Assessing terrestrial laser scanning for developing non-destructive biomass allometry

Assessing terrestrial laser scanning for developing non-destructive biomass allometry

https://ift.tt/2K2SaES

Publication date: 1 November 2018
Source:Forest Ecology and Management, Volume 427
Author(s): Atticus E.L. Stovall, Kristina J. Anderson-Teixeira, Herman H. Shugart

Forests provide essential ecosystem services and hold approximately 45% of global terrestrial carbon. Estimates of the quantity and spatial distribution of global forest carbon are built on the assumption that regional- or national-scale allometry accurately captures growth form across the wide spectrum of plant size. Allometry is painstaking and costly to create: trees must be cut, dried, and weighed, over the span of months. This bottleneck has left most equations low in sample size and without large trees (50 cm), which can contain over 40% of aboveground carbon. Terrestrial laser scanning (TLS) can potentially increase the range and sample size of allometric equations through non-destructive biomass estimation and must be evaluated in this context. We deployed TLS at the Center for Tropical Forest Science – Forest Global Earth Observatory (CTFS-ForestGEO) plot in Front Royal, Virginia and virtually reconstructed 329 trees with diameters up to 123 cm. Three-dimensional tree models were the basis for 22 local allometric relationships for comparison to the and equations. Overall, TLS allometry had lower RMSE and predicted higher tree-level biomass compared to the equivalent national equations. We evaluated site-wide allometry for errors from insufficient sample size and diameter range. Allometric equations did not stabilize to a consistent set of parameters until 100–200 samples were reached and exclusion of large trees severely limited prediction accuracy. This work implies that current biomass equations may be inadequate and highlights TLS stem modeling as an appropriate method of non-destructive allometric equation development for updating allometry and reducing uncertainty in landscape-level biomass estimates.

Superforest

via ScienceDirect Publication: Forest Ecology and Management https://ift.tt/xxwarn

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