Skip to main content

Advertisement

SpringerLink
Log in

A new algorithm for landslide geometric and deformation analysis supported by digital elevation models

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Geometric analysis of a landslide boundary, in particular, automatic determination of the length and width of landslide and classification is a challenge. In this regard, developing an integrated automatic algorithm to determine and measure length, width, area, failure flow direction, mass displacement material, and to classify a landslide, all at one time seem to be a useful method for updating landslide inventory with reliable outcomes and efficient time for disaster management. This study presents a new automatic mapping and modelling algorithm for landslide geometric analysis include calculating landslide displacement and failure flow direction. We utilized LiDAR high resolution digital elevation model (DEM) (5 m), ASTER DEM (30 m), and Unmanned Aerial Vehicle (UAV) associated with ground truth observations to support the geometric deformation measurements. This study aims to refurbish generating landslide inventory dataset of 2015 by implementing the proposed algorithm in a quicker time than existing and traditional methods. The proposed algorithm is scripted in MATLAB based on the DEMs of before and after a landslide. The proposed new automatic method contributes measure, determine, and calculate (a) length, width, area, (b) the flow direction of the material movement, (c) the volume of the material displacement after the onset of failure, and (d) type of a landslide, in an acceptable accuracy performance. I considered two study areas (1) Alborz Mountain of Iran and (2) Madaling of Guizhou Province in China. The proposed algorithm was validated by (a) the ground truth observations, (b) the existing inventory dataset and (c) implementing the same data in ArcGIS 10.4 to compute the relative measurement errors. The relative error for area, length, width, and volume is 0.16%, 1.67%, 0.30%, 5.50%, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Ali SA, Pirasteh S (2004) Geological applications of landsat Enhanced Thematic Mapper (ETM) data and Geographic Information System (GIS): mapping and structural interpretation in south-west Iran, Zagros structural belt. Int J Remote Sens 25(21):4715–4727

    Article  Google Scholar 

  • Ali SA, Rangzan K, Pirasteh S (2003a) Remote sensing and GIS study of tectonics and net erosion rates in the Zagros structural belt, southwestern Iran. GISci Remote Sens 40(4):253–262

    Google Scholar 

  • Ali SA, Rangzan K, Pirasteh S (2003b) Use of digital elevation model for study of drainage morphometry and identification stability and saturation zones in relations to landslide assessments in parts of the Shahbazan area, SW Iran. Cartography 32(2):71–76

    Article  Google Scholar 

  • Ardizzone F, Cardinali M, Carrara A, Guzzetti F, Reichenbach P (2002) Impact of mapping errors on the reliability of landslide hazard maps. Nat Haz Earth Syst Sci 2:3–14. https://doi.org/10.5194/nhess-2-3-2002

    Article  Google Scholar 

  • 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 Haz Earth Syst Sci 7:637–650. https://doi.org/10.5194/nhess-7-637-2007

    Article  Google Scholar 

  • Burden RL, Faires JD (2011) Numerical analysis, 9th edn. Brooks/Cole, Boston

    Google Scholar 

  • Cruden DM (1991) A simple definition of a landslide. Bull Int Assoc Eng Geol 43(1):27–29

    Article  Google Scholar 

  • Freeman GT (1991) Calculating catchment areas with divergent flow based on a regular grid. Comput Geosci 17:413–422. https://doi.org/10.1016/0098-3004(91)90048-I

    Article  Google Scholar 

  • Freeman H, Shapira R (1975) Determining the minimum-area encasing rectangle for an arbitrary closed curve. Commun ACM 18:409–413. https://doi.org/10.1145/360881.360919

    Article  Google Scholar 

  • Gaidzik K, Ramírez-Herrera MT, Bunn M, Leshchinsky BA, Olsen M, Regmi NR (2017) Landslide manual and automated inventories, and susceptibility mapping using LIDAR in the forested mountains of Guerrero, Mexico. Geomat Nat Haz Risk 8(2):1054–1079

    Article  Google Scholar 

  • Gholami M, Ghachkanlu EN, Khosravi K, Pirasteh S (2019) Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. J Earth Syst Sci 128:42–22. https://doi.org/10.1007/s12040-018-1047-8

    Article  Google Scholar 

  • Golovko D, Roessner S, Behling R, Kleinschmit B (2017) Automated derivation and spatio-temporal analysis of landslide properties in southern Kyrgyzstan. Nat Haz 85(3):1461–1488

    Article  Google Scholar 

  • Guo J, Zeng C, Xie M, Meng Y, Zhang L (2018) Failure mechanism of Madaling landslide in Guizhou Province and stability analysis of accumulation body [J]. Saf Environ Eng 25(02):48–59

    Google Scholar 

  • Hattanji T, Moriwaki H (2009) Morphometric analysis of relic landslides using detailed landslide distribution maps: implications for forecasting travel distance of future landslides. Geomorphology 103(3):447–454

    Article  Google Scholar 

  • Highland LM, Bobrowsky P, Kempthorne D, Myers MD (2008) The landslide handbook: a guide to understanding landslides. Department of the Interior U.S. Geological Survey, Reston

    Google Scholar 

  • Jaboyedoff M, Oppikofer T, Abella’n A, Marc-Henri D, Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in landslide investigations: a review. Nat Haz 61:5–28. https://doi.org/10.1007/s11069-010-9634-2

    Article  Google Scholar 

  • Kreyszig E, Kreyszig H, Norminton EJ (2011) Advanced engineering mathematics, 10th edn. Wiley, New York

    Google Scholar 

  • Liu JK, Hsiao KH, Shih Peter TY (2012) A geomorphological model for landslide detection using airborne LiDAR data. J Mar Sci Technol 20(6):629–638

    Google Scholar 

  • Lyons NJ, Mitasova H, Wegmann KW (2014) Improving mass wasting inventories by incorporating debris flow topographic signatures. Landslides. 11:385–397. https://doi.org/10.1007/s10346-013-0398-0

  • Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Process Land 29:687–711. https://doi.org/10.1002/esp.1064

    Article  Google Scholar 

  • Martha TR, Kerle N, Jetten V, Van Westen CJ, Vinod Kumar K (2010) Characterizing spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116:24–36. https://doi.org/10.1016/j.geomorph.2009.10.004

    Article  Google Scholar 

  • Mondini AC, Guzzetti F, Reichenbach P, Rossi M, Ardizzone F (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115:1742–1757. https://doi.org/10.1016/j.rse.2011.03.006

    Article  Google Scholar 

  • Niculitˇa M (2016) Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs. Nat Haz Earth Syst Sci 16:2021–2030. https://doi.org/10.5194/nhess-16-2021-2016

    Article  Google Scholar 

  • Niculiță M (2015) Automatic extraction of landslide flow direction using geometric processing and DEMs. Geomorpho Geosci 10(12):201–203

    Google Scholar 

  • Pirasteh S, Li J (2016) Landslides investigations from geo-informatics perspective: quality, challenges, and recommendations. Geomat Nat Haz Risk 8:1–18. https://doi.org/10.1080/19475705.2016.1238850

    Article  Google Scholar 

  • Pirasteh S, Li J (2018) Developing an algorithm for automated geometric analysis and classification of landslides incorporating LiDAR-derived DEM. Environ Earth Sci 77:414–415. https://doi.org/10.1007/s12665-018-7583-3

    Article  Google Scholar 

  • Pirasteh S, Woodbridge K, Rizvi SM (2009) Geo-information technology (GiT) and tectonic signatures: the River Karun & Dez, Zagros Orogen in south-west Iran. Int J Remote Sens 30(1–2):389–404

    Article  Google Scholar 

  • Pirasteh S, Li J, Chapman M (2017) Use of LiDAR-derived DEM and a stream length-gradient index approach to investigation of landslides in Zagros Mountains, Iran. Geocarto J. https://doi.org/10.1080/10106049.2017.1316779

