1
|
Mugani R, El Khalloufi F, Kasada M, Redouane EM, Haida M, Aba RP, Essadki Y, Zerrifi SEA, Herter SO, Hejjaj A, Aziz F, Ouazzani N, Azevedo J, Campos A, Putschew A, Grossart HP, Mandi L, Vasconcelos V, Oudra B. Monitoring of toxic cyanobacterial blooms in Lalla Takerkoust reservoir by satellite imagery and microcystin transfer to surrounding farms. HARMFUL ALGAE 2024; 135:102631. [PMID: 38830709 DOI: 10.1016/j.hal.2024.102631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 06/05/2024]
Abstract
Cyanobacterial harmful algal blooms (CyanoHABs) threaten public health and freshwater ecosystems worldwide. In this study, our main goal was to explore the dynamics of cyanobacterial blooms and how microcystins (MCs) move from the Lalla Takerkoust reservoir to the nearby farms. We used Landsat imagery, molecular analysis, collecting and analyzing physicochemical data, and assessing toxins using HPLC. Our investigation identified two cyanobacterial species responsible for the blooms: Microcystis sp. and Synechococcus sp. Our Microcystis strain produced three MC variants (MC-RR, MC-YR, and MC-LR), with MC-RR exhibiting the highest concentrations in dissolved and intracellular toxins. In contrast, our Synechococcus strain did not produce any detectable toxins. To validate our Normalized Difference Vegetation Index (NDVI) results, we utilized limnological data, including algal cell counts, and quantified MCs in freeze-dried Microcystis bloom samples collected from the reservoir. Our study revealed patterns and trends in cyanobacterial proliferation in the reservoir over 30 years and presented a historical map of the area of cyanobacterial infestation using the NDVI method. The study found that MC-LR accumulates near the water surface due to the buoyancy of Microcystis. The maximum concentration of MC-LR in the reservoir water was 160 µg L-1. In contrast, 4 km downstream of the reservoir, the concentration decreased by a factor of 5.39 to 29.63 µgL-1, indicating a decrease in MC-LR concentration with increasing distance from the bloom source. Similarly, the MC-YR concentration decreased by a factor of 2.98 for the same distance. Interestingly, the MC distribution varied with depth, with MC-LR dominating at the water surface and MC-YR at the reservoir outlet at a water depth of 10 m. Our findings highlight the impact of nutrient concentrations, environmental factors, and transfer processes on bloom dynamics and MC distribution. We emphasize the need for effective management strategies to minimize toxin transfer and ensure public health and safety.
Collapse
Affiliation(s)
- Richard Mugani
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000, Marrakech, Morocco; Department of Plankton and Microbial Ecology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Zur alten Fischerhuette 2, 14775, Stechlin, Germany
| | - Fatima El Khalloufi
- Natural Resources Engineering and Environmental Impacts Team, Multidisciplinary Research and Innovation Laboratory, Polydisciplinary Faculty of Khouribga, Sultan Moulay Slimane University of Beni Mellal, B.P.: 145, 25000, Khouribga, Morocco
| | - Minoru Kasada
- Graduate School of Life Sciences, Tohoku University 6-3, Aoba, Sendai, 980-8578 Japan
| | - El Mahdi Redouane
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco; UMR-I 02 INERIS-URCA-ULH SEBIO, University of Reims Champagne-Ardenne, Reims 51100, France
| | - Mohammed Haida
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco
| | - Roseline Prisca Aba
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000, Marrakech, Morocco
| | - Yasser Essadki
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco
| | - Soukaina El Amrani Zerrifi
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco; Higher Institute of Nurses Professions and Health Techniques of Guelmim, Guelmim, 81000, Morocco
| | - Sven-Oliver Herter
- Department of Water Quality Engineering, Institute of Environmental Technology, Technical University Berlin, Berlin, Germany
| | - Abdessamad Hejjaj
- National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000, Marrakech, Morocco
| | - Faissal Aziz
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000, Marrakech, Morocco
| | - Naaila Ouazzani
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000, Marrakech, Morocco
| | - Joana Azevedo
- CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208, Porto, Portugal
| | - Alexandre Campos
- CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208, Porto, Portugal
| | - Anke Putschew
- Department of Water Quality Engineering, Institute of Environmental Technology, Technical University Berlin, Berlin, Germany
| | - Hans-Peter Grossart
- Department of Plankton and Microbial Ecology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Zur alten Fischerhuette 2, 14775, Stechlin, Germany; Institute of Biochemistry and Biology, University of Potsdam, Maulbeeralle 2, 14469, Potsdam, Germany
| | - Laila Mandi
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000, Marrakech, Morocco
| | - Vitor Vasconcelos
- CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208, Porto, Portugal; Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007, Porto, Portugal.
