Two consecutive earthquakes with magnitudes exceeding 7 occurred in the Republic of Turkey on February 6, 2023, causing severe damage to cities along the causative fault lines. Many satellite images were provided soon after the earthquakes for supporting the emergency response. In this study, the authors conducted a quick extraction of collapsed individual buildings using two pre-event and one post-event TerraSAR-X intensity images. The differences of backscattering coefficients and the correlation coefficients were calculated for pre-event and co-event SAR data pairs. Collapsed buildings were then identified based on the thresholds defined by the pre-event pair. Compared with visual interpretation results from high-resolution optical images, 64% of the collapsed buildings were detected successfully, demonstrating the effectiveness of our proposed method at an early stage of disaster response.

Wide spread landslides were extracted from airborne LiDAR data acquired before and after the 6 September 2018 Hokkaido-Eastern-Iburi earthquake. By taking the difference of Digital Surface Models (DSMs) from the LiDAR datasets, the failed slopes and accumulated soils were recognized. Since the pre-event dataset was obtained 12 years before the earthquake, the growth of trees was estimated based on forest registry block data. The adjusted DSM difference was compared with the visual inspection result of aerial photos by the Geospatial Information Authority of Japan and the accuracy of the confusion matrix was discussed.

A series of earthquakes hit Kumamoto Prefecture, Japan, in April 2016. Numerous landslides occurred in the surroundings of the Aso volcano and in the areas within 10-km from the Futagawa fault. Two months after the earthquake, the stagnation of the rainy season front brought heavy rainfall to the Kyushu Island. The rainfall caused additional landslides and the expansion of the earthquake-induced landslides. In this study, three temporal Lidar data taken before the 2016 Kumamoto earthquake, after the earthquake and after the heavy rainfall, were used to estimate the earthquake-induced and rainfall-induced landslides in the outer ring of the Mt. Aso. The height difference between two temporal digital surface models (DSMs) and that of digital terrain models (DTMs) were calculated. They were combined to extract the collapse zones and sediments of landslides. The results were verified by comparing with the reports of visual interpretation of aerial optical images.

In the rainy season of July 2021, debris flow suddenly occurred in Atami City, Japan, after a heavy rainfall and it caused significant impacts to human lives and houses along the Aizome river. In this study, the pre- and post-event SAR images acquired from the ALOS-2 satellite were used to extract the affected area and the results were compared with optical images and field survey data. The change detection techniques could identify the hard-hit zone to some extent. But the mountainous topography and thick vegetation cover hindered the accurate extraction of the affected area only from the SAR imagery. To interpret the SAR intensity images, a simple SAR simulation was further carried out using DSMs obtained from pre- and post-event LiDAR data.

One span of an aqueduct bridge suddenly collapsed in Wakayama City in western Japan on October 3, 2021. This study investigates the use of remote sensing data for the assessment of bridge situations in the normal time and in accidents/disasters. A field survey was conducted by the authors with the aid of a small UA V. Google Street View photos taken before the accident were also used. Based on these data, it is estimated that more than 5 hangers out of 18 might have failed for the collapsed span when the bridge collapse occurred. The failure of 4 hangers was also confirmed in the adjacent surviving span from the UAV images. The pre- and post-event high-resolution TerraSAR- X intensity images were also introduced to extract the collapsed span from the SAR data.

Record-breaking heavy rainfall hit Japan from July 3 to 31, 2020. The water level of rivers rose rapidly, and many bridges were washed away. In this study, two temporal ALOS-2 PALSAR-2 intensity images were introduced to detect damaged bridges over the Kuma River in Yatsushiro City and Ashikita Town, Kumamoto Prefecture, Japan. First, the backscattering models of bridges were investigated. Then the damage conditions of twenty-five target bridges were examined by the difference and correlation coefficient in the backscatter intensity. In addition, the increases of water levels was estimated by the movements of bridges' SAR model. A field survey report and a post-event SPOT-7 satellite optical image were used to verify our results.

