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Volume 41 Issue 4
Aug.  2020
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Weiying Wu, Chong Xu, Xiaoqing Wang, Yingying Tian, Fei Deng. Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution. Journal of Earth Science, 2020, 31(4): 853-866. doi: 10.1007/s12583-020-1297-7
Citation: Weiying Wu, Chong Xu, Xiaoqing Wang, Yingying Tian, Fei Deng. Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution. Journal of Earth Science, 2020, 31(4): 853-866. doi: 10.1007/s12583-020-1297-7

Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution

doi: 10.1007/s12583-020-1297-7
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  • The 3 August 2014 Ludian, Yunnan, China Mw 6.2 (Ms 6.5) earthquake triggered a large number of coseismic landslides. Based on pre- and post-quake high-resolution optical satellite images, this study established a new, complete and objective database of these landslides with field investigations. The updated inventory shows that this earthquake triggered at least 12 817 landslides with a total occupation area of 16.33 km2, covering a nearly circular area about 600 km2, which all exceed those in our previous work and other relevant studies. In addition, we used this database to examine the correlations of the landslides with topographic, geologic, and seismic factors. Results show that the landslides occurred mostly at places with slope gradients 10°-40°, showing an increase tendency with steeper slopes. Affected by the propagation direction of the earthquake rupture, the eastward-facing slopes are more prone to landsliding. The differences between the landslide susceptibility in different strata indicate that lithology is also an important controlling factor. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16g is obviously larger than those with PGA less than 0.16g. Meanwhile, the greater the distance from the epicenter, the lower the susceptibility of landslides is. This study suggests that when using satellite images to create coseismic landslide inventories, it should meet certain conditions, including high resolution, whole coverage, and timely data collection. The correct criteria of coseismic landslide inventorying also should be followed. Such inventories can provide a reliable basis for hazard assessment of earthquake-triggered landslides and other quantitative studies.
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  • Chang, Z. F., Chen, X. L., An, X. W., et al., 2016. Contributing Factors to the Failure of an Unusually Large Landslide Triggered by the 2014 Ludian, Yunnan, China, Ms=6.5 Earthquake. Natural Hazards and Earth System Sciences, 16(2):497-507. https://doi.org/10.5194/nhess-16-497-2016 doi:  10.5194/nhess-16-497-2016
    Chang, Z., Zhou, R., An, X., Chen, Y., Zhou, Q., Li, J., 2014. Late Quaternary Activity of the Zhaotong-Ludian Fault Zone and Its Tectonic Implication. Seismology and Geology, 36(4):1260-1279. https://doi.org/10.3969/j.issn.0253-4967.2014.04.025 (in Chinese with English Abstract) doi:  10.3969/j.issn.0253-4967.2014.04.025
    Chen, X. L., Zhou, Q., Liu, C. G., 2015. Distribution Pattern of Coseismic Landslides Triggered by the 2014 Ludian, Yunnan, China Mw6.1 Earthquake:Special Controlling Conditions of Local Topography. Landslides, 12(6):1159-1168. https://doi.org/10.1007/s10346-015-0641-y doi:  10.1007/s10346-015-0641-y
    Gnyawali, K. R., Adhikari, B. R., 2017. Spatial Relations of Earthquake Induced Landslides Triggered by 2015 Gorkha Earthquake Mw=7.8. In: Mikoš, M., Casagli, N., Yin, Y., et al., eds., Advancing Culture of Living with Landslides. WLF 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-53485-5_10
    Gorum, T., Fan, X. M., van Westen, C. J., et al., 2011. Distribution Pattern of Earthquake-Induced Landslides Triggered by the 12 May 2008 Wenchuan Earthquake. Geomorphology, 133(3/4):152-167. https://doi.org/10.1016/j.geomorph.2010.12.030 doi:  10.1016/j.geomorph.2010.12.030
    Gorum, T., van Westen, C. J., Korup, O., et al., 2013. Complex Rupture Mechanism and Topography Control Symmetry of Mass-Wasting Pattern, 2010 Haiti Earthquake. Geomorphology, 184:127-138. https://doi.org/10.1016/j.geomorph.2012.11.027 doi:  10.1016/j.geomorph.2012.11.027
    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 doi:  10.1016/j.epsl.2009.01.005
    Guzzetti, F., Malamud, B. D., Turcotte, D. L., et al., 2002. Power-Law Correlations of Landslide Areas in Central Italy. Earth and Planetary Science Letters, 195(3/4):169-183. https://doi.org/10.1016/s0012-821x(01)00589-1 doi:  10.1016/s0012-821x(01)00589-1
    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 doi:  10.1016/j.earscirev.2012.02.001
    Harp, E. L., Jibson, R. W., 1995. Inventory of Landslides Triggered by the 1994 Northridge, California Earthquake. US Geological Survey, Washington DC. http://pubs.usgs.gov/of/1995/ofr-95-0213/plate1.gif
    Harp, E. L., Jibson, R. W., 1996. Landslides Triggered by the 1994 Northridge, California, Earthquake. Bulletin of the Seismological Society of America, 86(1B):S319-S332 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0e8d4da7340c683dcd79f9a771a26ba8
    Harp, E. L., Jibson, R. W., Schmitt, R. G., 2016. Map of Landslides Triggered by the January 12, 2010, Haiti Earthquake. US Geological Survey, Washington DC. https://pubs.er.usgs.gov/publication/sim3353
    Harp, E. L., Keefer, D. K., Sato, H. P., et al., 2011. Landslide Inventories:The Essential Part of Seismic Landslide Hazard Analyses. Engineering Geology, 122(1/2):9-21. https://doi.org/10.1016/j.enggeo.2010.06.013 doi:  10.1016/j.enggeo.2010.06.013
    Kamp, U., Growley, B. J., Khattak, G. A., et al., 2008. GIS-Based Landslide Susceptibility Mapping for the 2005 Kashmir Earthquake Region. Geomorphology, 101(4):631-642. https://doi.org/10.1016/j.geomorph.2008.03.003 doi:  10.1016/j.geomorph.2008.03.003
    Keefer, D. K., 1984. Landslides Caused by Earthquakes. Geological Society of America Bulletin, 95(4):406-421 doi:  10.1130/0016-7606(1984)95<406:LCBE>2.0.CO;2
    Keefer, D. K., 2002. Investigating Landslides Caused by Earthquakes——A Historical Review. Surveys in Geophysics, 23(6):473-510. https://doi.org/10.1023/A:1021274710840 doi:  10.1023/A:1021274710840
    Larsen, I. J., Montgomery, D. R., Korup, O., 2010. Landslide Erosion Controlled by Hillslope Material. Nature Geoscience, 3(4):247-251. https://doi.org/10.1038/ngeo776 doi:  10.1038/ngeo776
