Measurement of Road Surface Deformation Using Images Captured from UAVs
"> Figure 1
<p>Geographical location and geological setting: (<b>a</b>) Geographical location; (<b>b</b>) Landslide and study area; (<b>c</b>) Geological setting (taken from the Geological Map of Spain at scale 1:50,000, [<a href="#B65-remotesensing-11-01507" class="html-bibr">65</a>]). Coordinates are in ETRS89-UTM-30N.</p> "> Figure 2
<p>(<b>a</b>) Photorealistic reconstructions of the landslide foot with the roads affected: (<b>a</b>): 24 April 2013 frontal view; (<b>b</b>): 24 April 2013 lateral view; (<b>c</b>): 5 March 2014 frontal view; (<b>d</b>): 5 March 2014 lateral view; (<b>e</b>) 19 November 2015 frontal view; (<b>f</b>): 19 November 2015 lateral view (it can be observed the drainage wells and the retaining wall).</p> "> Figure 3
<p>Methodology flow chart.</p> "> Figure 4
<p>Landslide and affected roads. (<b>a</b>) surveyed area (red rectangle) on affected roads and access to highway at the landslide foot; (<b>b</b>) Mission planning and location of trigger points for the low height flights; (<b>c</b>) Mission planning and photo footprints of low height flights; (<b>d</b>) Main landslide affecting the slope (yellow dash line) and surveyed area (red rectangle); (<b>e</b>) Mission planning and location of trigger points for the general flights for the slope monitoring [<a href="#B42-remotesensing-11-01507" class="html-bibr">42</a>]; (<b>f</b>) photo footprints of the strips used in this paper from the general flights of the slope (19 November 2012; 16 July 2014). Coordinates are in ETRS89-UTM-30N.</p> "> Figure 5
<p>Examples of targets for GCP and CHK points as seen from 40 m flying height. (<b>a</b>) White circles on black background (inside the white circle there is small black circle for accurate image measurement) printed on PVC foam board. (<b>b</b>) Circle/sector target sprayed on the ground with reflective red paint and a cardboard template. (<b>c</b>) Natural and well defined natural points (road reflective markers and corners on road surface markings).</p> "> Figure 6
<p>Standard deviation of monitoring points. Horizontal component: (<b>a</b>) Map; (<b>c</b>) Histogram; Vertical component: (<b>b</b>) Map; (<b>d</b>) Histogram. Both maps are based on the orthophotograph of 2013/05/03. Coordinates are in ETRS89-UTM-30N.</p> "> Figure 7
<p>Maps of displacements of monitoring points for the different periods. (<b>a</b>) 19 November 2012–24 April 2013; (<b>b</b>) 24 April 2013–3 May 2013; (<b>c</b>) 3 May 2013–20 May 2013; (<b>d</b>) 20 May 2013–4 July 2013; (<b>e</b>) 4 July 2013–17 January 2014; (<b>f</b>) 17 January 2014–5 March 2014; (<b>g</b>) 5 March 2014–16 July 2014; (<b>h</b>) 16 July 2014–23 December 2014; (<b>i</b>) 23 December 2014–27 March 2015; (<b>j</b>) 27 March 2015–19 November 2015); (<b>k</b>,<b>l</b>) 19 November 2012–19 November 2015) (total period). Orthophotographs correspond to the first date, except (<b>l</b>) that corresponds to the end date. Coordinates are in ETRS89-UTM-30N.</p> "> Figure 7 Cont.
<p>Maps of displacements of monitoring points for the different periods. (<b>a</b>) 19 November 2012–24 April 2013; (<b>b</b>) 24 April 2013–3 May 2013; (<b>c</b>) 3 May 2013–20 May 2013; (<b>d</b>) 20 May 2013–4 July 2013; (<b>e</b>) 4 July 2013–17 January 2014; (<b>f</b>) 17 January 2014–5 March 2014; (<b>g</b>) 5 March 2014–16 July 2014; (<b>h</b>) 16 July 2014–23 December 2014; (<b>i</b>) 23 December 2014–27 March 2015; (<b>j</b>) 27 March 2015–19 November 2015); (<b>k</b>,<b>l</b>) 19 November 2012–19 November 2015) (total period). Orthophotographs correspond to the first date, except (<b>l</b>) that corresponds to the end date. Coordinates are in ETRS89-UTM-30N.</p> "> Figure 7 Cont.
<p>Maps of displacements of monitoring points for the different periods. (<b>a</b>) 19 November 2012–24 April 2013; (<b>b</b>) 24 April 2013–3 May 2013; (<b>c</b>) 3 May 2013–20 May 2013; (<b>d</b>) 20 May 2013–4 July 2013; (<b>e</b>) 4 July 2013–17 January 2014; (<b>f</b>) 17 January 2014–5 March 2014; (<b>g</b>) 5 March 2014–16 July 2014; (<b>h</b>) 16 July 2014–23 December 2014; (<b>i</b>) 23 December 2014–27 March 2015; (<b>j</b>) 27 March 2015–19 November 2015); (<b>k</b>,<b>l</b>) 19 November 2012–19 November 2015) (total period). Orthophotographs correspond to the first date, except (<b>l</b>) that corresponds to the end date. Coordinates are in ETRS89-UTM-30N.</p> "> Figure 7 Cont.
