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A risk assessment method for remote sensing of cyanobacterial blooms in inland waters
Abstract
Cyanobacterial blooms (CABs) of inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatial continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the CABs risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA were executed in three different lakes of Wuhan urban agglomeration (WUA) with different trophic state, and validated with water quality data and the results of relating studies. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in east part of Lake Longgan is high than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification results and results of existing study, including trophic level, ecology risk and algal extent, It showed that MIRA method is valuable for spatial continues and accurately identifying the risk of CABs in inland waters with potential eutrophication trends.
... In this kind of method, water samples were collected in the field and then analyzed in laboratory to obtain detailed characterization factors of HABs, e.g., Chl-a concentration and algal density. The present conditions of HABs in the whole lake can be finally assessed with spatial interpolation methods [14,15]. Although these methods have many advantages such as detailed monitoring parameters, high monitoring accuracy, etc., they consume a large amount of manpower, material and financial resources [16]. ...
... The DTLF design and application in Lake Chaohu demonstrated its high value in the monitoring and management of HABs in lakes. Firstly, different from traditional monitoring methods for HABs [14,18,24], this work aims to provide an efficient way to obtain Two thousand images captured with land-based video devices, i.e., validation data that do not participate in the training of the HAB monitoring algorithm, were used to evaluate the accuracy of HAB monitoring based on the video devices. The evaluation method will be introduced in Section 5.5. ...
... The DTLF design and application in Lake Chaohu demonstrated its high value in the monitoring and management of HABs in lakes. Firstly, different from traditional monitoring methods for HABs [14,18,24], this work aims to provide an efficient way to obtain the real-time situation of HABs in nearshore areas of lakes. From 8 a.m. to 6 p.m. every day, coverage ratios of HABs in key nearshore areas can be accurately and frequently monitored ( Figure 3). ...
Harmful algal blooms (HABs) caused by lake eutrophication and climate change have become one of the most serious problems for the global water environment. Timely and comprehensive data on HABs are essential for their scientific management, a need unmet by traditional methods. This study constructed a novel digital twin lake framework (DTLF) aiming to integrate, represent and analyze multi-source monitoring data on HABs and water quality, so as to support the prevention and control of HABs. In this framework, different from traditional research, browser-based front ends were used to execute the video-based HAB monitoring process, and real-time monitoring in the real sense was realized. On this basis, multi-source monitored results of HABs and water quality were integrated and displayed in the constructed DTLF, and information on HABs and water quality can be grasped comprehensively, visualized realistically and analyzed precisely. Experimental results demonstrate the satisfying frequency of video-based HAB monitoring (once per second) and the valuable results of multi-source data integration and analysis for HAB management. This study demonstrated the high value of the constructed DTLF in accurate monitoring and scientific management of HABs in lakes.
... These intensive blooms pose a major risk to water security, such as increasing turbidity, choking the growth of submerged aquatic vegetation, and producing toxicity in the water (Lin et al., 2021;Shi et al., 2018;Su et al., 2017). As a result, it threatens the safety of drinking water and affects the normal lives of the surrounding residents (Chen et al., 2020;Duan et al., 2017;Xu et al., 2016). Here, the term "blooms" refers to the surface scums of cyanobacteria or the aggregation of cyanobacteria near the water surface. ...
... Thus requires an advanced risk assessment of cyanobacterial blooms in eutrophic freshwater lakes (Chen et al., 2021;Zhang et al., 2019a) to guide water resource management (Xu et al., 2021b). Most studies have estimated cyanobacterial bloom risk levels using field-collected data from multiple indicators related to blooms or only one spatially continuous cyanobacterial product (Chen et al., 2020;Huang et al., 2020). Nevertheless, the occurrence of cyanobacterial blooms in lakes has clear spatial heterogeneity and is affected by various factors (Qi et al., 2018;Wang et al., 2022). ...
... Lake trophic states were classified as oligotrophic (0 ≤ TSI < 30), mesotrophic (30 ≤ TSI < 50), and eutrophic (50 ≤ TSI < 100). Previous studies have revealed that high nutrient levels may trigger cyanobacterial blooms (Chen et al., 2020;Xu et al., 2021a). Referring to the World Health Organization guidelines (2003), our risk assessment framework measured this risk using a TSI threshold of 30 to distinguish the trophic states below or over mesotrophic. ...
Climate change has catalyzed the global expansion of cyanobacterial blooms in eutrophic freshwater lakes and threatens water security. In most studies, the cyanobacterial bloom risk levels in lakes were evaluated using field-collected data from multiple indicators or spatially continuous data from one cyanobacteria-related indicator. Nevertheless, the occurrence of cyanobacterial blooms in lakes has clear spatial heterogeneity and is affected by numerous factors. Therefore, we developed a multivariable integrated risk assessment framework for cyanobacterial blooms in lakes using five spatially continuous datasets to estimate the risk level of cyanobacterial blooms at the pixel scale (250 m). The spatial and temporal variations in cyanobacterial bloom risk levels from May 1, 2002, to October 31, 2020, were investigated for three typical eutrophic lakes in China: Lake Taihu, Lake Chaohu, and Lake Dianchi. Seasons and regions of high cyanobacterial bloom risk were identified for each lake. Environmental characteristics were discussed. A long-term investigation revealed that owing to its warm climate, the cyanobacterial risk levels in summer and autumn were much higher than those in the other two seasons. At the synoptic scale, Lake Taihu had a lower cyanobacterial bloom risk than lakes Chaohu and Dianchi. A further comparison found that precipitation, wind speed, and temperature were responsible for the differences in cyanobacterial bloom risk levels among the three lakes. At the pixel scale, the risk map indicated that the cyanobacterial bloom risk levels of Lake Taihu were unevenly distributed, and the cyanobacterial bloom risk of the lakeshore was higher than that of the other subregions. Nutrient levels played the most critical role in the regional differences in cyanobacterial bloom risk levels in a lake. Bloom events were defined and classified as “long-term bloom” or “flash bloom” according to their duration (over or below a year). Overall, this study can assist in advanced water management with a pixel-scale evaluation of cyanobacterial bloom risk levels.
... Each FAI image (masked land and cloud pixels) can generate a gradient image. The pixel gradient is defined as the FAI difference from the adjacent pixels in the 3 × 3 window (Chen et al., 2020a;Hu et al., 2010). The extraction of CyanoHABs was disturbed by a high concentration of suspended solids in water (Hu et al., 2010). ...
... The gradient value of the remaining pixels is determined. Several studies have shown that the FAI value corresponding to the maximum gradient value in an image can be used as a threshold for distinguishing between phytoplankton and pure water pixels (Chen et al., 2020a;Hu et al., 2010;Zhang et al., 2014). This is understandable because there should be a maximum grayscale change in FAI values of the MODIS images (MODIS-FAI) at the boundary of bloom and non-bloom. ...
... After automatic generation of MODIS image FAI values (MODIS-FAI), the algal threshold for each image was determined by a histogram of the gradient distribution based on the sum of the FAI differences of each pixel and its 3 × 3 pixels on the adjacent boundary. The threshold for non-algae pixels was then defined as − 0.004 by subtracting two times the standard deviation from the average threshold of all MODIS-FAI images (Chen et al., 2020a;Hu et al., 2010;Zhang et al., 2014). ...
Cyanobacterial Harmful Algae Blooms (CyanoHABs) in the eutrophic lakes have become a global environmental and ecological problem. In this study, a CNN-LSTM integrated model for predicting the CyanoHABs area was proposed and applied to the prediction of the CyanoHABs area in Taihu Lake. Firstly, the time-series data of the CyanoHABs area in Taihu Lake for 20 years were accurately obtained using MODIS images from 2000 to 2019 based on the FAI method. Then, a principal component analysis was performed on the daily meteorological data for the month before the outbreak of CyanoHABs in Taihu Lake from 2000 to 2019 to determine the meteorological factors closely related to the outbreak of CyanoHABs. Finally, the features of CyanoHABs area and meteorological data were extracted by Convolutional Neural Networks (CNN) model and used as the input of Long Short Term Memory Network (LSTM). An integrated CNN-LSTM model approach was constructed for predicting the CyanoHABs area. The results show that high R² (0.91) and low mean relative error (17.42%) verified the validity of the FAI index to extract the CyanoHABs area in Taihu Lake; the meteorological factors closely related to the CyanoHABs outbreak in Taihu Lake are mainly temperature, relative humidity, wind speed, and precipitation; the CNN-LSTM integrated model has better prediction effect for both training and test sets compared with the CNN and LSTM models. This study provides an effective method for predicting temporal changes in the CyanoHABs area and offers new ideas for scientific and effective regulation of inland water safety.
... The constellation of Sentinel-2A/2B and Landsat-8/9 satellites is expected to afford a 2-3 day revisit globally (not accounting for clouds) when Landsat-9 becomes operational (Pahlevan et al., 2019). As a result, a number of studies have begun to explore the application of Landsat-8 and Sentinel-2 imagery to inland, estuarine, and coastal waters (e.g., Herrault et al., 2016;Kutser et al., 2016;Olmanson et al., 2016;Chen et al., 2019;Kuhn et al., 2019;Luis et al., 2019;Page et al., 2019;Chen et al., 2020aChen et al., , 2020bChunHock et al., 2020;Zhang et al., 2020). Yet, satellite imagery from these two missions, or their merged time-series products, has not been previously applied to study biogeochemical exchanges in highly dynamic, tidally influenced wetland-estuarine environments. ...
... OLI and MSI Level 1 data were processed using the open-source ACOLITE processor (v20190326) to generate ocean color remote sensing reflectance (R rs (λ)) after applying the default "dark spectrum fitting (DSF)" algorithm for atmospheric correction (Vanhellemont, 2019;Vanhellemont and Ruddick, 2018). Previous studies have demonstrated successful processing of Landsat and Sentinel satellite images using the DSF approach in both inland and coastal waters (e.g., Caballero and Stumpf, 2019;Chen et al., 2020aChen et al., , 2020b. For the Sentinel-2/MSI data, the 60 m resolution blue band (B1) was internally resampled to the 10 m grid in ACOLITE, generating R rs (443) at the finer 10 m spatial resolution. ...
... Many previous studies focusing on satellite retrievals of DOC in coastal environments have been based on first retrieving the absorption coefficient of the colored component of DOM (i.e., a CDOM ) and then estimating DOC concentrations either by using seasonally and regionally specific relationships between a CDOM and DOC (e.g., Mannino et al., 2008;Joshi et al., 2017;Chen et al., 2020aChen et al., , 2020b or, alternatively, linking the DOC-specific CDOM absorption to its spectral shape to account for variability in DOC quality in nearshore environments (e.g., Fichot and Benner, 2012;Cao et al., 2018). Other published studies, particularly in inland and estuarine waters, have successfully linked DOC concentrations directly to water reflectance (e.g., Hirtle and Rencz, 2003;Tehrani et al., 2013;Huang et al., 2017). ...
