Automatic Sargassum detection using spatial anomaly of ocean color reflectance: A case study with GOCI data

Automatic Sargassum detection using spatial anomaly of ocean color reflectance: A case study with GOCI data
김나은; 김원국; 이보람; 안재현; 박영제
KIOST Author(s)
Ahn, Jae Hyun(안재현)Park, Young Je(박영제)
Publication Year
Massive amount of Sargassum horneri (S.horneri) occurred in the west coastline of Korea in 2015. It has disruptive impacts on both fisheries and tourism, giving rise to fish kills and beach fouling in the coastal and near-shore areas. This harmful substance was detected by Geostationary Ocean Color Imager (GOCI) during the period between January and June. Since GOCI data is acquired every hour during the daytime, allowing near real-time ocean monitoring, it can be effectively used to observe the temporal variations of the S.horneri distribution. The reflectance of the S.horneri patch floating on the oceanic water is higher than that of the background water in the Near-infrared (NIR) band. Therefore it allows the S.horneri patches to be detected by anomaly detection techniques. In this study, we derived inherent optical properties (IOP) from water leaving reflectance to know the in-water constituents. Remote-sensing reflectance (Rrs) was calculated by using absorption and backscattering characteristic of water, phytoplankton, suspended sediment and colored dissolved organic material. Thus It was used to detect anomalies. After then, we used spectral angle mapper (SAM) to differentiate S.horneri from other features observed as anomalies. Ulva prolifera, species of green tides, has a very similar Rrs spectral pattern to that of S.horneri in GOCI bands. Therefore, further spectral analysis is required to detect S.horneri.
Bibliographic Citation
Ocean Optics XXIII, pp.1, 2016
The Oceanography Society
The Oceanography Society
Related Researcher
Research Interests

Ocean Color Remote Sensing,Satellite Applications,Ocean color Algorithm,해양원격탐사,위성활용,해색 알고리즘

Files in This Item:
There are no files associated with this item.


Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.