Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)

This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of mult...

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Autores principales:	Davoud Omarzadeh, Sadra Karimzadeh, Masashi Matsuoka, Bakhtiar Feizizadeh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	OBIA semi-automated approach WorldView-2 damage mapping Kermanshah earthquake Science Q
Acceso en línea:	https://doaj.org/article/62548aa19488436a97f7cf0a269e8261
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spelling	oai:doaj.org-article:62548aa19488436a97f7cf0a269e82612021-11-11T18:52:29ZEarthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)10.3390/rs132142722072-4292https://doaj.org/article/62548aa19488436a97f7cf0a269e82612021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4272https://doaj.org/toc/2072-4292This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies.Davoud OmarzadehSadra KarimzadehMasashi MatsuokaBakhtiar FeizizadehMDPI AGarticleOBIAsemi-automated approachWorldView-2damage mappingKermanshah earthquakeScienceQENRemote Sensing, Vol 13, Iss 4272, p 4272 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	OBIA semi-automated approach WorldView-2 damage mapping Kermanshah earthquake Science Q
spellingShingle	OBIA semi-automated approach WorldView-2 damage mapping Kermanshah earthquake Science Q Davoud Omarzadeh Sadra Karimzadeh Masashi Matsuoka Bakhtiar Feizizadeh Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
description	This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies.
format	article
author	Davoud Omarzadeh Sadra Karimzadeh Masashi Matsuoka Bakhtiar Feizizadeh
author_facet	Davoud Omarzadeh Sadra Karimzadeh Masashi Matsuoka Bakhtiar Feizizadeh
author_sort	Davoud Omarzadeh
title	Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
title_short	Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
title_full	Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
title_fullStr	Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
title_full_unstemmed	Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
title_sort	earthquake aftermath from very high-resolution worldview-2 image and semi-automated object-based image analysis (case study: kermanshah, sarpol-e zahab, iran)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/62548aa19488436a97f7cf0a269e8261
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Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)

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