    Lei, C. I., 2012. Earthquake-Triggered Landslides. Proceeding of the 1st Civil and Environmental Engineering Student Conference, 25-26 June 2012, London. 1-6
    Li, G., West, A. J., Densmore, A. L., et al., 2014. Seismic Mountain Building:Landslides Associated with the 2008 Wenchuan Earthquake in the Context of a Generalized Model for Earthquake Volume Balance. Geochemistry, Geophysics, Geosystems, 15(4):833-844. https://doi.org/10.1002/2013gc005067 doi:  10.1002/2013gc005067
    Liao, H. W., Lee, C. T., 2000. Landslides Triggered by the Chi-Chi Earthquake. Proceedings of the 21st Asian Conference on Remote Sensing, Taipei. 1/2: 383-388
    Lu, P., Stumpf, A., Kerle, N., et al., 2011. Object-Oriented Change Detection for Landslide Rapid Mapping. IEEE Geoscience and Remote Sensing Letters, 8(4):701-705. https://doi.org/10.1109/lgrs.2010.2101045 doi:  10.1109/lgrs.2010.2101045
    Ma, S. Y., Xu, C., 2019a. Assessment of Co-Seismic Landslide Hazard Using the Newmark Model and Statistical Analyses:A Case Study of the 2013 Lushan, China, Mw6.6 Earthquake. Natural Hazards, 96(1):389-412. https://doi.org/10.1007/s11069-018-3548-9 doi:  10.1007/s11069-018-3548-9
    Ma, S. Y., Xu, C., 2019b. Applicability of Two Newmark Models in the Assessment of Coseismic Landslide Hazard and Estimation of Slope-Failure Probability:An Example of the 2008 Wenchuan Mw 7.9 Earthquake Affected Area. Journal of Earth Science, 30(5):1020-1030. https://doi.org/10.1007/s12583-019-0874-0 doi:  10.1007/s12583-019-0874-0
    Martha, T. R., Roy, P., Mazumdar, R., et al., 2017. Spatial Characteristics of Landslides Triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) Earthquakes in Nepal. Landslides, 14(2):697-704. https://doi.org/10.1007/s10346-016-0763-x doi:  10.1007/s10346-016-0763-x
    Massey, C., Townsend, D., Rathje, E., et al., 2018. Landslides Triggered by the 14 November 2016 Mw 7.8 Kaikōura Earthquake, New Zealand. Bulletin of the Seismological Society of America, 108(3B):1630-1648. https://doi.org/10.1785/0120170305 doi:  10.1785/0120170305
    Moosavi, V., Talebi, A., Shirmohammadi, B., 2014. Producing a Landslide Inventory Map Using Pixel-Based and Object-Oriented Approaches Optimized by Taguchi Method. Geomorphology, 204:646-656. https://doi.org/10.1016/j.geomorph.2013.09.012 doi:  10.1016/j.geomorph.2013.09.012
    Rao, G., Cheng, Y. L., Lin, A. M., et al., 2017. Relationship between Landslides and Active Normal Faulting in the Epicentral Area of the AD 1556 M~8.5 Huaxian Earthquake, SE Weihe Graben (Central China). Journal of Earth Science, 28(3):545-554. https://doi.org/10.1007/s12583-017-0900-z doi:  10.1007/s12583-017-0900-z
    Sato, H. P., Harp, E. L., 2009. Interpretation of Earthquake-Induced Landslides Triggered by the 12 May 2008, M7.9 Wenchuan Earthquake in the Beichuan Area, Sichuan Province, China Using Satellite Imagery and Google Earth. Landslides, 6(2):153-159. https://doi.org/10.1007/s10346-009-0147-6 doi:  10.1007/s10346-009-0147-6
    Seed, H. B.. 1968. Landslides During Earthquakes Due to Soil Liquefaction. Terzaghi Lectures:1963-1972:191-261 https://www.mendeley.com/catalogue/1f6bb3aa-016a-3661-9c9a-10b3b2e39d3f/
    Shao, X. Y., Ma, S. Y., Xu, C., et al., 2019a. Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake. Remote Sensing, 11(8):978. https://doi.org/10.3390/rs11080978 doi:  10.3390/rs11080978
    Shao, X., Xu, C., Ma, S., et al., 2019b. Effects of Seismogenic Faults on the Predictive Mapping of Probability to Earthquake-Triggered Landslides. ISPRS International Journal of Geo-Information, 8(8):328. https://doi.org/10.3390/ijgi8080328 doi:  10.3390/ijgi8080328
    Shen, L. L., Xu, C., Liu, L. Y., 2016. Interaction among Controlling Factors for Landslides Triggered by the 2008 Wenchuan, China Mw 7.9 Earthquake. Frontiers of Earth Science, 10(2):264-273. https://doi.org/10.1007/s11707-015-0517-4 doi:  10.1007/s11707-015-0517-4
    Shi, Z. M., Xiong, X., Peng, M., et al., 2017. Risk Assessment and Mitigation for the Hongshiyan Landslide Dam Triggered by the 2014 Ludian Earthquake in Yunnan, China. Landslides, 14(1):269-285. https://doi.org/10.1007/s10346-016-0699-1 doi:  10.1007/s10346-016-0699-1
    Sotiris, V., George, P., Spyros, P., 2016. Map of Co-Seismic Landslides and Surface Ruptures for the M 7.8 Kaikoura, New Zealand Earthquake. http://eqgeogr.weebly.com
    Tian, Y. Y., Xu, C., Chen, J., et al., 2017a. Geometrical Characteristics of Earthquake-Induced Landslides and Correlations with Control Factors:A Case Study of the 2013 Minxian, Gansu, China, Mw 5.9 Event. Landslides, 14(6):1915-1927. https://doi.org/10.1007/s10346-017-0835-6 doi:  10.1007/s10346-017-0835-6
    Tian, Y. Y., Xu, C., Chen, J., et al., 2017b. Spatial Distribution and Susceptibility Analyses of Pre-Earthquake and Coseismic Landslides Related to the Ms 6.5 Earthquake of 2014 in Ludian, Yunan, China. Geocarto International, 32(9):978-989. https://doi.org/10.1080/10106049.2016.1232316 doi:  10.1080/10106049.2016.1232316
    Tian, Y. Y., Xu, C., Hong, H. Y., et al., 2019b. Mapping Earthquake-Triggered Landslide Susceptibility by Use of Artificial Neural Network (ANN) Models:An Example of the 2013 Minxian (China) Mw 5.9 Event. Geomatics, Natural Hazards and Risk, 10(1):1-25. https://doi.org/10.1080/19475705.2018.1487471 doi:  10.1080/19475705.2018.1487471
    Tian, Y. Y., Xu, C., Ma, S. Y., et al., 2019a. Inventory and Spatial Distribution of Landslides Triggered by the 8th August 2017 Mw 6.5 Jiuzhaigou Earthquake, China. Journal of Earth Science, 30(1):206-217. https://doi.org/10.1007/s12583-018-0869-2 doi:  10.1007/s12583-018-0869-2
    Tian, Y. Y., Xu, C., Xu, X. W., et al., 2016. Detailed Inventory Mapping and Spatial Analyses to Landslides Induced by the 2013 Ms 6.6 Minxian Earthquake of China. Journal of Earth Science, 27(6):1016-1026. https://doi.org/10.1007/s12583-016-0905-z doi:  10.1007/s12583-016-0905-z
    Tiwari, B., Ajmera, B., Dhital, S., 2017. Characteristics of Moderate-to Large-Scale Landslides Triggered by the M w 7.8 2015 Gorkha Earthquake and Its Aftershocks. Landslides, 14(4):1297-1318. https://doi.org/10.1007/s10346-016-0789-0 doi:  10.1007/s10346-016-0789-0
    Wang, H. B., Sassa, K., Xu, W. Y., 2007. Analysis of a Spatial Distribution of Landslides Triggered by the 2004 Chuetsu Earthquakes of Niigata Prefecture, Japan. Natural Hazards, 41(1):43-60. https://doi.org/10.1007/s11069-006-9009-x doi:  10.1007/s11069-006-9009-x