Estuarine and coastal wetlands are among the most productive, ecologically valuable, and economically important ecosystems on Earth. Through tidal inundation and lateral export, estuarine marshes exert a strong influence on the amount and quality of carbon and nutrients exchanged between the land and the ocean. Yet, current understanding of the role of these "blue carbon" ecosystems on estuarine optics, biogeochemistry, and ecology is largely based on limited field observations at point locations, due to the coarse resolution of existing and heritage satellite ocean color sensors. Here, for the first time, we merged high resolution data from the constellation of Landsat-8/Operational Land Imager (OLI) and Sentinel-2A/B MultiSpectral Instruments (MSI) to examine dissolved organic carbon (DOC) dynamics in a tidally influenced temperate marsh–estuarine system. Often referred to as the "Everglades of the North", the Blackwater National Wildlife Refuge is the largest marsh system in the Chesapeake Bay and one of the most ecologically important areas in the United States. A multiple linear regression approach, linking DOC to the spectral shape of water remote sensing reflectance, was used to capture spatial features and temporal changes in DOC over a broad range of conditions (DOC in the range of 2.32–19.9 mg/L), across multiple years, different seasons, and tidal regimes, with a mean average percent difference of 23%. The data consistency between satellite sensors was evaluated, followed by a demonstration of a consistent, multi-sensor DOC data record that provided more frequent observations. Combining data from the three satellites, revealed the strong impact of marsh outwelling at the larger ecosystem scale, its seasonal variability, and the importance of other environmental factors, including wind conditions, river discharge, and extreme weather events, in shaping DOC dynamics along complex, tidally influenced terrestrial–aquatic interfaces.
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... Various interdisciplinary approaches have been closely integrated with remote sensing technology to establish novel methodologies for the long-term monitoring and assessment of regional environments. Chen et al. (2020) developed a multivariate integrated risk assessment (MIRA) method for evaluating cyanobacterial blooms (CABs). This method was applied to Lake Liangzi and Lake FuTou, with its results corroborated by remote sensing data obtained from the Landsat 8 OLI Dataset (Chen et al., 2020). ...
... Chen et al. (2020) developed a multivariate integrated risk assessment (MIRA) method for evaluating cyanobacterial blooms (CABs). This method was applied to Lake Liangzi and Lake FuTou, with its results corroborated by remote sensing data obtained from the Landsat 8 OLI Dataset (Chen et al., 2020). Additionally, Zhou et al. (2021) employed the Forel-Ule Index (FUI) to assess the trophic level of five inland lakes in the vicinity of Wuhan, China (Zhou et al., 2021). ...
This study provides a comprehensive review of eutrophication assessment and simulation within surface water ecosystems. It is noted that the majority of contemporary models and assessment methodologies fail to account for variations in local stream characteristics and their consequent impacts on biotic communities. To visualize the progression and focal points of eutrophication research, a total of 12,409 articles and their associated references were analyzed with bibliometric tools. The review procedure clarified the approaches used in eutrophication assessment and predicted future development trends. Contemporary research on eutrophication simulation and assessment predominantly concentrates on nutrients, phytoplankton, life-cycle assessment, trophic indices, and agricultural impacts. Additionally, the research frontiers have transitioned from traditional focuses on water quality and ecological status to more specialized areas such as freshwater modeling, harmful algal blooms, and estuarine dynamics. Consequently, this review constitutes a significant resource for researchers aiming to utilize eutrophication simulators in the context of groundwater ecosystem protection and water quality management. The findings will facilitate the advancement of assessment methodologies for trophic levels and capture the progression of cutting-edge research developments.
... However, recent literature indicates that although Liangzi Lake has not yet become the most severely affected area by cyanobacterial blooms, there are signs suggesting its potential for high risk (N. Chen et al., 2020). The existing research on Liangzi Lake is predominantly focused on the assessment of water quality index (WQI) and their spatiotemporal evolution trends (Lin et al., 2023;Shen et al., 2023;Zhang et al., 2019a). ...
... Larger datasets offer more input information for training models, but increasing the sample size of the training dataset can incur significant computational costs (N. Chen et al., 2020). We input the data sequentially in order of parameters ranking and compared the model's performance in terms of R 2 , RMSE, sMAPE, and MAE. ...
... Here, the FAI (Hu 2009) was used for detecting floating algae in lakes. In addition, the CMI was chosen to distinguish waters with cyanobacterial scums from those dominated by aquatic macrophytes, following a baseline subtraction similar to the FAI index with clearly different band combinations (Chen et al. 2020a;Liang et al. 2017). The use of the baseline subtraction method removed additional impacts from atmospheric effects. ...
... The maximum category variance method (Otsu) is a global binarization algorithm and an adaptive threshold acquisition method, and it has been widely used in previous studies (Chen et al. 2020a;Liang et al. 2017). The otsu algorithm divides the image into two parts, the target (the proportion w 0 , and the average m 0 ) and the background (the proportion w 1 , and the average m 1 ), according to the grayscale characteristics of the image. ...
Water color is a crucial optical indicator of water quality, polluted water bodies often show water color anomalies. To comprehensively understand the occurrence of water color anomalies in inland lakes, an integrated method was designed using the hue angle based on the Forel-Ule Index (FUI) model, and other remote sensing indices, including the Turbid Water Index (TWI), Floating Algae Index (FAI), and Cyanobacteria and Macrophytes Index (CMI). Based on all available Landsat-8 OLI images from 2013 to 2020, continuous monitoring was conducted in three different lakes in the middle of the Yangtze River, China. The results demonstrated that: (1) The proposed method can accurately identify algal blooms, high sediment loads, and eutrophication from the abnormal water color areas; (2) The calculated hue angles of sediment-dominated water were significantly higher than those of algal blooms and aquatic vegetation, providing a noticeable visual discoloration of water; (3) These water color anomalies exhibited significant correlations with the water quality and environmental conditions. This study serves as an example for accurate and spatially continuous assessment of water color anomaly and supports practical information to facilitate local water environment conservation.
... This increase in Poland was not only related to the demand for ecosystem services of the local population due to the increasing mean temperature (especially in the summer period) but also due to changes in the law regulations related to bathing waters which encouraged the local administration, as well as private owners of water bodies, to register the bathing waters. However, climate change significantly affect the water bodies, providing optimal conditions for the occurrence of cyanobacterial blooms which are noticed more often (Paerl and Huisman 2008;Huisman et al. 2018;Mantzouki et al. 2018;Kataržytė et al. 2019;Chen et al. 2020;Overlingė et al. 2020). Cyanobacterial blooms, as shown in this study, were the main reason for the closing of many bathing waters (Fig. 3, Fig. S1 in Supplementary Materials 2; Table S1 in Supplementary Materials 1) to protect people from potentially toxic effects (WHO 2003;Poniedziałek et al. 2012;Rzymski and Poniedziałek 2014;Kataržytė et al. 2019;Overlingė et al. 2020). ...
... Thus, considering the higher sensitivity of small water bodies to changes in the catchment and the additional load of nutrients resulting from the recreational activities of people, small water bodies with bathing waters in particular should be additionally monitored with the obligatory assessment of chlorophyll-a concentration. This monitoring might be supported or even performed by the remote sensing methods to minimize the costs of the chlorophyll-a analysis as many authors were already suggested (Kataržytė et al. 2019;Chen et al. 2020;Overlingė et al. 2020). As pointed above, small water bodies are especially characterized by rapid changes and the water quality might transition from good ("the clear water state") to bad ("the turbid water state") (Scheffer 1989;Scheffer and Van Nes 2007;Janssen et al. 2014;Andersen et al. 2020). ...
The safety of beachgoers and swimmers is determined by the presence or absence of microbial contaminants and cyanobacterial toxins in the water. This study compared the assessment of bathing waters according to the Bathing Water Directive, which is based on the concentration of fecal contaminants, with some modifications, and a new method based on the concentration of chlorophyll-a, which corresponds to the World Health Organization (WHO) guidelines used for determining cyanobacterial density in the water posing threat to people health. The results obtained from the method based on chlorophyll-a concentration clearly showed that the number of bathing waters in Poland with sufficient and insufficient quality were higher in 2018 and 2019, compared to the method based on microbial contamination. The closing of bathing waters based only on the visual confirmation of cyanobacterial blooms might not be enough to prevent the threat to swimmers' health. The multivariate analyses applied in this study seem to confirm that chlorophyll-a concentration with associated cyanobacterial density might serve as an additional parameter for assessing the quality of bathing waters, and in the case of small water reservoirs, might indirectly inform about the conditions and changes in water ecosystems.
... Consequently, there is a compelling need to develop cost-effective and scalable monitoring solutions. Although remote sensing techniques utilizing satellite imagery (Ahn et al., 2006;Cannizzaro et al., 2019;Chen et al., 2020;Hu, 2009;Kutser et al., 2006) offer a non-invasive alternative, their efficacy is limited by meteorological constraints and the temporal resolution of satellite overpasses, rendering them inadequate for capturing the rapid temporal dynamics characteristic of cyanobacterial bloom events. The recent study by Barrientos-Espillco et al. (2023) addresses the detection of cyanobacterial blooms using Autonomous Surface Vehicles (ASVs) equipped with Machine Vision Systems (MVS). ...
Cyanobacterial blooms emerge unpredictably on the surface of lentic water bodies, posing both ecological threats and public health risks. To effectively monitor these events, this study introduces the use of Machine Vision Systems (MVS) integrated into Autonomous Surface Vehicles (ASVs). These ASVs are capable of autonomous and safe navigation, enabling them to detect cyanobacterial blooms while avoiding obstacles. Convolutional Neural Networks (CNNs) are employed for early detection and continuous monitoring, but their effectiveness hinges on access to large, high-quality training datasets. Due to the sporadic and uncontrollable nature of bloom occurrences, acquiring sufficient real-world images for training and validating CNN models is a significant challenge. To overcome this, the Stable Diffusion XL (SDXL) text-to-image generative model is utilized to produce realistic synthetic images, ensuring a sufficient dataset for training. However, SDXL alone struggles to accurately depict cyanobacterial blooms. To address this limitation, DreamBooth is used to fine-tune SDXL with a small set of real bloom-specific image patches. To ensure the diversity of the synthetic dataset, detailed prompts for SDXL are generated using a Large Language Model (LLM). The combination of SDXL fine-tuning with LLM-driven prompts design applied to environmental monitoring and autonomous navigation in lentic environments represents the core innovation of this work. A dual-task CNN model is then trained on the synthetic dataset to simultaneously detect blooms and obstacles. Experimental results demonstrate the effectiveness and novelty of the proposed approach, showing improvements of up to 15.74% in object detection and 6.48% in semantic segmentation compared to the baseline dataset.
... For instance, the decline in aquatic biodiversity and the degradation of aquatic macrophytes [15]. What is worse, the risk of cyanobacteria blooms has increased in recent years [17]. Thus, the risk assessment and management of plankton is necessary in Liangzi Lake. ...
Liangzi Lake, a typical shallow lake in the middle reaches of the Yangtze River, is important for water resource and biodiversity conservation. With the development of urbanization, anthropogenic activities have posed serious threats to the water quality and biodiversity of Liangzi Lake. To assess the aquatic ecosystem health of Liangzi Lake, the structure, the environmental response, and the interactions of plankton were investigated in 2022 and 2023. The results indicated that water temperature was a pivotal factor regulating plankton dynamics, with the assemblage patterns predominantly shaped by the phytoplankton species, which were Bacillariophyta in spring and Chlorophyta in summer. In terms of the phytoplankton, dissolved oxygen and the N:P ratio significantly affect cyanobacteria distribution. The high biomass and abundance of cyanobacteria in summer highlight the potential risk of harmful algal blooms. In contrast to the phytoplankton, the zooplankton exhibited enhanced resilience to changes in the surrounding environment. Rotifera was the dominant group in summer in terms of both abundance and biomass. Most core genera of plankton were jointly identified by eDNA metabarcoding and microscopical analysis, and eDNA metabarcoding had advantages in revealing a higher diversity. However, some taxa among rotifers such as Liliferotrocha were only identified using microscopical analysis. Therefore, a combination of both the methods is recommended to better understand the structuring mechanisms of plankton assemblages in lake ecosystems.
