Integration of satellite-based A-DInSAR and geological modeling supporting the prevention from anthropogenic sinkholes: a case study in the urban area of Rome. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Esposito, Carlo; Belcecchi, Niccolò; Bozzano, Francesca; Brunetti, Alessandro; Marmoni, Gian Marco; Mazzanti, Paolo; Romeo, Saverio; Cammillozzi, Flavio; Cecchini, Giancarlo; Spizzirri, Massimo
• Is Part Of: Geomatics, natural hazards & risk. Volume 12:Issue 1(2021)
• Page Start: 2835
• Page End: 2864
• Publication Date: 2021-01-01
• Subjects: Sinkhole -- susceptibility -- A-DInSAR -- data integration -- spatial hazard zoning -- Rome; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2021.1978562
• Languages: English
• ISSNs: 1947-5705

Abstract

Abstract: This paper presents a methodology tuned to support the management of underground pipelines (sewer and aqueduct networks) in Rome, often threatened by the sudden formation of sinkholes related to the upward migration of existing underground cavities. The methodology integrates data coming from the assessment of susceptibility to sinkhole formation and the advanced processing of satellite-based SAR imagery. The former, performed through the multivariate logistic regression technique, relies on a detailed database of stratigraphic and other thematic (i.e. sinkhole inventory and density of underground cavities) information. A-DInSAR processing of satellite images, for which we developed on-purpose algorithms to filter only data relevant for the process under study, allowed us to provide density maps of subsiding reflectors whose likelihood to be precursors of sinkhole collapses is rated based on the integration with the susceptibility map. The procedure is addressed to point out potentially critical 'hotspots' that the company managing the underground networks should pay attention to by means of further detailed investigations. Recent (i.e. occurred after the finalization of the products shown in this paper) sinkholes validated the reliability of the procedure adopted, whose strength is the data fusion able to produce refined and focused information starting from independent and more generic datasets.