Lithology Identification of the North Qilian Belt by Surface Temperature and Spectral Emissivity Inf

International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013 doi: 10.14355/ijrsa.2013.0304.09

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Lithology Identification of the North Qilian Belt by Surface Temperature and Spectral Emissivity Information Derived from ASTER TIR Data Qin Yang, Xiangnan Liu*, Xuqing Li, Chao Ding, Fan Yang School of Information Engineering, China university of Geoscience (Beijing), China, 100083 * liuxncugb@163.com Abstract

help for alteration mineral mapping.

Lithology units have been identified in northern Qilian belt by using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR)L1B data which contains both surface temperature and spectral emissivity information. As mafic-to-untramafic units have higher surface temperature than other geologic units, surface temperature images were generated to identify the dolerite and peridotite etc. Reference to analysis of thermal spectra characteristics of geological units from JHU spectral library, a new ASTER colored composite band ratio combination band 11/12: band 12/10: band 12/14 as R:G:B was applied successfully to distinguish quartz-rich felsic units and feldspathic rocks from the large meadow in the study area , with the band ratio and de-correlation techniques. To clarify the geological boundaries and remove the vegetation effect more effective, NDVI image and false color composite image composed of band 3: band 2 : band 1 visible to near infrared bands were also generated. The result indicated that combination of TIR FCC image, VNIR FCC image, temperature image and NDVI image for the study lithology mapping works well and matches the field geological mapping.

Recently, thermal infrared remote sensing was generally applied in lithology identification and mineral mapping, extraction and visual interpret of TIR data has made significant improvement. Igneous rock thermal infrared emissivity spectra with SiO2 composition content have a corresponding relationship between each other (Salisbury et al., 1988). Silicate, sulfate and abundance of carbonate minerals and chemical properties is closely related to the thermal infrared spectral characteristics. Therefore, the surface of minerals including igneous rock, carbonate rock and sulfate, clay magnesium iron silicate rocks can easily be identified by thermal infrared multispectral scanner (TIMS), and the remote sensing lithological mapping using aerial thermal infrared spectrum has made some progress (Kahle et al., 1983; Fu et al., 1998; Cudahy et al., 1999; Cudahy et al., 2000). ASTER thermal infrared is the first satellite thermal infrared multispectral remote sensing system, and its rich spectrum, space resolution and radiation are suitable for the geological application. Ninomiya (Ninomiya et al., 2005) used ASTER TIR data and the lithology index of arid to identify the lithologic of semi-arid areas. (Rowan et al., 2003; Rowan et al., 2005) used ASTER visible-near, short-wave infrared and thermal infrared data mapping in two research areas, and discussed the applicability in lithologic mapping.

Keywords ASTER; Thermal Infrared; Lithology identification; Temperature; Emissivity information

Introduction ASTER data can provide valuable information about earth surface and is useful for lithology identifying and mineral exploration. It obtains multispectral image of earth not only visible to near infrared (VNIR; 3 bands with 15 m resolution) and shortwave infrared (SWIR; 6 bands with 30 m resolution) spectral regions, but it has 5 bands in thermal infrared (TIR) region with 90 m resolution. Its VNIR bands and SWIR bands provide many kinds of alteration mineral identification information and determine alteration intensity and

Concerning the current study, TIR data cannot be used thoroughly in the lithology mapping area. As more and more methods of separated temperature information and emissivity information were proposed, surface temperature can be effectively abstracted to help identifying, for different rock types with different features in the temperature image. In addition, the research of geological mapping in Qinghai-Tibet 235

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Plateauand is not enough in current, for its high altitude and hard exploration conditions. In this paper, north Qilian, a part of Qinghai-Tibet Plateauand which is rich in mineral resource was selected and a simple and effective method to generate false color composite images from TIR data has been proposed based on surface temperature and spectral emissivity information. In particular, this method can be used to identify mafic-to-ultramafic units and felsic units from the plateau meadow, to remove the influence of vegetation, water and snow.

Geology and Ore Deposits of Study Area The study area located in north Qilian，a part of the Qinghai-tibet plateau(E98°30′-99°20′, N38°20′-38°40′).

The average elevation of the area is about 4 km and most of the land covered by plateau and stunning peak is annually snow-covered. Thus, it’s suitable for geological mapping with remote sensing and June and July is the best month.

