**4. Results**

### *4.1. Assessing the Accuracy of Image Classifications*

Seven land cover classes were generated in a supervised manner. Using the RF algorithm in the GEE, the accuracy of the results was evaluated when vegetation indices were not used (Table 4) and when indices were used (Table 5). Overall accuracies for image composites with and without vegetation indices range from 0.91 to 0.98, while Kappa ranges from 0.86 to 0.96.




**Table 5.** Accuracies obtained when classifications were made with vegetation indices (PA = Producer accuracy, UA = user accuracy, OA = overall accuracy, and K = Kappa coefficient).

After extracting these precision parameters from the confusion matrices of the composite classification of each target year, one of the target years (1985) without vegetation indices was presented as an example (Table A1) in Appendix A. Overall accuracies and Kappa coefficients for the classification of composite images with the original bands was very similar to those of composites made with the original bands and vegetation indices. Nevertheless, under the null hypothesis that their slopes do not differ from a 1:1 relationship, linear regressions between the values of these two types of data yield *p*-values much less than 0.001 for the OAs and *K*s. This indicates that these values for the original band classifications of the image composites are significantly different from those including the vegetation indices. In Appendix B, this same finding of a significant difference was verified between the UA and PA accuracies for all land cover classes in all image composites (Table A2).
