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Remote Sensing Technology and Application  2020, Vol. 35 Issue (4): 882-892    DOI: 10.11873/j.issn.1004-0323.2020.4.0882
Automatic Algorithm for Extracting Lake Boundaries in Qinghai-Tibet Plateau based on Cloudy Landsat TM/OLI Image and DEM
Xinrui Wang1,2(),Rui Jin1,3(),Jian Lin4,Xiangfei Zeng4,Zebin Zhao1,2
1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
4.Key Laboratory Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan 411201, China
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Lakes in the Qinghai-Tibet Plateau are numerous and widely distributed, accounting for 41% and 57% of the total number and area of lakes in China, which are very important for the study of lakes in the whole country and even in the whole world. Remote sensing has been used to monitor the lake distribution for a long time, but optical remote sensing images are often obscured by clouds, from which it’s impossible to automatically extract complete lake boundaries. An automatic interpolation algorithm for lake boundary generation based on cloudy Landsat TM/OLI image and Shuttle Radar Topography Mission (SRTM) 30 m resolution Digital Elevation Model (DEM) is proposed. Firstly, supported by the platform of Google Earth Engine, the tier1 data of Landsat TM/OLI images are used to eliminate the effects of cloud, cloud shadow, snow and mountain area, based on the Pixel Quality Assessment (pixel_qa) attribute and SRTM 30 m DEM. Then, the Modified Normalized Difference Water Index (MNDWI) is calculated, and the Canny edge detection algorithm are used to obtain the known part of the lake boundary (L) in cloud-free areas. The possible lake areas are obtained by range filtering of DEM locally. At the same time, DEM is used to generate contours with an isometric interval of 1 m, and a series of contours surrounding the possible lake area are automatically screened out. The tree structure is established according to the inclusion relationship between contours. The leaf nodes are the innermost contours, which are recorded as inner contours (C1). Because the acquisition time of Landsat and DEM is different, with the lake expanding or shrinking, the lake water surface will rise or fall relative to the inner contour. Different methods of determining the outer contour (C2) are adopted. Subsequently, the slope-aspect relationship between the inner contour C1 and the outer contour C2 and the known part of the lake boundary L is established, and the unknown lake boundary points are interpolated. Finally, the nearest neighbor method is used to connect the known lake boundary points with the interpolated Lake boundary points to form a complete lake boundary. The extracted lake boundaries were validated by visual digitized lake boundaries from ZiYuan-3 image or cloud-free Landsat image on the near date. It is found that they are basically coincided, and the percentage of differences in length and area are -6.81%~9.4% and -2.11%~2.7% respectively. It shows that this method is very effective for automatic extraction of Lake boundary from cloudy Landsat TM/OLI images, and provides a new method for automatic extraction of long time series Lake boundary and its temporal and spatial variation analysis in the Qinghai-Tibet Plateau on GEE and other big data platforms.

Key words:  Qinghai-Tibet plateau      Lake boundary      Landsat      Cloud      DEM     
Received:  24 September 2019      Published:  15 September 2020
ZTFLH:  P941.78  
Corresponding Authors:  Rui Jin     E-mail:;
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Xinrui Wang
Rui Jin
Jian Lin
Xiangfei Zeng
Zebin Zhao

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Xinrui Wang, Rui Jin, Jian Lin, Xiangfei Zeng, Zebin Zhao. Automatic Algorithm for Extracting Lake Boundaries in Qinghai-Tibet Plateau based on Cloudy Landsat TM/OLI Image and DEM. Remote Sensing Technology and Application, 2020, 35(4): 882-892.

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Fig.1  Research region
光学遥感数据Landsat-830 m2015年9月16日有云
ZY-32.1 m2015年9月28日无云
Landsat-530 m1989年9月24日有云
DEM数据SRTM 30 m DEM30 m2000年2月不受云的影响
Table1  Data source information
Fig.2  Technique flow chart
Fig.3  Edge detection results of Canny algorithm
Fig.4  Diagram of range filtering of DEM (Units: m)
Fig.5  Contour Diagram
Fig.6  Diagram of part contour tree
Fig.7  Interpolation of the unknown points in the lake boundary in the case of lake level rising relative to the inner contour
Fig.8  Validation of complete lake boundaries extracted from a cloudy Landsat-8 image on September 16, 2015



Table 2  Analysis of the differences of length and area between the automatically extracted lake boundaries by this algorithm and the manually digitized boundaries in 2015
Fig.9  Interpolation of the unknown points in the lake boundary in the case of lake level falling relative to the inner contour
Fig.10  Validation of complete lake boundaries extracted from a cloudy Landsat-5 image on September 24, 1989



Table3  Analysis of the differences of length and area between the automatically extracted lake boundaries by this algorithm and the manually digitized boundaries in 1989
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