Canadian Forest Service Publications
Commentary: An ecosystem context for global gross forest cover loss estimates. 2010. Kurz, W.A. Pages 9025-9026 (Vol. 107(20)) in Proceedings of the National Academy of Sciences (PNAS) of the United States of America. PNAS, Washington, DC.
Issued by: Pacific Forestry Centre
Catalog ID: 31683
Fundamental questions remain about the contributions of forests to the global carbon cycle and how these are affected by natural and anthropogenic drivers. Deforestation, the human-induced conversion of forests to non-forest land, currently accounts for an estimated 12% of anthropogenic carbon emissions (1). The future response of forests to global climate change could result in substantial positive or negative feedback to the carbon cycle, and this forest feedback will in turn affect the mitigation efforts required to reach stabilization targets for atmospheric CO2 concentrations (2, 3). Accurate quantification of land-use change involving forests (afforestation, deforestation), natural disturbances (fire, insects), and forest management (harvesting, fire suppression) is a prerequisite to estimating the net contribution of forests to the global carbon balance. However, globally consistent data on these processes are difficult and costly to obtain. Hansen et al. (4) address one of these issues with a synthesis of data from several continental-scale studies into a globally consistent estimate of gross forest cover loss (GFCL), defined by the authors as any “conversion of forest cover to non-forest cover.”
The authors first use coarse spatial resolution imagery from the Moderate Resolution Imaging Spectroradiometer to depict forest cover change globally by stratum. They estimate GFCL as the reduction in forest cover between 2000 and 2005 by sampling 541 randomly selected 18.5 × 18.5-km blocks extracted from Landsat-7 Enhanced Thematic Mapper Plus imagery. The difference in forest cover indicates a global average GFCL rate of 3.1% over 5 yr or 0.6% yr-1 relative to the forest area in 2000.
Although these new global statistics provide great value because of their globally consistent definitions and methodology, the estimates must be placed into context to assess their implications. GFCL provides important but incomplete information to infer knowledge …