Abstract
Rockfall poses risk to people, their properties and to transportation ways in mountainous and hilly regions. This catastrophe shows various characteristics such as vast distribution, sudden occurrence, variable magnitude, strong fatalness and randomicity. Therefore, prediction of rockfall phenomenon both spatially and temporally is a challenging task. Digital Terrain model (DTM) is one of the most significant elements in rockfall source identification and risk assessment. Light detection and ranging (LiDAR) is the most advanced effective technique to derive high-resolution and accurate DTM. This paper presents a critical overview of rockfall phenomenon (definition, triggering factors, motion modes and modeling) and LiDAR technique in terms of data pre-processing, DTM generation and the factors that can be obtained from this technique for rockfall source identification and risk assessment. It also reviews the existing methods that are utilized for the evaluation of the rockfall trajectories and their characteristics (frequency, velocity, bouncing height and kinetic energy), probability, susceptibility, hazard and risk. Detail consideration is given on quantitative methodologies in addition to the qualitative ones. Various methods are demonstrated with respect to their application scales (local and regional). Additionally, attention is given to the latest improvement, particularly including the consideration of the intensity of the phenomena and the magnitude of the events at chosen sites.
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Adopted from Messenzehl et al. (2016)
Modified after Pradhan and Sameen (2017)
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References
Abellán A, Oppikofer T, Jaboyedoff M, Rosser NJ, Lim M, Lato MJ (2014) Terrestrial laser scanning of rock slope instabilities. Earth Surf Process Landf 39(1):80–97. https://doi.org/10.1002/esp.3493
Agliardi F, Crosta G (2003) High resolution three-dimensional numerical modeling of rockfalls. Int J Rock Mech Min Sci 40(4):455–471. https://doi.org/10.1016/S1365-1609(03)00021-2
Agliardi F, Crosta GB, Frattini P (2009) Integrating rockfall risk assessment and countermeasure design by 3D modelling techniques. Nat Hazards Earth Syst Sci 9:1059–1073
Akgün A, Yakut M (2017) A rockfall hazard assessment for a residential area by using 2D and 3D simulation models: a case study from North Turkey. In: EGU general assembly conference abstracts, vol 19, p 2516
Ardizzone F, Cardinali M, Galli M, Guzzetti F, Reichenbach P (2007) Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne LiDAR. Nat Hazards Earth Syst Sci 7(6):637–650. https://hal.archives-ouvertes.fr/hal-00299461
Ashraf I, Hur S, Park Y (2017) An investigation of interpolation techniques to generate 2D intensity images from LIDAR data. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2699686
Azzoni A, La Barbera G, Zaninetti A (1995) Analysis and prediction of rockfalls using a mathematical model. Int J Rock Mech Min Sci & Geomech Abstr 32(7):709–724
Bornaz L, Lingua ARF (2002) Engineering and environmental applications of laser scanner techniques. Int Arch Photogramm Remote Sens Spat Inf Sci 34(3/B):40–43
Brabb E (1984) Innovative approaches for landslide hazard evaluation. IV International Symposium on Landslides, Toronto, pp 307–323
Budetta P, Nappi M (2013) Comparison between qualitative rockfall risk rating systems for a road affected by high traffic intensity. Nat Hazards Earth Syst Sci 13(6):1643–1653. https://doi.org/10.5194/nhess-13-1643-2013
Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378. https://doi.org/10.1007/s10346-015-0557-6
Bozzolo D, Pamini R, (1986) Simulation of rock falls down a valley side. Acta Mechanica 63(1–4):113–130
