Daily monitoring of shallow and fine-grained water patterns in wet grasslands combining aerial LiDAR data and in situ piezometric measurements

Abstract : The real-time monitoring of hydrodynamics in wetlands at fine spatial and temporal scales is crucial for understanding ecological and hydrological processes. The key interest of light detection and ranging (LiDAR) data is its ability to accurately detect microtopography. However, how such data may account for subtle wetland flooding changes in both space and time still needs to be tested, even though the degree to which these changes impact biodiversity patterns is of upmost importance. This study assesses the use of 1 m × 1 m resolution aerial LiDAR data in combination with in situ piezometric measurements in order to predict the flooded areas at a daily scale along a one-year hydrological period. The simulation was applied over 663 ha of wet grasslands distributed on six sites across the Marais Poitevin (France). A set of seven remote sensing images was used as the reference data in order to validate the simulation and provide a high overall accuracy (76-94%). The best results were observed in areas where the ditch density was low, whereas the highly drained sites showed a discrepancy with the predicted flooded areas. The landscape proportion index was calculated for the daily steps. The results highlighted the spatiotemporal dynamics of the shallow flooded areas. We showed that the differences in the flooding durations among the years were mainly related to a narrow contrast in topography (40 cm), and occurred over a short period of time (two months).
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Sustainability, MDPI, 2018, 10 (3), pp.708. 〈10.3390/su10030708〉
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Dernière modification le : jeudi 11 octobre 2018 - 01:08:58

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Sébastien Rapinel, Nicolas Rossignol, Olivier Gore, Olivier Jambon, Guillaume Bouger, et al.. Daily monitoring of shallow and fine-grained water patterns in wet grasslands combining aerial LiDAR data and in situ piezometric measurements. Sustainability, MDPI, 2018, 10 (3), pp.708. 〈10.3390/su10030708〉. 〈hal-01739504〉

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