A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China

Yujie Long; Weile Li; Runqiu Huang; Qiang Xu; Bin Yu; Gang Liu

doi:10.1007/s12583-021-1525-9

Volume 34 Issue 2

Apr 2023

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Journal of Earth Science > 2023 > 34(2): 316-329.

Yujie Long, Weile Li, Runqiu Huang, Qiang Xu, Bin Yu, Gang Liu. A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China. Journal of Earth Science, 2023, 34(2): 316-329. doi: 10.1007/s12583-021-1525-9

Citation:

Yujie Long, Weile Li, Runqiu Huang, Qiang Xu, Bin Yu, Gang Liu. A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China. Journal of Earth Science, 2023, 34(2): 316-329. doi: 10.1007/s12583-021-1525-9

Citation:

Yujie Long, Weile Li, Runqiu Huang, Qiang Xu, Bin Yu, Gang Liu. A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China. Journal of Earth Science, 2023, 34(2): 316-329. doi: 10.1007/s12583-021-1525-9

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A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China

doi: 10.1007/s12583-021-1525-9

Yujie Long^{1, 2
,},
Weile Li^{1
,
,
,},
Runqiu Huang¹,
Qiang Xu¹,
Bin Yu¹,
Gang Liu^{1, 3}

1.
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
2.
Sichuan Metallurgical Geological Survey and Design Group Co., Ltd, Chengdu 610081, China
3.
College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

Funds:

the National Key R & D Program 2018YFC1505402

the Key Research and Development Program of Sichuan Province 2023YFS0435

the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project SKLGP2014Z004

the Science and Technology Innovation Fund of Sichuan Earthquake Agency 201901

More Information

Corresponding author: Weile Li, whylwl01@163.com
Received Date: 06 Oct 2021
Accepted Date: 31 Jul 2022
Issue Publish Date: 30 Apr 2023

Abstract

Abstract

The M_s 8.0 Wenchuan earthquake of 2008 dramatically changed the terrain surface and caused long-term increases in the scale and frequency of landslides and debris flows. The changing trend of landslides in the earthquake-affected area over the decade since the earthquake remains largely unknown. In this study, we were able to address this issue using supervised classification methods and multitemporal remote sensing images to study landslide evolution in the worst-affected area(Mianyuan River Basin) over a period of ten years. Satellite images were processed using the maximum likelihood method and random forest algorithm to automatically map landslide occurrence from 2007 to 2018. The principal findings are as follows: (1) when compared with visual image analysis, the random forest algorithm had a good average accuracy rate of 87% for landslide identification; (2) postevent landslide occurrence has generally decreased with time, but heavy monsoonal seasons have caused temporary spikes in activity; and (3) the postearthquake landslide activity in the Mianyuan River Basin can be divided into a strong activity period (2008 to 2011), medium activity period (2012 to 2016), and weak activity period (post 2017). Landslide activity remains above the prequake level, with damaging events being rare but continuing to occur. Long-term remote sensing and on-site monitoring are required to understand the evolution of landslide activity after strong earthquakes.
- Wenchuan earthquake,
- Mianyuan River Basin,
- automatic detection,
- evolutionary trend,
- maximum likelihood method,
- random forest algorithm,
- engineering geology

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References(55)

References

Blaschke, T., 2010. Object Based Image Analysis for Remote Sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004

Breiman, L., 2001. Random Forests. Machine Learning, 45(1): 5–32. https://doi.org/10.1023/a:1010933404324

Chen, H., Hawkins, A. B., 2009. Relationship between Earthquake Disturbance, Tropical Rainstorms and Debris Movement: An Overview from Taiwan. Bulletin of Engineering Geology and the Environment, 68(2): 161–186. https://doi.org/10.1007/s10064-009-0209-y

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

Chen, X., Cui, P., Li, Y., et al., 2010. Mountain Hazard Induced by Wenchuan Earthquake and Its Long-term Development Trends of Ganxi Gully, Beichuan. Journal of Sichuan University (Engineering Science Edition), 42 (S1): 22–32 (in Chinese with English Abstract)

