conditions. Full article

The dynamic interplay between urban polycentric structure and habitat quality profoundly shapes the pathways and outcomes of urban sustainable development. Based on the nighttime light index and economic aggregate data of 289 county-level units in the Yangtze River Delta region (2008–2023), this study identifies a polycentric urban structure and measures the polycentricity index of 41 prefecture-level cities. An ecological environment quality evaluation index system is constructed to analyze their influencing factors, followed by an exploration of the trade-off and synergy relationship between the two systems. The main findings are: (1) Both the nighttime light-based urban structure index and economic aggregate index exhibited a circularly decreasing pattern centered on Shanghai. (2) The “nighttime light–economic aggregate” polycentricity index showed a significant spatial “point–core” distribution, with gradually expanding outward diffusion over time. (3) The ecological environment quality achieved significant overall improvement, with better conditions in the southeast than the northwest. (4) Pearson correlation analysis confirms a significant positive correlation between a polycentric urban structure and ecological environment quality; the relationship is categorized into four types: strong trade-off, weak trade-off, weak synergy, and strong synergy. This study provides solid theoretical support and scientific decision-making guidance for urban future development planning and ecological protection practices. Full article

Satellite remote sensing has rapidly evolved from an experimental support tool into a structural component of preventive archaeology and cultural heritage governance. Drawing on scientific publications and policy-oriented grey literature from 2010–2025, this study provides an integrated review of how optical, SAR, and multi-sensor satellite data are used to detect archaeological sites, monitor landscape and structural change, and support risk-informed planning across diverse legal and institutional contexts. A multi-platform workflow combines AI-assisted semantic querying (Consensus), bibliometric searches (Scopus), and the collaborative management and geospatial visualisation of references through Zotero, VOSviewer (1.6.19), and QGIS (3.44)-based literature mapping, thereby linking thematic trends, co-authorship networks, and geographical patterns of research and regulation. The results show non-linear but marked publication growth, a strongly interdisciplinary profile, and the consolidation of international hubs that drive advances in Sentinel-2-based prospection, Landsat and night-time lights urbanisation metrics, and SAR time series for deformation, looting, and conflict-damage mapping. Parallel analysis of grey literature and institutional initiatives (Copernicus Cultural Heritage Task Force, national “extraordinary plans”, regional declarations, and UNESCO guidelines) reveals the codification of satellite Earth observation within rescue archaeology protocols, emergency archaeology, and long-term conservation strategies. Overall, the evidence indicates a transition towards data-driven, multi-sensor, and multi-scalar research, underpinned by open satellite data, reproducible workflows, and AI-supported evidence synthesis. Full article

Under China’s “Dual Carbon” goals, the electric vehicle (EV) industry has expanded rapidly, while the imbalance between supply and demand in public charging infrastructure (PCI) has emerged as a critical bottleneck. Accordingly, a structural assessment of PCI demand potential is essential for improving planning effectiveness. Focusing on the seven municipal districts of Qingdao, this study developed a dual-dimensional framework integrating physical space and population activity. Five core factors were incorporated: road network accessibility, road network betweenness, POI functional mixing density, population distribution density, and nighttime light intensity. By integrating Spatial Design Network Analysis (sDNA), Kernel Density Estimation (KDE), and the entropy weighting method, we conducted a structural assessment of PCI demand potential and derived spatial demand tiers and hierarchy. The results indicate that: (1) road network betweenness had the highest weight (0.396), acting as the dominant driver of structural demand potential, followed by POI functional mixing density (0.271), whereas nighttime light intensity (0.151) and population distribution density (0.143) functioned as baseline supportive indicators; (2) spatial demand was classified into five levels (Levels 1–5), with Level 1 hotspots exhibiting a radial spatial structure characterized by “one primary core, four secondary cores, three corridors, and multiple nodes”; and (3) while the existing PCI distribution exhibited overall gradient consistency with the structurally derived demand tiers, quantitative deviation results indicated localized mismatches, including under-allocation in high-demand areas and over-allocation in selected lower-demand pockets. The proposed dual-dimensional framework facilitates the identification of structural demand gradients for PCI by explicitly incorporating traffic-flow potential, functional aggregation, and population concentration. These findings provide planning-oriented diagnostic support for PCI configuration and contribute to the sustainable transformation of urban transportation systems in megacities. Full article