We use remote sensing and computer modeling to study forest ecosystems as they are influenced by humans and their environment, from the 3D structure of a single tree to disturbances across whole regions.
Forests are structurally complex, dynamic systems that interact with air temperature, CO2, wind, solar radiation and humidity to assimilate carbon and grow. Our work focuses on three sets of studies.
We use ground-based lidar to describe the 3D geometry of forest canopies and link it, through physically based models, to light reflectance, interception and gas exchange. The goal is to improve leaf-to-canopy scaling in terrestrial biosphere models.
We develop multilayer canopy models, such as CanVeg2, that simulate how forests exchange CO2, heat and water with the atmosphere, including the heat stored in tree stems.
We use satellite time series and deep learning to map deforestation, insect outbreaks, windthrow and forest recovery, and to classify tree species across Canada’s boreal forest.
Thomas Rasquinet joined the DFL as a PhD student. Thomas will be working on the use of lidar to characterize wood quality.
Marie-Luce Presson joined the DFL as a PhD student. Marie-Luce will be working on the role of trees in urban microclimate.
Martin presented “Estimating leaf damage from hot drought events and predicting the effects on forest productivity from multilayer canopy models” remotely at the EGU General Assembly 2026 in Vienna, Austria.
There are no current openings, but qualified candidates will be considered.