Automatic red tide detection from MODIS satellite images

Red tides pose a significant environmental and economic threat in the Gulf of Mexico. Timely detection of red tides is important for understanding this phenomenon. In this paper, learning approaches based on k-nearest neighbors, random forests and support vector machines have been evaluated for red tide detection from MODIS satellite images. Detection results from our algorithms were compared with ground truth red tide data collected in situ. Our results show that red tide identification methods based on machine learning approaches outperform baseline algorithms based on bio-optical characterization.