BASICS OF GEOLOGICAL REMOTE SENSING

AN INTRODUCTION TO APPLICATIONS OF REMOTE SENSING IN GEOLOGICAL MAPPING AND MINERAL EXPLORATION

2014

Christopher Legg

CONTENTS

Introduction1.Principles of Remote Sensing1.1.Definitions1.2.The Electromagnetic Spectrum1.2.1. Ultraviolet1.2.2, Visible Wavelengths1.2.3. Near Infrared1.2.4. Mid-Infrared1.2.5. Thermal Infrared1.2.6. Microwave1.3. Orbits1.4. Satellites1.5. Sensor Systems1.6. Spatial and Spectral Resolution1.7. Data Reception1.8. Archiving and Distribution2. Satellite Systems2.1. Operational Systems2.1.1. Meteorological Satellites2.1.1.1. Geostationary Satellites2.1.1.2. Polar orbiting Meteorological Satellites2.1.2. Landsat MSS2.1.3. Landsat TM/ETM2.1.4. Landsat 82.1.5. Spot Satellites2.1.6. Brazil.Chinese Cooperative Satellites2.1.7. Indian Remote Sensing Satellites2.1.8. SSTL Satellites and Sensors2.1.9. Ultra-Fine Resolution Satellites2.1.10. Space Photography2.1.11. ASTER2.1.12. Spaceborne Radar2.1.13. Lidar2.1.14. Airborne Scanners2.2. Choosing the Appropriate System3. Image Processing3.1. Image Enhancement3.1.1. Contrast Stretching3.1.2. Density Slicing3.1.3. Colour Composites3.1.4. Ratio Images3.1.5. Principal Components Analysis3.1.5.1. Principal Components for Mapping Alteration Zones3.1.6. Convolution Filtering, Edge Enhancement3.1.7. Decorrelation Stretching3.1.8. Processing Digital Elevation Models3.2. Information Extraction

3.2.1. Visual Interpretation3.2.2. Classification3.2.3. Spectral Matching3.2.4. Change Detection3.3. Geometric Correction3.4. Image Processing Systems3.4.1. A Short History of Image Processing for Remote Sensing3.4.2. Hardware3.4.3. Software3.4.4. Image and Map Output4. Remote Sensing and GIS in Geological Mapping and Mineral Exploration4.1. Limitations of Remote Sensing4.2. Logistics4.3. Regional Geological Mapping – Lithology and Structure.4.3.1. Using Multi-Seasonal Imagery to Enhance Lithology4.4. Alteration Zones5. Environmental Planning,Management and Reporting for Mineral Exploration6. Remote Sensing and Mineral Intelligence6.1 Mineral Production6.2 Exploration Intelligence7. Sources of Satellite Imagery and other Data7.1 Satellite Imagery7.2 Other satellite-derived data7.3 Geological Data8. Some Useful Links8.1 GIS and Image Processing Software8.2. Datellite Imagery and Image-derived Products8.3 Other Important Data Sets9. Acknowledgements

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2.1.4. Landsat 8

A replacement for the Landsat series of satellites had been long awaited, especially since the failure of the SLC in the Landsat 7 ETM in 2003. Landsat 8 (provisionally named the Landsat Data Continuity Mission) was finally launched on 12th February 2013, acquired its first images in March 2013 and is now officially named Landsat 8. Imagery is available free of charge through the standard Landsat sites. For a comparison of different Landsat sensors click here

Figure 2.7. Comparison of Landsat 8 OLI and TIRS with ETM (courtesy NASA)

Landsat 8 carries two different earth-observation scanners, the Operational Land Imager (OLI) operating in the visible, NIR and SWIR portions of the spectrum and the Thermal InfraRed Scanner (TIRS) imaging in the thermal infrared. Both are

“pushbroom” scanners rather than opto-mechanical. A comparison of the OLI and TIRS wavebands with ETM is shown in Figure 2.7. The OLI has two new wavebands; band 1 is in the “deep blue” portion of the spectrum for water studies, while band 9 is in an atmospheric absorbtion window for better discrimination of cloud and data quality assessment. The other bands are re-numbered, and the near infrared (ETM band 4, OLI band 5), SWIR (ETM band 5, OLI band 6) and SWIR (both ETM and OLI band 7) are all narrower in OLI. The 15-metre resolution panchromatic channel (Band 8) is also spectrally narrower in OLI, excluding the NIR portion of the older panchromatic band. A Landsat 8 ratio image, showing the geology of part of the Danakil depression in Ethiopia and Eritrea, appears in Figure 2.8. Purple areas are mainly late Proterozoic volcanics, separated by the Danakil rift, with evaporite-bearing Tertiary sediments (yellow) and Recent volcanics (dark green).

