## 2 Materials and Methods
### Creation of Trees' Thermal Profile and Indices Using IRT  
We utilized IRT to capture infrared images of all the tree trunks. For this purpose, we employed a handheld thermal camera (Testo 875-1i, Testo SE & Co. KGaA, Lenzkirch, Germany) mounted on a stable tripod to ensure precise positioning and stability. In the field, we calibrated the camera using ambient temperature, relative humidity, solar irradiance, and emissivity values (e = 0.95), which were individually determined beneath their crowns. To obtain accurate measurements, we relied on a portable weather station and a solar radiation meter (Amprobe SOLAR-100) to acquire the necessary environmental data. The infrared images were captured from a standardized distance of 3.0 m and a height of 1.3 m to encompass the entire tree trunk, specifically targeting the part where the presence of defects was either known or suspected (e.g., presence of wood-decay fungi), to ensure focused temperature measurements on regions of interest. In cases where no defects were present, we captured the infrared images from the side of the trunk, aligning with the mean orientation of defects observed in neighboring trees. To minimize errors resulting from atmospheric composition [87], global radiation effects [88], and temperature inaccuracies caused by solar radiation penetrating through the tree canopy, we conducted the image capture process during the early morning hours. This timing helped to mitigate potential distortions in the thermal data.  
We processed the collected infrared images using the TESTO IRSoft(r) (v. 4.3) software package to extract temperature information. By applying a temperature palette, we generated a dataset of temperature values for the entire image (160 \(\times\) 120 pixels), where each pixel represented a specific temperature corresponding to the adaptation of tree trunks to environmental conditions. To isolate the areas of interest, namely the tree trunks, from the background and other objects, we employed the ArcGIS Analysis toolbox (v. 10.2) (ESRI Inc., Redlands, NY, USA). First, we exported the calibrated infrared images from the TESTO software and imported them into the ArcGIS software in text file format. Subsequently, we converted each text file into a raster layer, enabling us to define the boundaries of the tree trunks manually. This was accomplished by creating unique polygons in shapefile format to enclose the tree trunk areas. Finally, we extracted the temperature values specificallywithin the defined tree trunk areas (Figure 2) and organized them into a structured database for subsequent statistical analysis.  
To define the thermal profile of the trees and establish trunk thermal indices, we analyzed the trunk temperature histograms to extract a comprehensive set of thermal variables representing the response of the tree trunk to ambient temperature. We extracted the trunk temperature values from the histograms and thereafter we calculated typical measures of central tendency and variability. In order to evaluate the overall state of each tree, it was essential to mitigate the influence of outliers and skewed data. Therefore, we computed the interquartile trunk temperature range (T\({}_{\text{IQR}}\)) from each histogram. This range provided a reliable measure of the tree's thermal profile while minimizing the impact of extreme temperature values. However, when assessing specific traits on the tree trunk that directly relate to the tree's health, such as cavities, it was necessary to examine the extreme temperature values. To capture this information comprehensively, we employed the interperentile ranges of each trunk histogram, collectively defining the outer percentile range (T\({}_{\text{OPR}}\)). The T\({}_{\text{OPR}}\) encompassed the temperature values associated with any external or internal abnormalities present on the trunk.