A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery
(2018) In International Journal of Applied Earth Observation and Geoinformation 68. p.73-91- Abstract
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.... (More)
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.
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- author
- Wang, Xiaobiao ; Xie, Shunping ; Zhang, Xueliang ; Chen, Cheng ; Guo, Hao ; Du, Jinkang and Duan, Zheng LU
- publishing date
- 2018-06-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Information extraction, Landsat OLI, Low reflectance surface, Pure pixel, Water index
- in
- International Journal of Applied Earth Observation and Geoinformation
- volume
- 68
- pages
- 19 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85063466088
- ISSN
- 1569-8432
- DOI
- 10.1016/j.jag.2018.01.018
- language
- English
- LU publication?
- no
- id
- f2c624d9-60d9-4620-aa16-ab4d528017ef
- date added to LUP
- 2019-12-22 20:14:52
- date last changed
- 2022-04-18 19:43:59
@article{f2c624d9-60d9-4620-aa16-ab4d528017ef,
abstract = {{<p>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.</p>}},
author = {{Wang, Xiaobiao and Xie, Shunping and Zhang, Xueliang and Chen, Cheng and Guo, Hao and Du, Jinkang and Duan, Zheng}},
issn = {{1569-8432}},
keywords = {{Information extraction; Landsat OLI; Low reflectance surface; Pure pixel; Water index}},
language = {{eng}},
month = {{06}},
pages = {{73--91}},
publisher = {{Elsevier}},
series = {{International Journal of Applied Earth Observation and Geoinformation}},
title = {{A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery}},
url = {{http://dx.doi.org/10.1016/j.jag.2018.01.018}},
doi = {{10.1016/j.jag.2018.01.018}},
volume = {{68}},
year = {{2018}},
}