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ASTER DATA PROCESSING & ANALYSIS forPORPHYRY COPPER EXPLORATION
In SOUTHERN ARIZONA
Venessa Bennett
Project Sponsor: Jonathan Boswell
Advanced Diploma GIS – Remote Sensing Concentration
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Thankyou
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PROJECT OVERVIEW
Collaborative project involving COGS and Anglo American Exploration USA Inc. based in Tucson, Arizona
Focus – process and analyze ASTER‐level 1B multispectral data to assist porphyry copper exploration
Important objective of analysis of processed data – RANK targets in terms of priority based on interpreted level of exposure through idealized porphyry
copper deposit
Literature Review Data Acquisition + Pre‐Processing
Data Processing Data Post ‐ Processing Data Analysis and Ranking
PROJECT COMPONENTS
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BACKGROUND ‐ ASTER SENSOR Multispectral imaging instrument aboard the Terra Satellite, launched
December 1999
Acquires land surface temperature, emissivity, reflectance and elevation data.
Co‐operative between NASA and Japanese Ministry of Economy, Trade & Industry
ASTER – (Advanced SpaceborneThermal Emission and Reflection
Radiometer)
4 VNIR bands, 6 SWIR bands, 5 TIR bands
Backwards‐looking NIR band (3B) yields stereo coverage (DEM generation)
http://www.jspacesystems.or.jp/ersdac/GDEM/E/2.html
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Scene Footprint: 60 x 60 km2
BACKGROUND ‐ ASTER SENSOR
Spatial Resolution: NIR – 15 m, SWIR – 30 m, TIR – 90 m
http://vterrain.org/Imagery/Satellite/LS7‐vs‐ASTER.jpg
NIR SWIR TIR
On‐demand instrument with Global Coverage (average 650 scenes/day)
SWIR bands specifically designed for lithological and mineral alteration mapping
n.b: As of April 2008, SWIR acquisition ceased due to erroneous raw DN values
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VNIR 3‐2‐1 False Colour Composite – 15 m
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SWIR 4‐5‐6 False Colour Composite – 30 m
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TIR 10‐11‐12 False Colour Composite – 90 m
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Variety of processing levels available – Most common:
Level 1A – reconstructed instrument Digital Numbers (DN; unprocessed ‘raw’ data)
Level 1B: Registered radiance at sensor (radiometrically calibrated and geometricallyco‐registered) – THIS STUDY
BACKGROUND ‐ ASTER SENSOR
Level1B Images georeferenced to WGS 84 datum and AOI UTM projections
Data – Hierarchical Data Format (HDF‐EOS)
[AcquisitionDate
MMDDYYY]
[Processing Level]
[AcquisitionTime]
[UpdatedProcessing
Date YYYYMMDD]
[Sensor]
[UpdatedProcessingTime]
[Data GranuleSequentialNumber]
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BACKGROUND ‐ ASTER SENSOR
ASTER L1A and L1B are freely available for download for coverage across the USA
(purchase for international locations – $80 – 100)
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Surface expressions of mineralization can be remotely sensed due to
their CHEMICAL FOOTPRINT
MINERAL ZONATION typically occurs and yields
results in differing SPECTRAL SIGNATURES
that vary in space
APPLICATION to PORPHYRY COPPER EXPLORATION
(Source ‐ Pour and Hashim, 2012)
Same methods would be used to map vegetation differences and health
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PORPHYRY COPPER DEPOSIT MODELPhyllic
Propylitic
Potassic
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EXPOSURE LEVEL ZONAL VARIATIONS
CROSS SECTION
PLAN VIEWS
HIGH LEVEL
MID LEVEL
Most practical ASTER processing targets PROPYLITIC, PHYLLIC and ARGILLIC ZONES due to spectral resolveability
CORE LEVEL
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ZONAL VARIATIONS in PROCESSED ASTER DATA –HIGH LEVEL
2SD Threshold
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ZONAL VARIATIONS in PROCESSED ASTER DATA –CORE LEVEL
2SD Threshold
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RGB VISUALIZATIONS
Mine Extension
???
