Progress Report Form of WCoEs 2014

Transcription

1 Date of Submission 25/02/2015 Progress Report Form of WCoEs June 2014 to 31 December Short Title of WCoE: Advanced Technologies for LandSlides (ATLaS) 2. Name of Institution (Name of leader and ): Department of Earth Sciences, University of Florence (DST-UNIFI) Nicola Casagli University of Florence Via G. La Pira n.4 I FIRENZE (Italy) Phone: Fax: nicola.casagli@unifi.it 3. List of core members Filippo Catani Veronica Tofani Sandro Moretti Riccardo Fanti Giovanni Gigli Chiara Del Ventisette 4. Progress report of activities up to 31 December 2013 The research activities have been carried in the framework of the WPs proposed and are described below: WP1: Ground-based SAR interferometry for landslide monitoring and development of reliable procedures and technologies for early warning. DST-UNIFI has been carrying out monitoring activities in order to estimate the deformational evolution of the landslide masses and the successive operative implementation of Early Warning Systems (EWS) in several sites within the national territory of Italy. The sites that have been recently monitored are 37 (Figure 1). All these sites have been monitored integrating innovative

2 instrumentation, mainly ground-based synthetic aperture radar interferometer (GB-InSAR) but also terrestrial laser scanner (LIDAR), extensometers, satellite interferometry, infrared thermography, inclinometers, piezometers (Figure 1). For all the monitored sites daily and/or weekly reports are produced in order to make alert warnings as much as possible robust, reliable in time, and tailored on specific scenarios. Figure 1: San Leo landslide. Cumulative displacement map detected by GB-InSAR plotted on LIDAR image. WP2: EO (Earth Observation) data and technology to detect, map, monitor and forecast ground deformations: Research activities have dealt deal with the exploitation of large satellite data archives for the rapid mapping and better understanding of complex phenomena that result in ground deformations such as landslides. Different kind of data such as past information (i.e. pre-existing landslide inventory maps), auxiliary data (thematic maps, Digital Elevation Model, VHR optical photos) and updated remote sensing SAR (Synthetic Aperture Radar) data (both space-borne and ground-based) have been analyzed and exploited to produce landslide geo-database and maps, e.g. ground deformation velocity maps, damage assessment, susceptibility and risk zonation maps. In particular, remotely sensed data acquired through different SAR satellites (e.g. ERS1/2, ENVISAT, COSMO-SkyMed) are used in order to monitor the evolution of slope deformations of the study areas. The long time series of displacement provided by the Persistent Scatterer SAR Interferometry (PSInSAR) technique permit to highlight the most critical unstable zones (Fig.2), to update the landslide state of activity and to redefine the extension of landslides phenomena, by comparing the ground deformation velocities measured in different time periods. Usability of remote sensing data, comprising satellite,

3 optical and also ground-based interferometric data (GB-InSAR) data is exploited during all phases (i.e. prevention, crisis, post-crisis) of emergency management. For instance they are used for detecting early structural failure symptoms in order to promptly undertake alert procedures and to address suitable countermeasures. Figure 2: Volterra study area: Persistent Scatterer Interferometry (PSI) data derived from the processing of SAR scenes acquired by COSMO-SkyMed satellite in the period , overlapped on the pre-existing landslide inventory map of the area. WP3: Coupling of short-term weather forecasting with geotechnical modeling for shallow landslide prediction: Research activities have dealt with the development and refinement of statistical models and physically-based model for the prediction of rainfall-induced landslides. The physically-based model, namely HIRESS, has been recently developed by DST-UNIFI. HIRESS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and on large areas using parallel computational techniques. The physical model proposed is composed of two parts: hydrological and geotechnical. During the last year several improvements has been carried out. They mainly concern the algorithm enhancement and the development of a probabilistic approach for the treatment of the input data of model, both geotechnical and hydrological. The improvement has also concerned all the procedures necessary to transfer the model from experimental scientific software to an operative system for real-time applications at regional scale. DST-UNIFI has recently developed two statistical models (SIGMA, MACUMBA) based on rainfall thresholds for the prediction of landslides. During the last year these two models have been applied at the national level in order to find a rainfall threshold for the initiation of landslides for each alert zone defined by the Italian Civil Protection (Figure 3). This work has been carried out making use of

