Search Results (2,151)

Soil erosion assessment in Southeast Europe’s mountainous regions often lacks systematic field validation, limiting confidence in model-based predictions. This study integrates the Erosion Potential Model (EPM) with remote sensing and field verification across 26,570 km² in the Federation of Bosnia and Herzegovina (FBiH) and Brčko District (BD). We developed a two-stage framework: initial GIS-based assessment using digital elevation models, soil maps, climate data, CORINE Land Cover, and Landsat imagery, followed by field calibration at 190 representative sites. Spectral indices (NDVI, BSI) provided dynamic corrections for vegetation cover and visible erosion features. Field validation significantly improved model performance; the erosion coefficient increased from Z = 0.21 to Z = 0.24, while discriminatory power improved AUC from 0.82 to 0.85, with corresponding gains in overall accuracy from 0.78 to 0.84 and F1-score from 0.78 to 0.85. The field-validated model estimated mean annual sediment production of 546.60 m³·km⁻²·year⁻¹, with total erosion material production of 14,074,940.2 m³·year⁻¹. Field calibration revealed substantial spatial redistribution, with medium-to-excessive erosion categories expanding by 30.37%, affecting 1319.12 km² requiring priority intervention. The Kappa coefficient (0.81) confirms high classification reliability. This field-validated framework enables evidence-based identification of degradation hotspots and provides actionable guidance for soil conservation planning in geomorphologically heterogeneous, data-limited regions. Full article

The assessment of the hydromorphological state of a river is fundamental for both correctly evaluating its ecological conditions and planning its restoration. Despite this, there is a critical gap in studies on this topic in Southern Italy, although they are recommended by several EU Framework Directives. This research provides a contribution to filling this gap by assessing the hydromorphological quality of the Middle and Lower Sabato River (Southern Italy), by using the method officially adopted by the Italian Institute for Environmental Protection and Research (ISPRA), named IDRAIM. The method presents the advantage of considering the specific Italian context in terms of channel adjustments and anthropogenic impacts. However, it also considers pre-existing geomorphological approaches developed in other countries that make the method applicable at least in the entire Mediterranean area. To apply the method, in this study, we used data obtained by GIS analysis, remotely sensed data, and field-surveyed data. The study has highlighted that, in the Middle and Lower Sabato R., eight river reaches out-of-fifteen have displayed a “moderate or sufficient” morphological quality, five reaches a “good” morphological quality, while the remaining two reaches have been characterized by a “poor” morphological quality. Functional alterations have seemed to prevail over artificiality and intensity of short-term channel adjustments in conditioning hydromorphological quality. These results will be a key starting point for already planned studies dealing with both the restoration of the Sabato R. and flood hazard and risk assessment. Full article

Groundwater is a vital freshwater resource that supports domestic, agricultural, and industrial activities in many regions worldwide. Accurate groundwater potential mapping (GPM) is essential for sustainable water resource management; however, traditional empirical and statistical approaches often struggle to capture the complex, nonlinear relationships among hydrogeological variables. In recent years, machine learning (ML) has emerged as a powerful data-driven approach for improving GPM accuracy and efficiency. This review synthesizes findings from 83 peer-reviewed studies published between 2015 and 2025, focusing on widely used ML algorithms such as Random Forest, Support Vector Machines, Artificial Neural Networks, and hybrid models. The review evaluates key methodological aspects, including input parameter selection, data partitioning, integration with GIS and remote sensing, and model justification techniques. It also discusses common challenges such as data limitations, regional variability, and model interpretability. The results indicate that ML-based approaches can significantly enhance groundwater prediction when supported by appropriate data and validation strategies. Future research directions include explainable artificial intelligence, uncertainty quantification, multi-source data integration, and improved model transferability. This review provides a comprehensive reference for advancing reliable and sustainable groundwater potential mapping. Full article

