UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Assessment of bias in pan-tropical biomass predictions

Disney, M; (2020) Assessment of bias in pan-tropical biomass predictions. Frontiers in Forests and Global Change , 3 , Article 12. 10.3389/ffgc.2020.00012. Green open access

[thumbnail of Disney_ffgc-03-00012.pdf]
Preview
Text
Disney_ffgc-03-00012.pdf - Published Version

Download (1MB) | Preview

Abstract

Above-ground biomass (AGB) is an essential descriptor of forests, of use in ecological and climate-related research. At tree- and stand-scale, destructive but direct measurements of AGB are replaced with predictions from allometric models characterizing the correlational relationship between AGB, and predictor variables including stem diameter, tree height and wood density. These models are constructed from harvested calibration data, usually via linear regression. Here, we assess systematic error in out-of-sample predictions of AGB introduced during measurement, compilation and modeling of in-sample calibration data. Various conventional bivariate and multivariate models are constructed from open access data of tropical forests. Metadata analysis, fit diagnostics and cross-validation results suggest several model misspecifications: chiefly, unaccounted for inconsistent measurement error in predictor variables between in- and out-of-sample data. Simulations demonstrate conservative inconsistencies can introduce significant bias into tree- and stand-scale AGB predictions. When tree height and wood density are included as predictors, models should be modified to correct for bias. Finally, we explore a fundamental assumption of conventional allometry, that model parameters are independent of tree size. That is, the same model can provide predictions of consistent trueness irrespective of size-class. Most observations in current calibration datasets are from smaller trees, meaning the existence of a size dependency would bias predictions for larger trees. We determine that detecting the absence or presence of a size dependency is currently prevented by model misspecifications and calibration data imbalances. We call for the collection of additional harvest data, specifically under-represented larger trees.

Type: Article
Title: Assessment of bias in pan-tropical biomass predictions
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/ffgc.2020.00012
Publisher version: https://doi.org/10.3389/ffgc.2020.00012
Language: English
Additional information: © 2020 Burt, Calders, Cuni-Sanchez, Gómez-Dans, Lewis, Lewis, Malhi, Phillips and Disney. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: tropical forests, above-ground biomass, allometry, prediction, error, uncertainty
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography
URI: https://discovery.ucl.ac.uk/id/eprint/10091968
Downloads since deposit
110Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item