Reconstructing Flood Inundation Probability by Enhancing Near Real-Time Imagery With Real-Time Gauges and Tweets
Reconstructing Flood Inundation Probability by Enhancing Near Real-Time Imagery With Real-Time Gauges and Tweets
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DOI：
10.1109/tgrs.2018.2835306
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发表时间：
2018-08-01
影响因子：
8.2
通讯作者：
Li, Zhenlong
中科院分区：
文献类型：
--
作者：
Huang, Xiao;Wang, Cuizhen;Li, Zhenlong
洪水淹没概率对于态势感知、防洪减灾、应急响应和事后损失评估至关重要。根据数据采集的时间，当前的洪水淹没测绘方法可分为实时（RT）和近实时（NRT）过程。然而，每个类别的内在局限性很大程度上阻碍了它们在洪水测绘中的应用。本文以 2015 年南卡罗来纳州哥伦比亚市中心洪水为例，通过利用包括流量计读数和社交媒体（推文）在内的 RT 数据增强源自遥感图像的 NRT 归一化水差指数（NDWI），提出了洪水淹没重建模型。该模型分为三个模块：水位高度模块、全局增强模块和局部增强模块，首先结合仪表读数和NDWI图像来重建宏观洪水概率层，然后使用经过验证的洪水相关推文进行局部增强。该模型的最终输出与美国地质调查局的淹没图及其测量的高水位线非常吻合。结果表明，通过使用 RT 数据源增强 NRT 图像，所提出的洪水淹没概率重建模型为应急响应人员提供了更稳健、空间增强的洪水概率指数，以快速识别需要紧急关注的区域。
Flood inundation probability is critical for situation awareness, flood mitigation, emergency response, and postevent damage assessment. Current flood inundation mapping approaches can be categorized into real-time (RT) and near-RT (NRT) processes based on the timing of data acquisition. However, the intrinsic limitations of each category largely hamper their applications for flood mapping. Taking the 2015 South Carolina flood in downtown Columbia as a case study, this paper proposes a flood inundation reconstruction model by enhancing the NRT normalized difference water index (NDWI) derived from remote sensing imagery with the RT data including stream gauge readings and social media (tweets). Splitting into three modules: water height module, global enhancement module, and local enhancement module, the proposed model first incorporates the gauge readings and the NDWI image to reconstruct a macroscale flood probability layer, which is then locally enhanced using the verified flood-related tweets. The final output of the model matches well with the U.S. Geological Survey inundation map and its surveyed high-water marks. Results suggest that by enhancing NRT imagery with RT data sources, the proposed flood inundation probability reconstruction model renders a more robust, spatially enhanced flood probability index for emergency responders to quickly identify areas in need of urgent attention.