A STUDY ON DETECTION METHOD AND MIGRATION PROCESS OF FLOATING GREEN ALGAE AND BROWN ALGAE BLOOM USING MULTI-SENSOR DATA

Title
A STUDY ON DETECTION METHOD AND MIGRATION PROCESS OF FLOATING GREEN ALGAE AND BROWN ALGAE BLOOM USING MULTI-SENSOR DATA
Author(s)
신지선; 유주형; 이윤경; 민승환
KIOST Author(s)
Shin, Jisun(신지선)Ryu, Joo Hyung(유주형)
Publication Year
2016-04-20
Abstract
The historically massive bloom of the green macroalgae, Ulva prolifera, reported in June-August 2008 around the Quingao in China, Yellow Sea (YS) and East China Sea (ECS) has recurred in a similar season and region. It was recently reported that the massive bloom of the green macroalgae occurred in the southern part of Jeju Island during June 2015. On the other hand, the massive bloom of the brown macroalgae, Sargassum horneri, reported in May 2002 around ECS and appeared unusual in January 2015 around the southern part of the Korean Peninsula and Jeju Island. These phenomena can cause great damage to fishing operation and aquaculture facility. Traditional methods based on the measurement and chemical analysis in the laboratory for monitoring these phenomena have difficult to surveillance accurately and periodically wide areas. On the other hand, remote sensing method using spectral characteristics of floating algae makes it possible the accurate and fast detection of floating algae. However, the methods using traditional satellite data and algorithms have been difficult or even impossible accurate and timely detection due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. In particular, the detection method using single satellite data and simple algorithms have difficulty distinguishing green algae and brown algae due to the similarity of spectral characteristics. The aim of this study is to reveal spectral shape difference of U. prolifera and S. horneri and the migration processes using multi-sensor data. The images of GOCI, Landsat and Sentinel-1 were used for this study. The preliminary result showed that U. prolifera and S. horneri are separated by difference of NIR region due to color and degree of dense algae. The normalized difference vegetation index (NDVI) and floating algae index (FAI) were applied to Landsat ETM+ and OLI image in order to detect floating algae. Preliminary result of the migration processes using GOCI, Landsat and Sentinel-1 suggest that multi-sensor data are complementary to the detection of various floating algae.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/24895
Bibliographic Citation
International Symposium on Remote Sensing 2016, pp.1 - 4, 2016
Publisher
KSRS, RSSJ, CSPRS, EMSEA
Type
Conference
Language
English
Publisher
KSRS, RSSJ, CSPRS, EMSEA
Related Researcher
Research Interests

Coastal Remote Sensing,RS based Marine Surveillance System,GOCI Series Operation,연안 원격탐사,원격탐사기반 해양감시,천리안해양관측위성 운영

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