19 October 2012 An object-based method for mapping ephemeral river areas from WorldView-2 satellite data
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Abstract
Continuous monitoring of river basins has become a significant requirement of our times. Due to increasing water scarcity and unprecedented flood calamities, assessing existing water resources and gathering timely information on water increase are nowadays essential to develop suitable strategies in water resources management. Hydrological models are being studied to increase hydrological process understanding and to support decision making in this field. River basin management models typically operate on wide territories and, given the complexity of most river basins, they are based on semi-empirical lumped parameterizations of hydrological processes. To overcome the uncertainties inherent in such models and achieve acceptable model performance, calibration techniques are indispensable. Remote sensing and satellite-based data with high temporal resolution have the potential to fill such critical information gaps. With its nine spectral bands and very high resolutions (spectral and radiometric) WorldView-2 satellite sensor (WV-2) can provide new insights in the on-going debate comparing object-oriented and spectral-based classifications for the highest accuracy. This paper proposes an efficient object-based method for land cover mapping from Worldview-2 imagery in order to assess its potentiality in acquiring detailed basic information on an ephemeral river area (Lama di Castellaneta, Taranto, Italy), to support further studies in the field of hydrological processes modeling. The approach suggested was evaluated by estimating classification accuracy.
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B. Figorito, B. Figorito, E. Tarantino, E. Tarantino, G. Balacco, G. Balacco, U. Fratino, U. Fratino, } "An object-based method for mapping ephemeral river areas from WorldView-2 satellite data", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85310B (19 October 2012); doi: 10.1117/12.974689; https://doi.org/10.1117/12.974689
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