Spatially explicit analysis of field inventories for national forest carbon monitoring

Background: Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO₂ emissions. Policiesthat incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimatesof national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitationsof field inventory sampling, we tested whether two common field-plot sampling approaches could accuratelyestimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map ofcarbon stocks was used as a model of the country's carbon geography.Results: Both field inventory sampling approaches worked well in estimating total national carbon stocks, almostalways falling within 10 % of the model national total. However, the sampling approaches were unable to produceaccurate spatially-explicit estimates of the carbon geography of Perú, with estimates falling within 10 % of the modelcarbon geography across no more than 44 % of the country. We did not find any associations between carbon stockerrors from the field plot estimates and six different environmental variables.Conclusions: Field inventory plot sampling does not provide accurate carbon geography for a tropical country withwide ranging environmental gradients such as Perú. The lack of association between estimated carbon errors andenvironmental variables suggests field inventory sampling results from other nations would not differ from thosereported here. Tropical forest nations should understand the risks associated with primarily field-based samplingapproaches, and consider alternatives leading to more effective forest conservation and climate change mitigation.