Dissertation/Thesis Abstract

Fractional snow cover estimation in complex alpineforested environments using remotely sensed data and artificial neural networks
by Czyzowska-Wisniewski, Elzbieta Halina Magdalena, Ph.D., The University of Arizona, 2013, 258; 3609512
Abstract (Summary)

There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management.

To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ~ 0.09; mean error ~ 0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA.

Indexing (document details)
Advisor: Hirschboeck, Katherine K., van Leeuwen, Willem J. D.
Commitee: Hirschboeck, Katherine K., Hutchinson, Charles F., Marsh, Stuart E., Meko, David M., van Leeuwen, Willem J. D.
School: The University of Arizona
Department: Arid Lands Resource Sciences
School Location: United States -- Arizona
Source: DAI-B 75/05(E), Dissertation Abstracts International
Subjects: Hydrologic sciences, Water Resource Management, Remote sensing
Keywords: Alpine environments, Alpine snow cover, Artificial neural network, Fractional snow cover, Remote sensing, Water resources
Publication Number: 3609512
ISBN: 9781303684135
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