Casagli, Nicola

Del Ventisette, Chiara

Lombardi, Luca

Righini, Gaia

English

Florence Research

Geophysical Research Abstracts,Vol. 10, EGU2008-A-09110, 2008SRef-ID: 1607-7962/gra/EGU2008-A-09110EGU General Assembly 2008© Author(s) 2008Case study on the updating of landslide inventory mapwith remote sensing dataG. Righini, C. Del Ventisette, N. Casagli and L. LombardiEarth Sciences Department, University of Florence, Italy (e-mail: nicola.casagli@unifi.it)The analysis of remote sensing data was carried out in order to assess the contribu-tion of these information in support to the characterization and monitoring of instableslopes in Valfurva and Valdisotto area, East of Bormio in the Italian Alps, where sig-nificant rock slides and deep-seated gravitational slope deformations were recognised.Available landslide inventory map was updated in GIS environment with the informa-tion from radar interferometric SAR processing of ERS and Envisat satellite data aspoints (persistent scatterers) indicating mean annual velocity and displacement.Images from ESA satellites, with temporal range from 1995 to 2007, processed byTelespazio S.p.A. (a Finmeccanica company) by means of radar interferometric tec-nhnique, have been used. The images were acquired in descending mode by the ERS-1/2 satellites (63 images) in the period Apr. 1995 – Jan. 2000 and by the Envisatsatellite (19 images) in the period Aug. 2002 – Mar. 2007. From the interferometricprocessing on ERS-1 and ERS-2 images, 19524 coherent points were extracted (61.4points/km2). Instead, the coherent points extracted from Envisat images were 3896(12.3 points/km2).The investigated area is 30 km x 30 km wide. The landslides characterized by thepresence of persistent scatterers (both from ERS and Envisat images) are 108 of thetotal 869 (interested area 21.82 km2of 62.75 km2). Also 13 deep-seated landslidesin the investigated area were previously mapped; 10 of them (area 49.49 km2of 59.2km2) are characterized by the presence of coherent points.The characteristics of landslides are highlighted in the GIS database: geometry, stateof activity, typology, monitoring systems, interventions, source of information; theupdating actions and modifications coming from the interferometric data are recorded.Furthermore, for each landslides area, the occurrence of persistent scatterers and thestatistical description of their velocities is reported.Main changes recorded on the landslides regarded the state of activity while in somecases also geometry was modified. Moreover, the state of activity of deep-seated land-slides was investigated.The main criteria used to define the state of activity is related to the annual meanvelocity retrieved from persistent scatterers: e.g. if point velocity from Envisat dataare above 2 mm per year then landslides is classified as “active”.Summing up, 108 landslides (12% of the total number) contain persistent scatterers(35% of the total area): 44 were updated, while the others remain unchanged. Focusingon the state of activity, in more than 30 landslides it was changed from the original: e.g.4 relict to dormant, 7 from relict to active and 9 from dormant to active. Concerningthe deep-seated landslides, the state of activity was changed from relict to active for 6of them, while one was moved from relict to dormant.In conclusion, data from interferometric SAR processing is proper to monitor slowmoving landslides for risk assessment and hazard zonation, even if the integration withtraditional methods and field surveys is still necessary. The updating of a landslideinventory using SAR interferometric processing could be likely delivered quarterly orhalf-yearly depending on satellite data availability.This work was carried out in the framework of the PREVIEW (EU FP6 IP ProjectN. 516172) – Landslides Platform Service 1; for Italian test site, the end users in-volved are the Italian Civil Protection Department and the Civil Protection of RegioneLombardia.

Case study on the updating of landslide inventory map with remote sensing data

https://flore.unifi.it/bitstream/2158/384575/1/Righini%20et%20al%20GRA%20vol%2010%20EGU%202008.pdf

Case study on the updating of landslide inventory map with remote sensing data

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