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Universiti Putra (2013)

Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment

Al-Doski, Jwan M. Mohammed

Titre : Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment

Auteur : Al-Doski, Jwan M. Mohammed

Université de soutenance : Universiti Putra

Grade : Master of Science (MS) 2013

Résumé
The technique of remote sensing satellite imaging has played a significant role in facilitating the study of land use/land cover changes (LULC). This is because the information that can be extracted from images constitutes a fundamental key in many diverse applications such as Environment, Planning and Monitoring programs and others. LULC changes are mainly the result of human intervention and natural phenomena such as population growth, urbanization, wars and other factors. During the 1980-1988 Iraq-Iran war, many cities and villages in the north of Iraq were shelled several times with chemical weapons that caused many changes in land covers. Among the cities seriously affected by these chemical weapons is Halabja City (the study area for this research), which was shelled on 16 March 1988, leaving approximately 5,000 people dead and 7,000 injured with long-term damage to their health. In this study, vegetation indices, tasseled cap transformation, hybrid classification as a combination of k-means and support vector machine algorithms,and post-classification comparison were respectively implemented to detect and assess LULC in Halabja. Two Landsat 5 (Thematic Mapper - TM) images obtained in 1986, 1990 with one Landsat 7 (Enhanced Thematic Mapper Plus - ETM+) image acquired in 2000 were used. All images were geometrically corrected and projected to UTM, Datum WGS_84 and Zone 38N using automatic image to image registration with polynomial transformation equations and a nearest neighbor re-sampling algorithm. The root mean square (RMS) error was less than 0.5 pixels. Subsequently,all images were atmospherically corrected by applying dark object subtraction and sub-setted to (1400) samples, (999) lines. The hybrid classifier with the aid of visual interpretation tools, knowledge-based assignment and other supplementary data like Google earth images and vegetation indices were run on subsets to classify images into five thematic classes based on the NLCD 92 classification system scheme (Water Bodies ; Shrub Land ; Cultivated/Planted Area ; Low-Intensity Urban Area ; and Bare Land). To assess classification accuracy, the classified images were randomly sampled to produce confusion matrix which provided LULCC maps with an average overall accuracy of 95% and 0.94 Kappa statistic that tendered them deal for further qualitative and quantitative analysis of land cover changes through a postclassification

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