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

Tamkuan, Nopphawan; Nagai, Masahiko

doi:10.3390/ijgi6070188

Cited by 35 publications

(31 citation statements)

References 27 publications

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“…The results illustrate the efficiency of using those proxies for building-based damage assessment and classification when UAV data are available. Tamkuan and Nagai [68] used similar proxies: cracks, displaced and collapsed roofing tiles, wall mortar that is somewhat peeled off, or inclined buildings ( Figure 3D) to evaluate and classify the building-based damages, e.g., roof tile displacements and inclined buildings are used as proxies for detecting slightly and heavily damaged buildings, respectively. Rubble piles and debris ( Figure 3E) are the first features to draw attention and they have been frequently used as proxies for damage detection [76][77][78][79][80][81], while spalling of buildings ( Figure 3E) also is one of the features for heavy damages which has been used for damage detection with UAV [71,82] and satellite images [75].…”

Section: Building Morphologymentioning

confidence: 99%

“…Using changes to different components of the buildings, e.g., facade and wall, as a proxy to evaluate damages to the buildings in detail remains a challenge and continues to be actively studied (e.g., Duarte, et al [83]). [70], (D) inclined building [68,70], (E) debris, rubble piles, spalling [82], and (F) facade windows symmetry [86].…”

Section: Building Morphologymentioning

confidence: 99%

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Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods.While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery.2 of 30 threats, to reduce own overall vulnerability, and to allow the community to recover from adverse impacts when they occur. Decades of disaster research offer extensive findings in this respect [4][5][6][7]. Remote sensing (RS)-as an effective and rapid tool for monitoring large areas-is essential for the acquisition of geospatial data, which in turn constitutes the basis for risk assessment and management. RS is widely used for various aspects of the DRM, ranging from vulnerability [8] to rapid damage assessments [9], for diverse areas ranging from coastal ecosystems [10] to complex urban settings [11], and for disasters as diverse as landslides [12,13] or cyclones [14].Numerous methods have been developed to extract information from RS data to identify, characterize, or quantify different phases of the disaster risk cycle: response, recovery, prevention/mitigation, and preparedness [15]. However, early studies predominantly considered the physical side of the assessments for both ...

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Section: Building Morphologymentioning

confidence: 99%

Section: Building Morphologymentioning

confidence: 99%

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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“…Landslides are automatically identified on the basis of NDVI trajectories. In [6], the information obtained by two satellites, Landsat-8 and Advanced Land Observing Satellite No. 2 (ALOS-2), was combined to make damage maps after a disaster occurs.…”

Section: Introductionmentioning

confidence: 99%

“…Earthquakes and torrential rain cause landslides, but finding the areas of landslides is difficult because they often occur in areas that are not easily accessed.Research on landslide detection using satellite remote sensing has been actively conducted [2]- [4]. Many methods detect landslides on the basis of the amount of change in the normalized difference vegetation index (NDVI) calculated from optical satellite images before and after the disaster [5], [6]. NDVI…”

mentioning

confidence: 99%

Landslide Classification from Synthetic Aperture Radar Images Using Convolutional Neural Network with Multichannel Information

Mabu

Nakayama

Kuremoto

2020

Journal of Signal Processing

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Detection of disaster-stricken areas using synthetic aperture radar (SAR) images is important in countries and regions with heavy rain and earthquakes. Although it is important to immediately find disaster-stricken areas when a disaster occurs, it takes time to read SAR images and also needs experience and expertise. Therefore, machine learning, especially deep learning, is expected to be applied to the classification of disaster-stricken areas. Classification using deep learning is often executed on patch images of local areas. However, patch-based classification would miss information on the surrounding areas such as topographic features. In this study, a convolutional neural network (CNN) is applied to the classification of SAR images using the following techniques. When making the images input to a CNN, two multichannel image generation methods, i.e, a zero-padding method and map-concatenation method, are used, where the target areas to be classified and their surrounding areas are combined to form multichannel images. In the experiments, the zero-padding method and map-concatenation method are evaluated by the classification performance of SAR images that cover the northern Kyushu area in Japan, where large-scale landslides due to heavy rain occurred in 2017. Through the experiments, we clarify the appropriate CNN structures with multichannel information for landslide classification.ters such as the metropolitan-area earthquake and the Nankai Trough earthquake are imminent; thus, detecting disaster-stricken areas by satellite remote sensing is extremely important. Earthquakes and torrential rain cause landslides, but finding the areas of landslides is difficult because they often occur in areas that are not easily accessed.Research on landslide detection using satellite remote sensing has been actively conducted [2]- [4]. Many methods detect landslides on the basis of the amount of change in the normalized difference vegetation index (NDVI) calculated from optical satellite images before and after the disaster [5], [6]. NDVI

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“…Numerous articles were related this topic by a group of respectful researchers in which they applied the fusion of optical and SAR in several applications; e.g. estimating forest biomass (Choi and Sarker 2013) (Zhao et al 2016), road network extraction (Khesali et al 2016), assessment of water body structure (Hunger et al 2016) ,earthquake damage assessment (Tamkuan and Nagai 2017) , land use/ land cover classification (Sukawattanavijit et al 2017), maritime monitoring (Liu et al 2015), shoreline extraction (Abd Manaf et al 2015) and Flood detection (Ward et al 2014). This present research is arranged as follow:…”

Section: Introductionmentioning

confidence: 99%

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Mahyoub

Fadil

Mansour

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Remote sensing and image fusion have recognized many important improvements throughout the recent years, especially fusion of optical and synthetic aperture radar (SAR), there are so many published papers that worked on fusing optical and SAR data which used in many application fields in remote sensing such as Land use Mapping and monitoring. The goal of this survey paper is to summarize and synthesize the published articles from 2013 to 2018 which focused on the fusion of Optical and synthetic aperture radar (SAR) remote sensing data in a systematic literature review (SLR), based on the pre-published articles on indexed database related to this subject and outlining the latest techniques as well as the most used methods. In addition this paper highlights the most popular image fusion methods in this blending type. After conducting many researches in the indexed databases by using different key words related to the topic “fusion Optical and SAR in remote sensing”, among 705 articles, chosen 83 articles, which match our inclusion criteria and research questions as results ,all the systematic study ‘ questions have been answered and discussed.</p>

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

Cited by 35 publications

References 27 publications

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

Landslide Classification from Synthetic Aperture Radar Images Using Convolutional Neural Network with Multichannel Information

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

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