The difficulty of gathering information on field and coarse resolution of Landsat images
forced to use ancillary data in vegetation mapping. The aim of this study is to increase
the accuracy of species level vegetation classification incorporating environmental
variables in the Amanos region. In the first part of the study, coarse vegetation
classification is attained by using maximum likelihood method with the help of forest
management maps. Canonical Correspondence analysis is used to explore the
relationships among the environmental variables and vegetation classes. Discriminant
Analysis is used in the second part of the study in two different stages. Firstly Fisher&rsquo / s
linear equations for each of the previously defined nine groups calculated and the pixels
are included in one of these groups by looking at the probability of that pixel being in
that group. In the second stage Distance raster value of maximum likelihood
classification is used. Distance raster pixels having a value less than one is accepted as
misclassified and replaced with a value of first stage result of that pixel. As a result of
this study 19.6 % increase in the overall accuracy is obtained by using the relationships
between environmental variables and vegetation distribution.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12605683/index.pdf |
| Date | 01 December 2004 |
| Creators | Domac, Aysegul |
| Contributors | Suzen, Lutfi Mehmet |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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