A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing

Estimating biomass of terrestrial vegetation is not only a rapidly expanding research area, but also a subject of tremendous interest for reducing carbon emissions associated with deforestation and forest degradation (REDD). The accuracy of biomass estimates, and rate of biomass change, is not only important in the context of carbon markets emerging under REDD, but also for characterizing uncertainty in estimates of carbon cycling and the global carbon budget. There is particular interest in mapping biomass so that carbon stocks and stock changes can be monitored consistently across a range of scales – from relatively small projects (tens of hectares) to national or continental scales – but also so that other benefits of forest conservation can be factored into decision making (e.g. biodiversity and habitat corridors). We conducted an analysis of reported biomass accuracy estimates from more than 70 refereed articles using different remote sensing platforms (airborne and spaceborne) and sensor types (optical, radar, and lidar), with a particular focus on lidar since those papers reported the lowest errors when used in a synergistic manner with other coincident multi-sensor measurements. We show systematic differences in accuracy between different types of lidar systems flown on different platforms but, perhaps more importantly, differences between forest types (biomes) and plot sizes used for field calibration and assessment. We discuss these findings in relation to monitoring, reporting and verification under REDD, and also in the context of more systematic assessment of factors that influence accuracy and error estimation.