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Applying landsat date for mineral exploration

ByAhmad Jeli Rinaldi

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MINERAL EXPLORATION USING ASTER IMAGES

• Images Satellite remote sensing images have been widely and successfully used for mineral exploration since the launch of Landsat in 1972. Gold is one of the most important mineral commodities that have been searched with the use of satellite remote sensing images over the last 30 years.

• Although gold cannot be “seen” directly by any remote sensor, the presence of minerals which form in association with this precious metal can be detected based on their spectral signatures.

• For nearly 20 years, the workhorse of mineral exploration has been Landsat Thematic Mapper/Enhanced Thematic Mapper+ (TM/ETM+), with its six spectral bands recording energy reflected by surface materials between the visible/near infrared (VNIR) and shortwave infrared (SWIR) portions of the electromagnetic spectrum, plus one band in the thermal infrared (TIR).

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With the launch of EOS/Terra platform in December 1999, a new sensor with enhanced capabilities for mineral exploration became available. This is the Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER).

Mineral Exploration Using ASTER Images

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ASTER obtains high-resolution (15 to 90 square meters per pixel) images of the Earth in 14 different wavelengths of the electromagnetic spectrum, ranging from visible to thermal infrared light.

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Instrument Characteristics

60 km swath; <16 day repeat cycle; stereo

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Visible-NIR Visible-NIR

Short Wave IR Thermal IR

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MINERAL SPECTRAL LIBRARY: USGS LIBRARY

The purpose for this is to create a Spectral Library with known The minerals in the target area and then compare the spectral library with the ithe image and find image possible sites for further investigation.

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MINIMUM NOISE FRACTION (MNF) MNF (minimum noise fraction) is used to show the variation between bands en in an image. This is a statistical method which works out differences in an image based on pixel DNs in various bands. MNF determines the inherent in dimensionality of image data, to segregate noise in the data, and to reduce d the computational requirements for subsequent processing.

MNF SWIR band R 1,G 2, B 3

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PRINCIPAL COMPONENTS

The use of Principal Components is to produce uncorrelated The output bands, to segregate noise components, and to reduce the dimensionality of data sets. Because multispectral data bands are often highly correlated, the Principal Components (PC) Transformation is used to produce uncorrelated output bands.

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Supervised ClassificationASTER PC

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RESULTS There are a few things to note when using ASTER imagery for regional mineralogical mapping. First, cloud cover, vegetation and atmospheric effects can severely mask or alter surface signals. Secondly, bands and band ratios do not indicate the occurrence of a mineral the with absolute certainty or with any idea of quantity, so ground truthing and setting appropriate thresholds is essential. Thirdly, every terrain is different, so ratios which work in some areas for a particular mineral or assemblage may not show the same thing elsewhere.

he As a result of these factors, it is important not to look at ASTAs ASTER images in isolation from ER other data. If possible, datasets such as geology and structural maps, geochemistry, PIMA maps, analyses (ground truthing ), radiometrics, and any other available data should be used in , conjunction with ASTER for best results.

After analyzing the results from the supervised classification and overlaying the shape files containing ground information (like different minerals found in containing different areas of the area covered by the image), it was interesting to find that most of the areas where Muscovite was he the dominant mineral, were those where the highest concentrations of Gold (40ppb) were s found.

The information that came out after analyzing the Aster image was pretty significant but it can s not direct us exactly to what we are looking for. It gives us a good idea of where we should do further research so that we can focus on a specific area instead of wasting resources in of places where the possibility of any positive findings is very slim.

if we want to get more specific results, we need to use hyperspectral images like the ones images produced by AVIRIS which would give a much better spectral resolution.

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Terima kasih

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• Ada beberapa hal yang perlu diperhatikan ketika menggunakan citra ASTER untuk pemetaan mineralogi daerah. Pertama, awan, vegetasi dan atmosfer efek parah dapat menutupi atau mengubah sinyal permukaan. Kedua, band dan rasio Band tidak menunjukkan terjadinya mineral dengan kepastian yang mutlak atau dengan ide kuantitas, sehingga ground truthing dan pengaturan ambang tepat adalah penting. Ketiga, setiap daerah berbeda, sehingga rasio yang bekerja di beberapa daerah untuk mineral tertentu atau kumpulan mungkin tidak menunjukkan hal yang sama di tempat lain.ia Akibat faktor-faktor ini, penting untuk tidak melihat gambar ASTAs ASTER dalam isolasi dari ER data lainnya. Jika memungkinkan, dataset seperti geologi dan peta struktural, geokimia, peta PIMA, analisis (ground truthing), radiometrics, dan data lain yang tersedia harus digunakan dalam, bersama dengan ASTER untuk hasil terbaik.Setelah menganalisis hasil dari klasifikasi terbimbing dan overlay file bentuk yang berisi informasi dasar (seperti mineral yang berbeda ditemukan di mengandung berbagai wilayah daerah yang dicakup oleh gambar), itu menarik untuk menemukan bahwa sebagian besar daerah di mana Moskow dialah yang dominan mineral, adalah mereka di mana konsentrasi tertinggi Emas (40ppb) adalah s ditemukan.Informasi yang keluar setelah menganalisis gambar Aster cukup signifikan tetapi dapat s tidak mengarahkan kita persis apa yang kita cari. Ini memberi kita ide yang baik dari mana kita harus melakukan penelitian lebih lanjut sehingga kita dapat fokus pada area spesifik daripada membuang-buang sumber daya di tempat di mana kemungkinan temuan positif sangat tipis.jika kita ingin mendapatkan hasil yang lebih spesifik, kita perlu menggunakan gambar itt seperti yang gambar yang dihasilkan oleh AVIRIS yang akan memberikan resolusi spektral yang jauh lebih baik

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