The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Development of a global ~90m water body map using multi-temporal Landsat images
Open accessAbstract
Abstract This paper describes the development of a Global 3 arc-second Water Body Map (G3WBM), using an automated algorithm to process multi-temporal Landsat images from the Global Land Survey (GLS) database. We used 33,890 scenes from 4 GLS epochs in order to delineate a seamless water body map, without cloud and ice/snow gaps. Permanent water bodies were distinguished from temporal water-covered areas by calculating the frequency of water body existence from overlapping, multi-temporal, Landsat scenes. By analyzing the frequency of water body existence at 3 arc-second resolution, the G3WBM separates river channels and floodplains more clearly than previous studies. This suggests that the use of multi-temporal images is as important as analysis at a higher resolution for global water body mapping. The global totals of delineated permanent water body area and temporal water-covered area are 3.25 and 0.49 million km2 respectively, which highlights the importance of river-floodplain separation using multi-temporal images. The accuracy of the water body classification was validated in Hokkaido (Japan) and in the contiguous United States using an existing water body databases. There was almost no commission error, and about 70% of lakes > 1 km2 shows relative water area error
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Cites
Global hydrological cycles and world water resources
Oki, T., & Kanae, S. (2006) · Science · doi:10.1126/science.1128845 · Cited by 4,392
A global inventory of lakes based on high-resolution satellite imagery
Verpoorter, C., Kutser, T., Seekell, D. A., & Tranvik, L. J. (2014) · Geophysical Research Letters · doi:10.1002/2014gl060641 · Cited by 1,504
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A High-Accuracy Map of Global Terrain Elevations
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., & Bates, P. D. (2017) · Geophysical Research Letters · doi:10.1002/2017gl072874 · Cited by 1,326
Self-citationSurface Water Mapping by Deep Learning
Isikdogan, F., Bovik, A. C., & Passalacqua, P. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2735443 · Cited by 292
An approach for global monitoring of surface water extent variations in reservoirs using MODIS data
Khandelwal, A., Karpatne, A., Marlier, M. E., Kim, J., Lettenmaier, D. P., & Kumar, V. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.05.039 · Cited by 197
Darrah, S. E., Shennan-Farpón, Y., Loh, J., Davidson, N. C., Finlayson, C. M., Gardner, R. C., & Walpole, M. J. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2018.12.032 · Cited by 196
Metadata
- Authors
- Yamazaki, D., Trigg, M. A., & Ikeshima, D.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 171
- Pages
- 337-351
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2192883182
- Cited by
- 364
- References
- 2
- FWCI
- 32.66
- Type
- journal article
- Language
- en
- Open access
- green
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