Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Abstract

Abstract:In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

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

“…For the extraction of TOW, Z-Score VV was determined by a RF classifier as the most reliable time series feature, having the highest contribution in comparison to the VH based time series features. While this finding matches the results of other studies [4,47], some studies [25,37] prefer VH as a basis for the classification. This ambivalence can probably be explained by different sensor characteristics and environmental conditions in the studies.…”

“…Cazals et al [37] achieved a similar OA (82.0%) using backscatter intensities of S-1 time series data for the detection of flooded areas, comprising TOW and TFV. However, a sequential time series is necessary for this method.…”

“…Moreover, the developed method enables simple management of SAR time series data without the use of extensive methods to describe the seasonality or the typical vegetation conditions. In comparison to other studies [19,37], which are based on the dependency of the sequential dates order of the multi-temporal SAR data, the technique presented in this paper can deal with non-sequential or irregular times series data. Sentinel-1 scenes acquired within a period of two years, which are containing the flood event, the flood event made it possible to distinguish the natural range of variation for the analysed vegetation from the changes induced by a flood event.…”

“…In previous studies, absolute backscatter values are applied for the extraction of flood-related classes [10,37]. This can lead to underestimation or overestimation of the TFV areas and limits the use of the method.…”

“…The shape of multi-temporal radar backscatter profiles can be used to derive multi-temporal features enabling a more detailed extraction of flood-related classes [6,7]. Only few studies address the detection of TFV based on multi-temporal satellite imagery, which either depends on the chronological order and regularity of satellite images [19,37] or the use of a limited amount of SAR time steps [11,34]. Furthermore, the application of multi-temporal approaches is limited due to the low availability of respective SAR data in the past.…”