    Wang, W. N., Nakamura, H., Tsuchiya, S., et al., 2002. Distributions of Landslides Triggered by the Chi-Chi Earthquake in Central Taiwan on September 21, 1999. Landslides, 38(4):318-326. https://doi.org/10.3313/jls1964.38.4_318 doi:  10.3313/jls1964.38.4_318
    Wang, Z., Zhao, D. P., Wang, J., 2010. Deep Structure and Seismogenesis of the North-South Seismic Zone in Southwest China. Journal of Geophysical Research, 115(B12334):7797. https://doi.org/10.1029/2010jb007797 doi:  10.1029/2010jb007797
    Wu, W., Xu, C., 2018. A New Inventory of Landslides Triggered by the 2014 Ludian Mw6.2 Earthquake. Seismology and Geology, 40(5):1140-1148. https://doi.org/10.3969/j.issn.0253-4967.2018.05.013 (in Chinese with English Abstract) doi:  10.3969/j.issn.0253-4967.2018.05.013
    Xu, C., 2015. Preparation of Earthquake-Triggered Landslide Inventory Maps Using Remote Sensing and GIS Technologies:Principles and Case Studies. Geoscience Frontiers, 6(6):825-836. https://doi.org/10.1016/j.gsf.2014.03.004 doi:  10.1016/j.gsf.2014.03.004
    Xu, C., Dai, F., Xu, X., 2010. Wenchuan Earthquake Induced Landslides:An Overview. Geological Review, 56(6):860-874 (in Chinese with English Abstract) http://en.cnki.com.cn/article_en/cjfdtotal-dzlp201006014.htm
    Xu, C., Tian, Y., Shen, L., et al., 2018a. Database of Landslides Triggered by 2015 Gorkha (Nepal) Mw7.8 Earthquake. Seismology and Geology, 40(5):1115-1128. https://doi.org/10.3969/j.issn.0253-4967.2018.05.011 (in Chinese with English Abstract) doi:  10.3969/j.issn.0253-4967.2018.05.011
    Xu, C., Ma, S. Y., Tan, Z. B., et al., 2018b. Landslides Triggered by the 2016 Mj 7.3 Kumamoto, Japan, Earthquake. Landslides, 15(3):551-564. https://doi.org/10.1007/s10346-017-0929-1 doi:  10.1007/s10346-017-0929-1
    Xu, C., Xu, X. W., Shyu, J. B. H., 2015. Database and Spatial Distribution of Landslides Triggered by the Lushan, China Mw 6.6 Earthquake of 20 April 2013. Geomorphology, 248:77-92. https://doi.org/10.1016/j.geomorph.2015.07.002 doi:  10.1016/j.geomorph.2015.07.002
    Xu, C., Xu, X. W., Yao, X., et al., 2014a. Three (nearly) Complete Inventories of Landslides Triggered by the may 12, 2008 Wenchuan Mw 7.9 Earthquake of China and Their Spatial Distribution Statistical Analysis. Landslides, 11(3):441-461. https://doi.org/10.1007/s10346-013-0404-6 doi:  10.1007/s10346-013-0404-6
    Xu, C., Shyu, J. B. H., Xu, X., 2014b. Landslides Triggered by the 12 January 2010 Port-Au-Prince, Haiti, Mw=7.0 Earthquake:Visual Interpretation, Inventory Compiling, and Spatial Distribution Statistical Analysis. Natural Hazards and Earth System Sciences, 14(7):1789-1818. https://doi.org/10.5194/nhess-14-1789-2014 doi:  10.5194/nhess-14-1789-2014
    Xu, C., Xu, X. W., Shyu, J. B. H., et al., 2014c. Landslides Triggered by the 22 July 2013 Minxian-Zhangxian, China, Mw 5.9 Earthquake:Inventory Compiling and Spatial Distribution Analysis. Journal of Asian Earth Sciences, 92:125-142. https://doi.org/10.1016/j.jseaes.2014.06.014 doi:  10.1016/j.jseaes.2014.06.014
    Xu, C., Xu, X., Shen, L., et al., 2014d. Inventory of Landslides Triggered by the 2014 Ms6.5 Ludian Earthquake and Its Implications on Several Earthquake Parameters. Seismology and Geology, 36(4):1186-1203. https://doi.org/10.3969/j.issn.0253-4967.2014.04.020 (in Chinese with English Abstract) doi:  10.3969/j.issn.0253-4967.2014.04.020
    Xu, C., Xu, X. W., Pourghasemi, H. R., et al., 2014e. Volume, Gravitational Potential Energy Reduction, and Regional Centroid Position Change in the Wake of Landslides Triggered by the 14 April 2010 Yushu Earthquake of China. Arabian Journal of Geosciences, 7(6):2129-2138. https://doi.org/10.1007/s12517-013-1020-4 doi:  10.1007/s12517-013-1020-4
    Xu, C., Xu, X. W., Tian, Y. Y., et al., 2016a. Two Comparable Earthquakes Produced Greatly Different Coseismic Landslides:The 2015 Gorkha, Nepal and 2008 Wenchuan, China Events. Journal of Earth Science, 27(6):1008-1015. https://doi.org/10.1007/s12583-016-0684-6 doi:  10.1007/s12583-016-0684-6
    Xu, C., Xu, X. W., Shen, L. L., et al., 2016b. Optimized Volume Models of Earthquake-Triggered Landslides. Scientific Reports, 6(1):29797. https://doi.org/10.1038/srep29797 doi:  10.1038/srep29797
    Xu, X. W., Xu, C., Yu, G. H., et al., 2015. Primary Surface Ruptures of the Ludian Mw 6.2 Earthquake, Southeastern Tibetan Plateau, China. Seismological Research Letters, 86(6):1622-1635. https://doi.org/10.1785/0220150038 doi:  10.1785/0220150038
    Xu, X., Han, Z., Yang, X., et al., 2016. Seismotectonic Map in China and Its Adjacent Regions. Seismogical Press, Beijing (in Chinese)
    Xu, X., Jiang, G., Yu, G., et al., 2014. Discussion on Seismogenic Fault of the Ludian Ms 6.5 Earthquake and Its Tectonic Attribution. Chinese Journal of Geophysics, 57(9):3060-3068. https://doi.org/10.3969/10.6038/cjg20140931 doi:  10.3969/10.6038/cjg20140931
    Xu, X., Wen, X., Zheng, R., et al., 2003. Pattern of Latest Tectonic Motion and Its Dynamics for Active Blocks in Sichuan-Yunnan Region, China. Science in China Series D:Earth Sciences, 46(S2):210-226. https://doi.org/10.1360/03dz0017 doi:  10.1360/03dz0017
    Yang, X. J., Chen, L. D., 2010. Using Multi-Temporal Remote Sensor Imagery to Detect Earthquake-Triggered Landslides. International Journal of Applied Earth Observation and Geoinformation, 12(6):487-495. https://doi.org/10.1016/j.jag.2010.05.006 doi:  10.1016/j.jag.2010.05.006
    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 doi:  10.1007/s10346-009-0148-5
    Zhang, P., Deng, Q., Zhang, G., et al., 2003. Active Tectonic Blocks and Strong Earthquakes in the Continent of China. Science in China Series D:Earth Sciences, 46(S2):13-24. https://doi.org/10.1360/03dz0002 doi:  10.1360/03dz0002
    Zhou, J. W., Lu, P. Y., Hao, M. H., 2016. Landslides Triggered by the 3 August 2014 Ludian Earthquake in China:Geological Properties, Geomorphologic Characteristics and Spatial Distribution Analysis. Geomatics, Natural Hazards and Risk, 7(4):1219-1241. https://doi.org/10.1080/19475705.2015.1075162 doi:  10.1080/19475705.2015.1075162
    Zhou, S. H., Chen, G. Q., Fang, L. G., 2016. Distribution Pattern of Landslides Triggered by the 2014 Ludian Earthquake of China:Implications for Regional Threshold Topography and the Seismogenic Fault Identification. ISPRS International Journal of Geo-Information, 5(4):46. https://doi.org/10.3390/ijgi5040046 doi:  10.3390/ijgi5040046
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Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution

doi: 10.1007/s12583-020-1297-7

Abstract: The 3 August 2014 Ludian, Yunnan, China Mw 6.2 (Ms 6.5) earthquake triggered a large number of coseismic landslides. Based on pre- and post-quake high-resolution optical satellite images, this study established a new, complete and objective database of these landslides with field investigations. The updated inventory shows that this earthquake triggered at least 12 817 landslides with a total occupation area of 16.33 km2, covering a nearly circular area about 600 km2, which all exceed those in our previous work and other relevant studies. In addition, we used this database to examine the correlations of the landslides with topographic, geologic, and seismic factors. Results show that the landslides occurred mostly at places with slope gradients 10°-40°, showing an increase tendency with steeper slopes. Affected by the propagation direction of the earthquake rupture, the eastward-facing slopes are more prone to landsliding. The differences between the landslide susceptibility in different strata indicate that lithology is also an important controlling factor. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16g is obviously larger than those with PGA less than 0.16g. Meanwhile, the greater the distance from the epicenter, the lower the susceptibility of landslides is. This study suggests that when using satellite images to create coseismic landslide inventories, it should meet certain conditions, including high resolution, whole coverage, and timely data collection. The correct criteria of coseismic landslide inventorying also should be followed. Such inventories can provide a reliable basis for hazard assessment of earthquake-triggered landslides and other quantitative studies.

Weiying Wu, Chong Xu, Xiaoqing Wang, Yingying Tian, Fei Deng. Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution. Journal of Earth Science, 2020, 31(4): 853-866. doi: 10.1007/s12583-020-1297-7
Citation: Weiying Wu, Chong Xu, Xiaoqing Wang, Yingying Tian, Fei Deng. Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution. Journal of Earth Science, 2020, 31(4): 853-866. doi: 10.1007/s12583-020-1297-7
  • Major earthquakes in mountainous areas can induce massive landslides, leading to serious disasters (Shao et al., 2019a; Tian et al., 2019a; Lei, 2012; Xu et al., 2010; Yin et al., 2009; Keefer, 1984; Seed, 1968). A detailed and accurate inventory of earthquake-triggered landslides is an important basis for research on this issue (Ma and Xu, 2019a, b; Tian et al., 2019b; Xu et al., 2015, 2014a; Guzzetti et al., 2012; Harp et al., 2011). A high-quality landslide inventory should cover the whole earthquake area, spanning all scales of coseismic landslides that can be detected, mapping accurate locations and boundaries, providing polygon-based descriptions of the landslides, and separating individual landslides from contiguous landslides groups (Xu, 2015). Landslides triggered by large earthquakes are usually characterized by large scope, large number, and complex morphology. Therefore, it is impossible to complete a high-quality landslide inventory related to a large earthquake immediately solely based on field investigations, especially in affected areas with complicated terrain. Currently, a common method of coseismic landslide inventorying is to delineate landslides based on visual interpretation of optical satellite images with the aid of other higher-resolution images, such as aerial photographs, and in combination with field verifications. The quality of landslide inventories depends on the image quality, coverage, and methods of interpretation, which determines if the final landslide inventory is complete and accurate. In recent years, quite a few achievements about coseismic landslide inventories have been reported, such as the 1994 Northridge, California (USA) Mw 6.7 (Harp and Jibson, 1996, 1995), the 1999 Chi-chi, Taiwan (China) Mw 7.6 (Wang et al., 2002; Liao and Lee, 2000), the 2008 Wenchuan (China) Mw 7.9 (Li et al., 2014; Xu et al., 2014a; Gorum et al., 2011), the 2010 Port-au-Prince (Haiti) Mw 7.0 (Harp et al., 2016; Xu et al., 2014b; Gorum et al., 2013), the 2013 Minxian (China) Mw 5.9 (Tian et al., 2017a, 2016; Xu et al., 2014c), the 2013 Lushan (China) Mw 6.6 (Xu et al., 2015), the 2015 Gorkha (Nepal) Mw 7.8 (Xu et al., 2018a, 2016a; Gnyawali and Adhikari, 2017; Martha et al., 2017; Tiwari et al., 2017), the 2016 Kumamoto (Japan) Mw 7.0 (Xu et al., 2018b), the 2016 Kaikōura (New Zealand) Mw 7.8 (Massey et al., 2018; Sotiris et al., 2016), the 2017 Jiuzhaigou (China) Mw 6.5 (Tian et al., 2019a), and the 2018 Tomakomai (Japan) Mw 6.6 (Shao et al., 2019a) earthquakes. These data can facilitate studies of the spatial distribution of coseismic landslides, the subsequent susceptibility and hazard assessment, and the impact on geomorphic evolution in the earthquake-affected area. Therefore, it is the most important task to prepare a detailed and complete inventory of coseismic landslides and analyze their spatial distribution after a large mountainous earthquake occurs.

    At 16:30 on August 3, 2014 (Beijing time), an Mw 6.2 (Ms 6.5) earthquake occurred in Ludian County, Zhaotong City, Yunnan Province, China. Its epicenter is located at 27.099 4°N, 103.34°E with a focal depth of 12 km. As of 15 August 2014, 617 people were killed, 112 were missing, 3 143 were injured, 229 700 were relocated and 80 900 houses collapsed (Xu et al., 2014d). Because of the steep terrain, high topographic relief, fragile geological conditions, and strong ground shaking in the affected area, the earthquake triggered a large number of landslides (Xu et al., 2014d), including quite a few large-scale rock falls and avalanches (Shi et al., 2017; Chang et al., 2016; Zhou J W et al., 2016). Very soon after the earthquake, some researchers made investigations into its geologic effects and several inventories of coseismic landslides were released (Wu and Xu, 2018; Tian et al., 2017b; Zhou S H et al., 2016; Chen et al., 2015; Xu et al., 2014d), which provides general information on these landslides and facilitates further research. However, because of the limited resolution and quality of satellite images available then and other reasons, such as professional levels and experience of interpreters, all the released landslide inventories are incomplete and unsatisfactory. Most of them contain significant commission and omission errors, for instance, delineating quite a few medium- and small-scale landslides into a large individual one. Considering the importance of the basic data, this study attempts to establish a new and more objective landslide inventory related to the Ludian earthquake based on artificial visual interpretation of pre- and post-quake ultrahigh-resolution satellite images covering the whole affected area in combination with selected field investigations. Results show that this earthquake triggered at least 12 817 landslides in a suborbicular area about 600 km2. This number is much larger than previous studies (Zhou S H et al., 2016; Chen et al., 2015; Xu et al., 2014d). The reasons for this discrepancy may involve some aspects, such as the selection of remote sensing images, the coverage, and resolution of available post-quake satellite images, and the interpretation methods. Then, the conditions for establishing a detailed, accurate and objective landslide inventory are discussed. In addition, this study also examines the correlations between the coseismic landslides and several major factors, including topography, seismology, and geology.

