联合SAR影像相位和幅度信息的多形变量级滑坡监测——以金沙江白格滑坡为例

doi:10.6046/zrzyyg.2022467

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近年来,雷达遥感被广泛用于提取滑坡表面的高精度形变信息,所用技术包括基于相位的干涉测量和基于幅度的像素偏移量追踪; 然而,大型复杂滑坡的形变量在时间演化以及空间分布上都存在较大差异,单一雷达遥感手段难以实现滑坡形变的全面监测。该文在雷达遥感形变探测能力分析的基础上,提出联合SAR(synthetic aperture Radar)影像的相位和幅度信息进行滑坡的全过程监测。以2018年发生的金沙江白格滑坡为例,基于2014—2021年Sentinel-1数据和2014—2018年ALOS-2数据,联合时序InSAR(interferometric SAR)分析和时序像素偏移量追踪(pixel offset tracking, POT),获取滑坡灾前灾后时序形变。结果显示白格滑坡灾前后缘的年平均速率在20 mm/a,主滑坡区2014年12月—2018年7月形变量可达45 m左右; 灾后滑坡逐渐向后缘扩张,年平均形变速率可达200 mm/a,已经威胁到部分民房。2种方法的联合可以实现大型复杂滑坡的时空维多形变量级提取。

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	杨帆
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关键词 ：干涉测量, 像素偏移量追踪, 多形变量级, 大型滑坡

Abstract：

In recent years, radar remote sensing has been extensively applied to extract high-precision deformation information of landslide surfaces. The techniques used include phase-based interferometry and amplitude-based pixel offset tracking (POT). However, large complex landslides exhibit significantly different deformation magnitudes over the spatio-temporal evolution, complicating the comprehensive monitoring of landslide deformation via single radar remote sensing. Hence, by analyzing the deformation detection capability of radar remote sensing, this study proposed monitoring the whole process of a landslide combined with the phase and amplitude information of synthetic aperture radar (SAR) images. This study investigated the Baige landslide occurring in Jinsha River in 2018 based on Sentinel-1 data from 2014 to 2021 and ALOS-2 data from 2014 to 2018. Combined with time-series interferometric SAR (InSAR) analysis and POT, this study acquired the pre- and post-disaster time-series deformations of the landslide. The results are as follows. Pre-disaster, the trailing edge of the Baige landslide exhibited an average annual rate of 20 mm/a, with deformation of the main landslide area up to about 45 m from December 2014 to July 2018. Post-disaster, the landslide gradually expanded to the trailing edge, with an average annual deformation rate reaching 200 mm/a, threatening the safety of some civilian houses. Therefore, the combined method in this study can achieve the multi-deformation magnitude extraction of large complex landslides from spatio-temporal dimensions.

Key words： interferometry pixel offset tracking multi-deformation magnitude large landslide

收稿日期: 2022-12-05 出版日期: 2024-06-14

ZTFLH:

TP79

基金资助:四川省科技计划项目“四川省地质灾害隐患早期识别与监测预警”(2021JDR0394);国家自然科学基金青年项目“复杂山区滑坡形变监测的相干散射体InSAR方法”(41904001)

通讯作者: 董杰(1988-),男,特聘副研究员,研究方向为时间序列InSAR算法及其在地表形变监测中的应用。Email: dongjie@whu.edu.cn。

作者简介: 杨帆(1999-),女,硕士研究生,研究方向为雷达遥感在地质灾害中的应用。Email: yangfan_whu@whu.edu.cn。

引用本文:

杨帆, 马志刚, 文艳, 董杰, 姜清辉. 联合SAR影像相位和幅度信息的多形变量级滑坡监测——以金沙江白格滑坡为例[J]. 自然资源遥感, 2024, 36(2): 257-267.
YANG Fan, MA Zhigang, WEN Yan, Dong Jie, JIANG Qinghui. Monitoring a landslide with a multi-deformation magnitude based on the phase and amplitude information of SAR images: A case study of the Baige landslide in Jinsha River. Remote Sensing for Natural Resources, 2024, 36(2): 257-267.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022467 或 https://www.gtzyyg.com/CN/Y2024/V36/I2/257

Fig.1 基于卫星雷达遥感技术的多形变量级滑坡监测方法流程

Fig.2 研究区域概况

Fig.3 滑坡发生后产生的严重影响

Tab.1 SAR影像数据集参数

Fig.4 Sentinel-1数据SBAS-InSAR方法获得白格滑坡形变速率图

Fig.5 ALOS-2数据时序POT方法获得白格滑坡灾前累积形变量

Fig.6 灾前空间维联合2种方法累积形变结果

Fig.7 Sentinel-1数据时序InSAR方法获得白格滑坡灾后形变量及形变速率图

Fig.8 灾后现场考察图片

Fig.9 Sentinel-1升降轨数据灾后二维形变反演结果

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