WANG Changying, CHU Jialan, TAN Meng, SHAO Fengjing, SUI Yi, LI Shujing. An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image[J]. Acta Oceanologica Sinica, 2017, 36(11): 106-114. doi: 10.1007/s13131-017-1141-9
Citation:
WANG Changying, CHU Jialan, TAN Meng, SHAO Fengjing, SUI Yi, LI Shujing. An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image[J]. Acta Oceanologica Sinica, 2017, 36(11): 106-114. doi: 10.1007/s13131-017-1141-9
WANG Changying, CHU Jialan, TAN Meng, SHAO Fengjing, SUI Yi, LI Shujing. An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image[J]. Acta Oceanologica Sinica, 2017, 36(11): 106-114. doi: 10.1007/s13131-017-1141-9
Citation:
WANG Changying, CHU Jialan, TAN Meng, SHAO Fengjing, SUI Yi, LI Shujing. An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image[J]. Acta Oceanologica Sinica, 2017, 36(11): 106-114. doi: 10.1007/s13131-017-1141-9
School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China;Institute of Big Data Technology and Smart City of Qingdao, Qingdao 266071, China;Key laboratory of Marine Red Tide Disaster Three-dimensional Monitoring Technology and Application, East China Sea Branch, State Oceanic Administration, Shanghai 200080, China
2.
Key laboratory of Marine Red Tide Disaster Three-dimensional Monitoring Technology and Application, East China Sea Branch, State Oceanic Administration, Shanghai 200080, China;National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023, China
3.
North China Sea Data and Information Service Center, North China Sea Branch, State Oceanic Administration, Qingdao 266061, China
4.
Institute of Big Data Technology and Smart City of Qingdao, Qingdao 266071, China
Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship (y=0.723x+0.504) between detection threshold y and subtraction x (x=λnir-λred) is found from the comparing Landsat TM/ETM plus image with the field surveys. Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image. Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows (sub-images) with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class (green tide or sea water) according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.
Bao Min, Guan Weibing, Wang Zongling, et al. 2015. Features of the physical environment associated with green tide in the southwestern Yellow Sea during spring. Acta Oceanologica Sinica, 34(7):97-104
Hu Chuanmin. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environmen, 113:2118-2129
Hu Song, Yang Hong, Zhang Jianheng, et al. 2014. Small-scale early aggregation of green tide macroalgae observed on the Subei Bank, Yellow Sea. Marine Pollution Bulletin, 81:166-173
Keesing J K, Liu D Y, Fearns P, et al. 2011. Inter-and intra-annual patterns of Ulva prolifera green macroalgaes in the Yellow Sea during 2007–2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China. Marine Pollution Bulletin, 62:1169-1182
Liu D Y, Keesing J K, He P M, et al. 2013. The world's largest macroalgal bloom in the Yellow Sea, China:formation and implications. Estuar Coast Shelf Sci, 129:2-10
Lyons D A, Arvanitidis C, Blight A J, et al. 2014. Macroalgal blooms alter community structure and primary productivity in marine ecosystems. Global Change Biology, 20:2712-2724
Merceron M, Antoine V, Auby I, et al. 2007. In situ growth potenial of the subtidal part of green tide forming Ulva spp. Stocks. Since of The Total Environment, 384:293-305
Nelson T A, Haberlin K, Nelson A V, et al. 2008. Ecological and physiological controls of species composition in green macroalgal blooms. Ecology, 89:1287-1298
Smetacek W, Zingone A. 2013. Green and golden seaweed tides on the rise. Natrue, 504:84-88
Ye Naihao, Zhang Xiaowen, Mao Yuze, et al. 2011. "Green tides" are overwhelming the coastline of our blue planet:taking the world's largest example. Ecological Research, 26:477-485
Zhang Qingchun, Liu Qing, Yu Rencheng, et al. 2015. Application of a fluorescence in situ hybridization (FISH) method to study green tide in the Yellow Sea. Estuarine, Coastal and Shelf Science, 163:112-119
Zhou Mingjiang, Liu Dongyan, Donal M, et al. 2015. Introduction to the special issue on green tides in the Yellow Sea. Estuarine, Coastal and Shelf Science, 163:3-8