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APPLICATION OF REMOTE SENSING FOR
GOLD EXPLORATION IN THE NUBA MOUNTAINS, SUDAN
Cosmas Pitia Kujjo
A Thesis
Submitted to the Graduate College of Bowling Green
State University in partial fulfillment of the requirement for the degree of
MASTER OF SCIENCE
May 2010
Committee:
Robert Vincent, Advisor
Charles Onasch
Sheila Roberts
ii
ABSTRACT
Robert Vincent, Advisor
Gold exploration in the Sudan has been known since Pharaonic times (3,000 years), but
the sparse population and inhospitable climate caused the prospecting to be sporadic and
ephemeral in manner. Despite the earlier exploration for minerals in the Sudan, there still are no
proper estimates of reserves and many mineral occurrences remain unexploited due to
inappropriate prospecting methods (GRAS, 1990).
The proper use of mineral exploration methods was delayed until the 1970’s, during
which the Sudanese Geological Survey located more than 50 gold-producing sites. Subsequently,
joint ventures between Sudan and foreign companies in the 1980’s have provided opportunities
for the application of modern technologies to gold exploration. Consequently, substantial
discoveries of gold deposits in the Red Sea Hills encouraged the search for gold, not only in
areas of quartz veins, but also in rocks associated with gossans.
The present study is aimed at utilizing remote sensing technology for gold exploration
through an approach of establishing a relationship between the known deposits in the Red Sea
Hills and similar occurrences in the Nuba Mountains, Sudan.
The multispectral remotely sensing datasets that have been used in this research are
comprised of the LANDSAT Enhanced Thematic Mapping (ETM+) and the Advanced Space
borne Thermal Emission and Reflection Radiometer (ASTER). The applied enhancements
iii
techniques included color composite, band ratioing, principal components analysis (PCA), and
spatial filtering.
Multispectral remote sensing (LANDSAT+ and ASTER) image enhancement and
interpretation proved to be useful in identification, detection, and delineation of lithological rock
units, hydrothermal alterations, and geologic structures associated with auriferous sulphides
deposits in the research area of the Nuba Mountains, Sudan.
The results indicated matching of hydrothermal alteration zones with the locations of
known base metals (Cu, Zn, and Ni) in the area, which were mostly located in the vicinity of
Jebel Kurun in the SE, Jebel Tumluk , Jebel Umm Takatik, Jebel Uru, and along the flanks of a
domal feature that was identified by mapping of geologic lineaments in the study area.
iv
This work is dedicated to my beloved parents and community.
v
ACKNOWLEDEMENTS
My thanks and appreciations are due to Dr. R. Vincent, for his expert guidance and supervision. I acknowledge the significant contributions made to this research by Dr. C. Onasch and Dr. S. Roberts.
I would like to express my thanks and gratitude to the Geological Research Authority of Sudan (GRAS) and the United States Geological Survey (USGS) for providing the data necessary for the accomplishment of this work. Particular thanks are due to the administration, staff, and technician of the Geology Department at Bowling Green State University (BGSU) for their continuous assistance during my study.
The moral support of my family and their patience and consistent encouragement throughout the course of this study are gratefully acknowledged.
vi
TABLE OF CONTENTS
Page
CHAPTER 1 INTRODUCTION -----------------------------------------------------------------1
1.1 General ----------------------------------------------------------------------------- 1
1.2 Location of the study area -------------------------------------------------------- 2
1.3 Physiography ----------------------------------------------------------------------- 2
1.4 Previous Work --------------------------------------------------------------------- 4
1.5 Objectives of the present studies ------------------------------------------------ 5
CHAPTER 2 GEOLOGICAL SETTING ------------------------------------------------------ 6
2.1 Regional Geology ----------------------------------------------------------------- 6
2.2 Geology of the study area -------------------------------------------------------- 7
2.2.1 Volcano-sedimentary series ------------------------------------------------------ 7
2.2.2 Ophiolite Series ------------------------------------------------------------------- 7
2.2.3 Non-Ophiolite Igneous Rocks --------------------------------------------------- 10
2.2.4 Post-Basement Complex Formations ------------------------------------------- 11
2.3 Geologic Structural Setting ------------------------------------------------------ 11
2.4 Gold Mineralization --------------------------------------------------------------- 12
CHAPTER 3 METHODOLOGIES --------------------------------------------------------------15
3.1 Remote Sensing Data and Software ---------------------------------------------15
3.2 Processing Techniques ------------------------------------------------------------ 17
3.2.1 Color Composite Images --------------------------------------------------------- 18
3.2.2 Band Ratioing (BR) --------------------------------------------------------------- 20
3.2.3 Principal Component Analysis PCA -------------------------------------------- 24
vii
3.3 Spatial Filtering -------------------------------------------------------------------- 26
CHAPTER 4 RESULTS AND INTERPRETATIONS --------------------------------------- 27
4.1 General ------------------------------------------------------------------------------ 27
4.2 Single Band Combination -------------------------------------------------------- 27
4.3 Principal Component Analysis PCA -------------------------------------------- 31
4.3.1 Feature-Oriented Principal Component Selection (FPCS) ------------------- 32
4.4 Band Ratioing (BR) --------------------------------------------------------------- 40
