Search Results (1,548)

River and lake ice are sensitive indicators of climate change and important components of hydrological and ecological systems in cold regions. In this study, we develop a simple and transferable “surface water + land surface temperature (LST)” framework on Google Earth Engine to map potential winter ice area across China from 1990 to 2020. The framework enables consistent, large-scale, long-term monitoring without relying on complex remote sensing models or region-specific thresholds. Our results show that, despite a pronounced northwestward shift in the freezing-zone boundary, more than 400 km in the Northeast Plain and about 13 km per year along the eastern coast, the total ice-covered area increased by approximately 1.1% per year. At the same time, the average ice season became slightly shorter. This indicates asynchronous spatial and temporal responses of potential winter ice to warming. We identify a persistent “Northwest–Northeast dual-core” spatial pattern with strong positive spatial autocorrelation, characterized by increasing ice cover in Tibet, Qinghai, Xinjiang, Inner Mongolia, and Northeast China, and decreasing ice cover mainly in Beijing and Yunnan, where intense urbanization and low-latitude warming dominate. Random Forest modeling further shows that water area fraction, nighttime lights, built-up area, altitude, and water–heat indices are the main controls on potential winter ice. These findings highlight the combined influence of hydrological and thermal conditions and urbanization in reshaping potential winter ice patterns under climate change. Full article

Land surface temperature (LST) is essential for studying land–atmosphere energy exchange, the impact of climate change, and its influence on crop yields and hydrology. Although satellite remote sensing provides large-scale LST data, existing spatiotemporal fusion methods face challenges. Traditional algorithms have difficulty with heterogeneous surfaces, and deep-learning models often produce blurred details and inaccurate temperatures, which limits their use in high-precision applications. This study addresses these issues by developing a Deep-Learning Spatial and Temporal Fusion Model (DLSTFM) for Landsat-8 and MODIS LST imagery in Griffith, Australia. DLSTFM employs a dual-branch structure: one branch is dedicated to dual-temporal fusion, and the other branch is dedicated to multi-source feature fusion. Key innovations include the Spatial Adaptive Feature Modulation (SAFM) module, which performs adaptive multi-scale feature fusion, and the Temperature Adaptive Correction Module (TCM), which makes pixel-wise adjustments using reference data. Experiments demonstrate that DLSTFM significantly outperforms traditional methods and existing deep-learning fusion methods. DLSTFM achieves clearer surface features and a mean absolute temperature error of approximately 2.1 K. The model also demonstrated excellent generalization performance in another test area (Ardiethan) without retraining, showcasing its substantial practical value for high-accuracy LST fusion. Full article

Accurate estimation of hourly near-surface air temperature (NSAT) is critical for climate analysis, environmental monitoring, and urban thermal studies. However, existing temperature datasets remain constrained by coarse spatial resolution and limited hourly accuracy. This study systematically evaluates four widely used land surface temperature (LST) datasets—MODIS, ERA5-Land, FY-2F, and CGLS—and five machine learning models (RF, MDN, DNN, XGBoost, and GTNNWR) for NSAT estimation across two contrasting regions in Shaanxi, China: a complex-terrain region in southwestern Shaanxi and the urban area of Xi’an. Results demonstrate that single-source LST inputs outperform multi-source LST stacking, largely due to compounded systematic biases across heterogeneous datasets. MODIS provides the best performance in the mountainous region, while CGLS excels in the urban environment. Among all models, GTNNWR—which explicitly captures spatiotemporal non-stationarity—consistently achieves the highest accuracy, reducing RMSE by 44.8% and 44.2% relative to the second-best model in the two study areas, respectively, whereas the remaining four models exhibit broadly comparable performance. This work identifies effective data–model configurations for generating high-resolution hourly NSAT products and provides methodological insights for climate and environmental applications in regions with complex terrain or strong urban heterogeneity. Full article

Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (<0.079), high BSI (>0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning. Full article

