Space-based tools supporting earthquake damage detection and mapping: SAR and optical data.

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1 Space-based tools supporting earthquake damage detection and mapping: SAR and optical data. S. Stramondo, C. Bignami, M. Chini Remote Sensing Unit Istituto Nazionale di Geofisica e Vulcanologia Rome, Italy stramondo@ingv.it Abstract In the fieldwork of disaster management and mitigation, Space Remote Sensing has become a suitable tool able to generate products to be ingested in the crisis scenarios analysis. Today Synthetic Aperture Radar (SAR) data and Very High Resolution (VHR) optical images are available. SAR revealed itself a powerful instrument for change detection and damage evaluation purposes. In particular, interferometric features like the InSAR phase coherence and the intensity correlation of multi-look images collected before and after an earthquake have been used in previous works to detect and quantify damage in urban areas. On the other side, optical sensors have also been successfully used for damage estimation. Thanks to the strong increase of their spatial resolution, the new optical sensors have become reliable systems for assessing single buildings damage. When both optical and SAR are available, a damage classification can also be obtained by combining the two data types, leading to a more reliable result. In this work, we present the results of recent studies aimed to develop methodologies to generate damage maps for urban areas prone to seismic risk and affected by moderate-to-strong earthquakes. I. INTRODUCTION In the last years, remote sensing data have become important instruments to supply disaster management and mitigation. In particular, in case of a strong earthquake, the rapid detection of damaged buildings and infrastructures has assumed an important role for the Civil Protection rescue activities. Recently space missions have concerned either SAR or optical sensors. L, C and X-band SAR, with polarimetric capabilities as for the Japanese ALOS PALSAR, are now available. The consequence has been a significant increasing of data and a reduction of time delay for space images soon after the investigated disaster. Besides the old C-band ERS-2 and Envisat (ESA), the new C-band Radarsat- 2, L-band ALOS, Terrasar-X from DLR and CosmoSkymed from ASI at X-band are now acquiring the first high resolution data. Moreover, CosmoSkymed mission is a constellation composed by four satellites and is potentially able to reduce the revisiting time to less than 1 day, being time delay a key issue for prompt damage mapping. Concerning VHR sensors, Ikonos, QuickBird and EROS-1A have been joined by EROS- 1B and WorldView that will ensure increased performances either in terms of spatial resolution and revisiting times. SAR systems are widely used in environmental studies thanks to their peculiarities allowing a fairly synoptic view in almost N. Pierdicca Dept. of Electronic Engineering Sapienza University of Rome Rome, Italy pierdicca@mail.die.uniroma1.it completely weather and time independent conditions. The exploitation of SAR sensitivity to scenario changes is a suitable tool for damage evaluation purposes. In particular, interferometric approach by means of InSAR phase coherence and the intensity correlation of multi-look images collected before and after a seismic event have been tested in previous works to map urban damage. The damage estimation by means of optical data has also been used. In particular, the very high resolution (less than 1 meter) images available from the present optical sensors, offer a reliable tool for single building damage detection. However, the presence of clouds, shadows, variation in Sun illumination and geometric distortions are critical for this type of sensors and prevent a fully automatic change detection approach. In case of both optical and SAR data availability, a more reliable damage map can be derived by combining these two different data types. The results obtained for the Izmit earthquake (August 17 th 1999) applied to Adapazari city test site, and the well known Bam, Iran, earthquake are presented. Two different methodologies have been applied for the two case studies. In particular, we propose the results of recent studies concerning the exploitation of SAR and Optical data by means of an empirical data fusion approach. Such approach is based on the results obtained in [1], where optical and SAR data have been jointly exploited by using a supervised classification procedure. For the Bam test case, the use of QuickBird panchromatic images has led to detect very small details. If on one hand with VHR panchromatic image is possible to detect very small details, on the other hand the buildings have become rather complex structures with many architectural details. Moreover, they may be surrounded by scattering objects which make the contrast between the roof and the ground less evident and increase the difficulties in the segmentation process. Also the different shadows caused by seasonal sun illumination between images are a source of false alarms in the change detection process. For these reasons, automatic procedure are often hampered and the visual inspection approach is still the most used method to produce reliable inventory of damage [2],[3]. We propose a segmentation approach, based on the use of morphological operators, aiming to reduce such false alarm signals affecting the change detection process (cars, recovery tents).

