Enhancing Forest Attribute Prediction by Considering Terrain and Scan Angles From Lidar Point Clouds: A Neural Network Approach

Sensitivity of lidar metrics to scan angle can affect the robustness of area-based approach (ABA) models, and modelling the interplay of scan geometry and terrain properties can be complex. The study hypothesizes that neural networks can manage the interplay of lidar acquisition parameters, terrain properties, and vegetation characteristics to improve ABA models. The study area is in Massif des Bauges Natural Regional Park, eastern France, comprising 291 field plots in a mountainous environment with broadleaf, coniferous, and mixed forest types. Field plots were scanned with a high overlap from multiple flight lines and the corresponding point clouds were considered independently to expand the standard ABA dataset (291 observations) to create a dataset containing 1095 independent observations. Computation of lidar, terrain, and scan angle metrics for each point cloud associated each observation in the expanded dataset with the scan information in addition to the lidar and terrain information. A multilayer perceptron (MLP) was used to model basal area and total volume to compare the predictions resulting from standard and expanded ABA datasets. With expanded datasets containing lidar, terrain, and scan information, the R
²
for the median predictions per plot were higher (R
²
of 0.83 and 0.85 for
BA
and
V
_tot
) than predictions with standard datasets (R
²
of 0.66(
BA
) and 0.71(
V
_tot
)) containing only lidar metrics. It also outperformed an MLP model for a dataset with lidar and terrain information [R
²
of 0.77(
BA
and
V
_tot
)]. The MLP performed better than Random forest regression, which could not sufficiently exploit additional terrain and scan information.