Curvature derived from LiDAR digital elevation models as simple indicators of debris-flow susceptibility
- 58 Downloads
To mitigate the damage caused by debris flows resulting from heavy precipitation and to aid in evacuation plan preparation, areas at risk should be mapped on a scale appropriate for affected individuals and communities. We tested the effectiveness of simply identifying debris-flow hazards through automated derivation of surface curvatures using LiDAR digital elevation models. We achieved useful correspondence between plan curvatures and areas of existing debris-flow damage in two localities in Japan using the analysis of digital elevation models (DEMs). We found that plan curvatures derived from 10m DEMs may be useful to indicate areas that are susceptible to debris flow in mountainous areas. In residential areas located on gentle sloping debris flow fans, the greatest damage to houses was found to be located in the elongated depressions that are connected to mountain stream valleys. Plan curvature derived from 5m DEM was the most sensitive indicators for susceptibility to debris flows.
KeywordsDigital elevation model LiDAR Gridspacing Debris flow Geological hazard Curvature
Unable to display preview. Download preview PDF.
This research was supported by the Crisis Management division of Toho village, and JSPS KAKENHI Grant Number (18K04660). The LiDAR data were provided by the Kyushu branch of the Ministry of Land, Infrastructure, Transport and Tourism. Mr. Shuntaro Hayashi helped the research. Mr. Jason Murrin helped the English editing.
- Barták V (2009) How to extract river networks and catchment boundaries from DEM: a review of digital terrain analysis techniques. Journal of Landscape Studies 2: 57–68.Google Scholar
- Cabinet Office, Government of Japan (2017) Bousai. 88: 4–9. (In Japanese)Google Scholar
- ESCAP (2018) Disasters in Asia and the Pacific: 2015 Year in Review. https://doi.org/www.unescap.org/sites/default/files/2015_Year%20in%20Review_final_PDF_1.pdf, accessed on 27th May 2018.Google Scholar
- GSI (2006) Manual of airborne LiDAR altimetry method. (In Japanese)Google Scholar
- GSI (2017) Rainfall–induced disaster, in northern Kyusyu in 2017.https://doi.org/www.gsi.go.jp/BOUSAI/H29hukuoka_ooita-heavyrain.html#9, accessed on 27th May 2018. (In Japanese)Google Scholar
- GSI (2014) August 2014 Rainfall induced disaster. https://doi.org/www.gsi.go.jp/BOUSAI/h26-0816heavyrain-index.html, accessed on 27th May 2018. (In Japanese)Google Scholar
- International Federation of Red Cross and Red Crescent Societies (IFRC) (2014) Effective law and regulation for disaster risk reduction: a multi–country report. https://doi.org/www.ifrc.org/Global/Publications/IDRL/country%20studies/summary_report_final_single_page.pdf, accessed on 27th May 2018.Google Scholar
- LAWA (2010) Recommendations for the Establishment of Flood Hazard Maps and Flood Risk Maps. Adopted at the 139th LAWA General Meeting in Dresden on 25/26 March 2010.https://doi.org/www.lawa.de/documents/LAWA_HWGK15062010_Text_Germany_ENG_f72_4d8.pdf, accessed on 27th May 2018.Google Scholar
- Park D, Lee S, Nikhil NV, Kang S, Park Jet al. (2013) Debris flow hazard zonation by probabilistic analysis (Mt. Woomyeon, Seoul, Korea). International Journal of Innovative Research in Science, Engineering and Technology 2: 231–2390.Google Scholar
- Tarboton DG, Ames DP (2001) Advances in the mapping of flow networks from digital elevation data. Proceedings of World Water and Environmental Resources Congress, Orlando, Florida.Google Scholar
- Viet TT, Lee G, Thu TM, An HU (2017) Effect of digital elevation model resolution on shallow landslide modeling using TRIGRS. Natural Hazards Review 18 (2): 1–12.Google Scholar
- Ventura County (2018) USGS Debris Flow Hazard Map. https://doi.org/venturacountyrecovers.org/usgs-debris-flow-preliminary-hazard-assessment-map/, accessed on 27th May 2018.Google Scholar
- Wilson JP, Gallant JC (2000) Digital Terrain Analysis. In: Wilson JP and Gallant JC (eds.), Terrain Analysis: Principles and Applications, John Wiley & Sons, New York. pp1–27.Google Scholar