## 4 Discussion
Moreover, a higher LAI\({}_{\text{range}}\) signifies a greater variability in leaf area distribution throughout the tree crown, implying potential variations in tree productivity and health [71; 94]. After all, LAI plays a pivotal role as a crucial biophysical index in characterizing ecosystem structure and vegetation growth. It operates as a regulator of the microclimate within and below the canopy, facilitating essential processes such as water and carbon exchange while influencing canopy water interception [82; 95; 96]. By quantifying the extent of leaf area per unit ground area, LAI provides valuable information about a tree's ability to capture sunlight for photosynthesis and its overall productivity [97; 98]. The inclusion of these two LAI metrics in our study was driven by the presence of cavities, which can inflict damage on the xylem and phloem tissues across the tree. Such damage has direct consequences on the tree's crown shape and structural integrity. As a result, disruptions in water and nutrient transport occur due to impaired vascular systems [99], directly impacting the availability and distribution of resources within the tree, ultimately influencing LAI measurements. Reduced water and nutrient transport can lead to alterations in leaf distribution patternsas the tree attempts to optimize resource allocation to sustain growth and vitality in challenging conditions [89; 100; 101]. Consequently, the observed variations in LAI in trees with cavities or defects serve as valuable indicators of the tree's health status and its ability to cope with structural damage. In addition to thermal and LAI indices, the assessment of tree health also involves the consideration of indicators specifically related to structural defects, with a focus on characteristics associated with cavities. The cavities ratio, a vital bridge connecting tree vitality metrics with IRT, describes trunk growth patterns that may exhibit various types of defects. The FP represents the potential reduction in functional tissue due to the presence of cavities [73], while the SL evaluates the tree's mechanical integrity by considering the dimensions of these cavities [81]. These indices serve as valuable proxies for assessing tree health and contribute to our understanding of the impact of defects on overall tree condition, as they can lead to an increased structural instability and reduced resilience of trees [39], ultimately raising the risk of tree mortality [102; 103]. This is also in response to the statistically significant relationships, whether positive or negative, identified between these indicators, the LAI indices, and the trunk thermal indices (Figure 5). By considering the inter-relationships among the examined indices and taking into account the notable differences observed in T\({}_{\text{IQR}}\), T\({}_{\text{OPR}}\), LAI\({}_{\text{mean}}\), and LAI\({}_{\text{range}}\) between trees with and without defects, we can confidently conclude that these indices effectively enable the identification and quantification of tree health conditions. Building upon the comprehensive assessment of tree health, we sought to explore the relationship between LAI and the combined influence of phenotypic and trunk thermal indices. By employing weighted linear regression analysis, we aimed to elucidate the extent to which these indices contribute to LAI variation. The results of the linear models demonstrated a strong dependency of LAI on three key indices that can effectively explain tree productivity: T\({}_{\text{IQR}}\), LAI\({}_{\text{range}}\), and SL. Remarkably, the consistent inclusion of these three indices in all three models, whether combined or considered individually, indicated their significant contribution in predicting LAI, which in our case serves as an indirect metric of tree productivity. Moreover, T\({}_{\text{IQR}}\) emerged as a common feature in all models, suggesting its crucial role in describing the surface temperature of the tree trunk and its potential as a reliable predictor of tree productivity.