| Title | An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery |
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| Author | Li, J; Wang, S |
| Source | International Journal of Remote Sensing vol. 36, no. 5, 2015 p. 1367-1384, https://doi.org/10.1080/01431161.2015.1009653  Open Access |
| Image |  |
| Year | 2015 |
| Alt Series | Earth Sciences Sector, Contribution Series 20140113 |
| Publisher | Informa UK Limited |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Province | Manitoba |
| NTS | 62G/03; 62G/04; 62G/05 |
| Area | southwest Manitoba; Killarney; Holmfield; Cartwright |
| Lat/Long WENS | -100.0000 -99.0000 49.7500 49.0000 |
| Subjects | hydrogeology; geophysics; watersheds; buried valleys; surface waters; mapping techniques; satellite imagery; remote sensing; Spiritwood buried valley; Radarsat-2 |
| Illustrations | flow charts; satellite images |
| Program | Groundwater Geoscience Aquifer Assessment &
support to mapping |
| Released | 2015 02 25 |
| Abstract | The use of synthetic aperture radar (SAR) imagery is generally considered to be an effective method for detecting surface water. Among various supervised/unsupervised classification methods, a
SAR-intensity-based histogram thresholding method is widely used to distinguish waterbodies from land. A SAR texturebased automatic thresholding method is presented in this article. The use of texture images substantially enhances the contrast
between water and land in intensity images. It also makes the method less sensitive to incidence angles than intensity-based methods. A modified Otsu thresholding algorithm is applied to selected sub-images to determine the optimal threshold value.
The sub-images were selected using k-means results to ensure a sufficient number of pixels for both water and land classes. This is critical for the Otsu algorithm being able to detect an optimal threshold for a SAR image. The method is completely
unsupervised and is suitable for large SAR image scenes. Tests of this method on a Radasat-2 image mosaicked from 8 QuadPol scenes covering the Spritiwood valley in Manitoba, Canada, show a substantial increase in land-water classification accuracy
over the commonly used SAR intensity thresholding method (kappa indices are 0.89 vs. 0.79). The method is less computationally intensive and requires less user interaction. It is therefore well suited for detecting waterbodies and monitoring their
dynamic changes from a large SAR image scene in a nearreal time environment). |
| Summary | (Plain Language Summary, not published)
Surface waters are integral parts of groundwater flow systems. Understanding and monitoring Canada's surface water is fundamental to the responsible
management of groundwater resources. For effective management, it is essential to have up-to-date information of their spatial and temporal variability. Due to the large extent of surface water in Canada, satellite remote sensing is the only
practical approach that can map surface water cost-effectively in a timely manner. Unlike optical satellite sensors limited by the weather conditions, Synthetic Aperture Radar (SAR) satellite sensors can observe the earth's surface in almost all
types of weather conditions. In fact, SAR is considered to be good at detecting surface water due to a high contrast between water and land in a SAR image. In this paper, we present a novel SAR texture-based method for automatically detecting water
bodies from Radarsat-2 imagery. The method outperforms the commonly used SAR intensity-based method on water detection. |
| GEOSCAN ID | 294824 |
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