In Detail: Flood Mapping with Sentinel-1 Interferometric Coherence

Flood detection in urban areas is a major limitation of flood detection approaches using SAR backscatter. This is problematic for the disaster response community, as urban areas are also where most damage happens and the most people are exposed to the disaster, considering that the majority population lives nowadays in cities rather than rural areas, the trend increasing. 

Input Data:

  • Sentinel-1 SLC data
  • Download recommended via the Alaska Satellite Facility

Sofware:

  • SNAP
  • Google Earth Engine 

The proposed practice can be well applied in dense urban areas, like cities but also in areas with little to no vegetation cover. In these areas, mudflows, landslides, and flow paths of flash floods can be mapped, although this is not the objective of this practice. 

Strengths:

  • Independant of clouds
  • Detection of changes at sub-pixel level, which is a major advantage in dense urban settlements
  • Detection of floods, infrastructure damage and other surface changes

Limitations:

  • The practice is limited in detecting floods in vegetated areas like forests and agricultural areas
  • Short time span between the SAR image acquisitions essential
  • No cloud computing options available, selection of ROI needed, no large-scale computations on local machines feasible.
  • SLC scenes need a lot of storage capacity, i.e., Up to 5GB per scene. 

A close-up of several icons

Description automatically generated
 

  1. Downloading the Scenes from the Alaska Satellite Facility 

  2. Processing the Scenes with a pre-defined workflow in SNAP 

  3. Exporting the Coherence GeoTiff 

  4. Importing the Coherence raster into Google Earth Engine 

  5. Change Detection in Urban Areas 

  6. Map of flooded/damaged areas 

Pelich, Ramona; Chini, Marco; Hostache, Renaud; Matgen, Patrick; Pulvirenti, Luca; Pierdicca, Nazzareno (2022): Mapping Floods in Urban Areas from Dual-Polarization InSAR Coherence Data. In IEEE Geosci. Remote Sensing Lett. 19, pp. 1–5. DOI: 10.1109/LGRS.2021.3110132.

 

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Slides: RP_COH.pdf (3 MB) 3 MB