... Liu also investigated the eukaryotic phytoplankton structures using high-throughput sequencing technology in 2019, and found that pH, NH 4 + -N, and nitrate nitrogen (NO 3 − -N) were the most crucial environmental factors affecting the structure of the phytoplankton community in the Danjiangkou reservoir [16]. Along with its rapidly expanding economy, China is also facing the increasingly serious problem of eutrophication, especially in aquatic ecosystems of lakes and reservoirs [17,18]. Water bloom is the worst result of lake or reservoir eutrophication [19]. ...
Freshwater reservoirs serve as vital water sources for numerous residential areas. However, the excessive presence of nutrients, such as nitrogen and phosphorus, stimulates rapid algal proliferation, leading to the occurrence of algal blooms. To prevent this phenomenon, it is imperative to conduct regular ecological surveys aimed at assessing water quality and monitoring the dynamic composition of aquatic biological communities within the reservoir’s ecosystem. In this study, seasonal changes in water quality parameters and the spatial and temporal distribution of planktonic algae at 14 sampling sites in the Danjiangkou reservoir were analyzed. A total of 136 taxonomic units of planktonic algae were identified, belonging to 8 phyla, 41 families, and 88 genera, with the dominant algae belonging to the phyla Chlorophyta, Bacillariophyta, and Cyanophyta. The order of abundance of the algae was summer > autumn > spring > winter and Hanku > Intake > Danku > Outflow. WT, pH, DO, CODMn, and Chl a were the primary drivers influencing the changes in the planktonic algal community within the reservoir. Two dominant algae, Chlamydomonas debaryana and Scenedesmus quadricauda, were isolated and cultured indoors to simulate the growth behaviors of algae in the Danjiangkou reservoir. The results show that the growth of C. debaryana was severely limited by the temperature, light, and nutrient concentration, whereas the growth of S. quadricauda was slightly affected under different temperature and light conditions and could occur at low concentrations of nitrogen and phosphorus nutrients. With excess nutrient levels, excessive proliferation of S. quadricauda could potentially cause algal blooms. This study examined the growth characteristics of the dominant algae in the Danjiangkou reservoir under laboratory conditions and delved into their interdependencies with environmental factors, aiming to furnish a theoretical and experimental foundation for investigating algal community dynamics and preventing algal blooms within the freshwater reservoir.
... The aquatic plant system is an important species that maintains the ecological safety of reservoirs and ponds [37,38]. It could increase the dissolved oxygen and the number of plankton in the ponds [39], and reduce the level of inorganic nitrogen and the possibility of eutrophication of the water body [40]. Most importantly, aquatic plants can also provide a stable and high-quality food source for hairy crabs, trash fish, and other herbivorous or omnivorous animals in the food chain in the pond system [41]. ...
The lakes, including reservoirs and ponds in the Yellow River Delta, are characterized by many fragile ecosystems and low economic values. How to take into account both ecology restoration and the economic benefits of the lakes in this region is a complex problem. The Chinese mitten crab (Eriocheir sinensis)-aquatic plant system may have this potential. In this study, we planted aquatic plants, e.g., Elodea nuttallii, Hydrilla verticillate, and Vallisneria natans, with the crabs and investigated geochemical parameters in the ponds. The concentration of NH4+-N was lower than 0.5 mg/L, the pH of the breeding peiponds was 8.274-9.365, and the dissolved oxygen was 3.554-6.048mg/L, which was better than the class II environmental quality standards for surface water. The more extensive specifications ( > 150g/pcs) of the crab growth with the aquatic plants account for >35% of the total production. This model is significant to the ecological utilization of reservoirs in the Yellow River Delta but has low promotion. Therefore, some compulsory breeding policies and breeding standards must be proposed. It is the current ecological needs of the ecological protection Yellow River Delta.
... Vision-based remote-sensing monitoring studies of CyanoHABs use satellite with hyperspectral imagery [7][8][9][10][11]. The study in [12] mentions that the most commonly used remote sensing spectral indices to identify CyanoHABs are: band ratio index, normalized difference vegetation index (NDVI), maximum chlorophyll index (MCI), and floating algae index (FAI). ...
Cyanobacterial Harmful Algal Blooms (CyanoHABs) in lakes and reservoirs have increased substantially in recent decades due to different environmental factors. Its early detection is a crucial issue to minimize health effects, particularly in potential drinking and recreational water bodies. The use of Autonomous Surface Vehicles (ASVs) equipped with machine vision systems (cameras) onboard, represents a useful alternative at this time. In this regard, we propose an image Semantic Segmentation approach based on Deep Learning with Convolutional Neural Networks (CNNs) for the early detection of CyanoHABs considering an ASV perspective. The use of these models is justified by the fact that with their convolutional architecture, it is possible to capture both, spectral and textural information considering the context of a pixel and its neighbors. To train these models it is necessary to have data, but the acquisition of real images is a difficult task, due to the capricious appearance of the algae on water surfaces sporadically and intermittently over time and after long periods of time, requiring even years and the permanent installation of the image capture system. This justifies the generation of synthetic data so that sufficiently trained models are required to detect CyanoHABs patches when they emerge on the water surface. The data generation for training and the use of the semantic segmentation models to capture contextual information determine the need for the proposal, as well as its novelty and contribution.
Three datasets of images containing CyanoHABs patches are generated: (a) the first contains real patches of CyanoHABs as foreground and images of lakes and reservoirs as background, but with a limited number of examples; (b) the second, contains synthetic patches of CyanoHABs generated with state-of-the-art Style-based Generative Adversarial Network Adaptive Discriminator Augmentation (StyleGAN2-ADA) and Neural Style Transfer as foreground and images of lakes and reservoirs as background, and (c) the third set, is the combination of the previous two. Four model architectures for semantic segmentation (UNet++, FPN, PSPNet, and DeepLabV3+), with two encoders as backbone (ResNet50 and EfficientNet-b6), are evaluated from each dataset on real test images and different distributions. The results show the feasibility of the approach and that the UNet++ model with EfficientNet-b6, trained on the third dataset, achieves good generalization and performance for the real test images.
... In this study, the MODIS surface reflectance product (MOD09A1) was downloaded through a data platform named Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) from 2015 to 2020. MOD09A1 provides an estimate of surface reflectance (Terra Bands 1 through 7) which has been corrected for atmospheric conditions such as Rayleigh scattering and gasses (Chen et al., 2020b). For each pixel, the value is selected from the acquisitions within 8-day composite period with a spatial resolution of 500 m. ...
Surface soil moisture (SSM) is of great importance in understanding global climate change and studies related to environmental and earth science. However, neither of current SSM products or algorithms can generate SSM with High spatial resolution, High spatio-temporal continuity (cloud-free and daily), and High accuracy simultaneously (i.e., 3H SSM data). Without 3H SSM data, fine-scale environmental and hydrological modeling cannot be easily achieved. To address this issue, we proposed a novel and integrated SSM downscaling framework inspired by deep learning-based point-surface fusion, which was designed to produce 1 km spatially seamless and temporally continuous SSM with high accuracy by fusing remotely sensed, model-based, and ground data. First, SSM auxiliary variables (e.g., land surface temperature, surface reflectance) were gap filled to ensure the spatial continuity. Meanwhile, the extended triple collocation method was adopted to select reliable in-situ stations to address the scale mismatch issue in SSM downscaling. Then, the deep belief model was utilized to downscale the original 9 km SMAP SSM and 0.1º. ERA5-Land SSM to 1 km. The downscaling framework was validated over three ISMN soil moisture networks covering diverse ground conditions in Southwestern US. Three validation strategies were adopted, including in-situ validation, time-series validation, and spatial distribution validation. Results showed that the average Pearson correlation coefficient (PCC), unbiased root mean squared error (ubRMSE), and mean absolute error (MAE) achieved 0.89, 0.034 m3m−3, and 0.032 m3m−3, respectively. The use of point-surface fusion greatly improved the downscaling accuracy, of which the PCC, ubRMSE, and MAE were improved by 3.73, 20.93, and 39.62% compared to surface-surface fusion method, respectively. Comparative analyses have also been carefully conducted to confirm the effectiveness of the framework, in terms of other downscaling algorithms, scale variations, and fusion methods. The proposed method is promising for fine-scale studies and applications in agricultural, hydrological, and environmental domains.
... Meanwhile, the gapfill tool was used to remove black strips from the Landsat 7 ETM+ gap-off data. The FAI (Chen et al., 2020;Hu, 2009;Oyama et al., 2015) was used to identify algal bloom regions after land and cloud masking with visual analysis. The formulas are as follows: ...
As one of three top-priority eutrophic lakes in China, Dianchi Lake has received national attention due to its severe eutrophication in recent decades. Meteorological factors are the main factors driving the formation and persistence of algae blooms. In addition, meteorological variation-induced algal blooms usually have a hysteresis effect. However, there have been few quantitative studies on this hysteresis effect. In the present study, Landsat images were used to extract the dynamic characteristics of changes in algal blooms in Dianchi Lake from 1988 to 2020. The hysteresis effect of meteorological factors driving algal blooms was studied by employing the modified lag-correlation method. The results showed that the algal blooms in Dianchi Lake were most severe between 1998 and 2008. During the periods of algal blooms, the values of air temperature (AT) and precipitation (PP) were significantly higher, while those wind velocity (WV) and sunshine duration (SSD) were obviously lower, than the corresponding annual mean values. AT and PP were significantly positively correlated with algal bloom factors in both the formation and persistence stages of algal blooms, while SSD and WV both promoted their regression, but these effects were less significant in the persistence period than in the formation period. Moreover, rainfall led to a decrease in SSD and WV, indirectly contributing to algal blooms. Furthermore, AT, PP and SSD are the main factors impacting the duration of persistent blooms. The time periods during which each meteorological factor was most influential were as follows: 1) AT - 25-30 days before the maximum bloom. 2) PP - within the first 10 days before the maximum bloom. 3) Both SSD and WV - 15-20 days before the maximum bloom. The results of this study support the prediction of algal blooms in Dianchi Lake.
... Landsat-8 OLI is an imager with 9 bands [32]. Chinese GF-1 satellite carries four WFV sensors that cover 4 bands [33][34]. HJ-1 has two satellites: HJ-1A and HJ-1B, they are both equipped with two multispectral cameras that have 4 bands, as shown in Table 1. ...
