Urban and industrial activities have lasting effects on Earth ecosystems, impairing their functionality. Technosols offer a sustainable solution for restoring degraded urban and industrial ecosystems. In a 30-day microcosm experiment, a mixture of six pioneer plant species was sown in three substrates: a sewage sludge-based Technosol (GF), a zeolite-enriched Technosol (GZ), and a commercial potting mix control (GC). The plant population dynamics, final performance, and substrate biochemical properties were monitored. A strong environmental filter allowed only two species (Bromus inermis Leyss. and Lolium perenne L.) to establish. Technosols exerted a demographic bottleneck, delaying emergence and reducing the total biomass relative to the control. Zeolites in the GZ Technosol mitigated this delay, accelerating early establishment due to their microporous structure and high cation exchange capacity. However, GZ caused the greatest reduction in individual biomass and functional plant performance index, corresponding to a microbial shift toward oxidative activity at the expense of hydrolytic nutrient mineralization. These results show that sewage sludge Technosols can initiate functional ecological succession. While zeolites positively affect germination, their microbial interaction suggests a temporary decoupling between the establishment speed and final productivity. Integrated monitoring of demographic and biochemical dynamics is therefore essential to optimize Technosol-based environmental restoration.
Reliable coastal wetland habitat mapping is often constrained by the scarcity and the cost of reliable reference data, especially in data-limited coastal environments. We propose a validation-controlled, label-efficient framework pairing multi-season Sentinel-1 and Sentinel-2 predictors with a CatBoost teacher and a lightweight MLP student. A candidate is pseudo-labeled only when both separately calibrated models agree and exceed class-specific thresholds; accepted labels are class-balanced and down-weighted. The framework was evaluated at the Sidi Moussa–Oualidia wetland complex and Merja Zerga lagoon in Morocco. At Sidi Moussa–Oualidia, 62 configurations were compared through nested polygon-grouped validation and then frozen before a five-seed held-out evaluation. The supervised MLP and Agreement-augmented MLP achieved mean Macro-F1 values of 0.9518±0.0044 and 0.9509±0.0062, indicating that augmentation did not materially change the already strong full-data baseline. Under a stricter budget of 30 training and 20 validation observations per class, Agreement yielded a mean Macro-F1 of 0.9092±0.0102 compared with 0.9023±0.0093 for the supervised baseline and produced pseudo-labels in all five seeds. A spatial-range sensitivity analysis further showed that both models retained Macro-F1 values of 0.9391 and 0.9403 for test observations located beyond the largest estimated within-class autocorrelation range. At Merja Zerga, the native six-class supervised MLP achieved 0.9456±0.0050, compared with 0.9401±0.0047 after Agreement augmentation. Spatially blocked four-class experiments nevertheless showed that 20 to 30 local training labels per class recovered approximately 96–98% of the corresponding full-data performance. The framework therefore supplies an operational criterion for using unlabeled observations: augmentation is adopted only where calibrated filtering yields adequate class coverage, and validation confirms a downstream effect; otherwise the supervised model is retained. For the strict Sidi Moussa–Oualidia reduced-label experiment, the reported development budgets count every site-specific label used for fitting, early stopping, and calibration. The Merja Zerga blocked experiments separately quantify training-label sensitivity while retaining their blocked validation resources.
