Search Results (5,859)

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research. Full article

Coal spontaneous combustion in arid regions poses severe threats to both ecological security and resource sustainability. Focusing on the detection challenges in Fire Zone No. 1 of the Miaoergou Coalfield, Xinjiang, this study proposes an Integrated Geophysical Collaborative Detection Framework that combines high-precision magnetic surveys, spontaneous potential (SP) measurements, and transient electromagnetic (TEM) methods. This innovative framework effectively overcomes the limitations of traditional single-method detection approaches, enabling the precise delineation of fire zone boundaries and the accurate characterization of spatial dynamics of coal fires. The key findings of the study are as follows: (1) High-magnetic anomalies (with a maximum ΔT of 1886.3 nT) exhibit a strong correlation with magnetite-enriched burnt rocks and dense fracture networks (density > 15 fractures/m), with a correlation coefficient (R²) of 0.89; (2) Negative SP anomalies (with a minimum SP of −38.17 mV) can effectively reflect redox interfaces and water-saturated zones (moisture content > 18%), forming a “positive–negative–positive” annular spatial structure where the boundary gradient exceeds 3 mV/m; (3) TEM measurements identify high-resistivity anomalies (resistivity ρ = 260–320 Ω·m), which correspond to non-waterlogged goaf collapse areas. Spatial integration analysis of the three sets of geophysical data shows an anomaly overlap rate of over 85%, and this result is further validated by borehole data with an error margin of less than 10%. This study demonstrates that multi-parameter geophysical coupling can effectively characterize the thermo-hydro-chemical processes associated with coal fires, thereby providing critical technical support for the accurate identification of fire boundaries and the implementation of disaster mitigation measures in arid regions. Full article

The Antas North mine, located in the southeastern Amazonian Craton within the Carajás Mineral Province, is hosted by mafic and felsic metavolcanic rocks that have undergone extensive hydrothermal alteration. Field and petrographic data reveal a hydrothermal sequence comprising sodic (albite), potassic (biotite + scapolite), calcic (amphibole + apatite), silicification (quartz), and propylitic (chlorite + epidote + calcite) assemblages. Copper–gold mineralization, spatially associated with calcic alteration, occurs as massive sulfide lenses, breccia zones, and vein networks dominated by chalcopyrite, pyrrhotite, and pyrite. The absence of magnetite/hematite and the dominance of sulfides and ilmenite classify Antas North as an Iron Sulfide–Copper–Gold (ISCG) system, representing a reduced endmember within the broader IOCG spectrum. New U–Pb titanite geochronology yields two concordant age populations at ca. 2476.6 ± 15.9 Ma Ga and 2162.9 ± 28.1 Ma Ga, recording a late Archean mineralizing stage and subsequent Paleoproterozoic reactivation during the Transamazonian orogeny. These ages parallel the multistage evolution recognized in other Carajás IOCG deposits, where copper–gold-related mineralization was repeatedly overprinted by later tectono-hydrothermal events. The reduced character of Antas North, marked by ilmenite and sulfide dominance with scarce magnetite, demonstrates that reduced IOCG styles were already established in the Neoarchean–Paleoproterozoic transition and underscores the diversity of mineralizing processes within the Carajás IOCG–IOA spectrum. Full article

