Extracting Iron Information In Larsemann Hills Of East-antarctic Based On WorldView-â…¡ And ASTER Data

As an important research surface information technology, Remote sensing technology has been widely used in earth system science and various branches of science,such as geology, military, forestry, water conservancy and other areas. The geology is one of the oldest areas of remote sensing applications, as well as more successful and mature.In this paper, use the remote sensing technology to extract the iron information. In the study area- Larsemann Hills region of Antarctica.The study area is located in the Larsemann Hills of East Antarctic continental margin,and there emerging Precambrian metamorphic rocks and Paleozoic magmatic rocks. Due to the harsh weather conditions, poor cross condition and the difficult Field investigation of Antarctic, we can use remote sensing means to identify the regional iron information.For the extracting of information with ferric iron minerals, the previous used the TM(ETM +) and Aster data to identify. Because their spectral and spatial resolution is limited,the generated mixing pixels directly affect the extraction accuracy. In this paper, try to use WorldView-II remote sensing images,and it has eight bands with high spatial and spectral resolution. For the extracting of information with ferric iron minerals, based on the extraction result of Aster date by principal component analysis approach,try to use the WorldView-II data by the same means. For the extracting of information with divalent ferric iron minerals, use the ratio method of Aster data and the formula of Band7>Band8of WorldView-II data. Finally,we get the good experimental results. The results show that the iron minerals are mainly in mixed gneiss and paragneiss area accounting for79.44% and 17.44% of the total area of the iron information, which is consistent with the findings of the field survey results。For the extraction of alteration information, remote sensing technology can do good results, however, the data currently in use should be improved. For example,multi-spectral data has high spectral resolution with low space resolution, to extract the alteration information in the visible light, WorldView-II is of high quality, whose multi-spectral resolution 1.8 m; therefore, I try to use WorldView-II data to improve iron information extraction; For hyperspectral data, the spectral resolution has been well increased but its spectral resolution is limited, therefor, we need to make unremitting efforts to improve the quality of romote sense data.