Quantifying the effects of land-use changes on soil fertility is essential for agricultural planning, yet long-term analyses combining field and remote sensing data remain scarce in Hungary. This systematic review followed PRISMA 2020 guidelines to assess arable land fertility trends between 2000 and 2020. A comprehensive search of WoS, Scopus, and Google Scholar identified 202 records, with 106 studies meeting inclusion criteria. Eligibility required empirical soil data collected from Hungarian arable lands. Among these, 17% reported declines in SOC, 13% indicated nutrient depletion, 36% observed stable or lost fertility, and 34% documented improvements. Regarding monitoring methods, 41% relied solely on field sampling, 44% applied GIS or spatial analyses, and 15% incorporated remote sensing indices such as NDVI. Evidence revealed spatial–temporal heterogeneity: fertility declines occurred in intensively cultivated regions, while western Transdanubia showed stability. Trends were linked to land-use intensification and intermittent reductions in agricultural area. Integration of remote sensing indices, such as NDVI, with field observations enhanced detection of spatial and temporal patterns. These findings underscore the need for harmonised monitoring frameworks, precision agriculture tools, and predictive modelling to support sustainable soil management. Identifying fertility-decline zones informs policy aligned with the EU Soil Strategy 2030 and supports Hungary’s agricultural resilience. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Rapid urbanisation and climate change are intensifying urban heat stress, posing significant challenges for climate-responsive urban planning. Digital and data-driven approaches, including GIS, remote sensing, microclimate simulation, and artificial intelligence (AI), have advanced urban climate analysis; however, their capacity to support human-centred planning remains insufficiently synthesised. This review analyses 78 peer-reviewed studies (2015–2025) to evaluate how digital methods address urban microclimate and outdoor thermal comfort. The reviewed studies are classified into four methodological groups: spatial data analytics, simulation-based models, parametric and optimisation workflows, and AI-driven or hybrid approaches. The results show that the majority of studies rely on proxy indicators, such as land surface temperature and sky view factor, while physiologically based comfort indices (e.g., PET and UTCI) are applied in a limited proportion of studies and remain largely confined to microscale simulations. A persistent scale mismatch is identified between large-scale analytics and pedestrian-level thermal experience, alongside geographic and climatic biases, particularly in hot-arid regions. Unlike previous reviews, this study integrates digital methodologies, urban microclimate processes, and human-centred thermal comfort within a unified framework. The findings provide actionable insights for planners and designers by supporting the integration of thermal comfort into multi-scale, climate-responsive decision-making. Full article

Soil contamination in South African agriculture poses escalating threats to food security and ecosystem integrity, yet the geospatial and machine learning evidence base addressing this problem has never been systematically synthesised. This scoping review, conducted within the PRISMA-ScR framework, applied SVM-assisted screening to 2000 retrieved records, yielding a final corpus of 228 eligible studies published from 2003 to 2025. To characterise temporal, thematic, and geographic patterns in the corpus, we applied machine learning-assisted topic modelling (LDA, k = 7), logistic growth modelling, keyword co-occurrence network analysis, and technology–contaminant evidence gap matrices. Remote sensing was the dominant methodology throughout the review period (n = 142; 62.3% of studies), with machine learning rising to the highest adoption rank from approximately 2020 onwards. Logistic modelling estimated a carrying capacity of K = 292.3 (95% CI: 269–324) studies and an inflexion year of 2020.2 (95% CI: 2019.4–2021.1), projecting 90% saturation by 2028. Research effort was highly concentrated in KwaZulu-Natal and the Eastern Cape, while Pesticides/Herbicides and acid mine drainage each comprised only three corpus studies. Deep learning registered zero entries across all cells of both the technology–contaminant and technology–province evidence matrices. Targeted investment in field validation, hyperspectral and deep learning deployment for underrepresented contaminants, and interpretable modelling for regulatory defensibility are identified as priority actions for the next research cycle. Full article

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants. Full article

Peri-urban areas (PUAs), as transitional zones between urban and rural regions, play a critical role in supporting food systems and agricultural livelihoods, yet they are increasingly pressured by rapid urban expansion. Reliable spatial delineation of PUAs remains challenging, as administrative boundaries often fail to capture their functional and spatial heterogeneity. This study proposes a multi-dimensional, spatially explicit framework to delineate peri-urban areas using Indonesia as a case study. Eighteen indicators representing six analytical dimensions—land use/land cover, economic, demographic, infrastructural, spatial accessibility, and landscape structure—were derived from remote sensing and GIS-based data sources and integrated into a composite scoring system using equal-weighted and AHP-weighted approaches. The framework was applied to four major cities on Java Island (Jakarta, Surabaya, Bandung, and Yogyakarta) to generate continuous peri-urban probability surfaces, which were validated using expert surveys across 25 districts in the Jakarta and Bandung metropolitan areas. The results show that the framework effectively captures the spatial heterogeneity and gradients of peri-urban areas, with the equal-weighted approach exhibiting statistically significant agreement with expert assessments (Pearson’s r = 0.517, p = 0.008; Spearman’s ρ = 0.522, p = 0.008; Kendall’s τ = 0.387, p = 0.008), consistently outperforming the AHP-weighted model across all validation metrics. The proposed approach provides a transferable spatial mapping framework for monitoring peri-urban dynamics in rapidly urbanizing regions using remote sensing and GIS. Full article

