## 3 Materials and Methods
### Accuracy Assessment of the Supervised Classification  
The most used method to assess the accuracy supervised classification map is to generate a set of random of user points (Figure 2) from the ground truth data and compare these data to the classified data in a confusion matrix. The user point is a point that is randomly selected from the obtained land cover classification of Arcgis, and it is compared with what is actually (really or producer point) present by Google earth. In the present study, they were used 70 randomly user points to assess the supervised classification. The user accuracy, producer accuracy, overall accuracy and kappa coefficient calculated by the Equations (3)-(6), respectively. The producer accuracy is based on experimenter classification point of view (obtained from the software or the algorithm) and user accuracy shows the reality on ground, for example, if user accuracy is 100% means the experimenter classified item is 100% of mapped area in actually that items other may not referred to that item.  
\[\text{Users accuracy}=\frac{\text{Number of correctly classified pixels in each category}}{\text{Total number of classified pixels in that category (the row total)}}\times 100 \tag{3}\]  
\[\text{Producer accuracy}=\frac{\text{Number of correctly classified pixels in each category}}{\text{Total number of classified pixels in that category (the column total)}}\times 100 \tag{4}\]  
The overall accuracy is calculated by summing the number of correctly classified values and dividing by the total number of values. It is givens as follows:  
\[\text{Overall accuracy}=\frac{\text{Number of correctly classified pixels (Diagonal)}}{\text{Total number of reference pixels}}\times 100 \tag{5}\]  
The kappa coefficient is given by the Equation (6). If the kappa coefficient equals to 1, then the classified image and the ground truth image are totally identical.  
\[\text{Kappa coefficient}\left(\text{T}\right)=\frac{\left(\text{TS}\times \text{TCS}\right)-\sum\left(\text{column total}*\text{raw total}\right)}{\left(\text{TS}^{2}\right)-\sum\left(\text{column total}*\text{raw total}\right)}\times 100 \tag{6}\]  
Figure 2: Flowchart diagram used.  
where, TCS is the total number of correctly classified pixels (Diagonal) and TS is the number of user points (sampled points) used for the validation process.  
Figure 2 indicates the flowchart diagram used for assessing land and vegetation cover and more understanding the hydro-morphological (topographic features) of the basin or the study area (Figure 1), by downloading the digital elevation model (DEMs) from SRTM, where, the spectral bands and indices features were obtained from Landsat and Sentinel-2 images. After this, at the image processing stage, the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) algorithm were applied as a tool to remove the effects of the atmosphere and correct purposes from remote sensing databases imageries. The next stage is the composite analysis and computing of the spectral indices using ArcGIS to obtain the land use classification, where, the supervised classification has been applied by comparing the obtained results of land and that obtained from Google earth (ground truth data). Finally, the validation of classification accuracy was obtained using the error matrix and kappa test, which are discussed in the next section.