Earthquake damage mapping techniques using SAR and Optical remote sensing satellite data

Abstract

The objective of the chapter is to describe the major satellite techniques by using both SAR and optical images, for mapping damage caused by seismic events in urban areas. These type of techniques have revealed themselves a suitable monitoring tool for disaster management since they provide a quick detection of land changes in wide areas, especially in remote areas or where the infrastructures are not well developed to ensure the necessary communication exchanges. In fact, in the aftermath of these severe disastrous events the most urgent needs is to estimate with sufficient reliability and rapidity the amount of population and infrastructures affected for different degrees of damage. The contribution of space technologies has been demonstrated to be effective for regional/continental damage assessment using low- or medium-resolution remotely sensed data (ranging from 30m to 1 km), and both automatic and manual interpretation approaches have been successfully used for extraction of information at a nominal scale ranging from 1 : 100 000 to 1 : 1 000 000. Today’s challenge for space technologies is also to demonstrate their effectiveness for damage assessment at local scale, ranging from 1 : 10 000 to 1 : 25 000 nominal scales. The information extracted at this level is crucial for calibration and estimation of the reliability of low- and medium-resolution assessment, for planning logistics for relief action on the field immediately after the event, and for planning the resources needed for recovery and reconstruction. Local or detailed damage assessment can also be addressed using Very High Resolution (VHR) satellite data with a spatial resolution ranging from 0.6 to 1m. At this level, the operational methodology for extracting the information was based on manual photo-interpretation of the satellite images which are processed on the screen by the photo-interpreter as for any other aerial imagery. The drawbacks of traditional photo-interpretation methodology are firstly linked to the time (and cost) needed for manual processing of the data and, secondly, to the difficulty in maintaining coherent interpretation criteria in case a large number of photo-interpreters, working in parallel in wide areas in a short time, is available. The long required processing time is in conflict with the need for rapid damage estimation, and the solution to involve parallel photo-interpreter teams often leads to an increase of time-consuming organizational problems and additional coherency lack in the information produced. Accordingly, some automatic procedures for exploiting these kind of data are developing in order to give information at this scale of detail. The most innovative automatic approaches will be described in this chapter. The major limitation, the availability of the images within a short time to manage the crisis, for an operational use of this kind of techniques will be highlighted. This is a key point for Civil Protections who needs a fast and draft overview of the epicentral area, quick information relative to the extension and distribution of damages, and the evaluation of infrastructure (roads, bridges) conditions. A single satellite can provide access time to a specific site in the order of some days, as a result the necessity to use any type of satellites data available and an integration of those data is mandatory to increase the chance to collect information on near real time. A description of the major satellite missions, SAR and optical, that provide data for this application will be done, paying particular attention to satellite constellations which may reduce the access time to 12 hours using the same sensor, as in the case of the COSMO-Skymed system. The work is addressed to the analysis of the different aspects leading to obtain maps representative of damage caused by earthquakes. At this aim some case studies will be considered, being representative of some severe earthquakes occurred in the past: Izmit (Turkey) on 1999, Bam (Iran) on 2003, Pakistan on 2005 and Sichuan (China) on 2008.