The aim of the current study was to select and test the appropriate model and input parameters for remote sensing retrieval of surface soil moisture (SSM) in the case of bare Chernozems on flat and sloping terrains in northern Bulgaria under different tillage systems. Normalized synthetic aperture radar (SAR) measurements from Sentinel-1 C-band dual-pol products (Gamma-Nought in VV, ratio) were utilized in two ways to delineate SSM from environmental factors that bias determination. The accuracy of the obtained SSM prediction was evaluated against ground-based volumetric water content (VWC) measured in the 0–3.8 cm soil layer at multiple points using a TDR meter. The TDR VWC data were preliminarily calibrated against gravimetric measurements in the 0–5 cm soil layer. The obtained data for soil water retention curves in all studied variants were used to determine the range of soil moisture variation. The measured ground-based data for surface roughness generally correlate with the co-pol Gamma-Nought in VV. The data modeled with the surface soil moisture script in Sentinel Hub (SSM-SH) was calibrated using the ground-based data. Incidence angle normalization of Sentinel-1 products improved the relationship between SAR observables and SSM, when expressed as the ratio of soil moisture to total porosity (rVWC). The modeling indicated the highest importance of the optical indices, together with the temporal differences of radar descriptors sensitive to variations in soil moisture over time. Although the applied Random Forest Regression (RFR) model achieved higher accuracy during training (nRMSE of 7.27%, R² of 0.86), the Gaussian Process Regression (GPR) model provided better generalization performance on the independent validation dataset. The results proved the advantages of the joint utilization of temporal Sentinel-1 SAR measurements with Sentinel-2 optical acquisitions to determine SSM in different bare soil conditions for achieving high accuracy. Full article

Most of the dam structures around the world are approaching the end of their economic life of 50 to 70 years, especially due to sediment accumulation in reservoir areas. This situation necessitates the development of proactive infrastructure management strategies. This study presents an original framework for the process of renewal of aging dams that blends remote sensing techniques and meta-intuitive optimization methods. Within the scope of the study, the Hasanlar Dam located in Düzce was selected as a sample, and a new dam axis was determined in the upper part of the basin. A detailed volume–height curve was created using 12.5 m resolution ALOS PALSAR numerical height models (DEM) and GIS-based spatial data curation to calculate the reservoir storage capacity in precise increments of 2 m. To maximize the structural efficiency of the proposed “New Hasanlar Dam”, the cross-sectional area has been minimized through seven current algorithms such as Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Crayfish Optimization Algorithm (CAO), and Cheetah Optimizer (CO). The findings obtained prove that the PSO and CAOs achieved a significant reduction in cross-sectional area by 29.36% and successfully approached the global optimum. The replacement of the 55.5 million m³ capacity of the existing Hasanlar Dam with a new structure with a height of 78 m will guarantee sustainability and structural safety in water management. As a result, this study reveals that the integration of high-resolution remote sensing data and advanced heuristic methods is a cost-effective and powerful tool in the strategic renovation of aging hydraulic infrastructures. Full article

Agriculture in dryland regions faces increasing pressure from climate variability, water scarcity, and competing urban and environmental demands. A recent basin-wide technical analysis for the Rio Grande/Rio Bravo in the United States of America (USA) and Mexico shows that consumptive water use in the river system overall is on an unsustainable path. The Middle Rio Grande Basin (MRGB) of central New Mexico (USA) exemplifies these sustainability challenges, where irrigated agriculture persists despite low precipitation, high evaporative demand, and prolonged drought. This study provides analytical spatial description of irrigated agriculture in the MRGB, examining farm size distribution, crop composition, groundwater access, and consumptive water use measured by evapotranspiration (ET) and effective ET. Using 2021 remotely sensed crops and ET data, groundwater well records, and GIS-based aggregation to the irrigator farm level, the analysis reveals a highly fragmented agricultural landscape dominated numerically by micro-scale and small farms, which together account for 55.9% of total agricultural ET. Alfalfa and other hay crops occupy nearly three-quarters of irrigated acreage and consume 74% of total ET, reflecting the prevalence of forage production. Groundwater access is highly uneven, with most wells concentrated among large farms, creating resilient disparities. The findings highlight that consumptive agricultural water use in the MRGB is diffuse rather than concentrated: non-commercial farms (<12 hectares) account for 55.9% of basin-wide ET, while commercial farms contribute only 14.4% despite occupying about one-fifth of irrigated land. This complicates water conservation efforts. Resilient management strategies must therefore engage thousands of small, largely non-commercial irrigators through mechanisms that recognize both hydrological and spatial realities. The study provides an empirical basis for designing sustainable irrigation and water-management strategies in dryland agricultural systems facing increasing climatic and institutional pressures. Full article

