Objectives and hypotheses. Remote sensing: applications for landslide hazard assessment and risk management. Ping Lu (University of Firenze) Methods

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1 Topical Workshop Remote sensing: applications for landslide hazard assessment and risk management Ping Lu (University of Firenze) Supervisors: Prof. Nicola Casagli; Prof. Filippo Catani (Unifi) Dr. Veronica Tofani Period of time (January, 2008 December, 2010) Collaboration: Prof. Cees Van Westen (ITC); Prof. Michel Jaboyedoff (Quanterra) Objectives and hypotheses 1. To apply and improve the diverse techniques of remote sensing in the application of landslides hazard assessment. 2. To integrate the use of remote sensing techniques in all phases of landslide risk management. Methods Study area 1. Radar and InSAR techniques, Permanent Scatterers (PS) Analysis 2. Optical image and aerial photo visual interpretation 3. DEM and DEM derivatives (contour, shaded relief, slope, aspect, curvature ) analysis 4. Spatial statistics analysis 5. Object-based image segmentation and classification 6. Multi-temporal analysis: change detection 7. ArcGIS development (Python) 8. Field geomorphologic observation and mapping 9. Artificial Neural Network (ANN) and landslide susceptibility map analysis The Arno River Basin (Italy) 9130 km 2 total area Firenze 235 m a.s.l. Mean elevation MUGELLO Study area mapped landslides 8.8 % landslide density Pisa Firenze San Gimignano CHIANTI CHIANA = UNESCO World Heritage 74% earth slides and flows 19% shallow landslides and creep 5% debris flows 2% others (Farina et al., 2006)

2 Topical Workshop LANDSLIDES MOTION SURVEY landslide terraces Updating of the Arno Basin Authority landslide inventory map (from P.A.I.) where InSAR information is available for the whole territory (9130 km 2 ) 1. PSInSAR (Permanent Scatterers SAR Interferometry) 2. Optical images interpretation (aerial photo, HR-VHR images 3. GIS environment Information obtainable PSInSAR techniques RADARSAT 1 Spatial distribution of already mapped landslides State of activity of already mapped landslides (active, dormant, inactive) Detection of new unstable areas (not previously known ) Agency: Canadian Space Agency Data: Band Wavelength (cm): 5.6 (C) Polarization: HH Beam Mode: S3 Incidence angle: 37 Resolution range: 30 m Resolution azimuth: 30 m Scene width: 100 km 24 days Passage rate: 24 days multi-image processing Km 100 Km Legend Landslide polygons Landslide lines PS Distribution active attiv inactive instab dormant quiesc active attiv inactive inatt PS ascending PS Descending Vel (mm/y) Vel (mm/y) ") $ ") $ ") $ ") $ ") $ ") $ SAR images (RADARSAT-1) processed ( ) 724,867 total number of PS 230,364 PS in hilly and mountainous areas ") $

3 Topical Workshop Radar Satellites Hotspot analysis of PS SAR = Synthetic Aperture Radar θ=23 To identify and characterize the potential hazardous event of landslides in the study area. To facilitate the methodology for automated/semiautomated landslides mapping (rapid and instant mapping). WHY HOT SPOT? Detect Spatial Clustering of PS with High or Low Velocity. Statistics Definition: Effectively and Automatically. Embedded in ArcGIS 9.X. Regardless of Reference Point.

4 Topical Workshop Spatial statistics analysis DISTANCE THRESHOLD Getis-Ord Gi* Statistics (Getis and Ord, 1996) Semivariogram γ ,92 0,69 The spatial weights vector within distance d from the point with the value x. 0,46 0,23 Ridge/Valley? DEM! 0-0, Distance, h Results: river network and ridge Results: Hotspot analysis of PS Detect Spatial Clustering of PS with High or Low Velocity effectively and automatically. High Positive and Low Negative Gi* Values: the Clustering of rapid movement. Extracted river network from DEM Extracted mountain ridges from DEM Moving Direction: positive: along LOS; Negative: away from LOS Future Works Relation to other sub-projects within MOUNTAIN RISKS 1. Landslides inventory: DEM analysis 2. Image segmentation: object-based classification 3. ArcGIS development 1. Secondment institute Quanterra (terrain analysis using DEM) ITC (object-oriented classification) 2. Case studies in Italy UNIMIB-CNR, UNIMORE 3. Working Blocks (WB) WB1, WB3, WB4 4. Transfer of Knowledge ESR1, ESR5, ESR9, ER5

5 Topical Workshop Relation to other projects outside MOUNTAIN RISKS 1. End-user The Arno River Basin Authority 2. ESA project Terrafirma 3. Service Provider Tele-Rilevamento Europa (TRE) 4. Finished Project Service for Landslides Monitoring (SLAM) 5. Non-EU project Cooperation with China (Sichuan earthquake, landslides Dam ) Details on how YOU intend to address aspects of multi-hazard consequences, vulnerability and risk analysis in YOUR research 1. Update the landslide inventory. 2. Validate and update the susceptibility and hazard map. 3. Socio-economic approach in risk assessment of landslides at the basin scale. References Contact Details Farina, P., Colombo, D., Fumagalli, A., Marks, F. & Moretti, S Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project. Engineering Geology 88: Getis, A., Ord, J. K Local spatial statistics: an overview. Spatial Analysis: Modelling in a GIS Environment(Longley P, Batty M eds). John Wiley and Sons, New York: Ping Lu Department of Earth Sciences University of Firenze Via La Pira, Firenze, Italy Tel: Fax: lu.ping@unifi.it