<p>Maps of displacements of monitoring points for the different periods. (<b>a</b>) 19 November 2012–24 April 2013; (<b>b</b>) 24 April 2013–3 May 2013; (<b>c</b>) 3 May 2013–20 May 2013; (<b>d</b>) 20 May 2013–4 July 2013; (<b>e</b>) 4 July 2013–17 January 2014; (<b>f</b>) 17 January 2014–5 March 2014; (<b>g</b>) 5 March 2014–16 July 2014; (<b>h</b>) 16 July 2014–23 December 2014; (<b>i</b>) 23 December 2014–27 March 2015; (<b>j</b>) 27 March 2015–19 November 2015); (<b>k</b>,<b>l</b>) 19 November 2012–19 November 2015) (total period). Orthophotographs correspond to the first date, except (<b>l</b>) that corresponds to the end date. Coordinates are in ETRS89-UTM-30N.</p> "> Figure 8
<p>Relationships between the horizontal displacements and the rainfalls in the study period considered. (<b>a</b>) Displacements and rainfalls; (<b>b</b>) Displacements rates and rainfalls. Numbers indicate the measurement epoch (1 to 11).</p> ">
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
- Flight planning: selection of UAV equipment and mission planning.
- Field work: GNSS field survey and execution of flights.
- Photogrammetric processing 1: georeferencing and flight orientation with SfM techniques.
- Photogrammetric processing 2: DSM and orthophotograph generation.
- GIS procedures and deformation analysis: measurement of displacements at identifiable points between campaigns.
3.1. Data Capture: UAV Flights and Field Work
3.2. Georeferencing and Flight Orientation
3.3. DSM and Orthophotograph Generation
3.4. Measurement of Displacements
4. Results
4.1. Basic Statistics of Monitoring Points
4.2. Displacements of Monitoring Points
4.3. Displacements Rates of Monitoring Points
5. Discussion
5.1. Accuracies and Uncertainties
5.2. Displacements, Landslide Characterization and Road Surface Deformation
5.3. Displacements Rates and Relation to Rainfalls as a Triggering Factor
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Arbanas, S.M.; Arbanas, Ž. Landslide mapping and monitoring: Review of conventional and advanced techniques. In Proceedings of the 4th Symposium of Macedonian Association for Geotechnics, Struga, Macedonia, 25–28 June 2014; pp. 57–72. [Google Scholar]
- Savvaidis, P.D. Existing landslide monitoring systems and techniques. In Stars to Earth and Culture, in Honor of the Memory of Professor Alexandros Tsioumis; Dermanis, A., Ed.; Ziti Publications: Thessaloniki, Greece, 2003; pp. 242–258. [Google Scholar]
- Metternicht, G.; Hurni, L.; Gogu, R. Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens. Environ. 2005, 98, 284–303. [Google Scholar] [CrossRef]
- Tofani, V.; Hong, Y.; Singhroy, V. Introduction: Remote Sensing Techniques for Landslide Mapping and Monitoring. In Landslide Science for a Safer Geoenvironment; Sassa, K., Canuti, P., Yin, Y., Eds.; Springer International Publishing: Cham, Switzerland, 2014; Volume 2, pp. 301–303. [Google Scholar]
- Xhao, C.; Lu, Z. Remote Sensing of Landslides—A Review. Remote Sens. 2018, 10, 279. [Google Scholar]
- Walstra, J.; Chandler, J.H.; Dixon, N.; Dijkstra, T.A. Time for change—Quantifying land-slide evolution using historical aerial photographs and modern photogrammetric methods. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2004, XXXV, 475–480. [Google Scholar]
- Fernández, T.; Pérez, J.L.; Colomo, C.; Cardenal, J.; Delgado, J.; Palenzuela, J.A.; Irigaray, C.; Chacón, J. Assessment of the Evolution of a Landslide Using Digital Photogrammetry and LiDAR Techniques in the Alpujarras Region (Granada, Southeastern Spain). Geosciences 2017, 7, 32. [Google Scholar] [CrossRef]
- Angeli, M.G.; Pasuto, A.; Silvano, S. A critical review of landslide monitoring experiences. Eng. Geol. 2000, 55, 133–147. [Google Scholar] [CrossRef]
- Gili, J.A.; Corominas, J.; Rius, J. Using Global Positioning System techniques in landslide monitoring. Eng. Geol. 2000, 55, 167–192. [Google Scholar] [CrossRef]
- Mikkelsen, P.E. Field instrumentation. In Landslides—Investigation and Mitigation; Transportation Research Board Special Report; Turner, A.K., Schuster, R.L., Eds.; National Academy Press: Washington, DC, USA, 1996; Volume 247, pp. 278–316. [Google Scholar]
- Abramson, L.W.; Lee, T.S.; Sharma, S.; Boyce, G.M. Slope Stability and Stabilization Methods; John Wiley & Sons: New York, NY, USA, 2002. [Google Scholar]
- Gao, W.; He, T.Y. Displacement prediction in geotechnical engineering based on evolutionary neural network. Geomech. Eng. 2017, 13, 845–860. [Google Scholar]
