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Comparison on the Performance of Selected Image Classification Techniques on Medium Resolution Data Towards Highland Bamboo Resource Mapping

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dc.contributor.advisor Legesse, Dagnachew (PhD)
dc.contributor.author Chernet, Tezera
dc.date.accessioned 2018-07-11T08:56:55Z
dc.date.available 2018-07-11T08:56:55Z
dc.date.issued 2008-12
dc.identifier.uri http://localhost:80/xmlui/handle/123456789/7952
dc.description.abstract This study was conducted with the objective of comparing the performance of different image classification techniques in discriminating Yushania alpina K. Shmann Lin. (highland bamboo) from other land cover classes, with the use of medium resolution images in three Woredas of the Bale highland. A subset of four Landsat 7 ETM+ images acquired on the 28th of November and 5th of February 2000, (path 167 / rows 55- 56 and path 168 / rows 55-56), covering the study area were used for the classification. The image processing software employed was ENVI 4.3. After performing the necessary pre-classification processes, Decision Tree and Maximum Likelihood classification techniques were applied on the images and the results thereof were examined using confusion matrix. Landsat 7 ETM+ image derived data sets such as tasseled cap and as well the spectral values of the different bands of the images were used as the decision rules in the Decision Tree Classifier. Analysis of the accuracy assessment had revealed that it had an overall accuracy of 66.79% and a Kappa statistic of agreement of about 0.56. The technique also showed user’s accuracy of 23.05% for bamboo class. The other classification technique evaluated was maximum likelihood, for which region of interest, based on the field data was delineated as a training guide in the classification process. The Maximum likelihood classification showed an overall accuracy of 87.75% and a Kappa value of about 0.84. User’s accuracy for bamboo forest by this technique was 40.56%. The potential highland bamboo growing areas of Ethiopia was mapped in order to provide an indication of the suitable areas for the growth and future expansion of Y. alpine, to guide the establishment of observation plots from which detailed ecological requirements of the species can further be investigated and to direct future mapping of the resource. The existing temperature, rainfall and altitudinal information were used to identify the location of these areas and it covered an estimated 7,632,788ha representing about 6.74% of the total country. Classification results showed that the existing Highland bamboo land units found in the study area cover a total of 63,407.32ha. The dominant corresponding soil types of these areas are Orthic Luvisols, Chromic Luvisols and with not less correlation with Eutric Cambisols, Eutric Fluvisols and Eutric Nitosols. Keywords: Yushania alpina, Landsat 7 ETM+, remote sensing, Bale highland, bamboo en_US
dc.language.iso en en_US
dc.publisher Addis Ababa Universty en_US
dc.subject Yushania alpina en_US
dc.subject Landsat 7 ETM+ en_US
dc.subject Remote sensing en_US
dc.subject Bale highland en_US
dc.subject Bamboo en_US
dc.title Comparison on the Performance of Selected Image Classification Techniques on Medium Resolution Data Towards Highland Bamboo Resource Mapping en_US
dc.type Thesis en_US


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