The study area is also a mining district where copper, chromite, erythrozincite and gold are mined. It mainly contains many felsic units including quartz, potassium feldspar, albite, etc. and mafic-ultramafic rocks including olivine, gabbro, diabase and diabase etc.

Granit is also a host country rock in the study area.

Most of the ore deposits in the area are related to mafic-ultramafic pand granitic magmatism. Felsic granitic rocks are bound up with Au and Cu mineralization and mafic-ultramafic units are common host rocks for magmatic for magmatic platinum, chromium, and nickel deposits. (Partington and Williams, 2000; Naldrett, 2010).

Radiometer (ASTER) thermal infrared data with five spectral bands and a spatial resolution of 90 m. ASTER L1B data is utilized to create false color composite (FCC) image in order to identify geological units and generated surface temperature image to provide more information. NDVI images were also generated by VNIR data to clarify the effect of vegetation.

Thermal Spectra Characteristics of Felsic and Mafic-To-Ultramafic Quartz and unaltered igneous rocks with prominent spectral features are in TIR region due to Si–O–Si stretching vibrations. Minerals spectral graph was obtained from ASU spectral library hosted by the Mars Space Flight Facility at Arizona State University which provides thermal infrared spectra of variety of materials including minerals, rocks, soils, etc. As shown in Fig2, felsic including quartz and albite shows the minimum spectral emissivity in ASTER bands 10 and 12, resulting in higher emissivity in band 11 than ones in bands 10 and 12. In addition, mafic-to-ultramafic units as augite have a very strong absorption at around 8.6 l m (ASTER band 11), and as a result, it exhibits lower emissivity in band 11 than in bands 10 and 12, similar tendency to felsic. But olivine has lower emissivity in bands 13 and 14 and higher emissivity in the band 10, showing obvious differences with quartz.

FIG. 2 LABORATORY EMISSIVITY SPECTRA OF QUARTZ, AUGITE, OLIVINE, AUGITE AND WATER ICE of HIGH LATITUDE, FROM ASU SPECTRAL LIBRARY.

FIR FCC Image FIG . 1 TECTONIC OF STUDY AREA, MODIFIED BY THE GRAPH FROM ALAN (ALAN, 2006)

TIR Images for Geological Interpretation The data mainly used in this study is Advanced Spaceborne Thermal Emission and Reflection

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To enhance the spectral differences between the geological units, the Band Ratio techniques were respectively used to process the TIR L1B data before the bands were combined for FCC image. Based on the spectral emissivity property of quartz, band 11/12 was selected which is high in quartz and low in K-feldspar and gypsum. While based on the spectral emissivity

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property of olivine, band 12/14 is chosen to enhance the mafic-to-ultramafic units. In the band12/14 image, vegetation shows high value but not brighter than olivine. In this study area, main vegetation is meadow with scattered small grass which covered most of the area. Therefore, separating the meadow is significant in the data processing. Grass shows high emissivity and less volatile tendency in the spectral graph, and refers to the result of the experiment many times that band 12/10 works well. In the result, the red, green and blue(R:G:B)are respectively assigned to be band11/12:band12/10:band12/14(Fig.3(a)).

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decorrelation stretch is necessary to eliminate or weaken the correlation between these bands. As shown in Fig.2, the FCC image is composed of spectral bands with decorrelation stretch which have a stronger color contract and show different geological units more clear.

Comparison with a geological map, the blue regions match peridotite units and purple regions match amphibolite. Pink regions match felsic units, whereas lake and water show similar color too. Green region matches vegetation obviously.

NDVI Images To clarify the effect of vegetation on the TIR images, NDVI images are generated from the VNIR bands of ASTER L1B reflectance data. The NDVI computed by (band3 - band2)/(band3 + band2) (Yue,YM etc.,2013) is utilized. The bright regions with high NDVI values indicate vegetation (Fig.4). The NDVI image indicates that most of study area has sparse vegetation and quite fit in with FCC image above. Dense vegetation exists along the stream in the valley and gets scattered gradually with the increasing height. Low-albedo regions appear white in the NDVI image.

