MONITORING LANDSLIDES IN THE MUSSOORIE REGION, UTTARAKHAND USING MULTI-TEMPORAL SAR INTERFEROMETRY WITH SENTINEL-1 IMAGES

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Narayan, A. B.^1¹  Tiwari, A.^1² 

[1] Indian Institute of Technology, Civil Engineering, India^1;Motilal Nehru National Institute of Technology, GIS Cell, India^2

关键词: Landslide monitoring;    geodetic techniques;    LiDAR;    GNSS network adjustment;   

DOI  :  10.5194/isprs-archives-XLII-5-849-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

Occurrence of landslide events are common in the lesser Himalayan region which lie in a tectonically active zone with unstable slopes. The Mussoorie region is situated in the lesser Himalayas and is one of most visited tourist sites of the Uttarakhand state. The region is facing social and economic crisis due to the damage caused by multiple landslide events, which requires continuous monitoring for better planning and rescue operations. This study shows the time series analysis of landslide events occurring in the Mussoorie region using the Persistent Scatterer Interferometry (PSI) technique on Sentinel-1 synthetic aperture radar (SAR) images. The processing steps required to process the Sentinel-1 data stacks using both differential SAR interferometry (DInSAR) and PSI are presented. 13 Sentinel-1A C-band interferometric wide swath (IW) images covering a time period of one year are used for PSI processing, resulting in the generation of 12 differential interferograms. The PSI approach extracted 5593 measurement points in the study area. The one dimensional line of sight (1D LOS) time series displacement estimates obtained from PSI processing show a maximum velocity of −55.7 mm year⁻¹ in the radar line of sight (LOS) direction, indicating subsidence. The displacement map further used to characterize the landslide susceptible zones show higher displacement magnitude for areas near Jabarkhet, Bhataghat, Bansagad and Raipur range. Use of more Sentinel-1 images and better DInSAR processing algorithms can improve the displacement estimation and pattern detection.

【 授权许可】

CC BY