Vegetation phenology (VP) is a crucial biological indicator for monitoring terrestrial ecosystems and global climate change. However, VP monitoring using traditional remote sensing vegetation indices has significant limitations in precise analysis. Furthermore, most studies have overlooked the distinction between stable and short-term VP in relation to climate change and have failed to clearly identify the seasonal variation in the impact of climatic factors on stable VP (SVP). This study compared the accuracy of solar-induced chlorophyll fluorescence (SIF) and three traditional vegetation indices (e.g., Normalized Difference Vegetation Index) for estimating SVP in China, using ground-based data for validation. Additionally, this study employs Sen’s slope, the Mann–Kendall (MK) test, and the Hurst index to reveal the spatiotemporal evolution of the Start of Season (SOS), End of Season (EOS), and Length of Growing Season (LOS) over the past two decades. Partial correlation analysis and random forest importance evaluation are used to accurately identify the key climatic drivers of SVP across different climate zones and to assess the seasonal contributions of climate to SVP. The results indicate that (1) phenological metrics derived from SIF data showed the strongest correlation coefficients with ground-based observations, with all correlation coefficients (R) exceeding 0.69 and an average of 0.75. (2) The spatial distribution of SVP in China has revealed three primary spatial patterns: the Tibetan Plateau, and regions north and south of the Qinling–Huaihe Line. From arid, cold-to-warm, and humid regions, the rate of SOS advancement gradually increases; EOS transitions from earlier to nearly unchanged; and the rate of LOS delay increases accordingly. (3) The spring climate primarily drives the advancement of SOS across China, contributing up to 70%, with temperatures generally having a negative effect on SOS (r = −0.53, p < 0.05). In contrast, EOS is regulated and more complex, with the vapor pressure deficit exerting a dual ‘limitation–promotion’ effect in autumn (r = −0.39, p < 0.05) and summer (r = 0.77, p < 0.05). This study contributes to a deeper scientific understanding of the interannual variability in SVP under seasonal climate change. Full article

Over the past three decades, the Yangtze River Delta has undergone a rapid urbanization phenomenon, resulting in pronounced urban sprawl that has significantly impacted regional sustainable development and air quality. This study constructs an urban sprawl index based on nighttime light data spanning 2000–2020 and employs exploratory spatio-temporal analysis, panel data models, and spatial econometric models to examine the evolution of urban sprawl and its effects on PM_2.5 concentrations. The results reveal four key findings: (1) Urban sprawl is spatially heterogeneous, exhibiting a ‘high in the centre-east, low in the north-west’ pattern, with high-intensity sprawl expanding from the central region towards the north-west and south-west; (2) The dominant growth pattern is characterized by relatively rapid expansion. The global Moran’s I index fluctuates between 0.428 and 0.214, indicating a gradual decline in the global clustering effect of urban sprawl. Meanwhile, the share of local high–high agglomeration zones decreases to 21.9%, whereas low–low zones increase to 24.3%; (3) Spatio-temporal transitions of urban sprawl show strong spatial dependence while overall relocation exhibits inertia; (4) Before the implementation of the Ten Key Measures for Air Pollution Prevention and Control in 2013, urban sprawl significantly intensified PM_2.5 pollution. Following the policy, this relationship notably reversed, with sprawl exhibiting pollution-mitigating effects in certain regions. The spatial diffusion of pollution is evident, as urban sprawl influences air quality through both local development and inter-regional interactions. This study provides an in-depth analysis of the spatio-temporal evolution of urban sprawl and establishes a framework to examine the interactive mechanisms between urban expansion and air pollution, thereby broadening perspectives on atmospheric pollution research and offering scientific and policy guidance for sustainable land use and air quality management in the Yangtze River Delta. Full article

Classifying multiband images acquired by advanced sensors, including those mounted on satellites, is a central task in remote sensing and environmental monitoring. These sensors generate high-dimensional outputs rich in spectral and spatial information, enabling detailed analyses of Earth’s surface. However, the complexity of such data presents substantial challenges to achieving both accuracy and efficiency. To address these challenges, we tested the ensemble learning framework based on ResNet50, MobileNetV2, and DenseNet201, each trained on distinct three-channel representations of the input to capture complementary features. Training is conducted on the LCZ42 dataset of 400,673 paired Sentinel-1 SAR and Sentinel-2 multispectral image patches annotated with Local Climate Zone (LCZ) labels. Experiments show that our best ensemble surpasses several recent state-of-the-art methods on the LCZ42 benchmark. Full article

Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their spatial heterogeneity and the multivariate nature of pollution dynamics. This study presents an integrated framework for detecting spatiotemporal pollution patterns using satellite remote sensing, trend segmentation, hierarchical clustering and dimensionality reduction. Taking Horseshoe Lake (Illinois), a shallow eutrophic–turbid system, as a case study, we analyzed Sentinel-2 imagery from 2020–2024 to derive chlorophyll-a (NDCI), turbidity (NDTI), and total phosphorus (TP) across five hydrologically distinct zones. Breakpoint detection and modified Mann–Kendall tests revealed both abrupt and seasonal trend shifts, while correlation and hierarchical clustering uncovered inter-zone relationships. To identify lake-wide pollution windows, we applied Kernel PCA to generate a composite pollution index, aligned with the count of increasing trend segments. Two peak pollution periods, late 2022 and late 2023, were identified, with Regions 1 and 5 consistently showing high values across all indicators. Spatial maps linked these hotspots to urban runoff and legacy impacts. The framework captures both acute and chronic stress zones and enables targeted seasonal diagnostics. The approach demonstrates a scalable and transferable method for pollution monitoring in morphologically complex lakes and supports more targeted, region-specific water management strategies. Full article

This study presents the first comprehensive IUCN-based assessment for all 88 Limonium species occurring in Greece, aiming to close a critical conservation gap for this highly diverse and important genus in the country. To identify patterns of extinction risk, we applied the IUCN Red List Categories and Criteria, integrating data on endemism, ploidy, and anthropogenic threats. Moreover, we employed spatial analysis to identify conservation hotspots, and we statistically analyzed how threat status changes across geographic space. Our results show that 51 species (58.0%) are threatened, with endemics (62.3%) exhibiting a significantly higher risk than non-endemics. A greater proportion of diploid species were also found to be threatened compared to their polyploid counterparts. Longitude was identified as a key spatial predictor of threat, with risk concentrated in southern and western coastal zones. The most prevalent threats are coastal development (56.9% of threatened species) and invasive species (33.3%). This work provides a vital baseline for Limonium conservation, highlighting the urgent need for a dual conservation strategy that combines efficient in situ actions with ex situ measures for the most imperiled species. Full article

Urban parks play a crucial role in mitigating urban heat stress and maintaining ecological stability through their cold island effect (PCIE). However, studies examining how multidimensional urban morphology influences these effects, particularly from a diurnal perspective in island cities, remain limited. This study investigates 30 representative urban parks within a typical island city, exploring how two-dimensional and three-dimensional spatial morphological factors affect four key PCIE indicators: park cooling intensity (PCI), park cooling gradient (PCG), park cooling area (PCA) and park cooling efficiency (PCE) across different times of day and night. The results reveal that: (1) coastal zones exhibit significantly lower land surface temperature (LST) than inland zones, with peak LST occurring at 5:00 p.m.; (2) the four cold island indicators follow a diurnal pattern of 5:00 p.m. > 1:00 a.m. > 7:00 a.m.; (3) morphological construction factors—such as building density (BD) and built-up proportion (BP)—positively contribute to cooling effects at 7:00 a.m., while park perimeter (PP) enhances cooling performance at both 5:00 p.m. and 1:00 a.m. Additionally, vegetation characteristics surrounding parks, including the normalized difference vegetation index (NDVI) and green space proportion (GP), influence daytime cooling in directions opposite to those of the aforementioned construction-related factors. These findings offer valuable insights into the temporal dynamics and spatial determinants of urban park cooling in island cities, providing a scientific basis for scientifically informed park planning and contributing to healthier and more sustainable urban development. Full article

Drive-through establishments are becoming increasingly prominent in urban transport systems; however, their impacts on traffic generation, spatial form, and sustainability remain insufficiently understood. Conventional trip generation manuals often rely on static predictors, such as gross floor area, which can misrepresent demand in high-turnover, convenience-driven contexts and fail to capture operational, behavioral, and environmental effects. This knowledge gap underscores the need for an integrated framework that supports both effective planning and congestion mitigation, particularly in cities experiencing rapid motorization and shifting mobility behaviors. This study investigated the evolving dynamics in trip generation associated with drive-through services and their influence on urban development patterns. A mixed-methods approach was employed, combining a systematic literature review, meta-analysis of queue data, cross-comparison of trip generation rates from international and Asian datasets, and case-based scenario modeling. The results revealed that drive-throughs intensify high-frequency, impulse-driven vehicle trips, thereby causing congestion, reducing pedestrian accessibility, and reinforcing auto-centric land use configurations, while also enhancing consumer convenience and commercial efficiency. This study contributes to the literature by synthesizing inconsistencies in regional datasets; introducing a systems-based framework that integrates structural, behavioral, and environmental determinants with road network topology; and outlining policy applications that align trip generation with zoning, design standards, and sustainable infrastructure planning. Full article

