摘要
Increasing incidences and severity of algal blooms are of major concern in coastal waters around India. In this work an automatic algorithm has been developed and applied to a series of MODIS-Aqua ocean color data to classify and monitor four major algal blooms in these waters (i.e., Trichodesmium erythareum, Noctiluca scintillans/miliaris (green/brown), and Cochlodinium polykrikoides (red)). The algorithm is based on unique spectral signatures of these blooms previously reported by various field sampling programs. An examination of the algorithm results revealed that classified blooms agree very well with in-situ data in most oceanic waters around India. Accuracy assessment based on overall, user’s and producer’s accuracy and Kappa accuracy further revealed that the producer’s/user’s accuracy of the four algal blooms were 100% / 100%, 79.16% / 79.16%, 100% / 80%, 100% / 86.95%, respectively. The Kappa coefficient was 1.01. These results suggest that the new algorithm has the potential to classify and monitor these major algal blooms and such information is highly desired by fishermen, fish farmers and public health officials in this region. It should be noted that coefficients with the new algorithm may be finetuned based on more in-situ data sets and the optical properties of these algal blooms in oceanic waters around India.
Increasing incidences and severity of algal blooms are of major concern in coastal waters around India. In this work an automatic algorithm has been developed and applied to a series of MODIS-Aqua ocean color data to classify and monitor four major algal blooms in these waters (i.e., Trichodesmium erythareum, Noctiluca scintillans/miliaris (green/brown), and Cochlodinium polykrikoides (red)). The algorithm is based on unique spectral signatures of these blooms previously reported by various field sampling programs. An examination of the algorithm results revealed that classified blooms agree very well with in-situ data in most oceanic waters around India. Accuracy assessment based on overall, user’s and producer’s accuracy and Kappa accuracy further revealed that the producer’s/user’s accuracy of the four algal blooms were 100% / 100%, 79.16% / 79.16%, 100% / 80%, 100% / 86.95%, respectively. The Kappa coefficient was 1.01. These results suggest that the new algorithm has the potential to classify and monitor these major algal blooms and such information is highly desired by fishermen, fish farmers and public health officials in this region. It should be noted that coefficients with the new algorithm may be finetuned based on more in-situ data sets and the optical properties of these algal blooms in oceanic waters around India.
