## 4 Discussion

Table 4: Logistic regression models for the prediction of the presence of defects, cavities, and fungal infestation in the total trees of the five species under study (N = 543). B = logistic coefficient; S.E. = standard error of estimate; Wald = Wald chi-square; df = degree of freedom; \(p\)-value = significance. The observed variations in tree thermal patterns, assessed through the utilization of the derived trunk thermal indices, both within and among the five tree species, as well as across different categories based on the presence or absence of defects, provide compelling evidence that reaffirms the fundamental concept of tree health [67; 68; 92]. According to this, a healthy tree is characterized by a homogeneously distributed trunk temperature, reflecting a well-functioning thermoregulatory system. In contrast, an unhealthy tree exhibits temperature abnormalities, suggesting underlying physiological or structural issues [68]. Furthermore, our findings highlight the pivotal role of the selected indicators, namely T\({}_{\text{IQR}}\) and T\({}_{\text{OPR}}\), in comprehensively evaluating a tree's thermal profile. These indicators ensured the robustness of our analysis, as by avoiding the use of range metrics, as they would be highly influenced by outliers, and focusing on percentile-based measures, we provided a more accurate representation of the thermal characteristics and abnormalities observed on the tree trunks. Therefore, our results highlight the importance of adopting a \"thermal perspective\", among other approaches, when evaluating tree health and validating the effectiveness of T\({}_{\text{IQR}}\) and T\({}_{\text{OPR}}\) as essential indices for assessing overall tree vitality [71]. Based on the results obtained from the thermal profiles of the examined trees, it becomes evident that trees of similar or different species exhibited subtle variations in their thermal patterns, as discussed previously by [PERSON] and [PERSON] [70]. Consequently, individual species must be examined separately to capture these differentiations effectively, aligning with the approach proposed by [PERSON] and [PERSON] [68] (Figure 3). Notably, the differences observed were primarily between urban and agricultural trees, as well as between urban and forest, and agricultural and forest trees. Interestingly, no significant differences were found between urban tree species, implying that IRT can be effectively applied to a broad range of urban trees for estimating their health state, irrespective of their taxonomic classification. Among the trees examined, those exhibiting cavities in their trunks demonstrated the most pronounced deviation from the established criterion for a healthy tree in both trunk thermal indices (Figure 4). Following this, trees displaying a combination of defects exhibited a noticeable but limited deviation, specifically in relation to the outer percentile range index. This occurrence can be attributed to the fact that this index incorporates specific trunk traits directly associated with tree health, such as the presence of cavities, necessitating an examination of extreme temperature values. Consequently, these findings facilitate the categorization of the examined trees into distinct groups, as previously elucidated by [PERSON] et al. [72], despite certain contrasting outcomes reported by other studies [93]. These categories encompass trees with cavities, those with a combination of defects, and, finally, the category of healthy trees. Furthermore, in addition to the notable thermal pattern disparities observed among the different tree categories, there is a similar trend in the indices related to LAI, which serves as an indicator of productivity. Specifically, both LAI\({}_{\text{mean}}\) and LAI\({}_{\text{range}}\) show similar patterns across the tree species. Healthy trees exhibit higher LAI values, indicating a greater overall leaf area, while trees with defects display lower LAI values.