Advanced Search

Indexed by SCI、CA、РЖ、PA、CSA、ZR、etc .

Volume 31 Issue 1
Jan 2020
Turn off MathJax
Article Contents
Fermín Villalpando, José Tuxpan, José Alfredo Ramos-Leal, Simón Carranco-Lozada. New Framework Based on Fusion Information from Multiple Landslide Data Sources and 3D Visualization. Journal of Earth Science, 2020, 31(1): 159-168. doi: 10.1007/s12583-019-1243-8
Citation: Fermín Villalpando, José Tuxpan, José Alfredo Ramos-Leal, Simón Carranco-Lozada. New Framework Based on Fusion Information from Multiple Landslide Data Sources and 3D Visualization. Journal of Earth Science, 2020, 31(1): 159-168. doi: 10.1007/s12583-019-1243-8

New Framework Based on Fusion Information from Multiple Landslide Data Sources and 3D Visualization

doi: 10.1007/s12583-019-1243-8
More Information
  • Corresponding author: José Tuxpan
  • Received Date: 17 Mar 2019
  • Accepted Date: 25 Jun 2019
  • Publish Date: 01 Jan 2020
  • Recent monitoring techniques employ multiple sources of information for the characterization of the phenomenon to be studied, being the coupling and adjustment of multi-source data one of the first challenges to consider and solve. The authors propose a new framework of the multi-source and mul-ti-temporal data-oriented fusion for the characterization of landslide events. The main objective is to generate 3D virtual models (in the form of dense point clouds) and feed them back with the characteristic of soil and subsoil information. The scheme consists of three main steps. The first one is on-site data collection (geological characterization, geophysical measurements, GPS measurements, and UAV/drone mapping). The second step is generation of a high-resolution 3D virtual model (~1-inch spatial resolution) from the frames acquired through the UAV using the structure of motion (SfM) processing; the developed virtual model is optimized with GPS measurements to minimize geolocation error and eliminate distortions. The last step is assembling of the acquired data in the field and densified point cloud considering the different nature of the data, re-escalating procedure and the information stacking layer.

     

