Airborne LIDAR Data Measurement and Landform Classification Mapping in Tomari-no-tai Landslide Area, Shirakami Mountains, Japan View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007

AUTHORS

Hiroshi P. Sato , Hiroshi Yagi , Mamoru Koarai , Junko Iwahashi , Tatsuo Sekiguchi

ABSTRACT

Detailed landform classification is important if effective measures against landslides are to be taken. Conventional techniques can only measure the detailed terrain in vegetated areas with difficulty. Airborne light detection and ranging (LIDAR) is a promising tool to precisely and directly measure a digital elevation model (DEM). Using a two-meter-grid DEM we attempted to understand landslide characteristics, namely, we produced manual and automated landform classification maps in Tomari-no-tai area in Shirakami Mountains, Japan. In advance, 1 : 2500-scale two-meter-interval contour map was newly printed using the LIDAR-DEM. It was found that valleys and other geomorphological features could be seen in better detail in the airborne LIDAR contour map than in the existing photogram-metric contour map. The map and 1 : 8000-scale aerial photographs were interpreted, and manual landform classification map was produced. As a result, 17 classifications were identified in the map. In producing the automated landform classification map, in advance, three variables such as slope, surface texture (feature frequency, or spacing), and local convexity were calculated from the DEM. The three variables were subdivided into three, two, and two classes, respectively, and 12 classifications, which mean the combination of 3 × 2 × 2 classes, were identified in the map. The manual landform classification map can give useful information and ideas about landform evolution of the study area, but it may not fully extract geomorphological features. The automated landform classification map can objectively describe the surface morphology, but it of itself does not give information about landform evolution. Interpreting and extracting geomorphological features from the automated landform classification map will help us to revise the manual landform classification map and to comprehensively understand landform and landslide processes. More... »

PAGES

237-249

Book

TITLE

Progress in Landslide Science

ISBN

978-3-540-70964-0
978-3-540-70965-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-70965-7_17

DOI

http://dx.doi.org/10.1007/978-3-540-70965-7_17

DIMENSIONS

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