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal remote sensing data from 1985 to 2015, this study takes the Inner Mongolia Hetao Plain as the research area, constructs a “multifunctionality–dynamic evolution” dual-principle classification system for urban–agricultural–ecological space, and adopts the technical process of “separate interpretation of each single land type using the maximum likelihood algorithm followed by merging with conflict pixel resolution” to improve the classification accuracy to 90.82%. Through a land use transfer matrix, a standard deviation ellipse model, surface temperature (LST) inversion, and vegetation fractional coverage (VFC) analysis, this study systematically reveals the spatiotemporal differentiation patterns of spatial structure evolution and surface parameter responses throughout the project’s life cycle. The results show the following: (1) The spatial structure follows the path of “short-term intense disturbance–long-term stable optimization”, with agricultural space stability increasing by 4.8%, the ecological core area retention rate exceeding 90%, and urban space expanding with a shift from external encroachment to internal filling, realizing “stable grain yield with unchanged cultivated land area and improved ecological quality with controlled green space loss”. (2) The overall VFC shows a trend of “central area stable increase (annual growth rate 0.8%), eastern area fluctuating recovery (cyclic amplitude ±12%), and western area local improvement (key patches increased by 18%)”. (3) The LST-VFC relationship presents spatiotemporal misalignment, with a 0.8–1.2 °C anomalous cooling in the central region during the construction period (despite a 15% VFC decrease), driven by irrigation water thermal inertia, and a disrupted linear correlation after completion due to crop phenology changes and plastic film mulching. (4) Irrigation and drainage projects optimize water resource allocation, constructing a hub regulation model integrated with the Water–Energy–Food (WEF) Nexus, providing a replicable paradigm for ecological effect assessment of major water conservancy projects in arid regions. Full article

Rapid human population growth accelerates biodiversity loss through urban habitat fragmentation, yet ecologically informed urban planning can mitigate these effects. This study evaluates whether and how vegetation characteristics, as captured by Earth observation data varies across forest habitats in a small Mediterranean city in Italy. The Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) for the Functional Urban Area of Campobasso were derived from multitemporal Landsat 8 imagery (2020–2023) acquired during the growing season and combined with elevation data to account for topographic gradients. Different forest habitats were identified using the regional coeval Carta della Natura (Map of Nature) and were sampled by a random stratified strategy yielding more than 900,000 observations. A linear mixed-effects model was used to model NDVI as a function of NDMI, LST, elevation, and habitat type, while accounting for temporal and spatial dependencies. The model explained a large proportion of NDVI variability (marginal R² = 0.75; conditional R² = 0.85), with NDMI emerging as the strongest predictor, followed by weaker effects of LST and elevation. Habitat differences were also evident: oak-dominated forests (i.e., Quercus frainetto, Q. cerris, and Q. pubescens dominated habitats) exhibited the highest NDVI values, while coniferous plantations (i.e., Pinus nigra dominated habitat) had the lowest; forests dominated by Robinia pseudoacacia and riparian Salix alba showed intermediate vegetation greenness values. These results highlight the ecological importance of oak forests in Mediterranean urban landscapes and demonstrate the value of satellite-based monitoring for capturing habitat variability. The reproducible workflow applied here provides a scalable tool to support habitat conservation and planning in urban environments, also accounting for impending climate change scenarios. Full article

The Semipalatinsk Test Site (STS), one of the most heavily contaminated nuclear test sites globally, presents critical challenges for ecological monitoring and restoration due to long-term radioactive pollution and soil degradation. This study applied the Remote Sensing Ecological Index (RSEI) model to systematically evaluate the spatiotemporal changes in ecological quality at STS from 2003 to 2023. The RSEI model integrated multi-indicator data, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), WET (Wetness), and NDBSI (Normalized Difference Built-up and Soil Index), enabling a comprehensive assessment of ecological dynamics. Results demonstrated a significant improvement in ecological quality, with the RSEI increasing by 29.59% (from 0.345 in 2003 to 0.447 in 2023). PCA results indicated that ecological recovery was primarily influenced by surface temperature, vegetation cover, and soil moisture, with radioactive residues further hindering recovery in severely contaminated zones. The proportion of “Poor” areas declined from 14.99% to 0.61%, while “Moderate” and “Good” areas expanded to 55.76% and 8.87%, respectively. Peripheral regions showed faster recovery due to effective natural and management interventions, while core high-contamination zones (Sary-Uzen) exhibited slower recovery due to persistent radioactive residues. This study highlights the applicability of RSEI for assessing ecological recovery in nuclear test sites and emphasizes the need for targeted remediation strategies. These findings provide valuable insights for global ecological management of nuclear test sites, supporting sustainable restoration efforts. Full article

This study investigates the influence of scattered (irregular) and grid (regular) settlement layouts on local climate and thermal comfort versus rural open areas. Research in Erzurum, Türkiye, utilized 2022 year-round on-site measurements, satellite imagery, and statistical analysis of climatic parameters and the Physiologically Equivalent Temperature (PET) thermal comfort index. Findings reveal distinct climatic responses: scattered urban forms consistently created cooler conditions year-round, exhibiting a winter cold island effect (−1.8 °C in December) and lower summer air temperatures (−3.4 °C in July). According to land surface temperature (LST) results, the grid urban form (−12.1 °C) is 0.9 °C colder than the scattered urban form (−11.2 °C) in winter. The scattered urban form (27.9 °C) is 1.5 °C warmer than the grid urban form (26.4 °C) in summer. The grid urban form exhibits a wind velocity range from 0.2 m/s to 1.2 m/s, and the scattered urban form’s wind velocity ranges from 0.0 m/s to 0.5 m/s. On the other hand, PET analysis indicated scattered forms offered more favorable thermal comfort. Average PET for scattered forms was 16.6 °C in summer and −3.3 °C in winter, compared to grid forms’ 15.1 °C and −4.7 °C, respectively. Wind velocity was a primary determinant, with lower speeds reducing heat loss and improving comfort in cold regions. This highlights urban planning’s critical role in optimizing thermal comfort across climates. Full article

The effects of extreme heat events on Surface Urban Heat Island Intensity (SUHII) were investigated in Lecce (southern Italy) during the summer months (June–August) from 2018 to 2025. The analysis began with the identification of heatwave frequency, duration, and intensity using the Warm Spell Duration Index (WSDI), based on a homogenized long-term temperature record, which indicated a progressive increase in persistent extreme events in recent years. High-resolution ECOSTRESS land surface temperature (LST) data were then processed and combined with CORINE Land Cover (CLC) information to examine the thermal response of different urban fabrics, compact residential areas, continuous/discontinuous urban fabric, and industrial–commercial zones. SUHII was derived from each ECOSTRESS acquisition and evaluated across multiple diurnal intervals to assess temporal variability under both normal and WSDI conditions. The results show a consistent diurnal asymmetry: daytime SUHII becomes more negative during WSDI periods, reflecting enhanced rural warming under dry and highly irradiated conditions, despite overall higher absolute LST during heatwaves, whereas nighttime SUHII intensifies, particularly in dense urban areas where higher thermal inertia promotes persistent heat retention. Statistical analyses confirm significant differences between normal and extreme conditions across all classes and time intervals. These findings demonstrate that extreme heat events alter the urban–rural thermal contrast by amplifying nighttime heat accumulation and reinforcing daytime negative SUHII values. The integration of WSDI-derived heatwave characterization with multi-year ECOSTRESS observations highlights the increasing thermal vulnerability of compact urban environments under intensifying summer extremes. Full article