Mangroves are highly productive ecosystems due to their great capacity to store carbon, but they are also vulnerable to human activities and adverse environmental conditions. Their conservation is often constrained by the opportunity costs of shifting from traditional economic activities to blue carbon projects. This work analyzes the prices of the different carbon markets in ecosystems, compares them with the benefits obtained from other productive activities, and evaluates the viability of implementing carbon projects in mangroves. An exhaustive literature search is conducted to assess the carbon prices of ecosystems worldwide. The opportunity costs of mangrove carbon capture projects in Mexico are estimated from site-specific and regionally relevant economic data; additionally, broader national benchmarks are presented for contextual comparison but are not interpreted as direct opportunity costs where they do not represent realistic land-use alternatives for the mangrove sites analyzed. The price of carbon ranges from 4 to 86 USD per Mg CO2e. The most studied natural ecosystems are forests. The highest gross annual profit (GAP) from carbon sales is observed in Tabasco and Campeche. GAP with mangrove wood harvesting ranges from 628.0 USD ha−1 year−1 to 3917.7 USD ha−1 year−1. The highest GAP for crops is obtained for white corn in the state of Hidalgo. GAP of the economic activity of livestock ranges from 3167.59 USD ha−1 year−1 to 3365.71 USD ha−1 year−1. The blue carbon projects are competitive with other productive activities at relatively high prices (86 USD per Mg CO2e). In Tabasco, under certain high-price and high-sequestration scenarios, blue carbon projects can be competitive with local agricultural activities; however, this competitiveness is highly conditional on carbon price, sequestration rates, and local opportunity costs, and therefore cannot be generalized to all mangrove owners without site-specific appraisal. Fair carbon prices are required to make mangrove conservation projects attractive to producers.
The abundant rainfall and rugged topography characteristic of southern Zacatecas promoted soil leaching. This differentiation in soil physicochemical properties, driven by leaching, results in higher-altitude areas having soils with high sand and aluminum (Al3+) content. The influence of altitude and soil units on acidity levels and the quantity of amendments required for the study region (the municipality of Momax) was determined. Samples were collected from sixty-one agricultural sites, based on pH data reported by INEGI in 1979 and variance estimated from an interpolated map. Principal component analysis was used to divide the samples into four contrasting groups. Group 4 exhibited the highest values for clay, organic matter, exchangeable cations, and cation exchange capacity (p < 0.05). Group 1 and 2 served as a transition zone; Group 3 showed the lowest pH values (mean of 4.7) (p < 0.05) and the highest levels of sand (65.5%), aluminum (0.83 cmol kg-1), and hydrogen (0.09 cmol kg−1) (p < 0.05). Given that 80% of the study area contains exchangeable aluminum, it is necessary to implement a future technological intervention plan, incorporating this study’s recommendations, as well as, the possibility of reducing cost by applying less CO3 amendment rates within a range from 0 to 1.76 t ha−1.
Agricultural drought threatens rainfed agriculture in northeast Thailand, where variable monsoon rainfall, limited irrigation access, and extensive cropland increase vulnerability. This study developed a Google Earth Engine-based Agricultural Drought Risk Index (ADRI) for the Chi River Basin using six benchmark years (2000, 2005, 2010, 2015, 2020, and 2025). CHIRPS precipitation, MODIS-derived vegetation health, ERA5-Land soil moisture, irrigation accessibility, and agricultural land exposure were normalized and integrated by weighted linear combination. The analysis quantified risk-class areas, irrigated–rainfed contrasts, persistent hotspots, weight sensitivity, and spatial agreement with the official Land Development Department recurring-drought map. Moderate risk dominated most years, but high-risk area expanded to 60.7% in 2015, coincident with severe rainfall deficits during the 2015–2016 El Niño event. Conditions improved in 2020 and 2025 as rainfall, vegetation health, and soil moisture recovered. Rainfed areas consistently had higher ADRI values than irrigated areas, and persistent hotspots were concentrated in southeastern and downstream agricultural zones. The principal spatial and temporal patterns remained stable under ±10% weight perturbations. External validation identified ADRI > 2.90 as the optimal threshold, with raster-level precision, recall, and F1 of 0.779, 0.884, and 0.828, respectively; the 998-point sample produced an F1 of 0.832. ADRI therefore provides a practical basin-scale screening framework for drought monitoring, adaptation prioritization, and agricultural water-management planning.