  • The 2014 Ludian Mw 6.2 earthquake took place on the east edge of the Sichuan-Yunnan rhomboidal block, the southeast margin of the Tibet Plateau, and mid-south of the north- south seismic belt of Central China Mainland (Wang et al., 2010). This region is characterized by many active faults, intense tectonic deformation, and frequent major earthquake (Xu X W et al., 2015; Xu X et al., 2003; Zhang et al., 2003). The Zemuhe fault and Xiaojiang fault, which trend NNW with left-lateral strike-slip movement are the primary active structures in this area. Northeast of them, a series of nearly parallel NE-trending right-lateral strike-slip faults of relatively smaller scales are present (Fig. 1), where the epicenter of the Ludian earthquake is located between Lianfeng fault (LFF) and Zhaotong-Ludian fault (ZLF). In field investigations, Xu X W et al. (2015) found a 2-km-long, NNW trending surface rupture and a 6-km-long deformation belt associated with the earthquake. Combining aftershock distribution, they suggested that the NNW-trending Baogunao- Xiaohe fault (BXF) between the LFF and ZLF is the seismogenic structure of this earthquake. It is a left-lateral strike- slip fault with a direction of 330° that is composed of several secondary, discontinuous faults (Xu X W et al., 2015; Chang et al., 2014; Xu X et al., 2014). Due to strong tectonic uplift and stream incision, this area hosts steep slopes and deep valleys with maximum terrain relief over 1 500 m.

    Figure 1.  Map showing the tectonic setting of the Ludian region, Yunnan. Inset in the upper right corner shows the location of the study area. The green star is the epicenter of 2014 Ludian Mw 6.2 earthquake. Active fault data modified from Xu X et al. (2016). BXF. Baogunao-Xiaohe fault; ZLF. Zhaotong-Ludian fault; SMF. Shimen fault; LFF. Lianfeng fault; JYF. Jingyang fault; WLFF. Wulianfeng fault; JJHF. Jiaojiahe fault; XJF. Xiaojiang fault, including the east and west branches; ZMHF. Zemuhe fault; PDHF. Puduhe fault; HZF. Huize fault.

  • The pre- and post-quake, ultrahigh-resolution satellite images used in this study come from the Google Earth platform. Terrain data, including slope gradients and slope aspects, were from a 10 m-resolution DEM with interpolation and resampling on a one second of arc (about 30 m) resolution SRTM DEM (http://earthexplorer.usgs.gov/). Geological data are from a 1 : 200 000 digital geologic map. The seismic data, a distribution map of the PGA was downloaded from USGS website (http://earthquake.usgs.gov). The Google Earth platform can provide global high- and ultrahigh-resolution multi-temporal optical remote sensing images and display three-dimensional scene, which allows users directly to identify and depict landslides to establish inventories of landslides (Sato and Harp, 2009). Optical satellite images used in this study are multi- temporal images (Fig. 2), including pre- (taken on August 20, 2014 and January 14, 2017) and post-quake (taken on January 30, 2014, March 14, 2013 and December 6, 2011). These images are about 0.5 m resolution and cover the whole affected area (mainly seismic intensity Ⅷ and Ⅸ). The image of August 20, 2014, only 17 days after the earthquake, covers most of the affected area (Fig. 2), thus presumably there was less change of coseismic landslides by rainfall or other factors and the inventory does not include non-seismic landslides. The image taken on January 14, 2017 is a supplement to ensure the post-quake satellite images covering the whole affected area. The coverage of pre-quake images is also the whole affected area so as the landslides that already existed before the earthquake can be recognized and excluded from the database (Wu and Xu, 2018).

    Figure 2.  Isolines of Ludian earthquake intensity (blue concentric ellipses) and coverage of post-quake, remote sensing images (red and yellow boxes) used to interpret coseismic landslides. Coverage of images is from Wu and Xu (2018).

  • In general, there are five major methods for establishing regional landslide inventories: (1) field investigation; (2) visual interpretation of aerial photographs; (3) visual interpretation of satellite images; (4) visual interpretation of satellite images and aerial photographs; and (5) automatic extraction from satellite images or aerial photographs (Xu, 2015; Moosavi et al., 2014; Guzzetti et al., 2012; Lu et al., 2011; Yang and Chen, 2010; Keefer, 2002). Of them, visual interpretation of remote sensing images is a preferred one because it has advantages such as whole coverage of remote sensing images in the affected area, strong interpretation ability in small- and medium-scale landslides and the cheaper cost than aerial photographs. Visual interpretation of aerial photographs and field investigation are often regarded as alternatives to verify the reliability of results and improve the quality of the landslide inventory from satellite data. After the 2014 Ludian earthquake, some high- and ultrahigh-resolution aerial photographs and satellite images have been available. For instance, satellite images provided by Google Earth can be publicly accessed, having high-resolution and cover the entire affected zone. Aerial photographs taken by unmanned aircraft have an extremely high spatial resolution but only cover part of the affected area. Therefore, this study uses satellite images provided by Google Earth to prepare a new inventory of the landslides triggered by this earthquake and then verifies it by using aerial photographs and field investigations. It is necessary to establish the interpretation signs of the coseismic landslide before we interpret the satellite images. Ludian is located in a subtropical region, so that vegetation is flourishing in summer. Landslides triggered by the earthquake can destroy land vegetation causing the surface to be exposed, which can be clearly seen on high-resolution images. Hence, vegetation damage is the most significant sign in interpreting landslides triggered by the earthquake, and the landslides that existed before the earthquake are eliminated through comparing pre- and post-quake images. Monomer landslide boundaries are depicted on the Google Earth platform and then stored as polygons on a GIS platform. Each polygon represents a landslide occurrence range and then we establish the inventory of landslides triggered by the earthquake.

    Figure 3 shows the flowchart of this study. Topographic factors (gradient and slope aspect), the geologic factor (lithology) and seismic factors (PGA and distance from the epicenter) are considered in the subsequent analysis of spatial distribution. Landslide number, landslide area, landslide number density (LND) and landslide area density (LAD) are selected as four parameters to measure absolute and relative occurrence degrees (susceptibility) of landslides. The relationships between coseismic landslides and topographic, seismic and geologic factors are analyzed based on the GIS platform.

    Figure 3.  The flowchart of this study.

  • The results show that at least 12 817 landslides were triggered by the 2014 Ludian earthquake. A nearly circular area of 603 km2 is framed according to the distribution of these landslides. The total occupation area of these landslides is 16.33 km2, the area of the smallest landslide is 12 m2, and the largest one covers about 345 000 m2. Among them, 4 landslides are larger than 100 000 m2, 215 landslides are between 100 000 and 10 000 m2, 2 929 landslides are between 10 000 and 1 000 m2, and 8 836 landslides are between 1 000 and 100 m2. The distribution of these landslides is an ellipse with a long axis trending northwest-southeast, consistent with the inferred seismogenic structure. Most of these landslides occurred southeast of the epicenter, a few in the northwest, the majority of which were concentrated along the valleys with large topographic relief (Fig. 4).

    Figure 4.  (a) The distribution of coseismic landslides triggered by the 2014 Ludian earthquake (red patches) and seismic intensity isolines (dark green circles). BXF. Baogunao-Xiaohe fault. 1. Hongshiyan landslide; 2. Miaozhaizi landslide. (b) Enlarged view of the bright green box in (a).

    The volume of a landslide is usually estimated by using a landslide area-volume law from statistics (Xu et al., 2016b, 2014e; Larsen et al., 2010; Guzzetti et al., 2009). For example, for the 2008 Wenchuan earthquake, the area-volume formula (Xu et al., 2016b) is

    where A is the area of a single landslide, and V is its volume. The volume of each landslide triggered by the Ludian earthquake is calculated by using this formula. Then the total volume of the landslides triggered by the Ludian earthquake is calculated to be 132 million m3. According to the number, total area, total volume, and elliptic area of landslide distribution, the landslide number density is 12 817/603 km2=21.25 km-2, landslide area density is (16.33 km2/603 km2)×100%=2.71%, and landslide volume density is 132 million m3/603 km2=0.219 m.

