Jump to Main Content
Spatially explicit analysis of field inventories for national forest carbon monitoring
- Marvin, David C., Asner, Gregory P.
- Carbon balance and management 2016 v.11 no.1 pp. 9
- carbon, carbon dioxide, carbon sinks, climate change, environmental factors, forest conservation, geography, greenhouse gas emissions, issues and policy, models, monitoring, national forests, risk, tropical forests, Peru
- BACKGROUND: Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO₂ emissions. Policies that incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimates of national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitations of field inventory sampling, we tested whether two common field-plot sampling approaches could accurately estimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map of carbon 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, almost always falling within 10 % of the model national total. However, the sampling approaches were unable to produce accurate spatially-explicit estimates of the carbon geography of Perú, with estimates falling within 10 % of the model carbon geography across no more than 44 % of the country. We did not find any associations between carbon stock errors 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 with wide ranging environmental gradients such as Perú. The lack of association between estimated carbon errors and environmental variables suggests field inventory sampling results from other nations would not differ from those reported here. Tropical forest nations should understand the risks associated with primarily field-based sampling approaches, and consider alternatives leading to more effective forest conservation and climate change mitigation.