Hotspot analysis of spatial distribution of algae blooms in small and medium water bodies

Zabaleta, Bernardo; Achkar, Marcel; Aubriot, Luis

doi:10.1007/s10661-021-08944-z

Cited by 9 publications

(5 citation statements)

References 90 publications

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“…Once EI was obtained for each event, patterns in its spatial distribution were explored through hot spot analysis (Zabaleta et al, 2021;Getis & Ord, 1992). To identify the zones with the greatest risk of biomass export, EI interpolations were performed using the IDW method for each date and the average between dates.…”

Section: Discussionmentioning

confidence: 99%

Zoning the risk of transporting phytoplankton blooms from multiple reservoirs to the primary drinking water intake in Uruguay

Zabaleta,

Achkar,

Aubriot

2023

Preprint

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Cyanobacterial blooms pose potential health and economic risks when they reach drinking water intakes. In basins with intensive agricultural production, there are numerous small and medium-sized (0.25-60 ha) productive freshwater reservoirs. These reservoirs create favorable conditions for the excessive growth of phytoplankton, which can be transported downstream after heavy rainfall events. These environments have been identified as potential sources of cyanobacterial inoculum in Uruguay's main drinking water intake, located in the Santa Lucía River Basin. To pinpoint areas with the highest risk of cyanobacterial biomass export, this study integrated chlorophyll-a concentration monitoring in 325 reservoirs using Sentinel-2 satellite images, along with meteorological and structural information from the reservoirs. An Export Index (EI) for biomass was developed, and spatial distribution patterns were explored through geostatistical and multivariate analyses. According to the EI, the western and southern zones, characterized by intensive agricultural land uses such as dairy farming and horticulture, were the most at risk for biomass export. Conversely, the northeastern zone exhibited the lowest risk, aligning with extensive cattle ranching practices associated with low nutrient export to aquatic systems. This study marks the initial effort to assess the risk of transporting cyanobacterial inoculum from numerous reservoirs to Uruguay's primary drinking water intake, providing valuable input for the development of early warning systems for the occurrences of cyanobacterial blooms.

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Section: Discussionmentioning

confidence: 99%

Zoning the risk of transporting phytoplankton blooms from multiple reservoirs to the primary drinking water intake in Uruguay

Zabaleta,

Achkar,

Aubriot

2023

Preprint

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“…These three methods are frequently utilized in spatial analysis. Moreover, there are studies that apply these methods to investigate the spatial distribution of chlorophyll-a concentration [57]. Often, these three methods are used simultaneously [58][59][60].…”

Section: Spatial Distribution Analysismentioning

confidence: 99%

Inversion and Analysis of Global Ocean Chlorophyll-a Concentration Based on Temperature Zoning

He,

Leng,

et al. 2024

Remote Sensing

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In recent years, the frequent occurrence of eutrophication problems in water bodies has been caused by changes in the climate environment and overexploitation of natural resources by humans. Chlorophyll-a, as a key indicator for water body assessment, plays an important role in eutrophication research and has a profound impact on the global biogeochemical cycle of the climate process. Studies have shown that temperature can directly or indirectly affect the concentration of chlorophyll-a by influencing the growth of algae and water quality indicators in water bodies. Considering the temperature factor in the inversion of chlorophyll-a concentration is a novel research approach. Based on the influence of temperature on chlorophyll-a concentration, we propose the idea of inverting global ocean chlorophyll-a concentration based on temperature zoning. Using monthly average remote sensing reflectance data from VIIRS (Visible and Infrared Imaging Radiometer Suite), combined with the results of temperature zoning, the OC3V(SST) model was constructed to invert the monthly average chlorophyll-a concentration in the global ocean in October 2018. The OC3V(SST) model has been validated by applying it to the remaining 11 months of January, April, July, and October in 2017, 2018, and 2019, as well as the entire 31-day dataset of October 2018. The results indicate that temperature zonation can significantly improve the inversion accuracy of chlorophyll-a and further explore the spatial distribution patterns of global chlorophyll-a concentrations across various temperature ranges based on monthly averages from the global ocean. Additionally, the study investigates the continuity issues of various models and the correlation between temperature and chlorophyll-a.

