Search Results (8,162)

This study investigates the spatial relationship between population distribution and tree cover loss in Nepal from 2000 to 2020, using satellite-based forest cover and population data along with statistical and geospatial analysis. Two statistical methods—linear regression (LR) and Geographically Weighted Regression (GWR)—were used to assess the influence of population on forest cover change. The correlation between total population and forest loss at the national level suggested little to no direct impact of population growth on forest loss. However, sub-national analysis revealed localized forest degradation, highlighting the importance of spatial and regional assessments to uncover land cover changes masked by national trends. While LR showed a weak national-level correlation, GWR revealed substantial spatial variation, with the coefficient of determination values increasing from 0.21 in 2000 to 0.59 in 2020. In some regions, local R² exceeded 0.75 during 2015 and 2020, highlighting emerging hotspot clusters where population pressure is strongly linked to deforestation, especially along major infrastructure corridors. Using very high-resolution spatial data enabled pixel-level analysis, capturing fine-scale deforestation patterns, and confirming hotspot accuracy. Overall, the findings emphasize the value of spatially explicit models like GWR for understanding human–environment interactions guiding targeted land use planning to balance development with environmental sustainability in Nepal. Full article

Agriculture accounts for 29% of the Gross Domestic Product of the Nusa Tenggara Islands (NTIs). However, recurring agricultural droughts pose a major threat to the sustainability of agriculture in this region. The interplay between precipitation, solar radiation, and surface temperature as meteorological factors plays a key role in affecting vegetation (Soil-Adjusted Vegetation Index) and agricultural drought (Temperature Vegetation Dryness Index) in the NTIs. Based on the analyses of interplay with temporal lag, this study investigates the effect of each factor on agricultural drought and attempts to provide early warnings regarding drought in the NTIs. We collected surface information data from Moderate-Resolution Imaging Spectroradiometer (MODIS). Meanwhile, rainfall was estimated from Himawari-8 based on the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The results showed reliable performance for 8-day and monthly scales against gauges. The drought analysis results reveal that the NTIs suffer from mild-to-moderate droughts, where cropland is the most vulnerable, causing shifts in the rice cropping season. The driving factors could also explain >60% of the vegetation and surface-dryness conditions. Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. The low error rates and the ability to capture the spatial distribution of droughts, especially in open-land vegetation, highlight the potential of these models to provide an estimation of agricultural drought. Full article

Urban heat islands (UHIs) present a growing environmental issue in swiftly urbanizing regions, where impermeable surfaces and a lack of vegetation increase local temperatures. This research analyzes the spatial distribution of urban heat islands in Zagreb, Croatia, utilizing remote sensing data, urban planning metrics, and spatial-statistical analysis. Composite rasters of land surface temperature (LST) and the Normalized Difference Vegetation Index (NDVI) were generated from four cloud-free Landsat 9 images obtained in the summer of 2024. The data were consolidated into regulatory planning units through zonal statistics, facilitating the evaluation of the impact of built-up density and designated green space on surface temperatures. A composite UHI index was developed by combining normalized land surface temperature (LST) and normalized difference vegetation index (NDVI) measurements, while spatial clustering was examined with Local Moran’s I and Getis-Ord Gi*. The results validate spatial patterns of heat intensity, with high temperatures centered in densely built residential areas. This research addresses the gap in past UHI studies by providing a reproducible approach for detecting thermal stress zones, linking satellite data with spatial planning variables. The results support the development of localized climate adaptation methods and highlight the importance of integrating green infrastructure into urban planning methodologies. Full article

