The 2011 Tohoku (Japan) Tsunami Inundation and Liquefaction Investigated Through Optical, Thermal, and SAR Data

Chini, Marco; Piscini, Alessandro; Cinti, F. R.; Amici, S.; Nappi, Rosa; DeMartini, P. M.

doi:10.1109/lgrs.2012.2205661

Cited by 49 publications

(25 citation statements)

References 22 publications

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“…The recent access to a wide range of software, very high-resolution satellite imagery, and active and passive sensors facilitate the collection of data and the analysis and mapping of disaster events within a few hours. The application of remote sensing in disaster management follows the trend towards higher resolution (Ehrlich, Kemper, Blaes, & Soille, 2013), multidimensional (Kostyuchenko, 2015), and multi-technique (Chini et al, 2013;Pradhan et al, 2016). UAV networks serve as the most efficient situational awareness with higher resolution and faster capture and processing time (Ezequiel et al, 2014;Kruijff et al, 2012;Murphy & Stover, 2008;Robinson & Lauf, 2013).…”

Section: Hazards and Riskmentioning

confidence: 99%

Big Earth data analytics: a survey

Yang

et al. 2019

Big Earth Data

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Big Earth data are produced from satellite observations, Internet-of-Things, model simulations, and other sources. The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding, responding, and addressing challenges of Earth sciences and applications. In the past years, new technologies (such as cloud computing, big data and artificial intelligence) have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable. This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain. We first introduce the concepts of big Earth data. The architecture, various functionalities, and supporting modules are then reviewed from a generic methodology aspect. Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools. The driven questions are exemplified through cutting-edge Earth science researches and applications. A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.

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Section: Hazards and Riskmentioning

confidence: 99%

Big Earth data analytics: a survey

Yang

et al. 2019

Big Earth Data

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“…Based on ENVISAT ASAR data, Chini et al [121] investigated the 2011 Tohoku Earthquake, Japan, and reported a decrease of the SAR backscattering in damaged urban area caused by the decrease of the double bounce effect. However, one has to take into account that the tsunami, which reached the coast shortly after the Earthquake, washed numerous buildings away.…”

Section: Sar Amplitude Data With Focus On Intensity Correlationmentioning

confidence: 99%

“…Summarizing Section 3.2 we can distinguish between methods suited for damage assessment at the block level [9,97,121] and procedures enabling the damage analysis at the scale of single buildings [114,118]. The former is based on SAR data with a spatial resolution ranging from 8 to 30 m and coarser (Radarsat-1, ERS, ENVISAT, JERS and ALOS PALSAR, cf.…”

Section: Sar Amplitude Data With Focus On Intensity Correlationmentioning

confidence: 99%

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

2014

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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.

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“…Based on ENVISAT ASAR data, Chini at al. 12 reported a decrease of the SAR backscattering in a damaged urban area due to the 2011 Tohoku (Japan) earthquake that was caused by the decrease of the double bounce effect.…”

Section: Introductionmentioning

confidence: 99%

Earthquake damage mapping by using remotely sensed data: the Haiti case study

Romaniello

Piscini

Bignami

et al. 2017

J. Appl. Remote Sens

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Abstract. This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-auPrince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

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The 2011 Tohoku (Japan) Tsunami Inundation and Liquefaction Investigated Through Optical, Thermal, and SAR Data

Cited by 49 publications

References 22 publications

Big Earth data analytics: a survey

Big Earth data analytics: a survey

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

Earthquake damage mapping by using remotely sensed data: the Haiti case study

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