Back to Search Start Over

Landslide detection in La Paz City (Bolivia) based on time series analysis of InSAR data.

Authors :
Huang Lin, Chao
Liu, Dawei
Liu, Guang
Source :
International Journal of Remote Sensing. Sep2019, Vol. 40 Issue 17, p6775-6795. 21p. 2 Color Photographs, 1 Diagram, 2 Charts, 10 Maps.
Publication Year :
2019

Abstract

Geologically, La Paz City is located in an unstable area. During the history of La Paz city, many landslides have destroyed houses and valuable infrastructures. In the last decades, time series Interferometric Synthetic Aperture Radar (InSAR) technologies have demonstrated a great capacity for detecting slow ground displacement, achieving an accuracy of millimetre-level. In order to have a better landslide monitoring of La Paz city, in this study, the Sentinel-1 SAR images have been processed by Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS) techniques. The time span of the datasets is from March 2015 to August 2016. Both ascending and descending Synthetic Aperture Radar (SAR) images have been processed to obtain the line of sight (LOS) ground velocity, and then the results have been combined to estimate the up-down and east-west displacement. Several active movement areas have been identified, showing a surface velocity up to 158 mm year−1 westward and 49 mm year−1 eastward. Furthermore, two important findings have been discovered. First, the InSAR result has detected movement in Auquisamaa hill before the area collapsed (15 February 2017), where five houses are buried. Second, the InSAR result has identified that there are still some unstable sites in Callapa area, where a mega-landslide has destroyed more than a thousand of houses in February 2011. In conclusion, we have verified that the InSAR technology could be a very useful tool to help La Paz public institutions for a better management of urban planning, landslide areas delimitation and landslide risk mitigation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01431161
Volume :
40
Issue :
17
Database :
Academic Search Index
Journal :
International Journal of Remote Sensing
Publication Type :
Academic Journal
Accession number :
136237820
Full Text :
https://doi.org/10.1080/01431161.2019.1594434