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Geological structural interpretation of Qiangduo area in Tibet based on multi-source remote sensing data |
| LIU Xinxing1,2, CHEN Jianping1, ZENG Min3, DAI Jingjing2, PEI Yingru1,2, REN Mengyi1, WANG Na4 |
1. School of the Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China;
2. Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China;
3. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China;
4. Institnte of China Geological Environment Monitoring, Beijing 100081, China |
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Abstract Usually remote sensing data are set corresponding parameters to solve specific problems such as resource and environment; nevertheless, the geological structures from the regional scale to the hand specimen are divided into different scales, and hence a single kind of remote sensing data cannot meet the multi-scale geological structure interpretation. To solve this problem, the authors took Qiangduo area in Tibet as the study area, utilized the advantages of ETM+,ASTER, WorldView2 and DEM data of the study area to interpret the geological structures at two different levels from ETM+ data with 30 m resolution and ASTER data with 15 m resolution to WorldView2 data with 0.5 m resolution, and obtained a good result. Firstly, the interpretation of structural framework from ETM+ data was carried out by interpretation keys. At the same time, the ASTER band calculation was used to indirectly reflect structural information which could verify the results of the ETM+. Furthermore, the high spatial resolution WorldView2 data were integrated to analyze the structure. Finally, on the basis of the field validation, the interpretation results were revised. The geological structure interpretation results of Qiangduo area show that the integrated application of multi-source remote sensing data can improve the accuracy of structure interpretation and achieve a good practical application effect in a short period of time.
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| Keywords
algae bloom
sub-pixel
low-resolution
remote sensing
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Issue Date: 23 July 2015
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2015,
Vol. 27
