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dc.contributor.advisorRasmussen, Craigen_US
dc.contributor.authorNauman, Travis William
dc.creatorNauman, Travis Williamen_US
dc.date.accessioned2011-12-05T14:18:31Z
dc.date.available2011-12-05T14:18:31Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/10150/193446
dc.description.abstractDigital soil mapping supervised and unsupervised classification methods were evaluated to aide soil survey of unmapped areas in the western United States. Supervised classification of landscape into mountains and basins preceded unsupervised classification of data chosen by iterative data reduction. Principal component data reduction, ISODATA classification, Linear combination of principal components, Zonal averaging of linear combination by ISODATA class, Segmentation of the image into polygons, and Attribution of polygons by majority ISODATA class (PILZSA process) comprised steps isolating unique soil-landscape units. Input data included ASTER satellite imagery and USGS 30-m elevation layers for environmental proxy variables representing soil forming factors. Results indicate that PILZSA captured general soil patterns when compared to an existing soil survey while also detecting fluvial soils sourced from different lithologies and unique mountain areas not delineated by the original survey. PILZSA demonstrates potential for soil pre-mapping, and sampling design efforts for soil survey and survey updates.
dc.language.isoENen_US
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.subjectDigital Soil Mapen_US
dc.subjectGISen_US
dc.subjectRemote Sensingen_US
dc.subjectSpatial Analysisen_US
dc.subjectTerrain Analysisen_US
dc.titleDigital Soil-Landscape Classification for Soil Survey using ASTER Satellite and Digital Elevation Data in Organ Pipe Cactus National Monument, Arizonaen_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
dc.contributor.chairRasmussen, Craigen_US
dc.identifier.oclc659752084en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelmastersen_US
dc.contributor.committeemembervan Leeuwen, Willem J.en_US
dc.contributor.committeememberGuertin, Phillip D.en_US
dc.identifier.proquest10459en_US
thesis.degree.disciplineSoil, Water & Environmental Scienceen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.nameM.S.en_US
refterms.dateFOA2018-06-23T04:47:34Z
html.description.abstractDigital soil mapping supervised and unsupervised classification methods were evaluated to aide soil survey of unmapped areas in the western United States. Supervised classification of landscape into mountains and basins preceded unsupervised classification of data chosen by iterative data reduction. Principal component data reduction, ISODATA classification, Linear combination of principal components, Zonal averaging of linear combination by ISODATA class, Segmentation of the image into polygons, and Attribution of polygons by majority ISODATA class (PILZSA process) comprised steps isolating unique soil-landscape units. Input data included ASTER satellite imagery and USGS 30-m elevation layers for environmental proxy variables representing soil forming factors. Results indicate that PILZSA captured general soil patterns when compared to an existing soil survey while also detecting fluvial soils sourced from different lithologies and unique mountain areas not delineated by the original survey. PILZSA demonstrates potential for soil pre-mapping, and sampling design efforts for soil survey and survey updates.


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