1. SAR and Optical satellite data for earthquake damage mapping S. Stramondo 1, C. Bignami 1, M. Chini 1, N. Pierdicca 2 1 Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143, Rome, Italy (chini@ingv.it). 2 Dept. of Electronic Engineering, Sapienza University of Rome, Via Eudossiana 18, Rome, Italy.

2. Overview Introduction and objectives New space missions for EO and natural disaster mitigation SAR and optical feature extraction SAR and Optical data processing chain Damage assessment products Test cases: Izmit earthquake (1999): City block damage level Bam earthquake (2003): Single Building Damage Level China earthquake (2008): VHR X- and L-band SAR Conclusions

3. Introduction and Objectives - Satellite Remote Sensing has assumed an important role in supporting natural disaster management and mitigation. - It provides a synoptic view of surface effects due extreme events. Concerning earthquakes EO data are used to generate a detailed map of damage to urban structures. - Therefore the potential of SAR and Optical images can be exploited to support the decision chain of Civil Protection for crisis management and to provide a rapid mapping of damage to buildings and infrastructures. - Combining SAR and Optical images overcome some limitation as those due to weather conditions and Sun illumination. -Analyzing the capability of remote sensing techniques for damage detection and classification in urban areas -Exploiting an approach integrating pre- and post- seismic data of both optical and radar (SAR) data

4. New space missions for EO and natural disaster mitigation - The new generation of L, C and X-band SAR sensors, with polarimetric capabilities, is now available. - The Japanese ALOS PALSAR, or TerraSAR-X from DLR and Cosmo- SkyMed from ASI, or the new C-band Radarsat-2 are dramatically enforcing the role of SAR systems in disaster monitoring. - These sensors provide high performance in terms of spatial resolution and revisiting time (some of them are satellite constellations) thus matching most of the end user requirements. - Forthcoming studies will concern the full exploitation of VHR data, either Optical and SAR.

5. Optical Provides a prompt overview of damages and is correlated to damage level, derived by: - Histogram matching between the pre- and post-seismic acquisitions - Normalized Change Image (NCI) definition SAR and Optical features extraction SAR - Both complex coherence and intensity correlation decrease when derived from co-seismic image pairs with respect to pre- or post- seismic pairs. - This is due to phase changes of the scattered field components at subpixel scale i.e., changes in scatterers geometrical arrangement.

6. Example of SAR features Decrease of intensity correlation is quite apparent in Bam example InSAR coherence of urban areas low in pre-seismic pair because of high spatial baseline (450 meters) so that change is not significant Pre-seismic coherence Co-seismic coherence Pre-seismic intensity correlation Co-seismic intensity correlation

7. SAR and Optical data processing chain

8. ProductDescriptionToolsScale Potential damage snapshot Prompt overview of potentially damaged area (only qualitative) Medium res. OPT & SAR Small/ medium, tents to hundreds km Damage level at district scale Damage level (i.e., collapse ratio) estimated on homogeneous urban areas medium/high res.OPT & very- high res. OPT & SAR District, block of buildings Collapsed/heavy damaged buildings Identification of single buildings collapsed or heavily damaged VHR Optical & SAR single building Damage assessment products From our experience:

9. Izmit earthquake (1999): SAR and Optical data sensitivity to city block damage level Ground truth with five different damage levels.

10. Available ground truth, i.e. the collapse ratio in Golcuk city ranges from 0% to 100% and is a 5-level one. Available ground truth, i.e. the collapse ratio in Adapazari city ranges from 0% to 41% and is a 7-level one. Izmit earthquake (1999): City block damage level from ground survey

11. Izmit earthquake (1999): City block damage level from SAR and Optical data Adapazari satellite damage levels map

12. The ground truth ROIs extracted from KOERI damage map have been superimposed to the satellite dataset and the damage level estimation has been performed and compared with the ground truth Izmit earthquake (1999): City block damage level comparison

13. Now, the damage map is the result of the fusion of SAR Correlation and IRS Change Index. The Integrated Damage Index is then defined leading to the estimation of damage within certain regions (unaffected by clouds to maximize the role of both data). A non-linear fitting function is then used to define an empirical data fusion approach. Izmit earthquake (1999): SAR and Optical data Integrated Damage Index

14. VHR remote sensing data are potentially able to provide very detailed mapping of the urban environment with a sub-meter ground resolution. At the same time, the increase in volume of data creates additional problems in terms of automatic classification The urban area appears as a complex structure containing buildings, transportation systems, utilities, commercial buildings and recreational areas. At different scale, a simple building may appear as a complex structure also, with many architectural details surrounded by gardens, trees, buildings, roads, shadows, social and technical infrastructure and many temporary objects, such as cars, buses, etc… VHR image classification

15. Even though the very high resolution, panchromatic images are not used for automatic urban classification studies and multiscale contextual analysis are essential being applied. A multiscale morphological analysis is carried out in order to extract geometrical information from adjacent pixels in a panchromatic image; this allows to improve the building classification overcoming the spectral information deficit of panchromatic imagery. The previous outcomes are the basis to labeling each building and defining a Single Building Damage Level (SBDL) criteria. Test case: Bam city (IRAN), affected by December 26th, 2003, earthquake The main goal is to label each building based on an SBDL criteria. Bam earthquake (2003): Single Building Damage Level

