ForestPaths partners offer insights into forest biomass mapping for climate mitigation

23 May 2024

A study in 2023, by ForestPaths partner LUKE, offers critical insights into the role of forest lands in mitigating climate change, emphasising the importance of high-resolution biomass maps generated through remote sensing technologies, particularly in Finland. 

In the EU, forest land is crucial in the Land Use, Land-Use Change and Forestry (LULUCF) sector, absorbing nearly 9% of total emissions from other sectors in 2019. However, forest characteristics vary regionally and greenhouse gas (GHG) emissions are related to changes in biomass and, for example, to the soil type, with significant differences between mineral soils and peatlands. Peatland forests, covering nearly 8% of EU forest land and up to 27% in Finland (most of which is drained), hold substantial potential for climate change mitigation. 

This study analyses tree biomass changes from 2009 to 2015 using data from the Finnish multi-source national forest inventory (MS-NFI) raster layers, aiming to inform targeted forest management strategies for climate change mitigation tailored to Finland's diverse environmental landscape. LUKE’s paper demonstrates the creation of segmented maps for tree biomass and its changes by soil type, essential for optimal climate change mitigation strategies. While biomass stock values at regional and national levels are consistent with field data, discrepancies are noted at the forest stand level, particularly in disturbed areas where only stand-replacement changes are reliably detected. The maps, segmented by soil type, enhance the understanding of GHG emissions and aid in practical forestry management decisions by providing more accurate emissions estimates and are regarded as applicable for carbon stock change quantification.

To find out more about the importance of biomass maps for forest management, read LUKE’s study here.

Biomass in 2009 and 2015 for different soil types in southern (S) and northern (N) Finland, error bars are twice the standard error.