Cruce de variables

Per-building level damage assessments are the most intensive level of analysis, requiring high resolution imagery and significant processing or time in photo-interpretation. As a result, they are also expected to provide the most detailed level of damage information, identifying both location and severity of damage on a per-structure basis. Determination of damage severity requires an understanding of the mechanics of damage for a particular event and robust evaluation techniques have not yet been developed for high velocity flooding. Building characteristics have been found to play a key role in damage signatures, and evaluation techniques must be tailored to incorporate differences in construction types (Womble et al., 2008).

A synopsis of per-building damage assessments completed after the 2004 South Asian Tsunami and 2005 Hurricane Katrina is presented in Table 5.4. The studies presented here consist entirely of object-based analyses of vertical remote sensing imagery, utilizing either automated, semi-automated or manual techniques. Because per-building damage assessments for high velocity flood events have not been used for a large number of events and the damage signatures are significantly different from other hazards, a consistent scale has not been developed to categorize the location and severity of building damage using remote sensing. The studies in Table 5.4 assess building damage in two general ways: identification of collapse or non-collapse and differentiation of more intermediate levels of damage.

Studies by Pesaresi (2007), Tanathong et al. (2007), Gamba et al. (2007) and Adams et al. (2009) use damage criteria to classify buildings as either collapsed or non-collapsed, with two of the studies identifying whether the building size decreased and one identifying if the structure had moved. While change and movement of the building footprint segregate damage categories, the combination of these damage states only differentiate failure and non-failure of the building.

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Table 5.4 Per-Building Level Damage Assessments for High Velocity Flood Events

Study Remote Sensing Data, Spatial

Resolution and Analysis Technique Damage Categories Study Findings Validation

Pesaresi (2007), 2004 South Asian Tsunami, Meulaboh, Indonesia

Quickbird panchromatic (0.6 m) and multi-spectral (2.4 m) pre- and post- tsunami imagery

Multi-criteria automatic classification based on biomass, stressed biomass, shadows and debris

Not damaged

Just flooded, structure still standing Flooded, structure destroyed with debris in place

Flooded, structure completely wiped out with no traces of remaining debris

The classification system was found to distinguish well between flooded and non- flooded buildings. Destroyed structures with debris were also recognized but completely destroyed structures were not well recognized, with accuracy of 39%.

Visual interpretation data from European Commission Joint Research Centre (EC JRC).

Tanathong et al. (2007), 2004 South Asian Tsunami, Khao Lak, Thailand

IKONOS pre- and post-tsunami imagery (1 m)

Automatic object extraction and damage detection using classifier agents from pre- and post-event imagery

Partially collapsed Building size has changed Building is moved Building has disappeared

A test case resulted in the automatic identification of 21 similar pre-event bungalows in a resort area. Post-event analysis identified 17 buildings, indicating disappearance of four buildings. Three buildings were classified as partially collapsed and four others were changed in size. The study states that preliminary results indicate that identified changes were real changes and pointed out the dependence on classifier agents for success of the technique.

No validation information provided.

Gamba et al. (2007), 2004 South Asian Tsunami, Kalutara, Sri Lanka

Quickbird panchromatic (0.6 m) and multi-spectral (2.4 m) pre- and post- tsunami imagery

Semi-automatic object extraction of pre- and post-event imagery

Comparison of footprints extracted from pre- and post-event imagery to determine surviving/partially surviving structures.

Based on building counts in test areas, correct building detection was found to range between 90% and 96%. In some areas, the building footprint was significantly underestimated. Methodology is flexible and can be applied to both high resolution optical and radar images.

Visual interpretation of test areas.

Adams et al. (2009), 2004 South Asian Tsunami, Ban Nam Khem, Thailand

IKONOS (1 m) pre-tsunami imagery, Quickbird post-event imagery Visual interpretation

Collapsed Non-collapsed

The study noted known limitations in detecting intermediate damage levels from nadir remote sensing imagery.

Overall classification accuracy was

approximately 90%. Collapse class resulted in user and producer accuracy above 85% indicating that building collapse was well identified. Non-collapse producer accuracy was high, while user accuracy (79%) may have been affected by delay in field data collection.

Field survey conducted using

the VIEWSTM _system

conducted 7 months after the event.

Miura et al. (2005; 2006), 2004 South Asian Tsunami, Batticaloa, Sri Lanka

IKONOS pre- and post-tsunami imagery (1 m)

Visual interpretation

Condensed/Modified EMS-98 (Grünthal, 1998)

G-1 to G-3 negligibly to slightly damaged G-4 partially collapsed buildings G-5 totally collapsed buildings Washed Away

Underestimation of damage occurred when roofs appear undamaged in imagery. Buildings that were totally collapsed or washed away were well identified.

Overestimation of damage was found to occur as a result of to surrounding debris and its effect on roof pixels.

Field survey, classifying buildings using the EMS-98 damage criteria.

