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Landforms identification using neural network-self organizing map and SRTM data | ||
| Desert | ||
| مقاله 4، دوره 16، شماره 2، اسفند 2011، صفحه 109-120 اصل مقاله (3.04 M) | ||
| شناسه دیجیتال (DOI): 10.22059/jdesert.2012.24742 | ||
| نویسندگان | ||
| A. H. Ehsani؛ A Malekian | ||
| Assistant Professor, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic features is a challenging task and could provide useful information for landscape studies at different scales. In this study the 3 arc second SRTM digital elevation model was projected on a UTM grid with 90 meter spacing for a mountainous terrain at the Polish - Ukrainian border. Terrain parameters (morphometric parameters) such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 corresponding to 450 meters on the ground. These morphometric parameters are strongly related to topographic features and geomorphological processes. Such data allow us to enumerate topographic features in a way meaningful for landscape analysis. Kohonen Self Organizing Map (SOM) as an unsupervised neural network algorithm is used for classification of these morphometric parameters into 10 classes representing landforms elements such as ridge, channel, crest line, planar and valley bottom. These classes were analyzed and interpreted based on spectral signature, feature space, and 3D presentations of the area. Texture contents were enhanced by separating the 10 classes into individual maps and applying occurrence filters with 9×9 window to each map. This procedure resulted in 10 new inputs to the SOM. Again SOM was trained and a map with four dominant landforms, mountains with steep slopes, plane areas with gentle slopes, dissected ridges and lower valleys with moderate to very steep slopes and main valleys with gentle to moderate slopes was produced. Both landform maps were evaluated by superimposing contour lines. Results showed that Self Organizing Map is a very promising and efficient tool for land form identification. There is a very good agreement between identified landforms and contour lines. This new procedure is encouraging and offers new possibilities in the study of both type of terrain features, general landforms and landform elements. | ||
| کلیدواژه‌ها | ||
| landforms؛ Morphometric features؛ Neural Network؛ Self Organizing Map؛ SRTM | ||
| عنوان مقاله [English] | ||
| Landforms identification using neural network-self organizing map and SRTM data | ||
| نویسندگان [English] | ||
| A. H. Ehsani؛ A Malekian | ||
| چکیده [English] | ||
| During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic features is a challenging task and could provide useful information for landscape studies at different scales. In this study the 3 arc second SRTM digital elevation model was projected on a UTM grid with 90 meter spacing for a mountainous terrain at the Polish - Ukrainian border. Terrain parameters (morphometric parameters) such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 corresponding to 450 meters on the ground. These morphometric parameters are strongly related to topographic features and geomorphological processes. Such data allow us to enumerate topographic features in a way meaningful for landscape analysis. Kohonen Self Organizing Map (SOM) as an unsupervised neural network algorithm is used for classification of these morphometric parameters into 10 classes representing landforms elements such as ridge, channel, crest line, planar and valley bottom. These classes were analyzed and interpreted based on spectral signature, feature space, and 3D presentations of the area. Texture contents were enhanced by separating the 10 classes into individual maps and applying occurrence filters with 9×9 window to each map. This procedure resulted in 10 new inputs to the SOM. Again SOM was trained and a map with four dominant landforms, mountains with steep slopes, plane areas with gentle slopes, dissected ridges and lower valleys with moderate to very steep slopes and main valleys with gentle to moderate slopes was produced. Both landform maps were evaluated by superimposing contour lines. Results showed that Self Organizing Map is a very promising and efficient tool for land form identification. There is a very good agreement between identified landforms and contour lines. This new procedure is encouraging and offers new possibilities in the study of both type of terrain features, general landforms and landform elements. | ||
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