Risks for cyanobacterial harmful algal blooms due to land management and climate interactions

Weber, Samuel J.; Mishra, Deepak R.; Wilde, Susan B.; Krämer, Elizabeth

doi:10.1016/j.scitotenv.2019.134608

Cited by 44 publications

(22 citation statements)

References 60 publications

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“…The availability of climate products accompanied by surface products within GEE, make this platform a great tool for climate studies and air quality monitoring. These advantages create a rising interest in the research community to use GEE for air pollution analyses [142], [143], climate change and monitoring [144], [145], biophysical variable studies [21], [146], evapotranspiration estimation [147], and precipitation mapping [148]. For instance, GEE was employed to map exposed mine waste areas to estimate the corresponding emission of particulate matter to the atmosphere [142].…”

Section: G Atmosphere and Climatementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

601

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: G Atmosphere and Climatementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

601

216

View full text Add to dashboard Cite

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“…Cyanobacterial blooms remain a highly concerning water environmental issue around the world (Casillas-Ituarte et al 2020; Mellios et al 2020;Weber et al 2020). Outbreaks of cyanobacterial blooms consume a large amount of dissolved oxygen in the water, reduce the transparency of the water, and produce toxic and harmful substances, such as microcystins (Funkey et al 2014;Su et al 2019).…”

Section: Introductionmentioning

confidence: 99%

The proliferation rule of Microcystis aeruginosa under different initial pH conditions and its influence on the pH value of the environment

Wei

Zhuang

Cheng

et al. 2021

Environ Sci Pollut Res

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This study investigated the characteristics of the proliferation process of Microcystis aeruginosa and its changes to environmental pH values under different initial pH values and different initial inoculation densities. The results showed that although the initial pH value or the initial inoculation density was different, the pH values of the culture systems uctuated up and down throughout the proliferation of M. aeruginosa, both on a daily and hourly time scale, and then tended to stabilize around the same value of 10.0 at the end of proliferation. The optimal pH value for the proliferation of M. aeruginosa was 9.56. This study creatively proposes that the period when the environmental pH value starts to rise rapidly toward 9.0 could be selected as an early warning period for a cyanobacterial outbreak, and the environmental pH value could be adjusted to below 8.0 to delay the outbreak. These results provide a scienti c basis for further understanding the mechanism of cyanobacterial blooms and formulating pHbased control strategies.

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“…Recently, GEE has enabled a number of long-term large-scale inland water investigations. These include inland surface water dynamics [40,41], analysis of sediments in crater lakes [42] and rivers [43], quantification of colored dissolved organic matter (CDOM) and dissolved organic matter (DOC) in Arctic rivers [44], retrieval of river water Chl-a and turbidity [45], monitoring of HABs in lakes [46], and risk estimation of HABs [47], etc.…”

Section: Introductionmentioning

confidence: 99%

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Wang

Liu

et al. 2020

Remote Sensing

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Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

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Risks for cyanobacterial harmful algal blooms due to land management and climate interactions

Cited by 44 publications

References 60 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

The proliferation rule of Microcystis aeruginosa under different initial pH conditions and its influence on the pH value of the environment

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

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