| | - Brahim Oudra
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech, 40000, Morocco
| |
Collapse
|
2
|
Zhao D, Huang J, Li Z, Yu G, Shen H. Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169152. [PMID: 38061660 DOI: 10.1016/j.scitotenv.2023.169152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/11/2023] [Accepted: 12/04/2023] [Indexed: 01/18/2024]
Abstract
Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.
Collapse
Affiliation(s)
- Desong Zhao
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Jue Huang
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
| | - Zhengmao Li
- Shandong Marine Resource and Environment Research Institute, Shandong Key Laboratory of Marine Ecological Restoration, Yantai 264006, China
| | - Guangyue Yu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Huagang Shen
- Qingdao Topscomm Communication Co., Ltd, TOPSCOMM Industry Park, Qingdao 266109, China
| |
Collapse
|
3
|
Zheng Z, Huang C, Li Y, Lyu H, Huang C, Chen N, Liu G, Guo Y, Lei S, Zhang R, Li J. A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166785. [PMID: 37666339 DOI: 10.1016/j.scitotenv.2023.166785] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023]
Abstract
It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.
Collapse
Affiliation(s)
- Zhubin Zheng
- School of Geography and Environmental Engineering, Jiangxi Provincial Key Laboratory of Low-Carbon Solid Waste Recycling, Gannan Normal University, Ganzhou 341000, China.
| | - Chao Huang
- School of Geography and Environmental Engineering, Jiangxi Provincial Key Laboratory of Low-Carbon Solid Waste Recycling, Gannan Normal University, Ganzhou 341000, China
| | - Yunmei Li
- School of Geographic Science, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China
| | - Heng Lyu
- School of Geographic Science, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China
| | - Changchun Huang
- School of Geographic Science, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China
| | - Na Chen
- Department of Environmental Sciences, Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
| | - Ge Liu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Yulong Guo
- College of the Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Shaohua Lei
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
| | - Runfei Zhang
- School of Geographic Science, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China
| | - Jianzhong Li
- School of Geographic Science, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China
| |
Collapse
|
4
|
Ma J, Loiselle S, Cao Z, Qi T, Shen M, Luo J, Song K, Duan H. Unbalanced impacts of nature and nurture factors on the phenology, area and intensity of algal blooms in global large lakes: MODIS observations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163376. [PMID: 37031931 DOI: 10.1016/j.scitotenv.2023.163376] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 05/27/2023]
Abstract
Under the influence of climate warming and human activities, many large lakes have experienced an increase in eutrophication and algal blooms. Although these trends have been identified using low temporal resolution (~16 days) satellites such as those of the Landsat missions, the opportunity to compare high-frequency spatiotemporal variations of algal bloom characteristics between lakes has not been explored. In the present study, we explore daily satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics in large lakes (>500 km2) across the globe. Data from 161 lakes, taken from 2000 to 2020 showed an average accuracy of 79.9 %. Algal blooms were detected in 44 % of all lakes, with a higher incidence in temperate lakes (67 % of all temperate lakes), followed by tropical lakes (59 %) compared to lakes in arid climates (23 %). We found positive trends in bloom area and frequency (p < 0.05), as well as an earlier bloom time (p < 0.05). Climate factors were found to be linked to changes in annual initial bloom time (44 %); while an increase in human activities was associated to bloom duration (49 %), area (max percent: 53 %, mean percent: 45 %), and frequency (46 %). The study shows the evolution of daily algal blooms and their phenology in global large lakes for the first time. Such information enhances our understanding of algal bloom dynamics and their drivers, with important considerations to improve the management of large lake ecosystems.
