Executive Summary

In project 606962 North State of the EU Framework Program 7 novel approaches were developed for the prediction of carbon balance of the boreal forest ecosystems. Forest and land cover variable values and cover change were estimated with the help of satellite and ground reference data. The predictions were used as parameters of carbon flux models. The final result was primary production in a form of raster maps.

The principal satellite data were from the Sentinel-2 and Sentinel-1 satellites of the Copernicus program supported by Landsat satellite data. Imagery from Suomi NPP satellite of the United States National Oceanic and Atmospheric Administration represented Sentinel-3 data that were not available at the time of the project. The Sentinel data and Landsat data were used at four intensive study sites in Iceland, Finland (south boreal Hyytiälä and north boreal Sodankylä) and Komi in Russia. A map of the European part of the boreal forest up to the Ural Mountains was computed with Suomi NPP imagery. In total more than sixty satellite image or airborne spectrometer image based predictions were computed. Their accuracy was tested with random samples of very fine resolution satellite data and field plots. Self-learning, intelligent tools that are able to analyze big data masses were developed and applied in image analyses.

In Sentinel-2 and Sentinel-1 based predictions the forest and non-forest classification accuracy was 84 % when tested over whole Finland with national forest inventory sample plots. In growing stock volume estimation, the root mean square error at a forest stand level was 65 m3/ha or 47 % of the mean. At the county level the results matched with the field plot data. In the large boreal forest zone, the forest area was 70.4 % in Suomi NPP classification and 70.7 % from an independent sample of very fine resolution data.

Two vegetation models were applied with the satellite borne estimates and climatic data: Lund-Potsdam-Jena Dynamic Vegetation Model (DVM) and the semi-empirical Helsinki Forest Model. The computed carbon flux variables were Gross Primary Production (GPP) and Net Primary Production (NPP). With the Helsinki model also Net Ecosystem Exchange (NEE) and forest stem volume increment was computed. The NEE considers in addition to the primary production the carbon flux from the soil.

The Helsinki model could utilize satellite data at fine resolution better than the traditional DVM that is mainly using climatic data, leaf area index and land cover data at a coarse resolution in the order of one kilometer. With the Helsinki model the 10-meter resolution of the optical Sentinel-2 satellite and a higher number of forest variables could be utilized. Carbon and water fluxes and forest growth, estimated on the base of satellite data and the Helsinki model were within the limits from the flux tower measurements at Hyytiälä and Sodankylä, the exception was the Net Ecosystem Exchange at Sodankylä. NEE was higher in the flux tower data than from the Helsinki model. The modeled mean annual increment of growing stock volume was close to the Finnish national forest inventory results. After parameter calibration of the LPJ DVM model and using downscaled earth observation data, we obtained simulated carbon fluxes which largely agreed to data obtained from flux towers albeit a small number of sites where the model exhibited numerical instabilities due to its inherent complexity.

The project enables changing the paradigm in forest management, forest primary productivity and carbon cycle estimation and reporting. This means better decisions in forest management and benefits in carbon reporting which further leads to economic gains. The image and other data analyses procedures developed in the North State form a foundation for operational carbon balance prediction systems. Their development will be the next step after North State.