- Reichenbach, P.; Rossi, M.; Malamud, B.D.; Mihir, M.; Guzzetti, F. A review of statistically-based landslide susceptibility models. Earth Sci. Rev. 2018, 180, 60–91. [Google Scholar] [CrossRef]
- Chacón, J.; El Hamdouni, R.; Irigaray, C.; Fernández, T. Engineering geology maps: Landslides and GIS. Bull. Eng. Geol. Environ. 2006, 65, 341–411. [Google Scholar] [CrossRef]
- Brückl, E.; Brunner, F.K.; Kraus, K. Kinematics of a deep-seated landslide derived from photogrammetric, GPS and geophysical data. Eng. Geol. 2006, 88, 149–159. [Google Scholar] [CrossRef]
- Cardenal, J.; Delgado, J.; Mata, E.; González, A.; Olague, I. Use of historical flight for landslide monitoring. In Proceedings of the Spatial Accuracy 2006, Lisbonne, Portugal, 5–7 July 2006; pp. 129–138. [Google Scholar]
- Dewitte, O.; Jasselette, J.C.; Cornet, Y.; Van Den Eeckhaut, M.; Collignon, A.; Poesen, J.; Demoulin, A. Tracking landslide displacement by multi-temporal DTMs: A combined aerial stereophotogrammetric and LiDAR approach in Belgium. Eng. Geol. 2008, 99, 11–22. [Google Scholar] [CrossRef]
- Kasperski, J.; Delacourt, C.; Allemand, P.; Potherat, P. Evolution of the Sedrun landslide (Graubünden, Switzerland) with ortho-rectified air images. Bull. Eng. Geol. Environ. 2010, 69, 421–430. [Google Scholar] [CrossRef]
- Prokešová, R.; Kardoš, M.; Medveďová, A. Landslide dynamics from high-resolution aerial photographs: A case study from W Carpathians, Slovakia. Geomorphology 2010, 115, 90–101. [Google Scholar] [CrossRef]
- Fabris, M.; Menin, A.; Achilli, V. Landslide displacement estimation by archival digital photogrammetry. Ital. J. Remote Sens. 2011, 43, 23–30. [Google Scholar] [CrossRef]
- Kraus, K. Photogrammetry: Geometry from Images and Laser Scans; Walter de Gruyter: Berlin, Germany, 2007. [Google Scholar]
- Hartley, R.; Zisserman, A. Multiple View Geometry in Computer Vision; Cambridge University Press: Cambridge, UK, 2004; p. 655. [Google Scholar]
- Eltner, A.; Kaiser, A.; Castillo, C.; Rock, G.; Neugirg, F.; Abellán, A. Image-based surface reconstruction in geomorphometry—Merits, limits and developments. Earth Surf. Dyn. 2016, 4, 359–389. [Google Scholar] [CrossRef]
- AGISOFT. Agisoft PhotoScan. Available online: https://www.agisoft.com/ (accessed on 31 March 2019).
- PIX4D. Professional Photogrammetry and Drone Mapping Software. Available online: https://www.pix4d.com/ (accessed on 31 March 2019).
- González-Aguilera, D.; López-Fernández, L.; Rodriguez-Gonzalvez, P.; Guerrero, D.; Hernandez-Lopez, D.; Remondino, F.; Menna, F.; Nocerino, E.; Toschi, I.; Ballabeni, A.; et al. Development of an all-purpose free photogrammetric tool. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, XLI-B6, 31–38. [Google Scholar] [CrossRef]
- Wu, C. VisualSFM: A Visual Structure from Motion System. Available online: http://ccwu.me/vsfm/ (accessed on 31 March 2019).
- Yang, Z.; Lan, H.; Gao, X.; Li, L.; Meng, Y.; Wu, Y. Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat. Hazards 2015, 75, 2467–2487. [Google Scholar] [CrossRef]
- Fan, X.; Scaringi, G.; Xu, Q.; Zhan, W.; Dai, L.; Li, Y.; Pei, X.; Yang, Q.; Huang, R. Coseismic landslides triggered by the 8th August 2017 Ms 7.0 Jiuzhaigou earthquake (Sichuan, China): Factors controlling their spatial distribution and implications for the seismogenic blind fault identification. Landslides 2018, 15, 967–983. [Google Scholar] [CrossRef]
- Yeh, M.L.; Hsiao, Y.C.; Chen, Y.H.; Chung, J.C. A study on Unmanned Aerial Vehicle applied to acquire terrain information of landslide. In Proceedings of the 32 Asian Conference Remote Sensing, Taipei, Taiwan, 3–7 October 2011; Volume 3, pp. 2210–2215. [Google Scholar]
- Liu, C.; Chen, P.; Matsuo, T.; Chen, C. Rapidly responding to landslides and debris flow events using a low cost unmanned aerial vehicle. J. Appl. Remote Sens. 2015, 9, 096016. [Google Scholar] [CrossRef]
- Huang, Y.; Yi, S.; Lia, Z.; Shao, S.; Qin, X. Design of highway landslide warning and emergency response systems based on UAV. In Proceedings of the 17th China Conference on Remote Sensing, Hangzhou, China, 27–31 August 2010; SPIE: Bellingham, WA, USA, 2011; Volume 8203, 820317. [Google Scholar]
- Tahar, K.N.; Ahmad, A.; Akib, W.A.A.W.M.; Mohd, W.M.N.W. Unmanned Aerial Vehicle Photogrammetric Results Using Different Real Time Kinematic Global Positioning System Approaches. In Developments in Multidimensional Spatial Data Models; Lecture Notes in Geoinformation and Cartography; Raman, A.A., Bogulawski, P., Gold, C., Said, M.N., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 123–134. [Google Scholar]