As a strategic replacement area for hydrocarbon exploration in the Tarim Basin, the Kuqa Depression has been the subject of relatively limited research on the sedimentary characteristics of the Triassic strata within its western Wushi Sag, which constrains exploration deployment in this region. This study focuses on the Wushi Sag, systematically analyzing the sedimentary facies types, the evolution of sedimentary systems, and the distribution patterns of the Triassic Kelamayi and Huangshanjie formations. This analysis integrates field outcrops, drilling cores, wireline logs, and 2D seismic data, employing methodologies grounded in foreland basin theory and clastic sedimentary petrology. The paleo-geomorphology preceding sedimentation was reconstructed through balanced section restoration to investigate the controlling influence of foreland tectonic movements on the distribution of sedimentary systems. By interpreting key seismic profiles and analyzing vertical facies successions, the study classifies and evaluates the petroleum accumulation elements and favorable source–reservoir-seal assemblages, culminating in the prediction of prospective exploration areas. The research shows that: (1) The Triassic in the Wushi Sag mainly develops fan-delta, braided-river-delta, and lacustrine–shallow lacustrine sedimentary systems, with strong planar distribution regularity. The exposed strata in the northern part are predominantly fan-delta and lacustrine systems, while the southern part is dominated by braided-river-delta and lacustrine systems. (2) The spatial distribution of sedimentary systems was demonstrably influenced by tectonic activity. Paleogeomorphological reconstructions indicate that fan-delta and braided-river-delta sedimentary bodies preferentially developed within zones encompassing fault-superposition belts, fault-transfer zones, and paleovalleys. Furthermore, Triassic foreland tectonic movements during its deposition significantly altered basin configuration, thereby driving lacustrine expansion. (3) The Wushi Sag exhibits favorable hydrocarbon accumulation configurations, featuring two principal source–reservoir assemblages: self-sourced structural-lithologic gas reservoirs with vertical migration pathways, and lower-source-upper-reservoir structural-lithologic gas reservoirs with lateral migration. This demonstrates substantial petroleum exploration potential. The results provide insights for identifying favorable exploration targets within the Triassic sequences of the Wushi Sag and western Kuqa Depression. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

Italy strives to meet its renewable energy targets for 2030 and 2050, with photovoltaic (PV) technology playing a central role. However, the push for increased solar adoption, spurred by past incentive schemes such as “Conto Energia” and “Superbonus 110%”, raises long-term challenges related to PV waste management. In this study, we present a multi-scale approach to forecast End-of-Life (EoL) PV waste across Italy’s 20 regions, aiming to support national circular economy strategies. Historical installation data (2008–2024) were collected and combined with socio-economic and energy-related indicators to train a Backpropagation Neural Network (BPNN) for regional PV capacity forecasting up to 2050. Each model was optimised and validated using R² and RMSE metrics. The projections indicate that current trends fall short of meeting Italy’s decarbonisation targets. Subsequently, by applying a Weibull reliability function under two distinct scenarios (Early-loss and Regular-loss), we estimated the annual and regional distribution of PV panels reaching their EoL. This analysis provides spatially explicit insights into future PV waste flows, essential for planning regional recycling infrastructures and ensuring sustainable energy transitions. Full article

To investigate the slope instability caused by differential frost heaving mechanisms from the slope crest to the toe during frost heave processes, this study takes a typical silty clay slope in Xinjiang, China, as the research object. Through indoor triaxial consolidated undrained shear tests, eight sets of natural and frost-heaved specimens were prepared under confining pressure conditions ranging from 100 to 400 kPa. The geotechnical parameters of the soil in both natural and frost-heaved states were obtained, and a spatiotemporal thermo-hydro-mechanical coupled numerical model was established to reveal the dynamic evolution law of anchor rod axial forces and the frost heave response mechanism between the frame and slope soil. The analytical results indicate that (1) the frost heave process is influenced by slope boundaries, resulting in distinct spatial variations in the temperature field response across the slope surface—namely pronounced responses at the crest and toe but a weaker response in the mid-slope. (2) Under the coupled drive of the water potential gradient and gravitational potential gradient, the ice content in the toe area increases significantly, and the horizontal frost heave force exhibits exponential growth, reaching its peak value of 92 kPa at the toe in February. (3) During soil freezing, the reverse stress field generated by soil arching shows consistent temporal variation trends with the temperature field. Along the height of the soil arch, the intensity of the reverse frost heave force field displays a nonlinear distribution characteristic of initial strengthening followed by attenuation. (4) By analyzing the changes in anchor rod axial forces during frost heaving, it was found that axial forces during the frost heave period are approximately 1.3 times those under natural conditions, confirming the frost heave period as the most critical condition for frame anchor design. Furthermore, through comparative analysis with 12 months of on-site anchor rod axial force monitoring data, the reliability and accuracy of the numerical simulation model were validated. These research outcomes provide a theoretical basis for the design of frame anchor support systems in seasonally frozen regions. Full article

