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

• Main Authors: Shettles, M. (Author), Hilker, T. (Author), Temesgen, H. (Author)
• Format: Article
• Language: English
• Published: 2016-02-25.
• Subjects: Article
• Online Access: Get fulltext; Get fulltext

 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.