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Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features
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Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery

1
Department of Science and Technology, University of Sannio, 82100 Benevento, Italy
2
Department of Civil, Building and Environmental Engineering (DICEA), Sapienza University of Rome, 00184 Rome, Italy
*
Author to whom correspondence should be addressed.
Water 2019, 11(11), 2289; https://doi.org/10.3390/w11112289
Received: 24 September 2019 / Revised: 28 October 2019 / Accepted: 29 October 2019 / Published: 31 October 2019
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time.
Keywords: sentinel-1; SAR; flood; image classification; clustering; monsoon; Philippines sentinel-1; SAR; flood; image classification; clustering; monsoon; Philippines
MDPI and ACS Style

Ruzza, G.; Guerriero, L.; Grelle, G.; Guadagno, F.M.; Revellino, P. Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery. Water 2019, 11, 2289.

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