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Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis)
Carvalho, G.A.; Minnett, P.J.; Fleming, L.E.; Banzon, V.F.; Baringer, W. (2010). Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis). Harmful Algae 9(5): 440-448. dx.doi.org/10.1016/j.hal.2010.02.002
In: Harmful Algae. Elsevier: Tokyo; Oxford; New York; London; Amsterdam; Shannon; Paris. ISSN 1568-9883, more
Peer reviewed article  

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Keyword
    Marine
Author keywords
    Algorithm development; Central West Florida Shelf (Gulf of Mexico); Detection; Florida Red Tide (Karenia brevis); Harmful algal bloom (HAB); Hybrid Scheme; Ocean color (MODIS-Aqua); Satellite remote sensing

Authors  Top 
  • Carvalho, G.A.
  • Minnett, P.J.
  • Fleming, L.E., more
  • Banzon, V.F.
  • Baringer, W.

Abstract
    In a continuing effort to develop suitable methods for the surveillance of harmful algal blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote-sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002–2006; during the boreal Summer–Fall periods – July–December) along the Central West Florida Shelf between 25.75°N and 28.25°N. Algorithm validation was done with in situ measurements of the abundances of K. brevis; cell counts =1.5 × 104 cells l-1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (~80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (~20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: ~70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: ~86%). These results demonstrate an excellent detection capability, on average ~10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs.

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