## Materials and Methods
### Methods
#### 2.3.2 Close Range Photogrammetry  
Close range photogrammetry (CRP) is widely used in forestry inventory to compute plant models and to detect interesting biometric and physical parameters, such as stem diameter, bark characteristics and anomalies affecting trees and plants. With this method, a registered filtered unified point cloud representing the studied tree can be computed, starting from some tens of 2D high resolution photos taken in situ all around the studied plants at short distances (1-3 m) and with optimal exposure conditions. In fact, generally speaking, during a CRP survey, a set of good quality 2D photo images are taken, using consumer-grade cameras, all around a tree or plant under study, at short distances (1-3 m), in different station points, evenly spaced at an average mean distance of 1 m between them, with the camera on a tripod. Single lens commercial digital cameras can be used; images must be aligned in the best way and then used to compute dense point clouds representing the studied tree, texturized with natural colors [3; 4; 23; 24; 25]. These clouds can be successively post-processed with the aid of facilities for point cloud processing, derived from terrestrial laser scanner (TLS) methodology, for example. The resulting unified clouds are then converted into mesh models with the aid of 3D reconstruction algorithms in order to be useful as input for CADs(r) or other professional engineering software. In this study three photogrammetric campaigns were performed in three different periods--October 2018, December 2019 and January 2020--with the aid of two high-resolution single-lens 3D digital cameras: a Nikon D-300 (12.3 Mpc) (in the 2018 campaign) and a Nikon D5300 (24.2 Mpc) (for the 2019 and 2020 campaigns), suitably operated to detect some tens of high quality images of the studied holm oak (_Quercus ilex_ L.). We took 36 photographs in the first campaign of 2018, 66 during the December 2019 campaign and 99 in the January 2020 campaign. The images were collected with the camera on a tripod  
Figure 2: Bark darkening, splitting and detachment in the basal part of the different branches: (**a**) north side; (**b**) south side; (**c**) east side. equipped with a spherical level all around the target plant at fixed predetermined distances (1-3 m), as far as possible under homogeneous light conditions, and where possible after inserting reference markers in the scene. The 2D images were carefully selected and preprocessed with release 1.6 of Agisoft Metashape software (Agisoft LLC, St. Petersburg, Russia) based on Structure from Motion (SfM) methodology to avoid the use of noising data to finely align poses and generate a high dense points cloud textured with natural colors [6, 22]. Three high density (each of about 10 million points) unstructured, unified, registered point clouds were restituted after image processing. The colorized, high density, registered point clouds representing the tree in the different periods were format converted into the e57 format (namely, the file format for 3D laser scanner data exchange) and then processed with the JRC 3D Reconstructor (Gexcel, Brescia, Italy) [37] and with CloudCompare free software [38]. Clouds were manually edited and then filtered to remove noising data. We used the Poisson surface reconstruction algorithm [39], implemented in the meshing facility of the package Reconstruct to compute high density meshes (1 mm \(\times\) 1 mm step) starting from 3D registered point clouds relating to the 2018, 2019, and 2020 CRP campaigns [25]. In the final step of our procedure, we begun with the computation of the geometrical anomalies of the 2018, 2019, and 2020 3D models as residuals with respect to best fitted cylindrical high density meshes taken as reference; afterwards, we computed the displacement that occurred in the holm oak stem after one month and then after one year.