Red-bed desertification represents a critical form of land degradation in subtropical regions, yet the coupled soil–vegetation processes remain poorly understood. This study integrates Sentinel-2 vegetation indices with soil fertility gradients to assess vegetation–soil interactions in the Nanxiong Basin of South China. By combining Normalized Difference Vegetation Index (NDVI)-based vegetation classification with comprehensive soil property analyses, we aim to uncover the spatial patterns and driving mechanisms of degradation. The results revealed a clear gradient from intact forests to exposed red-bed bare land (RBBL). NDVI classification achieved an overall accuracy of 77.8% (κ = 0.723), with mixed forests being identified most reliably (97.1%), while Red-Bed Bare Land (RBBL) exhibited the highest omission rate. Along this gradient, soil organic matter, available nitrogen, and phosphorus declined sharply, while pH shifted from near-neutral in forests to strongly acidic in bare lands. Principal component analysis (PCA) identified a dominant fertility axis (PC1, explaining 56.7% of the variance), which clustered forested sites in nutrient-rich zones and isolated RBBL as the most degraded state. The observed vegetation–soil pattern aligns with a “weathering–transport–exposure” sequence, whereby physical disintegration and selective erosion during monsoonal rainfall drive organic matter depletion, soil thinning, and acidification, with human disturbance further accelerating these processes. To our knowledge, this study is the first to directly couple PCA-derived soil fertility gradients with vegetation patterns in red-bed regions. By integrating vegetation indices with soil fertility gradients, this study establishes a process-based framework for interpreting red-bed desertification. These findings underscore the utility of remote sensing, especially NDVI classification, as a powerful tool for identifying degradation stages and linking vegetation patterns with soil processes, providing a scientific foundation for monitoring and managing land degradation in monsoonal and semi-arid regions. Full article

The GFED (Global Fire Emissions Database) series products are widely used in global fire research, yet their applicability in mainland China remains insufficiently evaluated. Additionally, large fires and small fires are rarely studied separately. This study first evaluates GFED4’s applicability for monitoring forest fire burned areas in Chinese mainland (2001–2015) through multi-temporal (annual, seasonal, and monthly) and multi-spatial (national, regional, provincial, and 0.25° grid) analyses, using Pearson correlation (CC), root mean square error (RMSE), and mean error (ME) alongside official statistical data. Then, the forest fire-burned areas of small fires were extracted based on the difference between GFED4 and GFED5. The results show that GFED4 exhibits strong consistency at the national level and in key fire-prone regions such as Northeast, North, and Central South China, especially during high-fire years and in spring. However, systematic overestimation occurs in the Northwest, while underestimation or seasonal bias is observed in parts of East and Southwest China. The results show a clear decline in large-fire burned area, but a significant increase in small fires, particularly in Northeast, Central South, and East China. Spatial analysis indicates small fires exhibit strong clustering (Moran’s I = 0.270, p < 0.01), whereas large fires are spatially dispersed. The study concludes that GFED4 is reliable for monitoring large fires in forested zones but should be applied cautiously in non-forested and small-fire-dominated regions. Full article