    Figure 5 shows the frequency-area curve of the coseismic landslides on log axes, which represents the relationship between the cumulative landslide number (N) whose area is larger than or equal to the landslide area (A). This relationship is expressed as

    Figure 5.  Frequency-area curve of landslides triggered by the 2014 Ludian earthquake. Updated from Wu and Xu (2018).

    where a and b are constants. The landslide number data is shown as a line in a logarithmic coordinate system. Inventories of earthquake-triggered landslides often have omission of some small- or medium-scale landslides. Therefore, this line shows a significant tendency to bend down corresponding to the small-scale landslides. This phenomenon can be found in many previous case studies (Xu et al., 2015, 2014b; Guzzetti et al., 2002). It can be seen from Fig. 5 that the area of the landslides triggered by the earthquake began to obviously bend in the values of 200–300 m2. It indicates that there is almost no omission for the landslides with an area larger than these values. Some of those landslides with smaller area may have been omitted due to difficulty in delineation on satellite images. Therefore, the curve can be used to determine the integrity of the inventory of coseismic landslides.

  • The largest landslide triggered by the 2014 Ludian earthquake occurred at a place named Hongshiyan, 8.2 km southwest to the epicenter (27.038°N, 103.400 1°E, Fig. 4). The maximum elevation of its slip cliff is about 1 770 m, and the minimum elevation of the landslide accumulation area is 1 135 m. The main substance of the landslide slid in the S30°W direction, dominated by dolomite and limestone. The landslide area is about 3.45×105 m2, and the collapse volume is about 12 million m3. The landslide slid down the slope and blocked the Niulan River, where deposits formed a dam with a height of about 100 m and a dammed lake with a storage capacity more than 2 billion m3 (Fig. 6). This site became the most dangerous after the earthquake, because the dammed lake threatens the safety of nearly 10 000 people who live in downstream of the river (Shi et al., 2017; Xu et al., 2014d).

    Figure 6.  The Hongshiyan landslide triggered by the 2014 Ludian earthquake. (a) 3D Google Earth post-quake image taken on 20 August 2014 with resolution about 0.5 m, view to northeast. (b) Post-quake aerial orthophoto taken on 7 August, 2014 with resolution 0.2 m.

    Another big one, the Miaozhaizi landslide is located on the right bank of the Shaba River, which is a tributary of the right bank of the Niulan River (27.068 9°N, 103.380 8°E, Fig. 4). More than 50 people in 28 homes in the Miaozhaizi Village were buried by the slope failure. The landslide also destroyed the Ganjiazhai segment of the Zhaotong-Qiaojia Road (Fig. 7). The elevation of its slip cliff is 1 510 m, the elevation of the landslide lip is 1 210 m, with a southwestward direction of sliding. The landslide area is 16×104 m2 and the volume is about 1.3 million m3. Landslide material is mainly composed of rock masses, broken stones, and cohesive soil. Locally vegetation integrity remained good after the quake, indicating the damage is not particularly serious. The reason may be the movement of this landslide is rather coherent (Xu et al. 2014d).

    Figure 7.  The Miaozhaizi landslide triggered by the 2014 Ludian earthquake. (a) 3D Google Earth post-quake image taken on 20 August 2014 with resolution 0.5 m, view to north. (b) Post-quake aerial orthophoto taken on 7 August 2014 with resolution 0.2 m.

  • Earthquake-triggered landslides are affected by many factors, such as topography, geology, and seismology (Tian et al., 2016; Xu et al, 2014a; Gorum et al., 2011). This study selects the most important and common factors for statistical analysis. Slope gradient is undoubtedly one of the most important factors affecting earthquake-triggered landslides, because the greater the gradient, the stronger the gravity and more susceptible to landsliding. The slope aspect affects the occurrence of earthquake- triggered landslides through two ways: (1) slopes with different aspects may have different rainfall, temperature, sunshine, and vegetation coverage (Kamp et al., 2008), and (2) the different combinations of the slope aspect and the direction of seismic wave propagation or the sudden movement direction of the blocks have different effects (Xu et al., 2015, 2014a). Although the elevation is also a factor in landsliding, the ways it affects earthquake-triggered landslides also include rainfall, temperature, sunshine and vegetation coverage, which are similar to slope aspect. Therefore, this study does not consider the elevation factor. This study selects lithology as a major geological factor because it is easy to obtain and determines the rock-soil mechanical strength of slopes, thus strongly affecting the occurrence of landslides. PGA and epicentral distance are undoubtedly two of the most important controls for coseismic landslides (Xu et al., 2014a; Gorum et al., 2011). Therefore, we employ slope gradient, slope aspect, lithology, PGA and epicentraldistance as the influence factors to analyze the spatial distribution of the landslides triggered by the Ludian earthquake.

  • Taking slope gradient and slope aspect as topographic factors, we first examine their correlations with the spatial distribution of the coseismic landslides by a statistic analysis (Fig. 8). Results show that the range of the slope gradients in the study area is 0°–81°. The number and area of landslides increase and then decrease with slope gradients. The gradient range of 20°–30° corresponds to the largest landslide number 3 925; the largest landslide area occurs in the gradient range of 30°–40°, which is 4.81 km2. The LND and LAD generally increase with the growing gradient. It is only the abnormal point of LND value in the gradient range of 50°–60° (Fig. 8a) with the LND of 27.40 km-2. The LAD value corresponding to the gradient range of 60°–81° is only 18.09 km-2. This gradient range has a maximum LAD value of 8.1% (Fig. 8b) possibly because the gradient range of 60°–81° hosts some large landslides with less number and larger area. On the whole, landslides tend to occur in the gradient range of 10°–40°, and the susceptibility increases with the growing gradient. Due to the less area coverage, the number and area of landslides on high slopes are few, similar to the expressions in other earthquakes (Xu et al., 2015, 2014a; Wang et al., 2007).

    Figure 8.  Changes of landslide number and LND (a) and landslide area and LAD (b) with slope gradient.

    The slope aspect (or the facing direction of a slope) in the study area is divided into nine categories: flat, north, northeast, east, southeast, south, southwest, west and northwest. Figure 9 shows the statistical results of landslide number, landslide area, LND and LAD in each category. It is noted that a large number of landslides occurs in east, southeast, and west that has 2 155, 1 824 and 1 914 landslides, covering 2.68, 2.23 and 2.84 km2, respectively. The slope aspect of the east has the larger landslide number density and landslide area density, with LND and LAD values 24.83 km-2 and 3.09%, respectively. It indicates that the most susceptive slope aspects are east and southeast. This is probably due to the direction of earthquake rupture is from northwest to southeast, which exerted a significant influence on the occurrence of landslides. The slope aspect consistent with the direction of seismic energy transmission is more prone to slide, while that of the opposite aspect is not. This phenomenon has also been noted in other earthquakes (Shen et al., 2016; Xu et al., 2015, 2014a), which is a significant feature of earthquake-triggered landslides different from those induced by rainfall.

    Figure 9.  Changes of landslide number and LND (a) and landslide area and LAD (b) with slope aspect.

  • The lithology of a slope can exert an important influence on the occurrence of coseismic landslides because it determines the slope's strength to a large extent. In this work, the lithologic distribution in the study area is obtained from a 1 : 200 000 geological map. It is divided into 11 categories based on the geological ages (Table 1). The landslide number, landslide area, LND and LAD values of each category are statistically analyzed (Fig. 10). The results show that there are significant differences of landslide occurrence in different strata. Permian P2 strata have the largest landslide number 3 269; the maximum value of LND is in Permian P1 strata which is 28.22 km-2. The maximum area of landslides is in P1 strata, 3.75 km2 and the LAD value of Sinian Z1 strata is the highest which is 4.68%. In addition, the landslide number density is the largest in P1 strata, while the highest landslide area density is in Z1 strata. It is noted that a lot of large-scale landslides occurred in Z1 strata. The differences of landslide number density and landslide area density in different strata indicate that the lithology plays an important role in the occurrence of landslides triggered by earthquakes.

