Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR

Qing Xu, Albert Man, Mark Fredrickson, Zhengyang Hou, Juho Pitkänen, Brian Wing, Carlos Ramirez, Bo Li, Jonathan A. Greenberg

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)514-528
Number of pages15
JournalRemote Sensing of Environment
Volume216
DOIs
StatePublished - Oct 2018

Bibliographical note

Funding Information:
The study is funded by NASA Carbon Monitoring System (proposal number: 14-CMS14-0048 ) under the title of “Reducing uncertainties in estimating California's forest carbon stocks”. We thank James Balamuta, Jiancong Zhu and Zhen Zhuo at the University of Illinois at Urbana-Champaign for their work at the earlier stage of the project, and Kirk Evans and Carol Clark from USDA Forest Service for sharing knowledge and key datasets.

Publisher Copyright:
© 2018

Keywords

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

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