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

Ghanea, Mohsen; Moradi, Masoud; Kabiri, Keivan

doi:10.1016/j.asr.2016.06.005

Cited by 19 publications

(7 citation statements)

References 30 publications

(43 reference statements)

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“…A study in the Bohai Sea [71] showed that the attenuation coefficient can be used as a proxy for the growth of phytoplankton in turbid coastal waters given that the blue to green light attenuation positively correlates with scattering particles (e.g., HABs). A high correlation between chlorophyll-a concentration and diffuse attenuation coefficient was observed under harmful red tide conditions in the Persian Gulf using MODIS data [72]. In another study done in the coastal waters of India, HABs were detected using satellite derived chlorophyll-a and diffuse attenuation coefficient images and were also validated through in situ measurement [37].…”

Section: Diffuse Attenuation Coefficientmentioning

confidence: 99%

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

et al. 2018

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Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

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Section: Diffuse Attenuation Coefficientmentioning

confidence: 99%

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

et al. 2018

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show abstract

“…A study in the Bohai Sea [51] showed that the attenuation coefficient can be used as a proxy for the growth of phytoplankton in turbid coastal waters given that the blue to green light attenuation positively correlates with scattering particles (e.g., HABs). A high correlation between chlorophyll-a concentration and diffuse attenuation coefficient was observed under harmful red tide conditions in the Persian Gulf using MODIS data [52]. In another study done in the coastal waters of India, HABs were detected using satellite derived chlorophyll and diffuse attenuation coefficient images and were also validated through in situ measurement [53].…”

Section: Diffuse Attenuation Coefficientmentioning

confidence: 99%

Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

Karki¹,

Sultan²,

Elkadiri³

et al. 2018

Preprint

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Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

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“…Spectral band difference algorithms are often used to relate the reflectance peak in the red/NIR to phytoplankton biomass, the most well-known version of which is arguably the fluorescence line height (FLH) (Letelier and Abbott, 1996;Gower et al, 1999); FLH provides a quantification of the height of the Chla fluorescence peak above a baseline formed by the Chl-a absorption trough near 665 and a NIR wavelength (usually near 750 nm). Several studies have used a variant of this line height detection method, whether on its own or in combination with other optical properties (e.g., backscattering) or true color imagery, for the detection of HABs in coastal waters using ocean color remote sensing (Gower et al, 2005;Ryan et al, 2008;Matthews et al, 2012;Al Shehhi et al, 2013;Ghanea et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Satellite Ocean Color Based Harmful Algal Bloom Indicators for Aquaculture Decision Support in the Southern Benguela

Smith¹,

Bernard

2020

Front. Mar. Sci.

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

Cited by 19 publications

References 30 publications

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

Satellite Ocean Color Based Harmful Algal Bloom Indicators for Aquaculture Decision Support in the Southern Benguela

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