Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS

Matthews, Mark W.; Bernard, Stewart; Winter, Kevin

doi:10.1016/j.rse.2010.04.013

Cited by 216 publications

(114 citation statements)

References 65 publications

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“…Para o pigmento ficocianina, indicador da ocorrência de cianobactérias, foi possível visualizar em algumas curvas uma feição sutil de absorção próxima a 620 nm e um pico de espalhamento em torno de 650 nm, conforme relatado na literatura para ambientes aquáticos com presença de cianobactérias (Matthews et al, 2010;Marion et al, 2012). Na Figura 4 as curvas espectrais destacadas por cores apresentam, na legenda, as concentrações de clorofila a e ficocianina em μg L -1 , a fim de indicar a variação do comportamento espectral com oscilaçãoes na concentração desses pigmentos fitoplanctônicos.…”

Section: Resultsunclassified

“…A época do ano (verão) foi definida por ser a mais propícia para a proliferação do fitoplâncton, em virtude de altas temperaturas, aumento da descarga de nutrientes pelas chuvas e baixa velocidade dos ventos (Esteves, 1998). As imagens foram adquiridas em datas mais próximas possível do levantamento de campo a fim de se manter as mesmas condições ambientais (Matthews et al, 2010).…”

Section: Methodsunclassified

“…O pigmento fitoplanctônico clorofila a é o componente opticamente ativo mais estudado de vez que está presente em todos os organismos fitoplanctônicos permitindo estimar o total de biomassa (Vincent et al, 2004). No caso das cianobactérias sua discriminação é possível por meio do pigmento ficocianina que possui propriedades ópticas peculiares, inclusive fluorescentes (Matthews et al, 2010;Agha et al, 2012;Mishra et al, 2013;Song et al, 2013).…”

Section: Introductionunclassified

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Sensoriamento remoto multifonte aplicado na detecção do fitoplâncton em águas interiores

Cicerelli

Galo

2015

Rev. bras. eng. agríc. ambient.

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R E S U M OFlorações de fitoplanctôn podem constituir em riscos à saúde humana e biota aquática, sendo necessários o monitoramento da comunidade fitoplanctônica e a adoção de mecanismos visando à prevenção de sua ocorrência. Neste contexto métodos tradicionais de monitoramento podem ser mais efetivos se complementados por abordagens que utilizam as propriedades ópticas dos pigmentos fitoplanctônicos por meio do Sensoriamento Remoto. Com o objetivo de avaliar o potencial de dados espectrais multifonte na detecção remota do fitoplâncton, foi selecionada uma área de estudo no reservatório de Nova Avanhandava, SP, caracterizada por intensa atividade agrícola no seu entorno. Para esta análise foram adquiridos dados hiperespectrais em campo e imagens multiespectrais Modis e RapidEye, os quais foram relacionados a variáveis limnológicas indicadoras do comportamento fitoplanctônico; clorofila a e ficocianina. Os resultados mostram que imagens multiespectrais permitem uma avaliação da biomassa fitoplanctônica pela clorofila a; contudo, para ficocianina, pigmento fitoplanctônico diagnóstico da presença de cianobactérias, dados mais refinados são necessários, tais como os hiperespectrais.Multisource remote sensing applied to the detection of phytoplankton in inland waters A B S T R A C TBlooms of phytoplankton can be a risk to human health and aquatic biota, so the adoption of monitoring methods of phytoplankton and mechanisms for preventing its occurrence are needed. Thus, traditional monitoring methods could be more effective if complemented by approaches using the optical properties of phytoplankton pigments by means of Remote Sensing. In order to evaluate the potential of multi-scale remote sensing for detection of the phytoplankton activity, a study area was selected in Nova Avanhandava reservoir, located in the Tietê River, SP. For this analysis, hyperspectral field data and multispectral images of low and medium spatial resolution (Modis and RapidEye) were acquired and were related to indicator limnological variables of phytoplankton behavior; chlorophyll a and phycocyanin. The results show that a specific spectral band of RapidEye system (690-730 nm) allowed detect chlorophyll a and to evaluate the phytoplankton biomass, however hyperspectral data are needed to detect the phycocyanin pigment, indicative of cyanobacteria. Palavras-chave:sensoriamento remoto da água cianobactérias clorofila a ficocianina Key words: remote sensing of water cyanobacteria chlorophyll a phycocyanin

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Section: Resultsunclassified

Section: Methodsunclassified

Section: Introductionunclassified

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Sensoriamento remoto multifonte aplicado na detecção do fitoplâncton em águas interiores

Cicerelli

Galo

2015

Rev. bras. eng. agríc. ambient.

