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\hypersetup{pdftitle={Detection of Land Cover Changes Using Multi"=Temporal Satellite Imagery},pdfauthor={Wafa Nori},pdfsubject={Tropentag 2016: Abstract},pdfkeywords={Change detection, remote sensing},pdfpagemode=None,colorlinks=true}
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\parbox[b]{13.4cm}{\centering \large{\textbf{Tropentag, September 18-21, 2016, Vienna, Austria}}\\[1ex] \Large{``Solidarity in a competing world ---\\fair use of resources''\\[2ex]}}
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\Large{\textbf{Detection of Land Cover Changes Using Multi"=Temporal Satellite Imagery\footnote{\textbf{Contact Address:} Wafa Nori, University of Kordofan, Forest and Range Science, Algama Street, 160~Elobied, Sudan, \mbox{e-mail}: \email{waffanori@gmail.com}}\\[0.8ex]}}
\normalsize{\textsc{Wafa Nori}}
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\begin{itemize*}
\item[]{\small{\textit{University of Kordofan, Forest and Range Science, Sudan}}}
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\index[author]{Nori, Wafa}
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\textbf{Abstract}
\begin{abstract}
\normalsize{
Multi"=temporal Landsat ETM+ and Aster data acquired on early dry season dates in 2003 and 2006 have been used for classification of land cover. Processing and classification of data for late rainy season which will extend the temporal series is currently underway. For temporal analysis, image calibration was necessary to reduce or eliminate differences due to atmospheric or sensor variations between the two dates. Absolute calibration using atmospheric corrections and sensor drift calibration was conducted by the software Geomatica. In order to enhance the image features a PCA fusion technique were applied by using the panchromatic and multispectral bands of Landsat ETM and Aster data, as a following step supervised maximum likelihood classification of PCA 2003 and 2006 scenes was conducted. In this study training data were selected in easily identifiable areas of the following four classes: grassland, closed forest, open forest and bareland. Following classification of imagery from the individual years, a post"=classification approach comparing classification maps of 2003 and 2006 was applied. An advantage of the approach is that it provides ``from"=to'' change information. As part of our analysis we have compared area estimates from the Landsat classifications with aster classification.



Results of supervised classification of ETM and Aster imagery have been evaluated for the study area. For Landsat ETM data of the year 2003, the Kappa coefficient indicates substantial agreement with values of 88.8 percent and the overall accuracy is 91.9 percent. For Aster data of the year 2006, the Kappa coefficient indicates substantial agreement by a value of 89.4 percent and overall accuracy with 92.1 percent. A post"=classification result indicated that, closed forest and open forest are the most important change. From 2003 to 2006, closed forest increased by \mbox{8.9\,\%}, or 9825 hectares and open forest increased by \mbox{9.1\,\%} or 10074, while grassland decreased by \mbox{19.2\,\%} or 21165 hectares and bareland showed very slight changes.



}

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\noindent \textbf{Keywords:} Change detection, remote sensing
\index[key]{Change detection}
\index[key]{Remote sensing}
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