Abstract
Crowdsourced environmental data have the potential to augment traditional data sources during disasters. Traditional sensor networks, satellite remote sensing imagery, and models are all faced with limitations in observational inputs, forecasts, and resolution. This study integrates flood depth derived from crowdsourced images with U.S. Geological Survey (USGS) ground-based observation networks, a remote sensing product, and a model during Hurricane Florence. The data sources are compared using cross-sections to assess flood depth in areas impacted by Hurricane Florence. Automated methods can be used for each source to classify flooded regions and fuse the dataset over common grids to identify areas of flooding. Crowdsourced data can play a major role when there are overlaps of sources that can be used for validation as well providing improved coverage and resolution.
Highlights
Hurricanes can leave behind a devastating wake of destruction with heavy precipitation and flooding
Collection and calculation of flood depth prepared the datasets for analysis
The data are prepared through rasterization of point data and high-resolution raster data into aggregate standardizations at various scales
Summary
Hurricanes can leave behind a devastating wake of destruction with heavy precipitation and flooding. Extreme flooding events are expected to become more frequent and severe so there is a greater need for decision-makers to be able to closely monitor conditions. The scientific community deals with observational limitations to adequately predict, observe, and produce useful flood reports during hurricanes. Traditional sensor networks may be offline or provide data at an insufficient spatio-temporal resolution. Satellite remote sensing imagery of the Earth’s surface is limited by revisit time and cloud cover that is typically present when hurricanes make landfall. The chance of having data when and where it is needed to provide a flood maps is a product of how the data are produced. The collection of social data is characterized by different conditions, so crowdsourcing may be used to augment traditional sources
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