Search Results (88)

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO₄, NO₃, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies. Full article

Climate change and related weather extremes are increasingly having an impact on all aspects of life. The main objective of the research was to analyze the impact of the most important meteorological elements and the image data of various water bodies of the Kis-Balaton wetland, Hungary. The primary question was which meteorological elements have a positive or negative influence on vegetational surface cover. Drones have facilitated the visual surveying and monitoring of challenging-to-reach water bodies in the area, including a lake and multiple channels. The individual channels had different flow conditions. Aerial surveys were conducted monthly, based on pre-prepared flight plans. Images captured by a Mavic 3 drone flying at an altitude of 150 m and equipped with a multispectral sensor were processed. The time-series images were aligned and assembled into orthophotos. The image details relevant to the research were segregated and classified using Maximum Likelihood classification algorithm. The reliability of the image data used was checked by Shannon entropy and spectral fractal dimension measurements. The results of the classification were compared with the meteorological data collected by a QLC-50 automatic climate station of Keszthely. The investigations revealed that the surface cover of the examined water bodies was different in the two years but showed a kind of periodicity during the year. In those periods, where photosynthetic organisms multiplied in a higher proportion in the water body, higher monthly average air temperatures and higher monthly global solar radiation sums were observed. Full article

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R² = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques. Full article

The climbing perch, Anabas testudineus, is one of the most widely distributed freshwater amphibious fishes in South and Southeast Asia, inhabiting both natural and artificial water bodies polluted by plastic waste. Current mesocosm experimental study aimed to investigate behavioral responses of wild fish to floating expanded polystyrene (EPS) pellets, with a focus on the biofilm developing on their surface. For biofilm formation, the pellets (diameter 3–4 mm) were exposed for two, six, and fourteen days in an irrigation canal inhabited by climbing perch. Development of an intensive biofilm was observed on days 6 and 14 of exposure, characterized by a high diversity of organisms, including protozoa, cyanobacteria, algae, amoebae, and fungi. Fish feeding behavior was observed in the presence of feed pellets, clean EPS pellets, and three variants of EPS pellets with biofilm developed on their surfaces in the freshwater environment. The fish rapidly grasped and ingested feed pellets compared to all variants of plastic pellets. Climbing perch grasped all types of EPS pellets but always rejected them after oral cavity testing. The time to the first grasp was significantly longer for both clean EPS and EPS exposed for two days compared to feed pellets. Biofilm appeared to function as a taste deterrent for the fish: the duration of oral cavity testing was negatively correlated with the EPS pellet exposure timings in natural conditions. We suggest that floating plastic stimulates foraging behavior in the fish, and the duration of this behavior was significantly longer than that observed with feed pellets. The similarity of positive buoyant EPS pellets to natural food objects may stimulate the fish movements towards the water surface, which likely results in greater energy expenditure and increased risk of predation, without any apparent benefit to the individual. Full article

Algal blooms pose a serious threat not only to the lake ecosystem of Lake Bosten but also by negatively impacting its rapidly developing fisheries and tourism industries. This study focuses on Lake Bosten as the research area and utilizes multi-source remote sensing imagery from Landsat TM/ETM+/OLI and Sentinel-2 MSI. The Adjusted Floating Algae Index (AFAI) was employed to extract algal blooms in Lake Bosten from 2004 to 2023, analyze their spatiotemporal evolution characteristics and driving factors, and construct a Long Short Term Memory (LSTM) network model to predict the spatial distribution of algal-bloom frequency. The stability of the model was assessed through temporal segmentation of historical data combined with temporal cross-validation. The results indicate that (1) during the study period, algal blooms in Lake Bosten were predominantly of low-risk level, with low-risk bloom coverage accounting for over 8% in both 2004 and 2005. The intensity of algal blooms in summer and autumn was significantly higher than in spring. The coverage of medium- and high-risk blooms reached 2.74% in the summer of 2004 and 3.03% in the autumn of 2005, while remaining below 1% in spring. (2) High-frequency algal bloom areas were mainly located in the western and northwestern parts of the lake, and the central region experienced significantly more frequent blooms during 2004–2013 compared to 2014–2023, particularly in spring and summer. (3) The LSTM model achieved an R² of 0.86, indicating relatively stable performance. The prediction results suggest a continued low frequency of algal blooms in the future, reflecting certain achievements in sustainable water-resource management. (4) The interactions among meteorological factors exhibited significant influence on bloom formation, with the q values of temperature and precipitation interactions both exceeding 0.5, making them the most prominent meteorological driving factors. Monitoring of sewage discharge and analysis of agricultural and industrial expansion revealed that human activities have a more direct impact on the water quality of Lake Bosten. In addition, changes in lake area and water environment were mainly influenced by anthropogenic factors, ultimately making human activities the primary driving force behind the spatiotemporal variations of algal blooms. This study improved the timeliness of algal-bloom monitoring through the integration of multi-source remote sensing and successfully predicted the future spatial distribution of bloom frequency, providing a scientific basis and decision-making support for the sustainable management of water resources in Lake Bosten. Full article

