Large-Scale Extraction and Mapping of Small Surface Water Bodies Based on Very High-Spatial-Resolution Satellite Images: A Case Study in Beijing, China

Abstract

Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly implemented using moderate- and low-resolution satellite images. However, the detection and analysis of more detailed surface water structures and small water bodies necessitate the use of very high-resolution (VHR) satellite images. The large-scale application of VHR images for water extraction requires convenient and accurate methods. In this paper, a method combining a pixel-level water index and image object detection is proposed. The method was tested using 2018/2019 multispectral 4-m resolution images obtained from the Chinese satellite Gaofen-2 across Beijing, China. Results show that the automatic extraction of water body information over large areas using the proposed method and VHR images is feasible. Kappa coefficient and overall accuracy of 0.96 and 99.8% after post-classification improvement were obtained for testing images inside the Beijing area. The Beijing water body dataset obtained included a total of 489.53 km2 of surface water in 2018/2019, 108.01 km2 of which were ponds with an area smaller than 2 km2. This study can be applied for water body extraction and mapping in other large regions and provides a reference for other methods for using VHR images to extract water body information on a large scale.