Advisor

Jiunn-Der Duh

Date of Award

Fall 12-15-2015

Document Type

Thesis

Degree Name

Master of Arts (M.A.) in Geography

Department

Geography

Physical Description

1 online resource (vii, 118 pages)

Subjects

Plants -- California -- Owens River -- Remote sensing, Landsat satellites, Remote-sensing images, Riparian plants -- Monitoring -- California -- Owens River

DOI

10.15760/etd.2631

Abstract

Rehabilitating river corridors to restore valuable riparian habitat consumes significant resources from both governments and private companies. Given these considerable expenditures, it is important to monitor the progress of such projects. This study evaluated the utility of using Landsat Thematic Mapper remotely-sensed data from 2002 and 2009 to monitor vegetation change induced by instream flow restoration to the Lower Owens River in central California. This study compared the results of an unsupervised classification with an NDVI threshold classification to appraise the resources required and effectiveness of each analysis method. The results were inspected by creating standard remote sensing accuracy error matrices and by correlating landscape pattern metrics with bird indicator species. Both sets of classified maps show a noticeable increase in riparian vegetation in the study area following flow restoration in 2006, indicating an improvement of the quality of bird habitat. The study concluded that analyzing vegetation change using the unsupervised classification technique required more effort, expert knowledge, and supplementary data than using the NDVI threshold method. If these prerequisites are met, the output from the unsupervised classification process produces a more precise map of land cover change than the NDVI threshold method. However, if an analyst is lacking either resources or ground verification data, the NDVI threshold technique is capable of providing a generalized, but still valid evaluation of vegetation change. This conclusion is supported by higher correlations between indicator bird species under the unsupervised classification method than were found with the NDVI threshold method.

Persistent Identifier

http://archives.pdx.edu/ds/psu/16435

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