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Open AccessArticle

A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

1
Department of Geography and the Environment, University of North Texas, Denton, TX 76203, USA
2
Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1611; https://doi.org/10.3390/rs12101611
Received: 16 April 2020 / Revised: 15 May 2020 / Accepted: 16 May 2020 / Published: 18 May 2020
(This article belongs to the Section Environmental Remote Sensing)
A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods. View Full-Text
Keywords: Landsat; Google Earth; water index; unsupervised image classification; supervised image classification; relative error; overall error Landsat; Google Earth; water index; unsupervised image classification; supervised image classification; relative error; overall error
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MDPI and ACS Style

Pan, F.; Xi, X.; Wang, C. A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery. Remote Sens. 2020, 12, 1611.

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