High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations
Abstract
:1. Introduction
2. Study Site
3. Materials and Methods
3.1. Fixed Camera System
3.2. Data Acquisition and Quality Control
3.3. Workflows for Multi-Epoch Photogrammetric Model Construction
3.4. Change Detection, Clustering, and Volume Estimation
3.5. Identification and Removal of High-Noise Point Clouds and Anomalous M3C2 Results
3.6. Manual Cluster Validation
3.7. Magnitude–Cumulative Frequency Curves
3.8. Single Start-to-End Comparison
4. Results
4.1. Point Cloud Model Quality
4.2. MCF Curves
4.3. Comparison between High-Temporal-Resolution Monitoring Results and Single Start-to-End Change
5. Discussion
5.1. Volume Calculation Uncertainty
5.2. Comparison of MCF Scaling Parameter to Literature Values
5.3. High Temporal Resolution and SSEC Volume Discrepancy
5.4. Spatiotemporal Resolution Considerations for Rockfall Monitoring
5.5. Prevalence of False Positive Clusters and Monitoring Method Limitations
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Royán, M.; Abellán, A.; Jaboyedoff, M.; Vilaplana, J.; Calvet, J. Spatio-temporal analysis of rockfall pre-failure deformation using Terrestrial LiDAR. Landslides 2014, 11, 697–709. [Google Scholar] [CrossRef]
- Gabrieli, F.; Corain, L.; Vettore, L. A low-cost landslide displacement activity assessment from time-lapse photogrammetry and rainfall data: Application to the Tessina landslide site. Geomorphology 2016, 269, 56–74. [Google Scholar] [CrossRef]
- Vanneschi, C.; Di Camillo, M.; Aiello, E.; Bonciani, F.; Salvini, R. SFM-MVS photogrammetry for rockfall analysis and hazard assessment along the ancient roman via Flaminia road at the Furlo gorge (Italy). ISPRS Int. J. Geo-Inf. 2019, 8, 325. [Google Scholar] [CrossRef]
- Briones-Bitar, J.; Carrión-Mero, P.; Montalván-Burbano, N.; Morante-Carballo, F. Rockfall research: A bibliometric analysis and future trends. Geosciences 2020, 10, 403. [Google Scholar] [CrossRef]
- Kromer, R.; Walton, G.; Gray, B.; Lato, M.; Group, R. Development and optimization of an automated fixed-location time lapse photogrammetric rock slope monitoring system. Remote Sens. 2019, 11, 1890. [Google Scholar] [CrossRef]
- Blanch, X.; Eltner, A.; Guinau, M.; Abellan, A. Multi-Epoch and Multi-Imagery (MEMI) Ohotogrammetric Workflow for Enchanced Change Detection Using Time-Lapse Cameras. Remote Sens. 2021, 13, 1460. [Google Scholar] [CrossRef]
- Parente, L.; Chandler, J.; Dixon, N. Optimising the quality of an SfM-MVS slope monitoring system using fixed cameras. Photogramm. Rec. 2019, 34, 408–427. [Google Scholar] [CrossRef]
- Yakar, M.; Ulvi, A.; Yiğit, A.; Alptekin, A. Discontinuity set extraction from 3D point clouds obtained by UAV Photogrammetry in a rockfall site. Surv. Rev. 2022, 55, 416–428. [Google Scholar] [CrossRef]
- van Veen, M.; Hutchinson, D.J.; Kromer, R.; Lato, M.; Edwards, T. Effects of sampling interval on the frequency—Magnitude relationship of rockfalls detected from terrestrial laser scanning using semi-automated methods. Landslides 2017, 14, 1579–1592. [Google Scholar] [CrossRef]
- Barlow, J.; Lim, M.; Rosser, N.; Petley, D.; Brain, M.; Norman, E.; Geer, M. Modeling cliff erosion using negative power law scaling of rockfalls. Geomorphology 2012, 139–140, 416–424. [Google Scholar] [CrossRef]
- Strunden, J.; Ehlers, T.; Brehm, D.; Nettesheim, M. Spatial and temporal variations in rockfall determined from TLS measurements in a deglaciated valley, Switzerland. J. Geophys. Res. Earth Surf. 2015, 120, 1251–1273. [Google Scholar] [CrossRef]
- D’Amato, J.; Hantz, D.; Guerin, A.; Jaboyedoff, M.; Baillet, L.; Mariscal, A. Influence of meteorological factors on rockfall occurrence in a middle mountain limestone cliff. Nat. Hazards Earth Syst. Sci. 2016, 16, 719–735. [Google Scholar] [CrossRef]