Chen XL, Liu CG, Chang ZF, Zhou Q (2016) The relationship between the slope angle and the landslide size derived from limit equilibrium simulations. Geomorphology 253:547–550. https://doi.org/10.1016/j.geomorph.2015.01.036
Chen Z, Gao B, Devereux B (2017) State-of-the-art: DTM generation using airborne LIDAR data. Sensors 17(1):150. https://doi.org/10.3390/s17010150
Chow TE, Hodgson ME (2009) Effects of LiDAR post-spacing and DEM resolution to mean slope estimation. Int J Geogr Inf Sci 23(10):1277–1295. https://doi.org/10.1080/13658810802344127
Christen M, Bartelt P, Gruber U (2007) RAMMS-a modeling system for snow avalanches, debris flows and rockfalls based on IDL. Photogramm Fernerkund Geoinf 4:289
Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S et al (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73(2):209–263
Crosta GB, Agliardi F (2003) New methodology for physically-based rockfall hazard assessment. Nat Hazards Earth Syst Sci 3:407–422. https://hal.archives-ouvertes.fr/hal-00301598
Crosta GB, Agliardi F, Frattini P, Imposimato S (2004) A three dimensional hybrid numerical model for rockfall simulation. Geophys Res Abstr 6:04502
Crosta GB, Agliardi F, Frattini P, Lari S (2015) Key issues in rock fall modeling, hazard and risk assessment for rockfall protection. Eng Geol Soc Territ 2:43–58. https://doi.org/10.1007/978-3-319-09057-3_4
Cundall PA (1971) A computer model for simulating progressive, large-scale movements in blocky rock system. Geotechnique 29(1):47–65
Daehne A, Corsini A (2013) Kinematics of active earthflows revealed by digital image correlation and DEM subtraction techniques applied to multi-temporal LiDAR data. Earth Surf Process Landf 38:640–654. https://doi.org/10.1002/esp.3351
Dorren LKA (2016) A review of rockfall mechanics and modelling approaches. Prog Phys Geogr 27(1):69–87
Dorren LKA (2012) Rockyfor3D (v5.1) revealed—transparent description of the complete 3D rockfall model
Dorren LKA, Berger FPU (2006) Real-size experiments and 3-D simulation of rockfall on forested and non-forested slopes. Nat Hazards Earth Syst Sci 6:145–153. https://doi.org/10.5194/nhess-6-145-2006
Dorren LKA, Seijmonsbergen AC (2003) Comparison of three GIS-based models for predicting rockfall runout zones at a regional scale. Geomorphology 56(1–2):49–64. https://doi.org/10.1016/S0169-555X(03)00045-X
Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu Y et al (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78(3):1749–1776. https://doi.org/10.1007/s11069-015-1799-2
Evans JS, Hudak AT (2007) A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Trans Geosci Remote Sens 45(4):1029–1038. https://doi.org/10.1109/TGRS.2006.890412
Evans S, Hungr O (1993) The assessment of rockfall hazard at the base of talus slopes. Can Geotech J 30:620–636. https://doi.org/10.1139/t93-054
Fanos AM, Pradhan B (2016) Multi-scenario rockfall hazard assessment using LiDAR data and GIS. Geotech Geol Eng 34(5):1375–1393. https://doi.org/10.1007/s10706-016-0049-z
Fanos AM, Pradhan B, Aziz AA, Jebur MN, Park HJ (2016) Assessment of multi-scenario rockfall hazard based on mechanical parameters using high-resolution airborne laser scanning data and GIS in a tropical area. Environ Earth Sci 75(15):1129. https://doi.org/10.1007/s12665-016-5936-3
Fernandes NF, Guimarães RF, Gomes RA, Vieira BC, Montgomery DR, Greenberg H (2004) Topographic controls of landslides in Rio de Janeiro: field evidence and modeling. CATENA 55(2):163–181. https://doi.org/10.1016/S0341-8162(03)00115-2
Fityus S, Giacomini A, Buzzi O (2013) The significance of geology for the morphology of potentially unstable rocks. Eng Geol 162:43–52. https://doi.org/10.1016/j.enggeo.2013.05.007
