Search Results (7,172)

Flooding triggered by intense precipitation is a significant natural hazard affecting Mediterranean regions, where complex terrain, rapid hydrological response and increasing urbanization can amplify flood impacts. This study assesses flood susceptibility in two representative Mediterranean River catchments: the Koiliaris in Crete, Greece, and the Pediaios in Cyprus. A compact Flood Hazard Index (FHI) was developed by integrating the Topographic Wetness Index (TWI), Curve Number (CN), and R20 heavy rain frequency index, representing the principal geomorphological, hydrological and climatological controls of flood generation. Spatial datasets including EU-DEM elevation data, CORINE land cover, European soil databases, and Copernicus CERRA precipitation reanalysis were combined within a GIS-based multi-criteria framework using Analytic Hierarchy Process weighting. The resulting FHI maps identify high flood susceptibility along river corridors, low-lying accumulation zones, and urbanized areas. In the Koiliaris basin, 34% of the area fell within the high and very high susceptibility classes, mainly in downstream alluvial zones, whereas in the Pediaios basin, 29% of the area fell within the high and very high susceptibility classes, concentrated around the urbanized Nicosia corridor. The analysis of historical flood events provided a qualitative consistency assessment of the FHI patterns, acknowledging that the absence of spatially explicit flood-inundation footprints limits quantitative validation. Full article

Operational landslide susceptibility mapping (LSM) remains challenging in regions with pronounced geo-environmental heterogeneity, where single global models often overlook spatially variable landslide-environment relationships. Northern Guangdong, China, is a typical humid mountainous region where steep terrain, diverse lithology, and highly variable rainfall produce non-stationary landslide controls. To address this challenge, we develop a cluster-informed LSM framework that integrates unsupervised consensus K-means sub-zoning with localized Random Forest (RF) models and SHapley Additive exPlanations (SHAP). We use a harmonized inventory of 1510 landslides (2011–2022), together with twelve 30 m conditioning factors, for model training and validation. Compared with logistic regression, Support Vector Machines (SVM), and Light Gradient Boosting Machine (LightGBM), RF consistently achieves higher accuracy across clusters, and the cluster-wise RF ensemble attains pooled ACC = 0.8212, F1 = 0.8176, and AUC = 0.8956. SHAP highlights both regionally consistent predictors (e.g., NDVI, distance to road) and distinct cluster-specific controls linked to geomorphic and hydrologic settings. The proposed framework enhances predictive accuracy, produces finer susceptibility gradients, and yields better-calibrated probability estimates than a single global model. These results demonstrate that explicitly accounting for geo-environmental heterogeneity can generate interpretable, spatially adaptive susceptibility outputs. By identifying high-risk zones for priority monitoring, land-use regulation, infrastructure protection, and mitigation planning, the proposed framework provides a practical decision-support tool for sustainable mountain development and disaster risk reduction in heterogeneous mountainous regions. Full article

This study presents a multi-indicator remote sensing framework for assessing satellite-derived water-quality-related and trophic-state-related dynamics across four freshwater systems in Türkiye Egirdir Lake, Sapanca Lake, Catalan Dam, and Yuvacik Dam between the baseline (2015–2018) and recent (2023–2025) periods. Rather than providing a regulatory or use-specific satellite-based assessment of water-quality-related indicators, the study evaluates optically and thermally detectable surface water indicators derived from Sentinel-2 MSI and Landsat 8/9 imagery processed in Google Earth Engine. The Normalized Difference Chlorophyll Index (NDCI), the Normalized Difference Turbidity Index (NDTI), and land surface temperature (LST, applied to water surfaces) were used to detect change patterns through period-mean difference mapping (Δ-mask) and interannual time series analysis. Results reveal distinct spatial and temporal dynamics broadly consistent with the interplay of climatic, hydrological, and anthropogenic drivers. In the southern Mediterranean systems, positive ΔNDCI anomalies in littoral and inflow zones were associated with increasing summer LST, with Egirdir Lake exhibiting a statistically significant warming trend of +0.170 °C yr⁻¹ (Mann–Kendall τ = 0.53, p = 0.029), interpreted cautiously as a physically plausible signal consistent with regional climate trends, suggesting elevated thermally mediated eutrophication-related optical risk. In the northern Marmara systems, satellite-observed patterns were more strongly associated with anthropogenic nutrient loading and morphological constraints, with turbidity-related optical increases concentrated in western and marginal zones despite relatively stable thermal conditions. As concurrent in situ measurements were unavailable, cross-sensor consistency checks and literature-based benchmarking were applied as alternative validation strategies. Across all four systems, positive ΔNDCI anomalies were systematically concentrated in shallow marginal and inflow zones, while ΔNDTI patterns varied by system, underscoring the role of littoral dynamics as early indicators of optically detectable water-quality deterioration and trophic-state-related change. The proposed framework offers a scalable, cost-effective approach for freshwater quality surveillance in data-scarce environments and provides direct support for integrated water resource management under Türkiye’s National Water Plan (2026–2036). Full article

