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 journalArticle

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 1 2018

Fingerprint

aboveground biomass
footprint
Biomass
uncertainty
remote sensing
Remote sensing
Uncertainty
detection method
analytical methods
Ecosystems
analytical method
ecosystems
ecosystem
biomass

Keywords

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

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR. / Xu, Qing; Man, Albert; Fredrickson, Mark; Hou, Zhengyang; Pitkänen, Juho; Wing, Brian; Ramirez, Carlos; Li, Bo; Greenberg, Jonathan A.

In: Remote Sensing of Environment, Vol. 216, 01.10.2018, p. 514-528.

Research output: Contribution to journalArticle

Xu, Q, Man, A, Fredrickson, M, Hou, Z, Pitkänen, J, Wing, B, Ramirez, C, Li, B & Greenberg, JA 2018, 'Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR', Remote Sensing of Environment, vol. 216, pp. 514-528. https://doi.org/10.1016/j.rse.2018.07.022
Xu, Qing ; Man, Albert ; Fredrickson, Mark ; Hou, Zhengyang ; Pitkänen, Juho ; Wing, Brian ; Ramirez, Carlos ; Li, Bo ; Greenberg, Jonathan A. / Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR. In: Remote Sensing of Environment. 2018 ; Vol. 216. pp. 514-528.
@article{e58fa634dab54e8ea71f8b2cf8ed6e9f,
title = "Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR",
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.",
keywords = "Allometric equations, California forests, Individual tree detection, Omission and commission errors, Uncertainty decomposition",
author = "Qing Xu and Albert Man and Mark Fredrickson and Zhengyang Hou and Juho Pitk{\"a}nen and Brian Wing and Carlos Ramirez and Bo Li and Greenberg, {Jonathan A.}",
year = "2018",
month = "10",
day = "1",
doi = "10.1016/j.rse.2018.07.022",
language = "English (US)",
volume = "216",
pages = "514--528",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR

AU - Xu, Qing

AU - Man, Albert

AU - Fredrickson, Mark

AU - Hou, Zhengyang

AU - Pitkänen, Juho

AU - Wing, Brian

AU - Ramirez, Carlos

AU - Li, Bo

AU - Greenberg, Jonathan A.

PY - 2018/10/1

Y1 - 2018/10/1

N2 - 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.

AB - 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.

KW - Allometric equations

KW - California forests

KW - Individual tree detection

KW - Omission and commission errors

KW - Uncertainty decomposition

UR - http://www.scopus.com/inward/record.url?scp=85050213378&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050213378&partnerID=8YFLogxK

U2 - 10.1016/j.rse.2018.07.022

DO - 10.1016/j.rse.2018.07.022

M3 - Article

VL - 216

SP - 514

EP - 528

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -