Fractional snow cover estimation in complex alpine-forested environments using remotely sensed data and artificial neural networks

Persistent Link:
http://hdl.handle.net/10150/312503
Title:
Fractional snow cover estimation in complex alpine-forested environments using remotely sensed data and artificial neural networks
Author:
Czyzowska-Wisniewski, Elzbieta Halina
Issue Date:
2013
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
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.
Type:
text; Electronic Dissertation
Keywords:
alpine snow cover; artificial neural network; fractional snow cover; remote sensing; water resources; Arid Lands Resource Sciences; alpine environments
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Arid Lands Resource Sciences
Degree Grantor:
University of Arizona
Advisor:
Hirschboeck, Katherine K.; van Leeuwen, Willem J. D.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleFractional snow cover estimation in complex alpine-forested environments using remotely sensed data and artificial neural networksen_US
dc.creatorCzyzowska-Wisniewski, Elzbieta Halinaen_US
dc.contributor.authorCzyzowska-Wisniewski, Elzbieta Halinaen_US
dc.date.issued2013-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThere 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.en_US
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectalpine snow coveren_US
dc.subjectartificial neural networken_US
dc.subjectfractional snow coveren_US
dc.subjectremote sensingen_US
dc.subjectwater resourcesen_US
dc.subjectArid Lands Resource Sciencesen_US
dc.subjectalpine environmentsen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineArid Lands Resource Sciencesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHirschboeck, Katherine K.en_US
dc.contributor.advisorvan Leeuwen, Willem J. D.en_US
dc.contributor.committeememberHirschboeck, Katherine K.en_US
dc.contributor.committeemembervan Leeuwen, Willem J. D.en_US
dc.contributor.committeememberMarsh, Stuart E.en_US
dc.contributor.committeememberHutchinson, Charles F.en_US
dc.contributor.committeememberMeko, David M.en_US
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