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Application of drone for landslide mapping, dimension estimation and its 3D reconstruction

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Abstract

Dimension estimation of landslides is a major challenge while preparing the landslide inventory map, for which very high-resolution satellite data/aerial photography is required, which is very expensive. An alternative is the application of drones for such mapping. This study presents the utility of drone/unmanned aerial vehicle (UAV) for mapping and 3D reconstruction of two landslides near IIT Mandi, Himachal Pradesh. In this study, we used DJI Phantom 3 Advanced drone to collect high-resolution images of landslides. Features in the images were automatically detected, described, and matched among photographs using scale invariant feature transform (SIFT) technique. The 3D position and orientation of the cameras and the XYZ location of each feature in the photographs was estimated using bundle block adjustment. This resulted in sparse 3D point cloud, which was densified using Clustering View for Multi-View Stereo (CMVS) algorithm. Finally, surface reconstruction was done using Poisson Surface Reconstruction method, which was visualised in open source software CloudCompare. The 3D model, generated from drone images, was validated using field measurements of some objects, and 3D surface created from total station. Various quantities i.e. width (length), height and perimeter were measured in the 3D drone model and verified with total station data. The differences among all the measured quantities for both the landslides are less than 5% keeping the measurements of total station as reference. The 3D reconstructed from the sets of photographs is very accurate giving the measurements up to cm level and even small objects could be easily identified. In addition, digital elevation model (DEM) of sub meter resolution could be easily generated and used for various applications. Hence drone-based imagery in combination with 3D scene reconstruction algorithms provide flexible and effective tools to map and monitor landslide apart from accurately assessing the dimensions of the landslides.

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References

  • Attene, M., & Spagnuolo, M. (2000). Automatic surface reconstruction from point sets in space. Computer Graphics Forum, 19(3), 457–465.

    Article  Google Scholar 

  • Berger, M., Tagliasacchi, A., Seversky, L. M., Alliez, P., Levine, J. A., Sharf, A., et al. (2014). State of the art in surface reconstruction from point clouds. EUROGRAPHICS Star Reports, 1(1), 161–185.

    Google Scholar 

  • Brardinoni, F., Slaymaker, O., & Hassan, M. A. (2003). Landslide inventory in a rugged forested watershed: A comparison between air-photo and field survey data. Geomorphology, 54(3), 179–196.

    Article  Google Scholar 

  • Champati ray, P. K., & Chattoraj, S. (2014). Sunkoshi landslide in Nepal and its possible impact in India: A remote sensing based appraisal. ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 8, 1345–1351.

    Article  Google Scholar 

  • Favalli, M., Fornaciai, A., Isola, I., Tarquini, S., & Nannipieri, L. (2012). Multiview 3D reconstruction in geosciences. Computers & Geosciences, 44, 168–176.

    Article  Google Scholar 

  • Fuhrmann, S., Langguth, F., & Goesele, M. (2014). MVE—a multi-view reconstruction environment. In Proceedings of the eurographics workshop on graphics and cultural heritage (pp. 11–18). Eurographics Association.

  • Furukawa, Y., Curless, B., Seitz, S. M., & Szeliski, R. (2010). Towards Internet-scale multi-view stereo. In IEEE Computer Society conference on computer vision and pattern recognition (pp. 1434–1441). IEEE.

  • Ghosh, S., Van Westen, C. J., Carranza, E. J. M., Jetten, V. G., Cardinali, M., Rossi, M., et al. (2012). Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Engineering Geology, 128, 49–62.

    Article  Google Scholar 

  • Gupta, S. K., & Shukla, D. P., (2017). 3D reconstruction of a landslide by application of UAV and structure from motion. In 20th AGILE conference on geographic information science, 9–12 May 2017, Wageningen, The Netherlands. ISBN 978-90-816960-7-4. Accessible https://agile-online.org/index.php/conference/proceedings/proceedings-2017.

  • James, M. R., & Robson, S. (2012). Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application. Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2011JF002289.

    Google Scholar 

  • Javernick, L., Brasington, J., & Caruso, B. (2014). Modeling the topography of shallow braided rivers using structure-from-motion photogrammetry. Geomorphology, 213, 166–182.

    Article  Google Scholar 

  • Kaiser, A., Neugirg, F., Rock, G., Müller, C., Haas, F., Ries, J., et al. (2014). Small-scale surface reconstruction and volume calculation of soil erosion in complex moroccan Gully morphology using structure from motion. Remote Sensing, 6(8), 7050–7080.

    Article  Google Scholar 

  • Kazhdan, M., & Hoppe, H. (2013). Screened Poisson surface reconstruction. ACM Transactions on Graphics, 32(3), 1–13.

    Article  Google Scholar 

  • Kumar, A., Mukherjee, A. B., & Krishna, A. P. (2017a). Application of conventional data mining techniques and web mining to aid disaster management. In A. V. Senthil Kumar (Ed.), Web usage mining techniques and applications across industries (pp. 138–167). IGI Global: Hershey, PA.

    Chapter  Google Scholar 

  • Kumar, D., Thakur, M., Dubey, C. S., & Shukla, D. P. (2017b). Landslide susceptibility mapping & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology, 295, 115–125.

    Article  Google Scholar 

  • Lehtola, V., Kurkela, M., & Rönnholm, P. (2017). Radial distortion from epipolar constraint for rectilinear cameras. Journal of Imaging, 3(1), 8.

    Article  Google Scholar 

  • Lindner, G., Schraml, K., Mansberger, R., & Hübl, J. (2016). UAV monitoring and documentation of a large landslide. Applied Geomatics, 8(1), 1–11.

