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Hengjian, Lu; Kohiyama, Masayuki; Horie, Kei; Maki, Norio; Hayashi, H.; Tanaka, Satoshi

doi:10.1023/a:1024724524972

Cited by 21 publications

(7 citation statements)

References 1 publication

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“…Building damage is one of the primary causes of human casualties (Hengjian et al 2003). The ability to rapidly determine the impact of an earthquake on structures is very important for emergency planning, human recovery, subsequent reconstruction efforts, and future disaster risk preparedness.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Damage Assessment Maps Derived from Very High Spatial Resolution Satellite and Aerial Imagery Produced for the Haiti 2010 Earthquake

et al. 2011

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Following the devastating M7.2 earthquake that affected Haiti on 12 January 2010 two types of building damage assessment maps were produced: 1) area-based damage assessments using pre- and post-event satellite imagery and 2) detailed building-by-building damage assessments using post-event aerial photography. In this paper, we compare the reliability and the usability of area-based damage assessment maps from satellite imagery with respect to the detailed damage assessment from aerial data. The main objective is to better understand how cooperative rapid mapping can steer the more detailed assessments that are typical in determining postdisaster recovery and reconstruction efforts. The results of these experiments indicate that damage assessment maps based on satellite data are capable of capturing the damage pattern, mainly in areas with a high level of damaged and many collapsed structures. However, these maps cannot provide the level of information needed for the quantification of damage intensity.

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Section: Introductionmentioning

confidence: 99%

Comparison of Damage Assessment Maps Derived from Very High Spatial Resolution Satellite and Aerial Imagery Produced for the Haiti 2010 Earthquake

et al. 2011

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“…Categories of damage related to ground motion were modified from earthquake field guidelines issued by the Earthquake Engineering Research Institute [37] and damage patterns of buildings collected during the earthquake investigation by Okada and Takai [38,39]. There were three urban damage classes ( Figure 6) used in this study, and these included areas with…”

Section: Buildings Damaged By Ground Motionmentioning

confidence: 99%

“…Widespread liquefaction can occur and damage buildings and infrastructure long distances away from the rupture zone. Buildings on liquefaction-susceptible areas are mainly damaged by two processes: inclination and subsidence [39].…”

Section: Buildings Damaged By Liquefactionmentioning

confidence: 99%

Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment

Tamkuan

Nagai

2017

IJGI

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Earthquakes are one of the most devastating types of natural disasters, and happen with little to no warning. This study combined Landsat-8 and interferometric ALOS-2 coherence data without training area techniques by classifying the remote sensing ratios of specific features for damage assessment. Waterbodies and highly vegetated areas were extracted by the modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), respectively, from after-earthquake images in order to improve the accuracy of damage maps. Urban areas were classified from pre-event interferometric coherence data. The affected areas from the earthquake were detected with the normalized difference (ND) between the pre-and co-event interferometric coherence. The results presented three damage types; namely, damage to buildings caused by ground motion, liquefaction, and landslides. The overall accuracy (94%) of the confusion matrix was excellent. Results for urban areas were divided into three damage levels (e.g., none-slight, slight-heavy, heavy-destructive) at a high (90%) overall accuracy level. Moreover, data on buildings damaged by liquefaction and landslides were in good agreement with field survey information. Overall, this study illustrates an effective damage assessment mapping approach that can support post-earthquake management activities for future events, especially in areas where geographical data are sparse.

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“…Probably because the appraisal of damage is useful for defining insurance premiums, private buildings or both private buildings and people (Hengjian et al 2003) are the elements more often considered in damage assessment procedures.…”

Section: (3) Merging Of Landslide Damage With Other Costsmentioning

confidence: 99%

A simplified method for assessing landslide damage indices

Petrucci

Gullà

2009

Nat Hazards

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The Support Analysis Framework (SAF) is a tool that converts descriptions of landslide effects into numerical indices expressing direct, indirect and intangible damage. Sections assessing direct damage are made up of 'elements' characterised by relative values and the 'levels of loss' that they can suffer. By typing an x value into the cell representing an element and a level of loss, formulas multiply the value of the element by the level of loss, obtaining a value used in the damage indices assessing. Indirect damage deals with: (a) emergency response and (b) efforts to restore pre-landslide conditions. The level of loss depends on the number of people affected (a) or the cost of actions (b). For intangible damage, the level of loss depends on the number of people affected. We assess the damage indices for a landslide occurred in Calabria (Italy) in 2005, using both the SAF and the SAFL, a customised version that takes into account the local socio-economic framework by including only elements present in the analysed municipality. We assess the damage indices for three scenarios with different initial conditions and compare them to the 2005 event; the results would have been useful to mitigate damage. The SAF can be used by nonspecialists, and it allows the ranking of damage caused by different landslides, thus supporting local authorities in prioritising countermeasures and civil protection offices to speed up responses to refund requests. The sorting of dormant phenomena according to their possible damage helps with defensive measures, emergency plans and insurance purposes.

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Untitled

Cited by 21 publications

References 1 publication

Comparison of Damage Assessment Maps Derived from Very High Spatial Resolution Satellite and Aerial Imagery Produced for the Haiti 2010 Earthquake

Comparison of Damage Assessment Maps Derived from Very High Spatial Resolution Satellite and Aerial Imagery Produced for the Haiti 2010 Earthquake

Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment

A simplified method for assessing landslide damage indices

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