Search Results (9,459)

This paper provides geospatial and correlation analysis of heavy metal distribution in the soil cover of the city of Temirtau and its industrial zones. Based on 25 soil samples taken in 2024, concentrations of nine heavy metals (As, Pb, Zn, Cu, Ni, Co, Mn, Cr, Ba) were determined using X-ray fluorescence analysis. Spatial data interpolation was performed using the Kriging method in the ArcGIS Pro environment. The results showed the presence of localized extreme pollution zones, primarily near the Qarmet JSC metallurgical plant. The most significant exceedances of maximum permissible concentrations (MPC), up to 348× MPC for Cr, 160× MPC for Zn, and 72× MPC for As, were recorded at individual locations. Correlation analysis revealed a moderate positive relationship between several elements, particularly Mn and Cu (r = 0.64). Comparison of the spatial distribution of pollution with population data allowed for the assessment of potential environmental risks. This research emphasizes the need to implement systematic monitoring, sustainable land management practices, ecological maps, and preventive measures to reduce the long-term impact of heavy metals on ecosystems and public health, and to promote environmental sustainability in industrial regions. Full article

White-tailed deer (Odocoileus virginianus) are a cervid species native to the Americas with ecological, social, and economic significance. Managers must consider several factors when working to maintain the health and sustainability of these wild herds, including reproduction, particularly pregnancy and recruitment rates. White-tailed deer have a variable reproductive capacity, with age, health, and habitat influencing this variability. However, it is unknown whether chronic wasting disease (CWD) impacts reproduction and, more specifically, if CWD infection alters a female deer’s probability of pregnancy. Our study addressed this question using data from 9783 female deer culled in northern Illinois between 2003 and 2023 as part of the Illinois Department of Natural Resources’ ongoing CWD management program. Multilevel Bayesian logistic regression was employed to quantify the relationship between pregnancy probability and covariates like maternal age, deer population density, and date of culling. Maternal infection with CWD was found to have no significant effect on pregnancy probability, raising concerns that the equal ability of infected and non-infected females to reproduce could make breeding, which inherently involves close physical contact, an important source of disease transmission between males and females and females and their fawns. The results also identified that female fawns (<1 year old) are sensitive to county-level deer land cover utility (LCU) and deer population density, and that there was no significant difference in how yearlings (1–2 years old) and adult (2+ years old) responded to these variables. Full article

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

Floods are considered one of the most devastating natural hazards, frequently resulting in substantial loss of lives and widespread damage to infrastructure. In the period of 4–7 September 2023, the region of Thessaly experienced unprecedented rainfall rates due to Storm Daniel, which caused significant flooding and many damages and fatalities. The southeastern areas of Trikala were among the many areas of Thessaly that suffered the effects of these rainfalls. In this research, a flood susceptibility assessment (FSA) of the broader area surrounding the settlement of Megala Kalyvia is carried out through the analytical hierarchy process (AHP) as a multicriteria analysis method, using Geographic Information Systems (GIS). The purpose of this study is to evaluate the prolonged flood susceptibility indicated within the area due to the past floods of 2018, 2020, and 2023. To determine the flood-prone areas, seven factors were used to determine the influence of flood susceptibility, namely distance from rivers and channels, drainage density, distance from confluences of rivers or channels, distance from intersections between channels and roads, land use–land cover, slope, and elevation. The flood susceptibility was classified as very high and high across most parts of the study area. Finally, a comparison was made between the modeled flood susceptibility and the maximum extent of past flood events, focusing on that of 2023. The results confirmed the effectiveness of the flood susceptibility assessment map and highlighted the need to adapt to the changing climate patterns observed in September 2023. Full article