Semi-arid territories in Latin America face chronic water stress; limited observability and fragmented institutions constrain effective water resources management (WRM). This narrative review synthesizes peer-reviewed evidence (2020–2026) on smart technologies that strengthen basin- and utility-level WRM, using Peru (Piura-like coastal semi-arid contexts) as an anchor and Latin America as a comparative lens. We used a structured, traceable database-based workflow and synthesized studies reporting measurable outcomes across five application categories: drought/flood early warning, hydrometeorological forecasting, water quality surveillance, non-revenue water (NRW)/leakage, and allocation and compliance. Findings were organized into an application-oriented taxonomy spanning remote sensing (RS) and GIS, Internet of Things (IoT)/telemetry, analytics/AI-enabled decision support, and hybrid approaches. Evidence most consistently reports operational gains (coverage, timeliness, predictive performance), while governance outcomes are less frequently measured and appear contingent on interoperability, digital capacity, and sustainable operations and maintenance (O&M) conditions. We conclude with a territorial adoption agenda specifying minimum enabling conditions and a phased pathway from pilots to scalable, eco-efficient smart WRM in Peru and comparable semi-arid settings across Latin America. Full article

Sediment yield remains a major challenge in erosion-prone watersheds because it affects reservoir capacity, water quality, hydraulic infrastructure, and ecological stability. Although numerous studies have examined sediment yield estimation and sediment control, these topics are often treated separately, limiting the development of integrated watershed management strategies. Unlike many existing sediment yield review papers that focus primarily on predictive models, erosion processes, or management measures in isolation, this study provides an integrated synthesis of sediment yield estimation methods and sediment control strategies within a single watershed management framework for erosion-prone environments. The review covers empirical models, traditional sampling, physically based models, and emerging data-driven tools such as artificial intelligence, machine learning, remote sensing, and sensor-based monitoring, alongside structural, vegetative, and adaptive sediment control measures. The reviewed literature indicates three major trends: increasing integration of GIS and remote sensing with conventional models, wider use of process-based models for scenario analysis, and rapid growth of AI-based methods for real-time and nonlinear prediction. The findings further show that no single estimation or control strategy is universally applicable; performance depends strongly on watershed scale, sediment connectivity, land use, climatic regime, and data availability. Overall, the review highlights the need for integrated, adaptive, and site-specific sediment management frameworks that combine predictive modeling, monitoring technologies, and practical control interventions to improve long-term watershed resilience. Full article

In the face of the dual challenges of global climate change and rapid urbanization, optimizing the ecosystem services of urban green spaces has become a key strategy for building resilient and sustainable cities. This is particularly crucial in ecologically fragile arid and semi-arid regions. To accurately assess the thermal regulation function of urban green spaces, this study selected 20 parks in Xi’an, China. Combining remote sensing and Geographic Information System (GIS) technology, we adopted four established cooling indicators—Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Intensity (PCI), and Park Cooling Gradient (PCG)—to systematically evaluate the thermal regulation functions of urban parks and their landscape-driving mechanisms. The results indicated that the average cooling amplitude of the parks was 2.53 °C, with an effective influence distance reaching 323.9 m, exhibiting a significant spatial gradient decay. We found a non-linear trade-off between green space scale and efficiency: while large parks provided a wider absolute cooling range, small and medium-sized parks demonstrated higher efficiency per unit area. Furthermore, a blue-green synergistic configuration significantly enhanced the mitigation of the urban heat island effect. The study confirmed that Park Area (PA), Park Perimeter (PP), and the Normalized Difference Vegetation Index (NDVI) significantly promoted cooling effects, whereas landscape fragmentation inhibited ecological benefits. This study elucidates the comprehensive regulation mechanism of urban parks on the urban microclimate, providing planning guidance for implementing Nature-based Solutions (NbS) and achieving climate-adaptive development in arid and semi-arid cities within the context of urban renewal. Full article