4.5 Mapping Lithologic units ---------------------------------------------------------41
4.6 Mapping Hydrothermal Alteration ---------------------------------------------- 50
4.7 Mapping Geologic Structures ---------------------------------------------------- 57
4.8 Validation of Results -------------------------------------------------------------- 64
4.9 Mapping Ophiolites with ASTER (TIR) bands ------------------------------- 69
4.10 Comparison between the applied image enhancement methods ----------- 73
4.11 Updating the geological map of the study area -------------------------------- 75
4.12 Mineral potential map -------------------------------------------------------------78
CHAPTER 5 CONCLUTION AND RECOMMENDATIONS --------------------------- 82
5.1 Conclusion --------------------------------------------------------------------------82
5.2 Recommendations ----------------------------------------------------------------- 83
REFERENCE ---------------------------------------------------------------------------------------- 84
APPENDICES ---------------------------------------------------------------------------------------- 86
viii
List of figures
Figure Page
1. Location of the study area (after GRAS, 2004)------------------------------------------------------ 3
2. Geological map of the study area (after BGR, 1986) ---------------------------------------------- 9
3. Locations of gold occurrences in the Sudan (after GRAS, 1990) -------------------------------- 14
4. LANDSAT TM and ASTER bands(after Kalinowski and Oliver, 2004) ----------------------- 16
5. Reflectance spectra of the iron oxide and iron hydroxide (after Clark et al., 1993) ----------- 22
6. Reflectance spectra of Kaolinite (1.6 – 6.0 µm plot) ---------------------------------------------- 23
7. Correlation between LANDSAT TM bands, Nuba Mountains ----------------------------- 29
8. Correlation between ASTER bands, Nuba Mountains -------------------------------------------- 31
9. LANDSAT TM image showing FPCS for Ferric oxide minerals(PC3) as bright pixels ----- 34
10. LANDSAT TM image showing FPCS for Clay minerals (PC3) as bright pixels ------------- 35
11. ASTER image showing FPCS for Ferric oxide minerals (PC3) as bright pixels.-------------- 38
12. ASTER image showing FPCS for clay minerals (PC4) as bright pixels ------------------------ 40
13. Vegetation cover appears red in LANDSAT TM image of bands 4-3-2 (RGB) --------------- 41
14. ASTER bands 3-2-1 (RGB) showing vegetation cover as red, specially the Mango orchards
along Khor Tandik ------------------------------------------------------------------------------------- 42
15. LADSAT TM bands 3-2-1 (RGB) which is a true color composite image, Nuba
Mountains ---------------------------------------------------------------------------------------------- 43
16. LADSAT TM bands 6-4-1 (RGB) false color composite FCC of the study area ------------- 44
17. LADSAT TM bands 4-5-1 (RGB) FCC image clearly discriminates lithologies and defines
drainage patterns as well. ------------------------------------------------------------------------------ 45
18. ASTER image for bands 7-3-2 (RGB) of the Nuba Mountains. The image reveals
vegetation in light green, granitoids in pink, the ultramafic rocks in a variety of brown
colors, the graphitic schist in grayish or navy blue, and the quartz-mica-chlorite schist in
a variety of dark green colors. ------------------------------------------------------------------------ 46
ix
19. PC3 of ASTER bands 1-9, showing iron-bearing rocks as bright pixels ----------------------- 47
20. LANDSAT TM image of band ratios R (3, 1), R (5, 4), and R (5, 7) as RGB ----------------- 48
21. ASTER image of band ratios R (4, 7), R (4, 3), and R (2, 1) as RGB ------- ------------------- 49
22. ASTER band combinations 6-2-1 (RGB) illustrates gossans in reddish brown color -------- 51
23. Goethite mineral mapped in red color by ASTER band ratio codes 9, 4, and 4 as (RGB) --- 52
24. ASTER band ratio codes 8, 1, 1 (RGB) showing Kaolinite in red color ------------------------ 53
25. Alunite is mapped in brighter red color by ASTER band ratio codes 9, 0, 0 as (RGB) ------ 54
26. Muscovite (red) mapped using ASTER band ratio R (8, 6) R (6, 4), and R (6, 5),
respectively ---------------------------------------------------------------------------------------------- 55
27. Chlorite (red) occurrences in the study area -------------------------------------------------------- 56
28. Buddingtonite shown in red color was mapped by the ratio R (8,6) assigned to red, whereas
R (5, 4) and R (9, 3) are assigned to the green and blue colors, respectively ------------------ 57
29. Prominent geologic lineaments extracted from DEM image with an Image illumination of
120 degrees azimuth and inclination at 30 degrees. ----------------------------------------------- 58
30. ASTER FCC 1-4-5 showing geologic structures in the vicinity of the domal structure ----- 59
31. Image showing area A in Figure 30 above, where folds, foliation, and shearing are clearly
observed in the vicinity of the domal structure ---------------------------------------------------- 60
32. ASTER contrast stretched gray scale image of the study area showing
brighter and darker parts of the image corresponding to opposite verging orientations ----- 61
33. Delineated structural features in the study area identified by PC6 of ASTER bands 4-9 --- 63
34. Mineral inventory map, Nuba Mountains (After BGR, 1986) ---------------------------------- 65
35. Variability map showing mineral inventory locations superimposed on LANDSAT TM
image produced by the ratios R (4,1), R(3,1), and R(3,5) assigned to RGB
colors, respectively. Ferric oxides rocks (gossans) are in reddish brown color. -------------- 66
36. Discrimination mapping using ASTER band ratios R (4, 1), R (3, 1), and R (3, 5) as RGB,
respectively. The red color represents gossans and the quadrangles represent gold mines
x
in the Red Sea Hill ------------------------------------------------------------------------------------ 67
