Tropical cyclone disaster management using remote sensing and spatial analysis: A review

Hoque, MAA; Phinn, S; Roelfsema, C; Childs, I

Tropical cyclone disaster management using remote sensing and spatial analysis: A review

Hoque, MAA Phinn, S Roelfsema, C Childs, I

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: International Journal of Disaster Risk Reduction, 2017, 22, pp. 345-354
Issue Date:: 2017-06-01

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Field	Value	Language
dc.contributor.author	Hoque, MAA
dc.contributor.author	Phinn, S
dc.contributor.author	Roelfsema, C
dc.contributor.author	Childs, I
dc.date.accessioned	2022-09-16T03:41:32Z
dc.date.available	2022-09-16T03:41:32Z
dc.date.issued	2017-06-01
dc.identifier.citation	International Journal of Disaster Risk Reduction, 2017, 22, pp. 345-354
dc.identifier.issn	2212-4209
dc.identifier.issn	2212-4209
dc.identifier.uri	http://hdl.handle.net/10453/161845
dc.description.abstract	Tropical cyclones and their often devastating impacts are common in many coastal areas across the world. Many techniques and dataset have been designed to gather information helping to manage natural disasters using satellite remote sensing and spatial analysis. With a multitude of techniques and potential data types, it is very challenging to select the most appropriate processing techniques and datasets for managing cyclone disasters. This review provides guidance to select the most appropriate datasets and processing techniques for tropical cyclone disaster management. It reviews commonly used remote sensing and spatial analysis approaches and their applications for impacts assessment and recovery, risk assessment and risk modelling. The study recommends the post-classification change detection approach through object-based image analysis using optical imagery up to 30 m resolution for cyclone impact assessment and recovery. Spatial multi-criteria decision making approach using analytical hierarchy process (AHP) is suggested for cyclone risk assessment. However, it is difficult to recommend how many risk assessment criteria should be processed as it depends on study context. The study suggests the geographic information system (GIS) based storm surge model to use as a basic input in the cyclone risk modelling process due to its simplicity. Digital elevation model (DEM) accuracy is a vital factor for risk assessment and modelling. The study recommends DEM spatial resolution up to 30 m, but higher spatial resolution DEMs always performs better. This review also evaluates the challenges and future efforts of the approaches and datasets.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	International Journal of Disaster Risk Reduction
dc.relation.isbasedon	10.1016/j.ijdrr.2017.02.008
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0502 Environmental Science and Management, 1117 Public Health and Health Services, 1604 Human Geography
dc.title	Tropical cyclone disaster management using remote sensing and spatial analysis: A review
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	0502 Environmental Science and Management
utslib.for	1117 Public Health and Health Services
utslib.for	1604 Human Geography
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-09-16T03:41:31Z
pubs.publication-status	Published
pubs.volume	22

Abstract:

Tropical cyclones and their often devastating impacts are common in many coastal areas across the world. Many techniques and dataset have been designed to gather information helping to manage natural disasters using satellite remote sensing and spatial analysis. With a multitude of techniques and potential data types, it is very challenging to select the most appropriate processing techniques and datasets for managing cyclone disasters. This review provides guidance to select the most appropriate datasets and processing techniques for tropical cyclone disaster management. It reviews commonly used remote sensing and spatial analysis approaches and their applications for impacts assessment and recovery, risk assessment and risk modelling. The study recommends the post-classification change detection approach through object-based image analysis using optical imagery up to 30 m resolution for cyclone impact assessment and recovery. Spatial multi-criteria decision making approach using analytical hierarchy process (AHP) is suggested for cyclone risk assessment. However, it is difficult to recommend how many risk assessment criteria should be processed as it depends on study context. The study suggests the geographic information system (GIS) based storm surge model to use as a basic input in the cyclone risk modelling process due to its simplicity. Digital elevation model (DEM) accuracy is a vital factor for risk assessment and modelling. The study recommends DEM spatial resolution up to 30 m, but higher spatial resolution DEMs always performs better. This review also evaluates the challenges and future efforts of the approaches and datasets.

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http://hdl.handle.net/10453/161845