Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Jacob Bukoski; Angie Elwin; Richard MacKenzie; Sahadev Sharma; Joko Purbopuspito; Benjamin Kopania; Maybeleen Apwong; Roongreang Poolsiri; Matthew D. Potts
    Date: 2020
    Source: Environmental Research Letters. 15(8): 084019
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: Download Publication  (817.0 KB)

    Description

    Estimating baseline carbon stocks is a key step in designing forest carbon programs. While field inventories are resource-demanding, advances in predictive modeling are now providing globally coterminous datasets of carbon stocks at high spatial resolutions that may meet this data need. However, it remains unknown how well baseline carbon stock estimates derived from model data compare against conventional estimation approaches such as field inventories. Furthermore, it is unclear whether site-level management actions can be designed using predictive model data in place of field measurements. We examined these issues for the case of mangroves, which are among the most carbon dense ecosystems globally and are popular candidates for forest carbon programs. We compared baseline carbon stock estimates derived from predictive model outputs against estimates produced using the Intergovernmental Panel on Climate Change's (IPCC) three-tier methodological guidelines. We found that the predictive model estimates out-performed the IPCC's Tier 1 estimation approaches but were significantly different from estimates based on field inventories. Our findings help inform the use of predictive model data for designing mangrove forest policy and management actions.

    Publication Notes

    • You may send email to psw_communications@fs.fed.us to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Bukoski, Jacob J; Elwin, Angie; MacKenzie, Richard A; Sharma, Sahadev; Purbopuspito, Joko; Kopania, Benjamin; Apwong, Maybeleen; Poolsiri, Roongreang; Potts, Matthew D. 2020. The role of predictive model data in designing mangrove forest carbon programs. Environmental Research Letters. 15(8): 084019. https://doi.org/10.1088/1748-9326/ab7e4e.

    Cited

    Google Scholar

    Keywords

    blue carbon, climate change mitigation, carbon offsets, carbon accounting, wetlands

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/60849