Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas

Chen Guo; Qiang Xu; Xiujun Dong; Weile Li; Kuanyao Zhao; Huiyan Lu; Yuanzhen Ju

doi:10.1007/s12583-021-1467-2

Volume 32 Issue 5

Oct 2021

Turn off MathJax

Article Contents

Journal of Earth Science > 2021 > 32(5): 1079-1091.

Chen Guo, Qiang Xu, Xiujun Dong, Weile Li, Kuanyao Zhao, Huiyan Lu, Yuanzhen Ju. Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas. Journal of Earth Science, 2021, 32(5): 1079-1091. doi: 10.1007/s12583-021-1467-2

Citation:

Chen Guo, Qiang Xu, Xiujun Dong, Weile Li, Kuanyao Zhao, Huiyan Lu, Yuanzhen Ju. Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas. Journal of Earth Science, 2021, 32(5): 1079-1091. doi: 10.1007/s12583-021-1467-2

Citation:

Chen Guo, Qiang Xu, Xiujun Dong, Weile Li, Kuanyao Zhao, Huiyan Lu, Yuanzhen Ju. Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas. Journal of Earth Science, 2021, 32(5): 1079-1091. doi: 10.1007/s12583-021-1467-2

PDF( 8677 KB)

Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas

doi: 10.1007/s12583-021-1467-2

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China

More Information

Corresponding author: Qiang Xu: xq@cdut.edu.cn
Received Date: 29 Jan 2021
Accepted Date: 06 Apr 2021
Publish Date: 01 Oct 2021

Abstract

Abstract

Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging (LiDAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the LiDAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne LiDAR DEM derivatives. Quasi-3D relief image maps are generated based on the sky-view factor (SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km² study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of LiDAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m². Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of LiDAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.
- geohazard,
- geohazard inventory,
- airborne LiDAR,
- sky view factor,
- remote sensing interpretation,
- complex mountainous areas

FullText(HTML)

References(47)

References

Ardizzone, F., Cardinali, M., Galli, M., et al., 2007. Identification and Mapping of Recent Rainfall-Induced Landslides Using Elevation Data Collected by Airborne Lidar. Natural Hazards and Earth System Sciences, 7(6): 637-650. https://doi.org/10.5194/nhess-7-637-2007

Bai, Y. J., Wang, Y. S., Ge, H., et al., 2020. Slope Structures and Formation of Rock-Soil Aggregate Landslides in Deeply Incised Valleys. Journal of Mountain Science, 17(2): 316-328. https://doi.org/10.1007/s11629-019-5623-4

Bell, R., Petschko, H., Röhrs, M., et al., 2012. Assessment of Landslide Age, Landslide Persistence and Human Impact Using Airborne Laser Scanning Digital Terrain Models. Geografiska Annaler: Series A, Physical Geography, 94(1): 135-156. https://doi.org/10.1111/j.1468-0459.2012.00454.x

Chen, N. S., Li, T. C., Gao, Y. C., 2005. A Great Disastrous Debris Flow on 11 July 2003 in Shuikazi Valley, Danba County, Western Sichuan, China. Landslides, 2(1): 71-74. https://doi.org/10.1007/s10346-004-0041-1

Chen, R. F., Chang, K. J., Angelier, J., et al., 2006. Topographical Changes Revealed by High-Resolution Airborne LiDAR Data: The 1999 Tsaoling Landslide Induced by the Chi-Chi Earthquake. Engineering Geology, 88(3/4): 160-172. https://doi.org/10.1016/j.enggeo.2006.09.008

Chen, R. F., Lin, C. W., Chen, Y. H., et al., 2015. Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM. Remote Sensing, 7(11): 15443-15466. https://doi.org/10.3390/rs71115443

Chen, W. T., Li, X. J., Wang, Y. X., et al., 2014. Forested Landslide Detection Using LiDAR Data and the Random Forest Algorithm: A Case Study of the Three Gorges, China. Remote Sensing of Environment, 152: 291-301. https://doi.org/10.1016/j.rse.2014.07.004