Macroalgae are an integral part of estuarine primary production; however, their excessive growth may have severe negative impacts on the ecosystem. Although it is generally believed that algal blooms may be caused by a combination of excessive nutrients and temperature, their occurrences are hard to predict, and quantitative monitoring is a logistical challenge which requires the development of reliable and inexpensive techniques. This can be achieved by implementation of processing algorithms and indices on multi-spectral satellite images. Tuggerah Lakes estuary on the Central Coast of NSW was studied because of the regular occurrences of blooms, primarily of green filamentous algae. The detection of algal blooms based on the red-edge effect of the chlorophyll provided consistent results supported by direct observations. The Floating Algae Index (FAI) was identified as the most accurate index for detecting algal blooms in shallow areas, following a comparative analysis of six commonly used algae detection indices. Logistic regression was implemented where FAI was used as a predictor of two clusters, “bloom” and “non-bloom”. FAI was calculated for multi-spectral satellite images based on pixels of 20 × 20 m, covering the entire area of the Tuggerah Lakes. Seven sample points (pixels) were chosen, and the optimal threshold was found for each pixel to assign it to one of the two clusters. The logistic regression model was trained for each pixel; then the optimal parameters for its coefficients and the optimal classification threshold were obtained by cross-validation based on bootstrapping. Probabilities for classifying clusters as either “bloom” or “non-bloom” were predicted with respect to the optimal threshold. The resulting model can be used to estimate probability of macroalgal blooms in coastal estuaries, allowing quantitative monitoring through time and space. Full article

Floating photovoltaic (FPV) systems can play an important role in energy transition. Yet, so far, not much is known about the effects of FPV systems on water quality and ecology. A sun-tracking FPV system (24% coverage) was installed on a shallow drinking water reservoir. We observed for the first time that benthic cyanobacteria (blue-green algae), which can deteriorate water quality, developed massively under the FPV system, while macrophytes and benthic algae, such as Chara (stonewort), mostly disappeared. Calculations of light availability explain this shift. The natural mixing of the water column was hardly affected, and the average temperature of the reservoir was not altered significantly. Biofouling of the water-submerged part of the FPV system consisted mostly of a massive attachment of Dreissena mussels, which affected water quality. Water bird numbers and concentrations of faecal bacteria were similar after the installation of the FPV system. Especially in shallow, transparent water bodies, there is a significant risk of FPV systems promoting the growth of undesirable benthic cyanobacteria. Overall, these new insights can aid water managers and governmental institutions in assessing the risks of FPV systems on water quality and the ecology of inland waters. Full article

This study assesses the environmental status and water quality of the Saraswati River, an ancient and endangered waterway in Bengal, using an integrated approach. By combining traditional knowledge, advanced geospatial tools, and field analysis, it examines natural and human-induced factors driving the river’s degradation and proposes sustainable restoration strategies. Tools such as the Garmin Global Positioning System (GPS) eTrex10, Google Earth Pro, Landsat imagery, ArcGIS 10.8, and Google Earth Engine (GEE) were used to map the river’s trajectory and estimate its water quality. Remote sensing-derived indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Salinity Index (NDSI), Normalized Difference Turbidity Index (NDTI), Floating Algae Index (FAI), and Normalized Difference Chlorophyll Index (NDCI), Total Dissolved Solids (TDS), were computed to evaluate parameters such as the salinity, turbidity, chlorophyll content, and water extent. Additionally, field data from 27 sampling locations were analyzed for 11 critical water quality parameters, such as the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and microbial content, using an arithmetic weighted water quality index (WQI). The results highlight significant spatial variation in water quality, with WQI values ranging from 86.427 at Jatrasudhi (indicating relatively better conditions) to 358.918 at Gobra Station Road (signaling severe contamination). The pollution is primarily driven by urban solid waste, industrial effluents, agricultural runoff, and untreated sewage. A microbial analysis revealed the presence of harmful species, including Escherichia coli (E. coli), Bacillus, and Entamoeba, with elevated concentrations in regions like Bajra, Chinsurah, and Chandannagar. The study detected heavy metals, fertilizers, and pesticides, highlighting significant anthropogenic impacts. The recommended mitigation measures include debris removal, silt extraction, riverbank stabilization, modern hydraulic structures, improved waste management, systematic removal of water hyacinth and decomposed materials, and spoil bank design in spilling zones to restore the river’s natural flow. Full article

