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photo of Zheng Duan on Lund webpage

Zheng Duan

Associate senior lecturer

photo of Zheng Duan on Lund webpage

A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

Author

  • Xiaobiao Wang
  • Shunping Xie
  • Xueliang Zhang
  • Cheng Chen
  • Hao Guo
  • Jinkang Du
  • Zheng Duan

Summary, in English

Surface water is vital resources for terrestrial life, while the rapid development of urbanization results in diverse changes in sizes, amounts, and quality of surface water. To accurately extract surface water from remote sensing imagery is very important for water environment conservations and water resource management. In this study, a new Multi-Band Water Index (MBWI) for Landsat 8 Operational Land Imager (OLI) images is proposed by maximizing the spectral difference between water and non-water surfaces using pure pixels. Based on the MBWI map, the K-means cluster method is applied to automatically extract surface water. The performance of MBWI is validated and compared with six widely used water indices in 29 sites of China. Results show that our proposed MBWI performs best with the highest accuracy in 26 out of the 29 test sites. Compared with other water indices, the MBWI results in lower mean water total errors by a range of 9.31%–25.99%, and higher mean overall accuracies and kappa coefficients by 0.87%–3.73% and 0.06–0.18, respectively. It is also demonstrated for MBWI in terms of robustly discriminating surface water from confused backgrounds that are usually sources of surface water extraction errors, e.g., mountainous shadows and dark built-up areas. In addition, the new index is validated to be able to mitigate the seasonal and daily influences resulting from the variations of the solar condition. MBWI holds the potential to be a useful surface water extraction technology for water resource studies and applications.

Publishing year

2018-06-01

Language

English

Pages

73-91

Publication/Series

International Journal of Applied Earth Observation and Geoinformation

Volume

68

Document type

Journal article

Publisher

Elsevier

Topic

  • Oceanography, Hydrology, Water Resources

Keywords

  • Information extraction
  • Landsat OLI
  • Low reflectance surface
  • Pure pixel
  • Water index

Status

Published

ISBN/ISSN/Other

  • ISSN: 1569-8432