- Warrick, J.A.; Ritchie, A.C.; Schmidt, K.M.; Reid, M.E.; Logan, J. Characterizing the catastrophic 2017 Mud Creek landslide, California, using repeat structure-from-motion (SfM) photogrammetry. Landslides 2019, 16, 1201–1219. [Google Scholar] [CrossRef]
- Shi, B.; Liu, C. UAV for Landslide Mapping and Deformation Analysis. In Proceedings of the International Conference on Intelligent Earth Observing and Applications, Guilin, China, 23–24 October 2015; SPIE: Bellingham, WA, USA, 2015; Volume 9808, 98080P. [Google Scholar]
- Hsieh, Y.C.; Chan, Y.; Hu, J. Digital elevation model differencing and error estimation from multiple sources: A case study from the Meiyuan Shan landslide in Taiwan. Remote Sens. 2016, 8, 199. [Google Scholar] [CrossRef]
- Liu, C.; Li, W.; Lei, W.; Liu, L.; Hu, H. Architecture planning and geo-disasters assessment mapping of landslide by using airborne LiDAR data and UAV images. In Proceedings of the International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, Nanjing, China, 26–29 May 2011; SPIE: Bellingham, WA, USA, 2011; Volume 8286, 82861Q. [Google Scholar]
- Niethammer, U.; James, M.R.; Rothmund, S.; Travelletti, J.; Joswig, M. UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Eng. Geol. 2012, 128, 2–11. [Google Scholar] [CrossRef]
- Stumpf, A.; Malet, J.P.; Kerle, N.; Niethammer, U.; Rothmund, S. Image-based mapping of surface fissures for the investigation of landslide dynamics. Geomorphology 2013, 186, 12–27. [Google Scholar] [CrossRef] [Green Version]
- Turner, D.; Lucieer, A.; de Jong, S.M. Time series analysis of landslide dynamics using an Unmanned Aerial Vehicle (UAV). Remote Sens. 2015, 7, 1736–1757. [Google Scholar] [CrossRef]
- Fernández, T.; Pérez, J.L.; Cardenal, F.J.; López, A.; Gómez, J.M.; Colomo, C.; Sánchez, M.; Delgado, J. Use of a light UAV and photogrammetric techniques to study the evolution of a landslide. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, XL-3-W3, 241–248. [Google Scholar]
- Fernández, T.; Pérez, J.L.; Cardenal, F.J.; Gómez, J.M.; Colomo, C.; Delgado, J. Analysis of landslide evolution affecting olive groves using UAV and photogrammetric techniques. Remote Sens. 2016, 8, 837. [Google Scholar] [CrossRef]
- Peterman, V. Landslide activity monitoring with the help of unmanned aerial vehicle. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, XL-1/W4, 215–218. [Google Scholar] [CrossRef]
- Daakir, M.; Pierrot-Deseilligny, M.; Bosser, P.; Pichard, F.; Thom, C. UAV onboard photogrammetry and GPS positioning for earthworks. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, XL-3/W3, 293–298. [Google Scholar] [CrossRef]
- Al-Rawabdeh, A.; He, F.; Moussa, A.; El-Sheimy, N.; Habib, A. Using an unmanned aerial vehicle-based digital imaging system to derive a 3D point cloud for landslide scarp recognition. Remote Sens. 2016, 8, 95. [Google Scholar] [CrossRef]
- Lindner, G.; Schraml, K.; Mansberger, R.; Hübl, J. UAV monitoring and documentation of a large landslide. Appl. Geomat. 2016, 8, 1–11. [Google Scholar] [CrossRef]
- James, M.R.; Robson, S.; d’Oleire-Oltmanns, S.; Niethammerd, U. Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment. Geomorphology 2017, 280, 51–66. [Google Scholar] [CrossRef] [Green Version]
- Dang, K.; Sassa, K.; Fukuoka, H.; Sakai, N.; Sato, Y.; Takara, K.; Quang, L.H.; Loi, D.H.; Tien, P.V.; Ha, N.D. Mechanism of two rapid and long-runout landslides in the 16 April 2016 Kumamoto earthquake using a ring-shear apparatus and computer simulation (LS-RAPID). Landslides 2016, 13, 1525–1534. [Google Scholar] [CrossRef]
- Mozas-Calvache, A.T.; Pérez-García, J.L.; Fernández, T. Monitoring of landslide displacements using UAS and control methods based on lines. Landslides 2017, 14, 2115–2128. [Google Scholar] [CrossRef]
- Peternel, T.; Kumelj, S.; Oštir, K.; Komac, M. Monitoring the Potoška planina landslide (NW Slovenia) using UAV photogrammetry and tachymetric measurements. Landslides 2017, 14, 395–406. [Google Scholar] [CrossRef]
- Balek, J.; Blahůt, J. A critical evaluation of the use of an inexpensive camera mounted on a recreational unmanned aerial vehicle as a tool for landslide research. Landslides 2017, 14, 1217–1224. [Google Scholar] [CrossRef]
- Peppa, M.V.; Mills, J.P.; Moore, P.; Miller, P.E.; Chambers, J.E. Brief communication: Landslide motion from cross correlation of UAV-derived morphological attributes. Nat. Hazards Earth Syst. Sci. 2017, 17, 2143–2150. [Google Scholar] [CrossRef] [Green Version]
- Peppa, M.V.; Mills, J.P.; Moore, P.; Miller, P.E.; Chambers, J.E. Automated co-registration and calibration in SfM photogrammetry for landslide change detection. Earth Surf. Process. Landforms 2019, 44, 287–303. [Google Scholar] [CrossRef]