The objective of this study is to quantify and characterize ground deformations induced by underground coal mining in the Karaganda coal basin, Kazakhstan, in order to improve the understanding of subsidence processes and their long-term evolution. The SBAS-InSAR method was applied to Sentinel-1 (C-band) and TerraSAR-X (X-band) data from 2019–2021 to estimate the magnitude, extent, and temporal behavior of displacements over the Kostenko, Kuzembayev, Aktasskaya, and Saranskaya mines. The results reveal spatially coherent and progressive deformation, with maximum cumulative LOS displacements exceeding –800 mm in TerraSAR-X data within active longwall mining zones. Time-series analysis confirmed acceleration of displacement during active extraction and its subsequent attenuation after mining ceased. Comparative assessment demonstrated a strong agreement between Sentinel-1 and TerraSAR-X results (r = 0.9628), despite differences in resolution and acquisition geometry, highlighting the robustness of the SBAS-InSAR approach. Analysis of displacement over individual longwalls showed that several panels (3, 5, 8, 15, and 18) already exceeded their projected maximum subsidence values, underlining the necessity of continuous monitoring for ensuring safety. In contrast, other longwalls have not yet reached their maximum deformation, indicating potential for further activity. Overall, this study demonstrates the value of multi-sensor InSAR monitoring for reliable assessment of mining-induced subsidence and for supporting geotechnical risk management in post-industrial regions. Full article

Ecosystem services provide the scientific foundation and optimization objectives for constructing ecological security patterns, and their spatial characteristics directly affect planning decisions such as ecological source identification and corridor layout. However, current methods for constructing ecological security patterns rely excessively on static spatial optimization of landscape structure and ecological processes, while overlooking the dynamic variations in ecosystem service values under climate change. Taking Yunnan Province as a case study, this paper calculates ecosystem service values, analyzes their spatiotemporal variations, and based on ecosystem service value hotspots, applies the MSPA model and circuit theory to identify ecological sources, corridors, pinch points, barrier areas, and improvement areas. On this basis, we construct and optimize the ecological security pattern of Yunnan Province and propose ecological protection strategies. The results show that: (1) From 2000 to 2030, ecosystem service values in Yunnan exhibit significant spatiotemporal heterogeneity. From 2000 to 2020, they first declined and then increased, with aquatic ecosystems contributing the most. Under future climate scenarios, ecosystem service values continue to increase, with the greatest growth under the SSP2-4.5 scenario. The spatial pattern is characterized by higher values in the central region and lower values in the eastern and western areas. (2) In 2020, 56 ecological sources were identified; under the SSP1-1.9 scenario, 61 were identified, while 57 were identified under both SSP2-4.5 and SSP5-8.5 scenarios. These sources are mainly distributed in northwestern Yunnan and the Nujiang and Lancang River basins, presenting a “more in the west, fewer in the east” pattern. (3) In 2020, 132 ecological corridors and 74 pinch points were identified. By 2030, under SSP1-1.9, there are 149 corridors and 84 pinch points; under SSP2-4.5, 135 corridors and 55 pinch points; and under SSP5-8.5, 134 corridors and 60 pinch points. (4) By integrating results across multiple scenarios, an ecological security pattern characterized as “three screens, two zones, six corridors, and multiple points” is constructed. Based on regional ecological background characteristics, differentiated strategies for ecological security protection of territorial space are proposed. This study provides a scientific reference for the synergistic optimization of ecosystem services and ecological security patterns under climate change. Full article

Northern Taiwan is a tectonically and volcanically active region shaped by plate convergence, active faulting, and subsurface hydrological processes. To investigate surface deformation across this complex setting, we applied Persistent Scatterer InSAR (PSInSAR) to Sentinel-1A imagery acquired from 2017 to 2022. Using data from ascending and descending tracks, and removing GNSS-derived northward motion, we decomposed line-of-sight velocities into vertical and eastward components. The resulting deformation fields, validated by dense precision leveling and continuous GNSS observations, reveal consistent but minor (less than 1 cm/year) land subsidence in the Taipei Basin, spatially variable uplift near the Tatun Volcano Group, and a previously vaguely documented uplift zone in northeastern Taoyuan. InSAR-derived eastward motion is consistent with expected kinematics along the southern Shanchiao Fault and supports broader patterns of clockwise tectonic rotation near Keelung. Our InSAR results show the effectiveness of PSInSAR in resolving multidirectional surface motion and exemplifies the value of integrating satellite-based and ground-based geodetic data for fault assessment, hydrologic monitoring, and geohazard evaluation in northern Taiwan. Full article