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Open AccessArticle

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

1
Department of Geography, Ludwig Maximilian University of Munich, Luisenstr. 37, Munich 80333, Germany
2
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, 82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1286; https://doi.org/10.3390/rs10081286
Received: 15 July 2018 / Revised: 5 August 2018 / Accepted: 12 August 2018 / Published: 15 August 2018
The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV. View Full-Text
Keywords: temporary flooded vegetation (TFV); SAR; Sentinel-1; time series data; classification; flood mapping temporary flooded vegetation (TFV); SAR; Sentinel-1; time series data; classification; flood mapping
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MDPI and ACS Style

Tsyganskaya, V.; Martinis, S.; Marzahn, P.; Ludwig, R. Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data. Remote Sens. 2018, 10, 1286.

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