Comparison of Pca (Principal Component Analysis) and Band Ratio Methods Using Landsat 9 Images for Land Cover Visualization Mapping (Sumatra Journal of Disaster, Geography and Geography Education, 2023)
Identification of land cover using satellite imagery through supervised and unsupervised classifications has been successfully carried out in various locations. This study aims to assess the accuracy of Landsat-9 OLI imagery by identifying land cover by observant the spectral responses of settlements, industrial buildings, rice fields, forests, wetlands, and open land using Principle Component Analysis (PCA) and Band Ratio methods. The success of land cover mapping was evaluated statistically using a confusion matrix. The acceptable level of accuracy is 85% with a Cohen’s kappa coefficient greater than 0.8. The results showed that the PCA and Band Ratio methods were not successful in mapping land cover at the study site because the accuracy level of both was at the specified moderate level. PCA has 80.5% accuracy with 0.75 kappa better than Band Ratio (80.5% accuracy, 0.76 kappa). The level of spectral sensitivity in industrial buildings and open land on PCA is much better than Band Ratio. However, in the residential and rice fields the Band Ratio is much better at detecting than PCA.