Unsupervised classification of saturated areas using a time series of remotely sensed images

de Alwis Pitts, D. A., Easton, Z.M., Dahlke, H.E. , Philpot, W.D. and Steenhuis, T.S. (2007) Unsupervised classification of saturated areas using a time series of remotely sensed images. Hydrology and Earth System Sciences, 11 (5). pp. 1609-1620. ISSN 10275606

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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.

Additional Information:cited By 20 © Author(s) 2007. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Keywords:index method, Landsat, phenology, soil water, spatial distribution, temporal variation
Subjects:F Physical Sciences > F832 Remote Sensing
F Physical Sciences > F720 Hydrography
Divisions:College of Science > School of Geography
ID Code:29410
Deposited On:20 Aug 2018 14:20

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