  • loading
  • Abidin, H., Heri, A., Mai, G., et al., 2004. On the Use of GPS Methods for Studying Land Displacements on the Landslide Prone Area. FIG Working Week 2004, May 22-27, 2004, Athens, Greece. https://www.fig.net/resources/proceedings/fig_proceedings/athens/papers/ts16/TS16_6_Abidin_et_al.pdf
    Al-Rawabdeh, A., He, F. N., Moussa, A., et al., 2016. Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition. Remote Sensing, 8(2): 95. https://doi.org/10.3390/rs8020095
    Alsadik, A., 2014. Guided Close Range Photogrammetry for 3D Modelling of Cultural Heritage Sites: [Dissertation]. University of Twente, Enschede
    Arosio, D., Longoni, L., Papini, M., et al., 2014. Analysis of Microseismic Activity within Unstable Rock Slopes. In: Scaioni, M., ed., Modern Technologies for Landslide Investigation and Prediction. Springer, Berlin, Heidelberg. 141-154
    Auge, M., 2008. Métodos Geoeléctricos para la Prospección de Agua Subterránea: [Dissertation]. Universidad de Buenos Aires, Buenos Aires
    Bogoslovsky, V. A., Ogilvy, A. A., 1977. Geophysical Methods for the Investigation of Landslides. Geophysics, 42(3): 562-571. https://doi.org/10.1190/1.1440727
    Carrera, H. J. J., Levresse, G., Lacan, P., et al., 2016. A Low Cost Technique for Development of Ultra-High Resolution Topography: Application to a Dry Maar*s Bottom. Revista Mexicana de Ciencias Geológicas, 33(1): 122-133
    Dong, S. C., Samsonov, S., Yin, H. W., et al., 2018. Two-Dimensional Ground Deformation Monitoring in Shanghai Based on SBAS and MSBAS InSAR Methods. Journal of Earth Science, 29(4): 960-968. https://doi.org/10.1007/s12583-017-0955-x
    Ganz, J., 1914. Die Gipfelbewegung der Rosablanche. Swiss Journal of Surveying and Rural Engineering, 21(10): 233. https://doi.org/10.5169/seals-188068
    González, N., G. A., Molina Garza, R. S., Aranda Gómez, J. J., et al., 2012. Paleomagnetismo y edad de la Ignimbrita Panalillo Superior, Campo Volcánico de San Luis Potosí, México. Boletín de la Sociedad Geológica Mexicana, 64(3): 387-409. https://doi.org/10.18268/bsgm2012v64n3a9
    Grayson, B., Penna, N. T., Mills, J. P., et al., 2018. GPS Precise Point Positioning for UAV Photogrammetry. The Photogrammetric Record, 33(164): 427-447. https://doi.org/10.1111/phor.12259
    Labarthe, H. G., Jiménez López, L. S., Aranda, J. J., 1995. Reinterpretación de la Geología del Centro Volcanico de la Sierra de Ahualulco, S. L. P
    Niethammer, U., James, M. R., Rothmund, S., et al., 2012. UAV-Based Remote Sensing of the Super-Sauze Landslide: Evaluation and Results. Engineering Geology, 128: 2-11. https://doi.org/10.1016/j.enggeo.2011.03.012
    Othaman, Z., Wan, A. W., Anuar, A., 2011. Evaluating the Performance of GPS Survey Methods for Landslide Monitoring at Hillside Residential Area: Static vs Rapid Static. IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011. March 4-6, 2011, Penang. 453-459
    Pirotti, F., Guarnieri, A., Masiero, A., et al., 2014. Micro-Scale Landslide Displacements Detection Using Bayesian Methods Applied to GNSS Data. In: Scaioni, M., ed., Modern Technologies for Landslide Investigation and Prediction. Springer, Berlin, Heidelberg. 123-138
    Reshetyuk, Y., Mårtensson, S. G., 2016. Generation of Highly Accurate Digital Elevation Models with Unmanned Aerial Vehicles. The Photogrammetric Record, 31(154): 143-165. https://doi.org/10.1111/phor.12143
    Rodríguez, D. F., 2015. Estudio de Técnicas Electromagnéticas de Prospección de Subsuelo.[2019-7-27]. https://upcommons.upc.edu/bitstream/handle/2117/78151/memoria.pdf?sequence=1&isAllowed=y
    Sato, M., 2015. Near Range Radar and Its Application to near Surface Geophysics and Disaster Mitigation. Journal of Earth Science, 26(6): 858-863. https://doi.org/10.1007/s12583-015-0595-y
    Scaioni, M., 2015. Modern Technologies for Landslide Monitoring and Prediction. Springer. http://doi.org/10.1007/978-3-662-45931-7
    Stumpf, A., Malet, J. P., Allemand, P., et al., 2015. Ground-Based Multi-View Photogrammetry for the Monitoring of Landslide Deformation and Erosion. Geomorphology, 231: 130-145. https://doi.org/10.1016/j.geomorph.2014.10.039
    Telford, W. M., Geldart, L. P., Sheriff, R. E., 1990. Applied Geophysics (Vol. 1). Cambridge University Press, Cambridge
    Teixidó, T., Quintana, Á. R., 2013. Aplicación de la Tomografía Eléctrica en la Caracterización del Deslizamiento de Doña Mencía: [Dissertation]. Instituto Andaluz de Geofísica, Granada, Spain. 56
    Tian, Y. Y., Xu, C., Ma, S. Y., et al., 2019. Inventory and Spatial Distribution of Landslides Triggered by the 8th August 2017 MW 6.5 Jiuzhaigou Earthquake, China. Journal of Earth Science, 30(1): 206-217. https://doi.org/10.1007/s12583-018-0869-2
    Turner, D., Lucieer, A., Wallace, L., 2014. Direct Georeferencing of Ultrahigh- Resolution UAV Imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2738-2745. https://doi.org/10.1109/tgrs.2013.2265295
    Zhong, C., Li, H., Xiang, W., et al., 2012. Comprehensive Study of Landslides through the Integration of Multi Remote Sensing Techniques: Framework and Latest Advances. Journal of Earth Science, 23(2): 243-252. https://doi.org/10.1007/s12583-012-0245-6
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)

    Article Metrics

    Article views(421) PDF downloads(32) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return