This study assessed the concentrations and health risks of heavy metals in cabbage (Brassica oleracea) and lettuce (Lactuca sativa) cultivated across three urban land surface temperatures in the Greater Accra Metropolitan Area (GAMA): Atomic (low land surface temperature, LST), Ashaiman (moderate LST), and Korle-Bu (high LST). The objective was to assess the influence of urban land surface temperature on heavy metal accumulation and associated human health risks. Results revealed that arsenic (As) and mercury (Hg) levels were consistently low (≤0.002 mg/kg) and remained below the maximum residue limits (MRLs) at all sites. However, cadmium (Cd), lead (Pb), and nickel (Ni) concentrations exceeded MRLs in both vegetables. Cd ranged from 1.40 ± 0.27 mg/kg (lettuce, Ashaiman) to 3.13 ± 0.99 mg/kg (cabbage, Atomic), while Pb varied between 0.90 ± 0.84 mg/kg (lettuce) and 2.62 ± 1.22 mg/kg (cabbage). Ni concentrations exceeded the permissible limit (0.2 mg/kg) across all LST zones, with the highest at Korle-Bu (0.65 ± 0.07 mg/kg). Cumulative heavy metal concentrations increased significantly (p < 0.005) with rising LST, particularly in cabbage. Noncarcinogenic risk assessment indicated that Cd and Ni were the dominant contributors to human health risk, with target hazard quotients (THQ) and hazard indices (HI) exceeding the safety threshold (HI > 1) for both adults and children, especially in Atomic and Korle-Bu. Children were more vulnerable, exhibiting higher exposure levels. Carcinogenic risk (CR) analysis further identified As, Cd, and Ni as the main carcinogens, with total cancer risk (TCR) values across all sites and age groups exceeding the USEPA acceptable range (1 × 10⁻⁶–1 × 10⁻⁴). The findings suggest that increasing urban temperatures exacerbate heavy metal accumulation in leafy vegetables, posing significant noncarcinogenic and carcinogenic health risks, particularly to children. Full article

Mountain forests play a key role in buffering local climate, yet their climate sensitivity is seldom mapped in a way that is directly usable for spatial planning. This study investigates how phenological thermal and vegetation variability are organized within the forested landscape of Namyangju, a mountainous region in central Korea, and derives spatial indicators of forest climate sensitivity. Using monthly, cloud-screened Landsat-8/9 land surface temperature (LST) and normalized difference vegetation index (NDVI) images over a recent multi-year period, we calculated phenological coefficients of variation for 34,123 forest grid cells and applied local clustering analysis to identify belts of high and low variability. Forest areas where LST and NDVI variability simultaneously occupied the upper tail of their distributions (top 5%/10%/20%) were interpreted as climate-sensitivity hotspots, whereas co-located coldspots were treated as microclimatic refugia. Across the mountainous terrain, sensitivity hotspots formed continuous belts along high-elevation ridges and steep, dissected slopes, while coldspots were concentrated in sheltered valley floors. Notably, the most sensitive belts were dominated by high-elevation conifer stands, despite the limited seasonal fluctuation typically expected in evergreen canopies. This pattern suggests that elevation strongly amplifies the coupling between thermal responsiveness and vegetation health, whereas valley-bottom forests act as stabilizers that maintain comparatively constant microclimatic and phenological conditions. We refer to these patterns as “forest climate-sensitivity belts,” which translate satellite observations into spatially explicit information on where climate-buffering functions are most vulnerable or resilient. Incorporating climate-sensitivity belts into forest plans and adaptation strategies can guide elevation-aware species selection in new afforestation, targeted restoration and fuel-load management in upland sensitivity zones, and the protection of valley refugia that support biodiversity, thermal buffering, and hydrological regulation. Because the framework relies on standard satellite products and transparent calculations, it can be updated as new imagery becomes available and transferred to other seasonal, mountainous regions, providing a practical basis for climate-resilient forest planning. Full article

