Monitoring Ulva prolifera in the Yellow Sea and East China Sea derived from multi-source remote sensing images

Dingfeng Yu; Zhigang Gai; Xiangfeng Kong; Hui Liu; Enxiao Liu

doi:10.1117/12.2295278

15 November 2017 Monitoring Ulva prolifera in the Yellow Sea and East China Sea derived from multi-source remote sensing images

Dingfeng Yu, Zhigang Gai, Xiangfeng Kong, Hui Liu, Enxiao Liu

Author Affiliations +

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060535 (2017) https://doi.org/10.1117/12.2295278
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

Ulva prolifera is a new type of marine ecological disaster in China. Since 2007, macroalgal blooms of Ulva prolifera occurred every summer in the Yellow Sea and East China Sea, causing significant environmental damage and economic loss especially for coastal areas. Because of its distribution range covering tens of thousands of square kilometers during its outbreak, it’s obviously difficult to monitor using ship, and therefore the author of this article attempted to use remote sensing technology to monitor its temporal and spatial distribution.In the present study, CCD of China’s Environmental Satellite and EOS-MODIS were employed to detect distribution of Ulva prolifera in the Yellow Sea and East China Sea in 2014.The remote sensing monitoring results show that early enteromorpha prolifera appeared not only in the sea adjacent to Yan Cheng, but also in the sea offshore from the Yangtze River estuary. Under the influence of favorable conditions, it drifted from south to north with the area gradually increased. During the period , its maximum area of the distribution reched to 3.5 square kilometers.

Citation Download Citation

Dingfeng Yu, Zhigang Gai, Xiangfeng Kong, Hui Liu, and Enxiao Liu "Monitoring Ulva prolifera in the Yellow Sea and East China Sea derived from multi-source remote sensing images", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060535 (15 November 2017); https://doi.org/10.1117/12.2295278

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