联合光学和SAR遥感影像的山区公路滑坡易发性评价方法

余绍淮; 徐乔; 余飞

doi:10.6046/zrzyyg.2022320

联合光学和SAR遥感影像的山区公路滑坡易发性评价方法

中交第二公路勘察设计研究院有限公司,武汉 430056

基金项目:
湖北省重点研发计划项目(2021BAA185)

及中交第二公路勘察设计研究院有限公司自主研发科技项目(KJFZ-2020-012)

详细信息

作者简介: 余绍淮(1979-),男,本科,正高级工程师,主要从事3S技术与公路CAD的集成研究。Email: yushaohuai@163.com。

通讯作者: 徐乔(1991-),男,硕士研究生,工程师,主要从事公路遥感地质灾害监测研究。Email: xu_qiao_cug@126.com。

中图分类号: TP79

收稿日期: 2022-08-10

修回日期: 2023-03-22

刊出日期: 2023-12-21

Method for assessing landslide susceptibility of highways in mountainous areas based on optical and SAR remote sensing images

CCCC Second Highway Consultants Co., Ltd., Wuhan 430056,China

More Information

Corresponding author: XU Qiao

Received Date: 10 August 2022

Revised Date: 22 March 2023

Publish Date: 21 December 2023

摘要

摘要: 艰险山区公路的滑坡易发性评价能够为公路地质选线提供关键支撑信息。传统滑坡易发性评价方法存在忽略地表形变等动态数据的使用而导致评价结果精度不高的问题。针对此问题,该文提出一种联合光学和SAR遥感影像的山区公路滑坡易发性评价方法。以青海省沿黄公路隆务峡至公伯峡段为研究区,先利用高分辨率QuickBird卫星影像提取多种滑坡灾害静态因子,并采用随机森林模型计算路线区域内的滑坡易发性风险初始等级; 然后基于长时间序列的Sentinel-1A影像,获取直接反映滑坡动态变化的地表形变因子; 最后,利用地表形变因子对滑坡易发性风险初始等级进行修正,得到最终的滑坡易发性评价分区图。工程实践表明,该方法综合利用滑坡灾害静态与动态因子数据,所获取的山区公路滑坡易发性评价分区图更具准确性,可为后续的公路地质选线提供准确信息。
- 滑坡易发性评价 /
- 光学遥感 /
- SAR /
- 山区公路 /
- 随机森林
Abstract: Assessing the landslide susceptibility of highways in precarious mountainous areas can provide crucial information for the geologic route selection of highways. Conventional landslide susceptibility assessment methods ignore the application of surface deformation data and other dynamic data, leading to low-accuracy assessment results. Hence, this study proposed a landslide susceptibility assessment method for mountain highways based on optical and SAR remote sensing images. With the Longwuxia-Gongboxia section of the Yanhuang Highway in Qinghai Province as the study area, this study extracted various static factors of landslides from high-resolution QuickBird satellite images and calculated the initial risk level of landslide susceptibility within the route area using a random forest model. Afterward, this study obtained the surface deformation factors, which directly reflect the dynamic changes of landslides, based on the long-time-series Sentinel-1A images. Finally, this study corrected the initial landslide susceptibility risk level based on the surface deformation factors, generating a landslide susceptibility assessment zoning map. As demonstrated by engineering practice, the method proposed in this study yielded a high-accuracy landslide susceptibility assessment zoning map for the mountain highway by combining data on both static and dynamic factors of landslides, thus providing accurate information for subsequent geologic route selection of the highway.
- landslide susceptibility assessment /
- optical remote sensing /
- SAR /
- mountain highway /
- random forest