Extracting water-related features using reflectance data and principal component analysis of Landsat images |
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| Authors: | Boglárka Balázs Tibor Bíró Gareth Dyke Sudhir Kumar Singh |
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| Affiliation: | 1. Department of Physical Geography and Geoinformatics, Faculty of Science and Technology, University of Debrecen, Debrecen, Hungary;2. Faculty of Water Sciences, National University of Public Service, Baja, Hungary;3. Department of Evolutionary Zoology, University of Debrecen, Debrecen, Hungary;4. K. Banerjee Centre of Atmospheric &5. Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad, India |
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| Abstract: | This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4–5–7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach. |
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| Keywords: | multivariate analysis principal component analysis remote sensing Landsat classification uncertainty analysis spectral index |
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