Accurate land use/land cover (LULC) mapping from remotely sensed data remains challenging in arid regions, particularly for spatial and seasonal generalization. This work proposes a novel exclude-one-city-out (EOCO) framework based on machine learning (ML) to achieve LULC generalization across summer and winter in arid environments. Four cities in Saudi Arabia witnessing rapid urban growth were selected: Riyadh, Madinah, Jeddah, and Dammam. The ML models were trained on three cities and tested on the unseen city. Sentinel-2 surface reflectance data for the visible (Blue, Green, and Red) and near-infrared bands (NIR, SWIR1, and SWIR2) were used. Spectral indices, texture features, and topographical data were used to form five feature sets, which were utilized as inputs for four ML algorithms: random forest, support vector machine, classification and regression trees, and K-nearest neighbors. Statistical tests (Friedman, Kendall’s W, and Wilcoxon signed rank) were conducted to assess differences across ML models, feature sets, and seasons. The random forest model consistently outperformed other models across the five feature sets, while the spectral texture and combined feature sets outperformed other feature combinations. Significant differences in feature importance were observed across cities and seasons for spectral texture during summer and winter (p-values: 1.25 × 10⁻⁴ and 9.2 × 10⁻⁵, respectively), with strong agreement (Kendall’s W = 0.9212 and 0.9424). The findings can support urban environmental monitoring in arid regions, contributing to sustainable urban development. Full article

Accurate and scalable urban land cover mapping is critical for sustainable urban planning and environmental management. While deep learning models offer powerful tools for this task, their performance is often constrained by the need for vast, manually labeled datasets, which are costly and challenging to acquire for diverse urban environments. To address this limitation, we propose the Cross-Scale U-Net, an original, highly adaptable operational framework that systematically exploits the inherent scale effects of remote-sensing imagery to optimize transfer learning. By operationalizing prior theoretical findings on receptive fields, this workflow provides an actionable method for users to manipulate spatial resolution, identify an optimal scale to bridge the domain gap, and subsequently automate feature extraction with significantly reduced manual effort. Using the well-annotated ISPRS Potsdam dataset as the source domain, our framework transfers learned knowledge to classify National Agriculture Imagery Program (NAIP) data from Phoenix, AZ (2015), into four primary land cover classes. We systematically evaluated the framework’s performance across spatial resolutions ranging from 15 cm to 100 cm, achieving a peak overall accuracy (OA) of 82.45%. To assess generalizability, the model was applied in a label-free transfer scenario to NAIP imagery from Las Vegas, NV (2015), and Phoenix, AZ (2013 and 2019), consistently delivering OA values above 70%. In a comparative analysis, the Cross-Scale U-Net significantly outperformed traditional classification techniques. While our current empirical validation is focused on arid urban environments due to experimental constraints, the framework introduces a highly flexible, actionable scale-adjustment process. This approach offers a scalable workflow that can be tailored to various landscape scales—such as expanding to coarser resolutions for large-scale forests or protected areas—delivering high-fidelity maps while mitigating data scarcity. Full article