Natural resource management under climate change generates interdependent economic, social, and environmental impacts. However, the scientific evidence remains fragmented. This fragmentation limits the design of integrated policies capable of reducing vulnerability and preventing the degradation of natural capital. The objective of this study is to analyze recent scientific literature to assess how natural resource management in the context of climate change simultaneously influences economic stability, social resilience, and environmental sustainability. To this end, a systematic review of literature published in indexed journals on environmental economics, climate change, and natural resource management was conducted, selecting quantitative and mixed-methods studies that examine economic, social, or biophysical impacts associated with environmental degradation, extractive dependence, and adaptation and mitigation strategies. The review integrated research at macroeconomic, microeconomic, and ecological scales, organized using comparative matrices that allowed for the identification of common patterns in indicators of economic loss, emissions, natural capital depreciation, and effects on social welfare. Subsequently, a comparative analysis was conducted to detect relationships between management failures, social vulnerability, and long-term costs, as well as to identify conceptual, methodological, and geographical gaps in the literature. The results show that the degradation of natural resources under climate change produces simultaneous effects on macroeconomic stability, household income, and ecosystem resilience, increasing the costs of inaction when policies are designed sectorally. The evidence synthesized in this review indicates that dependence on extractive activities, limited productive diversification, and institutional weaknesses are frequently associated with greater economic and social vulnerability, particularly in communities dependent on natural resources. The reviewed studies also suggest that adaptation and mitigation strategies that incorporate participatory governance, social capital, and natural capital conservation may contribute to strengthening resilience. However, given the heterogeneity of methodologies, spatial scales, and indicators among the analyzed studies, these findings should be interpreted as evidence of consistent patterns rather than causal relationships. Therefore, integrated approaches that consider economic, social, and environmental dimensions represent a promising direction for sustainable natural resource management under climate change, although further empirical research is required to evaluate their effectiveness across different contexts.
This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries.
Climate change and glacial retreat in the tropical Andes—as evidenced by environmental changes in the Carihuairazo volcano area and human-induced alterations to the páramo—reveal complex environmental dynamics that require an integrated understanding. This study evaluates edaphic variation along a periglacial–agricultural spatial gradient, relating soil properties to sustainable management strategies. Using a methodological approach that includes multi-criteria spatial delineation, altitude-stratified sampling, and multivariate modeling via HJ-Biplot, the physical, chemical, and biological properties were analyzed, with a focus on basal microbial respiration. The data show that periglacial soils exhibit geochemical–mineral control and high basal respiration, potentially influenced by moisture peaks, despite their low organic matter content. In contrast, lowland andisols exhibit biological–structural control conditioned by organic matter accumulation, reflecting distinct conditions associated with agricultural management and land use. It is concluded that understanding these spatial edaphic patterns and their vulnerability to human intervention is essential for designing sustainable management and conservation frameworks that mitigate the impact of climate change on high-mountain ecosystems.
Rapid urbanization in tropical Southeast Asia is transforming pervious land into impervious surfaces, intensifying the surface urban heat island (SUHI) effect and increasing the need for consistent urban thermal monitoring. This study assesses how impervious surface area (ISA) expansion relates to the urban thermal environment across five tropical megacities (Bangkok, Jakarta, Manila, Kuala Lumpur, and Ho Chi Minh City). AlphaEarth geospatial foundation model embeddings were used to reduce observation gaps caused by persistent cloud-cover, while MODIS land surface temperature (LST) was used to quantify the thermal response. We compared AlphaEarth classification against conventional Sentinel-2/NDVI approaches and an additional fairer annual Sentinel-2 full-band-plus-index Random Forest baseline, quantified ISA expansion for 2017-2024, and related ISA fraction to dry-season LST at 1 km resolution. Repeated random-holdout tests based on Google Earth Engine samples showed AlphaEarth mean IoU = 0.866 (95% CI: 0.857-0.875), compared with 0.758 (0.749-0.767) for the annual Sentinel-2 full-band-plus-index baseline and 0.686 (0.674-0.698) for the best single-date 5-index baseline. Spatial-block holdout tests gave similar but slightly lower values (AlphaEarth IoU = 0.859; annual Sentinel-2 baseline = 0.747; best single-date baseline = 0.673). Ho Chi Minh City experienced the fastest ISA expansion (+11.0 percentage points; slope = 1.48 pp yr-1, 95% CI: 1.06-1.91), whereas Bangkok reached the highest ISA fraction (65.1%). ISA fraction and LST were consistently and positively associated across cities and years (Pearson r = 0.748-0.900), and mean SUHI intensity during 2017-2024 ranged from 4.01 degrees C in Bangkok to 8.51 degrees C in Manila. These results indicate that foundation model embeddings can support cloud-resilient mapping of impervious surface change and thereby improve assessment of tropical urban thermal environments, while also highlighting the need for independent ground-truth validation.
Satellite-observed greening in arid regions is often interpreted as ecological restoration success, yet this assessment may conflate natural recovery with agricultural expansion. We developed an Arid Remote Sensing Ecological Index (ARSEI) incorporating a Comprehensive Salinity Index (CSI) to address systematic biases in the traditional RSEI when applied to irrigated drylands. ARSEI scores were validated against MODIS Net Primary Production (NPP) (R-2>0.75 at the regional scale), confirming its reliability in capturing ecosystem productivity, while CSI effectively maps the upper-bound of surface salinization potential dictated by intrinsic soil properties. Applied to China's Mu Us Sandy Land (2000-2024), the ARSEI reveals that 2327 km(2) of sandy land-54% of current cropland-was converted to agriculture, creating "assessment-induced false greening" signals. While the traditional RSEI increased monotonically (+135%), the ARSEI shows a nuanced pattern with plateau (2010-2015) and decline (2015-2020) phases, reflecting salinization risks masked by high crop NDVI. Optimal Parameters-Based Geographical Detector analysis demonstrates that Land Cover & times; Precipitation interactions (q = 0.28) drive spatial heterogeneity through irrigation-mediated water redistribution. The ARSEI provides a dialectical evaluation framework: acknowledging agricultural greening's economic benefits while monitoring subsurface degradation risks. This study offers a critical methodological advance for sustainable land assessment in global drylands undergoing agricultural intensification.
Groundwater is one of the key factors affecting the changes and evolution of surface processes in arid regions, determining the direction and scope of the evolution of surface eco-hydrological processes. To achieve sustainable water resource management in arid areas, this study aims to systematically explore the dynamic changes in groundwater level and their ecological effects on the basis of multi-source remote sensing data by multivariate statistical methods. The results show that groundwater levels in the Bayin River Basin increased from 2895.35 m in 2005 to 2906.75 m in 2022 at a rate of 6.7 m/decade, driven by increased runoff and irrigation. Conversely, groundwater levels in urbanized areas near Delingha City slightly decreased by approximately 0.3 m/decade, with a general west-to-east declining spatial gradient. These changes have generated cascading ecological effects. Overall, rising groundwater has coincided with increased vegetation index, wetland extent, and soil moisture. Annual average NDVI rose from 0.18 in 2000 to 0.23 in 2022, an increase of 27.7%, and wetland area expanded from 349.25 km2 in 2005 to 355.25 km2 in 2022. Soil moisture content showed an insignificant upward trend form 0.14% in 2003 to 0.15% in 2022, with the slope of 0.01%/yr. However, soil salinization has exhibited an aggravating trend, with salinization index (SI) values of 0.25, 0.26, and 0.31 in 2000, 2010, and 2020, respectively. Affected by human activities and geological constraints, the ecological effects associated with groundwater level changes display pronounced regional heterogeneity. This study provides a solid basis for regional water resource regulation and further quantification of water conveyance benefits.
Rapid urbanization imposes significant pressure on riverine water environments, yet the evolution of hydrochemical characteristics and dissolved organic matter (DOM) in rivers across urbanization gradients within developing regions, such as the Huaibei Plain, remains inadequately understood. Thus, this study investigates the hydrochemical and DOM characteristics of rivers across distinct urbanization gradients (suburban, peri-urban, and urban) in this area. Using an excitation-emission matrix coupled with a parallel factor analysis (EEM-PARAFAC) and hydrochemical analyses, we found that while rock weathering is the primary major ion source, human activities distinctly alter water profiles. Agriculturally dominated suburban rivers had significantly higher nitrate (NO3-) concentrations than those in urban and peri-urban rivers. Their DOM was predominantly humic-like (C1, C3) with a high humification index (HIX), indicating a substantial input of soil-derived humic substances driven by runoff from the agricultural catchment. Conversely, urban and peri-urban rivers exhibited higher chloride (Cl-) concentrations due to domestic sewage. Their DOM was dominated by protein-like components (C2 and C4, averaging 65-68%), with high biological indices (BIX) reflecting autochthonous origins. Correlation analysis confirmed these anthropogenic impacts: NO3- positively correlated with humic-like components and HIX, while Cl- strongly correlated with protein-like components. These findings confirm that DOM components and spectral indices are effective tracers of anthropogenic disturbance and hold promise for monitoring and predicting water quality, thus providing a scientific basis for improved water resource management and restoration strategies.
