Exploring clustering methods for the analysis of InSAR time series data: a case study of landslides in the Palos Verdes Peninsula, California, USA
dc.contributor.advisor
Ou, Qi
dc.contributor.advisor
Watson, Andrew Robert
dc.contributor.author
Lo, Kevin Lok Hang
dc.date.accessioned
2025-11-14T10:36:57Z
dc.date.available
2025-11-14T10:36:57Z
dc.date.issued
2025-11-19
dc.description.abstract
Landslides are a persistent hazard along Palos Verdes Peninsula, California, USA, where complex terrain and urban development necessitate effective monitoring of ground deformation. This study investigates the application of unsupervised clustering technique to Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) time series data to classify displacement behaviours and support regional landslide assessment. Principal Component Analysis (PCA) and K-means clustering were applied to ascending and descending Line-of-Sight (LOS) measurements, followed by decomposition into vertical and slope-aligned horizontal components. The clustering outputs were compared against known landslide complexes, such as Portuguese Bend, Abalone Cove, and Klondike Canyon, revealing strong spatial agreement and enabling broader interpretation across the peninsula. Three kinematic behaviour types were defined based on combinations of horizontal and vertical cluster patterns, allowing differentiation between kinematically stable regions, active landsliding, and ambiguous zones. The results demonstrate that the clustering framework is time-efficient, scalable, and capable of highlighting both known and previously unreported deformation patterns. However, the approach has limitations due to coherence loss in highly active zones, assumptions made in LOS decomposition, and the potential influence of non-landslide processes such as seasonal change or anthropogenic activity. Despite these constraints, the method provides a practical and physically interpretable model for regional landslide screening, offering insights that support hazard identification and long-term monitoring across complex, landslide-prone terrain.
en
dc.identifier.uri
https://hdl.handle.net/1842/44202
dc.identifier.uri
http://dx.doi.org/10.7488/era/6725
dc.language.iso
en
en
dc.publisher
The University of Edinburgh
en
dc.subject
Palos Verdes Peninsula
en
dc.subject
Landslides
en
dc.subject
Hazard Assessment
en
dc.subject
InSAR Time Series
en
dc.subject
Principal Component Analysis
en
dc.subject
Unsupervised K-means Clustering
en
dc.title
Exploring clustering methods for the analysis of InSAR time series data: a case study of landslides in the Palos Verdes Peninsula, California, USA
en
dc.type
Thesis or Dissertation
en
dc.type.qualificationlevel
Masters
en
dc.type.qualificationname
MSc Master of Science
en
Files
This item appears in the following Collection(s)