Search Results (455)

Land surface temperature (LST) is a key indicator reflecting the ecological environmental disturbance caused by open-pit coal mining activities and determining the ecological status in alpine permafrost regions. Thus, it is crucial to study the spatiotemporal variations and influencing mechanisms of LST throughout all stages of small-scale mining–large-scale land surface damage–ecological restoration. Landsat imagery over nine periods was extracted from the growing seasons between 1990 and 2024. This study retrieved LST while simultaneously calculating albedo, soil moisture, and normalized difference vegetation index (NDVI) for each time phase. By integrating land use/cover (LUCC) data, the spatiotemporal evolution patterns of LST in the mining area throughout all stages were revealed. Based on the Geodetector method, an identification approach for factors influencing LST spatial differentiation was established. This approach was applicable to the entire process characterized by significant land type transitions. The results indicate that the spatiotemporal variations in LST were significantly correlated with land surface damage and restoration caused by human activities in the mining area. With the implementation of ecological restoration, high and ultra-high temperatures decreased by about 25.98% compared to the period when the surface damage was the most severe. The main influencing factors of LST differentiation were identified for different land use types, i.e., natural and restored meadows (soil wetness, albedo, and NDVI), mine pits (albedo, aspect, and elevation), and mining waste dumps (aspect and albedo before restoration; aspect and NDVI after restoration). This study can provide a reference for monitoring the ecological environment changes and ecological restoration of global coalfields with the same climatic characteristics. Full article

Urban green spaces (UGS) are crucial for mitigating rising urban land surface temperatures (LST). Rapid urbanization presents unresolved questions regarding (a) seasonal variations in the spatial co-distribution of UGS and LST, (b) the temporal and spatial changes in UGS cooling, and (c) the dominant factors driving cooling effects during different periods. This study focuses on Beijing’s Fifth Ring Road area, utilizing nearly 40 years of Landsat remote sensing imagery and land cover data. We propose a novel nine-square grid spatial analysis approach that integrates LST retrieval, profile line analysis, and the XGBoost algorithm to investigate the long-term spatiotemporal evolution of UGS cooling capacity and its driving mechanisms. The results demonstrate three key findings: (1) Strong seasonal divergence in UGS-LST correlation: A significant negative correlation dominates during summer months (June–August), whereas winter (December–February) exhibits marked weakening of this relationship, with localized positive correlations indicating thermal inversion effects. (2) Dynamic evolution of cooling capacity under urbanization: Urban expansion has reconfigured UGS spatial patterns, with a cooling capacity of UGS showing an “enhancement–decline–enhancement” trend over time. Analysis through machine learning on the significance of landscape metrics revealed that scale-related metrics play a dominant role in the early stage of urbanization, while the focus shifts to quality-related metrics in the later phase. (3) Optimal cooling efficiency threshold: Maximum per-unit-area cooling intensity occurs at 10–20% UGS coverage, yielding an average LST reduction of approximately 1 °C relative to non-vegetated surfaces. This study elucidates the spatiotemporal evolution of UGS cooling effects during urbanization, establishing a robust scientific foundation for optimizing green space configuration and enhancing urban climate resilience. Full article

With the rapid advancement of urbanization, urban heat environment issues have become increasingly severe, presenting significant challenges to sustainable urban development. Although previous research has demonstrated the substantial impact of urban morphology on land surface temperature (LST), there is still a lack of comprehensive research on the non-stationary effects of urban morphology on seasonal LST at the block scale. Therefore, this study establishes a comprehensive research framework, utilizing urban functional zones in the core area of Chongqing as the primary research unit, to investigate the seasonal fluctuations in the spatial distribution of LST across various functional zones. Combining Random Forest (RF) with multiscale geographically weighted regression methods (MGWR), the study systematically analyzes the numerical and spatial distribution characteristics of how urban morphology factors influence LST from global and local perspectives. The results indicate that (1) the LST in central Chongqing exhibits marked seasonal variation and a distinct “mountain-water pattern,” with industrial zones consistently hotter and public service areas cooler; (2) biophysical surface parameters and building morphological indicators make a high relative contribution to LST changes across seasons, particularly in public service and commercial areas; (3) building density (BD) and biophysical surface parameters primarily exert local impacts on LST changes, while floor area ratio (FAR) and building height range (RBH) have a global effect. These findings provide new insights into the driving mechanisms of urban heat environments and offer scientific evidence for regulating and mitigating urban heat environment issues across different seasons and urban types. Full article

