Recreating structurally realistic tree maps with airborne laser scanning and ground measurements

The estimation of forest attributes representing aspects of structural biodiversity from vast areas is difficult not only because of the lack of agreement on what constitutes a structurally diverse forest, but also due to difficulties in the detection of small trees. However, a structurally diverse forest should have large variation in tree size (i.e., height and diameter), as well as some clustering of trees, as regular tree pattern is typical for managed forests. In this work we developed a framework for building structurally representative tree maps using airborne laser scanning data and a limited number of ground measurements using data from two locations in Finland. In the proposed method, an individual tree detection algorithm is optimized so that the number of undetected trees and false detections is minimized. The ground measurements are then used to train models for predicting the number and location of undetected trees and false detections by resampling the attributes of undetected trees in the training data. This model can then be applied to other areas in the proximity of the ground measurements to build tree maps, with the location, height, and diameter for each tree. The methodology was shown to reproduce the number of trees, the height distribution, and the spatial pattern of the trees with sufficient accuracy for practical use in large-scale mapping of forest attributes. For example, the individual tree detection could find at best about 54% of the trees with an additional 7% of false detections, resulting in a bias of approximately −30%, while with the new method the bias was reduced to ±10% of the stem density. Further research is needed on how to account for tree species when building tree maps.