Lake Chaohu has been suffering from harmful cyanobacteria blooms, while the clouds pixels in satellite images are usually mistaken as cyanobacteria blooms by some traditional indicators, leading to the need for cloud masking in advance. In addition, atmospheric correction is another challenge due to lack of a general atmospheric correction method and the difficulties in evaluating its accuracy without in situ investigations. Fortunately, tasseled cap transformation (TCT) allows to extract vegetation properties directly from satellite imagery digital numbers (DN), which provides a perspective for extracting cyanobacteria blooms independent from atmospheric correction. This study focuses on how to use TCT to establish an indicator, which allows to extract cyanobacteria blooms directly from image DN values without conducting any atmospheric correction or cloud-masking. Training and test sets containing over 200,000 pixels are constructed from 18 Sentinel-2A/B MSI images acquired in different seasons in recent three years. Four components are derived from TCT and they could form up to 81 linear combinations. Experimental results performed on the training set show that the candidate, which combines the last three components with the coefficients of 1,-1 and 0, assigns cyanobacteria blooms pixels in a completely separated value range from water, cloud, cloud shadow and cloud edge pixels. The candidate is defined as ICW3C index. Its threshold value range of (175 330) is given and the pixels with ICW3C values greater than its threshold could be classified as cyanobacteria blooms. Comparisons between ICW3C and the floating algae index (FAI) on the test set show that ICW3C misclassifies 0.02% of cloud pixels and 1.55% of yellow cloud edge pixels as cyanobacteria blooms, however, 19.18% clouds, 13.74% yellow cloud edges and 19.34% blue-green cloud edges are incorrectly identified as cyanobacteria blooms by FAI. Comparisons between ICW3C and FAI performed on image regions over time show that, in clear-sky regions with cyanobacteria blooms, FAI extracts 5.81% more pixels, which mainly lay in the edge of cyanobacteria blooms. In cloud-covered image regions without cyanobacteria blooms, FAI misclassifies over 608 times as many cloud and cloud edge pixels as ICW3C. Sensitivity test results suggest that the change of ICW3C threshold within its value range (175 330) will not lead to serious increase in misclassification, and ICW3C performs stable to variations of viewing geometry. Extension tests indicate that ICW3C is applicable for several other sensors. Further researches are still needed to test whether ICW3C is suitable for other inland lakes or seas.
... Based on spectral characteristics, it can be found that the most obvious characteristic of aquatic plants is the steep slope effect in the near-infrared band, and the steep slope effect of aquatic plants with higher enrichment is more obvious [11]. Many remote sensing index methods have been proposed by scholars around the world, including Normalized Difference Vegetation Index (NDVI) [12][13][14], Floating Algae Index (FAI) [15,16], Normalized Difference Water Index (NDWI) [17,18], Enhanced Vegetation Index (EVI) [19], and Normalized Difference Index of Cyanobacteria Bloom (NDICB) [20]. Among them, NDVI is the most widely used remote sensing index method. ...
Remote sensing monitoring of aquatic vegetation is critical to the water quality evaluation of plateau lakes. To obtain a clear understanding of the water environment status of Dianchi Lake, a GF-5 hyperspectral characteristics-based optimal NDVI approach was employed to quantify the aquatic vegetation cover and analyze water quality. By characteristic bands recognition, the optimal NDVI was obtained; the spatial distribution of aquatic plants and water quality in Dianchi Lake were then analyzed. Results showed the following: (1) For Caohai, the optimal NDVI value was calculated by B86 in the red band range and B151 in the near-infrared band range, which achieve the best spectral response. For Waihai, the respective bands were B86 in the red band range and B99 in the near-infrared band range. (2) We also found significant regional differences in aquatic plants distribution for the study area. Caohai was dominated by aquatic plants and high-quality water areas only occurred in the northern tip. While the situation for Waihai was much optimistic, areas with poor water quality were mainly found in the north and south parts. Water quality also showed a descending trend from the lakeside zone to the lake center. (3) By comparing to previous studies, we concluded that policy interventions and water protection measures carried out by the government during the past years are extremely effective. The optimal NDVI method provides a reliable evaluation and is potentially transferable to other plateau lake areas as a robust approach for the rapid assessment of water quality.
River eutrophication is difficult to diagnose and estimate quantitatively because of its complex degradation mechanism in large river systems. Conventional monitoring and modeling methods are limited to accurately revealing the evolution process and trends of river aquatic organisms. In the present study, based on HJ-1A/1B CCD sensor, combined with genetic algorithm (GA) and regression tree (GART), a remote sensing inversion prediction model was established; the model can estimate algal blooms in the Han River affected by China’s Middle Route of the South-to-North Water Diversion Project (SNWTP). During the outbreak of algal blooms, the near-infrared band reflectance evidently increased between 2009 and 2015, with increasing algal density. The algal density in the downstream of the Han River has a nearly synchronous positive change with the reflectance in the B4 (near-infrared) band and a nearly synchronous reverse change with the B1 (blue) band. B1 and B4 screened by GA reduced redundancy by 14%, leading to a good prediction performance (R² = 0.88). According to GART and partial dependence analysis, the B4 band is a crucial characterization factor of algal blooms in the Han River. When the remote sensing band was in the range of B1 ⩾ 0.085 and B4 ⩽ 0.101, the algal density was lower than 0.15 × 10⁷ cells l⁻¹, indicating no algal bloom in the downstream of the Han River. When B4 was >0.103 and B1 ⩽ 0.076, algal density was higher than 1 × 10⁷ cells l⁻¹ and algal blooms were very likely to occur. These findings could provide a scientific reference for diagnosing and predicting large-scale water ecological degradation in similar watersheds.
Cyanobacterial blooms (CBs) are a growing concern for shallow plateau lakes, and numerous studies have investigated the relationship between CBs and meteorological factors. However, these studies have typically lacked comprehensive analyses and neglected the impact of lag effects. This study employed Landsat satellite imagery to extract CBs information from Xingyun Lake from 1990 to 2019 and conducted a lag correlation analysis between meteorological factors ranging from 1 to 30 days with a 1-day step and CBs. The result show that the Sunshine Duration displayed a negative correlation with CBs at an 8-day lag, but when Sunshine Duration was <6 h, it exhibited a positive relationship with CBs at a 1-day lag. And daytime precipitation had a more substantial positive link with CBs than nighttime precipitation at a lag of 13 days. The aforementioned conclusions deepen our comprehension of the meteorological forces that drive cyanobacterial blooms in plateau lakes. Moreover, the maximum and minimum wind velocities were negatively and positively associated with CBs with lags of 20 and 29 days, respectively. In addition, relative humidity and atmospheric pressure were positively associated with cyanobacterial blooms 13-19 days and 3 days after their onset, respectively. The relationship between air temperature and cyanobacterial blooms in Xingyun Lake was weak. Our research emphasizes the significance of incorporating delayed effects and refined meteorological factors for accurate cyanobacterial bloom forecasting.
The 21st century witnessed unprecedented development in Chinese cities and rapid urbanization has exerted substantial effects on regional environmental and climate change. While increased precipitation and temperature extremes have been widely observed under urbanization, whether increasing urbanization enhances or mitigates drought evolution is still unknown. By applying a series of trend analysis, nonstationary frequency analysis, and spatial characteristics analysis, this study investigates urbanization effects and contributions on drought development, taking the rapidly developing Yangtze River Basin (YRB) as an example. Results indicate that urbanization
leads to exacerbation of drought at three major urban agglomerations in YRB, which accounts for 46.62% of total variations for Standardized Precipitation Evapotranspiration Index (SPEI). Considering nonstationary features, urbanization appears to mitigate extreme drought conditions (1.14%) while drought duration and severity are increased (9.02%) and enhanced (9.12%) under 50-year return period over YRB, respectively. From the spatial perspective, area of urbanizing region in a drought event also indicates a significant increasing trend during 1981 to 2018. These findings further confirm that urbanization appears to be a notable local factor that leads to the modifications of regional drought development. The results are expected to provide implications for mitigating drought impacts and making related policy.
This study aims to simulate the watershed of the Mindanao River Basin (MRB) to enhance water resource management for potential hydropower applications to meet the power demand in Mindanao with an average growth of 3.8% annually. The soil and water assessment tool (SWAT) model was used with inputs for geospatial datasets and weather records at four meteorological stations from DOST-PAGASA. To overcome the lack of precipitation data in the MRB, the precipitation records were investigated by comparing the records with the global gridded precipitation datasets from the NCDC-CPC and the GPCC. Then, the SWAT simulated discharges with the three precipitation data were calibrated with river discharge records at three stations in the Nituan, Libungan and Pulangi rivers. Due to limited records for the river discharges, the model results were, then, validated using the proxy basin principle along the same rivers in the Nituan, Libungan, and Pulangi areas. The R2 values from the validation are 0.61, 0.50 and 0.33, respectively, with the DOST-PAGASA precipitation; 0.64, 0.46 and 0.40, respectively, with the NCDC-CPC precipitation; and 0.57, 0.48 and 0.21, respectively, with the GPCC precipitation. The relatively low model performances in Libungan and Pulangi rivers are mainly due to the lack of datasets on the dam and water withdrawal in the MRB. Therefore, this study also addresses the issue of data quality for precipitation and data scarcity for river discharge, dam, and water withdrawal for water resource management in the MRB and show how to overcome the data quality and scarcity.
Cyanobacterial harmful algal blooms are the most common form of harmful algal blooms in freshwater systems throughout the world. However, in situ sampling of cyanobacteria in inland lakes is limited both spatially and temporally. Satellite data has proven to be an effective tool to monitor cyanobacteria in freshwater lakes across the United States. This study uses data from the European Space Agency Envisat MEdium Resolution Imaging Spectrometer and the Sentinel-3 Ocean and Land Color Instrument to provide a national overview of the percentage of lakes experiencing a cyanobacterial bloom on a weekly basis for 2008-2011, 2017, and 2018. A total of 2321 lakes across the contiguous United States were included in the analysis. We examined four different thresholds to define when a waterbody is classified as experiencing a bloom. Across these four thresholds, we explored variability in bloom percentage with changes in seasonality and lake size. As a validation of algorithm performance, we analyzed the agreement between satellite observations and previously established ecological patterns, although data availability in the wintertime limited these comparisons on a year-round basis. Changes in cyanobacterial bloom percentage at the national scale followed the well-known temporal pattern of freshwater blooms. The percentage of lakes experiencing a bloom increased throughout the year, reached a maximum in fall, and decreased through the winter. Wintertime data, particularly in northern regions, were consistently limited due to snow and ice cover. With the exception of the Southeast and South, regional patterns mimicked patterns found at the national scale. The Southeast and South exhibited an unexpected pattern as cyanobacterial bloom percentage reached a maximum in the winter rather than the summer. Lake Jesup in Florida was used as a case study to validate this observed pattern against field observations of chlorophyll a. Results from this research establish a baseline of annual occurrence of cyanobacterial blooms in inland lakes across the United States. In addition, methods presented in this study can be tailored to fit the specific requirements of an individual system or region.
Freshwater phytoplankton blooms affect public health and ecosystem services globally1,2, with harmful impacts resulting either from a bloom’s high intensity or the presence of toxin-producing phytoplankton species. Freshwater blooms result in economic losses of over US$4 billion annually in the United States alone, primarily from harm to aquatic food production, recreation and tourism, and drinking-water supplies3. Studies documenting bloom conditions in lakes have either focused only on individual or regional subsets of lakes4–6, or have been limited by lack of long-term observations7–9. Here, we use three decades of high-resolution Landsat 5 satellite imagery to investigate long-term trends in intense summertime near-surface phytoplankton blooms for dozens of large lakes globally. We find that peak summertime bloom intensity has increased in a majority (68 per cent) of the lakes studied, revealing a global exacerbation of bloom conditions. Lakes that have experienced a significant (P < 0.1) decrease in bloom intensity are rare (8 per cent). The reason behind the increase in phytoplankton bloom intensity remains unclear, however, as temporal trends do not track consistently with temperature, precipitation, fertilizer-use trends, or other previously hypothesized drivers. We do find that lakes with a decrease in bloom intensity warmed less compared to other lakes, suggesting that lake warming may already be counteracting management efforts to ameliorate eutrophication10,11. Our findings support calls for water-quality management efforts to account better for the interactions between climate change and local hydrologic conditions12,13.
