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Environmental Earth Sciences

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01.04.2024 | Original Article

Using drone-based multispectral imaging for investigating gravelly debris flows and geomorphic characteristics

verfasst von: Ho-Wen Chen, Chien-Yuan Chen, Pei-Zhang Yang

Erschienen in: Environmental Earth Sciences | Ausgabe 8/2024

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Abstract

Field reconnaissance has difficulty in providing information on topographic features of debris flow in vegetation-covered mountain regions. The study used drone-captured multispectral imaging, digital terrain models, and geomorphic indices to investigate the sources and erosion patterns of gravelly debris flows in mountainous areas. Geomorphic indices, including the topographic wetness index (TWI), stream power index (SPI), sediment transport index, terrain ruggedness index (TRI), and the vegetation index normal differential water index (NDWI), were used to identify key topographic and hydraulic characteristics that contribute to the initiation of gravelly debris flows. The analysis revealed that the debris flows originated from the softening of yellow soil and the dislodging of gravel in loess areas, both induced by surficial water infiltration. Areas with high TWI and SPI values were vulnerable to erosion from surficial water, leading to the formation of erosion gullies. Likewise, regions with high TWI and NDWI values were likely starting points for upstream landslides. Conversely, areas characterized by low TWI and TRI values but high NDWI were prone to downstream sediment deposition. Drone-based multispectral imaging, augmented by geomorphic and vegetation indices, effectively captures the characteristics of vegetation-covered areas, thereby enhancing debris flow field investigations in an inaccessible mountain area.

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Arafat MY, Alam MM, Moh S (2023) Vision-based navigation techniques for unmanned aerial vehicles: review and challenges. Drones 7(2):89. https://doi.org/10.3390/drones7020089CrossRef

Badora D, Wawer R (2022) Effect of DTM resolution on the determination of slope values in an upland catchment using different computational algorithms. Polish J Agronomy 51:11–32. https://doi.org/10.26114/pja.iung.460.2022.51.02CrossRef

Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci J 24:43–69. https://doi.org/10.1080/02626667909491834CrossRef

Boccardo P, Comoglio G (1995) Integration of multispectral data acquired by aerial and satellite platforms for the study of the phenomena caused by the November 1994 flood in Piedmont (Italy). Proc. SPIE 2585, Remote Sensing for Agriculture, Forestry, and Natural Resources. https://doi.org/10.1117/12.227197

Bregoli F, Medina V, Chevalier G, Hürlimann M, Bateman A (2015) Debris-flow susceptibility assessment at regional scale: Validation on an alpine environment. Landslides 12:437–454. https://doi.org/10.1007/s10346-014-0493-xCrossRef

Brožová N, Baggio T, D’Agostino V, Bühler Y, Bebi P (2021) Multiscale analysis of surface roughness for the improvement of natural hazard modelling. Nat Hazard 21:3539–3562. https://doi.org/10.5194/nhess-2021-85CrossRef

Candiago S, Remondin F, De Giglio M, Dubbini M, Gattelli M (2015) Evaluating Multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens 7:4026–4047. https://doi.org/10.3390/rs70404026CrossRef

Casagli N, Fanti R, Nocentini M, Righini G (2005) Assessing the capabilities of vhr satellite data for debris flow mapping in the Machu Picchu Area (C101–1). In: Sassa K, Fukuoka H, Wang F, Wang G (eds) Landslides. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28680-2_6CrossRef

Casagli N, Intrieri E, Tofani V, Gigli G, Raspini F (2023) Landslide detection, monitoring and prediction with remote-sensing techniques. Nat Rev Earth Environ 4:51–64. https://doi.org/10.1038/s43017-022-00373-xCrossRef

Chen CY, Wang Q (2017) Debris flow-induced topographic changes: effects of recurrent debris flow initiation. Environ Monit Assess 189(9):449. https://doi.org/10.1007/s10661-017-6169-yCrossRef

Chen CY, Yu FC (2011) Morphometric analysis of debris flows and their source areas using GIS. Geomorphology 129(3–4):387–397. https://doi.org/10.1016/j.geomorph.2011.03.002CrossRef

Chen NS, Zhou W, Yang CL, Hu GS, Gao YC, Han D (2010) The processes and mechanism of failure and debris flow initiation for gravel soil with different clay content. Geomorphology 121:222–230. https://doi.org/10.1016/j.geomorph.2010.04.017CrossRef

