A Clustering Approach for Studying Ground Deformation Trends in Campania Region through PS-InSARTM Time Series Analysis

Milone, Gabriella; Scepi, Germana

doi:10.3923/jas.2011.610.620

Cited by 16 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One example is Milone and Scepi (2011), who apply a k-medoids clustering technique to time series of ground deformations obtained through InSAR processing. The application considered in the article poses challenges related to the dimensionality of the data set (which involves 18,452 time series).…”

Section: Time Series Analysismentioning

confidence: 99%

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

et al. 2021

View full text Add to dashboard Cite

Recent advances in satellite technologies, statistical and mathematical models, and computational resources have paved the way for operational use of satellite data in monitoring and forecasting natural hazards. We present a review of the use of satellite data for Earth observation in the context of geohazards preventive monitoring and disaster evaluation and assessment. We describe the techniques exploited to extract ground displacement information from satellite radar sensor images and the applicability of such data to the study of natural hazards such as landslides, earthquakes, volcanic activity, and ground subsidence. In this context, statistical techniques, ranging from time series analysis to spatial statistics, as well as continuum or discrete physics-based models, adopting deterministic or stochastic approaches, are irreplaceable tools for modeling and simulating natural hazards scenarios from a mathematical perspective. In addition to this, the huge amount of data collected nowadays and the complexity of the models and methods needed for an effective analysis set new computational challenges. The synergy among statistical methods, mathematical models, and optimized software, enriched with the assimilation of satellite data, is essential for building predictive and timely monitoring models for risk analysis.

show abstract

Section: Time Series Analysismentioning

confidence: 99%

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The goal of this work is to present a procedure based on a sequence of statistical characterization tests which allows one to automatically classify PSI time series into distinctive target trends and to retrieve, for each specific time series, descriptive parameters which can be used to characterize the magnitude and timing of changes in ground motion. To our best knowledge, one of the very few published attempts to propose a PSI time series clustering approach is that of Milone and Scepi (2011). They used the partitionbased clustering algorithms CLARA (Clustering for Large Applications) which defines "k" clusters in an entire dataset on the basis of the identification of "k" representative objects in a sub-dataset.…”

Section: Berti Et Al: Automated Classification Of Persistent Scatmentioning

confidence: 99%

Automated classification of Persistent Scatterers Interferometry time-series

Berti¹,

Corsini²,

Franceschini³

et al. 2013

Preprint

View full text Add to dashboard Cite

Abstract. We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends (such as uncorrelated, linear, quadratic, bilinear and discontinuous) that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, uplifts due to deep injections in reservoirs.

show abstract

“…The grater the DI, the more different are the trends. Other works [6] propose a classification of TS using models. In Fig.…”

Section: Post-processing Toolsmentioning

confidence: 99%

Non Linear PS Time Series: Analysis and Post-Processing for Landslides Studies

Notti

Meisina

Zucca

et al. 2013

Lecture Notes in Earth System Sciences

View full text Add to dashboard Cite

A Clustering Approach for Studying Ground Deformation Trends in Campania Region through PS-InSARTM Time Series Analysis

Cited by 16 publications

References 5 publications

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards

Automated classification of Persistent Scatterers Interferometry time-series

Non Linear PS Time Series: Analysis and Post-Processing for Landslides Studies

Contact Info

Product

Resources

About