Alpine permafrost plays a vital role in regional hydrology and ecology. Alpine permafrost is highly sensitive to climate change and human disturbance. The Muri area, which is located in the headwaters of the Datong River, northeast of the Tibetan Plateau, has undergone decadal mining, and the permafrost stability there has attracted substantial concerns. In order to decipher how and to what extent the permafrost in the Muri area has responded to the decadal mining in the context of climate change, daily MODIS land surface temperatures (LSTs) acquired during 2000–2024 were downscaled to 30 m × 30 m. The active layer thickness (ALT)–ground thaw index (DDT) coefficient was derived from in situ ALT measurements. An annual ALT of 30 m × 30 m spatial resolution was subsequently estimated from the downscaled LST for the Muri area using the Stefan equation. Validation of the LST and ALT showed that the root of mean squared error (RMSE) and the mean absolute error (MAE) of the downscaled LST were 3.64 °C and −0.1 °C, respectively. The RMSE and MAE of the ALT estimated in this study were 0.5 m and −0.25 m, respectively. Spatiotemporal analysis of the downscaled LST and ALT found that (1) during 2000–2024, the downscaled LST and estimated ALT delineated the spatial extent and time of human disturbance to permafrost in the Muri area; (2) human disturbance (i.e., mining and replantation) caused ALT increase without significant spatial expansion; and (3) the semi-arid climate, rough terrain, thin root zone and gappy vertical structure beneath were the major controlling factors of ALT variations. ALT, estimated in this study with a high resolution and accuracy, filled the data gaps of this kind for the Muri area. The ALT variations depicted in this study provide references for understanding alpine permafrost evolution in other areas that have been subject to human disturbance and climate change. Full article

Investigating the diurnal variation characteristics of precipitation (DVCP) in Xinjiang, an arid region of Northwest China, is essential for improving water resource management and disaster mitigation strategies. This study examines the DVCP associated with diverse underlying surfaces in Eastern Xinjiang (EX)—one of the most arid regions in China—during summer (June–August) from 2015 to 2019, using hourly simulation data from the real-time forecasting system of Nanjing University (WRF_NJU). Evaluation against automatic weather station (AWS) observations indicates that WRF_NJU outperforms reanalysis (ERA5), satellite (CMORPH), and MESWEP datasets, demonstrating its reliability for regional precipitation analysis. Further investigation reveals that in the Turpan-Hami Basin (THB), below 1000 m above sea level (ASL), peaks in precipitation amount (PA), intensity (PI), and frequency (PF) occur at 06 local solar time (LST), whereas in mountainous areas above 3000 m ASL, these peaks are delayed until 13 LST. Analysis of the coefficient of variation (CV) shows that the most pronounced differences in DVCP between mountainous and basin regions are associated with PF and PI. Specifically, regions with high CV for PF are concentrated in the central to northern parts of the THB, while high CV for PI is found in the eastern Mid-Tianshan Mountains (MTM) and East Tianshan Mountains (ETM). Moreover, significant differences in DVCP are observed across land surface types: PA peaks over grasslands, forests, and water bodies occur around noon, whereas over impervious surfaces, croplands, and barren areas, they occur during the early morning hours. Full article

The outburst of Zonag Lake in the permafrost region of the Qinghai-Tibet Plateau (QTP) has significantly altered the local environment, particularly affecting surface conditions and permafrost dynamics. By employing remote sensing and GIS tools, this study analyzed the spatial and temporal variations in surface environmental changes (surface temperature, vegetation, and dryness) within the Zonag–Salt Lake basin. The results indicate that the outburst caused higher surface temperatures and reduced vegetation cover around Zonag Lake. Analysis using the Temperature–Vegetation Dryness Index (TVDI) reveals higher dryness levels in downstream areas, especially from Kusai Lake to Salt Lake, compared to the upstream Zonag Lake. Temporal trends from 2000 to 2023 show a decrease in average Land Surface Temperature (LST) and an increase in the Normalized Difference Vegetation Index (NDVI). Geographical centroid shifts in environmental indices demonstrate migration patterns influenced by seasonal climate changes and the outburst event. Desertification around Zonag Lake accelerates permafrost development, while the wetting environment around Salt Lake promotes permafrost degradation. The Zonag Lake region is also an ecologically significant area, serving as a key calving ground for the Tibetan antelope (Pantholops hodgsonii), a nationally protected species. Thus, the environmental changes revealed in this study carry important implications for biodiversity conservation on the Tibetan Plateau. These findings highlight the profound impact of the Zonag Lake outburst on the surface environment and permafrost dynamics in the region, providing critical insights for understanding environmental responses to lake outbursts in high-altitude regions. Full article

