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

Remote Sensing

Jean-Paul Rudant¹  Grégoire Mercier²  Sébastien Rapinel³  Anne Bonis³  Samuel Corgne⁴  Clément Mallet⁵  Pierre-Louis Frison⁶  Cécile Cazals⁶ 

[1] ;CNRS UMR 6285 Lab-STICC, TELECOM Bretagne, Technopole Brest-Iroise, Brest Cedex 29238, France;CNRS UMR 6553 ECOBIO, Université de Rennes 1, Campus de Beaulieu, Rennes Cedex 35042, France;CNRS UMR 6554 LETG Rennes, Université Haute Bretagne, Place Henri Le Moal, Rennes Cedex 35043, France;IGN, Université Paris-Est Marne-la-Vallée, LaSTIG/MATIS, 73 avenue de Paris, 94160 Saint-Mandé, France;Université Paris-Est, IGN, LaSTIG//MATIS, 6-8 av. B. Pascal, Cité Descartes, Champs sur Marne, 77455 Marne la Vallée Cedex 2, France;

关键词: radar;    SAR;    Sentinel-1;    remote sensing;    hysteresis;    time series;    flood;    wetland;    marshes;    water management;   

DOI  :  10.3390/rs8070570

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown