The Application of Remote Sensing Technology in Post-Disaster Emergency Investigations of Debris Flows: A Case Study of the Shuimo Catchment in the Bailong River, China
Abstract
:1. Introduction
2. Study Area
3. Data and Methods
3.1. Data
3.2. Methods
4. Results
4.1. Topographic Characteristics of the Debris Flow
4.2. Characteristics of Debris Flow Material Sources
4.2.1. Gravity Erosion Supply
4.2.2. Channel Erosion Supply
4.2.3. Surface Erosion Supply
4.3. Dynamic Process of the Debris Flow
4.3.1. Rainfall Process
4.3.2. Debris Flow Process
4.4. Characteristics of the Debris Flow Disaster
5. Discussion
6. Conclusions
- The Shuimo catchment is a typical low-frequency debris flow catchment, characterized by its hidden nature. Shuimo catchment has a large area and a significant elevation difference, with a relatively long main channel that provides sufficient potential energy conditions; however, the confluence conditions are inadequate. The debris flow was influenced by previous rainfall and triggered by the subsequent intense rainfall. The initiation mechanism of the debris flow is channel blockage and failure amplification.
- Based on the interpretation of remote sensing images, it is known that the initiation point of the debris flow is located 8.5 km from the outlet, resulting in a long transportation distance. A total of 8 blockage points were identified. The channel has experienced severe erosion and widening. Using drone imagery, the area of the debris flow accumulation fan was determined to be 79,100 m². The area of the dammed lake is approximately 1.06 km², with the submerged area around 374,000 m², providing support for the rescue of trapped individuals.
- Based on InSAR technology, the number and distribution of unstable slopes within the catchment were determined. Combined with field investigations, it was found that channel erosion and slope erosion are the primary sources of material supply for the debris flow.
- The formation mechanism and dynamic characteristics of the Shuimo catchment debris flow can be summarized as follows: rainfall triggering → shallow landslides → slope debris flows → channel erosion → landslide damming → dam failure and increased discharge → deposition and river blockage.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Pastorello, R.; D’Agostino, V.; Hürlimann, M. Debris Flow Triggering Characterization through a Comparative Analysis among Different Mountain Catchments. Catena 2020, 186, 104348. [Google Scholar] [CrossRef]
- Zhao, Y.; Meng, X.; Qi, T.; Chen, G.; Li, Y.; Yue, D.; Qing, F. Modeling the Spatial Distribution of Debris Flows and Analysis of the Controlling Factors: A Machine Learning Approach. Remote Sens. 2021, 13, 4813. [Google Scholar] [CrossRef]
- Zhao, Y.; Meng, X.; Qi, T.; Chen, G.; Li, Y.; Yue, D.; Qing, F. Estimating the Daily Rainfall Thresholds of Regional Debris Flows in the Bailong River Basin, China. Bull. Eng. Geol. Environ. 2023, 82, 46. [Google Scholar] [CrossRef]
