Objective
Boreal forest is the largest terrestrial biome and will be strongly affected by climate warming, which is predicted to be strongest at high latitudes, with significant impacts on the European economy. North State will demonstrate how innovative methods applied to the new Sentinel data streams can be combined with models to monitor carbon and water fluxes for pan-boreal Europe, with intensive study sites in Finland, Iceland and Russia. This will reduce the high uncertainty in current estimates of flux rates.
Key model parameters will be derived from Sentinel and other EO data, in situ and ancillary data, including relevant FP7 and ESA CCI projects. Parameter types include forest characteristics such as area and species, model drivers such as incoming radiation, and indicators of the dynamic state of the forest, such as fAPAR and tree height. Many of these parameters will also be applicable for other purposes, such as operational forest management.
The project brings together leading experts and organisations, including an SME experienced in developing value adding services, needed to address key research challenges that require innovative remote sensing methods: adaptation of the carbon and water cycle models for effective use of EO data; effective pre-processing and data management techniques to exploit high temporal frequency time series; assessing the potential of hyper-spectral data; developing powerful data fusion techniques; and developing an intelligent, automated framework to learn from and interpret multi-source data to address a key societal problem.
It responds to the Lund declaration and the recommendations of the Space Advisory Group. It will strengthen European leadership in the provision of EO-based services and will be a paradigm for exploiting opportunities offered by the new generation of EO satellites in developing products for future GMES services.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- agricultural sciencesagriculture, forestry, and fisheriesforestrysilviculture
- engineering and technologyenvironmental engineeringremote sensing
- natural sciencesbiological sciencesecologyecosystems
Programme(s)
Call for proposal
FP7-SPACE-2013-1
See other projects for this call
Funding Scheme
CP-FP - Small or medium-scale focused research projectCoordinator
02150 Espoo
Finland