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R²) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R² of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R² of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article

The Minimum Spanning Tree (MST) problem addresses the challenge of identifying optimal network pathways for critical infrastructure systems, including transportation grids, communication backbones, power distribution networks, and reliability optimization frameworks. However, inherent uncertainties stemming from disruptive events demand robust analytical models for effective decision-making. This research introduces an uncertainty-theoretic framework to assess MST stability in uncertain network environments through novel constructs: lower set tolerance (LST) and dual lower set tolerance (DLST). Both LST and DLST provide quantifiable measures characterizing the resilience of element sets relative to edge-weighted MST configurations. LST captures the maximum simultaneous risk variation preserving current MST optimality, while DLST identifies the minimal variation required to invalidate it. We evaluate MST robustness by integrating uncertain reliability measures and risk factors, with emphasis on computational methods for set tolerance determination. To overcome computational hurdles in set tolerance derivation, we establish bounds and exact formulations within an uncertainty programming paradigm, offering enhanced efficiency compared with conventional re-optimization techniques. Full article

The climate and environment of metropolitan areas have been negatively impacted by swift urbanization and industrialization. Surface Urban Heat Islands (SUHIs) are among the most critical environmental phenomena. This research focused on the spatiotemporal analysis of land use/land cover (LULC) changes in relation to surface urban heat islands and their interconnections from 1992 to 2022. Land Surface Temperature (LST), LULC, and LULC indices, such as the Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI), were generated using Landsat data. Urban hot spots (UHSs) were identified, and the Urban Thermal Field Variance Index (UTFVI) was then used to evaluate the spatiotemporal variation in thermal comfort. The results indicated LST values between a low of 14.24 and a maximum of 46.30. Urban areas and exposed surfaces, such as open or bare soil, exhibit the highest surface radiant temperatures. Conversely, regions characterized by vegetation and water bodies have the lowest. Additionally, this study explored the correlation between LULC, LULC indices, LST, and SUHIs. LST and NDBI show a positive relationship because of urbanization and industrialization (R² = 0.57 for the year 1992, R² = 0.38 for the year 2010, and R² = 0.35 for the year 2022), while LST shows an inverse relationship with NDVI and NDMI. Urban development should account for thermal sensitivity in densely populated regions. This study introduced an innovative spatiotemporal framework for monitoring long-term changes in urban surface environments. Furthermore, this research can assist planners in creating urban green spaces in cities of developing nations to minimize the adverse impacts of urban heat islands and improve thermal comfort. Full article

Understanding the seasonal nonlinear relationship between urban heat island (UHI) and multidimensional urban morphological patterns is crucial for regulating the urban thermal environment. To address this, this study quantified the contributions and sensitivities of urban morphology to land surface temperature (LST) variations and revealed their influencing pathways across four seasons in Beijing, using automated machine learning, SHapley Additive exPlanations interpretation, partial dependence analysis, and structural equation modeling. The results showed significant seasonal variations at the grid scale of 200 m. It was revealed that Normalized Difference Vegetation Index (NDVI) emerged as the most significant indicator affecting LST, followed by building height (BH) and building coverage ratio (BCR), while sky view factor and frontal area index had the least impact. BH was more influential than NDVI, affecting LST during winter. Additionally, sensitivity analysis revealed that impervious surface area, BCR, and mean building volume had positive relationships with LST. In contrast, NDVI and BH negatively affected LST with a noticeable cooling effect, particularly in summer. Furthermore, the total effects of all indicators on LST were negative, with the greatest in spring and the least in winter. Three-dimensional indicators generally exhibited more pronounced direct and total effects than two-dimensional indicators, except in winter. These findings can offer valuable insights for regulating seasonal surface UHI to maximize thermal environmental benefits. Full article

Total column water vapor (TCWV) is essential for assessing Earth’s radiation budget and hydrological cycle and plays a crucial role in accurate Land Surface Temperature (LST) retrieval from thermal infrared (TIR) imagery. Although TCWV is commonly estimated using near-infrared or microwave observations, TIR-based methods offer an efficient alternative; however, their long-term validation remains limited. This study evaluates TCWV retrieval from Landsat 8/9 Thermal Infrared Sensor (TIRS) using an updated version of the Modified Split-Window Covariance-Variance Ratio (MSWCVR) method, implemented on the Google Earth Engine platform, across Europe. Validation is conducted using AERONET sun photometer measurements (2013–2024) and GPS-based TCWV estimates enhanced with meteorological inputs (2020). Retrieval accuracy is evaluated analyzed in relation to seasonal variations, surface characteristics (e.g., land cover, altitude) and background climate. Results demonstrate robust performance of the TIR-based method, with an average Mean Absolute Error (MAE) of 0.6 gr/cm² across stations and datasets, supporting its applicability for LST retrieval and broader environmental monitoring applications. Full article

