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      Low-cost UAV surveys of hurricane damage in Dominica: automated processing with co-registration of pre-hurricane imagery for change analysis

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          Abstract

          In 2017, hurricane Maria caused unprecedented damage and fatalities on the Caribbean island of Dominica. In order to ‘build back better’ and to learn from the processes causing the damage, it is important to quickly document, evaluate and map changes, both in Dominica and in other high-risk countries. This paper presents an innovative and relatively low-cost and rapid workflow for accurately quantifying geomorphological changes in the aftermath of a natural disaster. We used unmanned aerial vehicle (UAV) surveys to collect aerial imagery from 44 hurricane-affected key sites on Dominica. We processed the imagery using structure from motion (SfM) as well as a purpose-built Python script for automated processing, enabling rapid data turnaround. We also compared the data to an earlier UAV survey undertaken shortly before hurricane Maria and established ways to co-register the imagery, in order to provide accurate change detection data sets. Consequently, our approach has had to differ considerably from the previous studies that have assessed the accuracy of UAV-derived data in relatively undisturbed settings. This study therefore provides an original contribution to UAV-based research, outlining a robust aerial methodology that is potentially of great value to post-disaster damage surveys and geomorphological change analysis. Our findings can be used (1) to utilise UAV in post-disaster change assessments; (2) to establish ground control points that enable before-and-after change analysis; and (3) to provide baseline data reference points in areas that might undergo future change. We recommend that countries which are at high risk from natural disasters develop capacity for low-cost UAV surveys, building teams that can create pre-disaster baseline surveys, respond within a few hours of a local disaster event and provide aerial photography of use for the damage assessments carried out by local and incoming disaster response teams.

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          Most cited references17

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          ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications

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            An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds

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              Mitigating systematic error in topographic models derived from UAV and ground-based image networks

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                Author and article information

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                Journal
                Natural Hazards
                Nat Hazards
                Springer Science and Business Media LLC
                0921-030X
                1573-0840
                April 2020
                March 12 2020
                April 2020
                : 101
                : 3
                : 755-784
                Article
                10.1007/s11069-020-03893-1
                d3c2a6a1-2ae2-4ae5-82ad-0d1f67863937
                © 2020

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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