A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models

Hailemariam Temesgen, David Affleck, Krishna Poudel, Andrew Gray, John Sessions

    Research output: Contribution to journalReview articlepeer-review

    Abstract

    Accurate biomass measurements and analyses are critical components in quantifying carbon stocks and sequestration rates, assessing potential impacts due to climate change, locating bio-energy processing plants, and mapping and planning fuel treatments. To this end, biomass equations will remain a key component of future carbon measurements and estimation. As researchers in biomass and carbon estimation, we review the present scenario of aboveground biomass estimation, focusing particularly on estimation using tree-level models and identify some cautionary points that we believe will improve the accuracy of biomass and carbon estimates to meet societal needs. In addition, we discuss the critical challenges in developing or calibrating tree biomass models and opportunities for improved biomass. Some of the opportunities to improve biomass estimate include integration of taper and other attributes and combining different data sources. Biomass estimation is a complex process, when possible, we should make use of already available resources such as wood density and forest inventory databases. Combining different data-sets for model development and using independent data-sets for model verification will offer opportunities to improve biomass estimation. Focus should also be made on belowground biomass estimation to accurately estimate the full forest contribution to carbon sequestration. In addition, we suggest developing comprehensive biomass estimation methods that account for differences in site and stand density and improve forest biomass modeling and validation at a range of spatial scales.

    Original languageEnglish
    Pages (from-to)326-335
    Number of pages10
    JournalScandinavian Journal of Forest Research
    Volume30
    Issue number4
    DOIs
    StatePublished - May 19 2015

    Keywords

    • biomass allometries
    • sampling error
    • uncertainity

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