Fast crisis response after natural disasters, such as earthquakes and tropical storms, is necessary to support, for instance, rescue, humanitarian, and reconstruction operations in the crisis area. Therefore, rapid damage mapping after a disaster is crucial, i.e., to detect the affected area, including grade and type of damage. Thereby, satellite remote sensing plays a key role due to its fast response, wide field of view, and low cost. With the increasing availability of remote sensing data, numerous methods have been developed for damage assessment. This article gives a comprehensive review of these techniques focusing on multi-temporal SAR procedures for rapid damage assessment: interferometric coherence and intensity correlation. The review is divided into six parts: First, methods based on coherence; second, the ones using intensity correlation; and third, techniques using both methodologies combined to increase the accuracy of the damage assessment are reviewed. Next, studies using additional data (e.g., GIS and optical imagery) to support the damage assessment and increase its accuracy are reported. Moreover, selected studies on post-event SAR damage assessment techniques and examples of other applications of the interferometric coherence are presented. Then, the preconditions for a successful worldwide application of multi-temporal SAR methods for damage assessment and the limitations of current SAR satellite missions are reported. Finally, an outlook to the Sentinel-1 SAR mission shows possible solutions of these limitations, enabling a worldwide applicability of the presented damage assessment methods.

/pdf/rapid-damage-assessment-by-means-of-multi-temporal-sar-a-14rd97xaks.pdf

Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1

This book presents a unique collection of state-of-the-art contributions by international remote sensing experts focussing on methodologies to extract information about urban areas from Synthetic Aperture Radar (SAR) data. SAR is an active remote sensing technique capable to gather data independently from sun light and weather conditions. Emphasizing technical and geometrical issues the potential and limits of SAR are addressed in focussed case studies, for example, the detection of buildings and roads, traffic monitoring, surface deformation monitoring, and urban change. These studies can be sorted into two groups: the mapping of the current urban state and the monitoring of change. The former covers, for instance, methodologies for the detection and reconstruction of individual buildings and road networks; the latter, for example, surface deformation monitoring and urban change. This includes also investigations related to the benefit of SAR Interferometry, which is useful to determine either digital elevation models and surface deformation or the radial velocity of objects (e.g. cars), and the Polarization of the signal that comprises valuable information about the type of soil and object geometry. Furthermore, the features of modern satellite and airborne sensor devices which provide high-spatial resolution of the urban scene are discussed.

Radar Remote Sensing of Urban Areas

The automatic reconstruction of buildings for the generation of city models is of great interest for different tasks. Three-dimensional information can be directly obtained from both, laser (LIDAR) and radar (InSAR) measurements. The features of both sensors are compared. The data acquisition by SAR is described, with emphasis on the special properties of the interferometric SAR principle. A segmentation approach for building reconstruction is proposed. The results show that building reconstruction is possible from InSAR, but the achievable level of detail cannot compete with LIDAR. The main source of limitation is the inherent side-looking scene illumination of SAR, giving rise to disturbing phenomena interfering with often large parts of the scene. Geometric constraints for the location and size of such problem areas are derived. To identify areas of unreliable data in SAR images of a built-up area, corresponding elevation data are analysed. The impact of the phenomena layover, shadow and dominant scattering at building locations is considered. For this task, a hybrid elevation reference is required. The buildings and the surrounding ground are represented as CAD planes. Natural objects like trees and bushes remain in the raster representation.

http://www.charim.net/sites/default/files/handbook/methodology/5/5.2/reference/Potential%20and%20limits%20of%20InSAR%20data%20for%20building%20reconstruction.pdf

Potential and limits of InSAR data for building reconstruction in built-up areas

This study proposes a workflow that enables the accurate identification of earthquake-induced damage zones by using coherence image pairs of the Sentinel-1 satellite before and after an earthquake event. The workflow uses interferometric synthetic aperture radar (InSAR) processing to account for coherence variations between coseismic and preseismic image pairs. The coherence difference between two image pairs is useful information to detect specific disasters in a regional-scale area after an earthquake event. To remove background effects such as the atmospheric effect and ordinal surface changes, this study employs the two-step threshold method to develop the coseismic coherence difference (CCD) map for our analyses. Thirty-four Sentinel-1 images between January 2015 and February 2016 were collected to process 30 preseismic image pairs and two coseismic image pairs for assessing multiple types of disasters in Tainan City of southwestern Taiwan, where severe damages were observed after the Meinong earthquake event. The coseismic unwrapping phases were further calculated to estimate the surface displacement in east-west and vertical directions. Results in the CCD map agree well with the observations from post-earthquake field surveys. The workflow can accurately identify earthquake-induced land subsidence and surface displacements, even for areas with insufficient geological data or for areas that had been excluded from the liquefaction potential map. In addition, the CCD details the distribution of building damages and structure failures, which might be useful information for emergency actions applied to regional-scale problems. The conversion of 2D surface displacement reveals the complex behavior of geological activities during the earthquake. In the foothill area of Tainan City, the opposite surface displacements in local areas might be influenced by the axis activities of the Kuanmiao syncline.

Coherence difference analysis of sentinel-1 SAR interferogram to identify earthquake-induced disasters in urban areas

On 24 August 2016, the M 6.2 Amatrice earthquake struck central Italy, well-known as a seismically active region, causing considerable damage to buildings in the town of Amatrice and the surrounding area. Damage from this earthquake was assessed quantitatively by means of multitemporal synthetic aperture radar (SAR) coherence and SAR intensity methods using dual-polarized SAR data obtained from the Sentinel-1 (VV, VH) and ALOS-2 (HH, HV) satellites. We developed linear discriminant functions based on three items: (1) the differential coherence values; (2) the differential backscattering intensity values of pre- and post-event images; and (3) a binary damage map of the optical pre- and post-event imagery. The accuracy of the proposed model was 84% for the Sentinel-1 data and 76% for the ALOS-2 data. The damage proxy maps deduced from the linear discriminant functions can be useful in the parcel-by-parcel assessment of building damage and development of spatial models for the allocation of urban search and rescue operations.

Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy

The authors studied the applicability of interferometric SAR (InSAR) using ERS-2 SAR data for detecting damaged urban areas by the Great Taiwan Earthquake (Chi-Chi Earthquake) occurred on Sep. 21, 1999. Three temporal ERS-2 SAR data acquired by Taiwan ground station on Jan. 21, May 6, and Sep. 23, in 1999 were used as the test data. By overlaying two SAR multi-look intensity images before (May 6) and after (Sep. 23) the earthquake, it was found that the damaged urban areas by the earthquake were hardly detected only by the intensity change. On the other hand, the overlay of the two coherence images from the pairs (Jan.-May) and (May-Sep.) indicated that the urban areas damaged severely (e.g. Dongshi) resulted in clear decrease of coherence by the earthquake occurrence, while the coherence in the urban areas not damaged severely (e.g. Taichung) did not change even after the earthquake. The result of this study suggests that InSAR technology has a great potential for detecting urban disasters by earthquake.

Detection of urban disaster using InSAR. A case study for the 1999 Great Taiwan Earthquake

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Detection of urban disaster using InSAR. A case study for the 1999 Great Taiwan Earthquake