Privacy Policy Disclaimer
  Advanced SearchBrowse





Local and Large Scale InSAR Measurement of Ground Surface Deformation


Haghshenas Haghighi,  Mahmud
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Heipke,  Christian
External Organizations;


Motagh,  M.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Amelung,  Falk
External Organizations;

Schön,  Steffen
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)

PHD Mahmud.pdf
(Publisher version), 50MB

Supplementary Material (public)
There is no public supplementary material available

Haghshenas Haghighi, M. (2019): Local and Large Scale InSAR Measurement of Ground Surface Deformation, PhD Thesis, (Veröffentlichungen / Deutsche Geodätische Kommission. Reihe C, Dissertationen ; 840).

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_4917905
Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique frequently used formeasuring Earth’s surface deformation. InSAR time series approaches provide detailed informationabout the evolution of displacement in time and also overcome the limitations of conventional InSAR.Both InSAR and InSAR time series methods have been used for measuring tectonic and non-tectonicsurface deformations caused by natural and anthropogenic processes in local to regional scales. Inthe past, the availability of SAR data suitable for InSAR purpose was a major constraint for InSARapplications in many areas of the world. The launch of the Sentinel-1 mission in 2014 guaranteed aregular long-term acquisition plan of SAR data available all over the globe that enables us to increasethe scale of deformation mapping from regional to national. However, there are challenges in large-scale InSAR processing that needs to be addressed. In this thesis, InSAR time series approaches areused to obtain detailed maps of the time-varying surface displacement fields caused by non-tectonicprocesses at a local to regional scales and find the link between surface displacement and its drivers.Then, a workflow for large-scale InSAR time series analysis is suggested that benefits from extensivecollections of data provided by Sentinel-1 to identify and monitor displacement fields over extensiveareas.In the first part of this thesis, InSAR time series approaches are used at local to regional scales inthree independent case studies. In the first case study, a localized displacement of as much as 1 cm/yrattributed to slope movement of a landslide in Taihape, New Zealand is observed. Along with the long-term trend of displacement caused by slope movement, the analysis of time series reveals an accelerationin surface displacement due to a rainstorm as well as the seasonal variations of displacement linkedto groundwater variations. In the second study area, land subsidence in Tehran, Iran is analyzedat a regional scale. Using an extensive collection of SAR data, land subsidence areas with a peakexceeding 25 cm/yr are detected. After combining time series from different datasets, both long-termsubsidence trend due to the constant decline of groundwater level, and seasonal variations of surfacedisplacement linked to the discharge/recharge cycle of groundwater are investigated. In the third casestudy, localized displacement of a natural gas reservoir in Berlin is analyzed and a long-term upliftof approximately 0.2 cm/yr and 2 cm of seasonal variations linked to the injection of gas in summerand extraction in winter are observed. Furthermore, mining-induced displacements in an area nearLeipzig are measured and approximately 4 cm/yr of subsidence coupled with 5 cm/yr of east-westdisplacement are observed.To extend the InSAR measurement of non-tectonic displacement fields from local and regional tonational scale, a workflow is developed in this thesis that benefits from the vast amount of data col- 4lected by Sentinel-1 mission. The proposed workflow notably reduces the broad-scale and topography-dependent components of tropospheric errors in an adaptive approach and provides a quick, but robustdisplacement field map from large stacks of Sentinel-1 data. The proposed method is applied on twotracks of Sentinel-1 data across Iran and Germany and successfully obtained the displacement fieldsin large scales. In the first case study, more than 20 displacement areas mostly attributed to landsubsidence induced by over-extraction of groundwater are identified. In the second case study, approx-imately 10 displacement areas mainly related to anthropogenic activities including mining and gas/oilinjection/extraction are detected.