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Allometric equations for integrating remote sensing imagery into forest monitoring programmes.

ABSTRACT: Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

SUBMITTER: Jucker T

PROVIDER: S-EPMC6849852 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Allometric equations for integrating remote sensing imagery into forest monitoring programmes.

Jucker Tommaso T Caspersen John J Chave Jérôme J Antin Cécile C Barbier Nicolas N Bongers Frans F Dalponte Michele M van Ewijk Karin Y KY Forrester David I DI Haeni Matthias M Higgins Steven I SI Holdaway Robert J RJ Iida Yoshiko Y Lorimer Craig C Marshall Peter L PL Momo Stéphane S Moncrieff Glenn R GR Ploton Pierre P Poorter Lourens L Rahman Kassim Abd KA Schlund Michael M Sonké Bonaventure B Sterck Frank J FJ Trugman Anna T AT Usoltsev Vladimir A VA Vanderwel Mark C MC Waldner Peter P Wedeux Beatrice M M BM Wirth Christian C Wöll Hannsjörg H Woods Murray M Xiang Wenhua W Zimmermann Niklaus E NE Coomes David A DA

Global change biology 20160706 1

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height an ...[more]

PMID: 27381364

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Project description:BACKGROUND:Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation-errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n?=?285). RESULTS:Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30-75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25-70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE]?=?50%). This is likely due to low-sample size (10-20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE?=?97%) and nationwide (2.2 billion Mg; % RMSE?=?94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg; % RMSE?=?165%). CONCLUSIONS:Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.

Dataset Information

Allometric equations for integrating remote sensing imagery into forest monitoring programmes.

Publications

Allometric equations for integrating remote sensing imagery into forest monitoring programmes.

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