Figure 2.8. Landsat 8 (OLI) ratio image (4/2=red;6/7=green;6/5=blue) of part of the Danakil depression and highlands to the west, Eritrea and Ethiopia.

The single thermal channel in ETM is split into 2 bands in TIRS. This gives the potential for geological discrimination based on different thermal emissivities of rocks. An example of Landsat 8 imagery, combining data from OLI and TIRS (a ratio of bands 10 and 11), is shown in Figure 2.9. Purple areas are mainly Teriary basaltic volcanics (harrat), while paler areas are mainly late Proterozoic volcanics. Three circular structures in upper centre are probably post-tectonic granitiods.

Figure 2.9. Landsat 8 image of an area north of Jeddah, Saudi Arabia. Red=ratio thermal 10/11; green= Fe directed PC; blue= Hydrox directed PC. (see section 3.1.5. for explanation of PCA)

The main geological implications of the differences between OLI/TIRS and the older ETM+ are that lithological discrimination should be better with OLI because of the narrower band widths, and that the two thermal channels should permit crude lithological discrimination using ratioing techniques.***

3.1.5. A Principal Components Technique for mapping alteration zones

The "Crosta" technique is illustrated with reference to a TM subscene of an area on the borders of Ethiopia and Eritrea (northern Tigray province) where volcanogenic massive sulphides occur in late Proterozoic volcanic rocks. A number of companies are exploring in this area currently (2014).

Figure 3.12. Inverted “Hydroxyl” Image...............Figure 3.13. Inverted “Fe-Oxide” Image

Figure 3.14. Left - Composite “Crosta” Image. Northern Tigray, Ethiopia. Right -Ratio image (4/2=red; 6/7=green; 6/5=blue) of same area

Horizons within the meta-volcanics relatively high in clay minerals and low in iron oxides appear as orange and yellow in the colour composite, while potential mineralised zones, with both iron oxides and clay minerals, appear bright golden. Hazy blue areas are probable residual patches of laterite on an exhumed peneplain above the Proterozoic rocks, originally covered with Palaeozoic sediments. It is often useful to examine TM ratio images of the same areas as the Crosta image, as a cross-check on the geology and the possible presence of hydrothermal alteration.

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3.1.6. Convolution Filtering, Edge Enhancement and Linear Filtering

Amongst the range of pre-set filters available in most image processing packages, the Sobel filter is popular with many geologists. This is essentially non-directional, and is excellent at detecting and enhancing edges. Filtering can be applied to digital elevation models as well as to satellite imagery, and an example of the Sobel filter applied to an ASTER DEM is shown in Figure 3.19. This filter detects sharp changes in elevation due to differing resistance to erosion when applied to a DEM. Read more on processing of DEMs in section 3.1.8.

Figure 3.19. Sobel filtering of ASTER DEM overlain on original colour-coded DEM

Filtering of satellite imagery and DEMs may produce very different types of information for structural geology. DEMs are detailed images of topography, which is often strongly influenced by geology. Except in cases of major engineering works, DEMs are not much affected by human activities. Satellite imagery can reveal many geological features not shown in DEMs. Alignment of river courses is often more apparent in satellite imagery than in DEMs, and vegetation differences resulting from lithology are not usually visible in DEMs. Unfortunately, cultural artefacts such as roads, setlements and agricultural features often dominate satellite imagery while being almost invisible in digital elevation models.

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4.1. Limitations on Use of Remote Sensing

Figure 4.2. Image extract of an area NW of Kasempa,NWP Zambia ---------- A typical “dambo” near Kasempa, Zambia with savanna woodland in background

b) Open Savanna. Open-canopy low forest interspersed with grassland is characteristic of many areas of Africa, South America and Australia. Forest vegetation is commonly controlled to some extent by underlying geology, and structure is evident in orientation of drainage and linear features in forest and grassland. Geological remote sensing can be usefully employed in such areas for mapping, although identification of mineral anomalies, for example iron and hydroxyl alteration

associated with mineralisation is usually difficult. Most savannah areas experience marked dry and wet seasons and, as described in Section 4.3.1 below, imagery acquired at different seasons can assist in lithological interpretation.

Figure 4.3. Image of part of the escarpment zone south of Choma, Zambia ---------- Sparse forest in dissected Basement rocks near Masuku,Choma, Zambia

c) Dry forest in dissected terrain. In these areas, vegetation rarely reflects geology, although structure is clearly revealed by erosion. Lithological and alteration zone mapping is rarely possible.***

4.3. Regional Geological Mapping - Lithology and Structure

After a close study of all available geological information on the area, the next step is to overlay, in a GIS, any existing geological maps, in image or vector form, on false colour composites of Landsat imagery and on hill-shaded DEMs, either SRTM or ASTER. This will serve as a quick check on the reliability or otherwise of the existing maps, and the amount of information likely to be added by remote sensing interpretation.