Fornaciai A, Bisson M, Landi P, Mazzarini F, Pareschi MT (2010) A LiDAR survey of Stromboli volcano (Italy): digital elevation model-based geomorphology and intensity analysis. Int J Remote Sens 31(12):3177–3194. https://doi.org/10.1080/11431160903154416
Frattini P, Crosta G, Carrara A, Agliardi F (2008) Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. Geomorphology 94(3–4):419–437. https://doi.org/10.1016/j.geomorph.2006.10.037
Guzzetti F, Crosta G, Detti R, Agliardi F (2002) STONE: a computer program for the three dimensional simulation of rock-falls. Comput Geosci 28:1079–1093. https://doi.org/10.1016/S0098-3004(02)00025-0
Habib AF, Kersting AP, Shaker A, Yan WY (2011) Geometric calibration and radiometric correction of LiDAR data and their impact on the quality of derived products. Sensors 11(9):9069–9097. https://doi.org/10.3390/s110909069
Hungr O, Evans S (1988) Engineering evaluation of fragmental rockfall hazards. In: Proceedings of the fifth international symposium on landslides, Lausanne. AA Balkema, Rotterdam, pp 685–690
Jaboyedoff M, Labiouse V (2011) Technical note: preliminary estimation of rockfall runout zones. Nat Hazards Earth Syst Sci 11(3):819–828. https://doi.org/10.5194/nhess-11-819-2011
Jaboyedoff M, Oppikofer T, Abellán A, Derron MH, Loye A, Metzger R et al (2012) Use of LiDAR in landslide investigations: a review. Nat Hazards 61:5–28. https://doi.org/10.1007/s11069-010-9634-2
Jahromi AB, Zoej MJV, Mohammadzadeh A, Sadeghian S (2011) A novel filtering algorithm for bare-earth extraction from airborne laser scanning data using an artificial neural network. IEEE J Sel Top Appl Earth Obs Remote Sens 4(4):836–843. https://doi.org/10.1109/JSTARS.2011.2132793
Katz O, Morgan JK, Aharonov E, Dugan B (2014) Controls on the size and geometry of landslides: insights from discrete element numerical simulations. Geomorphology 220:104–113. https://doi.org/10.1016/j.geomorph.2014.05.021
Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439. https://doi.org/10.1007/s10346-013-0391-7
Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Remote Sens 53(4):193–203. https://doi.org/10.1016/S0924-2716(98)00009-4
Kritikos T, Davies T (2015) Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand. Landslides 12(6):1051–1075. https://doi.org/10.1007/s10346-014-0533-6
Lambert S, Bourrier F (2013) Design of rockfall protection embankments: A review. Eng Geol 154:77–88
Lambert S, Bourrier F, Toe D (2013) Improving three-dimensional rockfall trajectory simulation codes for assessing the efficiency of protective embankments. Int J Rock Mech Min Sci 60:26–36. https://doi.org/10.1016/j.ijrmms.2012.12.029
Lan HX, Martin CD, Lim CH (2007) RockFall analyst: a GIS extension for three-dimensional and spatially distributed rockfall hazard modeling. Comput Geosci 33:262–279. https://doi.org/10.1016/j.cageo.2006.05.013
Leine RI, Schweizer A, Christen M, Glover J, Bartelt P, Gerber W (2014) Simulation of rockfall trajectories with consideration of rock shape. Multibody Syst Dyn 32:241–271. https://doi.org/10.1007/s11044-013-9393-4
Li L, Lan H (2015) Probabilistic modeling of rockfall trajectories: a review. Bull Eng Geol Environ 74(4):1163–1176. https://doi.org/10.1007/s10064-015-0718-9
Messenzehl K, Meyer H, Otto JC, Hoffmann T, Dikau R (2016) Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps)—a multivariate modeling approach. Geomorphology 287:29–45. https://doi.org/10.1016/j.geomorph.2016.01.008
Mezaal MR, Pradhan B (2018) Data mining-aided automatic landslide detection using airborne laser scanning data in densely forested tropical areas. Korean J Remote Sens 34(1):45–74. https://doi.org/10.7780/kjrs.2018.34.1.4
Mezaal MR, Pradhan B, Shafri HZM, Yusoff ZM (2017a) Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos. Geomat Nat Hazards Risk 8(2):1935–1954