2 II. CASE STUDIES AND PROPOSED APPROACHES A. Adapazari test case:data and method The dataset available for Adapazari test case is composed by both SAR and optical images. In particular, two preseismic ERS SAR acquisitions, in tandem configuration, and one post-seismic have been used to extract InSAR feature (phase coherence and intensity correlation) by adopting the procedure described in [1]. As far as optical data are concerned, two images sensed by the Indian Remote Sensing satellite (IRS), one before and one after the seismic event, have been used to derive to the normalized change image (NCI) [1]. The approach used for damage evaluation is based on city blocks scale. Unfortunately, the pre-seismic images was affected by clouds (see fig. 1), thus preventing the damage evaluation for the complete urban area of Adapazari. For this reason, some city blocks have been discarded. A preliminary analysis has been carried out to compare the sensitivity to damage, of the derived SAR and optical parameters between the Izmit case [1] and Adapazari. For Adapazari case, we use the ground truth data provided by Kandilly Observatory for Earthquake Research Institute, in the framework of EURORISK-PREVIEW project. The corresponding damage map is shown in fig.2 and reports the collapse ratio index for some city blocks, with three damage levels (light, medium and heavy), superimposed on one IRS image. In order to perform the comparison, the mean value of SAR and optical parameters has been calculated within each polygon of each city block. The results of the comparison are presented in fig. 3. Despite the slight difference of the collapse ratio index for the two test cases, the sensitivity curves are in good agreement. Stemming from these, we have attempted to use the Izmit data to retrieve a simple empirical damage index model, by SAR and optical data fusion. The approach is based on a non linear fitting function, whose input data are the NCI Figure 1. Pre-seismic optical image (IRS) affected by clouds. Figure 2. Ground survey damage map of Adapazari city. The map has been kindly provided by KOERI institute of Istanbul and superimposed on IRS image. IRS normalized change image Intensity Correlation Difference ~ ~ Izmit 0.125~0.25 Adapazari Collapse ratio Izmit (a) ~0.5 Adapazari Collapse Ratio (b) Figure 3. Optical (a) and SAR (b) features vs damage level: comparison between data extracted from Adapazari (red points) and Izmit (blue points) test site. 0.5~

3 and the SAR intensity correlation (ρ I ). Hence, the damage index can be expressed by: IDI = A log 10 (NCI) + B log 10 (ρ I ) + C (1) where IDI means Integrated Damage Index and the coefficients A, B and C are obtained by a fitting procedure. The output value is an integer index ranging between 1 and 5 (damage grade). It is related to the five collapse ratio classes reported in the Izmit ground survey map. B. Bam test cas: data and method The case study is the well known Bam (Iran) earthquake occurred on December 26th, Quickbird captured two very high resolution images of Bam. The first one is eight days after the event (January 3, 2004) and the second one has been imaged on September 30, Both images are 0.6 meter/pixel panchromatic ones, with a wide difference in the viewing angle: 9.7 and 23.8 off-nadir angle pre-seismic and post-seismic acquisition respectively. A classification procedure has been applied to this dataset, aiming to cancel false alarms in the change detection procedure caused by shadow or other temporary objects like cars or recovery tents. A single band is not enough to classify such complex scene; thus further information, taking into account the geometrical shape of objects within a panchromatic image, can be exploited. Therefore, morphological profiles from the original panchromatic images taken before and after the earthquake have been carried out in order to classify only buildings in the scene. The procedure is composed of two processing steps. Firstly, morphological features have been extracted by applying the Open and Close operators [4] on the original panchromatic images by using different window sizes (spanning from 3x3 up to 125x125 pixels). After that, a morphological feature vector made of 37 different elements is obtained: 18 from Close operator, 18 from Open operator plus the original panchromatic image. Different morphological feature vectors, i.e. the morphological profiles, correspond to different classes in the image. In the second step of the procedure, the morphological feature vector is used as input to an unsupervised classifier. After the classification process, each building has been recognized and then labeled and the damage estimation can be computed by simply comparing the pixel belonging to the same building before and after the seismic event. The percentage of the damaged pixels respect to the total for each building is an estimation of the damage level. The higher number of damage pixels the higher damage level of the buildings. III. RESULTS C. Adapazari damage map The IDI map, obtained by applying (1) is shown in fig. 5. Firstly, we can note that some city blocks have not been classified. This is because, as mentioned in section II, the presence of clouds does not allow using optical information for the whole urban area. The IDI map and the ground survey map in fig. 2 appear quite in agreement. It is noteworthy that the heaviest damaged area is well identified (central block of Adapazari) and the value assigned is grade 4, perfectly in agreement with the collapse ratio of ground data (25-40 %). Moreover, no grade 5 areas have been detected and this is also compliant with the in situ survey map. However, some discrepancies exist. In particular, polygons of damage grade 2 and grade 3, in some cases do not match ground truth information. It could be partially explained by observing that probably some areas of light damage level (0-10%) and QB panchromatic image 09/30/2003 QB panchromatic image 01/04/2004 Morphological profile extraction Morphological profile extraction Unsupervised classifier Unsupervised classifier Building Map 09/30/2003 Building Map 01/04/2004 Single Building Identification (Labeling) Single Building Damage Level Map Figure 4. Scheme of the damage building procedure for Bam test case. Figure 5. Five level damage map obtained by Optical and SAR data fusion. The five damage grades are related to the five different collapse ratio values identified in the Izmit case study.