4 a network constituted by 2835 rainguage stations. Figure 3: An example of intensity-duration rainfall threshold for an alert zone in Italy. Plan of future activities The future activity in the framework of WP1 is the set-up of a fast monitoring system based on the integrated use of different equipment, technologies and software. In this system different types of sensors (radar, optical, thermal and so on) will be installed in different types of platforms (ground-based, aerial, satellite, marine and submarine). For this purpose DST-UNIFI has recently patented a drone structure (SATURN) and a ROV, Remotely operated underwater vehicle (NEMO). SATURN is capable of autonomous flight, from take-off to landing, and emergency management. Saturn drone has onboard a complete and fully configurable acquisition system with frame grabber for scientific instruments. It is possible to plug in a multispectral camera, mini-lidar and various sensors through the most common connections and protocols (Usb, Firewire, LAN, wifi, RS-232). The drone is developed to be used and provide data in emergency and calamity areas. Saturn is designed to be water resistant to fly into rain and harsh conditions. NEMO is a remote-control submarine, commonly called ROV (Remotely Operated underwater Vehicle). It is a mobile robot remotely piloted and used in underwater applications; these devices are widely used and suitable for different scenarios, e.g. for patrolling and monitoring but also for underwater interventions. A ROV allows investigating areas inaccessible to the human being or dangerous for himself. The technologies used in the monitoring system will be (Figure 4): ground-based radar, wireless sensor networks (WSN), Laser scanner (LIDAR), Infrared thermography (IRT), Automated total station, Multi-beam eco sounder.

5 Figure 4: Equipment and technologies available at DST-UNIFI for landslide monitoring system. The future activities in the framework of WP2 will be the exploitation of radar data coming from recently launched satellites such as COSMO-Skymed and SENTINEL-1. These satellites thanks to high spatial resolution and short revisiting time will provide new opportunities for earth surface monitoring and for the assessment of risk scenario connected to ground movements. The activity will deal also with the set-up of procedures for data fusion of radar data, high resolution optical data and hyper spectral and multispectral data for landslides rapid mapping. The future activities in the framework of WP3 will be the set-up of an integrated nowcasting system for regional early warning of rainfall-induced landslides, based on physically based model (HIRESSS) and statistical rainfall thresholds models (SIGMA and MACUMBA). This integrated system will be tested in the whole Italian national territory. In order to have a facilitated interpretation of the model outcomes and to have a synthetic framework for the visualization of the alerts, a specific web site will be implemented. 5. Main Publications Nocentini M., Tofani V., Gigli G., Fidolini F., Casagli N. (2014). Modeling debris flows in volcanic terrains for hazard mapping: the case study of Ischia Island (Italy). LANDSLIDES Gigli G., Morelli S., Fornera S., Casagli N. (2014). Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios. LANDSLIDES Uzielli M., Catani F., Tofani V., Casagli N. (2014). Risk analysis for the Ancona landslide I: characterization of landslide kinematics. LANDSLIDES

6 Uzielli M., Catani F., Tofani V., Casagli N. (2014). Risk analysis for the Ancona landslide II: estimation of risk to buildings. LANDSLIDES Segoni S., Lagomarsino D., Fanti R., Moretti S., Casagli N. (2014). Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system. LANDSLIDES Gigli G., Frodella W., Garfagnoli F., Morelli S., Mugnai F., Menna F., Casagli N. (2014). 3- D geomechanical rock mass characterization for the evaluation of rockslide susceptibility scenarios. LANDSLIDES , 11(1). Lu P., Catani F., Tofani V., Casagli N. (2014). Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry. LANDSLIDES, , 11(4) Lagomarsino D., Segoni S., Fanti R., Catani F., Casagli N. (2014). Regional scale landslide susceptibility mapping in Emilia Romagna (Italy) as a tool for early warning. Sassa K., Canuti P., Yueping Y.. In: Landslide Science for a Safer Geoenvironment. Springer International Publishing, , Tofani V., Raspini F., Catani F., Casagli N. (2014). Persistent Scatterer Interferometry (PSI) technique for landslide characterization and monitoring. Sassa K., Canuti P., Yueping Y.. In: Landslide Science for a Safer Geoenvironment - Volume 2: Methods of Landslide Studies. Springer International Publishing, , Tofani V., Agostini A., Nolesini T., Tanteri L., Rosi A., Casagli N. (2014). A new appraisal of the Ancona landslide. Sassa K., Canuti P., Yueping Y. In: Landslide Science for a Safer Geoenvironment - Volume 3: Targeted Landslides. Springer International Publishing, ,