    No. Stratum Lithology description
    1 T Triassic System. Siltstone, argillaceous siltstone with fine sandstone
    2 P2 Upper Permian System. Mudstone, porphyritic basalt, volcanic breccia
    3 P1 Lower Permian System. Siltstone, shale, limestone
    4 D Devonian System. Quartz sandstone, siltstone, dolomite
    5 S Silurian System. Shale, carbonatite, clastic rocks
    6 O2–3, O2 Upper–Middle Ordovician System. Dolomite, sandstone with shale and argillaceous limestone
    7 O1 Lower Ordovician System. Fine sandstone, dolomite, mica siltstone
    8 Є2, Є3 Cambrian Upper and Middle Cambrian System. Gray dolomite, shale with siltstone, clastic rock, argillaceous limestone
    9 Є1 Lower Cambrian System. Sandstone, shale, dolomite, argillaceous limestone
    10 Z2 Upper Sinian System. Dolomite, dolomite limestone, dolomitic shale
    11 Z1 Lower Sinian System. Basal conglomerate, pebbly sandstone, sandstone, quartz sandstone, silty mudstone

    Table 1.  Descriptions of categorized lithology in the study area

    Figure 10.  Changes of landslide number and LND (a) and landslide area and LAD (b) with lithology.

  • The PGA represents the intensity of ground shaking during an earthquake. In general, the vulnerability of the slope increases with the PGA. More landslides can be triggered by a major earthquake at a site with larger PGA and other similar conditions (Xu et al., 2015, 2014a). From data available, the PGA of the study area ranges 0.08g–0.36g, which is divided into 8 classes using an interval of 0.4g. The relationship between PGA and landslides is calculated on a GIS platform (Fig. 11). The results show that both landslide number and LND value increase first with the PGA and then decrease. The landslide number reaches the maximum when PGA is 0.2g, which is 3 085, and then begins to decrease. The LND value peaks at the PGA 0.28g that is 31.36 km-2, and then decreases. Both landslide area and LAD value increase at first and then decrease with the rise of the PGA, both of which reach the maximums when PGA is 0.2g, the values of which are 5.37 km2 and 6.16%, respectively. Landslide area and LAD value reduce after the PGA exceeds 0.2g. Obviously, the relationship between landslide and PGA for this event is different from other earthquakes. It is an obvious bias in the core area of coseismic landslide distribution (the center is roughly located at 27.074°N, 103.373°E) and maximum area of PGA distribution (the center is roughly located in 27.189°N, 103.409°E) when we overlay coseismic landslides and the PGA diagram from the USGS. Their difference in space is about 15 km. The area of landslide distribution is only about 600 km2 with a radius less than 14 km if we regard it as a circle. It may cause a large error because that the seismic stations used by USGS to locate the epicenter are relatively sparse. Therefore, the PGA distribution map obtained by simulation does not match the coseismic landslides. The mismatch between PGA and coseismic landslides should not be explained as an abnormal phenomenon which is different from previous earthquakes. The more reasonable explanation may be the error of the PGA data or the coseismic landslides are also affected by local topography and geological conditions.

    Figure 11.  Changes of landslide number and LND (a) and landslide area and LAD (b) with PGA.

  • Apparently seismic factors, such as the epicenter and seismogenic fault, can exert significant effects on the occurrence of the earthquake-triggered landslides. The statistical relationships between these factors and earthquake-triggered landslides were often analyzed in previous work (e.g., Shao et al., 2019b; Rao et al., 2017; Xu et al., 2014a; Gorum et al., 2011; Keefer 1984). As the magnitude of the 2014 Ludian earthquake is only Mw 6.2, and the surface rupture is only exposed for about 2 km at the southeastern end of the inferred seismogenic fault (Xu X W et al., 2015), it is difficult to clarify the relationship between earthquake-triggered landslides and the seismogenic fault. Therefore, in this study, the distance to the epicenter is used to examine the relationship between earthquake-triggered landslides and the seismic source. According to the China Earthquake Network Center, the epicenter of the Ludian earthquake is at 27.099 4°N, 103.34°E. The buffers of 2 km interval around the epicenter are established on the GIS platform and the whole study area is divided into 9 intervals according to the distance from the epicenter, i.e., 2–4, 4–6, 6–8, 8–10, 10–12, 12–14, 14–16, and 16–18 km. Then, relationships between landslide distribution and the distances from the epicenter are calculated (Fig. 12). In general, the landslide number and landslide area increase first and then decrease. The reason for this is that the buffer area of the same buffer distance rises gradually with the distance from the epicenter, thus landslide number and area increase. This is probably because fewer landslides occurred when the distance from the epicenter exceeds a certain value and the outer area is smaller because of cutting by the boundary of the study area. Landslide number and area peak at 8–10 and 4–6 km away from the epicenter, i.e., 2 597 landslides and 4.68 km2, respectively. In general, LND and LAD decrease with increasing distance from the epicenter. However, the maximum values of both are not located in the buffer 0–2 km far from the epicenter, instead of 2–4 and 4–6 km to the epicenter, which are 47.58 km-2 and 7.44%, respectively. On the whole, the farther from the epicenter, the lower susceptibility of landslides. This is likely because that seismic shaking reduced with the increase of distance from the epicenter, thus leading to fewer landslides. This is common in other cases (Xu et al., 2015, 2014a). Besides, the lower landslide density in 0–4 km distance from the epicenter is probably associated with the error in epicenter location or lower gradients in this area or unfavorable geological conditions for landsliding.

    Figure 12.  Changes of landslide number and LND (a) and landslide area and LAD (b) with distance from the epicenter.

  • After the 2014 Ludian earthquake, quite a few inventories of coseismic landslides were completed by some research teams, which differs from each other to some extent. Compared with these data, the resultant inventory of this study is more complete, detailed and objective. For example, the number of coseismic landslides is much greater than those in previous work. The reasons for this may include the quality, resolution, and coverage of remote sensing images used, as well as the landslide interpretation methods. Here we make a comparison of them in the following five aspects: types and resolution of remote sensing images, landslide number, landslide area, landslide distribution area and integrity of post-quake images (Table 2). Zhou S H et al. (2016) obtained 19.12 km2 of the landslide area that is the largest in previous results. Their work is based on pre- and post-quake Landsat-8 images (15-m resolution), combining with ultrahigh-resolution aerial photographs (0.2-m resolution) covering part the affected area (Fig. 13a). Apparently, such a low resolution of the satellite images can lead to commission and omission errors, i.e., missing a large number of small- and medium-landslides. Because ground objects identified are not clear, the landslides existing before the earthquake, residential areas and lakes might be recognized as coseismic landslides, leading to the low quality of the landslide inventory. For instance, as shown in Fig. 13b, a quarry and exposed slope have been interpreted as a fairly large coseismic landslide. Meanwhile, the whole township named Huodehong was depicted into several large-scale coseismic landslides (Fig. 13c). These are obviously severe commission errors in the work of Zhou S H et al. (2016).

