Remote Detection of Cyanobacteria Blooms in an Optically Shallow Subtropical Lagoonal Estuary Using MODIS Data

Jennifer P. Cannizzaro; Brian B. Barnes; Chuanmin Hu; Alina A. Corcoran; Katherine A. Hubbard; Eric Muhlbach; William C. Sharp; Larry E. Brand; Christopher R. Kelble

doi:10.1016/j.rse.2019.111227

Remote Detection of Cyanobacteria Blooms in an Optically Shallow Subtropical Lagoonal Estuary Using MODIS Data

Jennifer P. Cannizzaro, Brian B. Barnes, Chuanmin Hu, Alina A. Corcoran, Katherine A. Hubbard, Eric Muhlbach, William C. Sharp, Larry E. Brand, Christopher R. Kelble

College of Marine Science

Research output: Contribution to journal › Article › peer-review

Abstract

Widespread and persistent Ecosystem Disruptive Algal Blooms dominated by marine picocyanobacteria ( Synechococcus ) commonly occur in the subtropical lagoonal <a title="Learn more about estuary from ScienceDirect's AI-generated Topic Pages"> estuary </a> of Florida Bay (U.S.A). These blooms have been linked to a decline in natural sheet flow over the past century from upstream Everglades National Park. <a title="Learn more about Remote sensing from ScienceDirect's AI-generated Topic Pages"> Remote sensing </a> algorithms for monitoring cyanobacteria blooms are highly desired but have been mainly developed for freshwater and coastal systems with minimal bottom reflectance contributions in the past. Examination of in situ <a title="Learn more about optical properties from ScienceDirect's AI-generated Topic Pages"> optical properties </a> revealed that Synechococcus blooms in Florida Bay exhibit unique <a title="Learn more about spectral absorption from ScienceDirect's AI-generated Topic Pages"> spectral absorption </a> and reflectance features that form the basis for algorithm development. Using a large, multi-year match-up dataset (2002–2012; n = 682) consisting of in situ pigment concentrations and <a title="Learn more about Moderate Resolution Imaging Spectroradiometer from ScienceDirect's AI-generated Topic Pages"> Moderate Resolution Imaging Spectroradiometer </a> (MODIS) Rayleigh-corrected reflectance (R rc (λ)), classification criteria for detecting cyanobacteria blooms with chlorophyll- a concentrations (Chl- a ) ~5–40 mg m −3 were determined based on a new approach to combine the MODIS Cyanobacteria Index, CI MODIS , and spectral shape around 488 nm, SS(488). The inclusion of SS(488) was required to prevent false positive classifications in seagrass-rich, non-bloom waters with high bottom reflectance contributions. 75% of cyanobacteria blooms were classified accurately based on this modified CI approach with <1% false positives. A strong correlation observed between cyanobacteria bloom in situ Chl- a and CI MODIS (r 2 = 0.80, n = 32) then allowed cyanobacterial chlorophyll- a concentrations (Chl CI ) to be estimated. Model simulations and image-based analyses showed that this technique was insensitive to variable <a title="Learn more about aerosol properties from ScienceDirect's AI-generated Topic Pages"> aerosol properties </a> and sensor viewing geometry. Application of the approach to the entire MODIS time-series (2000–present) may help identify factors controlling blooms and system responses to ongoing management efforts aimed at restoring flow to pre-drainage conditions. The method may also provide insights for algorithm development for other lagoonal estuaries that experience similar blooms.

Original language	American English
Journal	Remote Sensing of Environment
Volume	231
DOIs	https://doi.org/10.1016/j.rse.2019.111227
State	Published - Jan 1 2019

Keywords

Algal bloom
Ocean color
Remote sensing
MODIS
Chlorophyll
Cyanobacteria
Synechococcus
Florida Bay

Disciplines

Life Sciences

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https://doi.org/10.1016/j.rse.2019.111227

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@article{f0ef1776d818460e990314ea6ed014a0,

title = "Remote Detection of Cyanobacteria Blooms in an Optically Shallow Subtropical Lagoonal Estuary Using MODIS Data",

abstract = " Widespread and persistent Ecosystem Disruptive Algal Blooms dominated by marine picocyanobacteria ( Synechococcus ) commonly occur in the subtropical lagoonal estuary of Florida Bay (U.S.A). These blooms have been linked to a decline in natural sheet flow over the past century from upstream Everglades National Park. Remote sensing algorithms for monitoring cyanobacteria blooms are highly desired but have been mainly developed for freshwater and coastal systems with minimal bottom reflectance contributions in the past. Examination of in situ optical properties revealed that Synechococcus blooms in Florida Bay exhibit unique spectral absorption and reflectance features that form the basis for algorithm development. Using a large, multi-year match-up dataset (2002–2012; n = 682) consisting of in situ pigment concentrations and Moderate Resolution Imaging Spectroradiometer (MODIS) Rayleigh-corrected reflectance (R rc (λ)), classification criteria for detecting cyanobacteria blooms with chlorophyll- a concentrations (Chl- a ) ~5–40 mg m −3 were determined based on a new approach to combine the MODIS Cyanobacteria Index, CI MODIS , and spectral shape around 488 nm, SS(488). The inclusion of SS(488) was required to prevent false positive classifications in seagrass-rich, non-bloom waters with high bottom reflectance contributions. 75% of cyanobacteria blooms were classified accurately based on this modified CI approach with <1% false positives. A strong correlation observed between cyanobacteria bloom in situ Chl- a and CI MODIS (r 2 = 0.80, n = 32) then allowed cyanobacterial chlorophyll- a concentrations (Chl CI ) to be estimated. Model simulations and image-based analyses showed that this technique was insensitive to variable aerosol properties and sensor viewing geometry. Application of the approach to the entire MODIS time-series (2000–present) may help identify factors controlling blooms and system responses to ongoing management efforts aimed at restoring flow to pre-drainage conditions. The method may also provide insights for algorithm development for other lagoonal estuaries that experience similar blooms. ",

keywords = "Algal bloom, Ocean color, Remote sensing, MODIS, Chlorophyll, Cyanobacteria, Synechococcus, Florida Bay",

author = "Cannizzaro, {Jennifer P.} and Barnes, {Brian B.} and Chuanmin Hu and Corcoran, {Alina A.} and Hubbard, {Katherine A.} and Eric Muhlbach and Sharp, {William C.} and Brand, {Larry E.} and Kelble, {Christopher R.}",

year = "2019",

month = jan,

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doi = "10.1016/j.rse.2019.111227",

language = "American English",

volume = "231",

journal = "Remote Sensing of Environment",

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TY - JOUR

T1 - Remote Detection of Cyanobacteria Blooms in an Optically Shallow Subtropical Lagoonal Estuary Using MODIS Data

AU - Cannizzaro, Jennifer P.

AU - Barnes, Brian B.

AU - Hu, Chuanmin

AU - Corcoran, Alina A.

AU - Hubbard, Katherine A.

AU - Muhlbach, Eric

AU - Sharp, William C.

AU - Brand, Larry E.

AU - Kelble, Christopher R.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Widespread and persistent Ecosystem Disruptive Algal Blooms dominated by marine picocyanobacteria ( Synechococcus ) commonly occur in the subtropical lagoonal estuary of Florida Bay (U.S.A). These blooms have been linked to a decline in natural sheet flow over the past century from upstream Everglades National Park. Remote sensing algorithms for monitoring cyanobacteria blooms are highly desired but have been mainly developed for freshwater and coastal systems with minimal bottom reflectance contributions in the past. Examination of in situ optical properties revealed that Synechococcus blooms in Florida Bay exhibit unique spectral absorption and reflectance features that form the basis for algorithm development. Using a large, multi-year match-up dataset (2002–2012; n = 682) consisting of in situ pigment concentrations and Moderate Resolution Imaging Spectroradiometer (MODIS) Rayleigh-corrected reflectance (R rc (λ)), classification criteria for detecting cyanobacteria blooms with chlorophyll- a concentrations (Chl- a ) ~5–40 mg m −3 were determined based on a new approach to combine the MODIS Cyanobacteria Index, CI MODIS , and spectral shape around 488 nm, SS(488). The inclusion of SS(488) was required to prevent false positive classifications in seagrass-rich, non-bloom waters with high bottom reflectance contributions. 75% of cyanobacteria blooms were classified accurately based on this modified CI approach with <1% false positives. A strong correlation observed between cyanobacteria bloom in situ Chl- a and CI MODIS (r 2 = 0.80, n = 32) then allowed cyanobacterial chlorophyll- a concentrations (Chl CI ) to be estimated. Model simulations and image-based analyses showed that this technique was insensitive to variable aerosol properties and sensor viewing geometry. Application of the approach to the entire MODIS time-series (2000–present) may help identify factors controlling blooms and system responses to ongoing management efforts aimed at restoring flow to pre-drainage conditions. The method may also provide insights for algorithm development for other lagoonal estuaries that experience similar blooms.

AB - Widespread and persistent Ecosystem Disruptive Algal Blooms dominated by marine picocyanobacteria ( Synechococcus ) commonly occur in the subtropical lagoonal estuary of Florida Bay (U.S.A). These blooms have been linked to a decline in natural sheet flow over the past century from upstream Everglades National Park. Remote sensing algorithms for monitoring cyanobacteria blooms are highly desired but have been mainly developed for freshwater and coastal systems with minimal bottom reflectance contributions in the past. Examination of in situ optical properties revealed that Synechococcus blooms in Florida Bay exhibit unique spectral absorption and reflectance features that form the basis for algorithm development. Using a large, multi-year match-up dataset (2002–2012; n = 682) consisting of in situ pigment concentrations and Moderate Resolution Imaging Spectroradiometer (MODIS) Rayleigh-corrected reflectance (R rc (λ)), classification criteria for detecting cyanobacteria blooms with chlorophyll- a concentrations (Chl- a ) ~5–40 mg m −3 were determined based on a new approach to combine the MODIS Cyanobacteria Index, CI MODIS , and spectral shape around 488 nm, SS(488). The inclusion of SS(488) was required to prevent false positive classifications in seagrass-rich, non-bloom waters with high bottom reflectance contributions. 75% of cyanobacteria blooms were classified accurately based on this modified CI approach with <1% false positives. A strong correlation observed between cyanobacteria bloom in situ Chl- a and CI MODIS (r 2 = 0.80, n = 32) then allowed cyanobacterial chlorophyll- a concentrations (Chl CI ) to be estimated. Model simulations and image-based analyses showed that this technique was insensitive to variable aerosol properties and sensor viewing geometry. Application of the approach to the entire MODIS time-series (2000–present) may help identify factors controlling blooms and system responses to ongoing management efforts aimed at restoring flow to pre-drainage conditions. The method may also provide insights for algorithm development for other lagoonal estuaries that experience similar blooms.

KW - Algal bloom

KW - Ocean color

KW - Remote sensing

KW - MODIS

KW - Chlorophyll

KW - Cyanobacteria

KW - Synechococcus

KW - Florida Bay

UR - https://digitalcommons.usf.edu/msc_facpub/2049

UR - https://doi.org/10.1016/j.rse.2019.111227

U2 - 10.1016/j.rse.2019.111227

DO - 10.1016/j.rse.2019.111227

M3 - Article

VL - 231

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

ER -

Remote Detection of Cyanobacteria Blooms in an Optically Shallow Subtropical Lagoonal Estuary Using MODIS Data

Abstract

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