The sustainable development of eco-tourism is significantly influenced by multiple conditions within spatiotemporally continuous geographic scenarios. However, existing evaluations of the development value of eco-tourism resources (Eco-TRDVs) are non-spatial and do not sensitively represent their complex relationships. This study proposed a GIS approach for evaluating regional Eco-TRDVs by mapping the complex interconnections with spatial distances. Inherent and external conditions for evaluating Eco-TRDVs were classified under three indicators and digitized using GIS and remote sensing technologies. Then, the analytic hierarchy process and GIS cost distance analysis were introduced to define the initial values and cumulate Eco-TRDVs with distances. Taking the Taihang Honggu National Forest Park, China, as the case area, the Eco-TRDVs over the entire area in 2017 and 2020 were mapped. The results present a continuous spatial variability of Eco-TRDVs and comprehensively reflect the complex interconnections of constraint elements with spatial distances. The evaluation is sensitive to the intrinsic value of poles, as evidenced by the high development values and high-density distribution of their contours. Source additions improve the evaluation considerably, with transportation networks having a greater impact than economic development zones and urban elements. Furthermore, aggravated fragmentation of the price flow field increases spatial heterogeneity. The development value shows a negative linear correlation with distance. The proposed approach handles the spatially oriented relationships of the multi-conditions, and supports future planning and monitoring of spatial-temporal changes in eco-tourism development. Full article

Investigating the spatial distribution of chromium (Cr) in soil is essential for understanding Cr pollution and accurately assessing associated environmental risks. However, field sampling is challenging due to limited sampling points, and the spatial distribution of Cr is affected by multiple complex environmental covariates, thereby restricting model development and prediction accuracy. This study selected the Chizhou–Xuancheng border area in southern Anhui Province as the research region and collected 2035 data points. Machine learning models, including AdaBoost, GBDT, XGBoost, and MLP, were employed to predict Cr concentrations in conjunction with environmental covariates. To address the challenges of sparse sampling data and complex data relationships for Cr prediction, the PHMS-Transformer model is proposed. Featuring a shallow encoder design, configurable pooling strategies, and a lightweight structure, the model significantly reduces the number of parameters and alleviates overfitting under sparse sampling conditions, while the incorporation of multi-head self-attention mechanisms captures complex nonlinear relationships among multi-source environmental variables relevant to Cr. To further enhance model interpretability for Cr prediction, the SHAP model was applied to identify key factors influencing Cr distribution. Comprehensive comparisons indicate that the PHMS-Transformer model achieves superior performance in predicting Cr, demonstrating high accuracy and generalization capability, with clear advantages over traditional methods. These findings offer valuable insights for soil environmental protection and Cr pollution control and possess significant theoretical and practical implications. Soil Cr pollution represents a global environmental challenge, where achieving accurate predictions for Cr is particularly crucial yet difficult in regions with constrained data accessibility. The lightweight, high-precision, and interpretable PHMS-Transformer framework proposed in this study provides an effective technical solution to the widespread challenges of sample sparsity and model complexity inherent in predicting the spatial distribution of soil Cr globally. Therefore, this work offers significant reference value for advancing global soil environmental risk assessment and Cr pollution remediation efforts. Full article

In recent years, the exploration and utilization of geothermal energy have received growing attention as a sustainable alternative to conventional energy sources. Reliable, data-driven identification of geothermal reservoirs, particularly in crystalline basement terrains, is crucial for reducing exploration uncertainties and costs. In such geological settings, magnetic susceptibility, radioactive heat production, and seismic wave characteristics play a vital role in evaluating geothermal energy potential. Building on this foundation, our study integrates in situ and laboratory measurements, collected using advanced sensors from spatially diverse locations, with statistical and unsupervised artificial intelligence (AI) clustering models. This integrated framework improves the effectiveness and reliability of identifying clusters of potential geothermal sites. We applied this methodology to the migmatitic gneisses within the Simav Basin in western Türkiye. Among the statistical and AI-based models evaluated, Density-Based Spatial Clustering of Applications with Noise and Autoencoder-Based Deep Clustering identified the most promising and spatially confined subregions with high geothermal production potential. The potential geothermal sites identified by the AI models align closely with those identified by statistical models and show strong agreement with independent datasets, including existing drilling locations, thermal springs, and the distribution of major earthquake epicenters in the region. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

