Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data
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Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data. / Breidenbach, Johannes; Ivanovs, Janis; Kangas, Annika; Nord-larsen, Thomas; Nilsson, Mats; Astrup, Rasmus.
In: Canadian Journal of Forest Research, Vol. 51, No. 10, 2021, p. 1472–1485.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data
AU - Breidenbach, Johannes
AU - Ivanovs, Janis
AU - Kangas, Annika
AU - Nord-larsen, Thomas
AU - Nilsson, Mats
AU - Astrup, Rasmus
PY - 2021
Y1 - 2021
N2 - Policy measures and management decisions aimed at enhancing the role of forests in mitigating climate change require reliable estimates of carbon (C)-stock dynamics in greenhouse gas inventories (GHGIs). The 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) estimators of living-biomass C-stock loss using only field data, by leveraging with remote sensing auxiliary data in model-assisted (MA) estimators. 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) 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 adding ALS. A doubling of efficiency was possible for national estimates and even larger efficiencies were observed at the subnational level. Average annual estimates were considerably more precise than pooled estimates of the NFI data from all years at once. The combination of remotely sensed and NFI field data yields reliable estimators, which is not necessarily the case when using remotely sensed data without reference observations.
AB - Policy measures and management decisions aimed at enhancing the role of forests in mitigating climate change require reliable estimates of carbon (C)-stock dynamics in greenhouse gas inventories (GHGIs). The 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) estimators of living-biomass C-stock loss using only field data, by leveraging with remote sensing auxiliary data in model-assisted (MA) estimators. 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) 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 adding ALS. A doubling of efficiency was possible for national estimates and even larger efficiencies were observed at the subnational level. Average annual estimates were considerably more precise than pooled estimates of the NFI data from all years at once. The combination of remotely sensed and NFI field data yields reliable estimators, which is not necessarily the case when using remotely sensed data without reference observations.
U2 - 10.1139/cjfr-2020-0518
DO - 10.1139/cjfr-2020-0518
M3 - Journal article
VL - 51
SP - 1472
EP - 1485
JO - Canadian Journal of Forest Research
JF - Canadian Journal of Forest Research
SN - 0045-5067
IS - 10
ER -
ID: 260303813