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

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.

River classification and change detection from landsat images by using a river classification toolbox

“…However, with continuous advances in remote sensing and computing technologies, processing of these hydrological data and relevant management processes can be executed ever much quickly, with virtually no need for human intervention. According to recent literature on remotely sensed data acquisition and processing, in particular, those based on image analysis, it was established that Landsat images were the 949 most preferred modality in handling river data [6]- [10]. This is mainly because their data services are provided with no charge to users.…”

“…This is mainly because their data services are provided with no charge to users. Besides, Landsat images contain sufficient information for characterizing water bodies and particularly main rivers, for classification purposes, at reasonably high accuracy [6]- [10].…”

“…The images can be acquired free of charge from the USGS website. In addition, with high spectral resolution, main rivers can be characterized at great accuracy [6]- [10]. Nonetheless, for sub-river classification, which required greater fidelty, Landsat images are usually considered jointly with other RS data [23]- [26].…”