Detection of chromite bearing mineralized zones in Abdasht ophiolite complex using ASTER and ETM+ remote sensing data

Pournamdari, Mohsen; Hashim, Mazlan

doi:10.1007/s12517-013-0927-0

Cited by 32 publications

(25 citation statements)

References 30 publications

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“…It is expected to be high for ultramafic rocks, and to move systematically lower as the rock type changes to felsic, and to be quite low for silicic rocks. Similarly, it has been confirmed high MI is a distinct indicator of ultramafic rocks [14][15][16][17][18][25][26][27][28][29][30]. Low MI is sensitive for high SiO2 content rocks with both crystalized and amorphous silica [14,17].…”

Section: Mineralogical Indices For Aster-tirmentioning

confidence: 91%

“…Especially, Quartz Index (QI), Carbonate Index (CI) and Mafic Index (MI) defined for the ASTER-TIR data are finely analyzed theoretically and practically, and the performance of the indices in extracting geological information is confirmed [14]. Many studies have applied the indices (e.g., [13][14][15][16][17][18][19]) and other workers have proved the usefulness and stability of the indices in the local geological case studies (e.g., [20][21][22][23][24][25][26][27][28][29]). Despite ASTER radiance registered at the sensor data without atmospheric corrections applied are adopted for the calculation of the indices, the recognized scene dependency is quite weak if the atmospherically well conditioning data are selected [17].…”

Section: Introductionmentioning

confidence: 99%

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Regional Lithological Mapping Using ASTER-TIR Data: Preliminary Case Study for the Tibetan Plateau and the Surrounding Area

Ninomiya

2016

Preprint

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Abstract:The mineralogical indices; the Quartz Index (QI), Carbonate Index (CI) and Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data were applied to various geological materials for regional lithological mapping on the Tibetan Plateau. Many lithological and structural features are not currently well understood in the central Tibetan Plateau, including the distribution of mafic-ultramafic rocks related to the suture zones, the quartzose and carbonate sedimentary rocks accreted to the Eurasian continent, and sulfate layers related to the Tethys and neo-Tethys geological setting. These rock types can now be mapped with the interpretation of the processed ASTER TIR images described in this paper. A methodology is described for the processing of ASTER TIR data applied to a very wide region of the Tibetan Plateau. The geometrical and radiometric performance of the processed images is discussed, and the advantages of using ortho-rectified data are shown. The challenges of using ASTER data with small footprint in addition to selecting an appropriate subset of scenes are also examined. ASTER scenes possess a narrow swath width when compared to LANDSAT data (60 km vs. 185 km respectively). Furthermore, the ASTER data archive is vast consisting of approximately 3 million images. These details can present an added level of complexity during an image processing workflow. Finally, geological interpretations made on the maps of the indices are compared with prior geological field studies. The results from the investigations suggest that the indices perform well in the classification of quartzose rocks based on the carbonate and the mafic mineral content, in addition to the granitic rocks based on the feldspar content.

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Section: Mineralogical Indices For Aster-tirmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Regional Lithological Mapping Using ASTER-TIR Data: Preliminary Case Study for the Tibetan Plateau and the Surrounding Area

Ninomiya

2016

Preprint

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“…It has lower signal-to-noise radiometer performance, allowing 8 bit quantification of data, but records solar radiation in more spectral bands (table 2) than Landsat-8 and it provides useful spectral information for geologists [9]. ASTER data have been successfully used for lithological and mineral mapping [10] to aid in exploration [11]. In the VNIR and SWIR bands, iron bearing minerals, carbonates, hydrate and hydroxide minerals display molecular absorption features related to their overtones and combination tones [12].…”

Section: Introductionmentioning

confidence: 99%

Application of remotely sensed data in detecting zinc-lead bearing mineralized zones in Westkunlun Huoshaoyun area

Yang

Zhuan

Yao

et al. 2018

J. Phys.: Conf. Ser.

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“…In thermal infrared (TIR) wavelengths, igneous rocks with relatively high SiO 2 contents have diagnostic emission spectral features due to the vibration of the Si-O bond, based on which, some rock indices have been constructed, such as the sulfuric acid rock index and the carbonate rock index [5][6][7]. Spectral-feature-based approaches for lithological identification mainly focus on the conversion and enhancement of spectral features, such as principal component analysis (PCA), spectral angle mapping (SAM), band ratio (BR), relative absorption band depth (RBD), false color composite (FCC), matched-filtering, and combinations of these methods [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

2016

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Abstract:Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries-Matusita (J-M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.

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Detection of chromite bearing mineralized zones in Abdasht ophiolite complex using ASTER and ETM+ remote sensing data

Cited by 32 publications

References 30 publications

Regional Lithological Mapping Using ASTER-TIR Data: Preliminary Case Study for the Tibetan Plateau and the Surrounding Area

Regional Lithological Mapping Using ASTER-TIR Data: Preliminary Case Study for the Tibetan Plateau and the Surrounding Area

Application of remotely sensed data in detecting zinc-lead bearing mineralized zones in Westkunlun Huoshaoyun area

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

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