Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China


Water is the most common and important resources on earth. In this paper, we tested and analyzed a variety of water indices for water surface extraction using Landsat TM/ETM+ images, and evaluated extraction accuracies over Ebinur Lake area in Xinjiang Uyghur Autonomous Region of China. Eleven algorithms found in literature on land surface water extraction, including normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automatic water extraction index with no shadow (AWEInsh), automatic water extraction with shadow (AWEIsh), vegetation index 1 (VI-1), vegetation index 2 (VI-2), vegetation index 3 (VI-3), water index (LBV_B), national wetland inventory (NWI), enhanced water index (EWI), and revised normalized different water index (RNDWI) were used. The results were validated by a maximum likelihood classification method, edge extraction accuracy assessment and extracted the area of Ebinur Lake from higher-resolution Landsat ETM+ panchromatic band. The results showed that these algorithms have different accuracies in extracting water information. We proposed VI-2 for KT3+ TM4>TM2+ TM7 and VI-3 for KT3+ TM2>TM4+ TM3 as an optimized and comprehensive method for land surface water extraction from Landsat TM/ETM+ Images (KT3, i.e. Wetness index from the Kauth-Thomas Transformation). The proposed VI-2 and VI-3 models demonstrated its potential in water body extraction with 92.95% and 85.04% accuracies, outperforming other algorithms. Using the optimal mask water model, the two models achieved up to 93.80% accuracy while minimizing the disturbance from vegetation and non-exploited land. Through comparison with other commonly used methods, it shows that the performance of the proposed method is superior to the others. Therefore VI-2 and VI-3 are the best indicators for water mapping using Landsat TM/ETM+ images. This study provided its great potential for quantitative evaluating of temporal changes of Ebinur Lake in Xinjiang Uyghur Autonomous Region of China.

Publication Title

Remote Sensing Applications: Society and Environment