Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery
Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery
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DOI：
10.3390/hydrology10080158
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发表时间：
2023-07
期刊：
影响因子：
3.2
通讯作者：
K. Wienhold;Dongfeng Li;Wenzhao Li;Zheng N. Fang
中科院分区：
文献类型：
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作者：
K. Wienhold;Dongfeng Li;Wenzhao Li;Zheng N. Fang
在飓风或山洪暴发等新出现的公共安全危机期间确定洪水危害是第一反应者和管理人员的宝贵工具，但由于云层覆盖和其他数据来源的限制，在使用传统遥感方法时，从任何全面意义上讲仍然无法实现。虽然有许多遥感技术可用于洪水识别和提取，但很少有研究表明，人们对从收集的数据中分离洪水光谱特性的技术有了更好的了解，这些数据因每次事件而异。本研究介绍了一种新的方法，划定近实时的洪水淹没范围和深度映射风暴事件，使用廉价的无人机（UAV）为基础的多光谱遥感平台，这是专为适用于城市环境，在广泛的大气条件下。该方法使用实际的逼近事件-2020年大西洋飓风季期间的飓风泽塔进行了演示。被称为无人机和洪水淹没和深度测绘仪（FIDM），该方法包括三个主要组成部分，包括航空数据收集，处理，洪水淹没（水面范围）和深度测绘。模型结果的淹没和深度进行了比较，验证数据集和地面实况数据，分别。结果表明，UAV-FIDM是能够预测洪水的总误差（遗漏和佣金误差的总和）为15.8%，并产生洪水深度估计是足够准确的，是可操作的，以确定道路封闭的一个真实的事件。
The identification of flood hazards during emerging public safety crises such as hurricanes or flash floods is an invaluable tool for first responders and managers yet remains out of reach in any comprehensive sense when using traditional remote-sensing methods, due to cloud cover and other data-sourcing restrictions. While many remote-sensing techniques exist for floodwater identification and extraction, few studies demonstrate an up-to-day understanding with better techniques in isolating the spectral properties of floodwaters from collected data, which vary for each event. This study introduces a novel method for delineating near-real-time inundation flood extent and depth mapping for storm events, using an inexpensive unmanned aerial vehicle (UAV)-based multispectral remote-sensing platform, which was designed to be applicable for urban environments, under a wide range of atmospheric conditions. The methodology is demonstrated using an actual flooding-event—Hurricane Zeta during the 2020 Atlantic hurricane season. Referred to as the UAV and Floodwater Inundation and Depth Mapper (FIDM), the methodology consists of three major components, including aerial data collection, processing, and flood inundation (water surface extent) and depth mapping. The model results for inundation and depth were compared to a validation dataset and ground-truthing data, respectively. The results suggest that UAV-FIDM is able to predict inundation with a total error (sum of omission and commission errors) of 15.8% and produce flooding depth estimates that are accurate enough to be actionable to determine road closures for a real event.