The urban heat island (UHI) effect has become a critical environmental issue affecting urban livability and public health, attracting widespread attention from both academia and society. Although numerous studies have examined the influence of urban characteristics on land surface temperature (LST), most have been restricted to single variables or single time points, and the traditional “urban–rural dichotomy” approach fails to capture intra-urban thermal heterogeneity. To address this limitation, this study integrates the Local Climate Zone (LCZ) framework with machine learning techniques to systematically analyze the diurnal variation patterns of LST across different LCZ types in Beijing and explore the interactive effects of urban characteristic variables on LST. The results show the following: (1) Compact building zones (LCZ 1–3) exhibit significantly higher daytime LST than open building zones (LCZ 4–6), with reduced differences at night; high-rise buildings cool daytime surfaces through shading but increase nighttime LST due to heat storage. (2) Blue–green space variables, such as NDVI and tree coverage (T_PLAND), substantially lower daytime LST through evapotranspiration, but their nighttime cooling effect is weak; cropland coverage (C_PLAND) plays a particularly important role in lowering nighttime LST. (3) Blue–green space and urban form variables exhibit significant interaction effects on LST, with contrasting impacts between day and night. (4) Population activity variables are strongly correlated with increased LST, especially at night, when their warming effects are more prominent. This study reveals the relative importance and nonlinear relationships of different variables across diurnal cycles, providing a scientific basis for optimizing blue–green space configuration, improving urban morphology, regulating human activity, and formulating effective UHI mitigation strategies to support the development of more sustainable urban environments. Full article

Homologous recombination deficiency (HRD) is a pivotal biomarker in precision oncology, driving therapeutic strategies for ovarian and breast cancers through impaired DNA double-strand break repair. This narrative review synthesizes recent advances (2021–2025) in HRD’s biological basis, prevalence, detection methods, and clinical implications, focusing on high-grade serous ovarian carcinoma (HGSOC; ~50% HRD prevalence) and triple-negative breast cancer (TNBC; 50–70% prevalence). HRD arises from genetic (BRCA1/2, RAD51C/D, PALB2) and epigenetic alterations (e.g., BRCA1 methylation), leading to genomic instability detectable via scars (LOH, TAI, LST) and mutational signatures (e.g., COSMIC SBS3). Advanced detection integrates genomic assays (Myriad myChoice CDx, Caris HRD, FoundationOne CDx), functional assays (RAD51 foci), and epigenetic profiling, with tools like HRProfiler and GIScar achieving >90% sensitivity. HRD predicts robust responses to PARP inhibitors (PARPi) and platinum therapies, extending progression-free survival by 12–36 months in HGSOC. However, resistance mechanisms (BRCA reversion, SETD1A/EME1, SOX5) and assay variability (60–70% non-BRCA concordance) pose challenges. We propose a conceptual framework in ^{Section 10}, integrating multi-omics, methylation analysis, and biallelic reporting to enhance detection and therapeutic stratification. Regional variations (e.g., Asian cohorts) and disparities in access underscore the need for standardized, cost-effective diagnostics. Future priorities include validating novel biomarkers (SBS39, miR-622) and combination therapies (PARPi with ATR inhibitors) to overcome resistance and broaden HRD’s applicability across cancers. Full article

This study investigates long-term land surface temperature (LST) trends along the shoreline of Lake Drūkšiai, a transboundary lake in eastern Lithuania that formerly served as a cooling reservoir for the Ignalina Nuclear Power Plant (INPP). Although the INPP was decommissioned in 2009, its legacy continues to influence the lake’s thermal regime. Using Landsat 8 thermal infrared imagery and NDVI-based methods, we analysed spatial and temporal LST variations from 2013 to 2024. The results indicate persistent temperature anomalies and elevated LST values, particularly in zones previously affected by thermal discharges. The years 2020 and 2024 exhibited the highest average LST values; some years (e.g., 2018) showed lower readings due to localised environmental factors such as river inflow and seasonal variability. Despite a slight stabilisation observed in 2024, temperatures remain higher than those recorded in 2013, suggesting that pre-industrial thermal conditions have not yet been restored. These findings underscore the long-term environmental impacts of industrial activity and highlight the importance of satellite-based monitoring for the sustainable management of land, water resources, and coastal zones. Full article

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development. Full article