Improving habitats mapping from remote sensing : a primordial step for allowing us to scale up existing biodiversity data sets and to evaluate the potential impacts of regional drivers such as climate, land use intensification, and dams upon wetland habitats.
Vegetation/inundation mapping of the entire lowland Amazon basin is available (Hess et al, 2015), but at a spatial resolution (~90 m) and temporal frequency (2 dates) insufficient for biodiversity scenarios; fine-scale mapping (~30m) is available only for a small number of sites, owing to the large number of scenes and dates required for inundation period mapping, and to the lack of cloud-free optical imagery.
- We will explore synergies between multidate optical and radar sensors to improve methods for capturing spatiotemporal variability of floodplain habitats.
- Lidar data from GEDi will be explore to better detecting vegetation structure and improve Digital elevation models
Mapping water optical properties and relate it with phytoplankton biomass is appraised by chlorophyll-a, phytoplankton taxonomy (functional groups), and support the analysis of fish-phytoplankton relationships.
Phytoplankton production in floodplain lakes is fed by the nutrient input of river waters during rising water, but increased phytoplankton blooms occur when suspended material has settled, allowing light to penetrate into the water column .Increased nutrient inputs from humans or cattle on or near the floodplain can contribute to increased phytoplankton biomass, eutrophication, and shifts in assemblage diversity toward toxic Cyanobacteria.
- We will gathered field data at contrasted hydrological seasons and relate these data with multi-spectral images from Sentinel-2 satellite to map seasonal chlorophyll-a concentrations during 2019 at 30 m scale in focus sites using and a semi-analytical approach to extend the mapping to the Study Region using Sentinel-3 (300 m).
- Because the phytoplankton community of Amazonian whitewater floodplains is mainly controlled by light availability, remote sensing of light availability can potentially be applied as a surrogate to map multi-temporal phytoplankton biodiversity at regional scale. To test the feasibility of this approach, we will use optical, limnological, and phytoplankton taxonomy measurements collected over the past decade in Amazonian floodplain lakes to develop a phytoplankton community model based on water transparency.