## 3 Results
### Classification Accuracy Assessment  
The accuracy assessment quantifies how well the pixels were sampled into appropriate land cover categories and is critical for analyzing and assessing remotely sensed data for land use/landcover change. Cross-tabulation, the confusion matrix, and the production of random samples were used to test classification accuracy [66; 67]. Overall classification accuracies (OA) of at least 85% are considered desirable in remote sensing applications and land management [66; 68]. For the 1980s, 2000s, and 2020, the statistical values of the overall accuracy, kappa coefficient, omission error (producer's accuracy), and commission error (user's accuracy) were calculated. According to the kappa statistics, which range from +1.0 to \(-\)1.0, the possibility of an agreement may be predicted to occur by chance. However, it varies from great agreement to weak agreement, correspondingly. Table 5 detail the 9 time periods from 1980 to 2020 with respect to producer and user accuracies, OA, kappa coefficients, accuracy, and F1-score. Table 5 shows that the OA and kappa for all datasets are pretty high, ranging from 0.90 to 0.96, respectively. The maximum overall accuracy of over 96% was obtained in 2000, followed by 2015 and 2020 compared with the other years. The forest cover and other land cover were most accurately classified compared with the woodland cover. The low UA and PA in the years 1980 and 1985 are responsible for these poor precisions. Classification statistics for LULC on 9 different periods from 1980 to 2020 are presented in Figure 7 and Table 6. The UA for forest and other land was approximately above 90%, while for the other land it was nearly 93%, and the PA for the forest was almost 92%. The model's performance dropped dramatically for the woodland. Other lands obtained the maximum UA of over 95% in 2020 compared with the other classes, but its PA was only 90%. The UA and PA of the grassland were 68% and 90%, respectively. The least value of the F1 score, about 83%, was obtained forest in the year 1980, and the maximum values were 95% in the year 2020 compared with the other classes and years. Table 5 indicates the kappa statistics and overall accuracy, which is a measure of agreement or reliability. The estimated error matrix of sample counts is demonstrated, along with the user's accuracy, the producer's accuracy, and the F1-score. classification periods enabled land cover classification. LULC data have been extracted from Sentinel-2 data recently. Sentinel-2 data have a high spatial resolution, but Landsat has monitored Earth for over 50 years, making it a preferred source for LULC change research. The relative importance of the spectral bands and the vegetative indices in classification accuracy were critical parameters investigated in this work. Using vegetation indices can improve the classification of forest areas and other land types in land cover classification studies [68; 69; 70; 71]. The NDVI's significance as a vegetation approximation was to be expected. As the SAVI adjusts for soil reflectance on vegetation reflectance, it is advantageous in sparsely vegetated places [72; 73]. Hence its relatively high importance was to be expected. It is quite remarkable that the GNDVI, which was designed to improve sensitivity to thick forests, has such a large impact [74; 75]. Following this, it was anticipated that vegetation indices would provide further support to the classification results. Overall, from the beginning of the 1980s to the end of 2022, there was a considerable amount of forest loss. About 26 km\({}^{2}\), 53 km\({}^{2}\), 101 km\({}^{2}\), and 84 km\({}^{2}\) of forest cover were lost in 1990, 2000, 2010, and 2020, respectively, and the maximum loss of forest cover happened between 2000 and 2010. Woodland cover decreased by about \(-\)3.1%, \(-\)16.7%, \(-\)30%, and \(-\)24% in 1990, 2000, 2010, and 2020. The maximum decline in wood cover happened between 2000 to 2010.