Abstract
Lidar-based models rely on an optimal relationship between the field and the lidar data for accurate predictions of forest attributes. This relationship may be altered by the variability in the stand growth conditions or by the temporal discrepancy between the field inventory and the lidar survey. In this study, we used lidar data to predict the timber merchantable volume (MV) of five sites located along a bioclimatic gradient of temperature and elevation. The temporal discrepancies were up to three years. We adjusted a random canopy height coefficient (accounting for the variability amongst sites), and a growth function (accounting for the growth during the temporal discrepancy), to the predictive model. The MV could be predicted with a pseudo-R2 of 0.86 and a residual standard deviation of 24.3 m3 ha−1. The average biases between the field-measured and the predicted MVs were small. The variability of MV predictions was related to the bioclimatic gradient. Fixed-effect models that included a bioclimatic variable provided similar prediction accuracies. This study suggests that the variability amongst sites, the occurrence of a bioclimatic gradient and temporal discrepancies are essential in building a generalized lidar-based model for timber volume.
Highlights
Forest stands often develop a diversity of canopy structures across space and time
Our study aimed at building a generalized lidar-based model to predict the timber merchantable volume of various study sites located along a gradient of bioclimatic factors
The addition of a growth adjustment function improved the model significantly (p-value < 0.0001). This function accounted for the merchantable volume (MV) growth that occurred during the temporal discrepancy
Summary
Forest stands often develop a diversity of canopy structures across space and time. The diversity may result from the natural dynamics in stands through time (e.g., growth, mortality, succession, stand composition), the variability of biophysical factors (e.g., soil, climate, elevation) or human activities [1,2]. Lidar is an active sensor which uses pulses of laser light to measure the distance to a target and record the strength of light backscattering from this target. It generates a point cloud which is a three-dimensional representation of the volumetric interaction between pulse
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