Collapse
Affiliation(s)
- Jinge Ma
- Key Laboratory of watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Steven Loiselle
- Dipartimento di Biotecnologie, Chimica e Farmacia, CSGI, University of Siena, 53100 Siena, Italy
| | - Zhigang Cao
- Key Laboratory of watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Tianci Qi
- Key Laboratory of watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Ming Shen
- Key Laboratory of watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Juhua Luo
- Key Laboratory of watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Hongtao Duan
- Key Laboratory of watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Nanjing 211135, China.
| |
Collapse
|
5
|
Luo A, Chen H, Gao X, Carvalho L, Xue Y, Jin L, Yang J. Short-term rainfall limits cyanobacterial bloom formation in a shallow eutrophic subtropical urban reservoir in warm season. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 827:154172. [PMID: 35231504 DOI: 10.1016/j.scitotenv.2022.154172] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/23/2022] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
The global increase in dominance of toxic blooms of cyanobacteria has severely impacted aquatic ecosystems and threatened human health for decades. Although it has been shown that high levels of rainfall may inhibit the growth of bloom-forming cyanobacteria, it is still unclear how cyanobacteria respond to short-term rainfall events. Based on five-year (2016-2020) high-frequency (half-week) sampling data from a shallow eutrophic urban reservoir in subtropical China, we explored the short-term effects of rainfall events on cyanobacterial biomass (CBB) by constructing generalized additive models of CBB in rainy periods during warm (April to September) and cool (December and January) months, respectively. We find evidence in support of the hypotheses that short-term rainfall events significantly reduce CBB in warm months, but the opposite response was observed in the cool months. We also highlight a difference in the factors explaining CBB decreases in warm months (precipitation, air temperature, relative humidity, dissolved oxygen and total phosphorus) compared with factors explaining the response of CBB in cool months (sunshine hours, pH and total carbon). In particular, meteorological factors (precipitation, wind speed and sunlight) might drive changes in water temperature and hydro-dynamics of the reservoir, thereby causing a rapid reduction of CBB after rainfall events in warm months. This varying response of cyanobacteria to short-term rainfall events in the shallow eutrophic subtropical reservoir may also be expected in temperate or cool lakes as climate change effects become stronger.
Collapse
Affiliation(s)
- Anqi Luo
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huihuang Chen
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaofei Gao
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Fisheries, Henan Normal University, Xinxiang 453007, China
| | - Laurence Carvalho
- UK Centre for Ecology & Hydrology, Penicuik EH45 8EP, United Kingdom; Norwegian Institute for Water Research, Oslo NO-0579, Norway
| | - Yuanyuan Xue
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Lei Jin
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Yang
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
| |
Collapse
|
6
|
Li J, Li Y, Bi S, Xu J, Guo F, Lyu H, Dong X, Cai X. Utilization of GOCI data to evaluate the diurnal vertical migration of Microcystis aeruginosa and the underlying driving factors. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 310:114734. [PMID: 35220103 DOI: 10.1016/j.jenvman.2022.114734] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/20/2022] [Accepted: 02/13/2022] [Indexed: 06/14/2023]
Abstract
Cyanobacterial blooms are one of the most severe ecological problems affecting lakes. The vertical migration of cyanobacteria in the water column increases the uncertainty in the formation and disappearance of blooms, which may be closely associated with light, temperature, and wind speed. However, it is difficult to quantitatively evaluate the influencing factors of cyanobacteria vertical movement in natural environment compared to the laboratory experimental environment. Besides, both field survey and laboratory experiment method have the difficulties in determining the diurnal vertical migration of cyanobacteria at the synoptic lake scale. In this study, based on the diurnal dynamics of cyanobacterial bloom intensity (CBI) observed by the Geostationary Ocean Color Imager (GOCI) from 2011 to 2019, the daily variations, floating rate, and sinking rate of Microcystis aeruginosa were calculated in the natural environment. Then, the effects of light, temperature, and wind speed on the vertical migration of M. aeruginosa were analysed from the perspectives of day, night, and season. The results are as follows: the records of three typical patterns of diurnal CBI exhibited strong seasonal variability from the 9-year statistics; at night, the buoyancy recovery rate of cyanobacterial colonies increased with temperature, so that at temperature >15 °C and wind speed <3 m s-1, CBI reached the maximum of the whole day at 08:16; the sinking rate of M. aeruginosa was positively correlated with the cumulated light energy at both synoptic and pixel scale; the upward migration speed of M. aeruginosa was positively correlated with the maximum wind speed of the day before cyanobacterial bloom. Therefore, the severer cyanobacterial blooms were often observed by satellite images after strong winds. The analysis of diurnal variation, floating rate, and sinking rate of M. aeruginosa will expand our knowledge for further understanding the formation mechanism of cyanobacterial blooms and for improving the accuracy of model simulation to predict the hourly changes in cyanobacterial blooms in Lake Taihu.