Mix of Blue and Red bands = purpleSilica
RGB layer stack important for identifying more discreteAlteration footprints NOT identified in 2 SD thresholds
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STUDY AREASouthern Arizona historic copper producing district
Study area (AOI) is region of both active copper mining and exploration
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PROJECT DATASETS9 Archival (pre April 2008) Level 1B ASTER datasets encompass the AOI
1/3 Arc Second National Elevation Data (NED) ‐ Orthorectification
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PROJECT COMPONENTS
Literature Review – Determine most applicable processing methods
Data Acquisition + Pre‐Processing ‐ Acquire and prepare data for processing and analysis
Data Processing – Various raster processing algorithms applied to emphasize spectral contrasts between PROPYLITIC, PHYLLIC and ARGILLIC
alteration (Band ratios and Principal Component Analysis)
Data Post ‐ Processing ‐ Final scene masking, trimming, thresholding and layerstacking automated using custom geoprocessing models (ASTER
toolbox)
Data Analysis and Ranking – Examination of data in geological context to identify and rank targets
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PRE ‐ PROCESSING WORK FLOW ASTER scene ACQUISITION CROSSTALK CORRECTION– Partial compensation for observed signal leakage from band 4
into bands 5 and 9 (open source software) LAYER STACKING 1: VNIR (15m), SWIR (30m) and TIR (90m) LAYER STACKING 2/RESAMPLING 15m ‐ VNIR + SWIR = 9 band stack RADIANCE CALIBRATION to relative surface REFLECTANCE(VNIR+SWIR) and EMISSIVITY (TIR)
– involves application of Atmospheric correction algorithm (FLAASH) Image ORTHORECTIFICATION ‐ 1/3 arc second NED DEM
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ASTER PROCESSING FOR MINERAL ALTERATION MAPPING
Groups 1 & 2: enhance spectral contrasts between diagnostic ABSORPTION FEATURES
between alteration minerals
Propylitic (chlorite, epidote) – 2.35 m absorptionPhyllic (kaolinite,alunite) – 2.20 m absorption
Three main groups of ASTER processing techniques: Band Ratios (Gp 1) , Data Transformations – PCA (Gp 2) and Spectral Mapping (Gp 3)
Group 3 ‐ supervised classification algorithms using end member spectra derived from imagery or laboratory &/or field based spectral libraries
Argillic (Muscovite‐sericite) – 2.17 m absorption
GROUP 1 Band Ratios & 2 PCA conducted in this study
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ASTER PROCESSING – BAND RATIOS
Technique in which DN (surface reflectance or emissivity) of > 1 band are divided by DN of > 1 other bands to ENHANCE spectral differences
Band Ratios used for Arizona Study area:
PROPYLITIC ‐ (7+9)/8PHYLLIC ‐ (5+7)/6ARGILLIC ‐ 4/5Silica ‐ 11/12
Iron Oxides ‐ 4/2Vegetation – 3/2
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BAND RATIOS ‐ PHYLLIC (5+7)/6 EXAMPLE
Threshold values only draped on Landsat
Ratio Image
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ASTER PROCESSING – FEATURE ORIENTED PRINCIPAL COMPONENT ANALYSIS
Orthogonal transformation of SELECTED ASTER bandsmost representation of alteration minerals – selected according to position of DIAGNOSTIC spectral
features (typically 2 reflective and absorption bands)
Helps separate noise from meaningful information content
Critical to examine resultant statistics (eigenvector values) to determine whether the mineral of interest will appear bright or
dark in the imagery
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Threshold values only draped on Landsat
Ratio Image – DARK pixels = Argillic alteration
FEATURE ORIENTED PRINCIPAL COMPONENT ANALYSIS
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POST‐ PROCESSING: MASKINGThe large number of scenes and types of analysis procedures used require automation of post‐processing procedures (custom ASTER
toolbox in ArcGIS)
Several features existent in each scene must be removed or minimized to ensure accurate statistical calculations when determining threshold cutoffvalues for each mineral species.
These include:
1. Scene edge effects (parallax correction applied to SWIR data causes offset on scene edge when VNIR and SWIR are layer stacked)
2. Water bodies (can distort min/max cell values)3. Urban areas (can distort min/max cell values)
4. Vegetation (can yield false positives)
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VEGETATION MASKING
A threshold cutoff value is applied to a 3/2 band ratio image to create a mask
Zero cell values – green vegetation1 cell values – no vegetation
The vegetation mask is then multiplied by the ASTER scene
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SCENE EDGE EFFECT, WATER BODIES & URBAN AREASGeoprocessing tool combines vegetation model into total scene masking model that accounts for edge effect, water, vegetation and urban areas
Scene edge, water body (shape file) and urban areas are removed using extract by mask tool
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ASTER MASK TOOLINPUT ASTER
SCENE
INPUT VEGETATION
MASK
OUTPUT MASKED IMAGE
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POST‐ PROCESSING: THRESHOLD CALCULATIONBand ratios and PCA transformation generate a ‘result’ for every cell, most of
which are false positives.
Each band ratio and PCA transformed ASTER scene must have thresholds applied.
1 and 2 Standard Deviations thresholds were calculated for each band ratio and PCA dataset for each aster scene using geoprocessing tools.
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POST ‐ PROCESSING: THRESHOLD CALCULATION
Higher thresholds = higher probability that mineral of interest occurs at that location
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POST ‐ PROCESSING: RGB VISUALIZATIONThree RGB datasets representing statistical ‘slices’ were created for each ASTER
scene.R – PHYLLIC G – ARGILLIC B – PROPYLITIC
Slice 1 : ENTIRE data with masks appliedSlice 2: 1 SDSlice 3: 2 SD
Slice 1: Total + Mask Slice 2: 1 SD Slice 3: 2 SD
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Slice 1: Total + Mask
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Slice 2: 1 SD
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Slice 3: 2 SD
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ANALYSIS AND RANKINGEach RGB slice was evaluated in terms of mineral zonation trends depicted by
the resultant ASTER thresholds and RGB slices.
BAND RATIO – RGB Slice 1
Silica (2SD threshold)
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ANALYSIS AND RANKINGTargets were then ranked in context of porphyry deposit model
CORE LEVEL
HIGH LEVEL
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IMPORTANT REFERENCES
http://www.ga.gov.au/webtemp/image_cache/GA7833.pdf ‐ ASTER Mineral Index Processing Manual
Crosta AP, Souza Filho CR, Azevedo F, Brodie C (2003). Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, Using ASTER imagery and principal component analysis. Int J Remote sens 24:4233–4240
Loughlin, W., (1991). Principal component analysis for alteration mapping. Photogrammetric Engineering and Remote Sensing, 57, 1163–1169.
Lowell, J.D., and Guilbert, J.M., (1970). Lateral and vertical alteration‐mineralization zoning in porphyry ore deposits: Economic Geology and the Bulletin of the Society of Economic Geologists, v. 65, no. 4, p. 373–408.
Mars, J.C., and Rowan, L.C. 2006. Regional mapping of phyllic‐ and argillic‐altered rocks in the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere 2: 161 – 186. doi: 10.1130/GES00044.1 v. 2 no. 3 p. 161‐186
Pour, B.A., and Hashim, M., 2012. The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geology Reviews 44, 1‐9. http://www.sciencedirect.com/science/article/pii/S0169136811001168
Sillitoe, R.H. (2010). Porphyry copper systems, Economic Geology, v. 105, pp. 3‐41