Chen NS, Gao YC, Yang CL, Hu GS (2018) Effect of clay content to the strength of gravel soil in the source region of debris flow. J Mt Sci 15(10):2320–2334. https://doi.org/10.1007/s11629-018-4911-8CrossRef

Chou HT, Lee CF, Huang CH, Chang YL (2013) The monitoring and flow dynamics of gravelly debris flows. J Chin Soil Water Conserv 44(2):144–157

Dias HC, Hölbling D, Dias VC, Grohmann CH (2023) Application of object-based image analysis for detecting and differentiating between shallow landslides and debris flows. GI Forum 11(1):34–44. https://doi.org/10.1553/giscience2023_01_s34CrossRef

Du J, Fan Z-J, Xu W-T, Dong L-Y (2021) Research progress of initial mechanism on debris flow and related discrimination methods: a review. Front Earth Sci. https://doi.org/10.3389/feart.2021.629567CrossRef

Elkadiri R, Sultan M, Youssef AM, Elbayoumi T, Chase R, Bulkhi AB, Al-Katheeri MM (2014) A remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling. IEEE J Sel Top Appl Earth Obs Remote Sens 7:4814–4835. https://doi.org/10.1109/JSTARS.2014.2337273CrossRef

Gao B-C (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266. https://doi.org/10.1016/S0034-4257(96)00067-3CrossRef

Giordan D, Hayakawa Y, Nex F, Remondino F, Tarolli P (2018) The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management. Nat Hazards Earth Syst Sci 18:1079–1096. https://doi.org/10.5194/nhess-18-1079-2018CrossRef

Giordan D, Adams MS, Aicardi I, Alicandro M, Allasia P, Baldo M, De Berardinis P, Dominici D, Godone D, Hobbs P (2020) The use of unmanned aerial vehicles (UAVs) for engineering geology applications. Bull Eng Geol Environ 79:3437–3481. https://doi.org/10.1007/s10064-020-01766-2CrossRef

Godt JW, Coe JA (2007) Alpine debris flows triggered by a 28 July 1999 thunderstorm in the central Front Range, Colorado. Geomorphology 84:80–97. https://doi.org/10.1016/j.geomorph.2006.07.009CrossRef

Hermle D, Keuschnig M, Hartmeyer I, Delleske R, Krautblatter M (2021) Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria. Nat Hazards Earth Syst Sci 21:2753–2772. https://doi.org/10.5194/nhess-21-2753-2021CrossRef

Hoffman DF, Gabet EJ (2007) Effects of sediment pulses on channel morphology in a gravel-bed river. GSA Bull 119:116–125. https://doi.org/10.1130/B25982.1CrossRef

Hu M, Pan H, Wei H, Wang R, Ying A (2013) Landslides & debris flows formation from gravelly soil surface erosion and particle losses in Jiangjia Ravine. J Mt Sci 10:987–995. https://doi.org/10.1007/s11629-012-2526-zCrossRef

Huang J, van Asch TWJ, Wang C, Li Q (2019) Study on the combined threshold for gully-type debris flow early warning. Nat Hazards Earth Syst Sci 19:41–51. https://doi.org/10.5194/nhess-19-41-2019CrossRef

Huang H, Wang Y, Li Y, Zhou Y, Zeng Z (2022) Debris-flow susceptibility assessment in china: a comparison between traditional statistical and machine learning methods. Remote Sens 14(18):4475. https://doi.org/10.3390/rs14184475CrossRef

Jebur MN, Pradhan B, Tehrany MS (2015) Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE J Sel Top Appl Earth Obs Remote Sens 8:674–690. https://doi.org/10.1109/JSTARS.2014.2341276CrossRef

Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics Nat Hazards Risk 9:49–69. https://doi.org/10.1080/19475705.2017.1407368CrossRef

Karimi-Sangchini E, Emami SN, Shariat-Jafari M, Rezazadeh F, Raeisi H (2020) Landslide hazard zonation using multivariate statistical models in the Doab Samsami watershed, Chaharmahal Va Bakhtiari Province, Iran. Jordan J Earth Environ Sci 11:174–182

Lai YS, Lo CM, Weng MC, Chung YH (2022) Characteristics of erosion development and gravelly debris flow hazard zoning in the Houyenshan of Taiwan. Environ Earth Sci 81:76. https://doi.org/10.1007/s12665-022-10211-4CrossRef

