Version 1
: Received: 18 November 2019 / Approved: 19 November 2019 / Online: 19 November 2019 (03:10:17 CET)
How to cite:
Pan, F.; Xi, X.; Wang, C. A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery. Preprints2019, 2019110218. https://doi.org/10.20944/preprints201911.0218.v1
Pan, F.; Xi, X.; Wang, C. A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery. Preprints 2019, 2019110218. https://doi.org/10.20944/preprints201911.0218.v1
Pan, F.; Xi, X.; Wang, C. A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery. Preprints2019, 2019110218. https://doi.org/10.20944/preprints201911.0218.v1
APA Style
Pan, F., Xi, X., & Wang, C. (2019). A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery. Preprints. https://doi.org/10.20944/preprints201911.0218.v1
Chicago/Turabian Style
Pan, F., Xiaohuan Xi and Cheng Wang. 2019 "A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery" Preprints. https://doi.org/10.20944/preprints201911.0218.v1
Abstract
To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEIns, and AWEIs) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEIns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEIns and AWEIs are better than NDWI, MNDWI and MNDWI2. AWEIns works better than AWEIs under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEIns.
Keywords
Landsat; Google Earth; water index; unsupervised image classification; supervised image classification; Kappa coefficient
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