Chen, Z., Zhang, Y. F., Ouyang, C., et al., 2018. Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery. Sensors, 18(3): 821. https://doi.org/10.3390/s18030821

China Earthquake Administration, 2008. Intensity Distribution Map of Wenchuan M_s 8.0 Earthquake [2008-9-1]. https://www.cea.gov.cn/cea/xwzx/fzjzyw/5189771/index.html

Cui, P., Zhuang, J. Q., Chen, X. C., et al., 2010. Characteristics and Countermeasures of Debris Flow in Wenchuan Area after the Earthquake. Journal of Sichuan University (Engineering Science Edition), 42(5): 10–19 (in Chinese with English Abstract)

Dai, F. C., Xu, C., Yao, X., et al., 2011. Spatial Distribution of Landslides Triggered by the 2008 M_s 8.0 Wenchuan Earthquake, China. Journal of Asian Earth Sciences, 40(4): 883–895. https://doi.org/10.1016/j.jseaes. 2010.04.010 doi: 10.1016/j.jseaes.2010.04.010

Dou, J., Chang, K. T., Chen, S. S., et al., 2015. Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm. Remote Sensing, 7(4): 4318–4342. https://doi.org/10.3390/rs70404318

Dou, J., Yunus, A. P., Tien Bui, D., et al., 2019. Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM. Remote Sensing, 11(6): 638. https://doi.org/10.3390/rs11060638

Fan, X. M., Scaringi, G., Korup, O., et al., 2019. Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts. Reviews of Geophysics, 57(2): 421–503. https://doi.org/10.1029/2018r g000626 doi: 10.1029/2018rg000626

Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., et al., 2019. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sensing, 11(2): 196. https://doi.org/10.3390/rs11020196

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

Guo, X. J., Cui, P., Li, Y., et al., 2016a. Intensity-Duration Threshold of Rainfall-Triggered Debris Flows in the Wenchuan Earthquake Affected Area, China. Geomorphology, 253(13): 208–216. https://doi.org/10.1016/j.geomorph.2015.10.009

Guo, X. J., Cui, P., Li, Y., et al., 2016b. Spatial Features of Debris Flows and Their Rainfall Thresholds in the Wenchuan Earthquake-Affected Area. Landslides, 13(5): 1215–1229. https://doi.org/10.1007/s10346-015-060 8-z doi: 10.1007/s10346-015-0608-z

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

Hong, H. Y., Pourghasemi, H. R., Pourtaghi, Z. S., 2016. Landslide Susceptibility Assessment in Lianhua County (China): A Comparison between a Random Forest Data Mining Technique and Bivariate and Multivariate Statistical Models. Geomorphology, 259(15): 105–118. https://doi.org/10.1016/j.geomorph.2016.02.012

He, X. L., Xu, C., Qi, W. W., et al., 2021. Landslides Triggered by the 2020 Qiaojia M_w 5.1 Earthquake, Yunnan, China: Distribution, Influence Factors and Tectonic Significance. Journal of Earth Science, 32(5): 1056–1068. https://doi.org/10.1007/s12583-021-1492-1

Huang, R. Q., Li, W. L., 2009. Analysis of the Geo-Hazards Triggered by the 12 May 2008 Wenchuan Earthquake, China. Bulletin of Engineering Geology and the Environment, 68(3): 363–371. https://doi.org/10.1007/s10064-009-0207-0

Huang, R. Q., Li, W. L., 2014. Post-Earthquake Landsliding and Long-Term Impacts in the Wenchuan Earthquake Area, China. Engineering Geology, 182: 111–120. https://doi.org/10.1016/j.enggeo.2014.07.008

Huang, Y. D., Xu, C., Zhang, X. L., et al., 2021. An Updated Database and Spatial Distribution of Landslides Triggered by the Milin, Tibet M_w 6.4 Earthquake of 18 November 2017. Journal of Earth Science, 32(5): 1069–1078. https://doi.org/10.1007/s12583-021-1433-z

Ji, S. P., Yu, D. W., Shen, C. Y., et al., 2020. Landslide Detection from an Open Satellite Imagery and Digital Elevation Model Dataset Using Attention Boosted Convolutional Neural Networks. Landslides, 17(6): 1337–1352. https://doi.org/10.1007/s10346-020-01353-2

