Combining remote sensing and 3D forest modelling to improve tropical forests monitoring of greenhouse gases emissions
Deforestation and forest degradation is considered the second largest source of global anthropogenic greenhouse gases (GHG) emissions. While there is a pressing need to go beyond satellite-based land-use and land-cover change (LULCC) survey to accurately monitor carbon stocks in the tropics, there is still no operational integrated framework to achieve this goal. In particular, much uncertainty comes from the difficult evaluation of forest degradation impact, which doesn't entail forest conversion. 3DForMod project aims at integrating advances in 3D forest modelling and very-high-resolution remote sensing technology to improve monitoring of forest aboveground biomass, especially in tropical countries that have signed the Paris Agreement. Our final goal is to supply stakeholders and decision-makers with reliable and accessible information on vegetation carbon stocks in forest territories along with simple predictive, GIS-based models on the consequences of forest degradation in terms of GHG emissions. The consortium of partners allows covering all steps that need to be considered to rigorously scale-up aboveground carbon estimates from tree to forest plot and region. We shall combine: advanced Terrestrial Laser Scanning technology to derive massive tree volume data (WP1) for allometry development without employing destructive harvesting (WP2); 3D forest modelling to link remote sensing information to ground data in order to improve capability of high-resolution satellite data to estimate biomass (WP3) and detect changes and emissions related to forest degradation for regional up-scaling (WP4); collaboration with developing country forest monitoring agencies for integrating project results into their national REDD+ monitoring system and related capacity building with international partners (WP5).
• Raphaël
Pélissier, Tropical Forest Ecology, IRD-AMAP lab,
Montpellier University, France (coordinator)
• Martin
Herold, Geoinformation Science and Remote
Sensing, Wageningen University and Research, The Netherlands
• Pasi
Raumonen, Applied Mathematics, Tampere University
of Technology, Finland
• Jerôme
Chave, Tropical Forest Biodiversity, University
of Toulouse, France
• Uta
Berger, Forest Biometrics and Forest System
Analysis, Dresden University of Technology, Germany
•
Grégoire Vincent, Tropical Forest Ecology and
Remote Sensing
• Jérôme Pérez, Data Management
• Maxime
Réjou-Méchain, Tropical Forest Ecology and Remote
Sensing
•
Nicolas Barbier, Tropical Forest Ecology and
Remote Sensing
•
Olivier Martin-Ducup , Forest Science
•
Philippe Verley, Computer Science
•
Pierre Couteron, Tropical Forest Ecology and
Remote Sensing
•
Pierre Ploton , Tropical Forest Ecology and
Remote Sensing
• Raphaël
Pélissier, Tropical Forest Ecology
•
Thierry Fourcaud, Applied Mathematics
• Alvaro
Lau Sarmiento, Tropical Forest Ecology and Remote
Sensing
• Harm
Bartholomeus, Ecology and Remote Sensing
• Martin
Herold, Geoinformation Science and Remote Sensing
• Pasi Raumonen, Applied Mathematics
•
Bonaventure Sonké, Ecology and Systematics
•
Stéphane Momo Takoudjou, Tropical Forest Ecology
and T-LiDAR Remote Sensing
• Guyana
Forestry Commission
• National REDD+ Coordination Unit, Cameroon
• Foundation
for Forest Management and Production Control, Suriname
• Centre
for International Forestry Research
• FAO
Global Observatory for Forest and Land Cover Dynamics
• ESA
Biomass mission