Detection of Macroalgal Blooms by SAR and Optical Remote Sensing

Abstract

Macroalgal bloom (MAB) is the vigorous proliferation of algae which is seen to be a global issue as it adversely affects the life and economy of coastal region’s people as well as the marine ecosystem. Therefore, the detection of MAB is crucial for monitoring their development from generation to dissipation so that appropriate remedial measures can be taken. In this study, Sentinel-1 Synthetic Aperture Radar (SAR), Landsat 8, Sentinel-2 and Geostationary Ocean Color Imager-II (GOCI-II) images were utilized for the MAB detection purpose in the Yellow Sea. For Sentinel-1, images were preprocessed by using the Sentinel Application Platform (SNAP) software which mainly covers orbit correction, radiometric calibration, speckle filtering, terrain correction and then adaptive threshold was applied to detect the MAB from the dual polarized images. Floating algae index (FAI) and normalized difference vegetation index (NDVI) were calculated for the preprocessed Landsat 8 and Sentinel-2 images, respectively. To detect the MAB adaptive threshold was then applied in both Landsat 8 and Sentinel-2 images. In the case of GOCI-II, floating algae area ratio product was used for mapping the MAB occurred in the study area. The study findings display that, areas measuring 56.4 km2, 13.62 km2 and 10.85 km2 from Landsat 8, Sentinel-1 and Sentinel-2 images, respectively, were detected as MAB for a common area. At present, finding out the effective method of MAB detection from Sentinel-1 SAR images compare with the detection results from other satellites are in progress.