FLOOD DETECTION IN NORWAY BASED ON SENTINEL-1 SAR IMAGERY

After large flood incidents in Norway, The Norwegian Water Resources and Energy Directorate (NVE), has the responsibility for documenting the flooded areas. This has so far mainly been performed by utilising aerial images and visual interpretation. Satellite images are a valuable source of additiona...

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Bibliographic Details
Main Authors: Reksten, J. H., Salberg, A.-B., Solberg, R.
Format: Conference Proceeding
Language:English
Subjects:
Backscatter
Backscattering
Detection
Flood control
Floods
Image acquisition
Imagery
Object recognition
Radar imaging
SAR (radar)
Satellite imagery
Satellites
Spaceborne remote sensing
Synthetic aperture radar
Training
Water resources
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Summary:After large flood incidents in Norway, The Norwegian Water Resources and Energy Directorate (NVE), has the responsibility for documenting the flooded areas. This has so far mainly been performed by utilising aerial images and visual interpretation. Satellite images are a valuable source of additional information as they are able to cover vast areas in each satellite pass. In this paper a fully automated system for detecting and delineating floods with the use of Synthetic Aperture Radar (SAR) images from the Sentinel-1 satellites is presented. In SAR images wet areas and water bodies usually show lower backscatter than dry areas. The flood detection system is thus based on comparing a reference image acquired before the flood with the flood event image. A Sentinel-1 training dataset has been obtained and manually annotated by NVE from three flood events in Norway. This training set has been used to train a random forest (RF) classifier, which outputs a score for each pixel in the SAR image. This score image is thresholded in order to obtain a crude flood detection. Unfortunately, changes in the backscatter may also be triggered by other events such as melting snow and harvested fields of crops. To mitigate such lookalikes, several techniques have been implemented and tested. This includes masking based on size, slope and height above nearest drainage (HAND). The experiments presented show that the system performance is very good. Of the 179 manually labelled flood objects, 168 are detected. The system is being applied operationally at NVE.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-3-W8-349-2019