An assessment of the Height Above Nearest Drainage terrain descriptor for the thematic enhancement of automatic SAR based flood monitoring services
Flood extent maps derived from Synthetic Aperture Radar (SAR) data can communicate spatially explicit information in a timely and cost effective manner to support disaster management. Automated processing chains for SAR based flood mapping have the potential to substantially reduce the critical time delay between the delivery of post event satellite data and the subsequent provision of satellite derived crisis information to emergency management authorities. However, the accuracy of SAR based flood mapping can vary drastically due to the prevalent land cover and topography of a given scene. While expert based image interpretation with the consideration of contextual information can effectively isolate flood surface features, a fully automated feature differentiation algorithm mainly based on the grey levels of a given pixel is comparatively more limited for features with similar SAR backscattering characteristics. The inclusion of ancillary data in the automatic classification procedure can effectively reduce instances of misclassification. In this work, a near global 'Height Above Nearest Drainage' (HAND) index [10] was calculated with digital elevation data and drainage directions from the HydroSHEDS mapping project [2] The index can be used to separate flood prone regions from areas with a low probability of flood occurrence. Based on the HAND index, an exclusion mask was computed to reduce water look alikes with respect to the hydrologictopographic setting. The applicability of this near global ancillary data set for the thematic improvement of Sentinel 1 and TerraSAR X based services for flood and surface water monitoring has been validated both qualitatively and quantitatively. Application of a HAND based exclusion mask resulted in improvements to the classification accuracy of SAR scenes with high amounts of water look alikes and considerable elevation differences.
Remote Sensing