Water quality assessment is an increasingly important area in environmental study. Assessment of water quality can be a process include multiple factors which can causing influence on water quality. Researchers have developed many evaluation indices In order to display the results of water quality evaluation more intuitively. Water quality index has been the important fields in sustainable water quality management. This research based on the papers published of 20 years from web of science, analyzed the data by using CiteSpace 5.0. The result shows the direction, frontiers and hotspots of water quality index. Research from institutes, research keywords, word frequency, quoted literature and Subjects. The result shows in view of the world, India, China, US, Brazil and Iran are major countries. From the hotspots and frontiers of research, key words such like water quality management, drinking water quality are the main research hotspots and frontiers of social network in the contamination of water and water quality problem in China and India. this study provide a method for scientists to keep up with the situation of the study on water quality management., and puts forward suggestions for the further research on sustainable water quality index.
The present study was conducted to analyze the suitability of groundwater and surface water of the Indus Delta, Pakistan for domestic and irrigation purposes based on the concentrations of arsenic (As), total dissolved solids (TDS), and chloride (Cl). Around 180 georeferenced groundwater and 50 surface water samples randomly collected were analyzed and mapped spatially using ArcGIS 10.5 software. The results were compared with their respective WHO and FAO guidelines. The analysis revealed that as in groundwater and surface water samples ranged up to 200, and 25 µg/L respectively. Similarly, the TDS in the groundwater and surface water ranged from 203 to 17, 664 mg/L and 378 to 38,272 mg/L respectively. The Cl in groundwater and surface water varied between 131 and 6,275 mg/L and 440 to 17,406 mg/L respectively. Overall, about 18%, 87% and 94% of the groundwater, and 10%, 92% and 56% of the surface waters possessed higher concentrations of As, TDS, and Cl, respectively. The higher levels of Cl in the samples are attributed to subsurface seawater intrusion in the delta. Analysis results and GIS mapping of water quality parameters revealed that in most of the delta, the quality of water was not suitable for drinking and agricultural purposes, thus should be properly treated before its use.
The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states.
A new atmospheric correction (AC) method for aquatic application of metre-scale resolution (MR) optical satellite imagery is presented in this article, and demonstrated using images from the Pléiades constellation. MR satellites are typically operated privately and imagery can be costly. However in recent years, the price of individual acquisitions has dropped and their revisit times have improved, making them promising tools for remote sensing of inland and coastal waters. Due to the spatial resolution requirements of these satellites, the bands on the sensors are relatively wide (60–140 nm on Pléiades) in order to achieve an acceptable signal to noise ratio. This bandwidth and the limited number of bands can pose problems for the AC as the water signal may not be negligible in any band, especially over turbid waters. Since the MR sensors have a relatively narrow swath (20 km for Pléiades) the atmosphere can generally be assumed to be homogeneous over a scene or subscene. This assumption allows the atmospheric path reflectance (ρpath) to be estimated from multiple targets in the scene, which are selected according to the lowest observed top-of-atmosphere reflectances (ρTOA) in all bands. Rather than using pre-defined “dark” bands (e.g. in the NIR and SWIR) such as is common in other water-focused AC methods, the best band is selected automatically, i.e. the one yielding the lowest ρpath. This criterion avoids unrealistic negative (“overcorrected”) reflectances after the AC. Furthermore, for inland waters the NIR bands are usually affected by scattering from adjacent land and vegetation pixels, resulting in unrealistic ρpath when used in the AC. The spatial resolution of the sensors is used as an advantage here, since ground-level object shadows (e.g. from trees and buildings) can be spatially resolved and are usually the pixels selected by the automated procedure for the determination of ρpath. In fact, it is proposed that using these shadow pixels gives better performance than using any kind of water pixel for these broad-band MR sensors. The method is demonstrated using several Pléiades images, showing good performance in retrieval of the aerosol optical thickness (τa) for an urban (Brussels) and a coastal (Zeebrugge) site. Match-ups with water reflectances measured at the Zeebrugge AERONET-OC station show promising performance, although there is a significant spectral mismatch between the bands on the satellites and the CIMEL radiometer. Pléiades imagery of Zeebrugge reveals a turbid wake associated with the MOW1 measurement station, which opens perspectives of using MR satellites for the characterisation of monitoring and validation sites. Future work includes the application to other MR satellites (e.g. WorldView) and the evaluation for mass processing of open access high resolution (10–60 m) satellite data from Landsat and Sentinel-2.
Water clarity (via the Secchi disk depth, SDD) is an important indicator of water quality and lake ecosystem health. Monitoring long-term SDD change is vital for water quality assessment and lake management. In this study, we developed and validated an empirical model for estimating the SDD based on Landsat ETM+ and OLI data using the combination of band ratio of the near-infrared (NIR) band to the blue band and the NIR band. Time series data of remotely estimated SDD in Lake Liangzi were retrieved from 2007 to 2016 using the proposed models based on forty Landsat images. The results of the Mann–Kendall test (p = 0.002) and linear regression (R2 = 0.352, p < 0.001) indicated that the SDD in Lake Liangzi demonstrated a significant decreasing trend during the study period. The annual mean SDD in Lake Liangzi was significantly negatively correlated with the population (R2 = 0.530, p = 0.017) and gross domestic product (R2 = 0.619, p = 0.007) of the Lake Liangzi basin. In addition, water level increase and the flood have an important effect on SDD decrease. Our study revealed that anthropogenic activities may be driving factors for the long-term declining trend in the SDD. Additionally, floods and heavy precipitation may decrease the SDD over the short term in Lake Liangzi. A declining trend in the SDD in Lake Liangzi may continue under future intense anthropogenic activities and climate change such as the extreme heavy precipitation event increase.
We investigated possibility of predicting whether blooms, if they occur, would be formed of microcystin-producing cyanobacteria. DGGE analysis of 16S-ITS and mcyA genes revealed that only Planktothrix and Microcystis possessed mcy-genes and Planktothrix was the main microcystin producer. qPCR analysis revealed that the proportion of cells with mcy-genes in Planktothrix populations was almost 100%. Microcystin concentration correlated with the number of potentially toxic and total Planktothrix cells and the proportion of Planktothrix within all cyanobacteria, but not with the proportion of cells with mcy-genes in total Planktothrix. The share of Microcystis cells with mcy-genes was low and variable in time. Neither the number of mcy-possessing cells, nor the proportion of these cells in total Microcystis, correlated with the concentration of microcystins. This suggests that it is possible to predict whether the bloom in the Masurian Lakes will be toxic based on Planktothrix occurrence. Two species of toxin producing Planktothrix, P. agardhii and P. rubescens, were identified by phylogenetic analysis of 16S-ITS. Based on morphological and ecological features, the toxic Planktothrix was identified as P.
agardhii. However, the very high proportion of cells with mcy-genes suggests P. rubescens. Our study reveals the need of universal primers for mcyA genes from environment.
The potential risk from cyanobacteria blooms is the basis for predicting, preventing, and managing eutrophication. Poyang Lake lies on the southern bank of the middle and lower reaches of the Yangtze River. This lake is a large shallow lake connected to the Yangtze River and is affected by monsoon. The comprehensive evaluation index system, evaluation model, and method of the potential risk from cyanobacteria blooms were constructed based on the nutrient zoning in Poyang Lake, and the potential risk from cyanobacteria blooms was evaluated in 2013. (1) The evaluation index system comprises physical, chemical, and biological indicators. The physical indicators consist of blocking degree, lake region location, transparency (Secchi disk depth, SD), and temperature; the chemical indicators consist of total nitrogen (TN) and total phosphorus (TP); and the biological indicators consist of chlorophyll a (Chla) and phytoplankton biomass. Among the indicators, blocking degree and lake region location along with the prevailing wind direction were selected to represent the indicators affected by water retention time and wind direction. (2) We established a comprehensive evaluation method for assessing the potential risk from cyanobacteria blooms by adopting both analytic hierarchy process weighting and a comprehensive evaluation method. (3) Results show that the high-risk periods for cyanobacteria blooms were August, July, and December, and the high-risk regions were in the Northeastern Lake Region, Western Lake Region and Northern Lake Region. The Northeastern Lake Region is particularly in high risk in August and July. These cyanobacteria blooms presented heavy risk or close to heavy risk. Based on the risk evaluation indicators, outbreaks of cyanobacteria blooms are limited by temperature and location. Chla and phytoplankton biomass were the key indices affecting the level of potential risk from cyanobacteria blooms during the high-water-level period (July and August). In contrast, TN and TP are the key indices affecting the level of harm during the low-water-level period. Within a year, Chla, phytoplankton biomass, and TP are key indicators for the prediction of cyanobacteria blooms in Poyang Lake.
Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km2 from 1987 to 2002, and it decreased to 2020.78 km2 from 2005 to 2015, with a loss of 241.39 km2 (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km2, 44.40 km2, 45.39 km2, and 31.18 km2, respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km2, 322.71 km2, 460.35 km2, 400.79 km2, 535.51 km2, and 635.42 km2 under water inundation frequencies of 5%–10%, 10%–20%, 20%–40%, 40%–60%, 60%–80%, and 80%–100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management.
Satellite remote sensing can be an effective alternative for mapping cyanobacterial scums and aquatic macrophyte distribution over large areas compared with traditional ship's site-specific samplings. However, similar optical spectra characteristics between aquatic macrophytes and cyanobacterial scums in red and near infrared (NIR) wavebands create a barrier to their discrimination when they co-occur. We developed a new cyanobacteria and macrophytes index (CMI) based on a blue, a green, and a shortwave infrared band to separate waters with cyanobacterial scums from those dominated by aquatic macrophytes, and a turbid water index (TWI) to avoid interference from high turbid waters typical of shallow lakes. Combining CMI, TWI, and the floating algae index (FAI), we used a novel classification approach to discriminate lake water, cyanobacteria blooms, submerged macrophytes, and emergent/floating macrophytes using MODIS imagery in the large shallow and eutrophic Lake Taihu (China). Thresholds for CMI, TWI, and FAI were determined by statistical analysis for a 2010-2016 MODIS Aqua time series. We validated the accuracy of our approach by in situ reflectance spectra, field investigations and high spatial resolution HJ-CCD data. The overall classification accuracy was 86% in total, and the user's accuracy was 88%, 79%, 85%, and 93% for submerged macrophytes, emergent/floating macrophytes, cyanobacterial scums and lake water, respectively. The estimated aquatic macrophyte distributions gave consistent results with that based on HJ-CCD data. This new approach allows for the coincident determination of the distributions of cyanobacteria blooms and aquatic macrophytes in eutrophic shallow lakes. We also discuss the utility of the approach with respect to masking clouds, black waters, and atmospheric effects, and its mixed-pixel effects.