Mezaal MR, Pradhan B, Sameen MI, Mohd Shafri HZ, Yusoff ZM (2017b) Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data. Appl Sci 7(7):730. https://doi.org/10.3390/app7070730
Nefeslioglu HA, Duman TY, Durmaz S (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94(3):401–418. https://doi.org/10.1016/j.geomorph.2006.10.036
Pantelidis L (2011) A critical review of highway slope instability risk assessment systems. Bull Eng Geol Environ 70(3):395–400. https://doi.org/10.1007/s10064-010-0328-5
Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology 204:287–301. https://doi.org/10.1016/j.geomorph.2013.08.013
Petley DN (2013) Characterizing giant landslides. Science 339(6126):1395–1396. https://doi.org/10.1126/science.1236165
Piteau DR, Clayton R (1976) Computer rockfall model. In: Proceedings of the meeting on rockfall dynamics and protective works effectiveness, Bergamo, Italy, ISMES Publication 90, pp 123–125
Pradhan B, Fanos AM (2017a) Rockfall Hazard Assessment: An Overview. In: Pradhan B (ed) Laser Scanning Applications in Landslide Assessment. Springer, Cham, pp 299–322. https://doi.org/10.1007/978-3-319-55342-9_15
Pradhan B, Fanos AM (2017b) Application of LiDAR in rockfall hazard assessment in tropical region. In: Pradhan B (ed) Laser scanning applications in landslide assessment. Springer, Cham, pp 323–359. https://doi.org/10.1007/978-3-319-55342-9_16
Pradhan B, Sameen MI (2017) Laser scanning systems in landslide studies. In: Pradhan B (ed) Laser scanning applications in landslide assessment. Springer, Cham, pp 3–19. https://doi.org/10.1007/978-3-319-55342-9_1
Pradhan B, Yusof NM (2017) Slope vulnerability and risk assessment using high-resolution airborne laser scanning data. In: Pradhan B (ed) Laser scanning applications in landslide assessment. Springer, Cham, pp 235–251. https://doi.org/10.1007/978-3-319-55342-9_12
Pradhan B, Jebur MN, Abdullahi S (2017) Spatial prediction of landslides along jalan kota in bandar seri begawan (Brunei) using airborne LiDAR data and support vector machine. In: Pradhan B (ed) Laser scanning applications in landslide assessment. Springer, Cham, pp 167–178. https://doi.org/10.1007/978-3-319-55342-9_9
Rammer W, Brauner M, Dorren LKA, Berger F, Lexer MJ (2010) Evaluation of a 3-D rockfall module within a forest patch model. Nat Hazards Earth Syst Sci 10:699–711. https://hal.archives-ouvertes.fr/hal-00585011
Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742. https://doi.org/10.1007/s12517-012-0807-z
Ritchie AM (1963) Evaluation of rockfall and its control. Highw Res Rec 17:13–28
RocPro3D (2014) RocPro3D software
Rocscience Inc (2013) RocFall 5.0
Roering JJ, Mackey BH, Marshall JA, Sweeney KE, Deligne NI, Booth AM et al (2013) Connecting the dots with airborne LiDAR for geomorphic fieldwork. Geomorphology 200:172–183. https://doi.org/10.1016/j.geomorph.2013.04.009
Rosser NJ, Brain MJ, Petley DN, Lim M, Norman EC (2013) Coastline retreat via progressive failure of rocky coastal cliffs. Geology 41(8):939–942. https://doi.org/10.1130/G34371.1
Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote sensing for landslide investigations: an overview of recent achievements and perspectives. Remote Sens 6(10):9600–9652
Scioldo G (1991) Rotomap: analisi statistica del rotolamento dei massi. Guida Informatica Ambientale, Patron, Milano, pp 81–84 (in Italian)
Sithole G, Vosselman G (2001) Filtering of laser altimetry data using a slope adaptive filter. Int Arch Photogramm Remote Sens Spat Inf Sci 34(3/W4):203–210
Slatton KC, Carter WE, Shrestha RL, Dietrich W (2017) Airborne laser swath mapping: achieving the resolution and accuracy required for geosurficial research. Geophys Res Lett 34:23. https://doi.org/10.1029/2007GL031939