Chiba, T., Kaneta, S., Suzuki, Y., 2008. Red Relief Image Map: New Visualization Method for Three Dimensional Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B2), 1071-1076. http://www.isprs.org/proceedings/xxxvii/congress/2_pdf/11_ths-6/08.pdf. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=0D311937E390C0CE6901A3533AD5F823?doi=10.1.1.184.1205&rep=rep1&type=pdf

Chigira, M., Duan, F. J., Yagi, H., et al., 2004. Using an Airborne Laser Scanner for the Identification of Shallow Landslides and Susceptibility Assessment in an Area of Ignimbrite Overlain by Permeable Pyroclastics. Landslides, 1(3): 203-209. https://doi.org/10.1007/s10346-004-0029-x

Comert, R., Avdan, U., Gorum, T., et al., 2019. Mapping of Shallow Landslides with Object-Based Image Analysis from Unmanned Aerial Vehicle Data. Engineering Geology, 260(1): 105264. https://doi.org/10.1016/j.enggeo.2019.105264

Conrad, O., Bechtel, B., Bock, M., et al., 2015. System for Automated Geoscientific Analyses (SAGA) V. 2.1.4. Geoscientific Model Development, 8(7): 1991-2007. https://doi.org/10.5194/gmd-8-1991-2015

Dong, J., Zhang, L., Tang, M. G., et al., 2018. Mapping Landslide Surface Displacements with Time Series SAR Interferometry by Combining Persistent and Distributed Scatterers: A Case Study of Jiaju Landslide in Danba, China. Remote Sensing of Environment, 205: 180-198. https://doi.org/10.1016/j.rse.2017.11.022

Eeckhaut, M. V. D., Poesen, J., Verstraeten, G., et al., 2007. Use of LIDAR-Derived Images for Mapping Old Landslides under Forest. Earth Surface Processes and Landforms, 32(5): 754-769. https://doi.org/10.1002/esp.1417

Fan, X. M., Scaringi, G., Xu, Q., et al., 2018. Coseismic Landslides Triggered by the 8th August 2017 M_s7.0 Jiuzhaigou Earthquake (Sichuan, China): Factors Controlling Their Spatial Distribution and Implications for the Seismogenic Blind Fault Identification. Landslides, 15(5): 967-983. https://doi.org/10.1007/s10346-018-0960-x

Fan, X. M., Xu, Q., Alonso-Rodriguez, A., et al., 2019. Successive Landsliding and Damming of the Jinsha River in Eastern Tibet, China: Prime Investigation, Early Warning, and Emergency Response. Landslides, 16(5): 1003-1020. https://doi.org/10.1007/s10346-019-01159-x

Fan, X. M., Xu, Q., Scaringi, G., et al., 2017. Failure Mechanism and Kinematics of the Deadly June 24th 2017 Xinmo Landslide, Maoxian, Sichuan, China. Landslides, 14(6): 2129-2146. https://doi.org/10.1007/s10346-017-0907-7

Fan, X., Xu, Q., Huang, R., et al., 2007. Dynamical Optimal Anchoring Design and Information Construction of Danba Landslide. Chinese Journal of Rock Mechanics and Engineering, 26 (S2): 4139-4146 (in Chinese with English Abstract) http://www.cnki.com.cn/Article/CJFDTotal-YSLX2007S2080.htm

Fiorucci, F., Cardinali, M., Carlà, R., et al., 2011. Seasonal Landslide Mapping and Estimation of Landslide Mobilization Rates Using Aerial and Satellite Images. Geomorphology, 129(1/2): 59-70. https://doi.org/10.1016/j.geomorph.2011.01.013

Gao, J., Maro, J., 2010. Topographic Controls on Evolution of Shallow Landslides in Pastoral Wairarapa, New Zealand, 1979-2003. Geomorphology, 114(3): 373-381. https://doi.org/10.1016/j.geomorph.2009.08.002

Görüm, T., 2019. Landslide Recognition and Mapping in a Mixed Forest Environment from Airborne LiDAR Data. Engineering Geology, 258: 105155. https://doi.org/10.1016/j.enggeo.2019.105155

