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

Citation data:

International Journal of Disaster Risk Reduction, ISSN: 2212-4209, Vol: 22, Page: 345-354

Publication Year:
2017
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DOI:
10.1016/j.ijdrr.2017.02.008
Author(s):
Muhammad Al-Amin Hoque; Stuart Phinn; Chris Roelfsema; Iraphne Childs
Publisher(s):
Elsevier BV
Tags:
Earth and Planetary Sciences; Social Sciences
review description
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.