Collapse
Affiliation(s)
- Junda Li
- Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China
| | - Yunmei Li
- Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China; Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing, 210023, PR China
| | - Shun Bi
- Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China; Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing, 210023, PR China; Institute of Carbon Cycles, Helmholtz-Zentrum Hereon, 21502, Geesthacht, Germany
| | - Jie Xu
- Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecological Environment, Wuhan, 430010, China
| | - Fei Guo
- Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China; Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing, 210023, PR China.
| | - Heng Lyu
- Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China; Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing, 210023, PR China
| | - Xianzhang Dong
- Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China
| | - Xiaolan Cai
- Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China
| |
Collapse
|
7
|
Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics. REMOTE SENSING 2021. [DOI: 10.3390/rs13214414] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field.
Collapse
|
8
|
A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective. REMOTE SENSING 2021. [DOI: 10.3390/rs13214347] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.
Collapse
|
9
|
Lei S, Xu J, Li Y, Li L, Lyu H, Liu G, Chen Y, Lu C, Tian C, Jiao W. A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 270:116288. [PMID: 33352484 DOI: 10.1016/j.envpol.2020.116288] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 06/12/2023]
Abstract
The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment.
Collapse
Affiliation(s)
- Shaohua Lei
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing, 210023, China; Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), IN, 46202, USA
| | - Jie Xu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Yunmei Li
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing, 210023, China.
| | - Lin Li
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), IN, 46202, USA
| | - Heng Lyu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Ge Liu
- Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Yu Chen
- Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China
| | - Chunyan Lu
- College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Chao Tian
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), IN, 46202, USA
| | - Wenzhe Jiao
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), IN, 46202, USA
| |
Collapse
|
10
|
Ha NT, Nguyen HQ, Truong NCQ, Le TL, Thai VN, Pham TL. Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:789. [PMID: 33241485 DOI: 10.1007/s10661-020-08731-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 11/03/2020] [Indexed: 06/11/2023]
Abstract
Surface water eutrophication due to excessive nutrients has become a major environmental problem around the world in the past few decades. Among these nutrients, nitrogen and phosphorus are two of the most important harmful cyanobacterial bloom (HCB) drivers. A reliable prediction of these parameters, therefore, is necessary for the management of rivers, lakes, and reservoirs. The aim of this study is to test the suitability of the powerful machine learning (ML) algorithm, random forest (RF), to provide information on water quality parameters for the Tri An Reservoir (TAR). Three species of nitrogen and phosphorus, including nitrite (N-NO2-), nitrate (N-NO3-), and phosphate (P-PO43-), were empirically estimated using the field observation dataset (2009-2014) of six surrogates of total suspended solids (TSS), total dissolved solids (TDS), turbidity, electrical conductivity (EC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD5). Field data measurement showed that water quality in the TAR was eutrophic with an up-trend of N-NO3- and P-PO43- during the study period. The RF regression model was reliable for N-NO2-, N-NO3-, and P-PO43- prediction with a high R2 of 0.812-0.844 for the training phase (2009-2012) and 0.888-0.903 for the validation phase (2013-2014). The results of land use and land cover change (LUCC) revealed that deforestation and shifting agriculture in the upper region of the basin were the major factors increasing nutrient loading in the TAR. Among the meteorological parameters, rainfall pattern was found to be one of the most influential factors in eutrophication, followed by average sunshine hour. Our results are expected to provide an advanced assessment tool for predicting nutrient loading and for giving an early warning of HCB in the TAR.
