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Comparison of uncertainty in per unit area estimates of aboveground biomass for two selected model sets

Comparison of uncertainty in per unit area estimates of aboveground biomass for two selected model sets
Comparison of uncertainty in per unit area estimates of aboveground biomass for two selected model sets
Uncertainty in above ground forest biomass (AGB) estimates at broad-scale depends primarily on three sources of error that interact and propagate: measurement error, model error, and sampling error. Using Monte Carlo simulations, we compare the total propagated error for two sets of regional-level component equations for lodgepole pine AGB, and for two sets of high-precision instruments by accounting for all three of these sources of error. The two sets of models compared included a set of newly-developed component ratio method (CRM) equations, and a set of component AGB equations currently used by the Forest Inventory and Analysis (FIA) unit of the United States Department of Agriculture (USDA) Forest Service.

Relative contributions for measurement, model, and sampling error using the current regional equations were 5{\%}, 2{\%} and 93{\%}, respectively, and 13{\%}, 55{\%} and 32{\%}, respectively using the CRM equations. Relative standard error (RSE) values for the current regional and CRM equations with all three error types accounted for were 20.7{\%} and 36.8{\%}, respectively. Results for the model comparisons indicate that per acre estimates of AGB using the CRM equations are far less precise than those produced with the current set of regional equations. Results for the instrument comparisons indicate the terrestrial lidar scanning reduce uncertainty in broad-scale estimates of AGB attributed to measurement error.
AGB, CRM, CRM-FIA, CV, DBH, DNF, DOB, deschutes national forest, FIA, forest inventory and analysis, HT, HTCB, measurement error, model error, NFI, national forest inventory, pacific northwest, RMSE, RRMSE, RSE, SE, STM, SUR, sampling error, TTWOF, WNF, willamette national forest, aboveground biomass, coefficient of variation, component ratio method, component ratio method used by FIA, diameter at breast height, diameter outside bark, height to the base of live crown, relative root mean square error, relative standard error, root mean square error, seemingly unrelated regression, standard error, standing tree measurements, total tree aboveground biomass without foliage, total tree height
0378-1127
18-25
Shettles, Michael
68bb7bad-d119-4606-a4ff-ca4d9fcab907
Temesgen, H.
62942b29-1190-48fc-a1d0-3e9b3e7dbcaf
Gray, Andrew N.
2f4da2dc-de67-4313-93d9-7f197c5cfb0b
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40
Shettles, Michael
68bb7bad-d119-4606-a4ff-ca4d9fcab907
Temesgen, H.
62942b29-1190-48fc-a1d0-3e9b3e7dbcaf
Gray, Andrew N.
2f4da2dc-de67-4313-93d9-7f197c5cfb0b
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40

Shettles, Michael, Temesgen, H., Gray, Andrew N. and Hilker, Thomas (2015) Comparison of uncertainty in per unit area estimates of aboveground biomass for two selected model sets. Forest Ecology and Management, 354, 18-25. (doi:10.1016/j.foreco.2015.07.002).

Record type: Article

Abstract

Uncertainty in above ground forest biomass (AGB) estimates at broad-scale depends primarily on three sources of error that interact and propagate: measurement error, model error, and sampling error. Using Monte Carlo simulations, we compare the total propagated error for two sets of regional-level component equations for lodgepole pine AGB, and for two sets of high-precision instruments by accounting for all three of these sources of error. The two sets of models compared included a set of newly-developed component ratio method (CRM) equations, and a set of component AGB equations currently used by the Forest Inventory and Analysis (FIA) unit of the United States Department of Agriculture (USDA) Forest Service.

Relative contributions for measurement, model, and sampling error using the current regional equations were 5{\%}, 2{\%} and 93{\%}, respectively, and 13{\%}, 55{\%} and 32{\%}, respectively using the CRM equations. Relative standard error (RSE) values for the current regional and CRM equations with all three error types accounted for were 20.7{\%} and 36.8{\%}, respectively. Results for the model comparisons indicate that per acre estimates of AGB using the CRM equations are far less precise than those produced with the current set of regional equations. Results for the instrument comparisons indicate the terrestrial lidar scanning reduce uncertainty in broad-scale estimates of AGB attributed to measurement error.

Full text not available from this repository.

More information

Accepted/In Press date: 3 July 2015
e-pub ahead of print date: 10 July 2015
Published date: 15 October 2015
Keywords: AGB, CRM, CRM-FIA, CV, DBH, DNF, DOB, deschutes national forest, FIA, forest inventory and analysis, HT, HTCB, measurement error, model error, NFI, national forest inventory, pacific northwest, RMSE, RRMSE, RSE, SE, STM, SUR, sampling error, TTWOF, WNF, willamette national forest, aboveground biomass, coefficient of variation, component ratio method, component ratio method used by FIA, diameter at breast height, diameter outside bark, height to the base of live crown, relative root mean square error, relative standard error, root mean square error, seemingly unrelated regression, standard error, standing tree measurements, total tree aboveground biomass without foliage, total tree height
Organisations: Geography & Environment

Identifiers

Local EPrints ID: 384653
URI: https://eprints.soton.ac.uk/id/eprint/384653
ISSN: 0378-1127
PURE UUID: c4f8ea20-70b0-4ed4-983a-0890aa67cf99

Catalogue record

Date deposited: 11 Jan 2016 16:22
Last modified: 17 Jul 2017 20:03

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