Application of unmanned aerial vehicle for detection and classification of benthic macroalgal bloom

Title
Application of unmanned aerial vehicle for detection and classification of benthic macroalgal bloom
Author(s)
김근용; 김범준; 김의현; 유주형
KIOST Author(s)
Kim, Keunyong(김근용)Kim, Euihyun(김의현)Ryu, Joo Hyung(유주형)
Publication Year
2019-10-24
Abstract
Green tide mainly caused by Ulva sp. have been frequently observed along the coast of Korea every year. In particular, Jeju Island is main tourist site in the Korea, spending a lot of cost to remove green algae every year. Although satellite images provide a clear picture of the distribution of these macroalgal blooms, there were many difficulties in detecting small scale and/or submerged algae. This research attempts to identify the distribution and areal extent of the benthic green tide using unmanned aerial vehicles (UAVs). In mid-May 2019, the study was conducted on the southeast coast of Jeju Island, where massive benthic Ulva blooms occurred. The UAV images were taken at an altitude of 250 m using the DJI’s Mavic 2 Pro with optical camera. Individual images taken by UAV were produced as orthophoto through geometric correction, and the green algae were classified using the Maximum Likelihood, Mahalanobis Distance and Minimum Distance classification algorithms. Overall accuracy of Maximum Likelihood, Mahalanobis Distance and Minimum Distance classification method showed 97.6, 88.0 and 91.1%, respectively. In Kappa value as well as in overall accuracy of error matrix, the Maximum Likelihood method showed the highest accuracy. Despite some green algae were submerged, this study confirmed that benthic green algae could be classified with relatively high precision through an UAV images analysis.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/20425
Bibliographic Citation
4th China-Korea Workshop on Marine Environment and Disaster Monitoring Using Remote Sensing in the Yellow Sea, pp.2, 2019
Publisher
Chnia-Korea Joint Ocean Research Center
Type
Conference
Language
English
Publisher
Chnia-Korea Joint Ocean Research Center
Related Researcher
Research Interests

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

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

qrcode

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

Browse