- Zhao, Y.; Meng, X.; Qi, T.; Qing, F.; Xiong, M.; Li, Y.; Guo, P.; Chen, G. AI-Based Identification of Low-Frequency Debris Flow Catchments in the Bailong River Basin, China. Geomorphology 2020, 359, 107125. [Google Scholar] [CrossRef]
- Zhou, W.; Tang, C.; Van Asch, T.W.J.; Chang, M. A Rapid Method to Identify the Potential of Debris Flow Development Induced by Rainfall in the Catchments of the Wenchuan Earthquake Area. Landslides 2016, 13, 1243–1259. [Google Scholar] [CrossRef]
- Tang, C.; Rengers, N.; van Asch, T.W.J.; Yang, Y.H.; Wang, G.F. Triggering Conditions and Depositional Characteristics of a Disastrous Debris Flow Event in Zhouqu City, Gansu Province, Northwestern China. Nat. Hazards Earth Syst. Sci. 2011, 11, 2903–2912. [Google Scholar] [CrossRef]
- Qing, F.; Meng, X.; Guo, F.; Zhao, Y. Characteristics Analysis of “8·7” Debris Flow Disaster in Wen County Disturbed by Wenchuan Earthquake: A Case Study of Yangtang Catchment. J. Lanzhou Univ. (Nat. Sci.) 2021, 57, 376–381. [Google Scholar]
- Glade, T. Linking Debris-Flow Hazard Assessments with Geomorphology. Geomorphology 2005, 66, 189–213. [Google Scholar] [CrossRef]
- Jakob, M.; Bovis, M.; Oden, M. The Significance of Channel Recharge Rates for Estimating Debris-Flow Magnitude and Frequency. Earth Surf. Process. Landf. 2005, 30, 755–766. [Google Scholar] [CrossRef]
- Qing, F.; Zhao, Y.; Meng, X.; Su, X.; Qi, T.; Yue, D. Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway. Remote Sens. 2020, 12, 2933. [Google Scholar] [CrossRef]
- Van Steijn, H. Debris-Flow Magnitude—Frequency Relationships for Mountainous Regions of Central and Northwest Europe. Geomorphology 1996, 15, 259–273. [Google Scholar] [CrossRef]
- Lorente, A.; Beguería, S.; Bathurst, J.C.; García-Ruiz, J.M. Debris Flow Characteristics and Relationships in the Central Spanish Pyrenees. Nat. Hazards Earth Syst. Sci. 2003, 3, 683–692. [Google Scholar] [CrossRef]
- Sterling, S.; Slaymaker, O. Lithologic Control of Debris Torrent Occurrence. Geomorphology 2007, 86, 307–319. [Google Scholar] [CrossRef]
- Cheng, W.; Wang, N.; Zhao, M.; Zhao, S. Relative Tectonics and Debris Flow Hazards in the Beijing Mountain Area from DEM-Derived Geomorphic Indices and Drainage Analysis. Geomorphology 2016, 257, 134–142. [Google Scholar] [CrossRef]
- Esper Angillieri, M.Y. Debris Flow Susceptibility Mapping Using Frequency Ratio and Seed Cells, in a Portion of a Mountain International Route, Dry Central Andes of Argentina. Catena 2020, 189, 104504. [Google Scholar] [CrossRef]
- Jomelli, V.; Pavlova, I.; Giacona, F.; Zgheib, T.; Eckert, N. Respective Influence of Geomorphologic and Climate Conditions on Debris-Flow Occurrence in the Northern French Alps. Landslides 2019, 16, 1871–1883. [Google Scholar] [CrossRef]
- Ghestem, M.; Veylon, G.; Bernard, A.; Vanel, Q.; Stokes, A. Influence of Plant Root System Morphology and Architectural Traits on Soil Shear Resistance. Plant Soil 2014, 377, 43–61. [Google Scholar] [CrossRef]
- Winter, M.G.; Smith, J.T.; Fotopoulou, S.; Pitilakis, K.; Mavrouli, O.; Corominas, J.; Argyroudis, S. An Expert Judgement Approach to Determining the Physical Vulnerability of Roads to Debris Flow. Bull. Eng. Geol. Environ. 2014, 73, 291–305. [Google Scholar] [CrossRef]
