Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery

Abstract

This article first examines three existing methods of delineating open water features, i.e. the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI) and a method combining the near-infrared (NIR) band and the maximum likelihood classification. We then propose two new methods for the fast extraction of water features in remotely sensed imagery. Our first method is a pixel-based procedure that utilizes indices and band values. Based on their characteristic spectral reflectance curves, waterbodies are grouped into three types – clear, green and turbid. We found that the MNDWI is best suited for identifying clear water. Green water has its maximum reflectance in Landsat Thematic Mapper (TM) band 4 (NIR band), whereas turbid water has its maximum reflectance in TM band 5 (mid-infrared band). Our second method integrates our pixel-based classification with object-based image segmentation. Two Landsat scenes in Shaanxi Province, China, were used as the primary data source. Digital elevation models (DEMs) and their derived slope maps were used as ancillary information. To evaluate the performance of the proposed methods, extraction results of the three existing methods and our two new methods were compared and assessed. A manual interpretation was made and used as reference data. Results suggest that our methods, which consider the diversity of waterbodies, achieved better accuracy. Our pixel-based method achieved a producer's accuracy of 92%, user's accuracy of 90% and kappa statistics of 0.91. Our integrated method produced a higher producer's accuracy (95%), but a lower user's accuracy (72%) and kappa statistics (0.72), compared with the pixel-based method. The advantages and limitations of the proposed methods are discussed.

Acknowledgements

This research was supported by the following grants: the National High Technology Research and Development Programme of China (No. 2009AA12Z1462 and No. 2008AA121702) and State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, the Chinese Academy of Sciences (No. OFSLRSS201015). The authors thank the technology research group of the National High Technology Research and Development Programme of China for data collection and image preprocessing. They also thank Professor Arthur Cracknell, Co-Editor-in-Chief of the International Journal of Remote Sensing, and two anonymous reviewers for their very helpful suggestions and comments on an earlier draft of this article.

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