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| Titel |
Unsupervised classification of saturated areas using a time series of remotely sensed images |
| VerfasserIn |
D. A. Alwis, Z. M. Easton, H. E. Dahlke, W. D. Philpot, T. S. Steenhuis |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 11, no. 5 ; Nr. 11, no. 5 (2007-09-27), S.1609-1620 |
| Datensatznummer |
250009471
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| Publikation (Nr.) |
 copernicus.org/hess-11-1609-2007.pdf |
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| Zusammenfassung |
| The spatial distribution of saturated areas is an important consideration in
numerous applications, such as water resource planning or siting of
management practices. However, in humid well vegetated climates where runoff
is produced by saturation excess processes on hydrologically active areas
(HAA) the delineation of these areas can be difficult and time consuming. A
technique that can simply and reliably predict these areas would be a
powerful tool for scientists and watershed managers tasked with implementing
practices to improve water quality. Remotely sensed data is a source of
spatial information and could be used to identify HAAs. This study describes
a methodology to determine the spatial variability of saturated areas using
a temporal sequence of remotely sensed images. The Normalized Difference
Water Index (NDWI) was derived from medium resolution Landsat 7 ETM+ imagery
collected over seven months in the Town Brook watershed in the Catskill
Mountains of New York State and used to characterize the areas susceptible
to saturation. We found that within a single land cover, saturated areas
were characterized by the soil surface water content when the vegetation was
dormant and leaf water content of the vegetation during the growing season.
The resulting HAA map agreed well with both observed and spatially
distributed computer simulated saturated areas (accuracies from 49 to
79%). This methodology shows that remote sensing can be used to capture
temporal variations in vegetation phenology as well as spatial/temporal
variation in surface water content, and appears promising for delineating
saturated areas in the landscape. |
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