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“…Algal bloom indicators derived from satellite remote sensing bands include normalized difference vegetation index (NDVI) (Van Der Wal et al, 2010;Lin et al, 2016), FAI (Hu, 2009;Zhang et al, 2014;Page et al, 2018), Chla (Hu, 2009;Zhang et al, 2014;Page et al, 2018;Guan et al, 2020;Pompeo et al, 2021), etc. On this basis, various analyses are conducted on algal bloom distribution from spatio-temporal (Lu and Tian, 2012;Zhang et al, 2015;Page et al, 2018;Zabaleta et al, 2021) and vertical (Bosse et al, 2019) points of view. In 2019, a global spatiotemporal algal blooms analysis covering 71 large lakes from 33 countries based on Landsat five satellite images (Ho et al, 2019) revealed that algal bloom in over 2/3 of lakes had been increasing during the last 30 years.…”

Section: Introductionmentioning

confidence: 99%

“…Current research focuses less on the quantitative assessment method of algal bloom distributions. Using hotspots is one of the quantitative assessment methods that has been applied in spatial distribution analysis (Wei et al, 2021;Zabaleta et al, 2021). Generally, a quantitative description of temporal and spatial distribution of algal blooms is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Assessing Spatial and Temporal Distribution of Algal Blooms Using Gini Coefficient and Lorenz Asymmetry Coefficient

Zhou

Zhang

et al. 2022

Front. Environ. Sci.

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Algal bloom in an inland lake is characterized by significant spatial and temporal dynamics. Accurate assessment of algal bloom distribution and dynamics is highly required for tracing the causes of and creating countermeasures for algal bloom. Satellite remote sensing provides a fast and efficient way to capture algal bloom distribution at a large scale, but it is difficult to directly derive accurate and quantitative assessment based on satellite images. In this study, the Gini coefficient and Lorenz asymmetry coefficient were introduced to examine the spatio-temporal algal bloom distribution of Chaohu Lake, the fifth largest inland lake in China. A total of 61 remote sensing images from three satellite sensors, Landsat, Gaofen, and Sentinel were selected to obtain algal bloom distributions. By dividing remote sensing images into 0.01°*0.01° grid cells, the normalized difference vegetation index (NDVI) for each grid cell was derived, forming a spatial and time series database for quantitative analysis. Two coefficients, Gini coefficient and Lorenz asymmetry coefficient, were used to evaluate the overall intensity, unevenness, and attribution of algal bloom in Chaohu Lake from 2011 to 2020. The Gini coefficient results show a large variety of algal bloom in the spatial and temporal scales of Chaohu lake. The lake edge and northwestern part had longer lasting and more severe algal bloom than the lake center, which was mainly due to nutrient import, especially from three northwestern tributaries that flow through the upstream city. The Lorenz asymmetry coefficient revealed the exact source of the unevenness. Spatial uncertainties were mostly caused by the tiny areas with high NDVI values, accounting for 53 cases out of 61 cases. Temporal unevenness in northwestern and northeastern parts of the lake was due to the most severe breakout occurrences, while unevenness in the lake center was mainly due to the large number of light occurrences. Finally, the advantage of Gini coefficient and Lorenz asymmetry coefficient are discussed by comparison with traditional statistical coefficients. By incorporating the two coefficients, this paper provides a quantitative and comprehensive assessment method for the spatial and temporal distribution of algal bloom.

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Hotspot analysis of spatial distribution of algae blooms in small and medium water bodies

Cited by 9 publications

References 90 publications

Zoning the risk of transporting phytoplankton blooms from multiple reservoirs to the primary drinking water intake in Uruguay

Zoning the risk of transporting phytoplankton blooms from multiple reservoirs to the primary drinking water intake in Uruguay

Inversion and Analysis of Global Ocean Chlorophyll-a Concentration Based on Temperature Zoning

Assessing Spatial and Temporal Distribution of Algal Blooms Using Gini Coefficient and Lorenz Asymmetry Coefficient

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