This article presents the results of a study assessing changes in climatic resources in various natural zones of the Zhetysu region, Republic of Kazakhstan, conducted based on long-term climate data for the period 1966 to 2024 (from 12 meteorological stations). The study examines current trends in climatic indicators in spatial and temporal aspects that influence agricultural land use within the region. The first part of this study examines current trends in climate indicators from both spatial and temporal perspectives within the Zhetysu Region of the Republic of Kazakhstan; the second part focuses on studying trends in climate indicators using the non-parametric Mann–Kendall test and the Sen’s slope test, as well as Fisher’s t-test. The authors identified divergent trends in relative air humidity and precipitation and detected a steady trend toward an increase in the average annual air temperature across the region. Based on the analysis of time series of climate-forming and climate–environment-forming indicators, a persistent increasing trend in mean annual air temperature was identified, while relative humidity, precipitation, and evaporation exhibited divergent (both positive and negative) trends across the territory of the region. The developed climate–resource-forming models and a series of estimated applied maps of climate indicators for 1966–1975 and 2016–2024 serve as the scientific basis for climate change forecasting and can be used by administrative bodies to improve agricultural land use strategies in the region. Full article

The 2019 Mississippi River floods were among the most severe in recent U.S. history, impacting 11 states and driven by multiple tributary flood events rather than a single episode. This study focuses on the Lower Mississippi River Basin in Mississippi, examining how flood frequency interacts with land ownership patterns to influence agricultural losses in the Yazoo–Mississippi Delta. Using Sentinel-2 imagery within Google Earth Engine, land use and land cover were classified with a random forest algorithm, followed by change detection and a flood recurrence–persistence modeling framework to map and characterize inundation. Results indicate that mid-year floods (April–July) caused the greatest crop losses, particularly in soybeans (4475 ha), cotton (501 ha), and corn (546 ha). Most impacts were associated with short-duration, low-recurrence floods, which affected many structures (1812) and extensive agricultural areas due to their broad spatial reach. Small agricultural parcels (≤48 ha) experienced the highest proportional exposure across flood zones, while medium and large parcels showed comparatively lower vulnerability. These findings highlight the importance of targeted resilience and mitigation strategies that account for flood frequency, land use, and land ownership patterns across the Delta. Full article

Land use and land cover change (LULCC) is increasingly recognized as a dominant driver of hydrological alteration in tropical watersheds, often exceeding the influence of climatic variability. This study evaluates the spatiotemporal dynamics of LULCC and their implications for hydrological sustainability in the Uberabinha River Basin, southeastern Brazil, between 1990 and 2020. Utilizing MapBiomas data and statistical analysis, the results reveal a marked expansion of mechanized agriculture, particularly soybean cultivation, which grew from 3426 ha to 54,162 ha, and urban areas, which expanded by approximately 89.4%. Conversely, natural vegetation and pasturelands decreased continuously, with pastures showing the sharpest absolute reduction, from 72,248 ha to 34,535 ha. Despite a 10.76% increase in annual precipitation between 1990 and 2020, the hydrological response exhibited a severe decline in streamflow, characterized by a 76.35% drop in minimum flow. Furthermore, the runoff index decreased from 0.0574 in 1990 to 0.0211 in 2020, indicating a critical loss in the basin’s capacity to convert rainfall into streamflow. These findings demonstrate a clear decoupling between precipitation and streamflow driven by LULCC, posing a severe threat to regional water security and highlighting the urgent need for integrated land–water management. Full article

Understanding the coordinated development of production, living, and ecological (P–L–E) functions is critical for sustainable watershed governance in rapidly transforming regions. Using the Chaohu Lake Basin, China, as a case study, this study developed a process–pattern–potential–driver framework for watershed-scale P–L–E coordination analysis from 2000 to 2020. Unlike previous studies that mainly assess coordination levels or map spatial patterns, this framework further identifies subsystem constraints, quantifies coordinated development potential, and determines key factors driving spatial differences. The results show that production and ecological functions remained weakly coordinated, indicating persistent tension between economic growth and ecological protection. In contrast, the relationships between production and living functions and between living and ecological functions improved from strong imbalance to moderate coordination. Spatially, higher coordination levels were concentrated in the southwestern basin. Decoupling analysis further reveals that production activities, especially the energy-intensive secondary industry, were the main constraint on ecological function. In addition, 88.2% of the basin showed an increasing trend in coordinated development potential. Land-use patterns, socioeconomic conditions, and eco-environmental quality were identified as direct drivers, whereas climate change mainly acted indirectly. By linking diagnostic results with spatially differentiated management needs, this study provides a basis for more targeted watershed governance. Full article