Lee G, Kim G, Min G, Kim M, Jung S, Hwang J, Cho S (2023) Vegetation classification in urban areas by combining UAV-based NDVI and thermal infrared image. Appl Sci 13(1):515. https://doi.org/10.3390/app13010515CrossRef

Lv J, Qin S, Chen J, Qiao S, Yao J, Zhao X, Cao R, Yin J (2023) Application of different watershed units to debris flow susceptibility mapping: a case study of Northeast China. Front Earth Sci. https://doi.org/10.3389/feart.2023.1118160CrossRef

Lyu L, Xu M, Wang Z, Cui Y, Blanckaert K (2022) A field investigation on debris flows in the incised Tongde sedimentary basin on the northeastern edge of the Tibetan Plateau. CATENA 208:105727. https://doi.org/10.1016/j.catena.2021.105727CrossRef

Ma S, Xu C, Xu X et al (2020) Characteristics and causes of the landslide on July 23, 2019 in Shuicheng, Guizhou Province, China. Landslides 17:1441–1452. https://doi.org/10.1007/s10346-020-01374-xCrossRef

McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432. https://doi.org/10.1080/01431169608948714CrossRef

Mehmood Q, Qing W, Chen J, Yan J, Ammar M, Rahman G, Nasrullah A (2021) Susceptibility assessment of single gully debris flow based on AHP and extension method. Civil Eng J 7:953–973CrossRef

Moore ID, Burch GJ (1986) Physical basis of the length slope factor in the universal soil loss equation. Soil Sci Soc Am 50:1294–1298. https://doi.org/10.2136/sssaj1986.03615995005000050042xCrossRef

Moore ID, Wilson JP (1992) Length-slope factors for the Revised Universal Soil Loss Equation: simplified method of estimation. J Soil Water Conserv 47(5):423–428

Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological and biological applications. Hydrol Process 5:3–30. https://doi.org/10.1002/hyp.3360050103CrossRef

Morino C, Conway SJ, Balme M, Hillier J, Jordan C, Sæmundsson Þ, Argles T (2019) Debris-flow release processes investigated through the analysis of multi-temporal LiDAR datasets in north-western Iceland. Earth Surf Proc Land 44:144–159. https://doi.org/10.1002/esp.4488CrossRef

Notti D, Giordan D, Cina A, Manzino A, Maschio P, Bendea IH (2021) Debris flow and rockslide analysis with advanced photogrammetry techniques based on high-resolution RPAS data, Ponte Formazza case study (NW Alps). Remote Sens 13(9):1797. https://doi.org/10.3390/rs13091797CrossRef

Pradhan SP, Panda SD, Roul AR, Pradhan SP, Thakur M (2019) Insights into the recent Kotropi landslide of August 2017, India: a geological investigation and slope stability analysis. Landslides 16:1529–1537. https://doi.org/10.1007/s10346-019-01186-8CrossRef

Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain J Sci 5:23–27

Roelofs L, Colucci P, de Haas T (2022) How debris-flow composition affects bed erosion quantity and mechanisms: an experimental assessment. Earth Surf Proc Land 47:2151–2169. https://doi.org/10.1002/esp.5369CrossRef

Roelofs L, Nota EW, Flipsen TCW, Colucci P, de Haas T (2023) How bed composition affects erosion by debris flows—an experimental assessment. Geophys Res Lett 50:e2023GL103294. https://doi.org/10.1029/2023GL103294CrossRef

Różycka M, Migoń P, Michniewicz A (2017) Topographic Wetness Index and Terrain Ruggedness Index in geomorphic characterisation of landslide terrains, on examples from the Sudetes, SW Poland. Z Geomorphol 61:61–80. https://doi.org/10.1127/zfg_suppl/2016/0328CrossRef

Sánchez-Ruiz S, Piles M, Sánchez N, Martínez-Fernández J, Vall-llossera M, Camps A (2014) Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates. J Hydrol 516:273–283. https://doi.org/10.1016/j.jhydrol.2013.12.047CrossRef

Santos VCD, Hage ME, Polidori L, Stevaux J (2017) Effect of digital elevation model mesh size on geomorphic indices: a case study of the IvaÍ river watershed—state of Parana, Brazil. Boletim De Ciencias Geodesicas 23:684–699. https://doi.org/10.1590/S1982-21702017000400045CrossRef

Serrano J, Shahidian S, da Silva JM (2019) Evaluation of normalized difference water index as a tool for monitoring pasture seasonal and inter-annual variability in a Mediterranean Agro-Silvo-Pastoral System. Water 11:62. https://doi.org/10.3390/w11010062CrossRef