16. September 30, 2003 Off-nadir angle: 9.7° January 4, 2004 Off-nadir angle: 23.8° Shadow effects Bam earthquake (2003): Single Building Damage Level

17. QB Pan1 09/30/2003 QB Pan2 01/04/2004 QB Change Pan1-Pan2 Bam earthquake (2003): Single Building Damage Level

18. Panchromatic image Panchromatic image filtered by Open Operator with 3x3 window Panchromatic image filtered by Open Operator with 5x5 window Morphological operators Bam earthquake (2003): Single Building Damage Level

19. Morphological profile of six different classes The window size spans from 3x3 up to 125x125 pixels The feature vector is composed of 37 elements Bam earthquake (2003): Single Building Damage Level

20. September 30, 2003 Buildings Map of Bam City on September 30, 2003 Input: Panchromatic image + Panchromatic Images filtered by Open and Close Operators with different window size (Morphological profile) The buildings have been classified using an unsupervised classifier Bam earthquake (2003): Single Building Damage Level

21. Building Map 09/30/2003 Building Map 01/04/2004 Single Building Identification (Labeling) Single Building Damage Level Map Single Building Damage Level (SBDL): ratio between number of pixels which compose a building before and after the earthquake SBDL spans from 0 to 1 values Bam earthquake (2003): Single Building Damage Level

22. Three different levels of damage: Light (or No Damage), Medium and Heavy. Bam earthquake (2003): Single Building Damage Level

23. Ground Survey Damage Level LightMediumHeavy Light1683928957 Medium911914955 Heavy190619282316 Total450037704228 Comparison between SBDL statistic and Ground Survey: Total Buildings: SBDL=12498 Ground survey=12063 Error=3,6% Heavy damaged: SBDL=6615 Ground survey=6150 Error=7% Bam earthquake (2003): Single Building Damage Level

24. Pan 09/30/2003Pan 01/04/2004 Bam earthquake (2003): Single Building Damage Level

25. The magnitude Mw=7.8, May 12, 2008 earthquake which struck the Sichuan region in China, has been imaged by the new COSMO-SkyMed radar satellites. On May 12, immediately after the mainshock, the Italian Department of Civil Protection requested ASI to program new image acquisitions over the Sichuan region, using stripmap (swath 40 km wide, ground resolution 3 m) and spotlight (swath 10 km, resolution 1 m) modes. Two images, acquired one month before and two days after the quake, have been used to generate the first ever X-band coseismic interferogram, which clearly shows part of the strong ground deformation caused by the fault dislocation. A different analysis of the same data also allowed to detect several changes in radar response presumably due to strong seismic damage as far as 80 km from the epicenter. China earthquake (2008):

26. Two COSMO-SkyMed stripmap images of Chengdu city. In the following some examples extracted from the 40 x 40 km area covered by the COSMO images. The pixel size is at 6 x 6 m. Buildings and infrastructures are viewed from WSW under an incidence angle of 45°. The differences (in red) between the pre- and post-event images are striking and affect mainly bridges along motorways and large buildings. China earthquake (2008): COSMO-SkyMed

27. Actually no ground truth maps have been used for satellite data validation. As soon as VHR optical images are available we will apply data fusion procedures to fully exploit the combination of SAR and optical features. As soon as further data available already tested damage classification schemes (SAR+VHR+Morphological operators+Neural Networks) will be applied. China earthquake (2008): COSMO-SkyMed

28. ALOS PALSAR Fine Beam Single Pol. Pre = orbita 10835 5/2/2008 Post= orbita 12845 22/6/2008 China earthquake (2008): ALOS - PALSAR

29. Conclusions: City block damage level The 1999 Turkey test case has been fully analyzed. Outcomes from Golcuk city have been used to obtain a general correlation between collaspe ratio and SAR and/or Optical data A damage map at building block scale has been then obtained An empirical model stemming from SAR+Optical fusion has been defined. It is a non linear fitting function An upgraded damage map, from three to five level, has been showed

30. Conclusions: Single Building Damage Level The approach is based on VHR optical images. Even though the very small details are detectable on the contrary the very high complexity of each image results in a large number of false alarms Stemming from VHR data and thanks to the application of morphological analysis damage at single building scale can be identified and classified Temporary objects that are not part of the earthquake damage and have been removed The SBDL index has been defined and applied to the Bam test case. Results are satisfactory.

31. The high flexibility of the Cosmo SAR sensor was originally designed mainly for disaster reconnaissance and assessment, and the Sichuan earthquake was one of its first test benches. Even in its presently incomplete, and not yet operational, configuration, Cosmo Skymed has successfully demonstrated to be able to acquire, in very short times high quality images of disaster areas useful for crisis management and scientific research. We stress that, for the most effective use of Cosmo imagery in disaster applications using the assessment of changes between pre- and post-event conditions, a background data acquisition policy will have to be devised to acquire and archive on a routine basis. Since June 2008 the first Cosmo Skymed satellite has been fully operational and the system will progressively reach its complete monitoring capacity with all four satellites by 2009. Conclusions: VHR X- and L-band SAR