We applied a model-based site-independent band selection analysis called contribution analysis, theoretically equivalent to a sensitive study, to optimize the selection of vegetation indices. The contribution analysis calculates the contribution index (CI), which provides detailed, quantitative measurements of the correlations between each observation band and the target parameters, solely based on the model that we use.31 For this study, we calculated the CI for each spectral band based on the PROSPECT leaf model32 due to the prevailing deciduous leaf type. PROSPECT is well validated and is known for successfully simulating deciduous trees situations. The model simulates leaf reflectance and transmittance at 400–2500 nm and takes four key input parameters, such as the leaf chlorophyll content, the water content, dry matter, and a parameter describing the internal structure of the leaf, denoted as N. We calculated CI using PROSPECT, which covers all the spectral bands for VIs listed in Table 2. CI is the most important criterion for VI band selection, and the bands that have higher CIs are preferred over the lower scoring ones. In addition to this, we also conducted an actual performance evaluation. We used half of the known infested trees as a training data set, described in detail in the training section below, and the remainders were used as validators to evaluate the performance of the various VIs. From these analyses, the three top performing indices were chosen for the final infestation evaluation based on the following criteria: (1) higher CI in those bands involved in the VI; (2) higher separability in different health states; and (3) lower in-class variation, which is quantified by the coefficient of variation (CV). CV measures the dispersion of the measurement, which is the extent of variability in relation to the mean of a population. For VI selection, this means that VI should have minimum class variation (e.g., low standard deviation) in each state. Essentially, the greater the separation shown between different stages, particularly the early and median stage, the better the candidate.