Babak Mansouri

Development of urban planning guidelines for improving emergency response capacities in seismic areas of Iran

This paper presents the results of research carried out to improve emergency response activities in earthquake-prone areas of Iran. The research concentrated on emergency response operations, emergency medical care, emergency transportation, and evacuation-the most important issues after an earthquake with regard to saving the lives of victims. For each topic, some guidelines and criteria are presented for enhancing emergency response activities, based on evaluations of experience of strong earthquakes that have occurred over the past two decades in Iran, notably Manjil (1990), Bam (2003), Firouz Abad-Kojour (2004), Zarand (2005) and Broujerd (2006). These guidelines and criteria are applicable to other national contexts, especially countries with similar seismic and social conditions as Iran. The results of this study should be incorporated into comprehensive plans to ensure sustainable development or reconstruction of cities as well as to augment the efficiency of emergency response after an earthquake.

Earthquake-Induced Change Detection in the 2003 Bam, Iran, Earthquake by Complex Analysis Using Envisat ASAR Data

The recently deployed Envisat satellite collected before- and after-event imagery on the Bam, Iran, earthquake that occurred on 26 December 2003. The majority of buildings in Bam were traditional one-story unreinforced adobe structures constructed of the indigenous land material of the region. As a result, the corresponding SAR imagery for Bam reflects less material dependence on object detection. For this study, two sets of before and after SAR data are used from the ASAR sensor onboard of the Envisat platform. The backscattering, complex coherence, self-power and cross-power values are computed for each respective co-registered data pairs. The change detection scheme evaluates these results using orbital information to assess the levels of change in different city zones. Such damage maps can potentially serve in disaster response/management and also in estimating economic losses (Eguchi et. al. 2000). Damage maps from field observations are used to validate these findings.

Building Seismic Loss Model for Tehran

In order to model the building seismic loss for Tehran, urban databases have been compiled and processed considering different census zones, city blocks, and parcel records. Aerial photos, together with stereo image processing and ground survey data, have provided parcel level geospatial information. These data sets include urban features, land uses, and building inventory with height information. This research also focuses on the selection and the development of structural vulnerability functions and risk algorithms. The damage curves are selected or modified according to some regional data, the ATC-13 report, and the functions obtained for Costa Rica. Also, analytical fragility curves are derived and adopted for the area of study after the HAZUS-FEMA methodology. Finally, an upgradeable seismic risk model is developed in GIS using all compiled input data and structural vulnerability functions.

Streamlining Post-Earthquake Data Collection and Damage Assessment for the 2003 Bam, Iran, Earthquake Using VIEWS™ (Visualizing Impacts of Earthquakes With Satellites)

Advanced technologies, such as remote sensing, have considerable potential for increasing the effectiveness of post-disaster reconnaissance. In the aftermath of the Bam earthquake, the EERI field team deployed the VIEWS™ (Visualizing Impacts of Earthquakes With Satellites) reconnaissance system to support urban damage assessment activities. This paper introduces the VIEWS™ system and describes its inaugural implementation for earthquake response. For the Bam deployment, VIEWS™ integrated city-wide base layers of 60 cm color QuickBird satellite imagery collected “before” and “after” the event, with a real-time GPS (Global Positioning System) feed. The satellite imagery helped direct team members to the hardest hit areas, and real-time tracking supported efficient route planning, progress monitoring, and the capture of geo-referenced digital photographs. Through the VIEWS™ visualization mode, researchers are able to replay and analyze the datasets that were collected. The VIEWS™ system was developed by ImageCat, Inc. in collaboration with the Multidisciplinary Center for Earthquake Engineering Research (MCEER).

Classification of polarimetric SAR images using Support Vector Machines

Recently, Support Vector Machines (SVMs) have been introduced as a promising tool for performing supervised classification. This approach has been applied in different contexts and applications, such as data mining, regression analysis, and the classification of remotely sensed data. The advantage of SVMs for data classification is their ability to be used as an efficient algorithm for nonlinear classification problems, particularly in the case of extracting feature vectors from fully polarimetric SAR data. In this research, a classification algorithm based on the SVMs technique is applied to the fully polarimetric AIRSAR L-band data from the San Francisco Bay area, with a spatial resolution of 10 m. Several parameters are extracted from SAR data, including the individual channel backscatter value, Pauli decomposition coefficients, Krogager decomposition coefficients, and eigenvector decomposition parameters. Different combinations of polarimetric parameters are considered to assess the accuracy of the classification results. The accuracy of the SVMs is then compared with that obtained from several conventional classifiers, including the Maximum Likelihood classifier, Minimum Distance classifier, Mahalanobis Distance classifier, and Wishart classifier. The accuracy analysis shows that, for classification of fully polarimetric data, SVMs perform more poorly than the Wishart classifier by approximately 16%, whereas they perform better than the Maximum Likelihood, Minimum Distance, and Mahalanobis Distance classifiers by approximately 4%, 17% and 14%, respectively. Moreover, the highest accuracy is achieved by using the coefficients of Krogager decomposition in the classification procedure. This evaluation demonstrates that the SVM classifier can be used as an effective method for analyzing fully polarimetric SAR images with acceptable levels of accuracy.

