KEYWORDS: Buildings, LIDAR, Earthquakes, Data modeling, Data acquisition, Synthetic aperture radar, Image resolution, Damage detection, Data centers, Geographic information systems
The 2016 Kumamoto earthquake was a series of earthquake events, including the moment-magnitude (Mw) 7.0 mainshock on April 16, 2016 and the Mw 6.2 foreshock on April 14. Due to the strong shaking, more than 8,000 buildings were collapsed and about 30,000 buildings were severely damaged. Geospatial Information Authority of Japan (GSI) acquired high density (5.93 point/m2 ) Lidar data on May 8, 2016, three weeks after the earthquakes. In this study, the pre- and postevent Lidar data were used to detect the collapsed buildings in Mashiki town, Kumamoto Prefecture, Japan, which was one of the most severely affected regions. The pre-event Lidar data were taken on May 15, 2006 with the 0.72 point/m2 density. A report of building damage grades obtained by the field surveys of the Architectural Institute of Japan (AIJ) was introduced as the reference. First, the statistics of height differences within each building outline were calculated. Then the characteristics of the different damage grades were investigated. As a result, the average values of the height differences were adopted to extract collapsed buildings. 618 buildings were extracted as collapsed from 3,408 buildings existed in 2006. Comparing with the reference, 91% collapsed buildings were detected successfully, and the F-score was 0.88.
Extraction of landslides from a pair of Lidar data taken before and after the 2016 Kumamoto, Japan, earthquake was carried out. The spatial correlation coefficient of the two Lidar data was calculated, and the horizontal shift of the April-23 DSM with the maximum correlation coefficient was considered as the crustal movement by the April-16 main-shock. By taking the difference of the co-registered DSMs, the change of the surface elevation was calculated. This elevation change includes many effects due to the earthquake, such as landslides and building collapses, and the other temporal changes, such as parking cars and construction/rescue activities. Thus in this study, only large-scale elevation changes more than plus and minus 2.0 m and the areas of larger than 200 square meters were extracted as possible landslides. The extracted areas were compared with aerial photos taken after the Kumamoto earthquake and other soil movement maps made for this event. The result shows that large-scale landslides were easily extracted by the difference of the DSMs and even ground deformations along surface ruptures, where trees were torn down, could be identified.
Extraction of collapsed buildings from a pair of Lidar data taken before and after the 2016 Kumamoto, Japan, earthquake was conducted. Lidar surveys were carried out for the affected areas along the causative faults by Asia Air Survey Co., Ltd. The density of the collected Lidar data was 1.5 - 2 points/m2 for the first flight on April 15, 2016 and 3 - 4 points/m2 for the second flight on April 23, 2016. The spatial correlation coefficient of the two Lidar data was calculated using a 101 x 101 pixels window (50 m x 50 m), and the horizontal shift of the April-23 digital surface model (DSM) with the maximum correlation coefficient was considered as the crustal movement by the April-16 main-shock. The horizontal component of the calculated coseismic displacement was applied to the post-event DSM to cancel it, and then the vertical displacement between the two DSMs was calculated. The both horizontal and vertical coseismic displacements were removed to extract collapsed buildings. Then building-footprints were employed to assess the changes of the DSMs within them. The average of difference between the pre- and post-event DSMs within a building footprint was selected as a parameter to evaluate whether a building is collapsed or not. The extracted height difference was compared with the spatial coherence value calculated from pre- and post-event ALOS-2 PALSAR-2 data and the result of field damage surveys. Based on this comparison, the collapsed buildings could be extracted well by setting a proper threshold value for the average height difference.
High-resolution commercial synthetic aperture radar (SAR) satellites with resolutions of several meters have recently been used for effective disaster monitoring. One study reported the earthquake’s displacement using the pixel matching method with both pre- and postevent TerraSAR-X data, with a validated accuracy of ∼30 cm at global navigation satellite system (GNSS) Earth observation network (GEONET) reference points. However, it is insufficient to determine the accuracy using analysis of only a couple of data points per orbit. In addition, the errors were not reported because the number of data samples was too small to discuss the statistics. In order to better understand displacement accuracy, we analyzed displacement features using the pixel matching method to evaluate the relative geolocation accuracies of the TerraSAR-X product. First, we used fast Fourier transform oversampling 16 times to develop the pixel matching method for estimating the displacement at the subpixel level using the TerraSAR-X StripMap dataset. Second, we applied this methodology to 20 pairs of images from the Tokyo metropolitan area and calculated the displacement for each image pair. Third, we conducted spatial and temporal analyses in order to understand the displacement features. Finally, we evaluated the displacement accuracy by comparison with GEONET and solid earth tide data as a reference.
