Low-cost UAV surveys of hurricane damage in Dominica: automated processing with co-registration of pre-hurricane imagery for change analysis
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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低成本无人机对多米尼加飓风损害进行调查：自动化处理并共同注册飓风前图像以进行变化分析
DOI：
10.1007/s11069-020-03893-1
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Schaefer M
中科院分区：
文献类型：
--
作者：
Schaefer M
2017年，飓风玛丽亚在加勒比岛国多米尼加造成了前所未有的破坏和死亡。为了“重建得更好”，并从造成损害的过程中吸取教训，必须迅速记录、评估和绘制多米尼克和其他高风险国家的变化。本文提出了一种创新的和相对较低的成本和快速的工作流程，准确量化地貌变化的自然灾害后。我们使用无人机（UAV）调查收集了多米尼加44个受飓风影响的关键地点的航空图像。我们使用运动结构（SfM）以及专用的Python脚本进行自动处理，从而实现快速的数据周转。我们还将这些数据与飓风玛丽亚前不久进行的早期无人机调查进行了比较，并建立了共同注册图像的方法，以提供准确的变化检测数据集。因此，我们的方法与以前的研究有很大的不同，这些研究评估了在相对不受干扰的环境中无人机数据的准确性。因此，这项研究为基于无人机的研究提供了一个原始的贡献，概述了一个强大的航空方法，这是潜在的灾后损害调查和地貌变化分析的巨大价值。我们的研究结果可用于（1）在灾后变化评估中利用无人机;（2）建立地面控制点，以便进行前后变化分析;（3）在可能发生未来变化的地区提供基线数据参考点。我们建议自然灾害风险高的国家发展低成本无人机调查的能力，建立能够进行灾前基线调查的团队，在当地灾害事件发生后几小时内做出反应，并提供航空摄影，供当地和即将到来的灾害应对团队进行损失评估。
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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