- Hu, S.; Qiu, H.; Wang, X.; Gao, Y.; Wang, N.; Wu, J.; Yang, D.; Cao, M. Acquiring high-resolution topography and performing spatial analysis of loess landslides by using low-cost UAVs. Landslides 2018, 15, 593–612. [Google Scholar] [CrossRef]
- Rossi, G.; Tanteri, L.; Tofani, V.; Vannocci, P.; Moretti, S.; Casagli, N. Multitemporal UAV surveys for landslide mapping and characterization. Landslides 2018, 15, 1045–1052. [Google Scholar] [CrossRef] [Green Version]
- Fan, X.; Xu, Q.; Scaringi, G.; Zheng, G.; Huang, R.; Dai, L.; Ju, Y. The “long” runout rock avalanche in Pusa, China, on August 28, 2017: A preliminary report. Landslides 2019, 16, 139–154. [Google Scholar] [CrossRef]
- Ma, S.; Xu, C.; Shao, X.; Zhang, P.; Liang, X.; Tian, Y. Geometric and kinematic features of a landslide in Mabian Sichuan, China, derived from UAV photography. Landslides 2019, 16, 373–381. [Google Scholar] [CrossRef]
- Stöcker, C.; Nex, F.; Koeva, M.; Gerke, M. Quality assessment of combined IMU/GNSS data for direct georeferencing in the context of UAV-based mapping. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, 42, 355–361. [Google Scholar] [CrossRef]
- Forlani, G.; Dall’Asta, E.; Diotri, F.; Cella, U.M.D.; Roncella, R.; Santise, M. Quality assessment of DSMs produced from UAV flights georeferenced with onboard RTK positioning. Remote Sens. 2018, 10, 311. [Google Scholar] [CrossRef]
- Rabah, M.; Basiouny, M.; Ghanem, E.; Elhadary, A. Using RTK and VRS in direct geo-referencing of the UAV imagery. NRIAG J. Astron. Geophys. 2018, 7, 220–226. [Google Scholar] [CrossRef] [Green Version]
- Colomo, C.M.; Pérez-García, J.L.; Gómez-López, J.; Fernández, T. Análisis de la actividad de deslizamientos mediante técnicas de LiDAR y fotogrametría en el entorno de la autovía A-44 (Jaén). In Proceedings of the IX Simposio Nacional Sobre Laderas y Taludes Inestables, Santander, Spain, 27–30 June 2017; Volume 1, pp. 492–503. [Google Scholar]
- Carpena, R.L.; Mellado, I.; Moya, F.; Colomo, C.; Bédmar, P.; Calero, J.; Pérez, A.; Fernández, T.; Sánchez-Gómez, M.; Tovas, J. Análisis de riesgos asociados a las infraestructuras viarias de la Diputación Provincial de Jaén. In Proceedings of the IX Simposio Nacional Sobre Laderas y Taludes Inestables, Santander, Spain, 27–30 June 2017; Volume 1, pp. 335–346. [Google Scholar]
- Varnes, D.J. Slope movement, types and processes. In Landslides: Analysis and Control; Transportation Research Board Special Report; Schuster, R.L., Krizek, R.J., Eds.; National Academy of Sciences: Washington, DC, USA, 1978; Volume 176, pp. 12–33. [Google Scholar]
- Hungr, O.; Leroueil, S.; Picarelli, L. The Varnes classification of landslide types, an update. Landslides 2014, 11, 167–194. [Google Scholar] [CrossRef]
- Roldán, F.J.; Lupiani, E.; Jerez, L. Mapa Geológico de España, Escala 1:50.000, Mapa y Memoria Explicativa; Instituto Geológico Nacional: Madrid, Spain, 1988. [Google Scholar]
- Pérez-Valera, F.; Sánchez-Gómez, M.; Pérez-López, A.; Pérez-Valera, L.A. An evaporite-bearing accretionary complex in the northern front of the Betic-Rif orogeny. Tectonics 2017, 36, 1006–1036. [Google Scholar] [CrossRef]
- Navarro, V.; Ruiz-Ortiz, P.A.; Molina, J.M. Birth and demise of a Middle Jurassic isolated shallow-marine carbonate platform on a tilted fault block: Example from the Southern Iberian continental palaeomargin. Sediment. Geol. 2012, 269, 37–57. [Google Scholar] [CrossRef]
- Sánchez-Gómez, M.; Peláez, J.A.; García-Tortosa, F.J.; Torcal, F.; Soler, P.; Ureña, M.A. Aproximación geológica, geofísica y geomorfológica a la actividad tectónica en el valle del alto Guadalquivir. In Proceedings of the 6th Asamblea Hispano Portuguesa de Geodesia y Geofísica, Tomar, Portugal, 5–8 September 2008. [Google Scholar]
- González-Díez, A.; Fernández-Maroto, G.; Doughty, M.W.; Díaz de Terán, J.R.; Bruschi, V.; Cardenal, J.; Pérez, J.L.; Mata, E.; Delgado, J. Development of a methodological approach for the accurate measurement of slope changes due to landslides, using digital photogrammetry. Landslides 2014, 11, 615–628. [Google Scholar] [CrossRef]
- AscTec. AscTec Falcon 8 + AscTec Trinity. Ascending Technologies. Available online: http://www.asctec.de/downloads/public/F8_AscTec-Falcon-8-AscTec-Trinity_safety-data-sheet.pdf (accessed on 31 March 2019).
- QGIS. A Free and Open Source Geographic Information System. Available online: https://www.qgis.org/en/site/ (accessed on 31 March 2019).