Singh P, Thakur JK, Singh UC (2013) Morphometric analysis of Morar River Basin, Madhya Pradesh, India, using remote sensing and GIS techniques. Environ Earth Sci 68:1967–1977. https://doi.org/10.1007/s12665-012-1884-8CrossRef

Sørensen R, Zinko U, Seibert J (2006) On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol Earth Syst Sci 10:101–112. https://doi.org/10.5194/hess-10-101-2006CrossRef

Uysal M, Toprak AS, Polat N (2015) DEM generation with UAV photogrammetry and accuracy analysis in Sahitler hill. Measurement 73:539–543. https://doi.org/10.1016/j.measurement.2015.06.010CrossRef

Vagnon F, Kurilla LJ, Clusaz A, Pirulli M, Fubelli G (2022) Investigation and numerical simulation of debris flow events in Rochefort basin (Aosta Valley—NW Italian Alps) combining detailed geomorphological analyses and modern technologies. Bull Eng Geol Environ 81:378. https://doi.org/10.1007/s10064-022-02871-0CrossRef

Vaze J, Teng J, Spencer G (2010) Impact of DEM accuracy and resolution on topographic indices. Environ Model Softw 25:1086–1098. https://doi.org/10.1016/j.envsoft.2010.03.014CrossRef

Walsh SJ, Butler DR (1997) Morphometric and multispectral image analysis of debris flows for natural hazard assessment. Geocarto Int 12(1):59–70. https://doi.org/10.1080/10106049709354574CrossRef

Walter F, Hodel E, Mannerfelt ES, Ackermann N, Cook K, Dietze M, Estermann L, Farinotti D, Fengler M, Hammerschmidt L, Hänsli F, Hirschberg J, McArdell B, Molnar P (2022) Brief Communication: an autonomous UAV for catchment-wide monitoring of a debris flow torrent. Egusphere. https://doi.org/10.5194/egusphere-2022-156

Wang G, Sassa K (2003) Pore-pressure generation and movement of rainfall-induced landslides: effects of grain size and fine-particle content. Eng Geol 69:109–125. https://doi.org/10.1016/S0013-7952(02)00268-5CrossRef

Wei F, Gao K, Hu K, Li Y, Gardner JS (2008) Relationships between debris flows and earth surface factors in Southwest China. Environ Geol 55:619–627. https://doi.org/10.1007/s00254-007-1012-3CrossRef

Wei ZL, Shang YQ, Liang QH, Xia XL (2023) A coupled hydrological and hydrodynamic modelling approach for estimating rainfall thresholds of debris-flow occurrence. Nat Hazards Earth Syst Sci Discuss. https://doi.org/10.5194/nhess-2023-180

WRA (2022) Hydro information system, Water Resources Agency, Ministry of Economic Affairs. Available at https://gweb.wra.gov.tw/Hydroinfo/ (Accessed 5 June 2022)

Xu F, Wang B, Han J (2023) A potential gully debris flows hazard assessment method: a CNN model based on multi-source data fusion. J Geo-Information Sci 25(3):588–605

Yan XF, Xu H, Lu H, Zhou JW, Wang XK, Wang L (2021) Assessment and analysis of a rainfall-time-lagging water-related disaster in mountainous areas. Front Earth Sci. https://doi.org/10.3389/feart.2021.659708CrossRef

Zanandrea F, Michel GP, Kobiyama M, Cardozo GL (2019) Evaluation of different DTMs in sediment connectivity determination in the Mascarada River Watershed, southern Brazil. Geomorphology 332:80–87. https://doi.org/10.1016/j.geomorph.2019.02.005CrossRef

Zhang W, Chen JP, Wang Q, An Y, Qian X, Xiang L, He L (2012) Susceptibility analysis of large-scale debris flows based on combination weighting and extension methods. Nat Hazards 66:1073–1100. https://doi.org/10.1007/s11069-012-0539-0CrossRef

Zhong YX, Lo CM (2017) Characteristics of the development of erosion and the dynamics of gravelly debris flow in the Houyenshan. J Chin Soil Water Conserv 48:56–62

Titel: Using drone-based multispectral imaging for investigating gravelly debris flows and geomorphic characteristics
verfasst von: Ho-Wen Chen
Chien-Yuan Chen
Pei-Zhang Yang
Publikationsdatum: 01.04.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 8/2024
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-024-11544-y

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