Mapping floods due to Hurricane Sandy using NPP VIIRS and ATMS data and geotagged Flickr imagery

Abstract

In this study, we present an approach to estimate the extent of large-scale coastal floods caused by Hurricane Sandy using passive optical and microwave remote sensing data. The approach estimates the water fraction from coarse-resolution VIIRS and ATMS data through mixed-pixel linear decomposition. Based on the water fraction difference, using the physical characteristics of water inundation in a basin, the flood map derived from the coarse-resolution VIIRS and ATMS measurements was extrapolated to a higher spatial resolution of 30 m using topographic information. It is found that flood map derived from VIIRS shows less inundated area than the Federal Emergency Management Agency (FEMA) flood map and the ground observations. The bias was mainly caused by the time difference in observations. This is because VIIRS can only detect flood under clear conditions, while we can only find some clear-sky data around the New York area on 4 November 2012, when most flooding water already receded. Meanwhile, microwave measurements can penetrate through clouds and sense surface water bodies under clear-or-cloudy conditions. We therefore developed a new method to derive flood maps from passive microwave ATMS observations. To evaluate the flood mapping method, the corresponding ground observations and the FEMA storm surge flooding (SSF) products are used. The results show there was good agreement between our ATMS and the FEMA SSF flood areas, with a correlation of 0.95. Furthermore, we compared our results to geotagged Flickr contributions reporting flooding, and found that 95% of these Flickr reports were distributed within the ATMS-derived flood area, supporting the argument that such crowd-generated content can be valuable for remote sensing operations. Overall, the methodology presented in this paper was able to produce high-quality and high-resolution flood maps over largescale coastal areas.

Probabilistic flood extent estimates from social media flood observations

“…In the context of flood mapping, some investigations used these data as auxiliary data. Examples include the assessment of the accuracy of remote-sensing-derived flood maps using Flickr data (Sun et al, 2015) and the selection of the most realistic result of a series of hydraulic model runs based on Twitter data (Smith et al, 2015). Others actually created flood maps directly from the data.…”

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

“…Multi-source satellite and Twitter data were combined to delineate the flood extent on a daily basis [33]. Similar studies can also be found in [34][35][36][37][38]. These studies showed that social media data is valuable in providing disaster-related information at locations that are not being sensed by satellite sensors.…”

Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

“…These new features make the VIIRS imagery very attractive for near real-time flood detection. Under the support of the NOAA JPSS program office, near real-time flood maps have been derived from the VIIRS [23][24][25][26]. The SNPP is thus able to provide a flood detection capability to support the National Weather Services (NWS), the U.S. Army Corps of Engineering (USACE), the Federal Emergency Management Agency (FEMA), the U.S. Geological Survey (USGS), and local agencies.…”

“…As the VIIRS is an optical sensor, it thus cannot penetrate thick clouds to detect the floodwaters. While microwave sensors can observe the Earth's surface through non-rainy clouds, but usually with a coarse spatial resolution, for example, for passive microwave sensors, the spatial resolution is in the range from 12 km to 50 km in channels suitable for flood mapping, which is too coarse to observe the small-scale features [25,28]. Radar sensors, such as the Radarsat, synthetic aperture radar (SAR), TerraSAR-X, Sentinel, and so on, can provide observations under cloudy conditions at a high spatial resolution, and therefore play a very important role in flood mapping and disaster relief [29][30][31][32][33][34][35][36].…”