The next step is to interpret geological structure. The main features visible in remotely sensed imagery and DEMs are faults, shears and fractures, plus lithological boundaries between rocks of differing resistance to erosion. The first steps in structural interpretation are shown in Figure 4.14, where linear features are interpreted from SRTM and Radar images. All interpretation is done on-screen within a simple GIS package.

Figure 4.14. Interpretation of structural lineaments from SRTM (left) and radar imagery (right)

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5. ENVIRONMENTAL MANAGEMENT AND REPORTING FOR MINERAL EXPLORATION

Table 5.4. Data Sources for Generic EPB

COMPONENT SOURCE OF DATA

Physical Environment

Topography and Geomorphology DEMs, satellite imagery, site photos

Geology and Soils Published Maps

Hydrology and Hydrogeology DEM-derived hydrology. Geological maps, local surveys for water quality

Climate and Rainfall Data from Internet

Air Quality and Noise Local surveys

Conservation Environment

Flora Satellite-derived maps. Published data

Fauna Published data

Protected Areas and Species Internet maps and data

Landscapes Ground observations and photos

Cultural and Historical Sites National archives, local knowledge

Socio-Economic Environment

Administrative environment Internet maps

Population Distribution Internet maps derived from satellite data

Ethnic Groups Government and academic reports

Land Use / Tenure Satellite-derived maps, local information

Employment / Livelihoods Government data

Education and Health Government data

Infrastructure and Communications Internet maps supplemented by remote sensing

Project Environment

Project Plan Company internal data

Alternative choices Internet search

Environmental Management Plan Prepared in house or by consultant

Satellite remote sensing, and products derived from remote sensing, can play a very important part in production of Environmental Project Briefs and Environmental Management Plans, as indicated in Table 5.4. For examples from Australia and Asia, see this link. In most countries it is possible to employ local or regionally based consultants specialising in preparation of EPBs and EMPs, but for exploration licences there is no legal requirement for this to be done by external consultants, and exploration organisations can produce perfectly acceptable reports "in house"***

6.1.MINERAL PRODUCTION

Remote sensing provides a low-cost and rapid method for monitoring the progress of mining operations. This can be of interest to other mining and governmental organisations, and also as part of environmental monitoring. As a simple example of the possibilities, we can look at the Kalgoorlie "Super Pit", currently (2013) the largest single producer of gold in Australia. Kalgoorlie has been one of Australia's most important gold mining centres since 1893, with production coming from a large number of underground mines, some very deep. During the 1980's, Western Australian entrepreneur Alan Bond attempted to consolidate numerous mining leases into a single block so as to enable low-cost open cast mining of numerous gold-bearing veins and disseminations which were not previously mined underground. The Finiston Pit (later known as the "Super Pit") was developed in the early 1990's, and is now the largest single gold producer in Australia, producing up to 850,000 ounces of gold per year. Total production before completion of the pit in 2018 will be 10 million ounces from 1,129 million tonnes of rock.

Figure 6.1. Simulated view of Kalgoorlie Super Pit from south-west from Google Earth, 2013

Figure 6.2.Map of Kalgoorlie, June 2013, with open pits (purple), rock dumps (brown) and tailings storage (green). Mining features from satellite imagery, roads from Open Street Maps

To illustrate the use of satellite imagery to monitor mining operations, five Landsat images have been downloaded and processed, from 1989 (before the new open pit was developed), from 1994, soon after production commenced, and from 2001, 2005 and 2013. Co-registered image extracts showing Kalgoorlie and the open-pits, rock dumps and tailings storage facilities have been prepared as false-colour composites, and are shown below in figures 6.3 to 6.7.

Figure 6.3. Landsat TM view, July 1989

The 1989 image (Figure 6.3) shows the mining area east of Kalgoorlie before development of the Super Pit. Eight small open pits, apparently on the sub-outcrop of major vein systems, together with rock dumps and small tailings ponds, can clearly be seen. In the next image, acquired in March 1994 (Figure 6.4), production had obviously started from a large but relatively shallow pit, and significant quantities of tailings had already been deposited in the two Finiston TSFs and at Kaltails. By March 2001 (Figure 6.5) the pit is significantly deeper, the three TSFs are well established, and rock dumps east and south-east of the pit are growing. The fourth image (Figure 6.6) shows the area in July 2005. The pit is now sufficiently deep that most is in shadow at the time of the satellite overpass - relatively low sun angle in winter - and rock dumps are extending around the southern end of the Super Pit.

Figure 6.7. Landsat 8. June 2013

The final image in the sequence (Figure 6.7, June 2013) shows that the three main tailings storage facilities have increased in height, as indicated by the illuminated northern and eastern slopes, and the rock dumps have extended further around the southern end of the pit.