For the past two decades, China’s urbanization has attracted increasing attention from scholars around the world. Numerous insightful studies have attempted to determine the socioeconomic causes of the rapid urban growth in Chinese cities. However, most of these studies regarded each city as a single entity, with few considering inter-city relationships. The present study uses a gravity-based model to measure the spatial interaction among city clusters in the Wuhan urban agglomeration (WUA), which is one of China’s most rapidly urbanizing regions. The effects of spatial interaction on urban growth area were also analyzed. Empirical results indicate that, similar to urban population or employment in secondary and tertiary industries in the WUA from 2000 to 2005, the spatial interaction among city clusters is one of the main drivers of urban growth. In fact, this study finds the effects of spatial interaction as the only socioeconomic factor that affected the spatial expansion from 2005 to 2010. This finding suggests that population migration and information and commodity flows showed greater influence than the socioeconomic drivers of each city did on promoting urbanization in the WUA during this period. We thus argue that spatial interaction among city clusters should be a consideration in future regional planning.
The large shallow eutrophic Lake Taihu in China has long suffered from eutrophication and toxic cyanobacterial blooms. Despite considerable efforts to divert effluents from the watershed, the cyanobacterial blooms still reoccur and persist throughout summer. To mitigate cyanobacterial bloom pollution risk, a large scale integrated monitoring and forecasting system was developed, and a series of emergency response measures were instigated based on early warning. This system has been in place for 2009-2012. With this integrated monitoring system, it was found that the detectable maximum and average cyanobacterial bloom area were similar to that before drinking water crisis, indicating that poor eutrophic status and cyanobacterial bloom had persisted without significant alleviation. It also revealed that cyanobacterial bloom would occur after the intense storm, which may be associated with the increase in buoyance of cyanobacterial colonies. Although the cyanobacterial blooms had persisted during the monitoring period, there had been a reduction in frequency and intensity of the cyanobacterial bloom induced black water agglomerates (a phenomenon of algal bloom death decay to release a large amount black dissolved organic matter), and there have been no further drinking water crises. This monitoring and response strategy can reduce the cyanobacterial bloom pollution risk, but cannot reduce eutrophication and cyanobacterial blooms, problems which will take decades to resolve.
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Relatively little attention has been paid to examining the spatial expansion features of cities at various tiers at the regional level in China, especially those located in central and western regions of the country. Based on Landsat satellite imagery from four years—1980, 1990, 2000, and 2010, this paper investigates the spatio-temporal pattern of urban land expansion and its influencing factors in the Wuhan Urban Agglomeration (WUA) in central China. The research found that the total area of urban land expanded from 203.66 km² in 1980 to 1370.07 km² in 2010, and that urban land areas increased by 423.82, 167.42, and 574.93 km² in the periods 1980–1990, 1990–2000, and 2000–2010 respectively, exhibiting significant fluctuation between the different periods studied. Geographically, this spatial expansion pattern was characterised by conspicuous concentrations and regional imbalances across the overall study period. Whilst these spatio-temporal differences were found to be closely related to industrialisation, urban population growth, land-use policies, urbanisation guidelines (governmental plans and regulations addressing urbanisation), and national development strategy, the dominant mechanisms driving those differences varied over time. In response, the paper presents an urban-rural and regional integration strategy, with the aim of avoiding economic gaps and the inefficient utilisation of various resources in the urban agglomeration areas.
A novel algorithm is presented for detecting trophic status (chlorophyll-a), cyanobacterial blooms, surface scum and floating vegetation in coastal and inland waters using top-of-atmosphere data from the Medium Resolution Imaging Spectrometer (MERIS). The maximum peak-height algorithm (MPH) uses a baseline subtraction procedure to calculate the height of the dominant peak across the red and near-infrared MERIS bands between 664 and 885 nm caused by sun-induced chlorophyll fluorescence (SICF) and particulate backscatter. Atmospheric correction of the MERIS TOA reflectance data for gaseous absorption and Rayleigh scattering proved adequate given the spectral proximity of the relevant bands and the sufficiently large differential spectral signal. This avoided the need to correct for atmospheric aerosols, a procedure which is typically prone to large errors in turbid and high-biomass waters. A combination of switching algorithms for estimating chl-a were derived from coincident in situ chl-a and MERIS bottom-of-Rayleigh reflectance measurements. These algorithms are designed to cover a wide trophic range, from oligotrophic/mesotrophic waters (chl-a < 20 mg m− 3), to eutrophic/hypertrophic waters (chl-a > 20 mg m− 3) and surface scums or dry floating algae or vegetation. Cyanobacteria-dominant waters were differentiated from those dominated by eukaryote algal species (dinoflagellates/diatoms) on the basis of the magnitude of the MPH variable. This is supported by evidence that vacuolate cyanobacteria (Microcystis aeruginosa) possess enhanced chl-a specific backscatter which is an important bio-optical distinguishing feature. This enables these phytoplankton groups to be distinguished from space. An algorithm derived from cyanobacteria-dominant waters had a r2 value of 0.58 for chl-a between 33 and 362 mg m− 3 and an error of 33.7% (N = 17). The operational algorithm for eukaryote-dominant algal assemblages gave a coefficient of determination of 0.71 and a mean absolute percentage error of 60% for chl-a in the range 0.5–350 mg m− 3 (N = 48). A flag based on cyanobacteria-specific spectral pigmentation and fluorescence features was used to identify cyanobacterial-dominance in eutrophic waters. Global applications demonstrate how the MPH algorithm can offer rapid and effective assessment of trophic status, cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters.
Increased frequency and extent of potentially harmful blooms in coastal and inland waters world-wide require the development of methods for operative and reliable monitoring of the blooms over vast coastal areas and a large number of lakes. Remote sensing could provide the tool. An overview of the literature in this field suggests that operative monitoring of the extent of some types of blooms (i.e. cyanobacteria) is relatively straightforward. Operative monitoring of inland waters is currently limited to larger lakes or using airborne and hand-held remote sensing instruments as there are no satellite sensors with sufficient spatial resolution to provide daily coverage. Extremely high spatial and vertical variability in biomass during blooms of some phytoplankton species and the strong effects of this on the remote sensing signal suggest that water sampling techniques and strategies have to be redesigned for highly stratified bloom conditions, especially if the samples are collected for algorithm development and validation of remote sensing data. Comparing spectral signatures of different bloom-forming species with the spectral resolution available in most satellites and taking into account variability in optical properties of different water bodies suggests that developing global algorithms for recognizing and quantitative mapping of (harmful) algal blooms is questionable. On the other hand some authors cited in the present paper have found particular cases where satellites with coarse spectral and spatial resolution can be used to recognize phytoplankton blooms even at species level. Thus, the algorithms and methods to be used depend on the optical complexity of the water to which they will be applied. The aim of this paper is to summarize different methods and algorithms available in an attempt to assist in selecting the most appropriate method for a particular site and problem under investigation.
A numerical trophic state index for lakes has been developed that incorporates most lakes in a scale of 0 to 100. Each major division ( 10, 20, 30, etc. ) represents a doubling in algal biomass. The index number can bc calculated from any of several parameters, including Secchi disk transparency, chlorophyll, and total phosphorus. My purpose here is to present a new ap- proach to the trophic classification of lakes. This new approach was developed because of frustration in communicating to the pub- lic both the current nature or status of lakes and their future condition after restoration when the traditional trophic classification system is used. The system presented hcrc, termed a trophic state index (TSI), in- volves new methods both of defining trophic status and of determining that status in lakes. All trophic classification is based on the division of the trophic continuum, howcvcr this is defined, into a series of classes termed trophic states. Traditional systems divide the continuum into three classes: oligotrophic, mesotrophic, and cutrophic. There is often no clear delineation of these divisions. Determinations of trophic state are made from examination of several di- verse criteria, such as shape of the oxygen curve, species composition of the bottom fauna or of the phytoplankton, conccntra- tions of nutrients, and various measures of biomass or production. Although each changes from oligotrophy to eutrophy, the changes do not occur at sharply defined places, nor do they all occur at the same place or at the same rate. Some lakes may be considered oligotrophic by one criterion and eutrophic by another; this problem is
The extent of cyanobacterial blooms has been mapped using different satellite sensors from weather satellites to synthetic aperture radars. Quantitative detection of chlorophyll in cyanobacterial blooms by remote sensing, however, has been less successful. The first civilian hyperspectral sensor in space, Hyperion, acquired an image of cyano- bacterial bloom in the western part of the Gulf of Finland on 14 July 2002. A chlorophyll concentration map was produced from this image using a spectral library that was created by running a bio-optical model with variable concentrations of chlorophyll. The results show that chlorophyll concentrations in the bloom area were much higher than reported by conventional water-monitoring programs, ships-of-opportunity, and satellite measurements. The reason why both in situ and satellite methods underestimate the amount of phytoplankton during cyanobacterial blooms is vertical and horizontal distribution of cyanobacteria, because cyanobacteria can regulate their buoyancy and are not uniformly distributed within the top mixed layer of water column in calm weather conditions. Aggre- gations of cyanobacteria form dense subsurface blooms and surface scums during extensive blooms. This study demonstrates that it is difficult to collect representative water samples from research vessels using standard methods because ships and water samplers destroy the natural distribution of cyanobacteria in the sampling process. Flow- through systems take water samples from the depths at which the concentration of cyanobacteria is not correlated with the amount of phytoplankton that remote sensing instruments detect. The chlorophyll estimation accuracy in cyanobacterial blooms by many satellites is limited because of spatial resolution, as significant changes in chloro- phyll concentration occur even at a smaller spatial scale than 30 m.
A novel approach was used with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to characterize the intense blooms of cyanobacteria (primarily Microcystis aeruginosa) in Taihu Lake, China's third largest freshwater lake. The approach involves first deriving a floating algae index (FAI) based on the medium-resolution (250 and 500 m) MODIS reflectance data at 645, 859, and 1240 nm after correction of the ozone/gaseous absorption and Rayleigh scattering effects and then objectively determining the FAI threshold value (-0.004) to separate the bloom and nonbloom waters. By definition, the term "bloom" or "floating algae" refers to bloom where cyanobacteria form floating scums on the water surface. The 9 year MODIS time series data showed bloom characteristics (annual occurrence frequency, timing, and duration) between 2000 and 2008. Assuming 25% area coverage as a gauge for significance, significant bloom events rarely occurred between 2000 and 2004 for the entire lake (excluding East Bay) or several lake segments (Northwest Lake, Southwest Lake, and Central Lake). In most lake segments, the annual frequency of significant blooms increased from 2000-2004 to 2006-2008, when they started earlier and had a longer duration. The year 2007 showed unique bloom characteristics due to conditions highly favorable for bloom development and proliferation. The results suggest that the long-term bloom patterns are driven by both nutrients and climatic factors. The multiyear series of consistent MODIS FAI data products provide baseline information to monitor the lake's bloom condition, one of the critical water quality indicators, on a weekly basis, as well as to evaluate its future water quality trends. U.S. NASA [NNX09AE17G, NNX09AV56G]; NOAA [NA06NES4400004]; National Natural Science Foundation of China [40871168]; Ministry of Education of China [B07034]
Abstract
Mass populations of toxin-producing cyanobacteria commonly develop in fresh-, brackish- and marine waters and effective strategies for monitoring and managing cyanobacterial health risks are required to safeguard animal and human health. A multi-interdisciplinary study, including two UK freshwaters with a history of toxic cyanobacterial blooms, was undertaken to explore different approaches for the identification, monitoring and management of potentially-toxic cyanobacteria and their associated risks. The results demonstrate that (i) cyanobacterial bloom occurrence can be predicted at a local- and national-scale using process-based and statistical models; (ii) cyanobacterial concentration and distribution in waterbodies can be monitored using remote sensing, but minimum detection limits need to be evaluated; (iii) cyanotoxins may be transferred to spray-irrigated root crops; and (iv) attitudes and perceptions towards risks influence the public's preferences and willingness-to-pay for cyanobacterial health risk reductions in recreational waters.
