An Algorithm for Classification of Algal Blooms Using MODIS-Aqua Data in Oceanic Waters around India

Simon, Arthi; Shanmugam, Palanisamy

doi:10.4236/ars.2012.12004

Cited by 33 publications

(18 citation statements)

References 34 publications

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“…While chl-a data for Case 1 coastal waters are readily available, our research shows these are not applicable to the Case 2 coastal waters around New Zealand without additional tuning. Several specific Case 2 algorithms have been developed for other studies that have successfully improved chl-a data retrieval for coastal waters (Ahn and Shanmugam, 2006;Cannizzaro and Carder, 2006;Shanmugam, 2011;Simon and Shanmugam, 2012;Le et al, 2013). While these models were successful, they are regionally specific and may be complex to calibrate locally without specialist equipment and additional targeted studies.…”

Section: Uptake Of Satellite Data In the New Zealand Contextmentioning

confidence: 99%

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

et al. 2017

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Monitoring of the phytoplankton pigment chlorophyll-a is often used as an indicator of eutrophication in coastal waters. Improved water quality monitoring using data sourced from MODIS (Moderate Resolution Imaging Spectroradiometer)-sourced data allows for infrequently sampled sites to be interrogated for long-term trends. Despite the wide availability and good spatial and temporal coverage of MODIS data, these data have had little use in operational coastal monitoring of chlorophyll-a in New Zealand. This is in part due to the poor performance of global oceanic algorithms applied in the coastal waters. Accessible algorithm tuning methods that can be validated by in situ measurements may assist the uptake of satellite data for coastal monitoring. This study presents results from regional tuning and validation of two empirical algorithm approaches, including a new simple exponential model, to estimate chlorophyll-a for two coastal locations in New Zealand. A novel method of training chlorophyll-a models using smoothed in situ data to match spatial scales of satellite observations was applied, and shows promise for improving tuned model performance. This approach shows potential for lowering barriers for researchers and coastal managers wishing to make use of the growing satellite data resource in their coastal environments.

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Section: Uptake Of Satellite Data In the New Zealand Contextmentioning

confidence: 99%

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

et al. 2017

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“…10a). C. polykrikoids and T. erythtraeum species with high/medium Chl-a (>5 mg m -3 ) have a smooth increment in the magnitude from R rs (443) to R rs (531), high R rs at 531 and 547 nm (Simon and Shanmugam, 2012), and HOCI > HNA.…”

mentioning

confidence: 96%

“…Instead, C. polykrikoids contains a distinct trough at 667 nm, a peak at 678 nm (Simon and Shanmugam, 2012), and R rs (667) > R rs (443). These spectral attributes have not been observed in other species such as N. millaris, N. scintillans, and diatoms (Fig.…”

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confidence: 96%

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A novel method for characterizing harmful algal blooms in the Persian Gulf using MODIS measurements

Ghanea

Moradi

Kabiri

2016

Advances in Space Research

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“…The use of remote sensing has been considered for such studies to increase the efficiency and effectiveness in the course of action. Increased occurrences, as well as the severity of algal blooms, could be due to augmentation of required nutrients which results in the flourishing of already existing algae forming major blooms 1 . These nutrients are mostly inorganic compounds consisting phosphorus, nitrogen, and carbon.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Identification of Algal Blooms in Inland Waters: A Post Cyclone Case Study of Chilika Lake, Odisha, India

2017

Rasayan J Chem

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Algal blooms are referred to overgrowths of algae in fresh or marine water systems and are caused due to various factors including natural physical and chemical processes and can have severe impacts on human health, aquatic life and even economy in some cases. They flourish in the presence of inorganic nutrients such as nitrogen and phosphorus. This study focuses on identification of algal blooms in inland waters and hence Chilika Lake of Odisha, India is considered as the study area. It is the largest brackish water lake in Asia which was designated as the first Indian wetland of international importance under the Ramsar Convention (1981) for its rich biodiversity. In October 2013, the ecosystem of the lake was affected by a severe cyclone named, "Phailin" leading to increased runoff resulting in the formation of algal blooms. This study has used remote sensing techniques to detect those algal blooms by utilizing satellite imagery which was obtained from EO-1, by the multispectral sensor Advanced Land Imager (ALI). A methodology has been developed by the combination of various image processing techniques like Principal Component Analysis and Normalized Difference Vegetation Index, which proved to be efficient in visualizing the spatial distribution of algal blooms on the lake surface.

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An Algorithm for Classification of Algal Blooms Using MODIS-Aqua Data in Oceanic Waters around India

Cited by 33 publications

References 34 publications

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

A novel method for characterizing harmful algal blooms in the Persian Gulf using MODIS measurements

Geospatial Identification of Algal Blooms in Inland Waters: A Post Cyclone Case Study of Chilika Lake, Odisha, India

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