Education for Sustainability (EfS) has emerged as a key framework through which higher education engages ecological, social, and civic challenges. Although EfS is well represented in policy and conceptual scholarship, few empirical studies have examined how sustainability is enacted in everyday teaching practice. This exploratory qualitative collective case study investigates the pedagogical experiences of four faculty members at a U.S. land-grant university. Data were collected through semi-structured interviews, supported by syllabi, observations, and student responses, and analyzed using cross-case thematic analysis. Four interconnected themes were identified: latent complexity, personal commitment, inclusive scholarship, and adaptability to student motivations and context. Taken together, the findings offer an initial mapping of how EfS is interpreted and enacted in faculty teaching, while also underscoring the context-bound nature of these cases. The study contributes an exploratory, practice-based account of sustainability teaching and provides a foundation for future comparative research across institutions, disciplines, and regions. Full article

Liquefaction phenomena are strongly influenced by the depositional evolution of the area, including sediment grain size, depositional age, shallow layering, and groundwater depth. This study focuses on a 560 km² wide sector of the eastern Po River Plain (northern Italy), encompassing part of the modern Po Delta, to evaluate the susceptibility of the different geological units to liquefaction. A comprehensive dataset was compiled, integrating lithological, chronological (¹⁴C), geomorphological, hydrological, and hydrogeological information, together with satellite imagery, historical and modern maps, archaeological evidence, and subsurface data from core drilling and CPTu tests. The integrated analysis allowed us to reconstruct a liquefaction susceptibility map recognizing four classes: very high (4% of the investigated area), high (26%), moderate (20%), and non-susceptible (50%). CPTu-based statistical analyses confirm that the Liquefaction Potential Index (LPI) increases with higher susceptibility classes and decreases with increasing groundwater depth (0.5, 1.5, and 3.0 m scenarios). These results provide a scientific basis to support sustainable land management and governance strategies in the Po Delta, an area of high environmental, cultural, and economic value, a large sector of which is included in the Natura 2000 network. Full article

Frequent seasonal phase transitions in cold and arid lakes require different remote sensing indices for frozen and open-water periods, complicating the use of traditional empirical indices for automated monitoring. To address this challenge, this study proposes an intelligent indexing framework integrating the heuristic reasoning of Large Language Models (LLMs) with Random Forest (RF) feature selection. Leveraging the Google Earth Engine (GEE) and Landsat 8 data from Ulansuhai Lake, five LLMs such as Gemini and ERNIE were employed to generate candidate spectral indices based on typical sample spectra. Optimal band combinations were identified via RF importance, and Land Surface Temperature (LST) was incorporated as a physical constraint for unified cross-seasonal classification and determine the optimal threshold. Results show that the LLM-derived ERNIE-WISI and Gemini-WISI exhibit high robustness. During the freezing period, ERNIE-WISI significantly outperformed other indices, achieving an Overall Accuracy (OA) of 89% and a Kappa of 0.86. Spatially, it yielded snow and ice mapping with clear textures and low commission errors. During the non-freezing period, ERNIE-WISI achieved an OA of 95% with a Kappa of 0.84. While Gemini-WISI achieved an OA of 94% with a Kappa of 0.80, performing comparably to MNDWI. Notably, ERNIE-WISI effectively suppressed background interference in complex landscapes like narrow channels and aquaculture areas, maintaining high geometric fidelity and spatial continuity. A key advantage of ERNIE-WISI is its consistent performance without seasonal threshold adjustments. Aligned with the AI for Science paradigm, this methodology bridges AI-driven heuristic discovery and physical remote sensing, offering a robust, transferable solution for long-term dynamic lake monitoring in extreme environments, thereby facilitating sustainable water management. Full article

