Skip to main content

Susceptibility assessment of small, shallow and clustered landslide

Abstract

Susceptibility assessment of landslides over a large area depends on the basic spatial unit of mapping, usually by using grid cell or slope unit. Both units are used in this study for the assessment of small shallow and clustered landslides in vegetated slopes in Malipo, southwest China. Information value (IV) model was used to generate landslide susceptibility assessment map, while improved information value (IIV) model was used to determine whether the mapping unit is at risk of landslide. Seven factors, including slope angle, slope aspect, elevation, normalized difference vegetation Index (NDVI), Soil Moisture Content (SMC), distance to river and road were used as landslide influence factors. The Area under curve (AUC) values of the slope unit IIV, IV and grid cell were 0.814, 0.802 and 0.702 respectively for success rate. For prediction rate, the AUC values of the slope unit and grid cell were 0.803(IIV), 0.790(IV) and 0.699 respectively. Our results showed slope unit is more suitable than grid cell for assessing susceptibility of Small, Shallow and Cluster Landslide. Improved information value model increases the accuracy of susceptibility assessment model for this characteristic landslide.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Ba Q, Chen Y, Deng S, Wu Q, Yang J, Zhang J (2017) An improved information value model based on gray clustering for landslide susceptibility mapping. ISPRS Int J Geo Inf 6(1):18. https://doi.org/10.3390/ijgi6010018

    Article  Google Scholar 

  2. Ba Q, Chen Y, Deng S, Yang J, Li H (2018) A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inf 11(3):373–388. https://doi.org/10.1007/978-94-015-8404-3_8

    Article  Google Scholar 

  3. Burnett AD, Brand EW, Styles KA (1987) Terrain classification mapping for a landslide inventory in hong Kong, 24(1).https://doi.org/10.1016/0148-9062(87)91365-9

  4. Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) Gis Technology in mapping landslide hazard, 135–175. https://doi.org/10.1007/978-94-015-8404-3_8

  5. Chen T, Niu R, Jia X (2016) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci 75(10):1–16. https://doi.org/10.1007/s12665-016-5317-y

    Article  Google Scholar 

  6. Chen Cao W, Qing J, Ruan et al (2016) Landslide susceptibility mapping in vertical distribution law of precipitation area: case of the xulong hydropower station reservoir, southwestern china. Water. https://doi.org/10.3390/w8070270

  7. Che VB, Kervyn M, Suh CE, Fontijn K, Ernst GGJ, del Marmol M-A, Jacobs P (2012) Landslide susceptibility assessment in Limbe (SW Cameroon): A field calibrated seed cell and information value method. Catena 92:83–98. https://doi.org/10.1016/J.CATENA.2011.11.014

    Article  Google Scholar 

  8. Chung C-JF, Fabbri AG, Westen CJV (1995) Multivariate regression analysis for landslide hazard zonation. Geographical Information Systems in Assessing Natural Hazards Selected Contributions from an International Workshop Held in Perugia on September 20–22, 1993. (Advances in Natural and Technological Hazards Research ; 5), 107–133. https://doi.org/10.1007/978-94-015-8404-3_7

  9. Chalkias C, Ferentinou M, Polykretis C (2014) GIS-based landslide susceptibility mapping on the peloponnese peninsula, Greece. Geosciences 4(3):176–190. https://doi.org/10.3390/GEOSCIENCES4030176

    Article  Google Scholar 

  10. Cooke RU, Doornkamp JC (1985) Geomorphology in environmental management

  11. Erener A, Düzgün HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? Environ Earth Sci 66(3):1–19. https://doi.org/10.1007/S12665-011-1297-0

    Article  Google Scholar 

  12. Eeckhaut MVD, Moeyersons J, Nyssen J, Abraha A, Poesen J, Haile M, Deckers J (2009) Spatial patterns of old, deep-seated landslides: a case-study in the northern Ethiopian highlands. Geomorphology 105(3):239–252. https://doi.org/10.1016/J.GEOMORPH.2008.09.027

    Article  Google Scholar 

  13. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/J.PATREC.2005.10.010

    Article  Google Scholar 

  14. 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):181–216. https://doi.org/10.1016/S0169-555 × (99)00078-1

