Abstract
ABSTRACTUnmanned aerial vehicle-based LiDAR survey provides very-high-density point clouds, which involve very rich information about forest detailed structure, allowing for detection of individual trees, as well as demanding high computational load. Single-tree detection is of great interest for forest management and ecology purposes, and the task is relatively well solved for forests made of single or largely dominant species, and trees having a very evident pointed shape in the upper part of the canopy (in particular conifers). Most authors proposed methods based totally or partially on search of local maxima in the canopy, which has poor performance for species that have flat or irregular upper canopy, and for mixed forests, especially where taller trees hide smaller ones. Such considerations apply in particular to Mediterranean hardwood forests. In such context, it is imperative to use the whole volume of the point cloud, however keeping computational load tractable. The authors propose the use of a methodology based on modelling the 3D-shape of the tree, which improves performance with respect to maxima-based models. A case study, performed on a hazel grove, is provided to document performance improvement on a relatively simple, but significant, case.
Highlights
Sustainable forests management (SFM) is emerging as an increasingly important activity both for economic and environmental purposes
We intend to address this kind of problems, starting from a relatively simple case study, yet significant, because we considered a hazel grove, made of trees that have no evident top and several small stems, with the crown extending practically down to ground level
We propose the use of a methodology based on fitting and modelling the 3D-shape of the tree using random sample consensus (RANSAC) (Fischler & Bolles, 1981) applied to primary raw data, in order to improve performance both in tree detection and crown reconstruction
Summary
Sustainable forests management (SFM) is emerging as an increasingly important activity both for economic and environmental purposes. It is important to underline that RANSAC is able to extract all the shapes in the point cloud that are consistent with the parametric model, but we set it in such a way as to consider only the most relevant present in a small area of the data. Application of RANSAC on RoI’s obtained by ABA produces evident improvement to the tree position detection, and to crown identification.
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