Guzzetti, F., Ardizzone, F., Cardinali, M., et al., 2009. Landslide Volumes and Landslide Mobilization Rates in Umbria, Central Italy. Earth and Planetary Science Letters, 279(3/4): 222-229. https://doi.org/10.1016/j.epsl.2009.01.005

Guzzetti, F., Mondini, A. C., Cardinali, M., et al., 2012. Landslide Inventory Maps: New Tools for an Old Problem. Earth-Science Reviews, 112(1/2): 42-66. https://doi.org/10.1016/j.earscirev.2012.02.001

Huang, R. Q., 2009. Some Catastrophic Landslides since the Twentieth Century in the Southwest of China. Landslides, 6(1): 69-81. https://doi.org/10.1007/s10346-009-0142-y

Jaboyedoff, M., Metzger, R., Oppikofer, T., et al., 2007. New Insight Techniques to Analyze Rock-Slope Relief Using DEM and 3D-Imaging Cloud Points. Rock Mechanics: Meeting Society's Challenges and Demands. In: Eberhardt, E., Stead, D., Morrison, T., eds., Rock mechanics: Meeting Society's challenges and demands. Proceedings of the 1st Canada——U.S. Rock Mechanics Symposium, Vancouver, Canada, 27-31 May 2007. Taylor & Francis, 61-68. https://doi.org/10.1201/noe0415444019-c8

Jaboyedoff, M., Oppikofer, T., Abellán, A., et al., 2012. Use of LIDAR in Landslide Investigations: A Review. Natural Hazards, 61(1): 5-28. https://doi.org/10.1007/s11069-010-9634-2

Li, M. H., Zheng, W. M., Shi, S. W., et al., 2008. The Revival Mechanism and Stability Analysis to Jiaju Landslide of Danba County in Sichuan Province. Journal of Mountain Science, 26(5): 577-582 (in Chinese with English Abstract) http://www.cnki.com.cn/Article/CJFDTotal-SDYA200805013.htm

Li, W., Xu, Q., Lu, H., et al., 2019. Tracking the Deformation History of Large-Scale Rocky Landslides and Its Enlightenment. Geomatics and Information Science of Wuhan University, 44 (7): 1043-1053 (in Chinese with English Abstract) http://www.researchgate.net/publication/339091214_Tracking_the_Deformation_History_of_Large-Scale_Rocky_Landslides_and_Its_Enlightenment

Li, X. J., Cheng, X. W., Chen, W. T., et al., 2015. Identification of Forested Landslides Using LiDar Data, Object-Based Image Analysis, and Machine Learning Algorithms. Remote Sensing, 7(8): 9705-9726. https://doi.org/10.3390/rs70809705

Lin, M. L., Chen, T. W., Lin, C. W., et al., 2013. Detecting Large-Scale Landslides Using Lidar Data and Aerial Photos in the Namasha-Liuoguey Area, Taiwan. Remote Sensing, 6(1): 42-63. https://doi.org/10.3390/rs6010042

Malamud, B. D., Turcotte, D. L., Guzzetti, F., et al., 2004. Landslides, Earthquakes, and Erosion. Earth and Planetary Science Letters, 229(1/2): 45-59. https://doi.org/10.1016/j.epsl.2004.10.018

McKean, J., Roering, J., 2004. Objective Landslide Detection and Surface Morphology Mapping Using High-Resolution Airborne Laser Altimetry. Geomorphology, 57(3/4): 331-351. https://doi.org/10.1016/s0169-555x(03)00164-8

Nichol, J., Wong, M. S., 2005. Detection and Interpretation of Landslides Using Satellite Images. Land Degradation & Development, 16(3): 243-255. https://doi.org/10.1002/ldr.648

Parker, R. N., Densmore, A. L., Rosser, N. J., et al., 2011. Mass Wasting Triggered by the 2008 Wenchuan Earthquake is Greater than Orogenic Growth. Nature Geoscience, 4(7): 449-452. https://doi.org/10.1038/ngeo1154

