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In many instances, disasters act as catalysts in the adoption of new and emerging technologies. Spawned by the need to rapidly collect vital information for disaster management, technology innovations have often helped emergency responders to assess the impact of large disasters more efficiently and rapidly, and to track and monitor progress in critical response and recovery operations. Some examples of where technology implementation has been driven by the occurrence of a major disaster include Hurricane Andrew in 1992, where the lack of rapid damage or situation assessment tools hindered the deployment of federal resources and thus identified the need for near real-time loss estimation methodologies; the 1994 Northridge Earthquake where GIS took center stage during the initial response and recovery periods by providing important visual and spatial information on critical operations; the World Trade Center attacks which demonstrated the potential use of remote sensing technologies for damage assessment and recovery; and Hurricane Charley in 2004 where the deployment of GPS-based, field survey technologies helped to freeze in time the damage and destruction of this disaster so that researchers could study the effects of significant wind hazards in a more comprehensive and complete manner. All of these events underscore the opportunities that emerge when time-critical information can be delivered more efficiently to users making critical decisions during the disaster.
One technology which has had an enormous impact on disaster management has been remote sensing. In the past decade, this technology has been used extensively to explain the extent of impacts caused by earthquakes, tsunamis, hurricanes, floods, wildfires and terrorist attacks. Through high-resolution optical imagery and active sensors (e. g. , synthetic aperture radar, or more commonly known as SAR, and light detection and ranging or LIDAR), remote sensing technologies have demonstrated significant efficacies in quantifying post-disaster damage, monitoring recovery and reconstruction progress after significant disasters, and more recently, in developing information on our urban infrastructure. One main reason for this rapid progress has been the introduction of high-resolution, commercially-available satellite imagery. Where these technologies used to be available to mainly government agencies (mostly military), they have now become readily accessible to the public. The impact of this development has been most noticeable – in our opinion – in the disaster management area. It focuses on the integration of remote sensing technologies in all aspects of disaster management, i. e. , disaster preparedness, mitigation, response and recovery. In order to demonstrate their efficacy in these four areas, cases histories and examples from recent disasters, including the Marmara, Turkey earthquake, the Bam, Iran earthquake, and the Indian Ocean earthquake and tsunami are discussed. Finally, the paper ends with a view towards the future. What new developments can be expected in technology development and implementation, and what future challenges must be overcome to realize broader application of these technologies in future disasters.
Compiling a comprehensive and accurate database of existing critical infrastructure is a priority in emergency management, since it provides a basis for simulating probable effects through scenario testing, while setting a baseline for determining the actual extent of damage and associated losses once an event has occurred. In the context of mitigation and preparedness, demand is increasing for accurate inventories of the built environment, in order to perform vulnerability assessments, estimate losses in terms of repair costs, assess insurers liability, and for relief planning purposes. In lesser developed regions of the world, inventories are often scarce. CEOS (2001) documents a program to compile comprehensive records of urban settlements that could be affected in the event of an earthquake, to avoid a repeat of the 1998 Afghanistan earthquake, when due to unavailability of even simple maps or images, relief workers experienced extreme difficulty locating affected villages.
Although the location of urban centers is generally well documented for developed nations, interest is growing in accurate low-cost methods for characterizing the built environment in more detail. Building inventories are a primary input to loss estimation models, such as the FEMA program HAZUS®MH (Hazards-US) and California (Governor’s Office of Emergency Services) system EPEDAT (Early Post-Earthquake Damage Assessment Tool). These are used as planning tools prior to an event and as a response tool once an event has occurred. Measures of interest include: building height; square footage; and structural type and occupancy (use). To a large degree, the accuracy of loss estimates depends on the quality of input data. Default datasets are often based on regional trends, rather than local data. Research being undertaken at the Multidisciplinary Center for Earthquake Engineering Research (MCEER), suggests that remote sensing data offers a detailed inventory of both height and square footage, which through supplementing existing datasets, may lead to more accurate loss estimates.
Based on the method developed by Huyck et al. (2002), this nDEM is obtained as the difference between a SAR-derived digital surface model (DSM) and a bare-earth digital terrain model (DTM). The former DSM represents the apparent ground surface, as a composite of superimposed features, such as buildings and underlying bare earth topography. The latter DTM is solely topographic, obtained from the same base data via a sequence of filters. Building heights are recorded as the local maxima within footprints delineated on high-resolution aerial photography. The heights are then translated to stories, using a conversion factor that corresponds with standard loss estimation software. Ground level square footage is also recorded on a per building basis, as the footprint area in pixel units. Using a scaling factor based on image resolution, this value is converted to single story square footage. Finally, the total square footage for each structure is computed as the product of the number of stories and ground level area. The efficacy of this methodology has been tested for case study areas in Los Angeles, where the values for building height and coverage correspond closely with independently derived tax assessor data. Moving forwards, methodological procedures are under development that use these results to update existing inventories within the HAZUS®MH program.
A significant advantage of remotely-derived inventories is the relative ease with which they can be updated. This is particularly important at a city-wide scale, where the overview offered by satellite imagery can be used by planning departments to track urban growth. Classifying imagery into vegetation, concrete, and buildings is a straight-forward task, which is readily applied to multi-temporal coverage. Growth is detected in terms of change between the scenes. In addition to using active sensors, such as IfSAR, new building inventory development techniques are emerging from the use of high-resolution optical satellite data. Research at Stanford University and ImageCat, Inc. have focused on the development an approach for rapidly obtaining spatial and structural information from a single high-resolution satellite image, using rational polynomial coefficients (RPC) as a camera replacement model.
Geometric information that defines the sensor’s orientation is used in conjunction with the RPC projection model to generate an accurate digital elevation model (DEM). The methodology described in Sarabandi, et al. (2005) shows how the location and height of individual structures area extracted by measuring the image coordinates for the corner of a building at ground level and its corresponding roof-point coordinates, and using the relationship between image-space and object-space together with the sensor’s orientation.
Real-time damage detection following a natural or man-made disaster initiates the response process, providing the information needed to: prioritize relief efforts; direct first responders to critical locations, thereby optimizing response times and ultimately saving lives; compute initial loss estimates; and determine whether the situation warrants national or international aid. Of particular importance is damage sustained by urban settlements, together with critical infrastructure, such as roads, pipelines and bridges. In this section, damage detection methodologies developed from the remote sensing area are described for highway bridges and buildings, drawing on research conducted following recent earthquake events and experience gained in the aftermath of the World Trade Center attack. The methodological process follows either a direct and indirect approach. In the former case, damage is detected by directly observing the characteristics of, or temporal changes to an object of interest. In the latter case, damage is detected through a surrogate indicator. In extreme events, such as natural disasters and terrorist attacks, the performance of critical transportation elements is a major concern.
Taking the U. S. as an example, the transportation network is vast, comprising over 500, 000 bridges and 4 million miles of road. When a disaster like the 1994 Northridge earthquake strikes, effective incident response demands a rapid overview of damage sustained by numerous elements, spread over a wide geographic area. Given the magnitude and complexity of transportation systems, near-real time field-based assessment is simply not an option. Taking the recent Indian Ocean earthquake and tsunami (2004) centered near Sumatra, the media reported damage to roads and bridges, with a number of villages cut off. Considering the critical 48 hour period that urban search and rescue teams have to locate survivors, accessibility must be quickly and accurately determined, in order to reroute response teams and avoid life threatening delays. Irrespective of whether the event occurred in Indonesia or the US, earth orbiting remote sensing devices like IKONOS and QuickBird present a high-resolution, synoptic overview of the highway system, which can be used to monitor structural integrity and rapidly assess the degree of damage.
Under the auspices of a DOT/NASA initiative promoting remote sensing applications for transportation, preliminary damage detection algorithms termed ‘Bridge Hunter’ and ‘Bridge Doctor’ have been developed for highway bridges. Phase 1 of the damage detection process employs Bridge Hunter to track down and compile a catalogue of remote sensing imagery, together with attribute information from Federal Highway Administration Databases (FHWA) databases. During Phase 2, Bridge Doctor diagnoses the ‘health’ of bridges, determining whether catastrophic damage has been sustained. In this case, the bridge damage state is quantified directly, in terms of the magnitude of change between a temporal sequence of images acquired ‘before’ (Time 1) and ‘after’ the event (Time 2). It is hypothesized that for collapsed bridges, where part of the deck fell or was displaced, substantial changes will be evident on the remote sensing coverage. However, where negligible damage was sustained, change should be minimal.
The Northridge earthquake was employed as a testbed for model development. Widespread damage was sustained by the transportation network when the 6. 7 magnitude event struck Los Angeles on January 17, 1994. Six examples of bridge collapse were available for model calibration and validation. Damage profiles obtained from SPOT imagery clearly distinguish between these extreme scenarios. Reflectance signatures for the non-damaged example are consistent at Time 1 (before earthquake) and Time 2 (afterearthquake), following a similar pattern along the highway and across the bridge. For the collapsed scenario, substantial changes are evident between the ‘before’ and ‘after’ earthquake scenes. The damage profiles no longer follow a similar trend, with abrupt divergence in signature around the collapsed span. Damage indices including difference and correlation offer a quantitative comparison. The bivariate damage plot clearly distinguishes between the low correlation and high difference associated with collapsed bridges, and high correlation and low difference of their non-damaged counterparts.
