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    Author(s): Qing Xu; Albert Man; Mark Fredrickson; Zhengyang Hou; Juho Pitkänen; Brian Wing; Carlos Ramirez; Bo Li; Jonathan A. Greenberg
    Date: 2018
    Source: Remote Sensing of Environment
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: Download Publication  (5.0 MB)

    Description

    To address uncertainty in biomass estimates across spatial scales, we determined aboveground biomass (AGB) in Californian forests through the use of individual tree detection methods applied to small-footprint airborne LiDAR. We propagated errors originating from a generalized allometric equation, LiDAR measurements, and individual tree detection algorithms to AGB estimates at the tree and plot levels. Larger uncertainties than previously reported at both tree and plot levels were found when AGB was derived from remote sensing. On average, per-tree AGB error was 135% of the estimated AGB, and per-plot error was 214% of the estimated AGB. We found that from tree to plot level, the allometric equation constituted the largest proportion of the total AGB uncertainty. The proportion of the uncertainty associated with remote sensing errors was larger in lower AGB forests, and it decreased as AGB increased. The framework in which we performed the error propagation analysis can be used to address AGB uncertainties in other ecosystems and can be integrated with other analytical techniques.

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    Citation

    Xu, Qing; Man, Albert; Fredrickson, Mark; Hou, Zhengyang; Pitkänen, Juho; Wing, Brian; Ramirez, Carlos; Li, Bo; Greenberg, Jonathan A. 2018. Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR. Remote Sensing of Environment. 216: 514-528. https://doi.org/10.1016/j.rse.2018.07.022.

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    Keywords

    California forests, Individual tree detection, Allometric equations, Uncertainty decomposition, Omission and commission errors

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https://www.fs.usda.gov/treesearch/pubs/57080