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Evaluation of hierarchical segmentation for natural vegetation: a case study of the Tehachapi Mountains, California


Abstract Two critical limitations for hyperspatial imagery are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are the solution, more data sources and large amounts of testing at high costs are required. In this study, I used tree density segmentation as the key element of a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects. A two-step procedure, which involved environmental stratifications and the random walker algorithm, was used for tree density segmentation. I determined whether variation in tone and texture could be reduced within environmental strata, and whether tree density segm... (more)
Created Date 2013
Contributor Liau, Yan-Ting (Author) / Franklin, Janet (Advisor) / Turner, Billie (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Subject Geography / environmental stratification / image segmentation / natural vegetation / species identification
Type Masters Thesis
Extent 45 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.A. Geography 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis