Skip to main content

Advertisement

SpringerLink
Log in

Identification of unstable bedrock promontory on steep slope based on UAV photogrammetry

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

Conventional, manual methods of surveying unstable bedrock promontories on high and steep slopes are very risky and time-consuming due to problems regarding steep terrain and traffic inconvenience. This paper describes an unmanned aerial vehicle (UAV)-based method for the identification of unstable bedrock promontories. The first step is the spatial position identification of bedrock promontories, which is implemented by processing point cloud models with the one-class support vector machine (OC-SVM) and clustering algorithm. The next step is the preliminary stability assessment of bedrock promontories. Kinematic analysis by mapping the orientations of penetrative discontinuities within bedrock promontories is used to evaluate the stability. The applicability of the method is assessed using the point cloud model of the bank slopes of the Lianghekou Hydropower Project. The results show that effective identification, and preliminary stability assessment of bedrock promontories can be carried out using UAV photogrammetry.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References 

  • Barlow J, Gilham J, Ibarra Cofrã I (2017) Kinematic analysis of sea cliff stability using UAV photogrammetry. Int J Remote Sens 38:2464–2479

    Article  Google Scholar 

  • Beretta F, Rodrigues AL, Peroni RL, Costa JFCL (2019) Automated lithological classification using UAV and machine learning on an open cast mine. Appl Earth Sci 128:79–88

    Article  Google Scholar 

  • Cazals F, Pouget M (2005) Topology driven algorithms for ridge extraction on meshes. [Research Report] RR-5526, INRIA. 2005, 29

  • Chudzian P (2011) Radial basis function kernel optimization for pattern classification. In, Berlin, Heidelberg, Computer Recognition Systems 4. Springer, Berlin Heidelberg, pp 99–108

    Google Scholar 

  • Fanti R, Gigli G, Lombardi L, Tapete D, Canuti P (2013) Terrestrial laser scanning for rockfall stability analysis in the cultural heritage site of Pitigliano (Italy). Landslides 10:409–420

    Article  Google Scholar 

  • Fazio NL, Perrotti M, Andriani GF, Mancini F, Rossi P, Castagnetti C, Lollino P (2019) A new methodological approach to assess the stability of discontinuous rocky cliffs using in-situ surveys supported by UAV-based techniques and 3-D finite element model: a case study. Engineering Geology 260:105205

  • Ferrero AM, Forlani G, Roncella R, Voyat HI (2009) Advanced geostructural survey methods applied to rock mass characterization. Rock Mech Rock Eng 42:631–665

    Article  Google Scholar 

  • Giordan D, Adams MS, Aicardi I et al (2020) The use of unmanned aerial vehicles (UAVs) for engineering geology applications[J]. Bull Eng Geol Env 79:3437–3481

    Article  Google Scholar 

  • Harwin S, Lucieer A (2012) Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing 4:1573–1599

    Article  Google Scholar 

  • Hennig T, Wang W, Feng Y, Ou X, He D (2013) Review of Yunnan’s hydropower development. Comparing small and large hydropower projects regarding their environmental implications and socio-economic consequences. Renew Sustain Energy Rev 27:585–595

    Article  Google Scholar 

  • Hoek E, Bray J (1981) Rock slope engineering, 3rd edn. Institution of Mining and Metallurgy, London, UK

    Book  Google Scholar 

  • Kriegel H-P, Kröger P, Sander J, Zimek A (2011) Density-based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1:231–240

    Google Scholar 

  • Lucieer A, Jong SMd, Turner D (2014) Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Progress in Physical Geography: Earth and Environment 38:97–116

    Article  Google Scholar 

  • Mayr A, Rutzinger M, Bremer M, Oude Elberink S, Stumpf F, Geitner C (2017) Object-based classification of terrestrial laser scanning point clouds for landslide monitoring. Photogram Rec 32:377–397

    Article  Google Scholar 

  • Menegoni N, Giordan D, Perotti C, Tannant DD (2019) Detection and geometric characterization of rock mass discontinuities using a 3D high-resolution digital outcrop model generated from RPAS imagery – Ormea rock slope. Ital Eng Geol 252:145–163

    Article  Google Scholar 

  • Niethammer U, James MR, Rothmund S, Travelletti J, Joswig M (2012) UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Eng Geol 128:2–11

    Article  Google Scholar 

  • Pauly M, Gross M, and Kobbelt LP (2002) Efficient simplification of point-sampled surfaces. In Proceedings of the conference on Visualization ’02 (VIS ’02). IEEE Computer Society, USA, 163–170

  • Pipaud I, Lehmkuhl F (2017) Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines. Geomorphology 293:178–200

    Article  Google Scholar 

  • Riquelme AJ, Abellán A, Tomás R, Jaboyedoff M (2014) A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Comput Geosci 68:38–52

    Article  Google Scholar 

  • Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443–1471

    Article  Google Scholar 

  • Slob S, van Knapen B, Hack R, Turner K, Kemeny J (2005) Method for automated discontinuity analysis of rock slopes with three-dimensional laser scanning. Transp Res Rec 1913:187–194

    Article  Google Scholar 

  • Tonini M, Abellan A (2014) Rockfall detection from terrestrial LiDAR point clouds: a clustering approach using R. J Spat Inf Sci 2014:95–110

    Google Scholar 

  • Tonkin TN, Midgley NG (2016) Ground-control networks for image based surface reconstruction: an investigation of optimum survey designs using UAV derived imagery and structure-from-motion. Photogramm Remote Sens 8:786

    Google Scholar 

  • Torrero L, Seoli L, Molino A, Giordan D, Manconi A, Allasia P, Baldo M The use of micro-UAV to monitor active landslide scenarios. In, Cham, 2015. Engineering Geology for Society and Territory - Volume 5. Springer International Publishing, pp 701–704

  • Vasuki Y, Holden E-J, Kovesi P, Micklethwaite S (2014) Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: an image analysis approach. Comput Geosci 69:22–32

    Article  Google Scholar 

  • Verhoeven G (2011) Taking computer vision aloft – archaeological three-dimensional reconstructions from aerial photographs with photoscan. Archaeol Prospect 18:67–73

    Article  Google Scholar 

  • Wang MX, Huang D, Wang G, Li DQ (2020) SS-XGBoost: a machine learning framework for predicting newmark sliding displacements of slopes. J Geotech Geoenviron Eng 146:04020074

    Article  Google Scholar 

  • Zhang Y-H, Geng G-H, Wei X-R (2015) Valley-ridge feature extraction from point clouds. Opt Precis Eng 23:310–318

    Article  Google Scholar 

Download references

Funding

This research was supported by the Joint Fund of the Natural Science Foundation of China and Yalong River Hydropower Development Co., Ltd. (grant no. U1765106).

Author information

Authors and Affiliations

  1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300350, China

    Wei Cui & Xuan-hao Wang

  2. Key Laboratory of Earthquake Engineering Simulation and Seismic Resilience of China Earthquake Administration, Tianjin University, Tianjin, 300350, China

    Wei Cui

  3. Yalong River Hydropower Development Company. Ltd, Chengdu, 610051, China

    Gui-ke Zhang & Hong-bi Li

Authors
  1. Wei Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuan-hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gui-ke Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong-bi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuan-hao Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, W., Wang, Xh., Zhang, Gk. et al. Identification of unstable bedrock promontory on steep slope based on UAV photogrammetry. Bull Eng Geol Environ 80, 7193–7211 (2021). https://doi.org/10.1007/s10064-021-02333-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-021-02333-z

Keywords

Search

Navigation