An Mw 6.6 earthquake struck the eastern Iburi area of Hokkaido, Japan, on September 6, 2018. Due to the strong shaking, more than 3000 landslides occurred around Atsuma Town and killed 36 people. In this study, two pre-event and one post-event ALOS-2 PALSAR-2 images were used to extract landslides in Abira, Atsuma and Mukawa Towns. The characteristics of landslides were investigated using the backscattering coefficient, the coherence and the slope of elevation. Then the landslides in the target area were extracted by the optimal threshold values. A procedure using both the difference of backscattering coefficient and the difference of coherence was proposed to extract landslides. Finally, the result was verified by comparing with a post-event optical satellite image and a landslide distribution map by the GSI.

Due to the huge tsunamis occurred in the 2011 Tohoku-Oki, Japan, earthquake, more than 100 bridges located in the Pacific coast of the Tohoku region were severely damaged. In this study, the extraction of the damaged bridges in Miyagi Prefecture, Japan, was conducted by two methods using two post-event TerraSAR-X (TSX) intensity images, respectively. First, the statistical features within the outlines of the target bridges were calculated. The thresholding method of the backscatter intensity was applied to extract the damaged bridges. Then the TSX image was transformed into a binary image including water and non-water regions. The percentages of no-water regions within the bridge outlines were used to classify the washed-away and survived bridges. By comparing with the optical images and the report of field surveys, the accuracies of the proposed two methods and the influence of the shooting date were investigated.

In the March 11, 2011 Tohoku-Oki, Japan, earthquake, many bridges in Iwate and Miyagi Prefectures were washed away by associated tsunamis. In this study, nine bridges in the inundated areas of Iwate Prefecture, Japan were selected as targets to estimate their damage status from post-event TerraSAR-X (TSX) satellite images and Pi-SAR-X2 airborne SAR images. By visual interpretation, washed-away bridges could be identified from both the satellite and airborne very high-resolution SAR images. The statistical analysis was carried out to classify non-damaged bridges, debris blocked bridges and washed-away bridges. Pre- and post-event optical images and filed survey reports were introduced as the truth data of bridges' situation.

Unmanned Aerial Vehicles (UAVs) are becoming an efficient tool of image collection for affected areas due to natural disasters. In this study, UAV flights were carried out over a landslide affected site due to the July 2017 Northern Kyushu heavy rainfall in Japan. The UAV flights captured high-resolution still photos, and using them, three-dimensional (3D) models were developed based on a SfM (Structure-from-Motion) technique. The developed models could depict the damage situations vividly, and the location accuracy was evaluated through the comparison with the result of GPS measurements on the site.

Center for Environmental Remote Sensing, Chiba University develops L Band circularly polarized synthetic aperture radar (SAR) sensor for microsatellite (150 kg class). This paper explains the project, specification, antenna deployment, RF system, and experiment of circularly polarized SAR in anechoic chamber. In the future, this sensor will be employed to monitor global land deformation.

Unmanned Aerial Vehicles (UAVs) are becoming an efficient tool of high-resolution image collection for the places that are difficult to access or observe from the ground. In this study, UAV flights were carried out by the authors over various damage sites due to the 2016 Kumamoto, Japan earthquake, such as surface faulting, overturned tombstones, landslides, collapsed buildings and a bridge. The UAV flights captured high-resolution video footages and photos, and using them, three-dimensional (3D) models were developed based on a SfM (Structure-from-Motion) technique. The developed models could depict the damage situations vividly, and the accuracy was evaluated through comparison with aerial photos and field measurement results.

A strong typhoon hit the Pacific coast of Japan from the night of October 15th to the morning of 16th, 2013, and caused huge damages, especially in Izu-Oshima island, Tokyo. Synthetic aperture radar (SAR), which can observe the earth surface despite of weather conditions, is an effective tool to grasp damage situation caused by typhoons. In this study, pre-event Pi-SAR-L and post-event Pi-SAR-L2 airborne radar images with full polarizations were used to detect landsides and debris flows in Izu-Oshima. First, the extraction of potential landslide areas was carried out using only the post-event image by land-cover classification and from the standardized difference polarization index (NDPI). Then the difference of backscattering intensity between the pre- and post-event SAR images was calculated to extract potential landslides. In addition, a 5-m resolution digital elevation model (DEM) was introduced to remove errors. Finally, the results were verified through the comparison with the result from visual interpretation.