Pedrazzini, A., Humair, F., Jaboyedoff, M., et al., 2016. Characterisation and Spatial Distribution of Gravitational Slope Deformation in the Upper Rhone Catchment (Western Swiss Alps). Landslides, 13(2): 259-277. https://doi.org/10.1007/s10346-015-0562-9

Peng, D. L., Xu, Q., Liu, F. Z., et al., 2018. Distribution and Failure Modes of the Landslides in Heitai Terrace, China. Engineering Geology, 236: 97-110. https://doi.org/10.1016/j.enggeo.2017.09.016

Petschko, H., Bell, R., Glade, T., 2016. Effectiveness of Visually Analyzing LiDAR DTM Derivatives for Earth and Debris Slide Inventory Mapping for Statistical Susceptibility Modeling. Landslides, 13(5): 857-872. https://doi.org/10.1007/s10346-015-0622-1

Roering, J. J., MacKey, B. H., Marshall, J. A., et al., 2013. 'You are HERE': Connecting the Dots with Airborne Lidar for Geomorphic Fieldwork. Geomorphology, 200: 172-183. https://doi.org/10.1016/j.geomorph.2013.04.009

Santangelo, M., Cardinali, M., Rossi, M., et al., 2010. Remote Landslide Mapping Using a Laser Rangefinder Binocular and GPS. Natural Hazards and Earth System Sciences, 10(12): 2539-2546. https://doi.org/10.5194/nhess-10-2539-2010

Tang, C. X., Tanyas, H., van Westen, C. J., et al., 2019. Analysing Post-Earthquake Mass Movement Volume Dynamics with Multi-Source DEMs. Engineering Geology, 248: 89-101. https://doi.org/10.1016/j.enggeo.2018.11.010

Tomás, R., Li, Z. H., 2017. Earth Observations for Geohazards: Present and Future Challenges. Remote Sensing, 9(3): 194. https://doi.org/10.3390/rs9030194

Trigila, A., Iadanza, C., Spizzichino, D., 2010. Quality Assessment of the Italian Landslide Inventory Using GIS Processing. Landslides, 7(4): 455-470. https://doi.org/10.1007/s10346-010-0213-0

Yamazaki, F., Kubo, K., Tanabe, R., et al., 2017. Damage Assessment and 3d Modeling by UAV Flights after the 2016 Kumamoto, Japan Earthquake. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). July 23-28, 2017, Fort Worth, TX, USA. IEEE, 3182-3185. https://doi.org/10.1109/igarss.2017.8127673

Yin, Y. P., Wang, F. W., Sun, P., 2009. Landslide Hazards Triggered by the 2008 Wenchuan Earthquake, Sichuan, China. Landslides, 6(2): 139-152. https://doi.org/10.1007/s10346-009-0148-5

Zakšek, K., Oštir, K., Kokalj, Ž., 2011. Sky-View Factor as a Relief Visualization Technique. Remote Sensing, 3(2): 398-415. https://doi.org/10.3390/rs3020398

Zhang, S. L., Yin, Y. P., Hu, X. W., et al., 2020. Initiation Mechanism of the Baige Landslide on the Upper Reaches of the Jinsha River, China. Landslides, 17(12): 2865-2877. https://doi.org/10.1007/s10346-020-01495-3

Zheng, G., Xu, Q., Ju, Y. Z., et al., 2018. The Pusacun Rockavalanche on August 28, 2017 in Zhangjiawan Nayongxian, Guizhou: Characteristics and Failure Mechanism. Journal of Engineering Geology, 26(1): 223-240 (in Chinese with English Abstract) http://en.cnki.com.cn/Article_en/CJFDTOTAL-GCDZ201801023.htm

Zhu, Y. Q., Xu, S. M., Zhuang, Y., et al., 2019. Characteristics and Runout Behaviour of the Disastrous 28 August 2017 Rock Avalanche in Nayong, Guizhou, China. Engineering Geology, 259:105154. https://doi.org/10.1016/j.enggeo.2019.105154

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(13)

Get Citation

PDF

XML

Article Metrics

Article views(505) PDF downloads(54)

Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas

doi: 10.1007/s12583-021-1467-2

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas

doi: 10.1007/s12583-021-1467-2

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content