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Open AccessArticle

Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery

1
Hydro-Climate Extremes Lab (H-CEL), Ghent University, Coupure Links 653, 9000 Ghent, Belgium
2
Remote Sensing|Spatial Analysis Lab (REMOSA), Ghent University, Coupure Links 653, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3611; https://doi.org/10.3390/rs12213611
Received: 30 September 2020 / Revised: 23 October 2020 / Accepted: 29 October 2020 / Published: 3 November 2020
(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)
The European Space Agency’s Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available. View Full-Text
Keywords: flood mapping; flooded vegetation; SAR; Sentinel-1; Sentinel-2; clustering flood mapping; flooded vegetation; SAR; Sentinel-1; Sentinel-2; clustering
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MDPI and ACS Style

Landuyt, L.; Verhoest, N.E.C.; Van Coillie, F.M.B. Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery. Remote Sens. 2020, 12, 3611. https://doi.org/10.3390/rs12213611

AMA Style

Landuyt L, Verhoest NEC, Van Coillie FMB. Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery. Remote Sensing. 2020; 12(21):3611. https://doi.org/10.3390/rs12213611

Chicago/Turabian Style

Landuyt, Lisa; Verhoest, Niko E.C.; Van Coillie, Frieke M.B. 2020. "Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery" Remote Sens. 12, no. 21: 3611. https://doi.org/10.3390/rs12213611

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