Date of Award
Spring 2019
Document Type
Open Access Thesis
Department
Computer Science and Engineering
Sub-Department
Civil Engineering
First Advisor
Dimitris Rizos
Abstract
Landslides are among the most dangerous natural disasters, as they generally follow other major disasters thereby causing significant damage to already weakened systems. In the U.S. alone they cause an excess of 1 billion in damages and an average 25-50 deaths annually(USGC). Remote sensing techniques for landslide monitoring and prediction has gained popularity in recent years since it enables hazard mitigation. Synthetic Aperture Radar (SAR) interferometry is among the remote sensing techniques used. DInSAR is an advanced interferometric technique which uses radar sensors to estimate the phase of a given area from which the landslide activity can be predicted. The analysis has been performed in 3 test areas each having unique conditions. These areas have been chosen to assess the effectiveness of the analysis in their respective conditions.
The data for this analysis is obtained using Sentinel-1 satellite which is a C-band SAR sensor. The 3 locations have been analyzed for a period of 36 days with sensor taking acquisitions every 12 days. From this analysis, we have found in the case of Etna slope instability due to volcanic action has been observed. In the case of California highway-1 a unique phase activity was observed in the landslide region which suggest additional analysis to be performed in similar conditions to validate the findings. Finally, in the case of Anargyroi Greece bad phase readings in the region resulted in uncertain analysis prompting for longer time series analysis to mitigate these problems.
Additional subsidence maps were also generated from the phase, but these results could not be calibrated and correlated with in situ readings. To assess the effectiveness of this method to quantify subsidence monitoring additional analysis must be done which take in to consideration old phase values for accurate results.
Rights
© 2019, Sumanth Varma Byrraju
Recommended Citation
Byrraju, S. V.(2019). Landslide Detection Using Remote Sensing Methods A Review of Current Techniques. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/5105