Comparison of the performance of L-band polarimetric parameters for land cover classification

L-band fully polarimetric Synthetic Aperture Radar (SAR) data, acquired in April 2009 by the Japanese Advanced Land Observing Satellite over Tehran, were used to investigate the effect of various polarimetric descriptors on the accuracy of land cover classification. Particular attention was paid to evaluating the differences between polarimetric parameters in the image classification and understanding the type of information they contain. For this purpose, several polarimetric parameters were first derived from SAR complex analysis and target decomposition techniques including backscattering cross sections, ratios of scattering matrix elements, polarimetric coherencies, Pauli coefficients, Krogager coefficients, Freeman coefficients, Yamaguchi coefficients, H/A/Alpha parameters, and Eigenvalues of the coherency matrix. These parameters were then employed in Maximum Likelihood Classification (MLC), Minimum Distance Classification (MDC), and Parallelepiped classification for the land cover classification. The final accuracy assessment of the classification was performed using several statistical tests. This evaluation demonstrates that the performance and accuracy of the classification depend both on the selection of the polarimetric parameters and the type of classifier used. Polarimetric parameters extracted from target decomposition techniques in MLC produce the highest accuracies (a Kappa coefficient of greater than 90%) and the best discrimination of natural and man-made targets. Additionally, higher accuracies are generally obtained when the polarimetric parameters are derived from SAR complex analysis in a circular rather than linear basis. Thus, changing the polarization basis can significantly improve the result of classification.

Use of Remote Sensing Technologies for Building Damage Assessment after the 2003 Bam, Iran, Earthquake—Preface to Remote Sensing Papers

This preface introduces a series of papers that describe the use of remote sensing technologies in quantifying the extent of building damage after the 2003 Bam, Iran, earthquake. These papers represent a significant milestone in post-earthquake loss estimation. For the first time, independent evaluations of regional damage are documented, which will ultimately allow an assessment of the efficacy of these technologies as tools for post-earthquake damage detection and quantification. Not only were different sensors used, but radically different approaches were implemented in quantifying damage. The conclusions and recommendations of the different papers are generally consistent and strongly suggest that regional damage assessment using remotely sensed data is highly feasible. The papers, however, acknowledge that more research is needed before these technologies can be used to make critical emergency response decisions. Finally, the role of the Earthquake Engineering Research Institute through its Learning From Earthquakes Program is acknowledged, largely for helping to promote the use of remote sensing technologies in earthquake studies and for recognizing the value of collaboration through its newly formed Subcommittee on Remote Sensing.

A Soft Computing Method for Damage Mapping Using VHR Optical Satellite Imagery

In this research, the feasibility of a method based on some soft computing algorithms for earthquake damage mapping is sought. The idea is to classify different patterns of change associated with building footprints and to detect distinct damage levels. A fuzzy inference methodology is employed to determine the damage grade for individual building roofs by the means of evaluating the contribution of different patterns of changes. For implementation, satellite images of before and after the 2003 Bam, Iran earthquake, are used in addition to some available ancillary data. Building footprint pixels were extracted from pre- and postimages using the ancillary building mask. Haralick second-order textural features were computed for the building objects and an optimum set of such features was selected using genetic algorithm (GA). Considering optimal indices, different parts of roofs were classified into three change patterns as “low change,” “moderate change,” and “severe change” employing a support vector machine (SVM) algorithm. For each building footprint, the contribution of each class was calculated as the input of a fuzzy inference system (FIS). Mamdani fuzzy engine was used to determine the damage grade of each building. The proposed algorithm was evaluated by comparing the produced damage map with a reference damage map (ground truth). The results demonstrated the efficacy of the method showing overall accuracies of 76% for detecting three levels of structural damage (no to slight, moderate, and heavy to destruction) and 89% for determining binary damage levels (no-collapsed, and collapsed) as suitable for such overall monitoring process.

Development of Residential Building Stock and Population Databases and Modeling the Residential Occupancy Rate for Iran

AbstractSeismic risk mitigation planning relies on the assessment of possible losses because of destructive scenario earthquakes. In turn, realistic risk assessment procedures must be supported by an adequate element at risk data sets. In this research, two major investigations in Iran examine (1) the development and augmentation of fine resolution population and building databases through data fusion and modeling, and (2) the derivation of the residential occupancy rate for a 24-h period for a normal working day in large cities. The basic population and building data sources are obtained from the national census program, which exhibits very limited spatial details. A fine resolution global dataset is fused together with the census data to gain much detailed spatial information. The residential occupancy modeling is completed according to the distribution of the residential population during normal working days. The empirical modeling was completed using two sets of temporal data for both census and traff...