Application of a cluster analysis-based methodology on InSAR data to detect ground deformations in the Po plain (Northern Italy)

The Po Plain basin in the Emilia Romagna Region (Italy) has been historically affected by strong land movements ascribed to both anthropogenic and natural sources, as well as their superposition. The paper aims to the identification, geolocation and quantification of the main land movement phenomena of the Region via the time-series decomposition and the clustering analysis on the vertical component of satellite DInSAR time-series. The results were interpreted on the basis of ancillary information, such as: land use maps, water production (in terms of wells positions and produced volumes) and position of underground gas storage sites. In particular, the analysis of the purely seasonal components allowed a straightforward correlation between the identified land movement phenomena and the gas storage operations or aquifer recharge/ground water productions seasonality.

The Po Plain basin in the Emilia Romagna Region (Italy) has been historically affected by strong land movements ascribed to both anthropogenic and natural sources, as well as their superposition. The paper aims to the identification, geolocation and quantification of the main land movement phenomena of the Region via the time-series decomposition and the clustering analysis on the vertical component of satellite DInSAR time-series. The results were interpreted on the basis of ancillary information, such as: land use maps, water production (in terms of wells positions and produced volumes) and position of underground gas storage sites. In particular, the analysis of the purely seasonal components allowed a straightforward correlation between the identified land movement phenomena and the gas storage operations or aquifer recharge/ground water productions seasonality.


ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

Supported by


Edited by


GEAM - Associazione Georisorse e Ambiente c/o Dipartimento di Ing.dell’Ambiente, del Territorio e delle infrastrutture Politecnico di Torino
Copyright @ GEAM - Designed by DESIGN GANG - Privacy Policy