We contribute to climate change mitigation activities by developing a platform for forest carbon monitoring with high accuracy, reach and frequency
Forests are essential in maintaining healthy ecosystem interaction and biodiversity on Earth. They are in a key role in the fight against climate change since they can restore increasing carbon stocks. Hence, several national and international initiatives (including the UNFCCC Paris Agreement and the EU LULUCF Regulation) require regular reporting of forest biomass and carbon. Robust, reliable and transparent monitoring approaches of forest biomass and carbon are needed to respond to reporting requirements.
The Forest Carbon Monitoring project is developing and implementing a set of tools for reliable and cost-efficient monitoring of forest carbon stock based on satellite remote sensing approaches supported by in-situ datasets.
Spatially detailed data from large forest areas — also regions without proper forest inventory data.
Openly available satellite datasets used together with published monitoring methods.
Enabling high resolution mapping and monitoring of global forests.
Reaching global scale with frequent data acquisition and long-term standardized systems at low cost.
Last autumn, we had a visiting scientist from the University of Oviedo staying here at VTT. This gave us a great opportunity to test some of the FCM tools in his study areas in northern Spain, and thereby provided valuable information on the applicability of the methods in new areas.
One of the main objectives of the continuation phase of the FCM project is to include a deep learning method in the tool portfolio. Based on earlier experiences, the UNet model family approaches were selected. A vanilla UNet model as well as a more advanced SeUNet model with a squeeze-excitation […]
EO data products are sensitive to variations caused by atmospheric or seasonal effects, sometimes significantly affecting forest variable predictions. When used repeatedly in the same area to monitor the development of forest resources these effects can cause inconsistencies in the time series. The Data Assimilation (DA) approach to be demonstrated […]