Spadari M, Kardani M, De Carteret R, Giacomini A, Buzzi O, Fityus S, Sloan SW (2013) Statistical evaluation of rockfall energy ranges for different geological settings of New South Wales, Australia. Eng Geol 158:57–65. https://doi.org/10.1016/j.enggeo.2013.03.007
van Dijke JJ, van Westen CJ (1990) Rockfall hazard: a geomorphologic application of neighbourhood analysis with ILWIS. ITC J 1:40–44
Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86:404–414. https://doi.org/10.1007/s005310050149
Varnes D (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides: analysis and control. Special Report No. 176. Transportation Research Board, National Academy of Science, Washington
Volkwein A, Schellenberg K, Labiouse V, Agliardi F, Berger F, Bourrier F, Dorren LKA, Gerber W, Jaboyedo M (2011) Rockfall characterisation and structural protection—a review. Nat Hazards Earth Syst Sci 11(9):2617–2651. https://hal.archives-ouvertes.fr/hal-00653458
Vosselman G (2000) Slope based filtering of laser altimetry data. Int Arch Photogramm Remote Sens 33(B3/2; PART 3):935–942
Wang G, Joyce J, Phillips D, Shrestha R, Carter W (2013) Delineating and de-fining the boundaries of an active landslide in the rainforest of Puerto Rico using a combination of airborne and terrestrial LiDAR data. Landslides 10(4):503–513. https://doi.org/10.1007/s10346-013-0400-x
Wendeler C, Leonhardt V, Luis R (2017) Flexible barriers composed of high-strength steel nets, as a solution to the near surface slides. In: Workshop on world landslide forum. Springer, Cham, pp 513–522. https://doi.org/10.1007/978-3-319-53487-9_60
Woltjer M, Rammer W, Brauner M, Seidl R, Mohren GMJ, Lexer MJ (2008) Coupling a 3D patch model and a rockfall module to assess rockfall protection in mountain forests. J Environ Manag 87:373–388. https://doi.org/10.1016/j.jenvman.2007.01.031
Wu SS (1985) Rockfall evaluation by computer simulation. Publisher: Transportation Research Board, ISSN: 0361-1981
Wyllie DC (2014) Calibration of rock fall modeling parameters. Int J Rock Mech Min Sci 67:170–180. https://doi.org/10.1016/j.ijrmms.2013.10.002
Yan WY, Shaker A, Habib A, Kersting AP (2012) Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction. ISPRS J Photogramm Remote Sens 67:35–44. https://doi.org/10.1016/j.isprsjprs.2011.10.005
Yan WY, Shaker A, El-Ashmawy N (2015) Urban land cover classification using airborne LiDAR data: a review. Remote Sens Environ 158:295–310. https://doi.org/10.1016/j.rse.2014.11.001
Zeng D, Wu L, Chen B, Shen W (2017) Slope-restricted multi-scale feature matching for geostationary satellite remote sensing images. Remote Sens 9(6):576. https://doi.org/10.3390/rs9060576
Zhan Z, Lai B (2015) A novel DSM filtering algorithm for landslide monitoring based on multiconstraints. IEEE J Sel Top Appl Earth Obs Remote Sens 8(1):324–331. https://doi.org/10.1109/JSTARS.2014.2319855
Zhang K, Chen SC, Whitman D, Shyu ML, Yan J, Zhang C (2003) A progressive morphological filter for removing nonground measurements from airborne LiDAR data. EEE Trans Geosci Remote Sens 41(4):872–882. https://doi.org/10.1109/TGRS.2003.810682
Zhang Y, Xiong X, Hu X (2013) Rigorous LiDAR strip adjustment with tri-angulated aerial imagery. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 5(w2):361–366. https://doi.org/10.5194/isprsannals-II-5-W2-361-2013
Zhang Y, Xiong X, Zheng M, Huang X (2015) LiDAR strip adjustment using multifeatures matched with aerial images. IEEE Trans Geosci Remote Sens 53(2):976–987. https://doi.org/10.1109/TGRS.2014.2331234
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Fanos, A.M., Pradhan, B. Laser Scanning Systems and Techniques in Rockfall Source Identification and Risk Assessment: A Critical Review. Earth Syst Environ 2, 163–182 (2018). https://doi.org/10.1007/s41748-018-0046-x
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DOI: https://doi.org/10.1007/s41748-018-0046-x