We have measured the water quality and bio-optical parameters of 94 samples from Taihu Lake in situ and/or in the lab between June 10-18, 2007. A transparency-assisted decision tree was developed to more accurately divide the aquatic vegetation zone into a floating vegetation-dominated zone and a submerged vegetation-dominated zone, whose respective present biomass retrieval models were easily developed with an empirical approach because of the quasi-concurrence of ground field investigations with the satellite sensor flight over the lake. The significant quantitative relationships between the vegetation index NDVI (Normalized Difference Vegetation Index) of different images at different times were used to help develop the past biomass retrieval model on the basis of the present developed model. In Taihu Lake, the total covering area of aquatic vegetations decreased from 454.6 km² in 2001 to 364.1 km² in 2007. Correspondingly, the total biomass decreased from 489,000 tons in 2001 to 406,000 tons in 2007, suggesting that a great change in the ecological environment has been taking place in Taihu Lake over this period.
Definitive data on the absorption spectrum of pure water from 380 to 700 nm have been obtained with an integrating cavity technique. The results are in good agreement with those recently obtained by our group with a completely independent photothermal technique. As before, we find that the absorption in the blue is significantly lower than had previously been generally believed and that the absorption minimum is at a significantly shorter wavelength, i.e., 0.0044 ± 0.0006 m⁻¹ at 418 nm. Several spectroscopic features have been identified in the visible spectrum to our knowledge for the first time.
The rapid industrialization and urbanization that have occurred in East China in recent decades pose a threat to the local aquatic ecosystem. Pollution by heavy metals is one of the factors that threaten the environment, and assessing the risk of heavy metals is essential for promoting proper freshwater management in aquatic ecosystems. In this study, 121 surface sediment samples were collected from 63 shallow freshwater lakes in the middle and lower reaches of the Yangtze and Huai River Basins (MLYHB) to study the spatial distribution of common heavy metals, including As, Cd, Cr, Cu, Ni, Pb, and Zn. Multivariate statistical analysis, which based on the metals' distribution and geoaccumulation index (Igeo), sediment quality guidelines (SQGs), potential ecological risk (PER) index, and risk index (RI), was conducted to elucidate the pollution source and evaluate the potential ecological risks. The average concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn in the surface sediments were 22.6, 1.1, 74.6, 47.6, 38.3, 37.3, and 141.9 mg/kg, respectively. The concentrations of these metals in the surface sediments from the middle reaches were generally higher than those in sediments from the lower reaches of MLYHB. The concentrations of Cr and Ni were similar to the background values, and they mostly originated from natural detritus. Cd pollution was predominant in MLYHB, and the Zn, Cd, Cu, and Pb in the surface sediments mainly originated from industrial and agricultural activities, while As originated from pollution due to coal combustion. According to the SQGs and PER, Cr, Cu, Ni, Pb, and Zn likely pose a low ecological risk, while As and Cd were identified as the priority pollutants by the two methods, respectively, and may pose a high ecological risk to the aquatic ecosystem in MLYHB. Moreover, the PER of heavy metals in surface sediments is more severe in the middle reaches of MLYHB than that in the lower reaches. The middle reaches of MLYHB should be the priority regions for environmental monitoring and management.
Denitrification (DNF) and dissimilatory nitrate reduction to ammonium (DNRA) are critical dissimilatory nitrate reduction pathways that determine nitrogen (N) removal and internal recycling in aquatic environments. However, the relative important of DNRA, and the influences of environmental factors on DNF and DNRA, have not been widely studied in freshwater lakes. In our study, we used N isotope-tracing to investigate the potential rates of DNF and DNRA in 27 lakes from the Eastern Plain Lake Zone (EPL), China. In the EPL lakes, DNF was the dominant nitrate reduction process, however DNRA was still important, accounting for around 4.3%-21.9% of total nitrate reduction. The sediment organic carbon was the primary factor controlling the rates of dissimilatory nitrate reduction, accounting for 28.3% and 37.9% of the variance in DNF and DNRA rates, respectively. High algal biomass accelerated DNF rates, while indirectly affected DNRA via changing the quality of organic carbon. The greater contributions of DNRA to dissimilatory nitrate reduction were found in lakes with higher sulfate concentrations. DNRA coupled to sulfur cycling may play an important role in lakes with high sulfate concentrations and high sediment organic carbon. This study highlights the important role played by DNRA in total nitrate reduction pathways of freshwater lakes. Mitigation strategies for N pollution and algal blooms should not only target decrease of nutrient input, strategies should also create a suitable environment for improving N removal and inhibit N recycling.
In cyanobacteria bloom-affected areas, drinking water treatment processes are optimized to ensure the absence of cyanotoxins in their finished water. A concern about the sludge generated from water treatment has emerged because the removed cyanotoxins and cyanobacteria can get concentrated in the sludge, called water treatment residuals (WTR), and these WTR are often applied on land for beneficial purposes. However, the impact of WTR from bloom-affected areas on the agricultural application and public health is hardly reported. The objective of this study was to characterize bloom-affected WTR by focusing on cyanotoxins, toxin-producing cyanobacteria, microbiomes, and resistome profiles. In addition, the fate of WTR-originated microcystin in crops and soil was examined.
WTR samples were obtained from a bloom-affected area in Ohio, USA in November 2017. Cyanotoxins and toxin-producing cyanobacteria were quantified with the enzyme-linked immunosorbent assay and droplet digital PCR, respectively. Microbiome and resistome were determined with Nanopore sequencing. Cyanotoxin concentrations were measured: microcystin (259 μg/kg), saxitoxin (0.16 μg/kg), anatoxin-a (not detected), and β-Methylamino-L-alanine (BMAA) (575 μg/kg). MC-producing cyanobacteria concentrations were determined: Planktothrix (5.3 × 10⁷ gene copies/g) and Microcystis (3.3 × 10³ gene copies/g). Proteobacteria was the most predominant and Planktothrix phage was a remarkably dominant virus in the WTR microbiome. Aminoglycoside resistance was the most abundant class, and antibiotic resistance was found in multiple pathogens (e.g. Mycobacterium).
WTR land application was simulated by growing carrots with a mixture of WTR and soil in a greenhouse. At harvest, ~80% of WTR-originated microcystin was found in the soil (83–96 μg/kg) and 5% accumulated in carrots (19–28 μg/kg). This study provides the first insight into the cyanotoxin, microbiome, and resistome profile of bloom-affected WTR. Our finding suggests that careful WTR management is needed for the beneficial use of WTR for protecting agricultural environments, especially soil and groundwater, and food safety.
Harmful algal blooms (HABs) have been investigated for their catastrophic effects on public health and aquaculture intensively, but the research about HABs effects on the diversity patterns and intrinsic functions of the plankton community based on a species identification with high resolution and accuracy has been scarce. We therefore investigated the shifts of plankton diversity via pyrosequencing during and around a natural dinoflagellate (Prorocentrum donghaiense) bloom and analyzed the effect of P. donghaiense abundance on the operationally-defined resource use efficiency (RUE) of plankton community to test our hypothesis that outbreaks of HABs will reduce RUE of the plankton community via shifting the plankton community structure, species composition in particular. We found that the species diversity of eukaryotic plankton community was significantly decreased during the bloom, as reflected in OTU (operational taxonomic unit) richness, and Pielou's evenness index. Principal coordinates analysis indicated significant difference in plankton community structure between blooming and non-blooming periods. As hypothesized, the species richness was positively correlated to RUE (defined as the ratio of phytoplankton biomass to total phosphorus), and more importantly, the cell density of P. donghaiense exhibited significant negative correlation with RUE. Our results explicitly demonstrated HABs reduce RUE via reducing species richness (corresponding to a less occupancy of the trophic niches), which supports the previously documented notion that niche partitioning enhances RUE (a key ecosystem function). Also, our work provides striking evidence for the relationship between plankton species richness (or diversity) and community function (resource use efficiency) via studying on HABs, a natural but exceptional phenomenon, in addition to revealing a profound consequence of HABs.
The trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Thus, information on the spatial and temporal distribution of TSI is critical for supporting scientifically sound water resource management decisions. We proposed a semi-analytical approach to remotely estimate TSI based on Landsat 8 OLI data for inland waters. The approach has two major steps: deriving the total absorption coefficient of optically active constituents (OACs) and building the relationship between the total absorption coefficient and TSI. First, version 6.0 of the Quasi-Analytical Algorithm (QAA_V6, developed by Zhongping Lee) was implemented with Landsat 8 OLI data to derive the total absorption coefficients of the OACs. Second, we modeled TSI using the total absorption coefficients of OACs at 440 nm based on a large in situ measurement dataset. The total absorption coefficient of OACs at 440 nm gave satisfactory validation results for modeling TSI with a mean absolute percent error of 6% and a root-mean-square error of 5.77. Then, we performed this approach in three inland waters with various eutrophic statuses to validate its results, and the approach demonstrated a robust and satisfactory performance. Finally, an application of the approach was demonstrated in Lake Qiandaohu. Our semi-analytical approach has a sound optical mechanism and extensive application for different trophic inland waters.
Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.
With increased global warming, cyanobacteria are blooming more frequently in lakes and reservoirs, severely damaging the health and stability of aquatic ecosystems and threatening drinking water safety and human health. There is an urgent demand for the effective prediction and prevention of cyanobacterial blooms. However, it is difficult to effectively reduce the risks and loss caused by cyanobacterial blooms because most methods are unable to successfully predict cyanobacteria blooms. Therefore, in this study, we proposed a new cyanobacterial bloom occurrence prediction method to analyze the probability and driving factors of the blooms for effective prevention and control. Dominant cyanobacterial species with bloom capabilities were initially determined using a dominant species identification model, and the principal driving factors of the dominant species were then analyzed using canonical correspondence analysis (CCA). Cyanobacterial bloom probability was calculated using a newly-developed model, after which, the probable mutation points were identified and thresholds for the principal driving factors of cyanobacterial blooms were predicted. A total of 141 phytoplankton data sets from 90 stations were collected from six large-scale hydrology, water-quality ecology, integrated field surveys in Jinan City, China in 2014–2015 and used for model application and verification. The results showed that there were six dominant cyanobacterial species in the study area, and that the principal driving factors were water temperature, pH, total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen. The cyanobacterial blooms corresponded to a threshold water temperature range, pH, total phosphorus (TP), ammonium nitrogen level, chemical oxygen demand, and dissolved oxygen levels of 19.5–32.5 °C, 7.0–9.38, 0.13–0.22 mg L ⁻¹ , 0.38–0.63 mg L ⁻¹ , 10.5–17.5 mg L ⁻¹ , and 4.97–8.28 mg L ⁻¹ , respectively. Comparison with research results from other global regions further supported the use of these thresholds, indicating that this method could be used in habitats beyond China. We found that the probability of cyanobacterial bloom was 0.75, a critical point for prevention and control. When this critical point was exceeded, cyanobacteria could proliferate rapidly, increasing the risk of cyanobacterial blooms. Changes in driving factors need to be rapidly controlled, based on these thresholds, to prevent cyanobacterial blooms. Temporal and spatial scales were critical factors potentially affecting the selection of driving factors. This method is versatile and can help determine the risk of cyanobacterial blooms and the thresholds of the principal driving factors. It can effectively predict and help prevent cyanobacterial blooms to reduce the global probability of occurrence, protect the health and stability of water ecosystems, ensure drinking water safety, and protect human health.
Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake. By means of a knowledge-supported way, physicochemical factors, Microcystis morphospecies, and MCs were integrated into different network structures. The sensitive analysis illustrated that Microcystis aeruginosa biomass was overall the best predictor of MCs risk, and its high biomass relied on the combined condition that water temperature exceeded 24 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/L) was higher under interactive effect of temperature increase and nutrients (nitrogen and phosphorus) imbalance than that of warming alone. Likewise, data-driven model development using a naïve Bayes classifier and equal frequency discretization resulted in a substantial technical performance (CCI = 0.83, K = 0.60), but the performance significantly decreased when model excluded species-specific biomasses from input variables (CCI = 0.76, K = 0.40). The BN framework provided a useful screening tool to evaluate cya-notoxin in three studied lakes in China, and it can also be used in other lakes suffering from cyanobacterial blooms dominated by Microcystis.
Toxic cyanobacterial harmful algal blooms (cyanoHABs) are one of the most significant threats to the security of Earth's surface freshwaters. In the United States, the Federal Water Pollution Control Act of 1972 (i.e., the Clean Water Act) requires that states report any waterbody that fails to meet applicable water quality standards. The problem is that for fresh waters impacted by cyanoHABs, no scientifically-based framework exists for making this designation. This study describes the development of a data-based framework using the Ohio waters of western Lake Erie as an exemplar for large lakes impacted by cyanoHABs. To address this designation for Ohio's open waters, the Ohio Environmental Protection Agency (EPA) assembled a group of academic, state and federal scientists to develop a framework that would determine the criteria for Ohio EPA to consider in deciding on a recreation use impairment designation due to cyanoHAB presence. Typically, the metrics are derived from on-lake monitoring programs, but for large, dynamic lakes such as Lake Erie, using criteria based on discrete samples is problematic. However, significant advances in remote sensing allows for the estimation of cyanoHAB biomass of an entire lake. Through multiple years of validation, we developed a framework to determine lake-specific criteria for designating a waterbody as impaired by cyanoHABs on an annual basis. While the criteria reported in this manuscript are specific to Ohio's open waters, the framework used to determine them can be applied to any large lake where long-term monitoring data and satellite imagery are available.
In this paper, the current situation and the carrying capacity of water resources in Wuhan urban agglomeration are analyzed. A comprehensive evaluation model is established to quantify the distribution of water resources and carrying capacity of cities in Wuhan urban agglomeration. The entropy method and synergistic theoretical model are applied to calculate parameters, and a series of indicators are also used to characterize the water resources carrying capacity in Wuhan urban agglomeration. The results show that water resources are in severe shortage and overloaded in Wuhan. It also tends to be overloaded in Ezhou.The larger protential of WRCC should be recognized in other cities of Wuhan urban agglomeration, such as Xiaogan, Huanggang, Qianjiang and Tianmen. Therefore, the rational planning and utilization of water resources, adjustment of industrial structure and the implementation of strict water-saving regulations are important for the protection of water resources and sustainable economic development of the urban agglomeration.
Cyanobacteria can form dense and sometimes toxic blooms in freshwater and marine environments, which threaten ecosystem functioning and degrade water quality for recreation, drinking water, fisheries and human health. Here, we review evidence indicating that cyanobacterial blooms are increasing in frequency, magnitude and duration globally. We highlight species traits and environmental conditions that enable cyanobacteria to thrive and explain why eutrophication and climate change catalyse the global expansion of cyanobacterial blooms. Finally, we discuss management strategies, including nutrient load reductions, changes in hydrodynamics and chemical and biological controls, that can help to prevent or mitigate the proliferation of cyanobacterial blooms.
Chromophoric dissolved organic matter (CDOM) is an important optically active substance in aquatic environments and plays a key role in light attenuation and in the carbon, nitrogen and phosphorus biogeochemical cycles. Although the optical properties, abundance, sources, cycles, compositions and remote sensing estimations of CDOM have been widely reported in different aquatic environments, little is known about the optical properties and composition changes in CDOM along trophic gradients. Therefore, we collected 821 samples from 22 lakes along a trophic gradient (oligotrophic to eutrophic) in China from 2004 to 2015 and determined the CDOM spectral absorption and nutrient concentrations. The total nitrogen (TN), total phosphorus (TP), and chlorophyll a (Chla) concentrations and the Secchi disk depth (SDD) ranged from 0.02 to 24.75 mg/L, 0.002-3.471 mg/L, 0.03-882.66 μg/L, and 0.05-17.30 m, respectively. The trophic state index (TSI) ranged from 1.55 to 98.91 and covered different trophic states, from oligotrophic to hyper-eutrophic. The CDOM absorption coefficient at 254 nm (a(254)) ranged from 1.68 to 92.65 m-1. Additionally, the CDOM sources and composition parameters, including the spectral slope and relative molecular size value, exhibited a substantial variability from the oligotrophic level to other trophic levels. The natural logarithm value of the CDOM absorption, lna(254), is highly linearly correlated with the TSI (r2 = 0.92, p < .001, n = 821). Oligotrophic lakes are distinguished by a(254)<4 m-1, and mesotrophic and eutrophic lakes are classified as 4 ≤ a(254)≤10 and a(254)>10 m-1, respectively. The results suggested that the CDOM absorption coefficient a(254) might be a more sensitive single indicator of the trophic state than TN, TP, Chla and SDD. Therefore, we proposed a CDOM absorption coefficient and determined the threshold for defining the trophic state of a lake. Several advantages of measuring and estimating CDOM, including rapid experimental measurements, potential in situ optical sensor measurements and large-spatial-scale remote sensing estimations, make it superior to traditional TSI techniques for the rapid monitoring and assessment of lake trophic states.
The mechanistic model reported in Lee et al. (2015) estimating the Secchi disk depth (ZSD) was applied to an oligo- to mesotrophic reservoir in Brazil. The model was originally validated with data covering lake, oceanic, and coastal waters; however, the model used the quasi-analytical algorithm (QAA) designed for optically deep waters as input and was applied to oceanic and coastal waters to derive absorption [a] and backscattering [bb] coefficients. The hypothesis is that the use of QAAv5 (http://www.ioccg.org/groups/Software_OCA/QAA_v5.pdf) to estimate both a and bb (step M1) to retrieve Kd (step M2) and ZSD (step M3) will lead to errors caused by M1 preventing an accurate estimate in oligo- to mesotrophic water. To test this hypothesis, data collected in three field trips were used to apply the mechanistic model based on the spectral bands from OLI/Landsat-8, (often applied to oceanic and coastal waters), and multispectral instrument (MSI)/Sentinel-2 bands (applied to QAA designed for very turbid inland water). The impact of step M1 over steps M2 and M3 was analyzed by the error analysis. The mean absolute percentage error (MAPE) for Kd using QAAv5 ranged between 10.35% and 19.76%, while the error using QAAM14 varied between 12.68% and 28.29%. Regarding the errors of step M3 and applying QAAv5, the total root-mean-square difference (RMSD) varied from 0.55 to 1.18 m and MAPE ranged between 12.86% and 31.17%, while the RMSD ranged between 0.70 and 1.50 m and MAPE varied from 14.33% to 39.13% when using QAAM14. However, the result from QAAv5 showed a better correlation with in situ data, although underestimating Kd and ZSD. Therefore, a modified version of QAAv5 (QAAR17) was evaluated. The results showed an improvement of Kd (MAPE ranging between 8.89% to 18.76%) and ZSD (RMSD ranging between 0.32 and 0.90 m and MAPE ranging between 8.65 and 19.75%), bringing the values close to the 1:1 line. The largest error was observed for the data of the second field trip, where the bio-optical properties showed a horizontal gradient along the reservoir. In addition, the magnitude of the remote sensing reflectance (Rrs) also varied depending on the water quality. Thus, with respect to ZSD mapping, this research showed that environments with a high variability in Rrs can limit the accurate estimation of inherent optical properties (IOPs) based on QAAv5. Therefore, the limiting step of the model was attributed to M1, which means that the mechanistic model from Lee et al. (2015) can be considered an universal approach if M1 is modified based on the type of water.
Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.
Nutrient enrichment of aquatic ecosystems caused dramatic increase in the frequency, magnitude and duration of cyanobacterial
blooms. Such blooms may cause fish kills, have adverse health effects on humans and contribute to the loss of biodiversity
in aquatic ecosystems. Some 50 eutrophic to hypereutrophic ponds from the Brussels Capital Region (Belgium) were studied between
2003 and 2009. A number of the ponds studied are prone to persistent cyanobacterial blooms. Because of the related health
concerns and adverse effects on ecological quality of the affected ponds, a tool for assessment of the risk of cyanobacterial
bloom occurrence was needed. The data acquired showed that cyanobacteria have threshold relationships with most of the environmental
factors that control them. This is negatively reflected on the predictive capacity of conventional statistical methods based
on linear relationships. Therefore, classification trees designed for the treatment of complex data and non-linear relationships
were used to assess the risk of cyanobacterial bloom occurrence. The main factors determining cyanobacterial bloom development
appeared to be phytoplankton biomass, pH and, to a lesser degree, nitrogen availability. These results suggest that to outcompete
eukaryotic phytoplankters cyanobacteria need the presence of environmental constraints: carbon limitation, light limitation
and nitrogen limitation, for which they developed a number of adaptations. In the absence of constraints, eukaryotic phytoplankters
appear to be more competitive. Therefore, prior build up of phytoplankton biomass seems to be essential for cyanobacterial
dominance. Classification trees proved to be an efficient tool for the bloom risk assessment and allowed the main factors
controlling bloom development to be identified as well as the risk of bloom occurrence corresponding to the conditions determined
by these factors to be quantified. The results produced by the classification trees are consistent with those obtained earlier
by probabilistic approach to bloom risk assessment. They can facilitate planning management interventions and setting restoration
priorities.
Various types of floating algae have been reported in open oceans and coastal waters, yet accurate and timely detection of these relatively small surface features using traditional satellite data and algorithms has been difficult or even impossible due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. Here, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments. FAI is defined as the difference between reflectance at 859 nm (vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-wave infrared band (1240 or 1640 nm). Through data comparison and model simulations, FAI has shown advantages over the traditional NDVI (Normalized Difference Vegetation Index) or EVI (Enhanced Vegetation Index) because FAI is less sensitive to changes in environmental and observing conditions (aerosol type and thickness, solar/viewing geometry, and sun glint) and can “see” through thin clouds. The baseline subtraction method provides a simple yet effective means for atmospheric correction, through which floating algae can be easily recognized and delineated in various ocean waters, including the North Atlantic Ocean, Gulf of Mexico, Yellow Sea, and East China Sea. Because similar spectral bands are available on many existing and planned satellite sensors such as Landsat TM/ETM+ and VIIRS (Visible Infrared Imager/Radiometer Suite), the FAI concept is extendable to establish a long-term record of these ecologically important ocean plants.
Predicting cyanobacteria dominance in lakes
- Downing
Downing, J.A., Watson, S.B., McCauley, E., 2001. Predicting cyanobacteria dominance in
lakes. Can. J. Fish. Aquat. Sci. 58 (10), 1905-1908. https://doi.org/10.1139/f01-143.