Canadian Forest Service Publications

Fine-scale three-dimensional modeling of boreal forest plots to improve forest characterization with remote sensing. 2018. Côté, J.-F.; Fournier, R.S.; Luther, J.E. van Lier, O.R. Rem. Sens. Environ. 219: 99-114.

Year: 2018

Issued by: Laurentian Forestry Centre

Catalog ID: 39359

Language: English

Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1016/j.rse.2018.09.026

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Improving the quality of information that can be obtained from forest inventories can enhance planning for the best use of forest resources. In this study, we demonstrate the capability to improve the characterization of forest inventory attributes using terrestrial laser scanner (TLS) data, a fine-scale architectural model (L-Architect), and airborne laser scanner (ALS) data. Terrestrial laser scanning provides detailed and accurate three-dimensional data and has the potential to characterize forest plots with comprehensive structural information. We use TLS data and in situ measurements as input to L-Architect to create reference plots. The use of L-Architect for modeling was validated by comparing selected attributes of the reference plots with validation plots produced using simulated TLS data, with normalized root-mean square error (NMRSE) values below 17%. Surrogate plots were then created using a library of tree models where individual trees were selected according to three attributes—tree height, diameter at breast height, and crown projected area—either measured from in situ plots or derived from ALS data. The accuracy of the surrogate plots was assessed by comparing several key forest attributes from the reference plots, including branching structure (e.g., number of whorls, knot surface), crown shape and size (e.g., base height, asymmetry), heterogeneity (e.g., lacunarity, fractal dimension), tree volume, and the spatial distribution of material (e.g., Weibull fit, leaf area index). Overall, the surrogate plots reproduced the attributes of the reference plots with NRMSE mean value of 17% (R2=0.68) using in situ ground measurements and 24% (R2=0.51) using inputs estimated with ALS. Some attributes, such as leaf area index, knot surface, and fractal dimension, were well predicted (R2 > 0.80), whereas others, like crown asymmetry and lacunarity, had weak correspondence (R2 < 0.16). The ability to create surrogate forest plots with L-Architect makes it possible to estimate detailed structural attributes that are difficult to measure with conventional forest mensuration techniques and that can be used for model calibration with above-canopy remote-sensing data sets.