- IAEG. Commission on Landslides. Suggested nomenclature for landslides. Bull. IAEG 1990, 41, 13–16. [Google Scholar]
- Hutchinson, J.N. General Report: Morphological and Geotechnical Parameters of Landslides in Relation to Geology and Hydrogeology. In Proceedings of the 5th International Symposium on Landslides, Lausanne, Switzerland, 10–15 July 1988. [Google Scholar]
- Crozier, M.J. Techniques for the morphometric analysis of landslips. Z. Geomorphol. 1973, 17, 78–101. [Google Scholar]
- International Union of Geological Sciences Working Group on Landslides. A suggested method for describing the rate of movement of a landslide. Bull. Eng. Geol. Environ. 1995, 52, 75–78. [Google Scholar]
- Finlay, P.J.; Fell, R.; Maguire, P.K. The relationship between the probability of landslide occurrence and rainfall. Can. Geotech. J. 1997, 34, 811–824. [Google Scholar] [CrossRef]
- Guzzeti, F. Landslide hazard assessment and risk evaluation: Limits and prospectives. In Proceedings of the 4th EGS Plinius Conference, Mallorca, Spain, 2–4 October 2002. [Google Scholar]
- Irigaray, C.; Lamas, F.; El Hamdouni, R.; Fernández, T.; Chacón, J. The importance of the precipitation and the susceptibility of the slopes for the triggering of landslides along the roads. Nat. Hazards 2000, 21, 65–81. [Google Scholar] [CrossRef]
- Chacón, J.; Irigaray, C.; El Hamdouni, R.; Jiménez, J.D. Diachroneity of landslides. In Geologically Active; Williams, A.L., Pinches, G.M., Chin, C.Y., McMorran, T.J., Massey, C.I., Eds.; Taylor & Francis Group: London, UK, 2010; pp. 999–1006. [Google Scholar]
- Trigo, R.M.; Pozo, D.; Timothy, C.; Osborn, J.; Castro, Y.; Gámiz, S.; Esteban, M.J. North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula. Int. J. Climatol. 2004, 24, 925–994. [Google Scholar] [CrossRef]
No. | Date | No. Images | GSD (m) | Flying Height (m) |
---|---|---|---|---|
1 | 19 November 2012 | 25 | 0.030 | 100 * |
2 | 24 April 2013 | 44 | 0.011 | 41 |
3 | 03 May 2013 | 50 | 0.011 | 39 |
4 | 20 May 2013 | 48 | 0.011 | 40 |
5 | 04 July 2013 | 47 | 0.014 | 50 |
6 | 17 January 2014 | 46 | 0.012 | 43 |
7 | 05 March 2014 | 43 | 0.013 | 45 |
8 | 16 July 2014 | 24 | 0.034 | 105 * |
9 | 23 December 2014 | 44 | 0.012 | 44 |
10 | 27 March 2015 | 44 | 0.013 | 46 |
11 | 19 November 2015 | 47 | 0.013 | 45 |
Date | GSD | GCP/CHK Number | Tie Point Number | RMS Pixel | CHK RMS (m) | |
---|---|---|---|---|---|---|
RMS XY (m) | RMS Z (m) | |||||
19 November 2012 | 30 | 11/5 | 151,635 | 0.37 | 0.018 | 0.012 |
24 April 2013 | 11 | 9/3 | 356,966 | 0.41 | 0.024 | 0.015 |
03 May 2013 | 11 | 11/4 | 367,430 | 0.39 | 0.014 | 0.014 |
20 May 2013 | 11 | 17/4 | 59,384 | 0.82 | 0.013 | 0.023 |
04 July 2013 | 14 | 12/6 | 9479 | 0.83 | 0.017 | 0.010 |
17 January 2014 | 12 | 7/3 | 423,343 | 0.43 | 0.026 | 0.005 |
05 March 2014 | 12.5 | 7/3 | 426,766 | 0.54 | 0.019 | 0.004 |
16 July 2014 | 33.8 | 7/4 | 247,888 | 0.86 | 0.030 | 0.032 |
23 December 2014 | 12.4 | 14/6 | 101,414 | 0.57 | 0.016 | 0.010 |
27 March 2015 | 12.7 | 13/9 | 62,348 | 0.81 | 0.011 | 0.010 |
19 November 2015 | 12.6 | 10/4 | 60,277 | 0.71 | 0.022 | 0.027 |
Mean | 0.66 | 0.020 | 0.016 | |||
STD | 0.25 | 0.007 | 0.009 |
Points | Number of Measurements | SDXY (m) | SDZ (m) |
---|---|---|---|
1 | 11 | 0.017 | 0.039 |
2 | 11 | 0.018 | 0.064 |
3 | 11 | 0.009 | 0.030 |
4 | 11 | 0.018 | 0.053 |
5 | 11 | 0.017 | 0.041 |
6 | 11 | 0.019 | 0.070 |
7 | 11 | 0.017 | 0.021 |
8 | 8 | 0.017 | 0.036 |
