Applicability of the Surface Water Extraction Methods Based on China’s GF-2 HD Satellite in Ussuri River, Tonghe County of Northeast China
2020
Wenfeng Gong, Tiedong Liu, Yan Jiang and Philip Stott
Surface water is the most important and common water resource on earth. Accurate and effective mapping and detecting of surface water have been made possible by remote sensing technology, highresolution satellite data, playing an important role in surface water monitoring and mapping, which has become the current hot research for water information extraction in recent decades. Therefore, in this paper, we tested and analysed four models to extract water bodies using China’s GF-2 HD satellite (GF- 2) image, including Normalized Difference Water Index (NDWI), Modified Shadow Water Index (MSWI), Support Vector Machine (SVM) and Object-Oriented Method (OOM). The results showed applying water extraction models can map surface water with an overall accuracy of 0.8935, 0.9256, 0.9467 and 0.9357, respectively. SVM owns the highest overall accuracy value of 0.9467, followed by OOM. SVM performed significantly better at surface water extraction with kappa coefficients improved by 9.00%, 5.00%, and 2.00%, respectively, which yielded the best results and used to map surfaces water bodies in the study region, while index methods (NDWI and MSWI) are mostly classified into the water and non-water information based on a threshold value, with higher total omission and commission errors at 12.45%, 25.64%, 6.38% and12.87%, respectively. Therefore, we proposed SVM as the best algorithm to identify water body and effectively detect surface water from the GF-2 image.
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