Cyanobacterial blooms are a widespread phenomenon in aquatic ecosystems worldwide, causing significant harm to the ecological environment. Lake Taihu is the third-largest freshwater lake in China. The region has been increasingly affected by cyanobacterial blooms, drawing greater attention from people. Currently, numerous models have been developed for detecting algal bloom based on spectral characteristics. However, the intuitive basis of optical detection lies in water color. Therefore, constructing an algal bloom detecting model from the perspective of chromaticity is worth exploring. This study constructed an algal bloom detecting model based on chromatic parameters, DFLH, and IAVW by using hyperspectral data from Lake Taihu. It further applied the model to the ZY-1E hyperspectral satellite for large-scale algal bloom monitoring. The threshold for detecting cyanobacterial blooms is defined as DFLH > 0.013 sr⁻¹ and Hue Angle > 170.58 degrees; the threshold for the normal water is defined as DFLH < 0.013 sr⁻¹. The parameter thresholds for the floating leaf vegetation range were defined as DFLH > 0.013 sr⁻¹, Saturation < 0.07, and IAVW > 598 nm. Through the validation, in the modeling dataset, the overall accuracy (OA) value is 0.81 and the F₁-score is 0.86. In the validation dataset, the overall accuracy (OA) value is 0.83 and the F₁-score is 0.89. The model demonstrates good detecting performance. Regarding its application on the ZY-1E satellite, we validated the detection results accuracy through matching synchronized in situ algal density data. The results are as follows: OA is 0.95, and the F₁-score is 0.95. The results above indicate that the algal bloom detection method developed in this study had a good accuracy in detecting algal blooms in Lake Taihu on 6 September 2020. This study provided the algae bloom detecting model based on water color characteristics in Lake Taihu, which had high detecting accuracy. Full article

Green tide area is a crucial indicator for monitoring green tide dynamics. However, scale effects arising from differences in image resolution can lead to estimation errors. Current pixel-level and sub-pixel-level methods often overlook the impact of morphological differences across varying resolutions. To address this, our study examines the influence of morphological diversity on green tide area estimation using GF-1 WFV data and the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index at a 16 m resolution. Green tide patches were categorized into small, medium, and large sizes, and morphological features such as elongation, compactness, convexity, fractal dimension, and morphological complexity were designed and analyzed. Machine learning models, including Extra Trees, LightGBM, and Random Forest, among others, classified medium and large patches into striped and non-striped types, with Extra Trees achieving outstanding performance (accuracy: 0.9844, kappa: 0.9629, F1-score: 0.9844, MIoU: 0.9637). The results highlighted that large patches maintained stable morphological characteristics across resolutions, while small and medium patches were more sensitive to scale, with increased estimation errors at lower resolutions. Striped patches, particularly among medium patches, were more sensitive to scale effects compared to non-striped ones. The study suggests that incorporating morphological features of patches, especially in monitoring striped and small patches, could be a key direction for improving the accuracy of green tide monitoring and dynamic change analysis. Full article

The increasing amount of plastic debris in water ecosystems provides a new substrate (epiplastic microhabitats) for aquatic organisms. The majority of research about epiplastic communities has focused on seawater environments, while research is still quite limited and scattered concerning freshwater systems. In this study, we analyze the first stages of colonization on different types of plastic by a periphytic algae community (its composition and dominant species complex) in freshwater bodies located in a nature reserve (within the Middle Volga Basin). A four-week-long incubation experiment on common plastic polymers (PET, LDPE, PP, and PS), both floating and dipped (~1 m), was conducted in two hydrologically connected karst water bodies in July 2023. The composition of periphytic algae was more diverse (due to the presence of planktonic, benthic, and periphytic species) than the phytoplankton composition found in the water column, being weakly similar to it (less than 30%). Significant taxonomic diversity and the dominant role of periphytic algae were noted for diatoms (up to 60% of the total composition), cyanobacteria (up to 35%), and green (including Charophyta) algae (up to 25%). The composition and structure of periphytic algae communities were distinct between habitats (biotope specificity) but not between the types of plastic, determined primarily by a local combination of factors. Statistically significant higher values of abundance and biomass were demonstrated for some species, particularly for Oedogonium on PP and Nitzschia on LDPE (p-value ≤ 0.05). As colonization progressed, the number of species, abundance, and dominance of individual taxa increased. In hydrologically connected habitats, different starts of colonization are possible, as well as different types of primary succession (initiated by potentially toxic planktonic cyanobacteria or benthic cyanobacteria and mobile raphid diatoms). Within the transparency zone, colonization was more active on the surface (for example, in relation to green algae on PP (p-value ≤ 0.05)). These results indicate a tendency for microalgae communities to colonize actively submerged plastic materials in freshwater, and they may be useful in assessing the ecological status of these aquatic ecosystems. Full article

