The remote sensing interpretation of active faults in Shannan region of Tibet was carried out by using multi-source remote sensing data, which showed remarkable difference from previous work. In addition, previous researches made by other people were used to study the distribution and activity of active faults synthetically. First, taking Sentinel-1 Radar images which have properties of all-weather, side looking and penetrating and Landsat ETM+ images that have abundant spectral information as master data sources, combined with high resolution GF-2 remote sensing image, the authors extracted and analyzed geological structure information at different scales. What is more, it was convenient to analyze tectonic geomorphology of active faults from different angles and levels with the help of the fusion of DEM data and ETM+ remote sensing images so as to make 3D visualization remote sensing images. Finally, the authors used a wide range of methods such as image preprocessing, image enhancement and multi-source remote sensing image fusion to reduce the multiple solutions and dubiety of active fault remote sensing interpretation. With the help of Radar and optical remote sensing respective imaging advantages, the authors clearly displayed the characteristics of active fault remote sensing image of the study area. According to the remote sensing interpretation marks of active faults, a total of 4 active faults were interpreted in the study area: the Yarlung Zangbo River fault, the Zanda-Lhaze-Qiongduojiang fault, the Sangri-Cona fault and the Darjeeling-Ngamring-Rinbung fault. The results of active fault interpretation in the study area show that the application of multi-source remote sensing data could greatly improve the accuracy and credibility of the interpretation of active faults and provide a reference for later researches on the study area.
王阳明, 张景发, 刘智荣, 申旭辉. 基于多源遥感数据西藏山南地区活动断层解译[J]. 国土资源遥感, 2018, 30(3): 230-237.
Yangming WANG, Jingfa ZHANG, Zhirong LIU, Xuhui SHEN. Active faults interpretation of Shannan area in Tibet based on multi-source remote sensing data. Remote Sensing for Land & Resources, 2018, 30(3): 230-237.
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