TY - JOUR
T1 - Long-term evaluation of three satellite ocean color algorithms for identifying harmful algal blooms (Karenia brevis) along the west coast of Florida
T2 - A matchup assessment
AU - Carvalho, Gustavo A.
AU - Minnett, Peter J.
AU - Banzon, Viva F.
AU - Baringer, Warner
AU - Heil, Cynthia A.
N1 - Funding Information:
Special thanks go to Lora Fleming, Edward Kearns, Robert Evans, Jennifer Cannizzaro, Kendall Carder, Chuanmin Hu, Angel Li, Julie Hollenbeck, Christina Plattner, Rafael Schiller, Lauren Zamora, Patricia Lee McCoy, Guilherme Castelao, Nancy Streb, Adrienne Romanski, Thiago Corrêa, Maria Villanueva and Gan Changlin for their contributions to this investigation. We acknowledge the thoughtful comments of four reviewers that contributed to improving this paper. Financial support was provided by NSF and NIEHS Oceans and Human Health Center grants: NSF#OCE0432368/0911373 and NIEHS#P50-ES12736 .
PY - 2011/1/17
Y1 - 2011/1/17
N2 - We present a simple algorithm to identify Karenia brevis blooms in the Gulf of Mexico along the west coast of Florida in satellite imagery. It is based on an empirical analysis of collocated matchups of satellite and in situ measurements. The results of this Empirical Approach is compared to those of a Bio-optical Technique - taken from the published literature - and the Operational Method currently implemented by the NOAA Harmful Algal Bloom Forecasting System for K. brevis blooms. These three algorithms are evaluated using a multi-year MODIS data set (from July, 2002 to October, 2006) and a long-term in situ database. Matchup pairs, consisting of remotely-sensed ocean color parameters and near-coincident field measurements of K. brevis concentration, are used to assess the accuracy of the algorithms. Fair evaluation of the algorithms was only possible in the central west Florida shelf (i.e. between 25.75°N and 28.25°N) during the boreal Summer and Fall months (i.e. July to December) due to the availability of valid cloud-free matchups. Even though the predictive values of the three algorithms are similar, the statistical measure of success in red tide identification (defined as cell counts in excess of 1.5×104 cells L-1) varied considerably (sensitivity-Empirical: 86%; Bio-optical: 77%; Operational: 26%), as did their effectiveness in identifying non-bloom cases (specificity-Empirical: 53%; Bio-optical: 65%; Operational: 84%). As the Operational Method had an elevated frequency of false-negative cases (i.e. presented low accuracy in detecting known red tides), and because of the considerable overlap between the optical characteristics of the red tide and non-bloom population, only the other two algorithms underwent a procedure for further inspecting possible detection improvements. Both optimized versions of the Empirical and Bio-optical algorithms performed similarly, being equally specific and sensitive (~70% for both) and showing low levels of uncertainties (i.e. few cases of false-negatives and false-positives: ~30%)-improved positive predictive values (~60%) were also observed along with good negative predictive values (~80%).
AB - We present a simple algorithm to identify Karenia brevis blooms in the Gulf of Mexico along the west coast of Florida in satellite imagery. It is based on an empirical analysis of collocated matchups of satellite and in situ measurements. The results of this Empirical Approach is compared to those of a Bio-optical Technique - taken from the published literature - and the Operational Method currently implemented by the NOAA Harmful Algal Bloom Forecasting System for K. brevis blooms. These three algorithms are evaluated using a multi-year MODIS data set (from July, 2002 to October, 2006) and a long-term in situ database. Matchup pairs, consisting of remotely-sensed ocean color parameters and near-coincident field measurements of K. brevis concentration, are used to assess the accuracy of the algorithms. Fair evaluation of the algorithms was only possible in the central west Florida shelf (i.e. between 25.75°N and 28.25°N) during the boreal Summer and Fall months (i.e. July to December) due to the availability of valid cloud-free matchups. Even though the predictive values of the three algorithms are similar, the statistical measure of success in red tide identification (defined as cell counts in excess of 1.5×104 cells L-1) varied considerably (sensitivity-Empirical: 86%; Bio-optical: 77%; Operational: 26%), as did their effectiveness in identifying non-bloom cases (specificity-Empirical: 53%; Bio-optical: 65%; Operational: 84%). As the Operational Method had an elevated frequency of false-negative cases (i.e. presented low accuracy in detecting known red tides), and because of the considerable overlap between the optical characteristics of the red tide and non-bloom population, only the other two algorithms underwent a procedure for further inspecting possible detection improvements. Both optimized versions of the Empirical and Bio-optical algorithms performed similarly, being equally specific and sensitive (~70% for both) and showing low levels of uncertainties (i.e. few cases of false-negatives and false-positives: ~30%)-improved positive predictive values (~60%) were also observed along with good negative predictive values (~80%).
KW - Algorithm development
KW - Chlorophyll
KW - Detection
KW - Florida red tide (Karenia brevis)
KW - Gulf of Mexico (west Florida shelf)
KW - Harmful algal bloom (HAB)
KW - Ocean color (MODIS)
KW - Satellite remote sensing
KW - Water-leaving radiance
UR - http://www.scopus.com/inward/record.url?scp=77958101880&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77958101880&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2010.07.007
DO - 10.1016/j.rse.2010.07.007
M3 - Article
AN - SCOPUS:77958101880
VL - 115
SP - 1
EP - 18
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
IS - 1
ER -