COSMO-SkyMed (CSK), a constellation composed of four Italian satellites equipped with X-band high-resolution SAR sensors, could capture the damage situation from the typhoon Haiyan throughout the event in spite of the frequent cloud cover condition. This study performs the investigation on the devastation in Tacloban City using a Multitemporal Correlation (MTR) map. Then calculate the Hyperboloid Change Index proposed in this study to indicate the effects by the typhoon. The damage was estimated by a thresholding operation and the result was compared with ThaiChote and GoogleEarth archive optical satellite images.

This paper examines an extraction method of widespread flooded areas in the Chao Phraya River basin of the central Thailand during the 2011 monsoon season. RADARSAT-2 imagery data were mainly used to extract affected areas while ThaiChote imagery data were aslo used as optical supporting data by the Thai government. In this study, the same data were used by a somewhat different method in more detail. The extracted results were validated by GeoEye-1, a high-resolution optical satellite image, water height data from gaging stations and a digital surface model (DEM) from LiDAR.

A method for capturing the two-dimensional (2D) surface movements from two temporal TerraSAR-X (TSX) intensity images has been proposed by the authors in previous research. However, it is impossible to detect the three-dimensional (3D) actual displacement from one pair of TSX images. Hence, three pairs of TSX images taken in ascending and descending paths were used to estimate 3D crustal movements in this study. First, the 2D crustal movements due to the 2011 Tohoku earthquake were detected from the three sets respectively. The relationship between the 3D actual displacement and 2D converted movement in SAR images was derived according to the observation model and shooting condition of the SAR sensor. Then the absolute 3D movements were estimated by the combination of the detected 2D movements that occurred within a short time interval. The results were verified by the GEONET observation records.

In this study, the flooded areas following the 2011 central Thailand flood were extracted using VNIR and TIR images of ASTER and ScanSAR-mode images of TerraSAR-X. The existence of water body was easily recognized for open spaces without trees and buildings from the NDVI value. The surface temperature was also found to be effective in detecting floods in a wide open space although it is limited by its coarse spatial resolution. The SAR intensity images were the most effective because water surfaces showed weak backscatter and they can be acquired at nighttime and under cloud-cover conditions. The extracted results were validated by a high-resolution optical satellite image.

Significant crustal movements were caused by the 2011 Tohoku, Japan earthquake. A method for capturing the surface movements from pre- and postevent TerraSAR-X (TSX) intensity images is proposed in this letter. Because the shifts of unchanged buildings were considered as crustal movements in the two synthetic aperture radar images, we first extracted buildings from the pre- and postevent images using a segmentation approach. Then, the unchanged buildings were detected by matching the buildings in the pre- and postevent images at similar locations. Finally, the shifts were calculated by area-based matching. The method was tested on the TSX images covering the Sendai area. Compared with GPS observation records, the proposed method was found to be able to detect crustal movement at a subpixel level.

The 11 March 2011 Tohoku, Japan earthquake caused gigantic tsunamis and widespread devastations. Various satellites quickly captured the details of affected areas, and were used for emergency response. In this study, high-resolution pre- and post-event TerraSAR-X (TSX) intensity images were used to identify damaged buildings. Since the damaged buildings show changes in backscattering intensity, they can be detected by calculating the difference. A GIS map was introduced to identify individual damaged buildings and investigate their characteristics. According to the side-looking nature of SAR sensors, the buildings' shapes obtained from the GIS map were converted to match their locations in the TSX images. Then washed-away and damaged buildings were extracted using the changed area of SAR intensity within a building's wall and outline. The results were compared with visual interpretation results, and the accuracy of the proposed method was confirmed.

Radar remote sensing, as in SAR, is an independent of daylight and cloud cover and hence has found wide applications including damage detection. The Haiti earthquake (Mw=7) on January 12, 2010 caused widespread casualty and extensive damage to structures including the capital city Port-au-Prince. 217,000 people lost their lives due to this event. Building damage detection was performed using very high-resolution pre- and post-event SAR imageries from TerraSAR-X. Radar characteristics like the correlation coefficient and the backscattering difference between the two SAR images taken in different times were calculated. To find out the building damage in the densely populated settlements as in Port-au-Prince, the threshold values of the correlation coefficient and backscattering difference were suggested and then efficiency of these thresholds was evaluated by overlaying on the optical satellite images. Then building damages could be observed even in the dense urban setting of Port-au-Prince.