    No. Source and resolution of images Landslide number Landslide area (km2) Landslide distribution area (km2) Integrity of images References
    1 SJ9A (Pan: 2.5 m; MSS: 10 m), 1 024 5.19 250 Yes Xu et al. (2014d)
    TH01-02 (Pan: 2 m)
    2 Aerial image (0.2 m) 1 053 2.36 44.13 No Tian et al. (2017b)
    3 Landsat-8 (Pan: 15 m; MSS: 30 m) 1 826 19.12 735 Yes Zhou S H et al. (2016)
    Aerial image (0.2 m)
    4 Not availible 114 4.2 368.2 Yes Chen et al. (2015)
    5 Google Earth (~0.5 m) 10 559 14.975 357 Yes Wu and Xu (2018)
    6 Google Earth (~0.5 m) 12 817 16.33 603 Yes This study

    Table 2.  Comparison of landslide inventories of the 2014 Ludian earthquake

    Figure 13.  Erroneous interpretation to seismic landslides by Zhou S H et al. (2016). (a) Distribution of coseismic landslides triggered by the Ludian earthquake (Zhou S H et al., 2016). (b) Quarry and exposed slope misjudged as coseismic landslides, image taken on February 17, 2015. Central coordinates: 27.067 4°N, 103.467 3°E; (c) Huodehong Township was misjudged as coseismic landslide, image taken on August 14, 2014, central coordinates: 27.040 1°N, 103.466 4°E.

    Xu et al. (2014d) established that the Ludian earthquake triggered at least 2014 individual landslides with a total distribution area of 5.19 km2. For comparison, we choose a rectangle 850 m×930 m with central coordinates 27.105 9°N, 103.311 2°E, where 81 landslides were interpreted by this study and 16 landslides were delineated by the study from Xu et al. (2014d), as shown in Fig. 14. Xu et al. (2014d) used the high-resolution satellite images that cover the whole region, with resolution 2.5 m that was fused with multi-spectrum images with 10 m resolution and a panchromatic image with 2.5 m resolution. In addition, the spectral difference between landslide regions and non-landslide regions is not so obvious that a number of small-scale landslides were unrecognized. Therefore, the results of Xu et al. (2014d) are notably different from this study.

    Figure 14.  Comparison between the work of Xu et al. (2014d) and this study. (a) Landslides interpreted by this study and pre-quake image from Google Earth (taken on December 6, 2011); (b) landslides interpreted by this study and post-quake image from Google Earth (taken on August 20, 2014); (c) landslides interpreted by Xu et al. (2014d) and pre-quake image taken by SJ9A; (d) landslides interpreted by Xu et al. (2014d), by this work and post-quake image of SJ9A. Modified after Wu and Xu (2018).

    Tian et al. (2017b) delineated the Ludian earthquake- triggered landslides on both sides of the Niulan River using ultrahigh-resolution aerial images. As their study area does not cover the whole affected area, hence the resulting landslide number in the inventory is less than the actual one. Nevertheless, landslide number and area density from their work are 1 053/44.13 km2=23.86 km-2 and 2.36 km2/44.13 km2×100%= 5.35%, close to those of this study which are 12 817/603 km2=21.25 km-2 and 16.33 km2/603 km2×100%=2.71%, respectively. Probably Tian et al. (2017b) adopted ultrahigh- resolution aerial photographs which have equivalent resolution of satellite images from Google Earth platform used in this study. On the other hand, the study area of Tian et al. (2017b) covers both sides of the Niulan River where slopes are highly prone to landsliding. Therefore the landslide density of Tian et al. (2017b) is higher than that in the whole range of this study. Besides, the study of Chen et al. (2015) did not mention the source of remote sensing images used; and they have delineated 114 landslides greater than 1 000 m2 and used them to analyze the special distribution of the coseismic landslides. Their study ignores a large number of small and medium-scale landslides triggered by the earthquake.

    Also based on visual interpretation of high-resolution satellite images from Google Earth, Wu and Xu (2018) constructed an inventory quite similar to this study, which determined 10 559 coseismic landslides with a total area of 14.975 km2 and a spatial distribution area of 357 km2. This study makes a new landslide inventory in a nearly circular area with the radius about 3 km larger than that of the study area in Wu and Xu (2018), and finds that there are still quite a few small landslides outside this area. Although the number of landslides in the area far from the epicenter is less and their scale is small, the new landslide distribution area in this study reaches 603 km2, which is 1.7 times the study area of Wu and Xu (2018). While the landslide number and distribution area of our new results are 12 817 pieces and 16.33 km2, respectively, which are only 1.2 and 1.1 times the results of Wu and Xu (2018).

    In this study, the landslide number is much greater than most of the previous work, because our work is strictly in compliance with the principles of establishing inventories of coseismic landslides (Xu, 2015; Guzzetti et al., 2012; Harp et al., 2011). Meanwhile, the coverage of pre- and post-quake satellite images that overlaps the whole affected area ensures the integrity of the inventory. Using post-quake high-resolution satellite images on Google Earth platform (resolution is about 0.5 m) permits landslides interpreted as many as possible, including small ones. Besides, in this work pre-quake satellite images are used to eliminate landslides that existed before the earthquake, avoiding the commission error when delineating coseismic landslides, and ensuring the accuracy of the inventory. We adopt artificial visual interpretation, in which coalescing landslides are divided into individual landslides according to the principle of interpretation (Xu, 2015), rather than complex landslides framed as individual ones, so that the resulting coseismic landslide number is objective. This is why the inventory of the landslides obtained in this study is relatively more complete and accurate. Comparing with the inventories of landslides triggered by other earthquakes can also explain why the inventory of this study is objective. For instance, the Minxian Mw 5.9 earthquake of June 22, 2013 triggered at least 6 478 landslides (Tian et al., 2016; Xu et al., 2014c). The Northridge Mw 6.7 earthquake in 1994 triggered more than 11 000 landslides in an area about 1 000 km2 (Harp and Jibson, 1996, 1995). The average gradient in the Ludian quake-affected area is larger than those of the Minxian and Northridge earthquake areas. Besides, Ludian is located in a subtropical region and the earthquake occurred in summer. Before the 2014 Ludian earthquake, continuous precipitation could have lowered the slope stability, thus more prone to landsliding. Hence, referring to the number of landslides triggered by the Minxian and Northridge earthquakes, it is unreasonable that the Ludian earthquake only triggered more than 1 000 major landslides as suggested by previous studies (Chen 2015; Xu et al., 2014d).

  • Based on artificial visual interpretation of pre- and post-quake high-resolution satellite images on Google Earth platform, a new, complete, and objective database of the landslides triggered by the 2014 Ludian, Yunnan (China) Mw 6.2 earthquake was established. The updated results show that this event triggered at least 12 817 landslides in a near-circular area of about 600 km2, with landslide number density 21.25 km2, the total area of the landslides 16.33 km2 and landslide area density 2.71%. They occurred mostly at the slope gradients of 10°–40°, exhibiting an increase tendency with steeper slopes affected by the propagation direction of the earthquake rupture, the east-facing slope is more prone to landsliding. The differences between the landslide susceptibility and landslide scale in different strata indicate that the lithology is an important controlling factor. The strata with more coseismic landslides are P1 and Z1, especially Z1 where large-scale landslides are common. In general, the susceptibility of the slopes to landsliding increases with PGA, as evidenced by landslide density in the areas with PGA greater than 0.16g is obviously larger than those with PGA less than 0.16g. In general, the greater the distance from the epicenter, the lower the susceptibility of landsliding is. In addition, this study suggests that when using satellite images to create complete and accurate coseismic landslide inventories, the images used should meet certain conditions, including high resolution, whole coverage, and timeliness of data collection. Meanwhile the correct criteria of landslide inventorying also should be followed.

  • This study was supported by the National Key Research and Development Program of China (No. 2017YFB0504104) and the National Natural Science Foundation of China (No. 41661144037). We thank the Google Earth platform for the free access satellite images used in this study. The final publication is available at Springer via https://doi.org/10.1007/s12583-020-1297-7.

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