Threshold stability of different algorithms for green tide detection base on geostationary ocean color imager

Xiaoguang Huang; Difeng Wang; Fang Gong; Xianqiang He; Yan Bai; Zhihong Wang; Qiankun Zhu

doi:10.1117/12.2532542

14 October 2019 Threshold stability of different algorithms for green tide detection base on geostationary ocean color imager

Xiaoguang Huang, Difeng Wang, Fang Gong, Xianqiang He, Yan Bai, Zhihong Wang, Qiankun Zhu

Author Affiliations +

Proceedings Volume 11150, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019; 1115018 (2019) https://doi.org/10.1117/12.2532542
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Since 2008, macroalgal blooms of Ulva Prolifera (also called green tide) have occurred every summer in the Yellow Sea (YS), which has caused environmental and economic problems. In recent years, a variety of detection algorithms for green tide have been proposed. However, the extraction thresholds of each algorithm are uncertain because of atmospheric conditions, the distribution of green tides, etc. In this paper, Geostationary Ocean Color Imager (GOCI) data and Landsat- 8 data were used to explore the threshold stability of some common detection algorithms for green tide, including the AFAI, DVI, EVI, IGAG, and NDVI. Four scenes of GOCI satellite data from 2016 to 2018 were selected for the experiments. The first step was to extract the green tide areas in one region to determine the threshold for each algorithm. In this step, the extraction results of the Landsat-8 data, which has a resolution of 30 m, was seen as the true value of the green tide coverage. Then, we determined the threshold value for each algorithm by visual inspection. The thresholds determined in the first step were used to extract the green tide area in the other three regions, and the extraction results were compared by visual contrast. A comparison of the extraction precision for each algorithm in the other three regions indicated that the threshold stability of the AFAI algorithm was the best among these data in the YS region.

Citation Download Citation

Xiaoguang Huang, Difeng Wang, Fang Gong, Xianqiang He, Yan Bai, Zhihong Wang, and Qiankun Zhu "Threshold stability of different algorithms for green tide detection base on geostationary ocean color imager", Proc. SPIE 11150, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019, 1115018 (14 October 2019); https://doi.org/10.1117/12.2532542

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