A bottom-up approach to compiling wood provision ecosystem service accounts with EO data

Quantifying and mapping the wood provisioning ecosystem service presents significant challenges to countries compiling ecosystem accounts because gathering data for large forested areas involves considerable resource investment. Modelling the growth and distribution of forests with Earth Observation (EO) data offers a scalable approach to estimating the Net Annual Increment of forests remotely in a consistent manner and mapping it at high resolution for large extents on a yearly basis. In this study, we introduce and assess a bottom-up approach to calculating Net Annual Increment (NAI) to generate high resolution (10 m) wood provisioning ecosystem accounts. We describe the theoretical reasoning and technical specifications of a novel method involving the identification of areas of living trees and forest available for wood supply, conversion to Dry Matter Productivity and calculation of total green biomass increment, accounting for multiple wood densities and fractional tree cover. We demonstrate the method by generating NAI maps for Slovakia and evaluate the results with comparisons to a reference dataset generated from public forestry measurements as well as to the ecosystem account output of the INCA tool. The maps generated by the Earth Observation (EO) approach captured a higher level of detail in forest and NAI distribution than INCA. NAI per NUTS region for the EO approach were more similar to the reference approach than were the INCA estimates. The INCA approach significantly overestimated NAI for beech and underestimated for spruce stands, although the greater range of values detected by the EO method meant that it had a marginally weaker correlation with the reference than INCA. The results indicate that the innovative EO method improves on existing top-down approaches while remaining scalable and flexible. It is an effective method for compiling wood provisioning ecosystem accounts with the potential for greater temporal resolution and spatial extent than would be possible with field measurements, producing realistic values and high-resolution maps. Its innovative bottom-up approach and foundation in open EO data give this method the potential for continental or global application (where input data are available), providing an accessible way for countries to compile ecosystem accounts to meet reporting obligations.