Protecting native coastal wetland vegetation and controlling the invasion of Spartina alterniflora (SA) have long been key ecological and management priorities in China. The accurate and rapid mapping of vegetation distribution is critical for effective invasion control and wetland restoration. While phenological information improves remote sensing classification, most studies rely on the Normalized Difference Vegetation Index (NDVI), which has limited capability to distinguish morphologically similar species in coastal wetlands. To better exploit the unique reddening phenology of one such species, Suaeda salsa (SS), this study builds on our previously developed Red Suaeda salsa Index (RSSI) and introduces two novel phenological indicators: the Redness Contribution Coefficient (RCC) and Reddening Rate Index (RCI). Using the coastal wetlands of Jiangsu Province as the study area, we employed multi-temporal Sentinel-2 image composites (spring, summer, autumn) from 2019, 2022, 2024, and 2025 to construct a multi-dimensional feature set and implemented classification using a random forest algorithm. Results showed that the feature scheme integrating SS reddening phenological parameters achieved the highest accuracy, with an overall accuracy of 97.32% and a Kappa coefficient of 0.9625 in 2019, confirming the method’s reliability at the provincial scale. Between 2019 and 2025, SA coverage in Jiangsu decreased by 90.8%, with most cleared areas converting to non-vegetated land, indicating the remarkable effectiveness of recent control projects. This study scales up a locally validated high-precision classification approach to the provincial scale, supporting sustainable coastal wetland management in line with United Nations (UN) SDG 14 (Life Below Water) and SDG 15 (Life on Land). Full article

In this study, a soil erosion intensity map for the territory of Serbia was produced using the Modernized Erosion Intensity Coefficient (MEIC-Z), combined with remote sensing data (Sentinel-2) and Geographic Information Systems (GIS). The analysis was based on contemporary geospatial data on lithology, land use, and terrain slope, with a spatial resolution of 30 m. Particular emphasis was placed on modifying the φ coefficient, which significantly improved estimates of erosion intensity. The average erosion intensity at the national level is 0.239, corresponding to the weak erosion class. Multivariate analysis of geographical conditions showed that the highest values of the erosion coefficient (Z) were determined by agricultural land (r = 0.826), while the lowest values were associated with terrain slope (r = −0.805) and forest cover (r = −0.767). In addition to the national-scale assessment, spatial differentiation of the results was performed at the local (municipal) level. Municipalities were differentiated into four clusters using Agglomerative Hierarchical Clustering. The advantage of the modified φ coefficient lies in the integration of land use and terrain slope, enabling a more realistic assessment of the intensity of erosion processes. Validation results demonstrated strong agreement between the modernized Z-derived erosion coefficient and the expert-defined erosion inventory, supporting the internal consistency of the model-derived erosion susceptibility patterns. This study significantly contributes to decision-making at both national and local levels by providing a scientific basis for developing strategies for sustainable forest management and soil conservation. Full article

Wildfires are a growing threat in Mediterranean regions where climate variability and land-use practices increase vulnerability to fire risk. Developing effective prediction models is essential for robust wildfire management, particularly in such data-scarce environments. Focusing on data-scarce Mediterranean environments, with reference to environmental conditions observed in Morocco, this review presents prediction models across three methodological categories: spatial risk mapping, temporal forecasting, and fire spread simulation, alongside the satellite data products that support their deployment. Each category is assessed in terms of predictive performance, data requirements, and adaptability to low-resource environments. XGBoost showed strong applicability in data-scarce Mediterranean contexts, while ARIMA was validated for forecasting fire-relevant time series under limited data resources. Freely accessible MODIS-derived products represent a significant asset to the region. Based on this synthesis, a hybrid XGBoost-ARIMA framework incorporating MODIS-derived inputs and SHAP-based interpretability is proposed as a promising candidate architecture to be validated after further investigation. The findings aim to support researchers, land managers, and policymakers in strengthening local wildfire prevention and mitigation efforts by aligning model capabilities with regional data and environmental constraints. Full article