Ogawa et al. (1999) and Ogawa and Yamazaki (2000) employ mono- and stereoscopic photo interpretation of vertical aerial photography to determine the damage sustained by wooden and non-wooden structures in Kobe. A ‘standard of interpretation’ was devised to distinguish between collapsed, partially collapsed, and nondamage structures, based on: the occurrence of debris; level of deformation; and degree of tilt. Success of this methodological approach is judged in terms of correspondence with ground truth observations. Chiroiu and Andre (2001), Chiroiu et al. (2002) use similar criteria to interpret building damage from high-resolution IKONOS satellite imagery of the city of Bhuj, which sustained extensive damage during the 2001 Gujurat earthquake. Similar work was done by Saito et al. (2005) for the Bam, Iran earthquake. High speed automated aerial television is also emerging as a useful tool for mono-temporal damage assessment. Ogawa et al. (1999) and Hasegawa et al. (2000) inventory building collapse from visual inspection of HTTV imagery for Kobe. Diagnostic characteristics of debris and structural building damage are expressed quantitatively by Hasegawa et al. (1999) and Mitomi et al. (2002). Their basic methodology recognizes collapsed and non-damage scenarios in terms of color, edge and textural information. Multi-level slice and maximum likelihood classifiers determine the spatial distribution of these classes (Mitomi et al. , 2001b, 2002). Although developed using imagery of Kobe, this methodology has successfully detected collapsed buildings in Golcuk, Chi Chi and Gujurat.
The damage map created for the town of Ban Nam Khem in Thailand was developed through expert interpretation of high-resolution pre- and post-tsunami imagery. Of the 761 structures sampled, 449 (59%) were classified as collapsed, with 312 sustaining a lesser damage state. The degree of damage is most extreme bordering the open coast and inlet, where between 50-100% of the houses were destroyed. The degree of damage captured by the remote sensing coverage rapidly diminishes moving inland, reaching 0-30% at a distance of approximately 500m from the shorelines
GPS-based technologies have been one of the reasons why field reconnaissance efforts after major disasters have improved significantly. Before this technology became available to the general public, documentation of field reconnaissance activities was cumbersome and time consuming. Now, with GPS-systems offering posi tional accuracies of about 1 to 3 meters anywhere in the world, it is possible to link photos and videos with actual points on the earth. This capability becomes even more important when this technology is integrated with GIS systems. The Indian Ocean event constituted the first deployment of VIEWS and high-resolution satellite imagery for post-tsunami field reconnaissance. The system was deployed to study several key sites from August 16-25th 2005, in order to “ground truth” the preliminary remote sensing results. Views was equipped with satellite base layers including the Landsat landuse classification, the mangrove change/loss map, and the QuickBird and IKONOS satellite imagery. The damage survey of impacted areas was conducted by a three member team from a moving vehicle, on foot, and by boat depending on vehicular access and type of landuse (for example mangrove). Fourteen (14) hours of geo-referenced digital video footage were recorded along the reconnaissance survey route that covered about 75 miles. Of this route, 50 miles were covered from a moving vehicle, 20 miles from a boat, and 5 miles as a walking tour. A library of approximately 550 digital photographs was also collected by the team.
The following recommendations are offered with regard to future directions for remote sensing applications in natural disasters:
Computer‐aided decision‐support tools are part and parcel of the emergency planning and management process today. Much is dependent on using modern technology to gather and analyse data on damage assessment, meteorology, demography, etc. and provide decision support for prevention/mitigation, response and recovery. Diverse technologies are merged to provide useful functions to aid the emergency planner/manager. Complexities arise when attempting to link several streams of technology to achieve a realistic, usable and reliable decision‐support tool. This discussion identifies and analyses the challenging issues faced in linking two technologies: simulation modelling and GIS, to design spatial decision‐support systems for evacuation planning. Experiences in designing CEMPS, a prototype designed for area evacuation planning, are drawn on to discuss relevant managerial, behavioural, processual and technical issues. Focus is placed on modelling evacuee behaviour, generating realistic scenarios, validation, logistics, etc. while also investigating future trends and developments.
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