Advertisement

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

  • 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.

Keywords

LIDAR landslide landform classification DEM Shirakami Mountains 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackermann F (1999) Airborne laser scanning — present status and future expectations. Isprs J Photogramm 54:64–67CrossRefGoogle Scholar
  2. Chigira M, Yagi H (2006) Distribution of landslides triggered by the 2004 Mid Niigata Prefecture earthquake. Eng Geol 45:83–90Google Scholar
  3. Hasegawa H, Okamatsu K (2001) Detailed landform feature and characteristics extraction with high dense DTM data. In: Proceedings of the autumn Conference of the Japan society of photogrammetry and remote sensing, pp 189–192 (in Japanese)Google Scholar
  4. Iwahashi J, Kamiya I (1995) Landform classification using digital elevation model by the skills of image processingainly — mainly using the digital national land information. Geoinformatics 6(2):97–108 (in Japanese with English abstract)Google Scholar
  5. Iwahashi J, Pike RJ (2007) Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology (in press)Google Scholar
  6. Keefer DV (2000) Statistical analysis of an earthquake-induced landslide distribution — the 1989 Loma Prieta, California event. Eng Geol 58:231–249CrossRefGoogle Scholar
  7. Lee S, Ryu JH, Min K, Wo JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Proc Land 28:1361–1376CrossRefGoogle Scholar
  8. Masaharu H, Hasegawa H, Ohtsubo K (2001) Three-dimensional city modeling from airborne laser scanning. In: Proceedings of the 20th International Cartographic Conference, International Cartographic Association, Beijing, 2, pp 1337–1343Google Scholar
  9. McKean J, Roering J (2004) Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology 57:331–351CrossRefGoogle Scholar
  10. Ozawa A, Tsuchiya N, Sumi K (1983) Geology of the Nakahama district. Geological Survey of Japan, 62 p (in Japanese with English abstract)Google Scholar
  11. Raber GT, Jensen JR, Schill SR, Schuckman K (2002) Creation of digital terrain models using an adaptive lidar vegetation point removal process. Photogramm Eng Remote Sens 68: 1307–1315Google Scholar
  12. Sassa K, Fukuoka H, Wang G, Ishikawa N (2004) Undrained dynamic-loading ring-shear apparatus and its application to landslide dynamics. Landslides 1:7–19CrossRefGoogle Scholar
  13. Sato HP, Sekiguchi T, Orimo K, Nakajima T (2004) Accuracy validation of airborne laser scanning DTM using the ground control points. J Jpn Soc Photogrammetry Remote Sensing 43(4):13–21 (in Japanese with English abstract)Google Scholar
  14. Sekiguchi T, Sato, HP (2004) Mapping of micro topography using airborne laser scanning. Landslides 1(3):195–202CrossRefGoogle Scholar
  15. Sekiguchi T, Sugiyama M (2003) Geomorphological features and distribution of avalanche furrows in heavy snowfall regions in Japan. Z Geomorphol NF 130:117–128Google Scholar
  16. Tamura T (1981) Multiscale landform classification study in the hills of Japan: Part II, application of the multiscale landform classification system to pure geomorphological studies of the hills of Japan. Science Report of Tohoku University 7th Series (Geography) 31:85–154Google Scholar
  17. Wehr A, Lohr U (1999) Airborne laser scanning — an introduction and overview. Isprs J Photogramm 54:68–82CrossRefGoogle Scholar
  18. Yagi H (1995) Landform and its evolution in Shirakami Mountains. In: National Parks Association of Japan (ed) Comprehensive research report on Shirakami Mountains Natural environment conservation region. pp 45–75 (in Japanese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hiroshi P. Sato
    • 1
  • Hiroshi Yagi
    • 2
  • Mamoru Koarai
    • 1
  • Junko Iwahashi
    • 1
  • Tatsuo Sekiguchi
    • 1
  1. 1.Geography and Crustal Dynamics Research Center, Geographical Survey InstituteMinistry of Land, Infrastructure and TransportTsukuba, IbarakiJapan
  2. 2.Yamagata UniversityJapan

Personalised recommendations