Assessing Classification Accuracy as a Criterion for Evaluating the Performance of Seven Topographic Correction Algorithms in the Trinidad Mountains, Cuba

To contribute to SDG-15 about the conservation of terrestrial ecosystems, the effective management of land resources is required. In this respect, determining the land use and cover (LUC) based on remote sensing constitutes a strength. For the Arimao watershed in the province Cienfuegos of Cuba, the main difficulty in determining the LUC is related to the topographic correction in the mountains of Trinidad. This study aims to validate the effectiveness of seven topographic correction methods using classification accuracy as a criterion. For this purpose, the mountain area was cut out on the Landsat-8 OLI image of December 2020, based on its physical–geographical and geological characteristics. Seven topographic correction algorithms were applied: Cosine correction, Improved cosine, C-correction, Minnaert, Minnaert with the slope, including Riano and others by Law, and the Normalization correction. To evaluate their performance, three criteria were used: visual interpretation, statistical analysis, and assessing classification accuracy taking into account eight cover classes. The obtained results showed a higher effectiveness of the Minnaert correction with slope and roughness coefficient k = 0.3, with an overall accuracy of 94.08%. The user and producer accuracies increased the performance for almost all forest classes. For the mountains of Trinidad, the non-forest classes were not affected by the topographic correction, so it was possible to apply the topographic correction algorithms to the entire area. The results have demonstrated the necessity of applying the criterion of accuracy assessment to select the best topographic correction.