基于InSAR与随机森林的滑坡敏感性评价与误差改正

doi:10.13474/j.cnki.11-2246.2022.0288

测绘通报 ›› 2022, Vol. 0 ›› Issue (10): 13-20.doi: 10.13474/j.cnki.11-2246.2022.0288

基于InSAR与随机森林的滑坡敏感性评价与误差改正

黄龙^1,2, 孙倩^1,2, 胡俊³

1. 湖南师范大学地理科学学院, 湖南长沙 410081;
2. 湖南师范大学地理空间大数据挖掘与应用重点实验室, 湖南长沙 410081;
3. 中南大学地球科学与信息物理学院, 湖南长沙 410083

收稿日期:2022-06-22 发布日期:2022-11-02
通讯作者: 孙倩。E-mail:sandra@hunnu.edu.cn
作者简介:黄龙(1997-),男,硕士生,研究方向为滑坡敏感性分析。E-mail:525422032@qq.com
基金资助:
国家重点研发计划(2018YFC1505101);国家自然科学基金(41704001;42030112);湖南省自然科学基金(2020JJ2043;2022JJ30031);云南省地质灾害隐患识别中心建设项目(2021年)(云财资环[2021]22号)

Landslide sensitivity assessment and error correction based on InSAR and random forest method

HUANG Long^1,2, SUN Qian^1,2, HU Jun³

1. College of Geographic Sciences, Hunan Normal University, Changsha 410081, China;
2. Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China;
3. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Received:2022-06-22 Published:2022-11-02

摘要/Abstract

摘要： 滑坡不仅影响社会经济的可持续发展,而且威胁人类的生命安全。滑坡敏感性图(LSM)被认为是预测滑坡空间位置的有效手段之一,但现有方法生成的LSM因受假阴性误差的影响,难以得到可靠的预测结果。针对该问题,本文提出了基于InSAR形变结果的LSM改进方法。在甘肃省舟曲县的试验结果表明,研究区范围内滑坡敏感性等级提升2.74%。对两个具体区域的原始LSM和改进后LSM进行比较,结果表明,利用改进后的方法,可在受滑坡蠕动现象影响的区域制作更可靠的LSM。

关键词: 随机森林, SBAS-InSAR, 滑坡敏感性, 假阴性误差

Abstract: Landslides not only affect the sustainable development of the social economy but also threaten the safety of human life. A landslide sensitivity map (LSM) is considered to be one of the effective means to predict the spatial location of landslides, but the LSM generated by existing methods is affected by false negative errors, so it is difficult to obtain reliable prediction results. This paper proposes an improved LSM method based on InSAR deformation results to solve this problem. The experimental results in Zhouqu county, Gansu province show that the landslide sensitivity grade within the study area has increased by 2.74%. The comparison results between the original LSM and the improved LSM in two specific regions show that the improved method can make a more reliable landslide sensitivity map in the area affected by landslide creep.

Key words: random forest, SBAS-InSAR, landslide sensitivity, false-negative error

中图分类号:

P237

黄龙, 孙倩, 胡俊. 基于InSAR与随机森林的滑坡敏感性评价与误差改正[J]. 测绘通报, 2022, 0(10): 13-20.

HUANG Long, SUN Qian, HU Jun. Landslide sensitivity assessment and error correction based on InSAR and random forest method[J]. Bulletin of Surveying and Mapping, 2022, 0(10): 13-20.

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基于InSAR与随机森林的滑坡敏感性评价与误差改正

Landslide sensitivity assessment and error correction based on InSAR and random forest method

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