Advances in the study of Ulvapolifera monitoring with remote sensing
YE Na1, JIA Jianjun2, TIAN Jing3, SU Hongbo3, LUO Weimin1, ZHANG Feng1, XIAO Kang1
1. East China Sea Data & Information Service of State Oceanic Administration, Shanghai 200137, China;
2. Headquarters of China Marine Surveillance, Beijing 100860, China;
3. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
The massive bloom of the green macroalgae Ulvaprolifera is called "green tide". The "green tide" event frequently happens in the world and is known as an oceanic disaster due to its bad effects on the marine ecological environment, coastal scene, seashore tourism and water sports. Remote sensing has the advantages over the conventional methods in oceanic monitoring because of its great capabilities of large-area, multi-resolution, multi-spectrum, quick and dynamic observations. Remote sensing has become a necessary method in the study of the origin, spatial pattern, evolution, size and movement of "green tide". At present, 3 main approaches detecting Ulvaprolifera have been developed, which are single band threshold image segmentation method, multi-band ratio method and radiation transmission model method. The multi-band ratio method is most widely used, which includes two-band ratio algorithm, normalized differential vegetation index, floating algae index and normalized difference algae index. The method of radiation transmission model is mainly used to monitor Ulvaprolifera suspended in water, and its study is just in the initial stage. Further researches on distinguishing the difference between the spectrum of Ulvaprolifera and other type of floating algae, monitoring Ulvaprolifera suspended in water, solving the contradiction between the deficiency of monitoring capability and the needs of users are the directions of Ulvaprolifera monitoring in the future.
叶娜, 贾建军, 田静, 苏红波, 雒伟民, 张峰, 肖康. 浒苔遥感监测方法的研究进展[J]. 国土资源遥感, 2013, 25(1): 7-12.
YE Na, JIA Jianjun, TIAN Jing, SU Hongbo, LUO Weimin, ZHANG Feng, XIAO Kang. Advances in the study of Ulvapolifera monitoring with remote sensing. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 7-12.
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2013, Vol. 25 