Land surface temperature (LST) is an important indicator of ecosystem sustainability and climate change resilience, particularly in highland watersheds characterized by fast land use and land cover (LULC) changes. In this research, the LST dynamics of the Laut Tawar Sub-watershed, Central Aceh, Indonesia, were investigated, based on Landsat 9 OLI/TIRS 2024 imagery. Supervised classification identified eight land cover categories, and their thermal contrasts were evident: built-up and plantation zones exhibited the highest LST values (25–32 °C), while water bodies and forests acted as natural coolers (9.5–17 °C), with elevation further modulating these patterns by creating cooler microclimates at higher altitudes (>2000 m), highlighting the impact of topography in generating microclimatic diversity. Intermediate values were shown for the moderate and sparse forest areas, which thus worked as transition zones with low cooling capabilities. Natural land covers contributed to thermal regulation, whereas built-up and agricultural expansion exacerbated surface heat and possible urban heat island (UHI) effects. This research highlights the importance of protecting forests and water bodies, controlling land conversion, and applying targeted green infrastructure informed by the thermal disparities and land cover dynamics observed. Full article

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R² = 0.27) but substantially stronger along arterial roads with continuous vegetation (R² = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning. Full article

Groundwater depletion is among the most critical hydrological threats to sustainable agriculture and water security in semi-arid regions. This study presents a high-resolution, multi-sensor assessment of groundwater storage (GWS) dynamics across the Lower Chenab Canal (LCC) command area in Punjab, Pakistan—an intensively irrigated agro-hydrological system within the Indus Basin. We integrated downscaled GRACE/GRACE-FO-derived total water storage anomalies with CHIRPS precipitation, MODIS evapotranspiration (ET) and vegetation indices, TerraClimate soil moisture, land surface temperature (LST), land use/land cover (LULC), and population density using the Google Earth Engine (GEE) platform to reconstruct spatiotemporal GWS changes from 2002 to 2020. The results reveal a persistent and accelerating decline in groundwater levels, averaging 0.52 m yr⁻¹, which intensified to 0.73 m yr⁻¹ after 2014. Cumulative GWS losses exceeded 320 mm yr⁻¹, with severe depletion (up to −3800 mm) in northern districts such as Sheikhupura, Gujranwala, and Narowal. Validation with borewell data (R² = 0.87; NSE = 0.85) confirms the reliability of the remote sensing estimates. Statistical analysis indicates that anthropogenic drivers (population growth, urban expansion, and intensive irrigation) explain over two-thirds of the observed variability (R² = 0.67), whereas precipitation contributes only marginally (R² = 0.28), underscoring the dominance of human-induced stress over climatic variability. The synergistic rise in evapotranspiration, land surface temperature, and cultivation of high-water-demand crops such as rice and sugarcane has further amplified hydrological imbalance. This study establishes an operational framework for integrating satellite and ground-based observations to monitor aquifer stress at basin scale and highlights the urgent need for adaptive, data-driven groundwater governance in the Indus Basin. The approach is transferable to other data-scarce semi-arid regions facing rapid aquifer depletion, aligning with the global targets of Sustainable Development Goal 6 on water sustainability. Full article

The urban heat island (UHI) effect poses significant challenges to cities worldwide, particularly in regions like Thessaloniki, Greece, where rising temperatures exacerbate urban living conditions. This study investigates the effectiveness of sustainable urban planning strategies in mitigating the UHI effect by analyzing the spatial distribution of Land Surface Temperature (LST) during the summer heatwave of 2023. Utilizing LANDSAT 8–9 satellite imagery processed with QGIS, we calculated LST, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). Additionally, urban structure data from OpenStreetMap (OSM) was integrated to assess the urban fabric. Our findings reveal significant spatial temperature variations, with densely built-up areas, such as the old town and industrial district, exhibiting higher LSTs compared to greener spaces. Based on these results, we propose targeted interventions, including the large-scale implementation of green roofs and the use of light-colored asphalts, which have shown potential for substantial LST reduction. This work underscores the importance of integrating these strategies into a standardized urban planning framework to enhance urban resilience, providing a model that can be applied to other European cities facing similar climate challenges. Full article