龙玉洁, 李为乐, 黄润秋, 许强, 余斌, 刘刚. 汶川地震震后10 a绵远河流域滑坡遥感自动提取与演化趋势分析[J]. 武汉大学学报 ( 信息科学版), 2020, 45(11): 1792-1800. DOI: 10.13203/j.whugis20200180
引用本文: 龙玉洁, 李为乐, 黄润秋, 许强, 余斌, 刘刚. 汶川地震震后10 a绵远河流域滑坡遥感自动提取与演化趋势分析[J]. 武汉大学学报 ( 信息科学版), 2020, 45(11): 1792-1800. DOI: 10.13203/j.whugis20200180
LONG Yujie, LI Weile, HUANG Runqiu, XU Qiang, YU Bin, LIU Gang. Automatic Extraction and Evolution Trend Analysis of Landslides in Mianyuan River Basin in the 10 Years After Wenchuan Earthquake[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1792-1800. DOI: 10.13203/j.whugis20200180
Citation: LONG Yujie, LI Weile, HUANG Runqiu, XU Qiang, YU Bin, LIU Gang. Automatic Extraction and Evolution Trend Analysis of Landslides in Mianyuan River Basin in the 10 Years After Wenchuan Earthquake[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1792-1800. DOI: 10.13203/j.whugis20200180

汶川地震震后10 a绵远河流域滑坡遥感自动提取与演化趋势分析

Automatic Extraction and Evolution Trend Analysis of Landslides in Mianyuan River Basin in the 10 Years After Wenchuan Earthquake

  • 摘要: 2008年汶川Ms 8.0级地震触发了大量的崩塌滑坡地质灾害,导致强震区震后地质灾害活动规模和频率显著增加。选取地质灾害频发的绵远河流域作为研究区,分别利用最大似然法和随机森林算法自动提取该区域2007—2018年的滑坡灾害信息。结果表明:随机森林算法提取效果较理想,能准确识别区域内的大部分滑坡,且与已有研究成果结果更吻合,正确率均值为86.73%。汶川地震震后10 a间研究区滑坡活动状态可分为3种:2008—2011年为强活动期,滑坡活动规模和频率较地震前显著增加,灾害损失严重;2012—2016年为中等活动期,滑坡活动规模和频率逐渐降低,灾害事件偶尔发生;2017年之后为弱活动期,滑坡活动规模和频率显著降低,灾害事件鲜有发生,但仍然没有恢复到震前水平。

     

    Abstract:
      Objectives  The 2008 Wenchuan Ms 8.0 earthquake triggered tens of thousands of landslides and produced about 10 billion cubic meters of loose material, resulted in a significant increase in the scale and frequency of landslides after the earthquake. The temporal and spatial evolution of post-earthquake landsliding has become the focus of attention of scholars and the public. The Mianyuan River basin is selected as a case study, and the temporal and spatial evolution trend of post-earthquake landsliding is quantitatively analyzed using multi-temporal satellite images.
      Methods  Firstly, the satellite images of Landsat 7, SPOT-5, RapidEye and Planet from 2007 to 2018 are collected and preprocessed such as atmospheric correction, ortho-rectification and image cutting, etc. Then, the maximum likelihood method and the random forest algorithm are engaged to automatically detect the landsliding in the Mianyuan River basin using the preprocessed satellite images. Finally, the landslide detection accuracy of the two methods is evaluated by comparing the landslides interpreted manually and the landslides detected automatically.
      Results  The average recognition accuracy of random forest algorithm is 86.73%, while the average recognition accuracy of maximum likelihood method is 73.11%. The total area of new landslides detected by the random forest algorithm in 2007, 2008, 2011, 2013, 2015, 2016, 2017 and 2018 are 0.24, 51.54, 19.25, 7.21, 6.39, 7.35, 3.51, 3.82 km2, respectively.
      Conclusions  The random forest algorithm method has higher detection accuracy than the maximum likelihood method. The landsliding activity in the study area during the 10 years after the Wenchuan earthquake can be divided into three stages: 2008 to 2011 is the strong activity period, the scale and frequency of landsliding activity are significantly increased than before the earthquake, and the disaster losses are serious; 2012 to 2016 is the moderate activity period, the scale and frequency of landsliding activity gradually decreased, and disaster events occurred occasionally; 2017 to 2018 is the weak activity period, the scale and frequency of landsliding activity decreased significantly. After 2017, disaster events rarely occurred in the study area, but landsliding activity still did not return to the pre-earthquake level.

     

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