Modeling Algae optical Properties for a large Scale application – MAPS
Remote sensing is an exceptional tool for mapping landscapes but needs empirical and theoretical knowledge to extract biophysical variables. The MAPS project is part of this process and proposes a generic model of alga biophysical and biochemical properties retrieval using remote sensing sensors. For scientists, managers and the general public, algae are an important heritage, and there is an increasing need for mapping and monitoring algae on large scales. The MAPS project explores the added value of polarimetry to hyperspectral imaging for algae mapping, and develops a new physics-based model of algae optical properties.
The methodology includes four steps:
(1) create a database of algae optical chemical and anatomical measurements
(2) develops the optical model of algae
(3) applies the model to real hyperspectral images and simulated hyperspectral-polarimetric images
(4) improves the model for a species of interest based on more realistic anatomy and 3D radiative transfer
The expected spin-offs concern the scientific community, economic actors and the general public. Companies could benefit from the tools developed in MAPS to have a better knowledge of the spatial distribution of macroalgae species on several Breton sites. A hackathon between France and Tunisia will be organized in order to disseminate the results to the student population and to generate ideas for startups or projects. Finally, we will participate in an event of scientific popularization, for example the European Researchers' Night.
Project coordination
Josselin Aval (Yncréa Ouest - ISEN / LSL (Light Scatter Learning))
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Partnership
ODE-DYNECO Dynamique de l'Environnement Côtier
CRNS / Remote Sensing for Smart Agriculture
Yncréa Ouest - ISEN / LSL (Light Scatter Learning)
Help of the ANR 193,994 euros
Beginning and duration of the scientific project:
October 2021
- 36 Months