The estimation of foliar traits has numerous potential applications, with the evaluation of ecosystem productivity generally regarded as one of the most widely used. In this study, we linked information regarding the concentration of various canopy traits to landscape-level forest health and pest susceptibility using remote sensing technology. Foliar traits that can affect insect herbivory, including nutritive elements such as nitrogen (N), phosphorous (P), potassium (K), and copper (Cu), non-nutritive elements such as iron (Fe) and calcium (Ca), and defensive parameters such as equivalent water thickness (EWT) and leaf mass per area (LMA), were estimated. We used Sentinel-2 and site data to develop trait estimation models in a forest dominated by spruce and fir. Several machine-learning algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) were tested. Based on the model performances, where normalized root mean square error (nRMSE) values were taken into account, XGB algorithm was selected to estimate Ca (nRMSE: 0.16), EWT (nRMSE: 0.12), Fe (nRMSE: 0.19), and K (nRMSE: 0.14). On the other hand, RF was used to model Cu (nRMSE: 0.18), LMA (nRMSE: 0.14), N (nRMSE: 0.16), and P (nRMSE: 0.22). Almost all best-performing models included Sentinel-2 red-edge indices and depth to water table (DWT) as the most important variables. We propose a novel framework to establish a connection between the concentration of foliar traits in SBW host foliage and tree susceptibility to the pest. This approach could allow for the assessment of host susceptibility on a landscape level based on the concentrations of foliar traits.
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