通过结合光学卫星、机载激光扫描和NFI数据改进活生物量碳储量损失估计
Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data
摘要：
旨在加强森林在减缓气候变化方面的作用的政策措施和管理决定需要对温室气体清单中的碳储量动态作出可靠的估计。本研究的目的是组装设计为基础的估计提供估计GHGI使用国家森林资源清查（NFI）数据。我们改进了基本扩展（BE）估计的活生物量C-股票损失仅使用现场数据，利用遥感辅助数据模型辅助（MA）的估计。我们对挪威、瑞典、丹麦和拉脱维亚的案例研究覆盖了超过70 Mha的面积。陆地卫星的森林覆盖损失数据和一次性的机载激光扫描数据作为辅助数据。ALS在FCL指示的潜在干扰之前提供了关于C库存的信息。使用FCL在MA估计导致了相当大的效率增益，在大多数情况下，进一步增加了使用ALS此外。国家估计数的效率可能提高一倍，国家以下一级的效率甚至更高。平均年度估计数比同时使用所有年份的NFI数据的汇总估计数要精确得多。将遥感数据与NFI实地数据相结合，可得出可靠的估计数，而在没有参考观测的情况下使用遥感数据时，情况就不一定如此。
英文摘要：
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.
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