    Article  Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Lu, P., Stumpf, A., Kerle, N., & Casagli, N. (2011). Object-oriented change detection for landslide rapid mapping. IEEE Geoscience and Remote Sensing Letters, 8(4), 701–705.

    Article  Google Scholar 

  • Lucieer, A., de Jong, S. M., & Turner, D. (2014). Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Progress in Physical Geography, 38(1), 97–116.

    Article  Google Scholar 

  • Martha, T. R., Kamala, P., Jose, J., Kumar, K. V., & Sankar, G. J. (2016). Identification of new landslides from high-resolution satellite data covering a large area using object-based change detection methods. Journal of the Indian Society of Remote Sensing, 44(4), 515–524.

    Article  Google Scholar 

  • Martha, T. R., Kerle, N., Jetten, V., Van Westen, C. J., & Kumar, K. V. (2010a). Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology, 116(1), 24–36.

    Article  Google Scholar 

  • Martha, T. R., Kerle, N., Jetten, V., Van Westen, C. J., & Kumar, K. V. (2010b). Landslide volumetric analysis using Cartosat-1-derived DEMs. IEEE Geoscience and Remote Sensing Letters, 7(3), 582–586.

    Article  Google Scholar 

  • Patwary, M. A. A., Champati ray, P. K., & Parvaiz, I. (2009). IRS-LISS-III and PAN data analysis for landslide susceptibility mapping using heuristic approach in active tectonic region of Himalaya. Journal of the Indian Society of Remote Sensing, 37(3), 493–509.

    Article  Google Scholar 

  • Poonam, C., Rana, N., Champati ray, P. K., Bisht, P., Bagri, D. S., Wasson, R. J., et al. (2017). Identification of landslide-prone zones in the geomorphically and climatically sensitive Mandakini valley, (central Himalaya), for disaster governance using the Weights of Evidence method. Geomorphology, 284, 41–52.

    Article  Google Scholar 

  • Saunders, G. M. (2014). Development of photogrammetric methods for landslide analysis. University of Oslo.

  • Shukla, D. P., Gupta, S., Dubey, C. S., & Thakur, M. (2016). Geo-spatial technology for landslide hazard zonation and prediction. In M. Marghany (Ed.), Environmental applications of remote sensing (pp. 281–308). InTech.

  • Siyahghalati, S., Saraf, A. K., Pradhan, B., Jebur, M. N., & Tehrany, M. S. (2016). Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images. Geomatics, Natural Hazards and Risk, 7(1), 326–344.

    Article  Google Scholar 

  • Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from internet photo collections. International Journal of Computer Vision, 80(2), 189–210.

    Article  Google Scholar 

  • Stöcker, C., Eltner, A., & Karrasch, P. (2015). Measuring gullies by synergetic application of UAV and close range photogrammetry—a case study from Andalusia, Spain. CATENA, 132, 1–11.

    Article  Google Scholar 

  • Triggs, B., Triggs, B., McLauchlan, P., Hartley, R., & Fitzgibbon, A. (2000). Bundle adjustment—a modern synthesis. In Vision algorithms: Theory and practice, LNCS, pp. 298–375.

  • Tsutsui, K., Rokugawa, S., Nakagawa, H., Miyazaki, S., Cheng, C. T., Shiraishi, T., et al. (2007). Detection and volume estimation of large-scale landslides based on elevation-change analysis using DEMs extracted from high-resolution satellite stereo imagery. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1681–1696.

    Article  Google Scholar 

  • Turner, D., Lucieer, A., & de Jong, S. M. (2015). Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sensing, 7(2), 1736–1757.

    Article  Google Scholar 

  • Van Westen, C. J., Ghosh, S., Jaiswal, P., Martha, T. R., & Kuriakose, S. L. (2013). From landslide inventories to landslide risk assessment; an attempt to support methodological development in India. In C. Margottini, P. Canuti, & K. Sassa (Eds.), Landslide science and practice: Landslide inventory and susceptibility and hazard zoning (Vol. 1, pp. 3–20). Berlin: Springer.

    Chapter  Google Scholar 

  • Van Westen, C. J., & Lulie Getahun, F. (2003). Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models. Geomorphology, 54(1–2), 77–89.

    Article  Google Scholar 

  • Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). “Structure-from-motion” photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314.

    Article  Google Scholar 

  • Wieczorek, G. F. (1984). Preparing a detailed landslide-inventory map for hazard evaluation and reduction. Bulletin Association of Engineering Geologists, 21(3), 337–342.

    Google Scholar 

  • Wu, C. (2007). SiftGPU: A GPU implementation of scale invaraint feature transform (SIFT). http://cs.unc.edu/~ccwu/siftgpu.

  • Wu, C. (2013). Towards linear-time incremental structure from motion. In Proceedings— 2013 international conference on 3D vision, 3DV 2013 (pp. 127–134). IEEE.

  • Wu, C., Agarwal, S., Curless, B., & Seitz, S. M. (2011). Multicore bundle adjustment. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (pp. 3057–3064). IEEE.

Download references

Acknowledgements

The author would like to thank Mr. Abhay Guleria (M.S. Scholar), Mr. M. Naresh (Ph.D. Scholar), Mr. Lokesh Tungariya and Mr Rakesh Meena (B.Tech Students) and and Mr. Sudhanshu Gautam (Intern) of IIT Mandi, for their help during the field work and data collection using total station. This paper would not come out in such a better shape without the suggestive and critical comments of both the reviewers.

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Correspondence to Dericks P. Shukla.

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Gupta, S.K., Shukla, D.P. Application of drone for landslide mapping, dimension estimation and its 3D reconstruction. J Indian Soc Remote Sens 46, 903–914 (2018). https://doi.org/10.1007/s12524-017-0727-1

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