A Flooding Probability Reconstruction Approach by Enhancing Near Real-Time Imagery with Real-Time Gauge and Tweets

Authors: Xiao Huang*, University of South Carolina, Cuizhen Wang, University of South Carolina, Zhenlong Li, University of South Carolina
Topics: Remote Sensing, Geographic Information Science and Systems, Hazards, Risks, and Disasters
Keywords: the 2015 SC flood; flood probability model; satellite imagery; crowdsourcing; rapid flood mapping
Session Type: Paper
Day: 4/12/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Studio 10, Marriott, 2nd Floor
Presentation File: No File Uploaded


Flood probability mapping is critical for situation awareness, flood mitigation, emergency response and post-event damage assessment. Current flood probability mapping approaches can be categorized into Real-Time (RT) and Near Real-Time (NRT) processes based on the timing of data acquisition. However, the intrinsic limitations of each category largely hamper their applications for flooding mapping. Taking the 2015 South Carolina Flood in downtown Columbia as a case study, this paper proposes a flooding reconstruction model by enhancing the NRT Normalized Difference Water Index (NDWI) derived from Remote Sensing (RS) imagery with the RT stream gauge readings and the RT social media (tweets). Splitting into three modules: Water Height Module, Global Enhancement Module and Local Enhancement Module, the proposed model firstly incorporates the gauge readings and the NDWI image to reconstruct a macro-scale flood probability layer, which is then locally enhanced using the verified flood-related tweets. The final output of the model matches well with the published USGS inundation map and the USGS surveyed High Water Marks (HWMs). Results suggest that, by enhancing NRT imagery with RT data sources, the proposed flooding probability reconstruction model compensates their own inherent limitations, rendering a more robust, spatially enhanced flooding probability index for emergency responders to quickly identify areas in need of urgent attention.

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