The present study analyzes the potential propagation trajectories and fate of floating microplastic particles released on the Kara Sea shelf. The transport of microplastics is described using a Lagrangian model based on daily 2016–2020 data obtained from numerical modeling of Arctic Ocean dynamics. A particle biofouling model is used to simulate the submergence of floating microplastic particles in the water column. The model includes a parameterization of the processes of biofilm accumulation (via collision with algae in surrounding water, algae growth) and degradation (via respiration, mortality). The behavior of microplastic particles of different sizes (0.5 and 0.01 mm) during the sinking process and subsequent rising due to biofilm degradation is examined. The simulation results reveal that particles of 0.01 mm in size display a tendency to sink immediately during the process of biofouling. However, when the biofilm degraded, the particles exhibited a rising velocity, comparable to the current vertical velocity, and the particles remained submerged in the water for long periods. In contrast, the 0.5 mm particles remained at the surface for a longer period before sinking, accumulating biofilm. Subsequently, their behavior was oscillatory in response to changes in the biofilm, rising rapidly when the biofilm decayed and sinking rapidly again as a result of biomass accumulation. In winter, the 0.5 mm particles were mostly frozen into the ice. The phenomenon of biofouling, whereby microplastic particles of various sizes sink at different depths, results in considerable variation in the subsequent pathways of these particles in the Arctic Ocean. Full article

The increasing water temperature due to climate change has led to more frequent algae blooms and deteriorating water quality in coastal areas and rivers worldwide. To address this, we developed a deep learning-based model for identifying floating algae blooms using PlanetScope optical images and the Shifted Windows (Swin) Transformer architecture. We created 1,998 datasets from 105 scenes of PlanetScope imagery collected between 2018 and 2023, covering 14 water bodies known for frequent algae blooms. The methodology included data pre-processing, dataset generation, deep learning modeling, and inference result generation. The input images contained six bands, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), enhancing the model’s responsiveness to algae blooms. Evaluations were conducted using both single-period and multi-period datasets. The single-period model achieved a mean Intersection over Union (mIoU) between 72.18% and 76.47%, while the multi-period model significantly improved performance, with an mIoU of 91.72%. This demonstrates the potential of our model and highlights the importance of change detection in multi-temporal images for algae bloom monitoring. Additionally, the padding technique proposed in this study resolved the border issue that arises when mosaicking inference results from individual patches, providing a seamless view of the satellite scene. Full article

Urban and storm water retention ponds eventually become eutrophic after years of receiving runoff water. In 2020, a novel biological and chemical treatment was initiated to remove accumulated nutrients from an urban retention pond that had severe algae and weed growth. Our approach installed two 6.1 m × 6.1 m floating treatment wetlands (FTWs) and two airlift pumps that contained slow-release lanthanum composites, which facilitated phosphate precipitation. Four years of treatment (2020–2023) resulted in median nitrate-N concentrations decreasing from 23 µg L⁻¹ in 2020 to 1.3 µg L⁻¹ in 2023, while PO₄-P decreased from 42 µg L⁻¹ to 19 µg L⁻¹. The removal of N and P from the water column coincided with less algae, weeds, and pond muck (sediment), and greater dissolved oxygen (DO) concentrations and water clarity. To quantify the sustainability of this bio-chemical approach, we focused on quantifying nitrate removal rates beneath FTWs. By enclosing quarter sections (3.05 × 3.05 m) of the field-scale FTWs inside vinyl pool liners, nitrate removal rates were measured by spiking nitrate into the enclosed root zone. The first field experiment showed that DO concentrations inside the pool liners were well below the ambient values of the pond (<0.5 mg/L) and nitrate was quickly removed. The second field experiment quantified nitrate loss under a greater range of DO values (<0.5–7 mg/L) by including aeration as a treatment. Nitrate removal beneath FTWs was roughly one-third less when aerated versus unaerated. Extrapolating experimental removal rates to two full-sized FTWs installed in the pond, we estimate between 0.64 to 3.73 kg of nitrate-N could be removed over a growing season (May–September). Complementary laboratory mesocosm experiments using similar treatments to field experiments also exhibited varying nitrate removal rates that were dependent on DO concentrations. Using an average annual removal rate of 1.8 kg nitrate-N, we estimate the two full-size FTWs could counter 14 to 56% of the annual incoming nitrate load from the contributing watershed. Full article