Jin, W., Zhang, G., Zou, Q., et al., 2019. A New Understanding of the Activity Behavior of Post-Earthquake Debris Flow—Taking the "8· 20" Event in Wenchuan, Sichuan, China as an Example. Mountain Research, 37 (5): 787–796 (in Chinese with English Abstract)

Lacroix, P., Zavala, B., Berthier, E., et al., 2013. Supervised Method of Landslide Inventory Using Panchromatic SPOT5 Images and Application to the Earthquake-Triggered Landslides of Pisco (Peru, 2007, M_w 8.0). Remote Sensing, 5(6): 2590–2616. https://doi.org/10.33 90/rs5062590 doi: 10.3390/rs5062590

Lee, S., Lee, M. J., 2006. Detecting Landslide Location Using KOMPSAT 1 and Its Application to Landslide-Susceptibility Mapping at the Gangneung Area, Korea. Advances in Space Research, 38(10): 2261–2271. https://doi.org/10.1016/j.asr.2006.03.036

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

Li, L. J., Yao, X., Zhang, Y. S., et al., 2014. RS-Based Extraction and Distribution Characteristics of Geo-Hazards Trggered by Wenchuan Earthquake in Mianyuan River Basin. Journal of Engineering Geology, 22(1): 46–55 (in Chinese with English Abstract)

Li, W. L., Huang, R. Q., Tang, C., et al., 2011. Landslide Triggered by "5·12" Wenchuan Earthquake in the Mianyuan River Basin, China. Journal of Mountain Science, 29(4): 483–492 (in Chinese with English Abstract)

Li, Z. H., Zhang, C. L., Chen, B., et al., 2022. A Technical Framework of Landslide Prevention Based on Multi-Source Remote Sensing and Its Engineering Application. Earth Science, 47(6): 1901–1916 (in Chinese with English Abstract)

Lin, Q. G., Zou, Z. H., Zhu, Y. Q., et al., 2017. Object-Oriented Detection of Landslides Based on the Spectral, Spatial and Morphometric Properties of Landslides. Remote Sensing Technology and Application, 32(5): 931–937 (in Chinese with English Abstract)

Liu, P. Y., Chang, M., Wu, B. B., et al., 2022. Route Selection of Landslide Prone Area in Wenchuan Section of Chengdu-Wenchuan Expressway Based on SBAS-InSAR. Earth Science, 47(6): 2048–2057 (in Chinese with English Abstract)

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.201 0.2101045 doi: 10.1109/lgrs.2010.2101045

Nakamura, H., Tsuchiya, S. K. I., 2000. Sabo Against Earthquakes. Tokyo, Kokon Shoin

Ni, Z., Yang, Z., Li, Y., et al., 2019. Decreasing Trend of Geohazards Induced by the 2008 Wenchuan Earthquake Inferred from Time Series NDVI Data. Remote Sensing, 11(19): 2192. https://doi.org/10.3390/rs11192192

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/ng eo1154 doi: 10.1038/ngeo1154

Prakash, N., Manconi, A., Loew, S., 2020. Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sensing, 12(3): 346. https://doi.org/10.3390/rs12030346

Qi, S. W., Xu, Q., Lan, H. X., et al., 2010. Spatial Distribution Analysis of Landslides Triggered by 2008.5.12 Wenchuan Earthquake, China. Engineering Geology, 116(1/2): 95–108. https://doi.org/10.1016/j.engg eo.2010.07.011 doi: 10.1016/j.enggeo.2010.07.011

Sansar, M. R., Ghorbanzadeh, O., Westen, C. J., et al., 2021. Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach. Landslides, 18(5): 1937–1950. https://doi.org/10.1007/s10346-020-016 02-4 doi: 10.1007/s10346-020-01602-4

Stumpf, A., Kerle, N., 2011. Object-Oriented Mapping of Landslides Using Random Forests. Remote Sensing of Environment, 115(10): 2564–2577. https://doi.org/10.1016/j.rse.2011.05.013