Urban areas grow and change rapidly all over the world. Hence, regular and up-to-date information on urban changes is required for urban planning and disaster management. In this study, two temporal TerraSAR-X images are used to monitor urban changes. The study area is focused on a part of central Tokyo, Japan. Firstly, the changes between two images are checked by color composition. Then the difference and the correlation coefficient between the two images are calculated with a sliding window. A new factor that combines the difference and the correlation coefficient is proposed to detect changed areas. Finally, two high resolution optical images are introduced to verify the accuracy of the detection results.

Shadows in remote sensing images often result in problems for many applications such as land-cover classification, change detection, and damage detection in disasters. Due to these reasons, it is very useful if the radiance of shadowed areas is corrected to the same radiance as shadow-free areas. In this study, a shadow detection and correction method is proposed. Shadowed areas are detected by object-based classification, using brightness values and a neighbor relationship. Then the detected shadowed areas are corrected by a liner function to produce a shadow-free image. The shadowed areas with different darkness are corrected with different ratios to improve the accuracy of the result. The spectral characteristics of sunlit and shadowed areas in several QuickBird images were studied and then the shadow-free radiance was obtained.

SAR images obtained before and after a natural disaster are considered to be useful for emergency response due to its all-weather and sunlight-independent characteristics. Recently, the spatial resolutions of SAR systems have been improved significantly. In this paper, SAR intensity images acquired before and after the 2008 Iwate-Miyagi, Japan, earthquake from ALOS/PALSAR (L-band) and TerraSAR-X (X-band) are employed to investigate the radar backscattering characteristics for various acquisition and surface conditions. The spatial resolution, radar frequency, flight path, and incidence angle were shown to affect SAR backscattering echo, depending on surface materials and roughness. It is also observed that the difference of the backscattering coefficients at the pre- and post-event times gets large and their correlation coefficient becomes small at the locations of landslides and slope failures.

The effects of shadow in remote sensing imagery are investigated. The measurement of radiance in sunlit and shadowed areas was carried out to investigate the spectral characteristics of sunlight. Based on this observation, it is found that the radiance ratio (shadow/sunlit) increases as the sunlight gets weaker and the ratio is dependent on the wavelength of sunlight. The darkness of shadow is also found to vary depending on the surrounding condition. Thus the condition to restore a shadow-free image depends on the spectral bands and the location even in one image. A QuickBird image is then introduced and the spectral characteristics of sunlit and shadowed areas are investigated. Based on these observations, a method to detect shadowed areas and restore the shadow-free radiance for the multi-spectral bands is proposed. The effectiveness of the shadow correction method is demonstrated for the QuickBird image.

A new object-based method is developed to extract the moving vehicles and subsequently detect their speeds from two consecutive digital aerial images automatically. Several parameters of gray values and sizes are examined to classify the objects in the image. The vehicles and their associated shadows can be discriminated by removing big objects such as roads. To detect the speed, firstly the vehicles and shadows are extracted from the two images. The corresponding vehicles from these images are linked based on the order, size, and their distance within a threshold. Finally, using the distance between the corresponding vehicles and the time lag between the two images, the moving speed can be detected. Our test shows a promising result of detecting the moving vehicles' speeds. Further development will employ the proposed method for a pair of QuickBird panchromatic and multi-spectral images, which are at a coarser spatial resolution.

The massive 2004 Indian Ocean tsunami caused vast devastation along the coastal areas in countries around the Indian Ocean rim. Satellite images of various spatial resolutions could quickly capture the affected areas and were used for emergency response after the catastrophe occurred. To figure out the extent of affected areas, moderate-resolution satellites (e.g., Terra-ASTER) images are more suitable than high-resolution satellites (e.g., Ikonos) images. Basically, tsunami-affected areas can be observed and detected through land cover changes. Based on the nature of the tsunami attack, we chose the normalized difference vegetation index, soil index, and water index as indicators to help detect changes. This paper first investigates the fluctuations of these indexes and their differences using ASTER images of southern Thailand. The investigation is carried out in two cases: one using only the data acquired after the tsunami, and the other using both data acquired before and after the tsunami. Consequently, the thresholds of index differences are set up for the detection of tsunami-affected areas. In addition, since landform is a significant factor to determine the extent of tsunami runup, Shuttle Radar Topography Mission data are employed to perform geomorphological classification and to assess its relationship with the tsunami-affected areas