4 6th International Workshop on Remote Sensing for Disaster Applications medium damage level (10-25%) have been assigned to grade 2, which is referred to the collapse ratio range %. D. Bam damage map In fig. 6 the final damage map obtained by the proposed procedure for VHR data is shown. The damage map concerns those buildings of Bam city labeled and classified with one of the following three damage levels: Light or No-Damage (Yellow), Medium (Red) and Heavy (Purple). These three classes have been assigned by setting two thresholds in the percentage of changed pixels within the single building. Then, the satellite based damage map has been compared with a map derived from ground survey, provided by the Geological Survey of Iran [5], reporting the map of Collapse Ratio (fig. 7). Note that the information is provided at district scale. Therefore the different spatial information scale of these two maps did not allow a direct comparison. Nevertheless a qualitative study has been attempted. A post classification analysis has been performed and the results are shown in fig. 8. The plot reports the distribution of the three damage classification level at building scale for each region extracted from the ground survey map (on x axis). It is worth noting that Figure 8. Plot of the post classification analysis. the percentage of heavy damaged buildings grows with the increase of ground truth damage. Apparent discrepancies can be ascribed to different approaches for the map generation. IV. CONLCUSION In this work, we present the results of recent studies aimed to develop new methodologies for generating damage maps for urban areas hit by strong earthquake. A novel approach for optical and SAR data fusion has been tested for the Adapazari 1999 earthquake. A new procedure for the generation of a damage map at single building scale has been also tested. It is based on the exploitation of very high resolution images acquired by QuickBird optical sensor. The procedure is addressed to the extraction of buildings map by means of contextual information provided by morphological operators applied to panchromatic data. Even though the comparison between our results and a damage map from ground survey is not an easy task, due to their different scales (ground survey provide damage level at district scale) the obtained results are encouraging. Figure 6. Bam city damage map at single building scale: yellow building represent light/no damage level, red buildings and purple buildings are related to medium and heavy damage level respectively. Future work will be focused on the refinement of the IDI model and on the validation of our methodology with a more detailed ground truth. AKNOLEDGMENTS The authors would like to thank DigitalGlobe for providing data that have been used in this work. The work has been partially supported by EURORISK-PREVIEW project. REFERENCES [1] [2] Light Medium Heavy Figure 7. Damage map from ground survey. [3] S. Stramondo, C. Bignami, M. Chini, N. Pierdicca, A. Tertulliani: The radar and optical remote sensing for damage detection: results from different case studies, International Journal of Remote Sensing, Vol. 27, N. 20, 20 October, K. Saito, R. J. S. Spence, C. Going, M. Markus, Using high-resolution satellite images for post-earthquake building damage assessment: a study following the 26 January 2001 Gujarat earthquake, Earthquake Spectra, vol. 20, pp. 1 25, F. Yamakaki, M. Matsuoka, K. Kouchi, M. Kohiyama, N. Muraoka, Earthquake damage detection using high-resolution satellite images, Proceeding of IEEE IGARSS, vol. 4, pp , 2004.

5 [4] P. Soille, Morphological Image Analysis Principles and Applications, 2nd Edition, Berlin: Springer Verlag, [5] ICG report , Bam_earthquake_report-ICG.pdf, 2004