9 | 8 | 0.027 | 0.017 |
10 | 8 | 0.037 | 0.061 |
11 | 10 | 0.030 | 0.050 |
12 | 8 | 0.012 | 0.015 |
13 | 8 | 0.012 | 0.024 |
14 | 11 | 0.030 | 0.045 |
15 | 11 | 0.026 | 0.068 |
16 | 11 | 0.623 | 0.062 |
17 | 10 | 0.616 | 0.114 |
18 | 11 | 0.026 | 0.056 |
19 | 11 | 0.022 | 0.030 |
20 | 10 | 0.611 | 0.070 |
21 | 10 | 0.554 | 0.098 |
22 | 11 | 0.609 | 0.090 |
23 | 11 | 0.621 | 0.070 |
24 | 10 | 0.628 | 0.070 |
25 | 10 | 0.071 | 0.019 |
26 | 5 | 0.025 | 0.019 |
27 | 10 | 0.482 | 0.082 |
28 | 10 | 0.020 | 0.051 |
29 | 7 | 0.231 | 0.122 |
30 | 6 | 0.371 | 0.192 |
Total | Mean (m) | 0.194 | 0.059 |
STD (m) | 0.257 | 0.038 | |
Stable area 1 | Mean (m) | 0.021 | 0.042 |
STD (m) | 0.007 | 0.018 | |
Unstable area 2 | Mean (m) | 0.492 | 0.090 |
STD (m) | 0.190 | 0.044 |
Periods | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total |
---|---|---|---|---|---|---|---|---|---|---|---|
Total area | |||||||||||
Mean | 0.306 | 0.092 | 0.091 | 0.058 | 0.048 | 0.104 | 0.089 | 0.039 | 0.018 | 0.017 | 0.675 |
SD | 0.355 | 0.098 | 0.103 | 0.059 | 0.020 | 0.114 | 0.080 | 0.018 | 0.008 | 0.017 | 0.896 |
Min | 0.005 | 0.006 | 0.007 | 0.003 | 0.015 | 0.005 | 0.001 | 0.011 | 0.006 | 0.000 | 0.006 |
Max | 0.838 | 0.253 | 0.277 | 0.172 | 0.093 | 0.289 | 0.231 | 0.085 | 0.033 | 0.058 | 2.005 |
Stable area | |||||||||||
Mean | 0.027 | 0.018 | 0.025 | 0.019 | 0.037 | 0.023 | 0.028 | 0.032 | 0.019 | 0.019 | 0.027 |
SD | 0.014 | 0.008 | 0.012 | 0.009 | 0.014 | 0.011 | 0.024 | 0.011 | 0.009 | 0.011 | 0.020 |
Min | 0.005 | 0.006 | 0.007 | 0.003 | 0.015 | 0.005 | 0.001 | 0.011 | 0.008 | 0.000 | 0.006 |
Max | 0.053 | 0.035 | 0.046 | 0.041 | 0.063 | 0.041 | 0.070 | 0.045 | 0.033 | 0.031 | 0.078 |
Unstable area | |||||||||||
Mean | 0.660 | 0.186 | 0.205 | 0.123 | 0.065 | 0.218 | 0.170 | 0.048 | 0.017 | 0.015 | 1.684 |
SD | 0.235 | 0.076 | 0.091 | 0.049 | 0.017 | 0.092 | 0.050 | 0.023 | 0.009 | 0.024 | 0.574 |
Min | 0.139 | 0.032 | 0.016 | 0.016 | 0.040 | 0.019 | 0.072 | 0.017 | 0.006 | 0.000 | 0.206 |
Max | 0.838 | 0.253 | 0.277 | 0.172 | 0.093 | 0.289 | 0.231 | 0.085 | 0.033 | 0.058 | 2.005 |
Periods | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total |
---|---|---|---|---|---|---|---|---|---|---|---|
Total area | |||||||||||
Mean | −0.027 | −0.020 | −0.013 | −0.002 | −0.005 | 0.004 | −0.019 | 0.001 | 0.011 | −0.018 | −0.084 |
SD | 0.132 | 0.037 | 0.046 | 0.042 | 0.051 | 0.027 | 0.102 | 0.091 | 0.041 | 0.021 | 0.156 |
Min | −0.269 | −0.089 | −0.098 | −0.074 | −0.115 | −0.039 | −0.267 | −0.227 | −0.153 | −0.055 | −0.315 |
Max | 0.253 | 0.082 | 0.100 | 0.139 | 0.057 | 0.075 | 0.226 | 0.232 | 0.057 | 0.024 | 0.145 |
Stable area | |||||||||||
Mean | 0.021 | −0.021 | −0.011 | −0.004 | 0.018 | −0.005 | −0.021 | 0.006 | 0.012 | −0.019 | 0.011 |
SD | 0.087 | 0.027 | 0.041 | 0.049 | 0.043 | 0.022 | 0.065 | 0.053 | 0.016 | 0.023 | 0.105 |
Min | −0.202 | −0.089 | −0.098 | −0.074 | −0.115 | −0.039 | −0.118 | −0.070 | −0.021 | −0.055 | −0.244 |
Max | 0.124 | 0.014 | 0.061 | 0.139 | 0.057 | 0.041 | 0.083 | 0.091 | 0.037 | 0.024 | 0.145 |
Unstable area | |||||||||||
Mean | −0.088 | −0.019 | −0.016 | 0.001 | −0.041 | 0.016 | −0.015 | −0.005 | 0.010 | −0.014 | −0.232 |
SD | 0.156 | 0.049 | 0.056 | 0.028 | 0.042 | 0.029 | 0.142 | 0.129 | 0.064 | 0.009 | 0.093 |
Min | −0.269 | −0.072 | −0.078 | −0.026 | −0.115 | −0.017 | −0.267 | −0.227 | −0.153 | −0.024 | −0.315 |
Max | 0.253 | 0.082 | 0.100 | 0.052 | 0.019 | 0.075 | 0.226 | 0.232 | 0.057 | −0.007 | −0.068 |