The Brazilian Amazon is recognized worldwide for its biodiversity and it plays a key role in maintaining the regional and global climate balance. However, it has recently been greatly impacted by changes in land use, such as replacing native forests with agricultural activities. These changes have resulted in serious environmental consequences, including significant alterations to climate and hydrological cycles. This study aims to analyze changes in land use and land covered in the Brazilian Amazon between 2001 and 2023, as well as the resulting effects on precipitation variability, land surface temperature, and evapotranspiration. Data obtained via remote sensing and processed on the Google Earth Engine platform were used, including MODIS, CHIRPS, Hansen products. The results revealed significant changes: forest formation decreased by 8.55%, while agricultural land increased by 575%. Between 2016 and 2023, accumulated deforestation reached 242,689 km². Precipitation decreased, reaching minimums of 772.7 mm in 2015 and 726.4 mm in 2020. Evapotranspiration was concentrated between 941 and 1360 mm in 2020, and surface temperatures ranged between 30 °C and 34 °C in 2015, 2020, and 2023. We conclude that anthropogenic transformations in the Brazilian Amazon directly impact vegetation cover and the regional climate. Therefore, conservation and monitoring measures are essential for mitigating these effects. Full article

Amid China’s national strategic prioritization of the Chengdu–Chongqing Economic Circle and accelerated territorial spatial planning, this study deciphered the synergistic evolution of Land Use Intensity (LUI) and Balance Degree of Land Use Structure (BDLUS) during rapid urbanization. Leveraging 1 km grid units and integrating emerging spatiotemporal hotspot analysis, BFAST, and geographic detectors, we systematically analyzed spatiotemporal patterns and drivers of LUI, BDLUS, and their Coupling Coordination Degree (CCD) from 2000 to 2022. Key findings: (1) LUI strongly correlated with economic growth, with core areas reaching high-intensity development (average > 2.96) versus ecologically constrained marginal zones (<2.42), marked by abrupt changes during 2011–2014; (2) BDLUS improvements covered 82.22% of the study area, driven by the Yangtze River Economic Belt strategy (21.96% hotspot concentration), yet structural imbalance persisted in transitional zones (18.81% cold spots); (3) CCD exhibited center-edge dichotomy, contrasting high-value cores (CCD > 0.68) with ecologically sensitive edges (9.80% cold spots), peaking in regulatory shifts around 2010; (4) terrain constraints and intensified human activities (the interaction effect between nighttime lighting and population density increased by 219.49% after 2020) jointly governed coupling mechanisms, with urbanization and industrial transition becoming dominant drivers. This research advances an “intensity–structure–coordination” framework and elucidates “dual-core resonance” dynamics, offering theoretical foundations for spatial optimization and ecological civilization. Full article

Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article

Due to increased precipitation intensity and sea-level rise, low-lying coastal roads are increasingly vulnerable to subbase saturation. Widely applied lumped hydrological approaches cannot accurately represent time and space-varying groundwater levels in some highly conductive coastal soils, calling for more sophisticated tools. This study assesses the suitability of the Gridded Surface Subsurface Hydrologic Analysis model (GSSHA) for representing hydrological processes and groundwater dynamics in a unique coastal roadway setting in Alabama. A high-resolution model was developed to assess a 2 km road segment and was calibrated for hydraulic conductivity and aquifer bottom levels using observed groundwater level (GWL) data. The model configuration included a fixed groundwater tidal boundary representing Mobile Bay, a refined land cover classification, and an extreme precipitation event simulation representing Hurricane Sally. Results indicated good agreement between modeled and observed groundwater levels, particularly during short-duration high-intensity events, with NSE values reaching up to 0.83. However, the absence of dynamic tidal forcing limited its ability to replicate certain fine-scale groundwater fluctuations. During the Hurricane Sally simulation, over two-thirds of the segment remained saturated for over 6 h, and some locations exceeded 48 h of pavement saturation. The findings underscore the importance of incorporating shallow groundwater processes in hydrologic modeling for coastal roads. This replicable modeling framework may assist DOTs in identifying critical roadway segments to improve drainage infrastructure in order to increase resiliency. Full article

