Mapping cyanobacterial blooms in Lake Champlain's Missisquoi Bay using QuickBird and MERIS satellite data

Wheeler, Sarah M.; Morrissey, L. A.; Levine, Suzanne N.; Livingston, Gerald P.; Vincent, Warwick F.

doi:10.1016/j.jglr.2011.06.009

Cited by 52 publications

(39 citation statements)

References 40 publications

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“…Maximum chl a concentrations in both years (> 100 µg L −1 ) were similar to other hypereutrophic systems (Zhang et al, 2011;Wheeler et al, 2012;Steffen et al, 2014b), as were large swings in NO − 3 concentrations in 2015 (Xu et al, 2010;Steffen et al, 2014b;McCarthy et al, 2016). Elevated nutrient concentrations were associated with high discharge events from the Sandusky River, demonstrating a strong watershed influence on Sandusky Bay.…”

Section: Nutrient Stoichiometrysupporting

confidence: 58%

Nitrogen cycling in Sandusky Bay, Lake Erie: oscillations between strong and weak export and implications for harmful algal blooms

Salk¹,

Bullerjahn²,

McKay³

et al. 2018

Biogeosciences

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Abstract. Recent global water quality crises point to an urgent need for greater understanding of cyanobacterial harmful algal blooms (cHABs) and their drivers. Nearshore areas of Lake Erie such as Sandusky Bay may become seasonally limited by nitrogen (N) and are characterized by distinct cHAB compositions (i.e., Planktothrix over Microcystis). This study investigated phytoplankton N uptake pathways, determined drivers of N depletion, and characterized the N budget in Sandusky Bay. Nitrate (NO − 3 ) and ammonium (NH + 4 ) uptake, N fixation, and N removal processes were quantified by stable isotopic approaches. Dissimilatory N reduction was a relatively modest N sink, with denitrification, anammox, and N 2 O production accounting for 84, 14, and 2 % of sediment N removal, respectively. Phytoplankton assimilation was the dominant N uptake mechanism, and NO − 3 uptake rates were higher than NH + 4 uptake rates. Riverine N loading was sometimes insufficient to meet assimilatory and dissimilatory demands, but N fixation alleviated this deficit. N fixation made up 23.7-85.4 % of total phytoplankton N acquisition and indirectly supports Planktothrix blooms. However, N fixation rates were surprisingly uncorrelated with NO − 3 or NH + 4 concentrations. Owing to temporal separation in sources and sinks of N to Lake Erie, Sandusky Bay oscillates between a conduit and a filter of downstream N loading to Lake Erie, delivering extensively recycled forms of N during periods of low export. Drowned river mouths such as Sandusky Bay are mediators of downstream N loading, but climate-change-induced increases in precipitation and N loading will likely intensify N export from these systems.

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Section: Nutrient Stoichiometrysupporting

confidence: 58%

Nitrogen cycling in Sandusky Bay, Lake Erie: oscillations between strong and weak export and implications for harmful algal blooms

Salk¹,

Bullerjahn²,

McKay³

et al. 2018

Biogeosciences

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“…SeaWiFS data were also used to establish the seasonal distribution pattern and intensity of phytoplankton and terrigenous input [17], and AHVRR to point out the behavior of main taxonomic groups of Baikal lake phytoplankton in relation to ice conditions [18], or to assess other water quality parameters [19,20]. Recently, QuickBird and MERIS data were also used to assess cyanobacterial blooms based on their specific pigment (phycocyanin) in Lake Champlain [21]. Thus, many semi-analytical algorithms [12,[22][23][24][25][26] and indexes [13] are now available in the literature to retrieve the Chl-a concentration of inland water bodies.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

et al. 2012

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Abstract:The occurrence and extent of intense harmful algal blooms (HABs) have increased in inland waters during recent decades. Standard monitor networks, based on infrequent sampling from a few fixed observation stations, are not providing enough information on the extent and intensity of the blooms. Remote sensing has great potential to provide the spatial and temporal coverage needed. Several sensors have been designed to study water properties (AVHRR, SeaBAM, and SeaWIFS), but most lack adequate spatial resolution for monitoring algal blooms in small and medium-sized lakes. Over the last decade, satellite data with 250-m spatial resolution have become available with MODIS. In the present study, three models inspired by published approaches (Kahru, Gitelson, and Floating Algae Index (FAI)) and a new approach named APPEL (APProach by ELimination) were adapted to the specific conditions of southern Quebec and used to estimate chlorophyll-a concentration (Chl-a) using MODIS data. Calibration and validation were provided from in situ Chl-a measured in four lakes over 9 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) and concurrent MODIS imagery. MODIS bands 3 to 7, originally at 500-m spatial resolution, were downscaled to 250 m. The APPEL, FAI, and Kahru models yielded satisfactory results and enabled estimation of Chl-a for heavy blooming conditions (Chl-a > 50 mg•m −3 ), with coefficients of determination reaching 0.95, 0.94, and 0.93, respectively. The model inspired from Gitelson did not provide good estimations compared to the others (R 2 = 0.77).However, the performance of all models decreased when Chl-a was below 50 mg•m −3 . OPEN ACCESSRemote Sens. 2012, 4 2374

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“…Backscattering signals perceived in the near-infrared band specified the surface index. Another study used MERIS radiance data and predicted pigment concentrations from empirically calibrated Quick Bird data using selected band ratios and principal components analysis 5 . In 1998, NOAA-12 AVHRR imagery was utilized to conduct a reflective investigation which pointed out that blooms spread across 20% of the surface of the Arabian Sea for approximately a week in May (1995) 6 .…”

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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Mapping cyanobacterial blooms in Lake Champlain's Missisquoi Bay using QuickBird and MERIS satellite data

Cited by 52 publications

References 40 publications

Nitrogen cycling in Sandusky Bay, Lake Erie: oscillations between strong and weak export and implications for harmful algal blooms

Nitrogen cycling in Sandusky Bay, Lake Erie: oscillations between strong and weak export and implications for harmful algal blooms

Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

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

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