Examination of uncertainty in per unit area estimates of above ground biomass using terrestrial LiDAR and ground data

Abstract

In estimating aboveground forest biomass (AGB), three sources of error that interact and propagate include: (1) measurement error, the quality of the tree-level measurement data used as inputs for the individual-tree equations; (2) model error, the uncertainty about the equations of the individual trees; and (3) sampling error, the uncertainty due to having obtained a probabilistic or purposive sample, rather than a census, of the trees on a given area of forest land. Monte Carlo simulations were used to examine measurement, model and sampling error, and to compare total uncertainty between models, and between a phase-based terrestrial laser scanner (TLS) and traditional forest inventory instruments. Input variables for the equations were diameter at breast height, total tree height and height to crown base; these were extracted from the terrestrial LiDAR data. Relative contributions for measurement, model and sampling error were 5%, 70% and 25%, respectively when using TLS, and 11%, 66% and 23%, respectively when using the traditional inventory measurements as inputs into the models. We conclude that the use of TLS can reduce measurement errors of AGB compared to traditional measurement approaches.

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