Dissertation/Thesis Abstract

Digital soil-landscape classification for soil survey using ASTER satellite and digital elevation data in Organ Pipe Cactus National Monument, Arizona
by Nauman, Travis, M.S., The University of Arizona, 2009, 169; 1464085
Abstract (Summary)

Digital 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.

Indexing (document details)
Advisor: Rasmussen, Craig
Commitee: Guertin, Phillip D., van Leeuwen, Willem J.
School: The University of Arizona
Department: Soil, Water & Environmental Science
School Location: United States -- Arizona
Source: MAI 47/05M, Masters Abstracts International
Subjects: Physical geography, Environmental science, Remote sensing
Keywords: Digital soil map, GIS, Remote sensing, Spatial analysis, Terrain analysis
Publication Number: 1464085
ISBN: 978-1-109-12961-8
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