Acta Geodaetica et Cartographica Sinica ›› 2015, Vol. 44 ›› Issue (3): 309-315.doi: 10.11947/j.AGCS.2015.20130438

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Lake Storage Change Automatic Detection by Multi-source Remote Sensing without Underwater Terrain Data

ZHU Changming1, ZHANG Xin2,3, LU Ming3, LUO Jiancheng2   

  1. 1. Department of Geography and Environment, Jiangsu Normal University, Xuzhou 221116, China;
    2. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China;
    3. School of Water Conservancy and Electic Power, Hebei University of Engnieering, Handan 056038, China
  • Received:2013-09-30 Revised:2014-07-10 Online:2015-03-20 Published:2015-04-01
  • Supported by:
    The International Science and Technology Cooperation Program of China(No.2010DFA92720);The National Natural Science Foundation of China (Nos.41201460;61375002;41271367);The Special Funds for Scientific Research on Public Causes of the Ministry of Water Resources of China(No.201201092)

Abstract: Focusing on lake underwater terrain unknown and dynamic storage that is difficult to obtain by the traditional methods, a new method is proposed for lake dynamic storage estimation by multi-source and multi-temporal remote sensing without underwater terrain data. The details are as follows. Firstly, extraction dynamic lake boundary through steps by steps adaptive iteration water body detection algorithm from multi-temporal remote sensing imagery. And then, retrieve water level information from ICESat GLAS laser point data. Thirdly, comprehensive utilizing lake area and elevation data, the lake boundary is converted to contour of water by the water level is assigned to the lake boundary line, according to the time and water level information. Fourthly, through the contour line construction TIN (triangulated irregular network) model and Kriging interpolation, it is gotten that the simulated three-dimensional lake digital elevation model. Finally, on the basis of simulated DEM, it is calculated that the dynamic lake volume, lake area distribution and water level information. The Bosten lake is selected as a case studying to verify the algorithm. The area and dynamic water storage variations of Bosten lake are detected since 2000. The results show that, the maximum error is 2.21× 108 m3, the minimum error is 0.00002× 108 m3, the average error is 0.044×108 m3, the root mean square is 0.59 and the correlation coefficient reached 0.99.

Key words: lake, dynamic storages, remote sensing detection, underwater terrain

CLC Number: