3 research outputs found
Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane harvey as a test case
This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission's six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency's (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission
Recommended from our members
Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept
Coupled hydrologic and hydraulic models represent
powerful tools for simulating streamflow and water levels
along the riverbed and in the floodplain. However, input
data, model parameters, initial conditions, and model structure
represent sources of uncertainty that affect the reliability
and accuracy of flood forecasts. Assimilation of satellitebased
synthetic aperture radar (SAR) observations into a
flood forecasting model is generally used to reduce such uncertainties.
In this context, we have evaluated how sequential
assimilation of flood extent derived from SAR data can help
improve flood forecasts. In particular, we carried out twin
experiments based on a synthetically generated dataset with
controlled uncertainty. To this end, two assimilation methods
are explored and compared: the sequential importance sampling
method (standard method) and its enhanced method
where a tempering coefficient is used to inflate the posterior
probability (adapted method) and reduce degeneracy. The experimental
results show that the assimilation of SAR probabilistic
flood maps significantly improves the predictions of
streamflow and water elevation, thereby confirming the effectiveness
of the data assimilation framework. In addition,
the assimilation method significantly reduces the spatially
averaged root mean square error of water levels with respect
to the case without assimilation. The critical success index of
predicted flood extent maps is significantly increased by the
assimilation. While the standard method proves to be more
accurate in estimating the water levels and streamflow at the
assimilation time step, the adapted method enables a more
persistent improvement of the forecasts. However, although
the use of a tempering coefficient reduces the degeneracy
problem, the accuracy of model simulation is lower than that
of the standard method at the assimilation time step