Rainwater harvesting for sustainable agriculture (RWHSA) offers a viable and eco-friendly strategy to alleviate water scarcity in semi-arid regions, particularly for agricultural use. This study aims to identify optimal sites for implementing RWH systems in northern Iraq to enhance water availability and promote sustainable farming practices. An integrated geospatial approach was adopted, combining Remote Sensing (RS), Geographic Information Systems (GIS), and Multi-Criteria Decision Analysis (MCDA). Key thematic layers, including soil type, land use/land cover, slope, and drainage density were processed in a GIS environment to model runoff potential. The Soil Conservation Service Curve Number (SCS-CN) method was used to estimate surface runoff. Criteria were weighted using the Analytical Hierarchy Process (AHP), enabling a structured and consistent evaluation of site suitability. The resulting suitability map classifies the region into four categories: very high suitability (10.2%), high (26.6%), moderate (40.4%), and low (22.8%). The integration of RS, GIS, AHP, and MCDA proved effective for strategic RWH site selection, supporting cost-efficient, sustainable, and data-driven agricultural planning in water-stressed environments. Full article

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

With the growing demand for ecological monitoring and geological exploration, high-quality satellite remote-sensing imagery has become indispensable for accurate information extraction and automated analysis. However, haze reduces image contrast and sharpness, significantly impairing quality. Existing dehazing methods, primarily designed for natural images, struggle with remote-sensing images due to their complex imaging conditions and scale diversity. Given this, we propose a novel Multi-Scale Contextual Attention Generative Adversarial Network (MCA-GAN), specifically designed for satellite image dehazing. Our method integrates multi-scale feature extraction with global contextual guidance to enhance the network’s comprehension of complex remote-sensing scenes and its sensitivity to fine details. MCA-GAN incorporates two self-designed key modules: (1) a Multi-Scale Feature Aggregation Block, which employs multi-directional global pooling and multi-scale convolutional branches to bolster the model’s ability to capture land-cover details across varying spatial scales; (2) a Dynamic Contextual Attention Block, which uses a gated mechanism to fuse three-dimensional attention weights with contextual cues, thereby preserving global structural and chromatic consistency while retaining intricate local textures. Extensive qualitative and quantitative experiments on public benchmarks demonstrate that MCA-GAN outperforms other existing methods in both visual fidelity and objective metrics, offering a robust and practical solution for remote-sensing image dehazing. Full article

Land use/land cover (LULC) change constitutes a critical driver influencing regional carbon cycling processes. Optimizing LULC structures represents a significant pathway toward the realization of carbon neutrality. This study takes Liaoning Province as a case area to analyze LULC changes from 2000 to 2020 and to assess their impacts on land use carbon emissions (LUCE) and ecosystem carbon storage (ECS). To accelerate the achievement of carbon neutrality, four development scenarios are established: natural development (ND), low-carbon emission (LCE), high-carbon storage (HCS), and carbon neutrality (CN). For each scenario, corresponding optimization objectives and constraint conditions are defined, and a multi-objective LULC optimization coupling model is formulated to optimize both the quantity structure and spatial pattern of LULC. On this basis, the model quantifies ECS and LUCE under the four scenarios and evaluates the economic value of each scenario and its contribution to the carbon neutrality target. Results indicate the following: (1) From 2000 to 2020, the extensive expansion of construction land resulted in a reduction in ECS by 12.72 × 10⁶ t and an increase in LUCE by 150.44 × 10⁶ t; (2) Compared to the ND scenario, the LCE scenario exhibited the most significant performance in controlling carbon emissions, while the HCS scenario achieved the highest increase in carbon sequestration. The CN scenario showed significant advantages in reducing LUCE, enhancing ECS, and promoting economic growth, achieving a reduction of 0.18 × 10⁶ t in LUCE, an increase of 118.84 × 10⁶ t in ECS, and an economic value gain of 3386.21 × 10⁶ yuan. This study optimizes the LULC structure from the perspective of balancing economic development, LUCE reduction, and ECS enhancement. It addresses the inherent conflict between regional economic growth and ecological conservation, providing scientific evidence and policy insights for promoting LULC optimization and advancing carbon neutrality in similar regions. Full article

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha⁻¹, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article