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A new slope unit extraction method for regional landslide analysis based on morphological image analysis

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Abstract

In landslide analysis it is necessary to first define and extract slope units from remotely sensed data. When this is done using hydrological process analysis, there can be complications caused by apparent sudden changes in slope gradient, resulting in the assumption of homogeneity of regional slope stability not being met. This issue is addressed in this paper with the proposal of a novel slope unit extraction method, which is derived from morphological image analysis (MIA) and results in the extraction of homogeneous slope units (HSU). It is thus referred to as the MIA-HSU method. In this method, slope units are defined as small, closed and homogeneous in 3D space. The MIA method uses logical algorithms, such as expansion and erosion, to extract slope units. Jiangjia Gully, Yunnan Province, China was selected to test the MIA-HSU method. Results indicate that slope units extracted by this method overcome defects of sudden changes in slope gradient and are able to identify geographical features meaningful for further landslide analysis. The MIA-HSU method presents a more uniform slope and aspect, and is better suited to regional landslide analysis than data obtained using the hydrological process analysis method.

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References

  • Aleotti P (2004) A warning system for rainfall-induced shallow failures. Eng Geol 73(3-4):247–265. https://doi.org/10.1016/j.enggeo.2004.01.007

    Article  Google Scholar 

  • Apip, Takara K, Yamashiki Y, Sassa K, Ibrahim AB, Fukuoka H (2010) A distributed hydrological-geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale. Landslides 7(3):237–258. https://doi.org/10.1007/s10346-010-0214-z

    Article  Google Scholar 

  • Baum RL, Savage WZ, Godt JW (2002) Trigrs: a fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis. Open-File Report.

  • Baum RL, Savage WZ, Godt JW (2008) TRIGRS—a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis. US Geological Survey Open File Report 2008-1159. 2.

  • Baum RL, Savage WZ, Godt JW (2008) Trigrs-a fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, version 2.0. Open-File Report

  • Blum H, Nagel RN (1978) Shape description using weighted symmetric axis features. Pattern Recogn 10(3):167–180. https://doi.org/10.1016/0031-3203(78)90025-0

    Article  Google Scholar 

  • Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, Guzzetti F (2010) Rainfall thresholds for the possible occurrence of landslides in Italy. Nat Hazards Earth Syst Sci 10(3):447–458. https://doi.org/10.5194/nhess-10-447-2010

    Article  Google Scholar 

  • Caine N (1980) The rainfall intensity: duration control of shallow landslides and debris flows. Geogr Ann 62(1/2):23–27. https://doi.org/10.2307/520449

    Article  Google Scholar 

  • Calcaterra D, Riso R, Di MD (2004) Assessing shallow debris slide hazard in the Agnano plain (Naples, Italy) using SINMAP, a physically based slope-stability model. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao ASF (eds) Landslides: evaluation and stabilization. Taylor & Francis Group, Rio de Janeiro, pp 177–183. https://doi.org/10.1201/b16816-24

    Google Scholar 

  • Carrara A, Guzzetti F (1995) Geographical information systems in assessing natural hazards. Adv Nat Technol Hazards Res 4(4):45–59. https://doi.org/10.1007/978-94-015-8404-3

    Google Scholar 

  • Dahal RK, Hasegawa S (2008) Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology 100(3–4):429–443. https://doi.org/10.1016/j.geomorph.2008.01.014

    Article  Google Scholar 

  • Daniel D, Rathje EM, Jibson RW (2013) The influence of different simplified sliding-block models and input parameters on regional predictions of seismic landslides triggered by the Northridge earthquake. Eng Geol 163:41–54

    Article  Google Scholar 

  • Giles PT (1998) Geomorphological signatures: classification of aggregated slope unit objects from digital elevation and remote sensing data. Earth Surf Process Landf 23(7):581–594

    Article  Google Scholar 

  • Godt JW, Schulz WH, Baum RL, Savage WZ (2008) Modeling rainfall conditions for shallow landsliding in Seattle, Washington. Denver Annual Meeting(08), pp 137–152. https://doi.org/10.1130/2008.4020(08)

  • Gonzalez RC, Woods RE (2013) Digital image processing (3rd Edition). Electric Machines & Drives Conference (IEMDC), 2013 IEEE International, vol 45, pp 1160–1165