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“…However, Case-2 waters, now frequently referred to as optically complex waters, present significant challenges for atmospheric correction and retrieval of in-water constituents concentration and many standard ocean colour algorithms perform poorly when applied to these waters (Alikas & Reinart, 2008;Witter et al, 2009;Binding et al, 2010). In recent years considerable effort has been invested in the development and validation of in-water algorithms tailored to optically complex waters and there is a rapidly growing body of literature demonstrating promising results for the retrieval of a range of biogeochemical constituents (Kutser et al, 2005;Giardino et al, 2010;Tarrant et al, 2010;Hunter et al, 2010;Matthews et al, 2010;Odermatt et al, 2010;Nechad et al, 2010;Dogliotti et al, 2015;Palmer et al, 2015;Ayana et al, 2015). Many of these studies have focused on individual lakes or small populations of lakes with comparable atmospheric and optical properties and it is almost certain that no single approach will perform adequately over the continuum of surface water bodies.…”

Section: Special Issue Science Of the Total Environmentmentioning

confidence: 99%

“…Many of these algorithms have found applications in optically complex waters (e.g. Ali et al, 2014;Bélanger et al, 2008;Kowalczuk et al, 2010;Matthews et al, 2010;Tyler et al, 2006). Regionallyspecific implementations and adaptations of already existing algorithms are also common in the literature (Budd & Warrington, 2004;Li et al, 2004;Yacobi et al, 2001;Palmer et al, 2015).…”

Section: Figurementioning

confidence: 99%

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

Tyler

Hunter

Spyrakos

et al. 2016

Science of The Total Environment

130

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The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production.Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelfsea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable.

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Evaluation and reformulation of the maximum peak height algorithm (MPH) and application in a hypertrophic lagoon

Pitarch

Ruiz‐Verdú

Sendra

et al. 2017

J. Geophys. Res. Oceans

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We studied the performance of the MERIS maximum peak height (MPH) algorithm in the retrieval of chlorophyll‐a concentration (CHL), using a matchup data set of Bottom‐of‐Rayleigh Reflectances (BRR) and CHL from a hypertrophic lake (Albufera de Valencia). The MPH algorithm produced a slight underestimation of CHL in the pixels classified as cyanobacteria (83% of the total) and a strong overestimation in those classified as eukaryotic phytoplankton (17%). In situ biomass data showed that the binary classification of MPH was not appropriate for mixed phytoplankton populations, producing also unrealistic discontinuities in the CHL maps. We recalibrated MPH using our matchup data set and found that a single calibration curve of third degree fitted equally well to all matchups regardless of how they were classified. As a modification to the former approach, we incorporated the Phycocyanin Index (PCI) in the formula, thus taking into account the gradient of phytoplankton composition, which reduced the CHL retrieval errors. By using in situ biomass data, we also proved that PCI was indeed an indicator of cyanobacterial dominance. We applied our recalibration of the MPH algorithm to the whole MERIS data set (2002–2012). Results highlight the usefulness of the MPH algorithm as a tool to monitor eutrophication. The relevance of this fact is higher since MPH does not require a complete atmospheric correction, which often fails over such waters. An adequate flagging or correction of sun glint is advisable though, since the MPH algorithm was sensitive to sun glint.

show abstract

Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS

Cited by 216 publications

References 65 publications

Sensoriamento remoto multifonte aplicado na detecção do fitoplâncton em águas interiores

Sensoriamento remoto multifonte aplicado na detecção do fitoplâncton em águas interiores

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

Evaluation and reformulation of the maximum peak height algorithm (MPH) and application in a hypertrophic lagoon

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