Skip to Main Content
Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDARAuthor(s): Qing Xu; Albert Man; Mark Fredrickson; Zhengyang Hou; Juho Pitkänen; Brian Wing; Carlos Ramirez; Bo Li; Jonathan A. Greenberg
Source: Remote Sensing of Environment
Publication Series: Scientific Journal (JRNL)
Station: Pacific Southwest Research Station
Download Publication (5.0 MB)
DescriptionTo 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.
- You may send email to email@example.com 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.
CitationXu, 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.
KeywordsCalifornia forests, Individual tree detection, Allometric equations, Uncertainty decomposition, Omission and commission errors
- Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference
- Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data
- Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta
XML: View XML