. pi for some distance d of a particular unit cell i as a proportion of the sum of the variable values over the
entire study area (Table 2). In this study, the Gi was used to analyze spatial autocorrelation characteristics
of forest health conditions. Toward this objective, the Gi statistics or features were computed with a
series of increasing distance value ranging from 1 pixel (i.e., window size of 3 × 3 pixels) and with seven
neighborhood rules (including Rook’s case, Bishop’s case, Queen’s case, Horizontal, Vertical, Positive
slope, and Negative slope) [34] from four IKONOS MS bands, respectively, using the ENVI software.
This procedure was repeated with increasing lag sizes until the distance value leading to the highest
classification accuracy could be identified. With this optimal distance value, we compared and selected
an optimal neighborhood rule that could lead to the highest classification accuracy.