Towards the automatic 3D characterization of forest plots

Ground-based point clouds (from laser scanning or photogrammetry, and from static or mobile devices) give very detailed 3D information of forest plots. Also, if this information is complemented with data gathered from aerial vehicles, some parts of the forest structure that are not visible from the terrain can be represented (e.g. treetops). However, the heterogeneity of the point clouds, the complexity of some forest plots and the limitations of some data gathering/processing techniques lead to some occlusions and misrepresentations of the features in the plot. Therefore, complete automation of very detailed characterizations of all the items/features/structures in a forest plot is, most of the times, not possible yet.

On one hand, single trees (or small groups of them) can be modelled in detail from dense point clouds (e.g. using quantitative structure models), but this processes usually require  complete absence of leaves and  intense and/or active operator labouring. On the other hand, many methods automate the location of the trees in a plot and the estimation of basic parameters, like the diameters and, sometimes, the total tree height.

We are developing a fully automatic method that lies in between some very accurate but labour-intensive single-tree models, and the mere location and diameter calculation of the trees in a plot. Our method is able to automatically detect and locate the trees in a plot and calculate diameters, but it is also able to characterize the 3D tree structure: stem model, inclination and curvature; inclination and location of the main branches (in some cases); and tree crown individualization and diameter estimation. In addition, our method also classifies the points on understory vegetation.

Our method relies on the integration of algorithms that have been developed by our team, and includes the development of new modules. The first step consists in an initial classification of the point cloud using a multiscale approach based on local shapes. As a result, the point cloud is preliminarily classified into three classes: stems, branches and leaves, and ground. After that, a series of geometric operations lead to the final 3D characterization of the plot structure: (i) stem axes and section modelling (from the pre-classified points on the stems), (ii) distance points-closest stem axis and tree individualization, (iii) extraction and characterization of the main branches, and (iv) final classification of the points laying on stems, main branches, rest of the canopy, understory and ground.

We are testing the algorithm in several forest plots with coniferous and broadleaf trees. Initial results show values of completeness and correctness for tree detection and point classification over 90%.

Currently, there are already several cross-cutting projects using our method´s results as inputs: (i) Automatic calculation of taper functions (use: diameters along the stem and tree height), (ii) wood quality based on shape (use: diameters along the stem and insertion of main branches), and (iii) wildfire behaviour models (use: fuel classification and 3D structure to adapt the data to the format of the existing 3D fuel standard models).