Modeling for multi-temporal cyanobacterial bloom dominance and distributions using landsat imagery

Isenstein, Elizabeth M.; Kim, Dae-Young; Park, Mi-Hyun

doi:10.1016/j.ecoinf.2020.101119

Cited by 14 publications

(4 citation statements)

References 46 publications

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“…Gaining an understanding of the drivers of Cyanobacterial Harmful Algal Blooms (CyanoHABs) and understanding the influence of physiochemical and environmental variables is essential for optimizing water resource management [15]. As evidenced by the number of variables that are potentially important to understand and include, there are many, including physical-chemical variables, such as water temperature, ambient temperature, Secchi disk depth, turbidity, wind speed and direction, phosphates, total phosphorus, total nitrogen, nitrate, nitrite, ammonium ion, dissolved oxygen, conductivity, calcium, iron, alkalinity, pH, and more, indicative of the challenges of understanding CyanoHABs [15][16][17][18][19][20][21][22].…”

Section: Scope Of Contributing Variablesmentioning

confidence: 99%

Climate Change Impacts on Water Temperatures in Urban Lakes: Implications for the Growth of Blue Green Algae in Fairy Lake

Bhatti,

Singh,

McBean

et al. 2024

Water

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Cyanobacteria, also known as blue-green algae, are photosynthetic bacteria that play a crucial role in aquatic ecosystems and are susceptible to changes in temperature. Hence, as global temperatures rise due to climate change, some Cyanobacterial species will thrive in warmer temperatures, which will result in increased blooms during the growing season. The MIKE-3 model is calibrated to existing (2022) conditions and is used to assess the impact of the RCP 4.5 scenario for the year 2050 in Fairy Lake (a shallow urban lake in Ontario). The simulations projected indicate that in 2050, in the central parts of Fairy Lake’s central basin, water temperatures will be above 20 °C for 2281 h compared to 2060 h in 2022. This situation indicates there will be a 10.7% increase in the duration of Cyanobacteria blooms in the central area of Fairy Lake. Similarly, in the northern area of Fairy Lake, the MIKE-3 model results indicate that surface temperature durations above 20 °C will increase from 1628 h to 2275 h for the year 2050, resulting in an additional 647 h of increased temperatures at the surface under RCP 4.5 scenario conditions. This situation indicates there will be a 39.7% increase in the duration of Cyanobacteria blooms in the northern portion of Fairy Lake. These modeling conditions indicate there will be significantly more habitat amenable to Cyanobacteria growth when surface water temperatures are above 20 °C, indicating substantial increases in the available growth time of Cyanobacteria due to climate change, all of which translate to major concerns caused by climate change.

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Section: Scope Of Contributing Variablesmentioning

confidence: 99%

Climate Change Impacts on Water Temperatures in Urban Lakes: Implications for the Growth of Blue Green Algae in Fairy Lake

Bhatti,

Singh,

McBean

et al. 2024

Water

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“…Moreover, the use of different satellite source data for modelling is also risky due to the lack of data homogeneity as a consequence of the diversity of resolution and spectra from the different sensors, as well as the interferences from clouds, atmosphere and water reflectance [158]. For inland waters, Landsat satellite imagery provides valuable continuous time-series data sets to estimate PC and turbidity, already available for almost four decades and have been used to study cyanobacterial blooms at big lakes such as Lake Erie [159], Taihu Lake [160] or Lake Champlain [161], taking advantage of the fine resolution (about 30 m). However, under a rapid change into a cyanobacterial bloom development, a coarse revisit time of 16 days is not sufficient.…”

Section: Current Monitoring and Assessmentmentioning

confidence: 99%

Harmful Cyanobacterial Blooms: Going beyond the “Green” to Monitor and Predict HCBs

de Figueiredo

2024

Hydrobiology

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Under the Climate Change scenario, the occurrence of Harmful Cyanobacterial Blooms (HCBs) is an increasingly concerning problem. Particularly for inland freshwaters, that have human populations depending on them for consumption or recreation, HCBs can lead to serious ecological damages and socio-economic impacts, but also to health risks for local communities. From satellite imagery to molecular data, there is an increasing number of methodological approaches that can help improve the monitoring and prediction of cyanobacterial blooms. However, although each methodology has its own strengths and limitations, generally there is a lack of data addressing specific and intraspecific information, which has implications for the modelling and prediction of the real dynamics and toxicity of HCBs. The present review intends to make a quick overview on current approaches to monitor cyanobacterial blooms and provide a tier-based integrative perspective for their application. A transversal monitoring at a wide scale should be enhanced but cannot rely only on pigment levels but rather include the specific and intraspecific diversity information that can be obtained from modern molecular tools. This is crucial to achieve the effective prediction, monitoring and management of HCBs under their increasing occurrence and severity trends in freshwaters.

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“…As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. Water environment issues can have a huge impact ( Isenstein, Kim & Park, 2020 ; Nowruzi et al, 2021 ), not only lead to local water shortage ( Ibelings et al, 2016 ), but also affect local economic development. Research shows that economic development is closely related to environmental standards ( Bhatti et al, 2022 ; Li et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Wang

et al. 2023

PeerJ Computer Science

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Background As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.

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Modeling for multi-temporal cyanobacterial bloom dominance and distributions using landsat imagery

Cited by 14 publications

References 46 publications

Climate Change Impacts on Water Temperatures in Urban Lakes: Implications for the Growth of Blue Green Algae in Fairy Lake

Climate Change Impacts on Water Temperatures in Urban Lakes: Implications for the Growth of Blue Green Algae in Fairy Lake

Harmful Cyanobacterial Blooms: Going beyond the “Green” to Monitor and Predict HCBs

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

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