Description du projet
Découvrir l’importance économique et écologique des arbres non forestiers
L’étude des arbres non forestiers a été largement négligée par les chercheurs. Il s’avère que l’on sait peu de choses sur leur densité et leur taille, alors qu’ils jouent un rôle crucial dans la biodiversité et offrent une grande variété de services d’écosystèmes. Le projet TOFDRY, financé par l’UE, se concentrera sur les arbres dans les zones arides du monde entier. Il vise à mettre en lumière l’impact des interventions humaines et du changement climatique sur les arbres des zones arides, ainsi que la manière dont ces arbres peuvent contribuer à atténuer la détérioration, le changement climatique et la pauvreté. Pour apporter des réponses, le projet étudiera les arbres sur une base individuelle, en enregistrant leur aire de répartition mais aussi leur densité, la taille de leur couronne, leurs principaux services écologiques et déterminants socio-environnementaux. L’imagerie satellitaire et de nombreuses données de terrain seront utilisées conjointement à des techniques d’apprentissage profond qui permettront d’identifier les objets au sein de l’imagerie.
Objectif
Drylands cover approximately 65 million km² of the Earth’s land surface but their tree and shrub cover is a major unknown in terrestrial research. This is because a large proportion of dryland trees grow isolated without canopy closure and most scientific and non-scientific interest is devoted to forests, while the density and size of trees outside of forests is not well documented. However, these non-forest trees play a crucial role for biodiversity and provide ecosystem services such as carbon storage, food resources, livelihoods and shelter for humans and animals. The limited attention devoted to the quantification of dryland trees leads to an underrepresentation of non-forest trees in development strategies and climate\vegetation models, and the economic and ecological importance of non-forest trees is largely unknown at large scale.
Through this project I will work towards a wall-to-wall identification of trees in global drylands, and study their ecological services and socio-environmental determinants. The breakthrough is that trees are not assessed as canopy fraction of an area, but as individuals, allowing to identify not only their coverage but also their density, crown size, and key ecological services. I will apply a new generation of satellite imagery at sub-meter resolution and extensive field data in conjunction with fully convolutional neural networks, a deep learning technique being able to identify objects within imagery at an unprecedented accuracy. In doing so, I will lay the groundwork for new insights into the contribution of human agency and climate change to the distribution of dryland trees and their role in mitigating degradation, climate change and poverty.
Champ scientifique
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programme(s)
Thème(s)
Régime de financement
ERC-STG - Starting GrantInstitution d’accueil
1165 Kobenhavn
Danemark