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Open AccessArticle

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Global Land Cover Facility, Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
4
College of Information and Electrical Engineering, China Agricultural University, No. 17 Qinghua East Road, Haidian District, Beijing 100083, China
5
Department of Ecological Remote Sensing, Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(6), 5067-5089; https://doi.org/10.3390/rs6065067
Received: 14 February 2014 / Revised: 19 May 2014 / Accepted: 20 May 2014 / Published: 30 May 2014
The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and commission errors, edge position accuracy and completeness, were calculated to assess the method’s performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed. View Full-Text
Keywords: feature extraction; lake; mixed pixels; remote sensing; river; water index feature extraction; lake; mixed pixels; remote sensing; river; water index
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Jiang, H.; Feng, M.; Zhu, Y.; Lu, N.; Huang, J.; Xiao, T. An Automated Method for Extracting Rivers and Lakes from Landsat Imagery. Remote Sens. 2014, 6, 5067-5089.

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