  • Raucoules D, Michele M, Aunay B (2018) Landslide displacement mapping based on ALOS-2/PALSAR-2 data using image correlation techniques and SAR interferometry: application to the Hell-Bourg landslide (Salazie circle, La Réunion Island). Geocarto Int https://doi.org/10.1080/10106049.2018.1508311

  • Ren F, Wu X, Zhang K, Niu R (2014) Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the three gorges, China. Environ Earth Sci https://doi.org/10.1007/s12665-014-3764-x

  • Roering JJ, Stimely LL, Mackey BH, Schmidt DA (2009) Using DInSAR airbone LIDAR and archival air photos to quantify landsliding and sediment transport. Geophys Res Lett 36:L19402. https://doi.org/10.1029/2009GL040374

  • Su JG, Bork EW (2006) Influence of vegetation, slope and LiDAR sampling angle on DEM accuracy. Photogramm Eng Remote Sens 72:1265–1274

    Article  Google Scholar 

  • Su Wen J, Stohr C (2000) Aerial photointerpretation of landslides along the Ohio and Mississippi Rivers. Environ Eng Geosci VI(4):311–323

    Google Scholar 

  • Tarchi D, Casagli N, Fanti R, Leva DD, Luzi G, Pasuto A, Pieraccini M, Silvano S (2003) Landslide monitoring by using ground-based SAR interferometry: an example of application to the Tessina landslide in Italy. Eng Geol 68:15–30. https://doi.org/10.1016/S0013-7952(02)00196-5

    Article  Google Scholar 

  • Taylor Faith E, Malamud Bruce D, Witt A (2015) What shape is a landslide? Statistical patterns in landslide length to width ratio. Geophy Res Abstr 17:EGU2015-10191 EGU General Assembly

    Google Scholar 

  • Teza G, Galgaro A, Zaltron N, Genevois R (2007) Terrestrial laser scanner to detect landslide displacement fields: a new approach. Int J Remote Sens 28:3425–3446. https://doi.org/10.1080/01431160601024234

    Article  Google Scholar 

  • Travelletti J, Oppikofer T, Delacourt C, Malet J, Jaboyedoff M (2008) Monitoring landslide displacements during a controlled rain experiment using a long-range terrestrial laser scanning (TLS). Int Arch Photogr Remote Sens 37(B5):485–490

    Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Special report 176: landslides: analysis and control. National Academy of Science, Washington, DC, pp 11–33

    Google Scholar 

  • Wehr A, Lohr U (1999) Airborne laser scanning–an introduction and overview. ISPRS J Photogr Remote Sens 54:68–82. https://doi.org/10.1016/S0924-2716(99)00011-8 http://support.esri.com/technical-article/000006109

    Article  Google Scholar 

Download references

Acknowledgements

We greatly appreciate Professor Jonathan Li from the University of Waterloo, for his support. We are also thankful to Dr. Seyed Ali Ashrafizadeh, the Visiting Professor of the University of Waterloo for his reflections in writing the script. We also appreciate Natural Resources of Iran for the valuable comments and providing me with the landslide inventory data. The authors also appreciate Chengdu University of Technology for providing us with information from Madaling in Guizhou Province.

Code availability

The MATLAB stat code of the script which implements the algorithm is available upon request to s2pirast@uwaterloo.ca

Author information

Authors and Affiliations

  1. Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University (SWJTU), The Western Park of the Hi-Tech Industrial Development Zone, Chengdu, 611756, Sichuan, China

    Saied Pirasteh, Ghazal Shamsipour, Gouxiang Liu & Qing Zhu

  2. Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu, 610059, China

    YE Chengming

Authors
  1. Saied Pirasteh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghazal Shamsipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gouxiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qing Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. YE Chengming
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Saied Pirasteh or Ghazal Shamsipour.

Ethics declarations

Disclosure statement

The authors do not have any potential conflict of interest.

Additional information

Communicated by: H. Babaie

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pirasteh, S., Shamsipour, G., Liu, G. et al. A new algorithm for landslide geometric and deformation analysis supported by digital elevation models. Earth Sci Inform 13, 361–375 (2020). https://doi.org/10.1007/s12145-019-00437-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-019-00437-5

Keywords

Search

Navigation