- Williams, J.; Rosser, N.; Hardy, R.; Brain, M. The Importance of Monitoring Interval for Rockfall Magnitude-Frequency Estimation. J. Geophys. Res. Earth Surf. 2019, 124, 2841–2853. [Google Scholar] [CrossRef]
- Williams, J.; Rosser, N.; Hardy, R.; Brain, M.; Afana, A. Optimising 4-D surface change detection: An approach for capturing rockfall magnitude-frequency. Earth Surf. Dyn. 2018, 6, 101–119. [Google Scholar] [CrossRef]
- Birien, T.; Gauthier, F. Assessing the relationship between weather conditions and rockfall using terrestrial laser scanning to improve risk management. Nat. Hazards Earth Syst. Sci. 2023, 23, 343–360. [Google Scholar] [CrossRef]
- Giacomini, A.; Thoeni, K.; Santise, M.; Diotri, F.; Booth, S.; Fitvus, S.; Roncella, R. Temporal-spatial frequency rockfall data from open-pit highwalls using a low-cost monitoring system. Remote Sens. 2020, 12, 2459. [Google Scholar] [CrossRef]
- Hartmeyer, I.; Delleske, R.; Keuschnig, M.; Krautblatter, M.; Lang, A.; Otto, J.C.; Schrott, L. Current glacier recession causes significant rockfall increase: The immediate paraglacial response of deglaciating cirque walls. Earth Surf. Dyn. 2020, 8, 729–775. [Google Scholar] [CrossRef]
- Graber, A.; Santi, P. Power law models for rockfall frequency-magnitude distributions: Review and identification of factors that influence the scaling exponent. Geomorphology 2022, 418, 108463. [Google Scholar] [CrossRef]
- Graber, A.; Santi, P.; Arestegui, P.M. Constraining the critical groundwater conditions for initiation of large, irrigation-induced landslides, Siguas River Valley, Peru. Landslides 2021, 18, 3753–3767. [Google Scholar] [CrossRef]
- Wei, X.; Garcia-Chevesich, P.; Alejo, F.; García, V.; Martínez, G.; Daneshvar, F.; Bowling, L.; Gonzáles, E.; Krahenbuhl, R.; McCray, J. Hydrologic analysis of an intensively irrigated area in southern peru using a crop-field scale framework. Water 2021, 13, 318. [Google Scholar] [CrossRef]
- Flamme, H.; Krahenbuhl, R.; Li, Y.; Dugan, B.; Shragge, J.; Graber, A.; Sirota, D.; Wilson, G.; Gonzales, E.; Ticona, J.; et al. Integrated geophysical investigation for understanding agriculturally induced landslides in southern Peru. Environ. Earth Sci. 2022, 81, 309. [Google Scholar] [CrossRef]
- Lacroix, P.; Araujo, G.; Hollingsworth, J.; Taipe, E. Self-Entrainment Motion of a Slow-Moving Landslide Inferred From Landsat-8 Time Series. J. Geophys. Res. Earth Surf. 2019, 124, 1201–1216. [Google Scholar] [CrossRef]
- Lacroix, P.; Dehecq, A.; Taipe, E. Irrigation-triggered landslides in a Peruvian desert caused by modern intensive farming. Nat. Geosci. 2019, 13, 56–60. [Google Scholar] [CrossRef]
- Araujo, G.; Taipe, E.; Miranda, R.; Valderrama, P. Dinamica y Monitoreo Del Deslizamiento De Siguas; Instituto Geólgico, Minero y Metalúrgico: Arequipa, Peru, 2017. [Google Scholar]
- Garcia-Chevesich, P.; Wei, X.; Ticona, J.; Martínez, G.; Zea, J.; García, V.; Alejo, F.; Zhang, Y.; Flamme, H.; Graber, A.; et al. The impact of agricultural irrigation on landslide triggering: A review from Chinese, English, and Spanish literature. Water 2020, 13, 10. [Google Scholar] [CrossRef]
- Planet Team, Planet Application Program Interface: In Space for Life on Earth. 2023. Available online: https://api.planet.com (accessed on 21 April 2023).
- Harbortronics Cyclapse Time Lapse Camera System. 2018. Available online: https://cyclapse.com (accessed on 12 March 2023).
- Agisoft Metashape Professional Edition. 2022. Available online: http://www.agisoft.com/downloads/installer (accessed on 12 November 2023).
- Python Core Team, Python: A Dynamic, Open Source Programming Language. Python Software Foundation. 2022. Available online: https://www.python.org/ (accessed on 7 February 2022).