  • Gruber S, Peckham S (2009) Chapter 7 land-surface parameters and objects in hydrology. Dev Soil Sci 33(08):171–194. https://doi.org/10.1016/s0166-2481(08)00007-x

    Google Scholar 

  • Gu T, Wang J, Fu X, Liu Y (2015) GIS and limit equilibrium in the assessment of regional slope stability and map** of landslide susceptibility. Bull Eng Geol Environ 74(4):1105–1115. https://doi.org/10.1007/s10064-014-0689-2

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1–4):181–216. https://doi.org/10.1016/s0169-555x(99)00078-1

    Article  Google Scholar 

  • Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorog Atmos Phys 98(3):239–267. https://doi.org/10.1007/s00703-007-0262-7

    Article  Google Scholar 

  • Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910. https://doi.org/10.1029/2000wr900090

    Article  Google Scholar 

  • Jolliffe IT, Trendafilov NT, Uddin M (2003) A modified principal component technique based on the lasso. Journal of Computational & Graphical Statistics 12(3):531–547

  • Kanungo DP, Sharma S (2014) Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region, Garhwal Himalayas, India. Landslides 11(4):629–638. https://doi.org/10.1007/s10346-013-0438-9

    Article  Google Scholar 

  • Kim J, Lee K, Jeong S, Kim G (2014) GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model. Eng Geol 182:63–78

    Article  Google Scholar 

  • Lan HX, Zhou CH, Wang LJ, Zhang HY, Li RH (2004) Landslide hazard spatial analysis and prediction using GIS in the xiaojiang watershed, Yunnan, China. Eng Geol 76(1–2):109–128

    Article  Google Scholar 

  • Long NT, Smedt FD (2014) Slope stability analysis using a physically based model: a case study from A Luoi district in Thua Thien-Hue Province, Vietnam. Landslides 11(5):897–907

    Article  Google Scholar 

  • Maidment DR (2002) Arc hydro : GIS for water resources. ESRI Press, Redlands

    Google Scholar 

  • Michel GP, Kobiyama M, Goerl RF (2014) Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility map** in the Cunha River basin, southern Brazil. J Soils Sediments 14(7):1266–1277. https://doi.org/10.1007/s11368-014-0886-4

    Article  Google Scholar 

  • Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171. https://doi.org/10.1029/93wr02979

    Article  Google Scholar 

  • Montrasio L, Schilirò L, Terrone A (2015) Physical and numerical modelling of shallow landslides. Landslides 1–11

  • Moon HS, You T, Yoo HW, Sohn MH, Dong SJ (2005) A recovery system of broken relics using least squares fitting and vector similarity techniques. Expert Syst Appl 28(3):469–481. https://doi.org/10.1016/j.eswa.2004.12.009

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30. https://doi.org/10.1002/hyp.3360050103

    Article  Google Scholar 

  • Muntohar AS, Liao HJ (2009) Analysis of rainfall-induced infinite slope failure during typhoon using a hydrological-geotechnical model. Environ Geol 56(6):1145–1159. https://doi.org/10.1007/s00254-008-1215-2

    Article  Google Scholar 

  • Nery TD, Vieira BC (2015) Susceptibility to shallow landslides in a drainage basin in the Serra do Mar, São Paulo, Brazil, predicted using the sinmap mathematical model. Bull Eng Geol Environ 74(2):369–378. https://doi.org/10.1007/s10064-014-0622-8

    Article  Google Scholar 

  • Pack RT, Tarboton DG, Goodwin CN (1998) The SINMAP approach to terrain stability map**. In: Moore D, Hungr O (eds) Proceedings of the 8th congress of the International Association of Engineering Geology. AA Balkema Publisher, Rotterdam, pp 1157–1165. https://doi.org/10.1016/0148-9062(89)92793-9

    Google Scholar 

  • Park DW, Nikhil NV, Lee SR (2013) Landslide and debris flow susceptibility zonation using trigrs for the 2011 Seoul landslide event. Nat Hazards Earth Syst Sci 13(11):2833–2849. https://doi.org/10.5194/nhess-13-2833-2013

    Article  Google Scholar 

  • Pellicani R, Spilotro G (2014) Geomorphological complexity in landslide susceptibility modeling. In: Lollino G et al (eds) Engineering geology for society and territory, vol 5, pp 415–419. https://doi.org/10.1007/978-3-319-09048-1_80