Integrated water resources management uses decision-making and planning techniques in developing long-term strategies to ensure the sustainability of water resources and the resulting water security of future generations. Policy formulation through such integrated planning interlinks with indicators serving as an information channel to decision-makers. The present effort aims to develop a specific methodology using technical, environmental, and social indicators, formulating composite indices to identify vulnerability to changing water conditions. Thus, a set of indices developed through a multiyear research effort in Latin America, namely Drought Vulnerability Index (DVI), Water Stress Vulnerability Index (WSTVI), Water Scarcity Vulnerability Index (WSCVI), and Water Changing Conditions Vulnerability Index (WCCVI). Time series analysis covered the years 1991-2020, whereas the reference period was 1961-2020. Climate and water resources information is mainly obtained from ERA5-Land reanalysis; social, economic, infrastructure, and institutional data derived from harmonized sources (COROADO Project-EU, FAO, The World Bank, WHO/UNICEF JMP). Statistical tests and Principal Component Analysis (PCA) identified the indicators included in the equations for each index. Expert knowledge played an important role in the development as data were collected according to known local specificities and global trends, as well as scientific criteria and methodological rigor regarding the proposed new indices. Finally, application of such a framework for spatially explicit analysis indicated higher levels of vulnerability to changing water conditions in the northern part of Mexico, the Andes, Bolivia, Paraguay, and Central America, and lower levels in Chile, Brazil, Uruguay, and Argentina. This application demonstrates that the produced composite indices may be implemented with matching success all over Latin America and, therefore, in diversified natural, technical, environmental, social and economic conditions.
Vapor Pressure Deficit (VPD) is a critical determinant of atmospheric evaporative demand and plant water stress in tropical agricultural systems. This study applied a Gaussian Mixture Model (GMM) and K-Means clustering to 36,528 hourly meteorological observations collected from Eastern Thailand between August 2021 and September 2025, with the objective of identifying distinct atmospheric moisture regimes relevant to precision irrigation management in durian cultivation. Two input configurations were evaluated: a multivariate feature space comprising air temperature, relative humidity, wind speed, solar radiation, and VPD; and a univariate input consisting of VPD alone. Model selection for GMM was guided by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), while K-Means performance was assessed using the Elbow method, Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index. For the multivariate input, GMM identified K = 7 as the optimal number of clusters, supported by the largest single-step reduction in both AIC and BIC at this transition point. For the univariate VPD input, K = 5 was selected as the most parsimonious and agriculturally interpretable solution. The seven clusters derived from the multivariate GMM were organized into four atmospheric moisture regimes, such as very low, moderate, high, and very high evaporative demand, capturing the full spectrum of diurnal and seasonal VPD variability characteristic of Eastern Thailand. The results demonstrate that GMM-based probabilistic clustering applied to multivariate meteorological inputs provides a more comprehensive characterization of atmospheric moisture dynamics than univariate or geometric clustering approaches, offering a practical framework for tiered irrigation scheduling and drought stress early warning systems in tropical fruit cultivation.
The mineral resources sector has faced intense social and environmental scrutiny in recent years, often driven by a perceived lack of information and transparency regarding projects. Due to a perceived lack of information and transparency regarding projects, local communities have increasingly opposed them, leading to the revision of the Portuguese Decree-Law N degrees. 30/2021, which now mandates public clarification sessions for communities in affected territories. This study reviews the state of the art concerning socio-environmental conflicts and analyses the role of social awareness within the context of Corporate Social Responsibility (CSR). A survey was conducted in the parish of Alqueid & atilde;o da Serra (Central Portugal), a community historically exposed to stone extraction, to assess perceptions of sustainability and the sector's impact. The methodology combined the literature review with a statistical analysis of the population's views. Results indicate that the community recognises both the economic relevance and necessity of the sector, while simultaneously expressing concerns regarding local impacts. In this context, an exploratory Not In My Back Yard (NIMBY) tendency is identified in 45 +/- 6% of the population, with women showing a greater propensity. The study concludes that socio-environmental issues are the primary drivers of conflict. These findings support recommendations for enhanced population sensitivity studies and structured public clarification sessions to mitigate conflict.
