Using high-resolution topography for advancing the understanding of mass and energy transfer across landscapes

dc.contributor.advisorPassalacqua, Paolaen
dc.contributor.committeeMemberMaidment, Daviden
dc.creatorKladzyk, Anna Gabrielleen
dc.date.accessioned2015-10-19T16:32:46Zen
dc.date.available2015-10-19T16:32:46Zen
dc.date.issued2015-05en
dc.date.submittedMay 2015en
dc.date.updated2015-10-19T16:32:46Zen
dc.descriptiontexten
dc.description.abstractChannel networks are a critical means of mass and energy transfer across the landscape. High resolution topographic imagery derived from lidar scans, provides new opportunities in the observation and analysis of these processes, especially as the resolution of these data is proportionate to channel and hillslope process scales. Channel feature extraction algorithms supply a method with which to analyze hydrologic and geomorphic processes; automatic, open-source frameworks such as GeoNet aim to provide a reliable platform for this task to the academic, public service, and professional communities. In this thesis, the GeoNet algorithm is tested across different types of landscapes and data resolutions. Innovative analysis methods are also assessed within the framework in order to advance the method in parallel with advancing understanding of channel processes and improving technologies. The goal of this research is to assess GeoNet performance across landscape type in terms of relief, vegetation, and anthropogenic influence and make recommendations for future development of the algorithm and the GeoNet user community. An alternate spectral analysis-based filtering method is tested, as well as curvature-based filtering of erroneously identified channel heads. Results from alternate filtering testing indicate that the nonlinear Perona-Malik filter is superior to a spectral-based filtering approach. The use of a contour curvature threshold is not wholly successful at removing spuriously identified channel heads in natural landscapes. This research also introduces the analysis of urban landscapes into the GeoNet repertoire, and examines the effects of data resolution on feature extraction therein. An urban dataset from the Austin, TX area is tested and the optimal settings for GeoNet are identified; field work at the site is used to validate the results. The recommendations resulting from this work aim to improve in the functionality and versatility of GeoNet, and enhance accessibility for the user-community.en
dc.description.departmentCivil, Architectural, and Environmental Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifierdoi:10.15781/T2NS4Den
dc.identifier.urihttp://hdl.handle.net/2152/31773en
dc.language.isoenen
dc.subjectHigh resolution topographyen
dc.subjectChannel feature extractionen
dc.titleUsing high-resolution topography for advancing the understanding of mass and energy transfer across landscapesen
dc.typeThesisen
thesis.degree.departmentCivil, Architectural, and Environmental Engineeringen
thesis.degree.disciplineEnvironmental and Water Resources Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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