A new approach to retrieve leaf normal distribution using terrestrial laser scanners

Abstract

Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners (TLS) have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood (Cinnamomum camphora) using TLS point cloud data. First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.

Keywords

Leaf normal distribution / Leaf inclination angle / Terrestrial laser scanner / Point cloud data / Curvature / Clustering

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