## 5 Discussion  
The low relationship between rainfall and NDVI indicates that there is no direct relationship between them and that rainfall does not influence vegetation cover directly. This result agrees with [PERSON] et al. (2018), who explains that the effect of rainfall on vegetation was small and low in desert areas and forests. Moreover, [PERSON] et al. (2018) reported that NDVI negatively correlated with rainfall in the oasis area. Conversely, for high rainfall, [PERSON], and [PERSON] (2018) reported that there is a good relationship between rainfall and the NDVI that may be observed when rainfall is below about 1000/year and does not exceed ~200 mm/month. From the analysis, the accuracy results obtained from Esri Sentinel-2 data are higher than the accuracy results of Landsat-8 data. This can be explained by the high resolution of Sentinel 2 data compared to Landsat 8 data. These results agree with results obtained by [PERSON] et al. (2018), which assessed the classification accuracies of Landsat-8 and Sentinel-2 data for land cover / use mapping using support vector machine (SVM) and maximum likelihood (MLC) classification. The results of classification accuracy indicate that the Landsat-8 image gives an accuracy of 70.60% and 81.67% for MLC and SVM, respectively. While Sentinel-2 image -8 image gives 76.40% and 84.17% for MLC and SVM, respectively. [PERSON] and [PERSON] (2018) studied Sentinel-2 and Landsat 8 images for land cover and land use mapping in wetlands areas. The results revealed that for most LULC classes that the overall accuracy was around 87-88% with slightly enhanced results when using Sentinel-2. In the Brazilian Amazon, [PERSON] et al. (2018) compared Landsat 8 OLI and Sentinel-2 MSI imagery for monitoring selective logging. In terms of accuracy, both indicates the same performances for detecting the area-adjusted of selective logging by approximately 95.7% and 96.7% for Landsat 8 and Sentinel-2, respectively. In a Mediterranean pine ecosystem in Greece, [PERSON] et al. (2018) assessed and compared Landsat-8 OLI and Sentinel 2A spectral indices for estimating fire severity. The comparison results show a slightly greater classification accuracy for Sentinel 2A (73.33%) than Landsat-8 OLI (71.11%), which indicates the adequacy of Sentinel 2A for forest fire severity mapping and assessment in Mediterranean pine ecosystems. According to results obtained from the land cover assessments, the area of water bodies in the 2017/2018 period was 5.74 km\({}^{2}\) most localized only on Alaqiq, Baljurish and Elmelkhwah, this may be linked to water reservoir dam that exceeds 17 dams as Madhas and Beedah dams with a capacity of 1,500,000 and 3,000,000 m\({}^{3}\) respectively, where 86% of these water storage are addressed to irrigation purposes [55]. In addition, more than 14 dams in El Baha region with years of construction between 2008-2012 have been reported in the paper of [PERSON] [56]. This explains the great efforts that have be carried out by the Kingdom of Saudi Arabia in the framework of sustainable water resources management. The urban environment of the Middle East and North Africa (MENA) countries is today under great pressure due to the very rapid rate of urbanization recorded over the past 10 years. The Al Baha region is one of the large part of Saudi Arabia that has been known by its great development in the last ten years. In the Al Baha region, the development analysis of the built area during the study period (2017/2021) shows a great increase exceeding 650 km\({}^{2}\). This increase can be explained by Figures 1c and 4, which indicate that most of the built area is localized in the mountain area of high altitude, confirming that the decrease in bare ground/rocky mountain area and desert rangeland area can be interpreted by growing urbanization. According to [PERSON] and [PERSON] [57], the urban expansion had negative effects on agricultural areas and vegetation cover in forests.