(a)

(b) FIG. 3(a) TIR FCC IMAGE COMPOSITED OFBAND 11/12: BAND 12/10: BAND 12/14 (b) TIR FCC IMAGE COMPOSITED OF BAND 14: BAND 12: BAND 10;

Compared with the FCC image (Fig. 3(b)) composed of band 14: band 12: band 10 (Hatem Aboelkhair etc., 2010), color contract is increased in the image generated after band ratio. However, for adjacent ASTER TIR bans are highly correlated with each others, and these bands with band ratio processing also have a strong correlation between each other. For this reason,

FIG. 4 NDVI IMAGE COMPUTED BY NVIR BANDS (BAND3-BAND2)/(BAND3+BAND2)

Surface Temperature Image In this study, a surface temperature image was generated by ASTER TIR L1B data, not the standard product derived from TIR L2 data. Therefore, we preprocessed the L1B data, including radiometric calibration and atmospheric correction. After that, temperature and emissivity were separated by a authority algorithm called TES (Emissivity/ Temperature separation) model to inverse surface temperature (Fig. 6). Compared with the FCC images,

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International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013

temperature image indicates that different geological units show different features in the TIR temperature image. The mafic-to-ultramafic units have higher surface temperatures than the other geological units.

Taro’s study indicated that temperature dominates over spectral emissivity information in band 13 and 14 (Taro Yajima and Yasushi Yamaguchi, 2013), and just right mafic-to-untramafic units have high digital value in band 13 and 14. Therefore, the results of the FCC image and temperature image matches well. In the contrast, felsic units appear dark in color. The darkest color matches with lakes and water drainage areas.

Fig.6 obtained by respectively assigning R:G:B to band3:band2:band1, because the resolution of this image is higher than that of TIR FCC image.

Discussion and Conclusion

FIG. 7 RESULT of GEOLOGICAL MAPPING of THE STUDY AREA

FIG. 5 FIR SURFACE TEMPERATURE IMAGE WHICH SEPARATED WITH EMISSIVITY INFORMATION

FIG. 6 NVIR FCC IMAGE COMPOSITED OF BAND 3: BAND 2: BAND 1.

VNIR FCC Image The TIR FCC images and mineral index images are highly affected by the vegetation and land use of the surface. Some of the study areas are mix of rock, soil, and vegetation, and it is not clear how to distinguish the boundary by means of the TIR data. To clarify the geological boundaries, VNIR FCC image is applied in 238

Accurate and advanced interpret ion is expected to be carried out by combination with TIR FCC image, NDVI image, surface temperature image and VNIR FCC image (Fig. 7). Mafic-to-ultramafic units exhibit quite bright in the temperature image and blue in the TIR FCC image, indicating that mafic-to-ultramafic rocks have a high correlation with temperature and temperature dominates the spectral information emissivity, as expected. Felsic units exhibit dark in the temperature image and reddish or pink in the TIR FCC image, whereas lake and water area have similar features and obscure discriminating felsic units from the surface materials. In this study area, as granite is rich in quartz and anorthose, its digital value falls between quartz and mafic-to-ultramafic units. Alkali granite exhibits light blue and syenite exhibits purple in the FCC image with different proportion of quartz and feldspar. It is worth mentioning that lake and water areas are darker than felsic units, as water has the high albedo and low temperature. To remove the effect of the water and snow, the high resolution VNIR FCC image can provide more detail information to interpret water bodies. In the study area, most of rivers are all in the valley and along river bank vegetation is dense. For the study area being high and cold, meadow is the main surface material and key interpretation unit. From the NDVI image, we can acquire the boundaries of the meadow and it is indicated that some light green region fails to match the meadow in the FIR FCC image.

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In conclusion, combination with surface temperature image and TIR FCC image showed better results for mapping mafic-unmatrafic units like dolerite, olivine etc. NDVI and VNIR images also showed better effect on clarifying the boundaries of meadow and bareness rocks. As a result, combination of TIR FCC image, VNIR FCC image, temperature image and NDVI image for high altitude region lithology mapping works well and matches the field geological mapping.

The result will be useful for mineral resource prediction.

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ACKNOWLEDGMENT

and

We thank China university of Geoscience(Beijing) geology investigation institute for their providing of geological data. This study was conducted on the project of Qinghai chaidamu peripheral metallogenic belt and its adjacent area of geology and mineral resources investigation and evaluation (1212011121188), hosted

by China university of Geoscience (Beijing) geology investigation institute.

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