Assessing Macrophyte Seasonal Dynamics

Thanks to the improved spatial and temporal resolution of new generation Earth Observation missions, such as Landsat 8 and Sentinel-2, the capabilities demonstrated in the last decades by remote sensing in mapping land surface phenology of terrestrial biomes can now be tested in inland water systems. We assessed the capabilities of dense time series of medium resolution satellite data to deliver information about quantitative macrophyte phenology metrics, focusing on three temperate European shallow lakes with connected wetlands: Mantua lakes system (Italy) Lac de Grand-Lieu (France), and Fundu Mare Island (Romania). Macrophyte leaf area index (LAI) maps were derived from semi-empirical regression modelling based on the best performing spectral index, with an error level around 0.1 m2 m-2. Phenology metrics computed from LAI time series using TIMESAT code were used to analyse macrophyte seasonal dynamics across the three study areas in terms of spatial patterns and species-dependent variability for the year 2015. These peculiar dynamicity patterns of autochthonous and allochthonous species were related to the environmental characteristics of each area in terms of ecological, hydrological and meteorological conditions. In addition, the influence of cloud cover thresholding, temporal resolution and missing acquisitions was assessed in terms of phenology timing metrics retrieval, thus providing quantitative information on the expected variability of TIMESAT outputs when time series with reduced resolution are used, i.e. if 16-day time revisit Landsat data were used for retrospective study of macrophyte phenology during the last three decades.

Assessing Macrophyte Seasonal Dynamics

Authors: Paolo Villa | Monica Pinardi | Rossano Bolpagni | Jean-Marc Gillier | Peggy Zinke | Florin Nedelcuţ | Mariano Bresciani

Organizations: Institute for Electromagnetic Sensing of the Environment, National Research Council of Italy | Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma | SNPN, Réserve naturelle du lac de Grand-Lieu | Norwegian University of Science and Technology | Dunărea de Jos University of Galati

Keywords: #Vegetation #vegetationphenology #LAI #ShallowLakes #SpectralIndices #Sentinel-2 #Landsat 8

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