In situ leaching is increasingly used for rare earth element (REE) extraction because of its operational efficiency; however, acidic and chemically reactive leaching solutions may generate substantial environmental risks in riverine systems. This study evaluated water contamination and screening-level ecological risk following a cyanide leakage incident associated with a pilot REE mining operation in Houaphanh Province, northern Lao PDR. Surface water samples were collected from 12 downstream monitoring locations between February and April 2024. Physicochemical parameters, free cyanide (CN-), and dissolved metals, including arsenic (As), lead (Pb), copper (Cu), manganese (Mn), aluminum (Al), zinc (Zn), and iron (Fe), were analyzed using portable multiparameter probes, colorimetric cyanide determination, and ICP-OES. Contamination severity was interpreted using Pollution Index (PI) and Hazard Quotient (HQ) indicators based on Lao national standards and international guideline values. Results showed severe downstream contamination, with free cyanide and several dissolved metals substantially exceeding permissible thresholds. Observed elevated concentrations of As (30.29 mg/L), Pb (10.38 mg/L), Cu (14.97 mg/L), and CN- (0.51 mg/L) indicated elevated ecological risk conditions, while acidic pH conditions may have enhanced metal mobilization and downstream transport. Descriptive spatial observations indicated apparent downstream contaminant dispersion within affected downstream river communities reliant on river water for domestic use, irrigation, and fisheries. Field observations additionally documented fish mortality, reduced irrigation usability, and deterioration of river water quality conditions in affected downstream communities. The findings suggest the potential vulnerability of Mekong-connected river systems to chemically intensive REE extraction activities and highlight the importance of preventive environmental governance, continuous monitoring, and operational risk management in emerging rare earth mining regions.
Floods are among the most catastrophic natural disasters globally, causing significant damage to both life and infrastructure. Consequently, immediate and accurate assessment of inundated areas is critical for effective emergency response. While optical remote sensing is typically used for flood assessment, it is often ineffective during active flood events due to persistent cloud cover and precipitation. To address this, this research develops a deep learning method utilizing Synthetic Aperture Radar (SAR), which offers all-weather, 24 h imaging capabilities. Specifically, an attention-based differential Siamese U-Net was developed to detect temporal changes in bi-temporal SAR imagery (e.g., Sentinel-1) acquired before and after flood events. The method was evaluated on the S1GFloods dataset, comprising 5360 bi-temporal Sentinel-1 SAR image pairs across 46 flood incidents on six continents. Experimental results demonstrate a flood Intersection over Union (IoU) of 92.43%, an F1 score of 96.07%, and a recall of 97.64%. These metrics rank the proposed approach third overall among top-performing methods on this dataset. Notably, the high recall rate indicates the model is particularly beneficial for emergency response, as it minimizes the number of undetected flooded areas. Despite utilizing a CNN-based architecture that is less complex than Vision Transformer models, this method achieves results comparable to the state-of-the-art DAM-Net, with a performance difference of only 0.77%.
