## 3 Results
### Feasibility for Creating Prescription Maps  
A 10 m buffer was added to the infested areas of the airborne and Sentinel-2A classification maps in this study to generate prescription maps. Table 7 lists the treatment areas of the prescription maps generated from the airborne and Sentinel-2A images for Fields 1-8. For the airborne prescription map, the total treatment area reached 31.2% with a 10 m buffer. The average treatment percentages for the spectral model, texture model and spectral-texture model were 27.58%, 36.64%, and 32.14%, respectively, for all the fields, and these percentages were similar to the percentage (31.20%) from the airborne image. Clearly, the treatment area for each field was significantly increased with the addition of the buffer zones. The reason for the large increase was that the small polygons representing root rot-infested areas on the classification maps expanded in all directions and filled the gaps between the polygons with addition of the buffer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Actual Classification Area (m\({}^{2}\))**} & \multirow{2}{*}{**UA**} & \multirow{2}{*}{**OA**} \\ \cline{3-3} \cline{5-6}  & & **Noninfested** & & & **Infested** & & **Total** \\ \hline \multirow{3}{*}{Spectral} & Noninfested & 3,548,172 & 57,534 & 3,605,706 & 98.40\% & 92.95\% \\  & Infested & 249,872 & 502,752 & 7,522,624 & 66.80\% & \\  & Total & 3,798,044 & 560,286 & 4,358,330 & & \\  & PA & 93.42\% & 89.73\% & & & \\ \hline \multirow{3}{*}{Texture} & Noninfested & 3,177,595 & 41,717 & 3,219,312 & 98.70\% & 84.81\% \\  & Infested & 620,449 & 518,569 & 1,139,018 & 45.53\% & \\  & Total & 3,798,044 & 560,286 & 4,358,330 & & \\ \cline{1-1}  & PA & 83.66\% & 92.55\% & & & \\ \hline \multirow{3}{*}{Spectral-texture} & Noninfested & 3,495,409 & 51,491 & 3,546,900 & 98.55\% & 91.87\% \\  & Infested & 302,635 & 508,795 & 811,430 & 62.70\% & \\ \cline{1-1}  & Total & 3,798,044 & 560,286 & 4,358,330 & & \\ \cline{1-1}  & PA & 92.03\% & 90.80\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Error matrices for classification maps generated by binary logistic regression (BLR) models for the whole study area. Figure 7: Comparison of classification accuracy of classification maps generated by binary logistic regression (BLR) models: (**a**) overall accuracy (OA); (**b**) producer’s accuracy (PA) for the infested category; (**c**) user’s accuracy (UA) for the infested category. Figure 8 shows the results of overlaying the prescription maps from the airborne and Sentinel-2A images for Fields 1-8, and Table 8 shows the accuracy assessment error matrix for the prescription maps from the Sentinel-2A images for all the eight fields. The spectral-texture model achieved the best OA (91.40%), followed by the spectral model and the texture model with an OA of 90.83% and 87.14%, respectively. The PA of the spectral model, texture model, and spectral-texture model was 95.97%, 86.70%, and 93.06%, respectively, for the nontreatment category and 79.50%, 88.10%, and 87.72%, respectively, for the treatment category. The treatment areas that were not generated by the Sentinel-2A image were mainly composed of some small root rot-infested areas (\(<10\) m) and their buffered areas on the airborne prescription map (the orange zones in Figure 8). The UA of the texture model for the treatment category was 75.05%, indicating that 24.95% of the infested areas on the prescription map were actually nontreatment areas. The UA of the spectral model and the spectral-texture model was 89.94% and 85.15%, respectively. Therefore, the commission error of the texture model was the highest, followed by the spectral-texture model and the spectral model. Above all, it was feasible to create the prescription maps with the Sentinel-2A images. Moreover, the spectral-texture model had the highest accuracy.