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

Abstract

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 multi-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 (~l-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.

Keywords

3D model / natural risk / geophysical/geological techniques / unmanned aerial vehicle / fusion data

References

[1] Abidin H, Heri A, Mai G, . On the Use of GPS Methods for Studying Land Displacements on the Landslide Prone Area. FIG Working Week 2004, 2004.

[2] Al-Rawabdeh A, He F N, Moussa A, . Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition. Remote Sensing, 2016, 8 2 95

[3] Alsadik A. Guided Close Range Photogrammetry for 3D Modelling of Cultural Heritage Sites, 2014

[4] Arosio D, Longoni L, Papini M, . Scaioni M, . Analysis of Microseismic Activity within Unstable Rock Slopes. Modern Technologies for Landslide Investigation and Prediction, 2014, Berlin, Heidelberg: Springer, 141-154.

[5] Auge M. Metodos Geoelectricos para la Prospeccion de Agua Sub-terranea, 2008, Buenos Aires: Universidad de Buenos Aires

[6] Bogoslovsky V A, Ogilvy A A. Geophysical Methods for the Investigation of Landslides. Geophysics, 1977, 42(3): 562-571.

[7] Carrera H J J, Levresse G, Lacan P, . A Low Cost Technique for Development of Ultra-High Resolution Topography: Application to a Dry Maar’s Bottom. Revista Mexicana de Ciencias Geológicas, 2016, 33(1): 122-133.

[8] Dong S C, Samsonov S, Yin H W, . Two-Dimensional Ground Deformation Monitoring in Shanghai Based on SBAS and MSBAS InSAR Methods. Journal of Earth Science, 2018, 29(4): 960-968.

[9] Ganz J. Die Gipfelbewegung der Rosablanche. Swiss Journal of Surveying and Rural Engineering, 1914, 21 10 233.

[10] González N G A, Molina Garza R S, Aranda Gómez J J, . Pa-leomagnetismo y edad de la Ignimbrita Panalillo Superior, Campo Volcánico de San Luis Potosí, México. Boletín de la Sociedad Geológica Mexicana, 2012, 64(3): 387-409.

[11] Grayson B, Penna N T, Mills J P, . GPS Precise Point Positioning for UAV Photogrammetry. The Photogrammetric Record, 2018, 33(164): 427-447.

[12] Labarthe H G, Jiménez López L S, Aranda J J. Reinterpretación de la Geología del Centra Volcanico de la Sierra de Ahualulco, S. L. P, 1995.

[13] Niethammer U, James M R, Rothmund S, . UAV-Based Remote Sensing of the Super-Sauze Landslide: Evaluation and Results. Engineering Geology, 2012, 128: 2-11.

[14] Othaman Z, Wan A W, Anuar A. 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, 2011, 453-459.

[15] Pirotti E, Guarnieri A, Masiero A, . Scaioni M, ed, . Micro-Scale Landslide Displacements Detection Using Bayesian Methods Applied to GNSS Data. Modern Technologies for Landslide Investigation and Prediction, 2014, Berlin, Heidelberg: Springer, 123-138.

[16] Reshetyuk Y, Mårtensson S G. Generation of Highly Accurate Digital Elevation Models with Unmanned Aerial Vehicles. The Photogrammetric Record, 2016, 31(154): 143-165.

[17] Rodríguez D E. Estudio de Técnicas Electromagnéticas de Prospec-ción de Subsuelo, 2015.

[18] Sato M. Near Range Radar and Its Application to near Surface Geophysics and Disaster Mitigation. Journal of Earth Science, 2015, 26(6): 858-863.

[19] Scaioni M. Modern Technologies for Landslide Monitoring and Prediction, 2015

[20] Stumpf A, Malet J P, Allemand P, . Ground-Based Multi-View Photogrammetry for the Monitoring of Landslide Deformation and Erosion. Geomorphology, 2015, 231: 130-145.

[21] Telford W M, Geldart L P, Sheriff R E. Applied Geophysics (Vol. 1), 1990, Cambridge: Cambridge University Press

[22] Teixidó T, Quintana Á R R. Aplicacion de la Tomografía Eléctrica en la Caracterización del Deslizamiento de Doña Mencía. Instituto Andaluz de Geofísica, 2013, 56.

[23] Tian Y Y, Xu C, Ma S Y, . Inventory and Spatial Distribution of Landslides Triggered by the 8th August 2017 M _w 6.5 Jiuzhaigou Earthquake, China. Journal of Earth Science, 2019, 30(1): 206-217.

[24] Turner D, Lucieer A, Wallace L. Direct Georeferencing of Ultra-high-Resolution UAV Imagery. IEEE Transactions on Geoscience and Re-mote Sensing, 2014, 52(5): 2738-2745.

[25] Zhong C, Li H, Xiang W, . Comprehensive Study of Landslides through the Integration of Multi Remote Sensing Techniques: Framework and Latest Advances. Journal of Earth Science, 2012, 23(2): 243-252.