    Article  Google Scholar 

  15. Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y (2015) Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging 33(1):1–25. https://doi.org/10.1016/J.MRI.2014.09.004

    Article  Google Scholar 

  16. Hansen A, Franks CAM, Kirk PA, Brimicombe AJ, Tung F (1995) Application of GIS to hazard assessment, with particular reference to landslides in Hong Kong, 273–298. https://doi.org/10.1007/978-94-015-8404-3_14

  17. Huabin W, Gangjun L, Weiya X, Gonghui W (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29(4):548–567. https://doi.org/10.1191/0309133305pp462RA

    Article  Google Scholar 

  18. Kreuzer TM, Wilde M, Terhorst B, Damm B (2017) A landslide inventory system as a base for automated process and risk analyses. Earth Sci Inf 10(4):507–515. https://doi.org/10.1007/S12145-017-0307-5

    Article  Google Scholar 

  19. Meijerink AMJ (1988) Data acquisition and data capture through terrain mapping unit. ITC J 1:23–44

    Google Scholar 

  20. Pasang S, Kubicek P (2018) Information value model based landslide susceptibility mapping at Phuentsholing, Bhutan. AGILE conference 2018

  21. Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenviron Disasters 3(1):19

    Article  Google Scholar 

  22. Sarkar S, Patra AK, Kumar P (2006) GIS based landslide susceptibility mapping — A case study in Indian Himalaya. Proc Interpraevent Int, 617–624

  23. Sharma LP, Patel N, Ghose MK, Debnath P (2015) Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India. Nat Hazards 75(2):1555–1576. https://doi.org/10.1007/S11069-014-1378-Y

    Article  Google Scholar 

  24. Speight JG (1977) Landform pattern description from aerial photographs. Photogrammetria 32(5):161–182. https://doi.org/10.1016/0031-8663(77)90012-6

    Article  Google Scholar 

  25. Steger S, Brenning A, Bell R, Glade T (2017) The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements. Landslides 14:1767–1781. https://doi.org/10.1007/s10346-017-0820-0

    Article  Google Scholar 

  26. Sun X, Chen J, Bao Y, Han X, Zhan J, Peng W (2018) Landslide susceptibility mapping using logistic regression analysis along the Jinsha River and its tributaries close to Derong and Deqin County Southwestern China. ISPRS Int J GeoInformation. https://doi.org/10.3390/ijgi7110438

  27. Sun X, Chen J, Han X, Bao Y, Zhou X, Peng W (2020) Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification. Bull Eng Geol Env 79(9):4657–4670. https://doi.org/10.1007/s10064-020-01849-0

    Article  Google Scholar 

  28. Verstappen HT (1983) Applied geomorphology. Geomorphological surveys for environmental development

  29. Wang F, Xu P, Wang C, Wang N, Jiang N (2017) Application of a GIS-based slope unit method for landslide susceptibility mapping along the Longzi River, southeastern Tibetan plateau, China. ISPRS Int J Geo-Inf 6(6):172. https://doi.org/10.3390/ijgi6060172

    Article  Google Scholar 

  30. Yang H, Yang T, Zhang S, Zhao F, Hu K, Jiang Y (2020) Rainfall-induced landslides and debris flows in Mengdong Town Yunnan Province China. Landslides 17(4):931–941. https://doi.org/10.1007/S10346-019-01336-Y

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA23090202), Key consulting projects of Chinese Academy of Engineering(2019-XZ-18), Foundation of Department of Land and Resources of Tibetan autonomous region ([2020] 0890-2), National Natural Science Foundation of China (Grant No. 41877261), West Young Scholars Program of the Chinese Academy of Sciences, CAS Key Technology Talent Program, Youth Natural Science Foundation of China (41704014), Special Seismic Science and Technology Project of Sichuan Earthquake Administration (LY1814). 

Author information

Affiliations

Authors

Corresponding author

Correspondence to Pengcheng Su.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Communicated by H. Babaie.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Su, P., Li, Y. et al. Susceptibility assessment of small, shallow and clustered landslide. Earth Sci Inform 14, 2347–2356 (2021). https://doi.org/10.1007/s12145-021-00687-2

Download citation

Keywords

  • Landslide susceptibility assessment
  • Slope unit
  • Grid cell
  • Information value