Automated Sargassum Detection for Landsat Imagery

We implemented a system to automatically detect Sargassum, a floating seaweed, in 30-meter LANDSAT-8 Operational Land Imager (OLI) imagery. Our algorithm for Sargassum detection is an extended form of Hu’s approach to derive a floating algae index (FAI) [1]. Hu’s algorithm was developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data, but we extended it for use with the OLI bands centered at 655, 865, and 1609 nm, which are comparable to the MODIS bands located at 645, 859, and 1640 nm. We also developed a high resolution true color product to mask cloud pixels in the OLI scene by applying a threshold to top of the atmosphere (TOA) radiances in the red (655 nm), green (561 nm), and blue (443 nm) wavelengths, as well as a method for removing false positive identifications of Sargassum in the imagery.

Hu’s algorithm derives a FAI for each Sargassum identified pixel. Our algorithm is currently set to only flag the presence of Sargassum in an OLI pixel by classifying any pixel with a FAI > 0.0 as Sargassum. Additionally, our system geo-locates the flagged Sargassum pixels identified in the OLI imagery into the U.S. Navy Global HYCOM model grid. One element of the model grid covers an area 0.125 degrees of latitude by 0.125 degrees of longitude. To resolve the differences in spatial coverage between Landsat and HYCOM, a scheme was developed to calculate the percentage of pixels flagged within the grid element and if above a threshold, it will be flagged as Sargassum.

This work is a part of a larger system, sponsored by NASA/Applied Science and Technology Project at J.C. Stennis Space Center, to forecast when and where Sargassum will land on shore. The focus area of this work is currently the Texas coast. Plans call for extending our efforts into the Caribbean.

References: [1] Hu, Chuanmin. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment 113 (2009) 2118-2129.