- Guo, X.; Chen, X.; Song, G.; Zhuang, J.; Fan, J. Debris Flows in the Lushan Earthquake Area: Formation Characteristics, Rainfall Conditions, and Evolutionary Tendency. Nat. Hazards 2021, 106, 2663–2687. [Google Scholar] [CrossRef]
- Rengers, F.K.; McGuire, L.A.; Coe, J.A.; Kean, J.W.; Baum, R.L.; Staley, D.M.; Godt, J.W. The Influence of Vegetation on Debris-Flow Initiation during Extreme Rainfall in the Northern Colorado Front Range. Geology 2016, 44, 823–826. [Google Scholar] [CrossRef]
- Lorente, A.; García-Ruiz, J.M.; Beguería, S.; Arnáez, J. Factors Explaining the Spatial Distribution of Hillslope Debris Flows: A Case Study in the Flysch Sector of the Central Spanish Pyrenees. Mt. Res. Dev. 2002, 22, 32–39. [Google Scholar] [CrossRef]
- Frank, F.; McArdell, B.W.; Huggel, C.; Vieli, A. The Importance of Entrainment and Bulking on Debris Flow Runout Modeling: Examples from the Swiss Alps. Nat. Hazards Earth Syst. Sci. 2015, 15, 2569–2583. [Google Scholar] [CrossRef]
- Frank, F.; Huggel, C.; McArdell, B.W.; Vieli, A. Landslides and Increased Debris-Flow Activity: A Systematic Comparison of Six Catchments in Switzerland. Earth Surf. Process. Landf. 2019, 44, 699–712. [Google Scholar] [CrossRef]
- Chang, T.C.; Chao, R.J. Application of Back-Propagation Networks in Debris Flow Prediction. Eng. Geol. 2006, 85, 270–280. [Google Scholar] [CrossRef]
- Kovanen, D.J.; Slaymaker, O. The Morphometric and Stratigraphic Framework for Estimates of Debris Flow Incidence in the North Cascades Foothills, Washington State, USA. Geomorphology 2008, 99, 224–245. [Google Scholar] [CrossRef]
- Bertrand, M.; Liébault, F.; Piégay, H. Debris-Flow Susceptibility of Upland Catchments. Nat. Hazards 2013, 67, 497–511. [Google Scholar] [CrossRef]
- Liu, J.J.; Li, Y.; Su, P.C.; Cheng, Z.L. Magnitude–Frequency Relations in Debris Flow. Environ. Geol. 2008, 55, 1345–1354. [Google Scholar] [CrossRef]
- Johnson, P.A.; McCuen, R.H.; Hromadka, T.V. Magnitude and Frequency of Debris Flows. J. Hydrol. 1991, 123, 69–82. [Google Scholar] [CrossRef]
- Hu, G.; Huang, H.; Tian, S.; Rahman, M.; Shen, H.; Yang, Z. Method on Early Identification of Low-Frequency Debris Flow Gullies along the Highways in the Chuanxi Plateau. Remote Sens. 2023, 15, 1183. [Google Scholar] [CrossRef]
- Tian, S.; Hu, G.; Chen, N.; Rahman, M.; Han, Z.; Somos-Valenzuela, M.; Maurice Habumugisha, J. Extreme Climate and Tectonic Controls on the Generation of a Large-Scale, Low-Frequency Debris Flow. Catena 2022, 212, 106086. [Google Scholar] [CrossRef]
- Liu, M.; Deng, M.; Chen, N.; Tian, S.; Wang, T. Analysis of the Low-Frequency Debris Flow Disaster Induced by a Local Rainstorm on 12 July 2022, in Pingwu County, China. Remote Sens. 2024, 16, 1547. [Google Scholar] [CrossRef]
- Zhou, W.; Zou, Q.; Chen, S.; Jiang, H.; Zhou, B.; Yao, H.; Yang, T. Extreme Climate and Human Activities Contribute to Low-Frequency, Large-Scale Catastrophic Debris Flow: A Case Study in the Heishui Gully. Geomat. Nat. Hazards Risk 2024, 15, 2316719. [Google Scholar] [CrossRef]
