An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

TLDR

The water index (WI) is used to highlight inland water bodies in remotely sensed imagery, but conventional thresholding suffers from mixed pixels, noise confusion, and variable thresholds across locations and times. This study proposes an automated method that combines WI with digital image processing to extract rivers and lakes while addressing these issues. Using Landsat TM and ETM+ images from three Chinese regions, the method was compared to existing thresholding techniques and evaluated with Kappa, omission/commission errors, edge accuracy, and completeness metrics across different WIs. The new approach generally outperformed thresholding methods, with improvement varying by WI, and its strengths and limitations are discussed.

Abstract

The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and commission errors, edge position accuracy and completeness, were calculated to assess the method’s performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed.