## 4 Results
Moreover, crops and desert rangeland are observed as good with an acceptable accuracy equal to 84.62 and 75%, respectively (Figure 5c). The overall accuracy was 87%,  
Figure 5: Land cover classification assessment based on producer accuracy, overall accuracy, and kappa test for **(a,b)** Landsat 8 and **(c,d)** Esri Sentinel-2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Water Bodies** & **Trees** & **Flooded** & **Crops** & **Bult Area** & **Bare Ground** & **Desert** \\  & **and River** & **Vegetation** & **and Road** & **Mountain** & **Rangeland** & **Total User** \\ \hline
**Water bodies and river** & 8 & 1 & 0 & 0 & 0 & 0 & 0 & 9 \\ \hline
**Trees** & 0 & 3 & 0 & 0 & 0 & 0 & 0 & 3 \\ \hline
**Flooded vegetation** & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 \\ \hline
**Crops** & 0 & 0 & 0 & 11 & 1 & 1 & 0 & 13 \\ \hline
**B Built area and road** & 0 & 0 & 1 & 15 & 0 & 1 & 17 \\ \hline
**Bare ground Mountain** & 0 & 0 & 0 & 0 & 11 & 0 & 11 \\ \hline
**Desert Rangeland** & 0 & 0 & 0 & 0 & 0 & 4 & 12 & 16 \\ \hline
**Total producer** & 8 & 4 & 1 & 12 & 16 & 16 & 13 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error matrix for Esri Sentinel-2 classification using user and producer accuracy based on Google earth. whereas the Kappa test value reached 0.84, respectively (Figure 5d). From the obtained results, the Esri Sentinel-2 imagery will be used in the next sections for land cover change and assessment for the Al Baha region. Based on existing artificial intelligence (AI) and a massive and large training dataset of billions of human-labeled image pixels, these models were applied to the entire Sentinel-2 scene collection for each year from 2017 to 2021. A 9-class map of the surface was elaborated including water, vegetation types, cropland, barre surface and built areas. In addition, to Figure 4b of the land cover for the year 2021/2022, Figure 6 explains the land cover classification change based Esri Sentinel-2 imagery for the year 2017/2018 (Figure 6a), 2018/2019 (Figure 6b), 2019/2020 (Figure 6c) and 2020/2021 (Figure 6d). Thus, in this section, land cover change will be assessed using databases of five years. Figure 6: Land cover classification assessment based on producer accuracy, overall accuracy, and kappa test for (**a,b**) Landsat 8 and (**c,d**) Esri Sentinel-2. Figure 7 explains the general change at the scale of all Al Baha region in km\({}^{2}\) from 2017 to 2021. Where the mapping of area in (km\({}^{2}\)) for each land cover class at the scale of each district of Al Baha was represented in Figure 8a-g. The change in the area (km\({}^{2}\)) of each class by the district for the years 2018/2019, 2019/2020, 2020/2021, and 2021/2022 compared to the year 2017/2018 are explained in Figure 9a-f. According to Figure 7, the area of water bodies in the year 2017/2018 was 5.74 km\({}^{2}\), mostly localized only on Alaqiq, Baljurish, and Elmelkhwah, which accounted for 37, 36, and 21% (2.15, 2.07 and 1.23 km\({}^{2}\)) of the water bodies area (Figure 8a). This is linked to a large water reservoir dam. Moreover, the analysis indicates that there are no water bodies at Alatawluh, ElBahah, and Elmandaq. Between the year 2018/2019 to 2021/2022 the water bodies area of the study region have been increased from 0.73 to 1.75 km\({}^{2}\) most observed at Baljurish by 1.13 km\({}^{2}\) (50%) in the year 2021/2022, where the water bodies have shown some increase was observed in 2019/2020 (Figures 7, 8a and 9a). Between 2018/2019 and 2021/2021 small area of water bodies has been observed at Alatawluh ELBahah, Elmandaq, and Qelwah (Figures 8a and 9a). The built area at a scale of Al Baha region is estimated in the year 2017-2018 to be 516.5 km\({}^{2}\), wherein the most built areas have been concentrated in ELBahah, Baljurish and Alaqiq by 30, 26, and 20% (159, 135.8 and 104.9 km\({}^{2}\)), respectively. In addition, the built areas in Alatawluh, Elmelkhwah, Qelwah, Elmandaq are 63.69, 24.26, 16.97, 12.01 km\({}^{2}\), respectively (Figures 7 and 8b).