Su, F. H., Liu, H. J., Han, Y. S., 2008. The Extraction of Mountain Hazard Induced by Wenchuan Earthquake and Analysis of Its Distributing Characteristic. Journal of Remote Sensing, 12(6): 956–963 (in Chinese with English Abstract)

Tang, C., 2010. Activity Tendency Prediction of Rainfall Induced Landslides and Debris Flows in the Wenchuan Earthquake Areas. Journal of Mountain Science, 28(3): 341–349 (in Chinese with English Abstract)

Tang, C. X., van Westen, C. J., Tanyas, H., et al., 2016. Analysing Post-Earthquake Landslide Activity Using Multi-Temporal Landslide Inventories near the Epicentral Area of the 2008 Wenchuan Earthquake. Natural Hazards and Earth System Sciences, 16(12): 2641–2655. https://doi.org/10.5194/nhess-16-2641-2016

Tang, C., Liang, J. T., 2008. Characteristics of Debris Flows in Beichuan Epicenter of the Wenchuan Earthquake Triggered by Rainstorm on September 24, 2008. Journal of Engineering Geology, 16(6): 751–758 (in Chinese with English Abstract)

van der Linden, S., Rabe, A., Held, M., et al., 2015. The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing. Remote Sensing, 7(9): 11249–11266. https://doi.org/10.33 90/rs70911249 doi: 10.3390/rs70911249

Watts, J. D., Lawrence, R. L., Miller, P. R., et al., 2009. Monitoring of Cropland Practices for Carbon Sequestration Purposes in North Central Montana by Landsat Remote Sensing. Remote Sensing of Environment, 113(9): 1843–1852. https://doi.org/10.1016/j.rse.2009.04.015

Wei, X. L., Chen, N. S., Cheng, Q. G., et al., 2014. Long-Term Activity of Earthquake-Induced Landslides: A Case Study from Qionghai Lake Basin, Southwest of China. Journal of Mountain Science, 11(3): 607–624. https://doi.org/10.1007/s11629-013-2970-4

Xu, C., 2013. Automatic Extraction of Earthquake-Triggered Landslides Based on Maximum Likelihood Method and Its Validation. The Chinese Journal of Geological Hazard and Control, 24(3): 19–25 (in Chinese with English Abstract)

Xu, C., Xu, X. W., Yao, X., et al., 2014. Three (Nearly) Complete Inventories of Landslides Triggered by the May 12, 2008 Wenchuan M_w 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

Xu, Q., Zhang, S., Li, W. L., et al., 2012. The 13 August 2010 Catastrophic Debris Flows after the 2008 Wenchuan Earthquake, China. Natural Hazards and Earth System Sciences, 12(1): 201–216. https://doi.org/10.5194/nhess-12-201-2012

Yu, B., Chen, F., Xu, C., 2020. Landslide Detection Based on Contour-Based Deep Learning Framework in Case of National Scale of Nepal in 2015. Computers & Geosciences, 135: 104388. https://doi.org/10.10 16/j.cageo.2019.104388 doi: 10.1016/j.cageo.2019.104388

Yunus, A. P., Fan, X. M., Tang, X. L., et al., 2020. Decadal Vegetation Succession from MODIS Reveals the Spatio-Temporal Evolution of Post-Seismic Landsliding after the 2008 Wenchuan Earthquake. Remote Sensing of Environment, 236: 111476. https://doi.org/10.1016/j.rse.2019.111476

Zhang, L. M., Zhang, S., Huang, R. Q., 2014. Multi-Hazard Scenarios and Consequences in Beichuan, China: The First Five Years after the 2008 Wenchuan Earthquake. Engineering Geology, 180: 4–20. https://doi.org/10.1016/j.enggeo.2014.03.020

Zhu, J., Tang, C., Chang, M., et al., 2015. Field Observations of the Disastrous 11 July 2013 Debris Flows in Qipan Gully, Wenchuan Area, Southwestern China. Engineering Geology for Society and Territory, 2: 531–535. https://doi.org/10.1007/978-3-319-09057-3_88

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A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China

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