110 (Table 5.4 continued)

Study Remote Sensing Data, Spatial

Resolution and Analysis Technique Damage Categories Study Findings Validation

Magsud et al. (2005), 2004 South Asian Tsunami, Galle, Sri Lanka

Pan-sharpened post-tsunami Quickbird Imagery (0.6 m), pan- sharpened pre-tsunami IKONOS imagery (1 m), pre-event building footprints and GIS layers Visual interpretation

Completely destroyed

Partially collapsed with roof intact Partially damaged, mainly interior Slightly damaged

Totally or partially collapsed buildings were accurately identified. Single collapsed buildings were easy to identify, while buildings with undamaged roofs could not be accurately assessed. Detailed ground truthing was cited as necessary to increase the accuracy of RS assessment results

Field survey conducted, collecting GPS coordinates of building corners and photographs of damaged buildings Friedland et al. (2008a)1_, 2005 Hurricane Katrina, Mississippi Coast

NOAA, USACE ADS40 post-Katrina aerial imagery (1 ft), NAIP pre- Katrina aerial imagery (1 m), pre- event building footprints Visual interpretation

No Damage to Minor Damage Moderate to Severe Damage Destruction

Roof damage alone was not a robust indicator of storm surge damage. Completely destroyed buildings were well identified, although the “destruction” category used in the field validation included lower levels of damage which were not well identified in the remote sensing damage assessment. Revisions to the wind and flood damage criteria were recommended to be more readily adaptable to remote sensing damage assessment.

Field survey conducted using

the VIEWSTM _system,

classifying buildings using wind and flood damage criteria

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Tanathong et al. (2007) and Adams et al. (2009) relied on a priori knowledge of pre- event building inventory through object extraction and visual analysis to assess building damage in post-event imagery. Both indicated good results in identifying damaged structures, but limitations exist in Tanathong et al.’s methodology as the study area consisted of 21 nearly identical buildings, which were identified based on object classifiers. Pesaresi (2007) and Gamba et al. (2007) did not rely on a pre-event assessment of the location of structures, instead identifying spectral signatures (e.g. shadows, biomass, linear features) to assess changes in pre- and post-event imagery. While overall Pesaresi had good results in assessing damage, completely destroyed structures without debris were not well recognized, with accuracy of only 39%. Gamba et al. correctly detected over 90% of the buildings in the study area, although building footprints were significantly underestimated in some areas.

Studies by Miura et al. (2005; 2006), Magsud et al. (2005) and Friedland et al. (2008a) attempted to differentiate intermediate states of damage between collapse and non-collapse, achieving strikingly similar results. The three studies used visual interpretation of vertical (nadir) remote sensing imagery and were able to identify completely destroyed buildings very well. Two of the studies used a priori data (building footprint polygon shapefiles) to aid building damage assessment. These studies also identified areas of difficulty in estimating high velocity flood damage to buildings using vertical remote sensing, including underestimation of damage to buildings with undamaged roofs and overestimation of damage from nearby debris for 1 m resolution imagery.

5.7 Chapter Summary

Methodologies currently utilized for assessment of buildings damaged by high velocity flood events have been explored in a tiered spatial framework. The studies presented show good results in identifying the general location of damage at the regional level, primarily using

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standard automated pixel-based methodologies such as band algebra and thematic classification. Neighborhood level damage assessments also yielded good results in differentiating locations of very severe damage through object extraction and visual analysis techniques, although some studies did not quantitatively evaluate the effectiveness of assessment with validation datasets. The debris line left as a result of damage from Hurricanes Katrina and Rita was identified by three studies as an important indicator of damage, and quantification of the spatial distribution of damage with respect to the debris line has been demonstrated for Hurricane Katrina. Per- building damage assessment methodologies are still in the development phase, with collapse and non-collapse categories or multi-level scales being employed. Both automated or semi- automated and manual techniques have been used for per-building level damage assessments, and a priori knowledge of building locations was noted as improving assessment of building damage for both types of analysis. Using visual analysis, completely collapsed buildings are well identified, although significant improvements are needed to overcome difficulties identifying intermediate states of damage, as damage to building roofs does not necessarily serve as a robust indicator of overall damage state.

The following two chapters assess the suitability of using remote sensing for storm surge damage detection at the neighborhood and per-building levels. Regional level damage assessment provides an overall understanding of damage from high velocity flood events; however, it is not intended to provide detailed information and is generally not employed for urban damage assessment, where information about building performance is desired. Current neighborhood and per-building level damage assessment methodologies have been described in this chapter, and Chapters 6 and 7 contain portions of the Hurricane Katrina damage assessments referenced in this chapter by Friedland et al. Both neighborhood (Chapter 6) and per-building (Chapter 7) assessment methodologies have been expanded to address shortcomings identified in

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the original assessments and have been generalized to account for the hazard, building characteristics and building response conditions that were observed in Hurricanes Katrina and Ike. Chapter 7 also investigates the utility of multiple remote sensing data sources for assessment of storm surge damage, including oblique remote sensing imagery.

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