## 3 Results
### Accuracy of the Detection of the Analyzed Land Cover
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Years** & **Class** & **UA (L)** & **UA (V)** & **PA (L)** & **PA (V)** & **OA (L)\%** & **OA (V)\%** & **Kappa (L)\%** & **Kappa (V)\%** \\ \hline \multirow{8}{*}{MNDWI} & \multirow{2}{*}{2000} & Lakes & 98 & 96 & 94 & 93 & 96 & 93 & 94 & 92 \\ \cline{2-9}  & Land coverage & 90 & 87 & 92 & 87 & & & & \\ \cline{2-9}  & \multirow{2}{*}{2010} & Lakes & 97 & 93 & 92 & 90 & 95 & 91 & 93 & 90 \\ \cline{2-9}  & Land coverage & 89 & 87 & 90 & 88 & & & & \\ \cline{2-9}  & \multirow{2}{*}{2020} & Lakes & 95 & 93 & 93 & 90 & 94 & 90 & 93 & 91 \\ \cline{2-9}  & Land coverage & 90 & 88 & 91 & 88 & & & & \\ \hline \multirow{8}{*}{NDWI} & Years & CLASS & UA (L) & UA (V) & PA (L) & PA (V) & OA (L)\% & OA (V)\% & Kappa (L)\% & Kappa (V)\% \\ \hline \multirow{8}{*}{NDWI} & \multirow{2}{*}{2000} & Lakes & 96 & 94 & 95 & 90 & 90 & 86 & 87 & 85 \\ \cline{2-9}  & Land coverage & 78 & 75 & 86 & 83 & & & & \\ \cline{2-9}  & \multirow{2}{*}{2010} & Lakes & 94 & 92 & 93 & 88 & 88 & 85 & 85 & 83 \\ \cline{2-9}  & Land coverage & 76 & 73 & 84 & 82 & & & & \\ \cline{2-9}  & \multirow{2}{*}{2020} & Lakes & 94 & 91 & 94 & 88 & 87 & 84 & 85 & 83 \\ \cline{2-9}  & Land coverage & 76 & 74 & 86 & 81 & & & & \\ \hline \multirow{8}{*}{AWEI} & Years & CLASS & UA (L) & UA (V) & PA (L) & PA (V) & OA (L)\% & OA (V)\% & Kappa (L)\% & Kappa (V)\% \\ \cline{2-9}  & \multirow{2}{*}{2000} & Lakes & 90 & 85 & 90 & 85 & 85 & 80 & 83 & 78 \\ \cline{2-9}  & Land coverage & 75 & 70 & 81 & 78 & & & & \\ \cline{2-9}  & \multirow{2}{*}{2010} & Lakes & 95 & 90 & 91 & 84 & 83 & 78 & 87 & 81 \\ \cline{2-9}  & Land coverage & 78 & 71 & 80 & 77 & & & & \\ \cline{2-9}  & \multirow{2}{*}{2020} & Lakes & 90 & 80 & 90 & 86 & 87 & 83 & 82 & 75 \\ \cline{2-9}  & Land coverage & 73 & 70 & 80 & 77 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy of lake classification using spectral indices. In 2000, 2010 and 2020, through the analysis of the producer accuracy (PA) and user accuracy (UA), it was found that, both in the learning phase and the validation phase, the NDWI and the SVMs classifiers were the most accurate in detecting water bodies. Although the NDWI and SVMs classifiers obtained the best results, the NDWI presented fewer errors than the SVMs algorithm in the recognition of water and soil surfaces based on the control points added by the operator. The same performance trend was present in the recognition of classes based on the total number of objects classified by the program. The efficiency of the SVMs algorithm in the recognition of the terrestrial categories, W, C, Mo, Pz, S, We and PF, was efficient in both PA and UA. The overall accuracy (OA) indicated that the proportion of objects correctly classified by NDWI, NDWI and AWEI for the validation phase was 93%, 86% and 80%, respectively, with a Kappa of 92%, 85% and 78% for 2000, 2010 and 2020, respectively. For its part, the overall ranking of the SVMs algorithm in the classification coverages involved in the validation phase was 85%, 86% and 83% with a Kappa of 80%, 85% and 83% for 2000, 2010 and 2020, respectively.