Satellite remote sensing is recognized as one of the effective tools for detecting and monitoring affected areas due to natural disasters. Since SAR sensors can capture images not only at daytime but also at nighttime and under cloud-cover conditions, they are especially useful at an emergency response period. In this study, multi-temporal high-resolution TerraSAR-X images were used for damage inspection of the Kathmandu area, which was severely affected by the April 25, 2015 Gorkha Earthquake. The SAR images obtained before and after the earthquake were utilized for calculating the difference and correlation coefficient of backscatter. The affected areas were identified by high values of the absolute difference and low values of the correlation coefficient. The post-event high-resolution optical satellite images were employed as ground truth data to verify our results. Although it was difficult to estimate the damage levels for individual buildings, the high resolution SAR images could illustrate their capability in detecting collapsed buildings at emergency response times.
Using a dataset from the 2013 IEEE data fusion contest, a basic study to classify urban land-cover was carried out. The spectral reflectance characteristics of surface materials were investigated from the airborne hyperspectral (HS) data acquired by CASI-1500 imager over Houston, Texas, USA. The HS data include 144 spectral bands in the visible to near-infrared (380 nm to 1050 nm) regions. A multispectral (MS) image acquired by WorldView-2 satellite was also introduced in order to compare it with the HS image. A field measurement in the Houston’s test site was carried out using a handheld spectroradiometer by the present authors. The reflectance of surface materials obtained by the measurement was also compared with the pseudo-reflectance of the HS data and they showed good agreement. Finally a principal component analysis was conducted for the HS and MS data and the result was discussed.
Building damage such as to side-walls or mid-story collapse is often overlooked in vertical optical images. Hence, in
order to observe such building damage modes, high-resolution SAR images are introduced considering the side-looking
nature of SAR. In the 2011 Tohoku, Japan, earthquake, a large number of buildings were collapsed or severely damaged
due to repeated tsunamis. One of the important tsunami effects on buildings is that the damage is concentrated to their
side-walls and lower stories. Thus this paper proposes the method to detect this kind damage from the change in layover
areas in SAR intensity images. Multi-temporal TerraSAR-X images covering the Sendai-Shiogama Port were employed
to detect building damage due to the tsunamis caused by the earthquake. The backscattering coefficients in layover areas
of individual buildings were extracted and then, the average value in each layover area was calculated. The average value
was seen to decrease in the post-event image due to the reduced backscatter from building side-walls. This example
demonstrated the usefulness of high-resolution SAR intensity images to detect severe damage to building side-walls
based on the changes of the backscattering coefficient in the layover areas.
The Tohoku earthquake on March 11, 2011 caused widespread devastation and significant crustal movements.
According to the GPS Earth Observation Network System (GEONET) operated by Geospatial System Institution (GSI)
of Japan, crustal movements with a maximum of 5.3 m to the horizontal direction (southeast) and a maximum of 1.2 m to
the vertical direction (down) were observed over wide areas in the Tohoku (north-western) region of Japan. A method
for capturing the two-dimensional (2D) surface movements from pre- and post-event TerraSAR-X (TSX) intensity
images has been proposed by the present authors in our previous research. However, it is impossible to detect the threedimensional
(3D) actual displacement from one pair of TSX images. Hence, two pairs of pre- and post-event TSX
images taken in ascending and descending paths respectively were used to detect 3D crustal movements in this study.
First, two sets of 2D movements were detected by the authors’ method. The relationship between the 3D actual
displacement and 2D converted movement in SAR images was derived according to the observation model of the TSX
sensor. Then the 3D movements were calculated from two sets of detected movements in a short time interval. The
method was tested on the TSX images covering the Sendai area. Comparing with the GEONET observation records, the
proposed method was found to be able to detect the 3D crustal movement at a sub-pixel level.
Earthquakes that have caused large-scale damage in developed areas, such as the 1994 Northridge and 1995 Kobe events, remind us of the importance of making quick damage assessments in order to facilitate the resumption of normal activities and restoration planning. Synthetic aperture radar (SAR) can be used to record physical aspects of the Earth's surface under any weather conditions, making it a powerful tool in the development of an applicable method for assessing damage following natural disasters. Detailed building damage data recorded on the ground following the 1995 Kobe earthquake may provide an invaluable opportunity to investigate the relationship between the backscattering properties and the degree of damage. This paper aims to investigate the differences between the backscattering coefficients and the correlations derived from pre- and post-earthquake SAR intensity images to smoothly detect areas with building damage. This method was then applied to SAR images recorded over the areas affected by the 1999 Kocaeli earthquake in Turkey, the 2001 Gujarat earthquake in India, and the 2003 Boumerdes earthquake in Algeria. The accuracy of the proposed method was examined and confirmed by comparing the results of the SAR analyses with the field survey data.
This paper presents a newly developed multi-level detection methodology using high-resolution optical satellite images.
It aims to balance the quick response requirement and the details of detected results and hence, to satisfy various user
demands. Damage extent is firstly detected from only post-disaster image on the first level, texture-based processing.
This level quickly maps the damage extent and damage distribution but not in details. In some focused areas, the second
level with object-based processing will derive further details of the damage using both pre- and post- data. The
methodology is demonstrated on QuickBird images acquired over the damage areas of Bam, Iran, which was extensively
devastated by the December 2003 earthquake. The detected results show a good agreement with the ones by visual
detection and field survey.
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