Collapse
Affiliation(s)
- Nam-Thang Ha
- Environmental Research Institute, School of Science, The University of Waikato, Hamilton, 3216, New Zealand
- Faculty of Fisheries, The University of Agriculture and Forestry, Hue University, Hue, 530000, Vietnam
| | - Hao Quang Nguyen
- Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan
| | | | - Thi Luom Le
- Dong Nai Technical Resources and Environment Center, Dong Khoi Street, Tan Hiep Ward, Bien Hoa City, Dong Nai Province, 810000, Vietnam
| | - Van Nam Thai
- Ho Chi Minh City University of Technology (HUTECH), 475A Dien Bien Phu Street, Binh Thanh District, Ho Chi Minh City, 700000, Vietnam
| | - Thanh Luu Pham
- Institute of Tropical Biology, Vietnam Academy of Science and Technology (VAST), 85 Tran Quoc Toan Street, District 3, Ho Chi Minh City, 700000, Vietnam.
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet Street, Cau Giay district, Hanoi, 100000, Vietnam.
| |
Collapse
|
11
|
Chen Q, Huang M, Tang X. Eutrophication assessment of seasonal urban lakes in China Yangtze River Basin using Landsat 8-derived Forel-Ule index: A six-year (2013-2018) observation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 745:135392. [PMID: 31892484 DOI: 10.1016/j.scitotenv.2019.135392] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 11/04/2019] [Accepted: 11/04/2019] [Indexed: 06/10/2023]
Abstract
Lakes eutrophication have been a complex and serious problem for China's Yangtze River Basin. A series of algorithms based on different remote sensing dataset have been proposed to simulate the lakes trophic state. However, these algorithms are often targeted at a particular lake and cannot be applied to a watershed management. In this study, a Forel-Ule index (FUI) method based on Landsat 8 OLI image is proposed to simulate trophic state index (TSI) in three typical urban lakes (Dianchi, Donghu, and Chaohu) from 2013 to 2018. The results show that the Landsat 8 derived FUI can well represent the lake TSI with an accuracy of R2 = 0.6464 for the in situ experimental TSI dataset (N = 115) and R2 = 0.8065 for the lake average TSI dataset (N = 315). In the study period 2013-2018, the order of the simulated TSI is Dianchi > Chaohu > Donghu. Seasonal dynamics show differences where the percentage of eutrophic area in summer is significantly lower than the other seasons for Lake Dianchi and Chaohu. However, the percentage of eutrophic area for Lake Donghu is highest in summer and lowest in winter. To further detect the driving factors of eutrophication in study lakes, the Pearson correlation and multiple linear regression analyses were conducted. The results show that sunshine and temperature are, respectively, the most and the second most significant factors for Lake Dianchi with explanations of 14.8% and 22.0%; temperature and pollution are the main influencing factors for Lake Donghu (39.2% and 10.9% explanation, respectively) and Chaohu (57.2% and 60.7% explanations, respectively). In addition, the wind is another negatively significant factor for Lake Chaohu with an explanation of 31.3%. Our results serve as an example for other lakes in the Yangtze River Basin and support the formulation of effective strategies to reduce seasonal eutrophication.
Collapse
Affiliation(s)
- Qi Chen
- College of Hydropower and Information Engineering, Huazhong University of Science and Technology, NO. 1037, Luoyu Road, Wuhan 430074, China
| | - Mutao Huang
- College of Hydropower and Information Engineering, Huazhong University of Science and Technology, NO. 1037, Luoyu Road, Wuhan 430074, China.
| | - Xiaodong Tang
- College of Hydropower and Information Engineering, Huazhong University of Science and Technology, NO. 1037, Luoyu Road, Wuhan 430074, China
| |
Collapse
|
12
|
Nguyen HQ, Ha NT, Pham TL. Inland harmful cyanobacterial bloom prediction in the eutrophic Tri An Reservoir using satellite band ratio and machine learning approaches. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:9135-9151. [PMID: 31916153 DOI: 10.1007/s11356-019-07519-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 12/23/2019] [Indexed: 06/10/2023]
Abstract
In recent years, Tri An, a drinking water reservoir for millions of people in southern Vietnam, has been affected by harmful cyanobacterial blooms (HCBs), raising concerns about public health. It is, therefore, crucial to gain insights into the outbreak mechanism of HCBs and understand the spatiotemporal variations of chlorophyll-a (Chl-a) in this highly turbid and productive water. This study aims to evaluate the predictable performance of both approaches using satellite band ratio and machine learning for Chl-a concentration retrieval-a proxy of HCBs. The monthly water quality samples collected from 2016 to 2018 and 23 cloud free Sentinel-2A/B scenes were used to develop Chl-a retrieval models. For the band ratio approach, a strong linear relationship with in situ Chl-a was found for two-band algorithm of Green-NIR. The band ratio-based model accounts for 72% of variation in Chl-a concentration from 2016 to 2018 datasets with an RMSE of 5.95 μg/L. For the machine learning approach, Gaussian process regression (GPR) yielded superior results for Chl-a prediction from water quality parameters with the values of 0.79 (R2) and 3.06 μg/L (RMSE). Among various climatic parameters, a high correlation (R2 = 0.54) between the monthly total precipitation and Chl-a concentration was found. Our analysis also found nitrogen-rich water and TSS in the rainy season as the driving factors of observed HCBs in the eutrophic Tri An Reservoir (TAR), which offer important solutions to the management of HCBs in the future.
