Can UAVs fill the gap between in situ surveys and satellites for habitat mapping?

Abstract

Habitat mapping is an essential descriptor to monitor and manage natural or semi-natural ecosystems. Habitats integrate both the environmental conditions and the related biodiversity. However, it remains challenging to map certain habitats such as inland wetlands due to spectral, spatial and temporal variability in the vegetation cover. Currently, no satellite constellations optimize the spectral, spatial and temporal resolutions required to map wetlands according to the habitats discriminated from in situ surveys. Our approach aims to combine satellite and unmanned aerial vehicle (UAV) data to exceed their respective limitations. Both data sources were combined through a spectral unmixing algorithm with the hypothesis that endmembers from UAV data are pure enough to enhance plant community abundances estimated from satellite data. The experiment was conducted on the regional preserve of the Sougéal marsh, a wet grassland of 174 ha located upstream of the Mont-Saint-Michel Bay. Two satellite data sources-Sentinel-2 and Pleiades-and three acquisition periods-November 2017, April 2018 and May 2018-were considered. A reference map of plant community distribution was produced from UAV multitemporal data and floristic surveys to validate the unmixing of satellite data. This study shows innovative results and perspectives: while UAV can improve habitat discrimination, results vary among acquisition periods and habitats. Results illustrate well the great potential of combined UAV and satellite data but also demonstratethe influence of endmembers on the unmixing process and technical limitations (e.g. spectral mismatches between sensors), which can be overcome using domain adaptation.

Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

“…It allows for creating orthophotomosaics and DSMs at VHSR and very high temporal resolution (VHTR) [17]. This is highly relevant to monitoring the changes in plant communities depending on the seasonality or after a punctual event [8,18]. The monitoring of these changes is crucial owing to the temporal impact in the services that can be furnished by the ecosystems.…”

Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

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AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages

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