- Chou, T.; Yeh, M.; Chen, Y.; Chen, Y. Disaster Monitoring and Management by the Unmanned Aerial Vehicle Technology. ISPRS TC VII Symp. 2010, XXXVIII, 137–142. [Google Scholar]
- Delaney, K.B.; Evans, S.G. The 2000 Yigong Landslide (Tibetan Plateau), Rockslide-Dammed Lake and Outburst Flood: Review, Remote Sensing Analysis, and Process Modelling. Geomorphology 2015, 246, 377–393. [Google Scholar] [CrossRef]
- Salvini, R.; Mastrorocco, G.; Seddaiu, M.; Rossi, D.; Vanneschi, C. The Use of an Unmanned Aerial Vehicle for Fracture Mapping within a Marble Quarry (Carrara, Italy): Photogrammetry and Discrete Fracture Network Modelling. Geomat. Nat. Hazards Risk 2017, 8, 34–52. [Google Scholar] [CrossRef]
- Dominici, D.; Alicandro, M.; Massimi, V. UAV Photogrammetry in the Post-Earthquake Scenario: Case Studies in L’Aquila. Geomat. Nat. Hazards Risk 2017, 8, 87–103. [Google Scholar] [CrossRef]
- Tuckey, Z.; Stead, D. Improvements to Field and Remote Sensing Methods for Mapping Discontinuity Persistence and Intact Rock Bridges in Rock Slopes. Eng. Geol. 2016, 208, 136–153. [Google Scholar] [CrossRef]
- Qi, T.; Meng, X.; Zhao, Y.; Su, X.; Chen, G.; Zeng, R.; Zhang, Y.; Li, Y.; Yue, D. Formation and Distribution of Landslides Controlled by Thrust-Strike-Slip Fault Zones and Fluvial Erosion in the Western Qinling Mountains, China. Eng. Geol. 2023, 323, 107209. [Google Scholar] [CrossRef]
- Qi, T.; Meng, X.; Qing, F.; Zhao, Y.; Shi, W.; Chen, G.; Zhang, Y.; Li, Y.; Yue, D.; Su, X.; et al. Distribution and Characteristics of Large Landslides in a Fault Zone: A Case Study of the NE Qinghai-Tibet Plateau. Geomorphology 2021, 379, 107592. [Google Scholar] [CrossRef]
- Zhang, J.; Yang, R.; Qi, Y.; Zhang, H.; Zhang, J.; Guo, Q.; Ma, C.; Wang, H. A Study on the Monitoring of Landslide Deformation Disasters in Wenxian County, Longnan City Based on Different Time-Series InSAR Techniques. Nat. Hazards 2024, 2024, 1–25. [Google Scholar] [CrossRef]
- Zhang, S.; Xia, M.; Li, L.; Yang, H.; Liu, D.; Wei, F. Quantify the Effect of Antecedent Effective Precipitation on Rainfall Intensity-Duration Threshold of Debris Flow. Landslides 2023, 20, 1719–1730. [Google Scholar] [CrossRef]
- Melton, M.A. An Analysis of the Relations among Elements of Climate, Surface Properties, and Geomorphology; Technical Report No. 11; Columbia University, Department of Geology, Office of Naval Research: New York, NY, USA, 1957. [Google Scholar]
- Strahler, A.N. Hypsometric (Area-Altitude) Analysis of Erosional Topography. Bull. Geol. Soc. Am. 1952, 63, 1117–1142. [Google Scholar] [CrossRef]
- Jackson, L.E.; Kostaschuk, R.A.; MacDonald, G.M. Identification of Debris Flow Hazard on Alluvial Fans in the Canadian Rocky Mountains. GSA Rev. Eng. Geol. 1987, 7, 115–124. [Google Scholar] [CrossRef]
- Shi, W.; Chen, G.; Meng, X.; Jiang, W.; Chong, Y.; Zhang, Y.; Dong, Y.; Zhang, M. Spatial-Temporal Evolution of Land Subsidence and Rebound over Xi’an in Western China Revealed by SBAS-InSAR Analysis. Remote Sens. 2020, 12, 3756. [Google Scholar] [CrossRef]
- Zhao, Y.; Meng, X.; Qi, T.; Li, Y.; Chen, G.; Yue, D.; Qing, F. AI-Based Rainfall Prediction Model for Debris Flows. Eng. Geol. 2022, 296, 106456. [Google Scholar] [CrossRef]
- Zhao, Y.; Meng, X.; Qi, T.; Chen, G.; Li, Y.; Yue, D.; Qing, F. Extracting More Features from Rainfall Data to Analyze the Conditions Triggering Debris Flows. Landslides 2022, 19, 2091–2099. [Google Scholar] [CrossRef]
- Bovis, M.J.; Jakob, M. The Role of Debris Supply Conditions in Predicting Debris Flow Activity. Earth Surf. Process. Landf. 1999, 24, 1039–1054. [Google Scholar] [CrossRef]
Data Type | Spectral Features | Spatial Resolution | Acquisition Time |
---|---|---|---|
Pleiades | Visible light: 4 near-infrared bands, 1 panchromatic band | 2 m multispectral, 0.5 m panchromatic spectrum | 24 August 2020 |
Gaofen-1 | Visible light: 4 near-infrared bands, 1 panchromatic band | 8 m multispectral, 2.0 m panchromatic spectrum | 20 August 2020 |
Gaofen-2 | Visible light: 4 near-infrared bands, 1 panchromatic band | 4 m multispectral, 0.8 m panchromatic spectrum | 24 August 2020 |
UAV image | True color | Better than 0.2 m | 17 August 2020 |
Branch Channel Name | Accumulation Thickness (m) | Length along Path (m) | Volume (104 m3) |
---|---|---|---|
Middle and upper reaches of the main channel (above Lishuxia Village) | 3–8 | 5880 | 352.8 |
Middle reaches of the main channel (Lishuxia Village–Wenjiagou Village) | 5–10 | 3980 | 477.6 |
Lower reaches of the main channel (below Wenjiagou Village) | 5–15 | 4654 | 1047.1 |
Total | 1877.5 |
Gravity Erosion | Channel Erosion | Slope Erosion | Total Volume (104 m3) | Area (km2) | Supplementary Amount per Unit Area 104 m3/km2 | |||
---|---|---|---|---|---|---|---|---|
Landslide | Collapse | Slope Debris Flow | Channel Deposits | Residual Slope Deposits | ||||
Material reserves (104 m3) | 1560 | 150 | 78 | 1878 | 942 | 4608 | 31.26 | 147.4 |
Transformation rate (%) | 10 | 70 | 90 | 68 | 55 | 46.14 | ||
Available supplementary amount (104 m3) | 156 | 105 | 70 | 1277 | 518 | 2126 | 31.26 | 68.2 |
Proportion (%) | 7.34 | 4.94 | 3.29 | 60.07 | 24.36 | 100 |
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Huo, F.; Guo, F.; Shi, P.; Gao, Z.; Zhao, Y.; Wang, Y.; Meng, X.; Yue, D. The Application of Remote Sensing Technology in Post-Disaster Emergency Investigations of Debris Flows: A Case Study of the Shuimo Catchment in the Bailong River, China. Remote Sens. 2024, 16, 2817. https://doi.org/10.3390/rs16152817
Huo F, Guo F, Shi P, Gao Z, Zhao Y, Wang Y, Meng X, Yue D. The Application of Remote Sensing Technology in Post-Disaster Emergency Investigations of Debris Flows: A Case Study of the Shuimo Catchment in the Bailong River, China. Remote Sensing. 2024; 16(15):2817. https://doi.org/10.3390/rs16152817
Chicago/Turabian StyleHuo, Feibiao, Fuyun Guo, Pengqing Shi, Ziyan Gao, Yan Zhao, Yongbin Wang, Xingmin Meng, and Dongxia Yue. 2024. "The Application of Remote Sensing Technology in Post-Disaster Emergency Investigations of Debris Flows: A Case Study of the Shuimo Catchment in the Bailong River, China" Remote Sensing 16, no. 15: 2817. https://doi.org/10.3390/rs16152817