  • Qiu C, **e MW, Tetsuro E (2005) Landslide hazard assessment on highway slope in weathered granite zone—an example of no. 49 national highway in Hehu area, Japan. Chinese Journal of Geological Hazard and Control 16(1):23–28. (In Chinese)

  • Rotigliano E, Cappadonia C, Conoscenti C, Costanzo D, Agnesi V (2012) Slope units-based flow susceptibility model: using validation tests to select controlling factors. Nat Hazards 61(1):143–153. https://doi.org/10.1007/s11069-011-9846-0

    Article  Google Scholar 

  • Salciarini D, Godt JW, Savage WZ, Conversini P (2006) Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria region of Central Italy. Landslides 3(3):181–194. https://doi.org/10.1007/s10346-006-0037-0

    Article  Google Scholar 

  • Schmidt J, Evans I S, Brinkmann J (2003) Comparison of polynomial models for land surface curvature calculation. International Journal of Geographical Information Science,17(8):797–814. https://doi.org/10.1080/13658810310001596058

  • Seibert J, McGlynn B L (2007) A new triangular multiple flow direction algorithm for computing upslope areas from gridded digital elevation models. Water Resources Research, 43 (4), 306-320.

  • Soille P (2004) Morphological image analysis: principles and applications. Springer, Berlin. https://doi.org/10.1007/978-3-662-05088-0

    Book  Google Scholar 

  • Tsai TL, Yang JC (2006) Modeling of rainfall-triggered shallow landslide. Environ Geol 50(4):525–534. https://doi.org/10.1007/s00254-006-0229-x

    Article  Google Scholar 

  • Turel M, Frost JD (2011) Delineation of slope profiles from digital elevation models for landslide hazard analysis. Am Soc Civil Eng (224):829–836. https://doi.org/10.1061/41183(418)87

  • Wang X, Zhang L, Wang S, Lari S (2014) Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors. Landslides 11(3):399–409. https://doi.org/10.1007/s10346-013-0392-6

    Article  Google Scholar 

  • Wei FQ, Gao KC, Jiang YH, Jia SW, Cui P, Xu J et al (2007) GIS-based prediction of debris flows and landslides in southwestern China. International conference on debris-flow hazards mitigation- mechanics, prediction, and assessment. https://doi.org/10.2495/deb060041

  • Westen CJV (1993) Application of geographic information systems to landslide hazard zonation. Doctoral dissertation, International Institute for Aerospace Survey and Earth Sciences, Enschede

  • **e M, Zhou G, Tetsuro E (2003) GIS component based 3d landslide hazard assessment system: 3dslopegis. Chin Geogr Sci 13(1):66–72. https://doi.org/10.1007/s11769-003-0087-3

    Article  Google Scholar 

  • ** of landslide hazard using a three-dimensional deterministic model. Nat Hazards 33(2):265–282. https://doi.org/10.1023/b:nhaz.0000037036.01850.0d

    Article  Google Scholar 

  • Zevenbergen LW, Thorne CR (2010) Quantitative analysis of land surface topography. Earth Surf Process Landf 12(1):47–56

    Article  Google Scholar 

  • Zhang S, Yang H, Wei F, Jiang Y, Liu D (2014a) A model of debris flow forecast based on the water-soil coupling mechanism.Journal of Earth Science,25(4):757–763. https://doi.org/10.1007/s12583-014-0463-1

  • Zhang SJ, Wei FQ, Liu DL, Yang HJ (2014b) A regional-scale method of forecasting debris flow events based on water-soil coupling mechanism. Journal of Mountain Science,11(6):1531–1542. https://doi.org/10.1007/s11629-013-2906-z

  • Zhang SJ, Wei FQ, Liu DL, Jiang YH (2016) Analysis of slope stability based on the limit equilibrium equation and the hydrological simulation. Journal of Basic Science and Engineering 12:1182–1192. (In Chinese)

    Google Scholar 

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Acknowledgements

This work was supported by the Science and Technology Service Network Initiative (No. KFJ-SW-STS-180) and the Chongqing Municipal Bureau of Land, Resources and Housing Administration (no. KJ-2018005). We thank Warwick Hastie, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

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Correspondence to Shaojie Zhang.

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Wang, K., Zhang, S., DelgadoTéllez, R. et al. A new slope unit extraction method for regional landslide analysis based on morphological image analysis. Bull Eng Geol Environ 78, 4139–4151 (2019). https://doi.org/10.1007/s10064-018-1389-0

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