The MISTRALE project proposes to address soils moisture management in agriculture as well as wetlands or flooded areas monitoring by using Global Navigation Satellite Systems reflected signals (GNSS-R) as a powerful technology for humidity or flooded mapping. The detection by GNSS-R is known to be much more reliable than visible/NIR imagery, and will be usable even under a cloud cover, during the night and even under vegetation (bushes, grass, trees) when passive remote sensing is not applicable. The main objective is to demonstrate a service chain in different use cases : pilot projects will be carried out in soil humidity mapping for agriculture (optimizing the water resource management), wetlands and flooded areas (risk management, flood-prone areas, damages evaluation). In order to meet the objectives, a GNSS-R receiver embedded into a small RPAS (Remotely Piloted Aircraft System) will be developed and implemented into an operational chain to provide the service. GNSS-R technology aims at measuring the GNSS signals reflected on the ground, and, compared to the direct signals, permits a measurement of the soil humidity (from 0 to 100%) as well as flooded extend. The use of GALILEO signals will significantly improve the precision of mapping. The operational system will integrate three main axes of development: adapting the GNSS-R technology for the requirements, making a compact GNSS-R receiver and optimizing an existing RPAS. Using EGNOS and GALILEO in the project will also improve navigation capabilities of small RPAS (<4Kg) and contribute to the development of regulations for their integration in airspace. We assembled a consortium that addresses all aspects of the project : four SME specialized in GNSS receivers, GNSS-R technology, operational applications and dissemination, two labs for RPAS and GNSS-R technology. An advisory board composed of agronomy and environment specialists as well as end users will complete the skills of the consortium.

Mine tailings are witnesses of exploitation of ore bodies which took place several decades ago. These tailings are an important part of the 100 000 heavy-metal polluted sites which require urgent rehabilitation in Europe. These tailings represent a sizeable source of contaminated material spreadable in the environment. Despite their toxicity for non-adapted species, rare heritage plant communities, metallicolous grasslands, established on them gradually over many years. Six-P project aims to assess the role of plant-plant interactions in these specific plant communities, both as a key system to understand variation in plant-plant interactions along stress gradients and as a possible restoration tool. In mountainous areas, tailings form particularly harsh environments for plant growth (metal toxicity, climatic constraints). In such conditions, positive plant-plant interactions are expected according to a dominant ecological theory: The Stress Gradient Hypothesis (SGH). However, this hypothesis has been poorly investigated along (metal) pollution gradients. In addition, and regardless of pollution gradients, there is a need to better define its conditions of application. SixP aims to contribute to this active field of research by focusing on four directions: i) to characterize the variation of plant-plant interactions along gradients of metal phyto-availability, while explaining the specific role of metallicolous species in these interactions; ii) to better identify the effects of multiple stress factors on these interactions; iii) to specify the plant functional strategies at stake; and iv) to assess the effect of plant-plant interactions at the community scale. The project will be implemented in several mine tailings in the Pyrénées at different altitudes (in the montane zone, and at the subalpine-alpine zone). At each site, several areas will be specified from peripheral low-contaminated areas towards tailings centers corresponding to a gradient of metal phyto-availability. The first three research directions will then be addressed by experimentations manipulating species in interaction. As for the last direction, the combination of very high resolution airborne data (lidar, multispectral images) covering the studied areas with in situ observations in a deep learning framework will be used to map species distribution and their geomorphological position. Spatial patterns of the different interacting species (aggregation vs repulsion) will exhibit the effects of plant-plant interactions on the long-term. Six-P relies on a multidisciplinary consortium with expertise in ecology, metals biogeochemistry, airborne data acquisition, computer vision, machine learning and management of post-mining sites. In addition to the valuable and general knowledge acquired on plant-plant interactions, benefits are expected in the phyto-management domain, by proposing the use of several species associations as viable alternative to the already available techniques. Management of rare metallicolous grasslands of heritage value could also be improved thanks’ to project results. Finally, Six-P will increase the interest of computer vision and artificial intelligence groups on ecological issues. The deep neural network models designed in SixP could also be applied to other problems in ecology, since transferability of deep networks is improving regularly.