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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (4) : 130-136     DOI: 10.6046/gtzyyg.2019351
A study on water information extraction method of cyanobacteria lake based on Landsat8
WANG Lin(), XIE Hongbo, WEN Guangchao(), YANG Yunhang
Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo 454000, China
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Since the 21st century, the outbreak of cyanobacteria in the Taihu Lake has seriously affected the development and utilization of local water resources. Based on Landsat8 imagery, this paper analyzes the spectral reflection characteristics of non-cyanobacteria water and cyanobacteria water. Cyanobacteria water shows strong reflectance characteristics in the near-infrared band, but the reflectance characteristics in the blue, green, red and shortwave-infrared bands are the same as those in non-cyanobacteria water. On such a basis, a method for extracting cyanobacteria water information, i.e., double infrared band water index (DIBWI), is proposed. On the basis of the Landsat8 imageries of 2014 and 2017 in Taihu Lake area, the comparison and analysis were made with the results of normalized difference water index (NDWI), modified normalized difference water index (MNDWI), new water index (NWI), multi-band water index (MBWI) and water index 2015 (WI2015), and the data of 2013, 2016 and 2018 were used for verification. The results show that DIBWI can extract the cyanobacteria water information, effectively eliminate the influence of cyanobacteria and better inhibit the background features. The overall accuracy is above 98%, and the Kappa coefficient is more than 0.95, which can provide technical support for the protection and reasonable development and utilization of water resources in Taihu Lake area.

Keywords Landsat8      cyanobacteria      water information extraction      DIBWI     
:  TP79  
Corresponding Authors: WEN Guangchao     E-mail:;
Issue Date: 23 December 2020
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Hongbo XIE
Guangchao WEN
Yunhang YANG
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Lin WANG,Hongbo XIE,Guangchao WEN, et al. A study on water information extraction method of cyanobacteria lake based on Landsat8[J]. Remote Sensing for Land & Resources, 2020, 32(4): 130-136.
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Fig.1  Study area
水体指数 参考文献 公式 理论阈值
NDWI McFeeters[10] (ρB3-ρB5)/(ρB3+ρB5) 0
MNDWI 徐涵秋[11] (ρB3-ρB6)/(ρB3+ρB6) 0
NWI 丁凤[12] [ρB2-(ρB5+ρB6+ρB7)]/[ρB2+(ρB5+ρB6+ρB7)] 0
WI2015 Fisher等[13] 1.720 4+171ρB3+3ρB4-
MBWI Wang等[14] 2ρB3-ρB4-ρB5-ρB6-ρB7 0
Tab.1  Typical water indexes
Fig.2  Average reflectance of surface features in Taihu Lake area
Fig.3  Modified surface feature spectrum
Fig.4  Water index values of various features under different coefficients a
Fig.5  Water extraction results based on different methods on October 26,2014
Fig.6  Water extraction results based on different methods on May 27,2017
Fig.7  Consistency test of extraction results based on different methods
时相 指数 总体精
20141026 DIBWI 98.83 0.976 6 0.58 0
MNDWI 95.17 0.903 4 8.79 0
NDWI 73.12 0.462 4 10.72 47.44
NWI 73.58 0.471 7 1.24 52.56
MBWI 76.25 0.525 1 1.04 46.94
WI2015 89.55 0.791 0 6.17 15.33
20170527 DIBWI 99.37 0.985 4 0.25 0
MNDWI 95.52 0.892 6 6.17 0
NDWI 83.32 0.645 0 5.81 19.50
NWI 58.45 0.289 1 0.92 60.83
MBWI 85.43 0.700 1 0.25 21.17
WI2015 94.05 0.862 4 4.70 3.98
Tab.2  Accuracy evaluation results
时相 指数 总体精度/% Kappa系数
20131210 DIBWI 98.52 0.970 5
MNDWI 92.21 0.844 2
20160621 DIBWI 98.38 0.967 6
MNDWI 95.12 0.902 4
20180428 DIBWI 98.06 0.960 5
MNDWI 95.60 0.909 9
Tab.3  Validation
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