Collapse
Affiliation(s)
- Hao-Quang Nguyen
- Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, 3058573, Japan
| | - Nam-Thang Ha
- Environmental Research Institute, School of Science, University of Waikato, Hamilton, 3260, New Zealand
- Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Thua Thien Hue, 530000, Vietnam
| | - Thanh-Luu Pham
- Ho Chi Minh City University of Technology (HUTECH), 475A Dien Bien Phu Street, Ward 25, Binh Thanh District, Ho Chi Minh City, 70000, Vietnam.
- Vietnam Academy of Science and Technology (VAST), Institute of Tropical Biology, 85 Tran Quoc Toan Street, District 3, Ho Chi Minh City, 70000, Vietnam.
| |
Collapse
|
13
|
Bi S, Li Y, Xu J, Liu G, Song K, Mu M, Lyu H, Miao S, Xu J. Optical classification of inland waters based on an improved Fuzzy C-Means method. OPTICS EXPRESS 2019; 27:34838-34856. [PMID: 31878664 DOI: 10.1364/oe.27.034838] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 11/04/2019] [Indexed: 06/10/2023]
Abstract
Water optical clustering based on water color information is important for many ecological and environmental application studies, both regionally and globally. The fuzzy clustering method avoids the sharp boundaries in type-memberships produced by hard clustering methods, and thus presents its advantages. However, to make good use of the fuzzy clustering methods on water color spectra data sets, the determination of the fuzzifier parameter (m) of FCM (fuzzy c-means) is the key factor. Usually, the m is set to 2 by default. Unfortunately, this method assigned some membership degrees to non-belonging water type, failing to obtain the unitarity of cluster structure in some cases, especially in inland eutrophic water. To overcome this shortcoming, we proposed an improved FCM method (namely FCM-m) for water color spectra classification by optimizing the fuzzifier parameter. We collected an inland data set containing 1280 in situ spectral data and co-measured water quality parameters with a wide range of biogeochemical variability in China. Using FCM-m, seven spectrally distinct water optical clusters on Sentinel-3 OLCI (Ocean and Land Colour Imager) bands were obtained with the optimized fuzzifier (m=1.36), and the well-performed clustering result is assessed by the validated index (Fuzzy Silhouette Index=0.513). Also, the FCM-m-based soft classification framework was successfully applied to the atmospherically corrected OLCI images, which was evaluated by previous case studies. Besides, by testing FCM-m on three coastal and oceanic data sets, we verified that the optimized m should be adjusted based on the data set itself, and in general, the value gradually approaches 1 with the increase of the band number (or dimension). Finally, the effect of the improved method was tested by Chlorophyll-a concentration estimation. The results show that the algorithm------- blending by FCM-m performs better than that by original FCM, which is mainly because the FCM-m reduces the estimation error from non-belonging clusters by a stricter membership value assignation. To sum up, we believe that FCM-m is an adaptive algorithm, whose R codes are available at https://github.com/bishun945, and needs to be tested by more public data sets.
Collapse
|
14
|
Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges. Sci Bull (Beijing) 2019; 64:1540-1556. [PMID: 36659563 DOI: 10.1016/j.scib.2019.07.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 06/18/2019] [Accepted: 06/23/2019] [Indexed: 01/21/2023]
Abstract
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.
Collapse
|