Sea fog is a natural phenomenon that significantly reduces visibility, posing navigational hazards for ships and impacting coastal activities. Geostationary meteorological satellite data have proven to be indispensable for sea fog monitoring due to their large spatial coverage and spatiotemporal consistency. However, the spectral similarities between sea fog and low clouds result in omissions and misclassifications. Furthermore, high clouds obscure certain sea fog regions, leading to under-detection and high false alarm rates. In this paper, we present a novel sea fog detection method to alleviate the challenges. Specifically, the approach leverages a fusion of spectral, motion, and spatiotemporal texture consistency features to effectively differentiate sea fog and low clouds. Additionally, a multi-scale self-attention module is incorporated to recover the sea fog region obscured by clouds. Based on the spatial distribution characteristics of sea fog and clouds, we redesigned the loss function to integrate total variation loss, focal loss, and dice loss. Experimental results validate the effectiveness of the proposed method, and the detection accuracy is compared with the vertical feature mask produced by the CALIOP and exhibits a high level of consistency. Full article

To ensure food security in Xinjiang, scientifically conducting land suitability evaluation is of significant importance. This paper takes an arid and ecologically fragile region of southern Xinjiang—Qiemu County—as an example. Based on the optimized Maximum Entropy (MaxEnt) model, 14 multi-source environmental variables including climate, soil, hydrology, and topography are integrated. The ENMeval package is used to optimize the model parameters, and Spearman’s rank correlation analysis is employed to screen key variables. The spatial distribution of land suitability and the dominant factors are systematically assessed. The results show that the model AUC values for the mountainous and plain areas are 0.987 and 0.940, respectively, indicating high accuracy. In the plain area, land suitability is primarily influenced by the soil sand content, while in the mountainous region, the annual accumulated temperature plays a leading role. The highly suitable areas are mainly distributed in the northern plains and parts of the southern mountains. This study clarifies the suitable areas for land development and environmental thresholds, providing a scientific basis for the development of land resources in arid regions and the implementation of the “store grain in the land” strategy. Full article

Cities house more than half of the world’s population and are responsible for more than 70% of the world anthropogenic CO₂ emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, should allow us to monitor changes in global fossil fuel CO₂ emissions in an independent, objective way. The study adopted a high-spatiotemporal-resolution regional assimilation method using satellite observation data and atmospheric transport model WRF-Chem/DART to assimilate CO₂ concentration and fluxes in Lisbon, a major city in Portugal. It is based on Zhang’s assimilation method, combined OCO-2 XCO₂ retrieval data, ODIAC 1 km anthropogenic CO₂ emissions and Ensemble Adjustment Kalman Filter Assimilation. By employing three two-way nested domains in WRF-Chem, we refined the spatial resolution of the CO₂ concentrations and fluxes over Lisbon to 3 km. The spatiotemporal distribution characteristics and main driving factors of CO₂ concentrations and fluxes in Lisbon and its surrounding cities and countries were analyzed in March 2020, during the period affected by COVID-19 pandemic. The results showed that the monthly average CO₂ and XCO₂ concentrations in Lisbon were 420.66 ppm and 413.88 ppm, respectively, and the total flux was 0.50 Tg CO₂. From a wider perspective, the findings provide a scientific foundation for urban carbon emission management and policy-making. Full article

Cultural ecosystem services (CES) play a vital role in rural well-being, yet their spatial patterns and local perceptions remain underexplored in many regions, including Türkiye. This study aims to assess the social values of CES in rural landscapes by focusing on the Şarkikaraağaç and Yenişarbademli districts of Isparta Province. Using Participatory Geographic Information Systems (PGIS) and the Social Values for Ecosystem Services (SolVES) models, we collected and analyzed spatial data from 836 community surveys, mapping 3771 CES value points. Sentinel-2A imagery and derived indices (NDVI, NDWI, SAVI, NDBI) were used to classify landscape infrastructures into green, blue, yellow, and grey categories. The results show that aesthetic and recreational services were most highly valued, followed by biodiversity, spiritual, and therapeutic values. Chi-square and Kruskal–Wallis tests revealed significant demographic and spatial variation in CES preferences, while Principal Component Analysis highlighted two key dimensions of value perception. MaxEnt-based modeling within SolVES confirmed the spatial distribution of CES with high predictive accuracy (AUC > 0.93). Our findings underscore the importance of integrating CES into sustainable land-use planning and suggest that infrastructure type and proximity to natural features significantly influence CES valuation in rural settings. Full article