TETROFOR - Remote sensing of tropical vegetations

THR images
Time series
Tropical rainforests

To characterise the role of natural tropical vegetation as a reservoir of biodiversity and an essential component in the regulation of biogeochemical (water, carbon) and energy cycles on a global scale.

This theme brings together research actions that implement remote sensing tools and ad hoc methodological developments.
Remote sensing approaches are generally based on the use of passive or active signals of high resolution (temporal, spatial or spectral). LiDAR (terrestrial or airborne) plays a predominant role among these tools, thanks to the unique link it establishes between tree architecture and stand organisation, as well as its potential for quantifying carbon acquisition and allocation. Our approaches are also based on (i) mapping of tropical forests at different scales (local, regional) using multi-sensor, multi-temporal and multi-spatial resolution approaches, (ii) canopy modelling (based on archi-FSPM models or 3D digitisation) and (iii) modelling of radiative transfer, both from a functional (photosynthesis, allocation, etc.) and instrumental (sensor sensitivity, cal/val) perspective.

We are involved in research projects, impact studies, development studies, or sensitivity studies, through services for public or private partners. This involves producing vegetation maps or providing field data or compatible population models for radiative transfer modelling. Our main projects are in Central Africa (LMI Dycofac) and in French Guiana (Labex CEBA) but also in New Caledonia, India and Thailand. Access to ground truth data relies on plot networks developed in partnership with local institutions. We regularly mobilise close remote sensing equipment (6 drones, 5 qualified pilots, 3 TLS scanners, 1 UAV-LS scanner, DGNSS systems), a spectroradiometer, canopy climbing equipment and trained climbers. We also have an archive of airborne and/or satellite images in our regions of intervention allowing us to develop analyses of cover dynamics.

Acronym Title Duration
ADMIREPartenariat pour l’Analyse des DynaMIques de REforestation et de la résilience forestière
Project PI: Philippe BIRNBAUM 
2021 - 2024
ALTAmazonian Landscapes in Transition
Project PI: Jérôme CHAVE (EDB, Toulouse)  
2022 - 2025
DESSFORDegraded Stable States in Tropical Forests
Project PI: Maxime REJOU-MECHAIN 
2021 - 2024
IFIHSAInventaires forestriers par imagerie hyperspectrale aéroportée
Project PI: Olivier BRUNAUX (ONF)  
2023 - 2024
PhenobsTowards a phenology observatory in French Guiana to study climate-vegetation feedbacks and the diversity of plant strategies
Project PI: Nicolas BARBIER 
2020 - 2024
TROPECOSBilans de carbone des écosystèmes côtiers tropicaux dans l’Anthropocène
Project PI: Gwenaël ABRIL (CNRS)  
2023 - 2027

LEBLANC César 2022 - 2025. Prédiction des trajectoires futures de la biodiversité par apprentissage machine. Ecole doctorale : I2S / Université de Montpellier. Dir : JOLY Alexis

PRIEUR Colin 2022 - 2025. Télédétection hyperspectrale pour l’exploitation soutenable des forêts tropicales: quand la modélisation physique rencontre l'apprentissage profond.. Ecole doctorale : GAIA / Université de Montpellier. Dir : VINCENT Grégoire / Co-dir. : CHANUSSOT jocelyn

  • JB Feret (Tetis MPL),
  • D Coomes (Cambridge U),
  • S Saatchi (NASA JPL),
  • R Valbuena (Wales University),
  • J Chanussot (GIPSA Grenoble),
  • M Disney (UCL),
  • V Deblauwe (CBI),
  • B Sonké (LaboSYstE, ENS-Univ Yaoundé I),
  • JP Gastellu Etchegorry, T Le Toan (Cesbio),
  • H Poilvé (Airbus DS),
  • M Herold (WUR),
  • T Stévart (MBG),
  • S Gourlet Fleury, E Forni (CIRAD Forêts et Sociétés).