- Feurer, D.; Vinatier, F. Joining multi-epoch archival aerial images in a single SfM block allows 3-D change detection with almost exclusively image information. ISPRS J. Photogramm. Remote Sens. 2018, 146, 495–506. [Google Scholar] [CrossRef]
- Cook, K.; Dietze, M. Short Communication: A simple workflow for robust low-cost UAV-derived change detection without ground control points. Earth Surf. Dyn. 2019, 7, 1009–1017. [Google Scholar] [CrossRef]
- Zhang, L.; Rupnik, E.; Pierrot-Deseilligny, M. Feature matching for multi-epoch historical aerial images. ISPRS J. Photogramm. Remote Sens. 2021, 182, 176–189. [Google Scholar] [CrossRef]
- Li, J.; Yang, B.; Chen, C.; Habib, A. NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement. ISPRS J. Photogramm. Remote Sens. 2019, 158, 123–145. [Google Scholar] [CrossRef]
- Butcher, B. Rockslope And Landslide Monitoring Using High Temporal Resolution Terrestrial Structure From Motion Photogrammetry: A Case Study of a Landslide in Majes Zone, Peru Using Multi-Epoch Photogrammetric Techniques; Colorado School of Mines: Golden, CO, USA, 2023. [Google Scholar]
- Lague, D.; Brodu, N.; Leroux, J. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS J. Photogramm. Remote Sens. 2013, 82, 10–26. [Google Scholar] [CrossRef]
- Bonneau, D.; DiFrancesco, P.; Hutchinson, D.J. Surface reconstruction for three-dimensional rockfall volumetric analysis. ISPRS Int. J. Geo-Inf. 2019, 8, 548. [Google Scholar] [CrossRef]
- Ester, M.; Kriegel, H.; Sander, J.; Xu, X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, 2–4 August 1996; pp. 226–231. [Google Scholar]
- Tonini, M.; Abellan, A. Rockfall detection from terrestrial lidar point clouds: A clustering approach using R. J. Spat. Inf. Sci. 2013, 8, 95–110. [Google Scholar] [CrossRef]
- Guerin, A.; Rossetti, J.; Hantz, D.; Jaboyedoff, M. Estimating rock fall frequency in a limestone cliff using LIDAR measurements. In Proceedings of the First International Conference Landslides Risk, Tabarka, Tunisia, 14–16 March 2013; pp. 293–301. Available online: https://hal.archives-ouvertes.fr/hal-00808577 (accessed on 4 December 2022).
- Pedregosa, F.; Thirion, B.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; et al. Scikit-learn: Machine Learning in {P}ython. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
- DiFrancesco, P.; Bonneau, D.; Hutchinson, D. The implications of M3C2 projection diameter on 3D semi-automated rockfall extraction from sequential terrestrial laser scanning point clouds. Remote Sens. 2020, 12, 1885. [Google Scholar] [CrossRef]
- Wang, C.; Ji, M.; Wang, J.; Wen, W.; Li, T.; Sun, Y. An improved DBSCAN method for LiDAR data segmentation with automatic Eps estimation. Sensors 2019, 19, 172. [Google Scholar] [CrossRef] [PubMed]
- Walton, G.; Weidner, L. Accuracy of Rockfall Volume Reconstruction from Point Cloud Data—Evaluating the Influences of Data Quality and Filtering. Remote Sens. 2022, 15, 165. [Google Scholar] [CrossRef]
- DiFrancesco, P.; Bonneau, D.; Hutchinson, D. Computational geometry-based surface reconstruction for volume estimation: A case study on magnitude-frequency relations for a LiDAR-derived rockfall inventory. ISPRS Int. J. Geo-Inf. 2021, 10, 157. [Google Scholar] [CrossRef]
- Janeras, M.; Lantada, N.; Núñez-Andrés, M.; Hantz, D.; Pedraza, O.; Cornejo, R.; Guinau, M.; García-Sellés, D.; Blanco, L.; Blanco, J.; et al. Rockfall Magnitude-Frequency Relationship Based on Multi-Source Data from Monitoring and Inventory. Remote Sens. 2023, 15, 1981. [Google Scholar] [CrossRef]
- Zoumpekas, T.; Puig, A.; Salamo, M.; Garcia-Selles, D.; Nunez, L.; Guinau, M. An intelligent framework for end-to-end rockfall detection. Int. J. Intell. Syst. 2021, 36, 6471–6502. [Google Scholar] [CrossRef]
- Farmakis, I.; DiFrancesco, P.; Hutchinson, D.; Vlachopoulos, N. Rockfall detection using LiDAR and deep learning. Eng. Geol. 2022, 309, 106836. [Google Scholar] [CrossRef]
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Butcher, B.; Walton, G.; Kromer, R.; Gonzales, E.; Ticona, J.; Minaya, A. High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations. Remote Sens. 2024, 16, 66. https://doi.org/10.3390/rs16010066
Butcher B, Walton G, Kromer R, Gonzales E, Ticona J, Minaya A. High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations. Remote Sensing. 2024; 16(1):66. https://doi.org/10.3390/rs16010066
Chicago/Turabian StyleButcher, Bradford, Gabriel Walton, Ryan Kromer, Edgard Gonzales, Javier Ticona, and Armando Minaya. 2024. "High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations" Remote Sensing 16, no. 1: 66. https://doi.org/10.3390/rs16010066