This study investigates the spatial distribution of ambient dose equivalent rates (ADER) on Avala and Kosmaj mountains, two protected landscapes located within the territory of the City of Belgrade, Serbia. Both sites, characterized by rich biodiversity and cultural heritage, were analyzed to assess their radiological safety and suitability for outdoor recreation. In mid-October 2025, in situ measurements were conducted at 42 sampling points using the Radex RD1503+ GM counter. The recorded values ranged from 0.085 to 0.2 & micro;Sv/h, remaining below the recommended safety threshold of 0.2 & micro;Sv/h. To visualize the gamma dose spatial variability, all field data were georeferenced and processed in QGIS 3.28.10 using the Inverse Distance Weighting (IDW) interpolation method. Integration of GIS and Remote Sensing techniques enabled the correlation between gamma radiation patterns, land cover, and elevation gradients derived from digital elevation models (DEMs). The comprehensive GIS-based approach confirms that Avala and Kosmaj maintain low natural background radiation levels comparable to global averages for similar geomorphological settings, and therefore are safe and suitable for sports, tourism and recreation. The applied combination of field dosimetry, Remote Sensing, and geostatistical modeling provides a valuable framework for continuous environmental monitoring and sustainable landscape management in protected mountainous landscapes in Central Serbia.
Wetlands are among the planet’s most productive ecosystems, yet they are increasingly imperiled by intersecting global challenges, particularly agricultural expansion, food security demands, and climate change. 1 This study investigated the spatial extent of floodplain wetlands and assesses Land Use/Land Cover (LULC) dynamics in the uMngeni catchment using multi-temporal Landsat imagery for the years 2000, 2010, 2020, and 2024. 2 Seven key land cover classes were classified, which included agriculture, bare land, built-up areas, forest, grassland, wetlands, and water bodies, using the Random Forest (RF) classification incorporating spectral indices (NDVI, NDWI) and topographic variables (slope and aspect) on Google Earth Engine (GEE). The overall accuracies for the respective years were 88.98% (2000), 91.23% (2010), 84.21% (2020), and 86.55% (2024), with corresponding Kappa coefficients of 0.82, 0.84, 0.78 and 0.80. 3 The findings show a significant 37% decline in wetland area from 2000 (2978 ha) to 2024 (1874 ha), with the most pronounced loss (46%) occurring between 2000 and 2010. Built-up areas increased by 38% over the same period, while agriculture peaked in 2010 (9312 ha) before declining to 7632 ha by 2024. The dominant transitions involved wetlands and grasslands being replaced by urban land and bare surfaces, particularly along the floodplain edges. 4 These patterns reflect intensifying human pressure on wetland ecosystems. Targeted interventions, such as enforcing buffer zones, regulating land use near water bodies, and restoring degraded wetlands, are critical to conserving ecosystem services and achieving sustainability outcomes aligned with the Sustainable Development Goals.
Understanding spatio-temporal rainfall variability is critical for water resource management, especially for climate-sensitive river basins. This study examines rainfall trends and variability in the Baitarani River Basin (eastern India) using high-resolution gridded data for 1979-2020. Rainfall trends were investigated using non-parametric Mann-Kendall test (MK test) and Sen's slope estimator (SSE). The shift point was detected using multiple homogeneity tests [Pettitt test, Standard Normal Homogeneity Test (SNHT), and Buishand test], while rainfall variability was quantified using an entropy-based Marginal Disorder Index (MDI). The analyses were performed at annual and seasonal scales. MK Z-statistic indicates the increasing or decreasing nature of a series, whereas Sen's beta slope provides the rate of change in that particular series. The MK test and SSE were applied again to examine trends before and after the identified change point. Finally, maps illustrating spatial trends and percentage changes were produced using ArcGIS 10.6. Over the 42-year period, the MK test revealed significant increasing annual trends in both districts, Keonjhar (Z = +2.4, beta = 0.7 mm/year), with a percentage change of around +21.8%, and Mayurbhunj (Z = +2.4, beta = 0.7 mm/year), with a percentage change of around +19.2%. During 1979-2020 post-monsoon rainfall showed the highest increase (62-70%) while, post 2001, monsoon rainfall declined substantially (1.7-3.3 mm/year) across all districts, with Balasore showing the largest decrease (-3.3 mm/year). The earlier period (1979-2001) had stable monsoon rainfall but greater variability in retreating monsoon, especially in northern regions. Entropy-based variability analysis indicated the Bhadrak and Balasore districts as having maximum variability with an MDI value of 1.44 and 1.35, respectively, for monsoon and annual rainfall series. These findings underscore the importance of incorporating changing seasonal dynamics into water-resource planning and flood-risk management for the Baitarani River Basin in the context of climate change.
