Contour Connection Method for automated identification and classification of landslide deposits

Computers and Geosciences

Volumn 74, Issue , 2015, Pages 27-38

  Leshchinsky, Ben A ^a   Olsen, Michael J ^b   Tanyu, Burak F ^c  

^a OREGON STATE UNIVERSITY   (United States)

^b 1148 Kelley Engineering Center Corvallis   (United States)

^c George Mason University   (United States)

Author keywords

Algorithm; Automation; Detection; Landslides; Lidar

Indexed keywords

AUTOMATION; ERROR DETECTION; LANDSLIDES; OPTICAL RADAR;

AUTOMATED IDENTIFICATION; CONNECTION METHOD; LANDSLIDE DEPOSITS;

ALGORITHMS;

ALGORITHM; AUTOMATION; DIGITAL TERRAIN MODEL; LANDSLIDE; LIDAR; MAPPING; REMOTE SENSING;

OREGON; UNITED STATES; WASHINGTON [UNITED STATES];

EID: 84910092994     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2014.10.007     Document Type: Article

References (29)

1. BBC News, 2014. Afghanistan Landslide'Kills at Least 350'. 2 May 2014 (retrieved 2 May).
2. Belardinelli, M.E., Antonioli, A., Bizzarri, A., Vogfjord, K.S., 2005. The early events after the June 17 2000 mainshock in South Iceland: constraints for instantaneous dynamic triggering with rate-and state-dependent friction, EGU.
3. Booth A.M., Roering J.J., Perron J.T. Automated landslide mapping using spectral analysis and high-resolution topographic data: puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology 2009, 109(3):132-147.
4. Burns, W.J., Madin, I.A., 2009. Protocol for Inventory Mapping of Landslide Deposits From Light Detection and Ranging (lidar) Imagery. DOGAMI Special Paper 42.
5. Burns W.J., Mickelson K.A., Saint-Pierr E.C. SLIDO-2, Statewide Landslide Information Database for Oregon, Release 2. 2011, Oregon Department of Geology and Mineral Industries, Portland, OR.
6. Burns W.J., Mickelson K.A., Saint-Pierre E.C. Landslide Inventory Maps of the Southern Half of the Pittsburg Quad, 2013, Oreg Dept Geol and Min Ind, Columbia County, Oregon, (IMS-55, 2 pls., scale 1:8000).
7. Conner J., Olsen M.J. Automated quantification of distributed landslide movement using circular tree trunks extracted from terrestrial laser scan data. Comput. Geosci. 2014, 67:31-39.
8. Cornforth D.H. Landslides in Practice 2005, Wiley, Hoboken.
9. Cruden D.M. A simple definition of a landslide. Bull. Eng. Geol. Environ. 1991, 43(1):27-29.
10. Dietrich, W.E., Bellugi, D., Real De Asua, R., 2001. Validation of the shallow landslide model, SHALSTAB, for forest management. Land Use and Watersheds: Human Influence on Hydrology and Geomorphology in Urban and Forest Areas, pp. 195-227.
11. Haugerud, R.A., 2014, Preliminary interpretation of pre-2014 landslide deposits in the vicinity of Oso. Washington, U.S. Geological Survey Open-File Report 2014-1065, 4 p., http://dx.doi.org/10.3133/ofr20141065.
12. Jaboyedoff M., Oppikofer T., Abellán A., Derron M.H., Loye A., Metzger R., Pedrazzini A. Use of lidar in landslide investigations: a review. Nat. Hazards 2012, 61(1):5-28.
13. Leshchinsky, B., 2013. Comparison of limit equilibrium and limit analysis for complex slopes. Proceedings of GeoCongress.
14. Ling H.I., Wu M.H., Leshchinsky D., Leshchinsky B. Centrifuge modeling of slope instability. J. Geotech. Geoenviron. Eng. 2009, 135(6):758-767.
15. Meng X., Currit N., Zhao K. Ground filtering algorithms for airborne lidar data: a review of critical issues. Remote Sens. 2010, 2(3):833-860.
16. Mora, O.E., Toth, C.K., Grejner-Brzezinska, D.A., Lenzano, M.G., 2014. A probabilistic approach to landslide susceptibility mapping using multi-temporal airborne lidar data.
17. National Research Council Partnerships for Reducing Landslide Risk: Assessment of the National Landslide Hazards Mitigation Strategy 2004, 143. The National Academies Press, Washington, DC.
18. Niebergall S., Loew A., Mauser W. Object-oriented analysis of very high-resolution QuickBird data for mega city research in Delhi/India. InUrban Remote Sensing Joint Event, 2007 2007, 1-8. (IEEE).
19. Niethammer U., et al. UAV-based remote sensing of the Super-Sauze landslide: evaluation and results. Eng. Geol. 2012, 128:2-11.
20. Olsen, M.J., Allan, J.C.Priest, G.R., 2012. Johnson Creek Landslide Movement and Erosion Quantification Through 3D Laser Scanning. ASCE GeoCongress 2012, Oakland, California.
21. Olsen M. In Situ Change Analysis and Monitoring through Terrestrial Laser Scanning. J. Comput. Civ. Eng. 2013, 04014040. 10.1061/(ASCE)CP.1943-5487.0000328.
22. Puget Sound Lidar Consortium. Puget Sound Lidar Consortium. Puget Sound Lidar Consortium, 31 March 2014, Web 26 May 2014.
23. Schulz W.H. Landslide susceptibility revealed by lidar imagery and historical records, Seattle, Washington. Eng. Geol. 2007, 89(1):67-87.
24. The Seattle Times. 14 Dead: 176 Reports of People Missing in Mile-wide Mudslide. March 24, 2014 (retrieved March 24, 2014).
25. Snyder, G.I., 2012. The 3D Elevation Program: Summary of Program Direction. US Department of the Interior, US Geological Survey.
26. Turner, A.K., Schuster, R.L., 1996. Landslides: Investigation and mitigation. Special Report 247 Transportation Research Board and National Research Council. Washington, DC.
27. US Geological Survey. "Landslide Hazards Program." Landslide Hazards Program. US Geological Survey, n.d. Web. 24 Oct. 2014.
28. van Westen C.J., Castellanos E., Kuriakose S.L. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng. Geol. 2008, 102(3):112-131.
29. Wang G., Joyce J., Phillips D., Shrestha R., Carter W. Delineating and defining the boundaries of an active landslide in the rainforest of Puerto Rico using a combination of airborne and terrestrial lidar data. Landslides 2013, 10(4):503-513.