  Policy measures and management decisions aiming at enhancing the role of
forests in mitigating climate-change require reliable estimates of C-stock
dynamics in greenhouse gas inventories (GHGIs). Aim of this study was to
assemble design-based estimators to provide estimates relevant for GHGIs using
national forest inventory (NFI) data. We improve basic expansion (BE) estimates
of living-biomass C-stock loss using field-data only, by leveraging with
remotely-sensed auxiliary data in model-assisted (MA) estimates. Our case
studies from Norway, Sweden, Denmark, and Latvia covered an area of >70 Mha.
Landsat-based Forest Cover Loss (FCL) and one-time wall-to-wall airborne laser
scanning (ALS) data served as auxiliary data. ALS provided information on the
C-stock before a potential disturbance indicated by FCL. The use of FCL in MA
estimators resulted in considerable efficiency gains which in most cases were
further increased by using ALS in addition. A doubling of efficiency was
possible for national estimates and even larger efficiencies were observed at
the sub-national level. Average annual estimates were considerably more precise
than pooled estimates using NFI data from all years at once. The combination of
remotely-sensed with NFI field data yields reliable estimates which is not
necessarily the case when using remotely-sensed data without reference
observations.
