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Study on the applicability of multi-source high-resolution satellite images for monitoring black and odorous water body

  • role： First author第一作者
  • Affiliation：

    School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China

  • Email：1059134050@qq.com
  • Introduction：王茹，1995年生，女，硕士研究生，研究方向为水环境遥感。E-mail：1059134050@qq.com
WANG Ru1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Introduction：申茜，1981年生，女，副研究员，研究方向为水环境遥感与黑臭水体遥感、内陆水体水质参数遥感反演。E-mail：shenqian@aircas.ac.cn
SHEN Qian2*,  
  • Affiliation：

    School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China

PENG Hongchun1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

YAO Yue2,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Junsheng2,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

WANG Mingxiu2,  
  • Affiliation：

    School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China

SHI Jiarui1,  
  • Affiliation：

    Faculty of Geomatics, East China University of Technology, Nanchang 330105, China

XU Wenting3

Resümee

The width of high-resolution satellite is generally very small. Affected by cloud and rain and orbital return visit cycle, the coverage capacity of a single satellite is limited in a short period of time. Therefore, A single high-resolution satellite is often unable to meet the needs of black and odorous water monitoring in a certain period of time, and multi-source satellites are needed to monitor black and odorous water. In order to analyze the applicability of multi-source high-resolution image to the remote sensing monitoring of black and odorous water, based on the water remote sensing reflectance data measured by the surface object spectrometer, the equivalent calculation was carried out with GeoEye-1, WorldView-2,DMC3, SuperView-1 (SV1) and GF-PMS series (GF-1/1B/1C/1D, GF-2, GF-6) sensor bands.Take the multi-source sensor remote sensing images as the research object. First compared the GeoEye-1, WorldView-2, DMC3, SuperView (SV1) and GF-PMS series (GF-1/1B/1C/1D, GF-2, GF-6) image spatial resolution, spectral response function, and band Settings; then, based on the BOI (Black and Odorous Water Index) recognition model, the applicability of multi-source sensor image monitoring is analyzed with the same threshold value, and a new model is proposed for DMC3 which is not suitable for BOI model, and the high-quality multi-source images are selected and applied; Finally, some suggestions are put forward for cooperative monitoring of black and smelly water with multi-source sensor remote sensing image.The research results show that: (1) It is found that GeoEye-1, WorldView-2, SuperView-1 and GF-1/1B/1C/1D/2/6 images can use the same threshold for black and odorous water Monitoring, with good identification accuracy; The normalized differential water body index (NDWI) for DMC3 can effectively identify the general water body and the black and smelly water body. (2) High quality multi-source images were selected with the threshold of BOI=0.05 and NDWI=0.55 for the application of black and smelly water monitoring. It was found that the collaboration of multi-source remote sensing images could provide continuous supervision for river water quality monitoring. (3) In the process of black smelly water monitoring, comprehensive consider price and spatial resolution image, when the river width in 2—10 meters, select GF-2, SV1 or DMC3 image as a conventional remote sensing image, GeoEye-1, WorldView-2 images as a supplement; When most of the river width is more than 10 meters, GF-1 or GF-6 images are selected as conventional remote sensing images, and the supplementary data sources are GF-2, SV1, DMC3, GF-1B/1C/1D, Geoeye-1, and WorldView-2 images, respectively.

Schlüsselwort

multi-source image;black-odor water monitoring;applicability;BOI;threshold

References

  1. 1.
    Bai H W, Qiu Y Z, Li Q J, Zhang T and Yang L Q. 2019. Discussion on water environment management mechanism based on river chief system in Beijing. Beijing Water, (2): 39-45
  2. 2.
    Bai Y, He X Q, Pan D L, Zhu Q K and Gong F. 2009. The black water around the Changjiang (Yangtze) Estuary in the spring of 2003. Acta Oceanologica Sinica, 28(4): 23-31
  3. 3.
    Black and smelly water treatment project for the Qijicun river and Xihe roadside ditch [EB/OL]. (2017-04-16)[2020-08-20].
  4. 4.
    China Centre for Resource Satellite Data and Application. 2016. Calibration[EB/OL]. (2016-10-10)[2020-08-20].
  5. 5.
    Guo L F, Gao X H, Kang J and Meng X Q. 2009. Application of the pseudo-invariant feature in normalization process of the remote sensing images. Remote Sensing Technology and Application, 24(5): 588-595
  6. 6.
    Hu M, Zhang N, Wang L J and Peng Y Y. 2017. Remote sensing monitoring of black smelly water controlling by multi-source data. Environment and Development, 29(9): 159-161
  7. 7.
    Huang Z Q and Zheng J C. Extraction of black and odorous water based on aerial Hyperspectral CASI image//Proceedings of IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE
  8. 8.
    Jin H X and Pan J. 2017. Urban black-odor water body remote sensing monitoring based on GF-2 satellite data fusion. Scientific and Technological Management of Land and Resources, 34(04): 107-117
  9. 9.
    Kutser T, Paavel B, Verpoorter C, Ligi M, Soomets T, Toming K and Casal G. 2016. Remote sensing of black lakes and using 810 nm reflectance peak for retrieving water quality parameters of optically complex waters. Remote Sensing, 8(6): 497
  10. 10.
    Li J Q, Li J G, Zhu L, Shen Q, Dai H Y and Zhu Y F. 2019. Remote sensing identification and validation of urban black and odorous water in Taiyuan city. Journal of Remote Sensing, 23(4): 773-784
  11. 11.
    Li X W, Niu Z C, Jiang S and Jin Y. 2012. Remote sensing monitoring of black color water blooms in Lake Taihu based on HT satellite CCD data. Environmental Monitoring and Forewarning, 4(3): 1-9
  12. 12.
    Liu F, Chen T, He J J, Wen Q, Qiao Y X, Wu W B and Wang Z Y. 2017. The research on extraction method of black-odor water body based on Triplesat constellation remote sensing data//Proceedings of the 38th Asian Conference on Remote Sensing (ACRS 2017). New Delhi: [s.n.]
  13. 13.
    Long T F, Jiao W L, He G J and Zhang Z M. 2016. A fast and reliable matching method for automated Georeferencing of remotely-sensed imagery. Remote Sensing, 8(1): 56
  14. 14.
    McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432
  15. 15.
    Ministry of Housing and Urban-Rural Development of the People’s Republic of China and Ministry of Environmental Protection of the People's Republic of China. 2015. Urban blackodor water remediation work guide[EB/OL]. [2020-08-20].
  16. 16.
    Mobley C D. 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38(36): 7442-7455
  17. 17.
    Nichol J E. 1993. Remote sensing of tropical blackwater rivers: a method for environmental water quality analysis. Applied Geography, 13(2): 153-168
  18. 18.
    Pixel-Knife GF Satellite Processing Software. 2018. Image sharpening[EB/OL]. (2018-03-15)[2020-08-20].
  19. 19.
    Shen Q, Yao Y, Li J S, Zhang F F, Wang S L, Wu Y H, Ye H P and Zhang B. 2019. A CIE color purity algorithm to detect black and odorous water in urban rivers using high-resolution Multispectral Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 57(9): 6577-6590
  20. 20.
    Shen Q, Zhu L and Cao H Y. 2017. Remote sensing monitoring and screening for urban black and odorous water body: a review. Chinese Journal of Applied Ecology, 28(10): 3433-3439
  21. 21.
    Sun W W, Yang G, Chen C, Chang M H, Huang K, Meng X Z and Liu L Y. 2020. Development status and literature analysis of China’s earth observation remote sensing satellites. Journal of Remote Sensing, 24(5): 479-510
  22. 22.
    Tang J W, Tian G L, Wang X Y, Wang X M and Song Q J. 2004. The methods of water spectra measurement and analysis I: above-water method. Journal of Remote Sensing, 8(1): 37-44
  23. 23.
    Wen S, Wang Q, Li Y M, Zhu L, Lü H, Lei S H, Ding X L and Miao S. 2018. Remote sensing identification of urban black-odor water bodies based on high-resolution images: a case study in Nanjing. Environmental Science, 39(1): 57-67
  24. 24.
    Yao H M, Lu Y N and Gong Z Q. 2019. Remote sensing identification of urban black and odorous water body based on PlanetScope images: A case study in Qinzhou, Guangxi. Environmental Engineering, 37(10):35-43
  25. 25.
    Yao Y, Shen Q, Zhu L, Gao H J, Cao H Y, Han H, Sun J G and Li J S. 2019. Remote sensing identification of urban black-odor water bodies in Shenyang city based on GF-2 image. Journal of Remote Sensing, 23(2): 230-242
  26. 26.
    Zhao J, Hu C M, Lapointe B, Melo N, Johns E M and Smith R H. 2013. Satellite-observed black water events off southwest Florida: implications for coral reef health in the Florida keys national marine sanctuary. Remote Sensing, 5(1): 415-431
  27. 27.
    Zhang X, Lai J B, Li J G,Wang L,Zhu L and Chen Y J. 2019. Remote sensing recognition of black-odor waterbodies in Shenzhen city based on GF-1 satellite. Science Technology and Engineering, 19(04): 268-274

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