An Open-Source Approach to Modelling and Analysing a Tree Detected with a Mobile Laser Scanner

For many applications, in both forestry and urban environments, knowledge of the biometric parameters of trees is essential. In this sense the use of the Quantitative Structure Models (QSMs), generated from Terrestrial Laser Scanners (TLSs), to recreate the structure of trees has increased in recent years. However, the utilisation of TLS has two main limitations which can be summarized as follows: (i) very long acquisition time and (ii) limited portability. The use of Mobile Laser Scanners (MLSs) with Simultaneous Localization and Mapping (SLAM) technology can overcome these kinds of limitations, allowing both complex environments to be detected in a short time and surveys to be made possible in impervious areas. Therefore, in this research we illustrate a workflow based on an open-source software to model and analyse a tree using the data produced by a MLS. In addition, the model of the same tree is also generated on the basis of the TLS-derived point cloud, in order to evaluate the differences. The process of modelling the tree, starting from theMLSdata, leads to the creation of a model that fits the original point cloud very well, with an average distance from it of 0.30 cm. However, it was not always possible, especially in the summit parts, to reconstruct the tree structure. In addition, it was noted that, due to the noisiness of the source cloud, this model also tends to overestimate the diameter and, consequently, the biomass of the tree. In order to reduce this overestimation, a corrective factor will be identified in the future. Our study confirms that open-source software-based reconstruction of a tree model from an MLS-derived point cloud is possible. With the help of this method, it is possible to extract numerous biometric and structural information about several trees in a short time. Such information can help to carry out fast broad-scale analyses, such as assessing plant growth, or provide basic information for the development of automatic pruning technologies.