An earthquake occurred in the coast of Algeria on May 21, 2003. The cities of Boumerdes and Zemmouri were the most extensively damaged areas. Canadian SAR satellite, RADARSAT, observed Boumerdes area by the fine-beam mode, on 4 days after the event. European SAR satellite, ERS, also observed the same area on June 7, 2003. On December 26, 2003, another strong earthquake occurred beneath the city of Bam, Iran. Severely damaged areas were found widely being distributed in the city from high-resolution optical satellite images obtained after the event. ENVISAT also captured the hard-hit areas on January 7, 2004. In this paper, we investigated the characteristics of damaged areas in these SAR images by visual interpretation and clarified the effect of spatial resolution for the detection of damaged buildings. Then, we applied our automated damages detection technique, which was developed based on the data set of the 1995 Kobe earthquake, to the SAR images of Algeria and Iran

In this paper a new transformation which enables us to detect boundaries of cast shadows in high resolution satellite images is introduced. The transformation is based on color invariant indices. Different radiometric restoration techniques such as gamma correction, linear-correlation correction and histogram matching are introduced in order to restore the brightness of detected shadow area

An automatic method for LIDAR-based (Light Detection And Ranging) change detection is proposed. Highly dense LIDAR point clouds are recommended as the most suitable gathered data for dense urban areas. The main goal is to develop an up-to-date building inventory database, which is in great demand for the earthquake-prone areas like Japan, using LIDAR as primary data. Two LIDAR surveying flights in 1999 and 2004 provide the test data over Roppongi, Tokyo, Japan. Detected results are visual evaluation using orthophoto produced by LIDAR surveying flights. The highly automated processing proved the efficiency of using LIDAR for a quick and reliable updating. Moreover, it also implies the feasibility for detection of damaged buildings due to earthquake.

QuickBird observed the city of Zemmouri, Algeria, before and after the May 21, 2003 Algeria earthquake. Using the pre-event and post-event pan-sharpened images, visual inspection of building damage was carried out by the five authors of this paper individually. A total 1,399 buildings were classified into five damage levels of European Micro-seismic Scale. The results from the different interpreters were reasonably close for collapsed buildings but the difference becomes larger for smaller damage levels. The locations of refugee tents in the two post-event images were also identified. These observations indicate that high-resolution satellite images can provide quite useful information to emergency management after natural disasters.

A new semi-automatic processing system for classification of airborne laser scanner cloud points is developed. To mitigate the difficulty caused by the complex distribution of objects on Earth's surface, wavelet was adopted in size-based clustering of laser points. A hybrid method of processing laser scanner data in both grid and raw formats was also adopted to speed up the processing time and adjust the smoothing effect of interpolation. The processing focused on processing the data acquired over urban area. This paper presents and explains the components of the system using the test data acquired over Shinjuku area, Tokyo, Japan.

One of the remarkable characteristics of synthetic aperture radar (SAR) is to record physical value called the backscattering coefficient of the Earth's surface not depending on weather conditions and Sun illumination. Therefore, SAR could be a powerful tool and be used to develop a universal method for grasping damaged areas by disasters such as earthquakes, forest fires and floods. Detailed ground truth data for building damage due to the 1995 Kobe earthquake provided us the opportunity to investigate the relationship between the backscattering property from SAR images and the degree of damage. From the above analysis we have already developed a method to detect areas of building damage. In this paper, we applied this method to the images taken over the area hit by the 1999 Kocaeli, Turkey and the 2001 Gujarat, India earthquakes, and then the accuracy of the proposed method was examined by comparing the results of the analyses with those from the damage surveys.

In multi-stage remote sensing performed by NASDA just after the 1995 Hyogoken-Nanbu (Kobe) earthquake, stricken areas were observed by the airborne MSS, which has twelve bands between visible and thermal infrared. In this study, after some training areas were selected in Nada Ward using GIS data based on a field damage survey, spectral characteristics of damaged and non-damaged buildings were investigated. Then, the distribution corresponding to the damage level of buildings in the hard-hit area of Kobe City was estimated by the maximum likelihood classifier. The estimated result of areas with burned and severely damaged buildings was relatively in good agreement with the field survey data. An application of this method, based solely on the post-event image, to early damage assessment systems can be expected.