## 4 Results  
The results of MK and Sen's slope tests indicate negative trends of NDVI for the years 2014, 2015, 2016, 2017, and 2021, and a positive trend of NDVI in 2018, 2019, 2020, where the trend is significant in the years 2014, 2018, and 2021 (Table 1). Figure 2(a) illustrates the monthly temporal fluctuations of NDVI and rainfall in the study area between 31 September 2013 and 1 July 2020, while Figure 2(b) indicates the relationship between NDVI and rainfall. Figure 2(a) revealed positive values of NDVI values at the scale of Al Baha region with high positive fluctuation observed in 2014, 2016 and 2020, which may indicate the increase of vegetation cover in these years. The rainfall in Al Baha region is very low, and does not exceed 25 mm/month. On the other hand, the relationship between NDVI and rainfall indicates a poor relationship with a low determination coefficient of 0.0022. In this section, high-resolution 30 m Landsat 8 OLI and Esriv10 m Sentinel-2 satellite imagery downloaded on 5 March 2022 and 1 January 2022, respectively, were assessed using GIS, Google Earth pro, error matrix and kappa test, where the best satellite imagery will use for land cover classification and assessment of the Al Baha region. User accuracy, producer accuracy, overall accuracy, and kappa test were calculated based on error matrix (Figures 4 and 5; Tables 2 and 3) using 70 supervised random points from Google Earth professional. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time Series** & **N** & **Test Z** & **Signific. ** & **Q** & **Qmin95** & **Qmax95** & **B** & **Bmin95** & **Bmax95** \\ \hline
**2013** & 8 & & * & 0.006 & - & - & 0.10 & - & - \\ \hline
**2014** & 36 & \(-2.17\) & * & \(-0.001\) & \(-0.002\) & 0.000 & 0.13 & 0.15 & 0.11  
Figure 4: Land cover classification using (**a**) Landsat 8 OLI and (**b**) Esri Sentinel-2 for the year 2022. Figure 3: Evolution of (**a**) rainfall and NDVI of Al Baha region and (**b**) relationship between them. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Water Bodies**} & **Desert** & **Bare** & **Small** & **Built** & \\  & **Grassland** & **Ground/Mountain** & **Trees/Flooded** & **Area/Roads/Dry** & **Vegetation/Grass** & **Total User** \\ \hline
**Water bodies** & 2 & 0 & 0 & 0 & 0 & 0 & 2 \\ \hline
**Desert grassland** & 0 & 15 & 16 & 0 & 0 & 0 & 31 \\ \hline
**Bare ground/Mountain** & 0 & 0 & 9 & 0 & 0 & 0 & 9 \\ \hline
**Small Trees/** & & & & & & & \\
**Flooded vegetation** & 0 & 0 & 3 & 10 & 0 & 1 & 14 \\ \hline
**Built area/roads/** & & & & & & & \\
**Dry Wadi** & 0 & 1 & 1 & 2 & 3 & 1 & 8 \\ \hline
**Vegetation/Grass** & 0 & 0 & 0 & 0 & 0 & 6 & 6 \\ \hline
**Total producer** & 2 & 16 & 29 & 12 & 3 & 8 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error matrix for Landsat-8 classification using user and producer accuracy based on Google earth. The use points were selected from the various locations representing various land cover/use classes (Figure 4a,b). The Error matrix was elaborated for each land cover class, where the classification accuracies were assessed using total, user, and producer accuracy values. The results of the error matrix of Landsat 8 and Sentinel 2 imagery are shown on Table 3, respectively. Figure 5a,b and Figure 5b,c explain in detail producer accuracy, overall accuracy and kappa test for Landsat 8 and Esri Sentinel 2, respectively. The overall accuracy using the supervised Google earth method for Landsat-8 data was found to be 100, 93.75, 83.33, 100 and 75% for water bodies, desert grassland, small trees/flooded vegetation, built area/roads/dry wadi, and vegetation/grass, respectively, where the lowest accuracy value was obtained at bare ground/Mountain by 31% (Figure 5a). Which are influences on overall accuracy and Kappa test values by 64% and 55%, respectively (Figure 5b). For Esri Sentinel-2, data was found as 88.89, 100, 100, 88.24, and 100% for water bodies/river, trees, flooded vegetation, built area, and road and bare ground/rocky mountain, respectively.