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

TL;DR

This study enhances estimates of forest biomass carbon loss by integrating satellite, airborne laser scanning, and national forest inventory data, significantly improving accuracy for climate policy assessments.

Contribution

It introduces a model-assisted estimation approach combining remote sensing and field data to improve carbon stock loss estimates in forests.

Findings

FCL and ALS data significantly increased estimation efficiency.

Combining remote sensing with NFI data yields more reliable C-stock loss estimates.

Efficiency gains were larger at sub-national levels.

Abstract

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.