Periods | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total |
---|---|---|---|---|---|---|---|---|---|---|---|
Total area | |||||||||||
Mean | 0.059 | 0.305 | 0.161 | 0.040 | 0.007 | 0.062 | 0.020 | 0.007 | 0.006 | 0.002 | 0.019 |
SD | 0.069 | 0.328 | 0.182 | 0.040 | 0.003 | 0.071 | 0.018 | 0.004 | 0.003 | 0.002 | 0.025 |
Min | 0.001 | 0.020 | 0.012 | 0.002 | 0.002 | 0.000 | 0.000 | 0.002 | 0.002 | 0.000 | 0.000 |
Max | 0.162 | 0.844 | 0.489 | 0.117 | 0.015 | 0.181 | 0.053 | 0.016 | 0.011 | 0.008 | 0.056 |
Stable area | |||||||||||
Mean | 0.005 | 0.059 | 0.045 | 0.013 | 0.006 | 0.013 | 0.006 | 0.006 | 0.006 | 0.002 | 0.001 |
SD | 0.003 | 0.026 | 0.020 | 0.006 | 0.002 | 0.008 | 0.005 | 0.002 | 0.003 | 0.001 | 0.001 |
Min | 0.001 | 0.020 | 0.012 | 0.002 | 0.002 | 0.000 | 0.000 | 0.002 | 0.002 | 0.000 | 0.000 |
Max | 0.010 | 0.117 | 0.081 | 0.028 | 0.010 | 0.026 | 0.016 | 0.009 | 0.010 | 0.004 | 0.002 |
Unstable area | |||||||||||
Mean | 0.128 | 0.619 | 0.362 | 0.084 | 0.010 | 0.136 | 0.039 | 0.009 | 0.005 | 0.002 | 0.047 |
SD | 0.045 | 0.253 | 0.160 | 0.033 | 0.003 | 0.058 | 0.011 | 0.004 | 0.003 | 0.003 | 0.016 |
Min | 0.027 | 0.108 | 0.028 | 0.011 | 0.006 | 0.012 | 0.017 | 0.003 | 0.002 | 0.000 | 0.006 |
Max | 0.162 | 0.844 | 0.489 | 0.117 | 0.015 | 0.181 | 0.053 | 0.016 | 0.011 | 0.008 | 0.056 |
Periods | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total |
---|---|---|---|---|---|---|---|---|---|---|---|
Total area | |||||||||||
Mean | −0.005 | −0.067 | −0.023 | −0.002 | −0.001 | 0.002 | −0.004 | 0.000 | 0.004 | −0.002 | −0.002 |
SD | 0.025 | 0.125 | 0.082 | 0.028 | 0.008 | 0.016 | 0.023 | 0.017 | 0.013 | 0.003 | 0.004 |
Min | −0.052 | −0.296 | −0.172 | −0.050 | −0.018 | −0.024 | −0.061 | −0.043 | −0.049 | −0.007 | −0.009 |
Max | 0.049 | 0.275 | 0.177 | 0.095 | 0.009 | 0.047 | 0.052 | 0.044 | 0.018 | 0.003 | 0.004 |
Stable area | |||||||||||
Mean | 0.004 | −0.069 | −0.020 | −0.003 | 0.003 | −0.003 | −0.005 | 0.001 | 0.004 | −0.002 | 0.000 |
SD | 0.017 | 0.090 | 0.072 | 0.033 | 0.007 | 0.013 | 0.015 | 0.010 | 0.005 | 0.003 | 0.003 |
Min | −0.039 | −0.296 | −0.172 | −0.050 | −0.018 | −0.024 | −0.027 | −0.013 | −0.007 | −0.007 | −0.007 |
Max | 0.024 | 0.047 | 0.108 | 0.095 | 0.009 | 0.026 | 0.019 | 0.017 | 0.012 | 0.003 | 0.004 |
Unstable area | |||||||||||
Mean | −0.017 | −0.064 | −0.028 | 0.001 | −0.006 | 0.010 | −0.003 | −0.001 | 0.003 | −0.002 | −0.006 |
SD | 0.030 | 0.164 | 0.099 | 0.019 | 0.007 | 0.018 | 0.033 | 0.025 | 0.020 | 0.001 | 0.003 |
Min | −0.052 | −0.241 | −0.138 | −0.018 | −0.018 | −0.010 | −0.061 | −0.043 | −0.049 | −0.003 | −0.009 |
Max | 0.049 | 0.275 | 0.177 | 0.035 | 0.003 | 0.047 | 0.052 | 0.044 | 0.018 | −0.001 | −0.002 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Cardenal, J.; Fernández, T.; Pérez-García, J.L.; Gómez-López, J.M. Measurement of Road Surface Deformation Using Images Captured from UAVs. Remote Sens. 2019, 11, 1507. https://doi.org/10.3390/rs11121507
Cardenal J, Fernández T, Pérez-García JL, Gómez-López JM. Measurement of Road Surface Deformation Using Images Captured from UAVs. Remote Sensing. 2019; 11(12):1507. https://doi.org/10.3390/rs11121507
Chicago/Turabian StyleCardenal, Javier, Tomás Fernández, José Luis Pérez-García, and José Miguel Gómez-López. 2019. "Measurement of Road Surface Deformation Using Images Captured from UAVs" Remote Sensing 11, no. 12: 1507. https://doi.org/10.3390/rs11121507