Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data

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