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http://dx.doi.org/10.7780/kjrs.2021.37.3.10

Green Algae Detection in the Middle·Downstream of Nakdong River Using High-Resolution Satellite Data  

Byeon, Yugyeong (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Seo, Minji (Center of Remote Sensing and GIS, Korea Polar Research Institute)
Jin, Donghyun (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Jung, Daeseong (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Woo, Jongho (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Jeon, Uujin (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Han, Kyung-soo (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.3, 2021 , pp. 493-502 More about this Journal
Abstract
Recently, because of changes in temperature and rising water temperatures due to increased pollution sources, many algae have been produced in the water system. Therefore, there has been a lot of research using satellite images for the generation and monitoring of green algae. However, in prior studies, it is difficult to consider the optical properties of the local water system by using only a single index, and by using medium and low-resolution satellite images to conduct large-scale algae detection, there is a problem of accuracy in narrow, broad rivers. Therefore, in this work, we utilize high-resolution images of Sentinel-2 satellites to perform green algae detection on a single index (NDVI, SEI, FGAI) and development index (NDVI & SEI, FGAI & SEI) that mixes single indices. In this study, POD, FAR, and PC values were utilized to evaluate the accuracy of green algae detection algorithms, and the FGAI & SEI index showed the highest accuracy with 98.29% overall accuracy PC.
Keywords
Climate change; Green Algae; Sentinel-2; NDVI; SEI; FGAI;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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