37. Location of gold mines in the Red Sea Hills of Sudan in relation to fault / shear zones.
Ariab Mineral District, AMD is defined by red quadrangles (after AMC, 2002). ----------- 68
38. ASTER 1-4-5 (RGB) image showing cell value sampling sites
(marked as XXX and ZZZ) at jebel Togla and Jebel Umm Sanagir, respectively ----------- 70
39. Comparison of ratio codes for unknown rock candidate (Ophiolites) versus. talc mineral -- 71
40. Comparison of ratio codes for unknown rock candidate (Ophiolites) versus. smithsonite -- 72
41. Simplified geological map of the study area (after BGR, 1986). (original map
is shown in Figure 3) ------------------------------------------------------------------------------------77
42. Updated geological map of the study area (after BGR, 1986) ---------------------------------- 78
43. Mineral potential map showing locations of iron (blue) and clay (red) alteration zones,
overlain on LANDSAT TM (Kernel edge) filtered image, aimed at identifying spatial
relation between structural lineaments and alteration zones, as favorable sites for gold
exploration in the study area ------------------------------------------------------------------------- 80
44. Mineral potential map showing locations of iron (blue lines) and clay (red lines) alterations
derived from PCA, overlain on ASTER 6-2-1 FCC image representing gossans (red),
hydrothermal alteration (green), and host rock (blue) as means of establishing a conceptual
model for gold occurrences in the area ------------------------------------------------------------- 81
xi
List of tables
Table Page
1. LANDSAT TM bands and their applications ----------------------------------------------------- 17 2. OIF values for LANDSAT TM band composites ------------------------------------------------ 28
3. OIF values for ASTER band composites ---------------------------------------------------------- 30
4. FPCS for iron oxide minerals using LANDSAT TM bands (1,3,4,and 7) -------------------- 33
5. FPCS for clay minerals using LANDSAT TM bands (1,4,5,and 7) ---------------------------- 35
6. FPCS for iron oxide minerals using ASTER bands (1,3,4,and 7) ------------------------------ 37
7. FPCS for clay minerals using ASTER bands (1,4,6,and 7) ------------------------------------- 39
8. Structural lineament analysis for the study area -------------------------------------------------- 62
1
CHAPTER ONE
INTRODUCTION
1.1 General
Gold exploration in the Sudan has been known since Pharaonic times
(3,000 years), but the sparse population and inhospitable climate caused the
prospecting to be sporadic and ephemeral in manner. The most noted exploration
period by British enterprises was between 1900 and 1954. Other attempts by the
local natives have been along the border with Ethiopia, Uganda, Central African
Republic, and The Democratic Republic of Congo, based on artisanal mining.
Despite the fact that mineral exploration started earlier in Sudan than in its
neighbors, and the presence of a wide range of minerals (gold, silver, copper, iron,
chromite, manganese, mica, and graphite), there still are no proper estimates of
reserves and many mineral occurrences remain unexploited due to inappropriate
prospecting methods (GRAS, 1990).
The proper use of mineral exploration methods was delayed until the
1970’s, during which the Sudanese Geological Survey located more than 50 gold
producing sites. Subsequently, joint ventures between Sudan and foreign
companies in the 1980’s have provided opportunities for the application of
modern technologies to gold exploration. Consequently, substantial discoveries of
gold deposits in the Red Sea Hills encouraged the search for gold, not only in
areas of quartz veins, but also in rocks associated with gossans.
2
The present study is aimed at utilizing remote sensing technology for gold
exploration through an approach of establishing a relationship between the known
deposits in the Red Sea Hills and similar occurrences in the Nuba Mountains,
Sudan.
1.2 Location of the study area
The study area is located in central Sudan and bounded by latitudes 10°
45' - 12° 15' N, and longitudes 31° 00' - 31° 30' E. (Fig. 1). It includes the eastern
part of the Nuba Mountains, in Southern Kordofan State.
The main town in the area is Abu Jubeiha, located at about 600 km south
of Khartoum. It is easily accessible and connected to several parts of the area
through a network of unpaved seasonal roads. Although these roads are unpaved,
they are well maintained; thus, are easily traveled by vehicles throughout the year,
except for some roads during the rainy season.
1.3 Physiography
Generally, the area is mountainous, comprising massif plateaus with some
surrounding plains that drop gradually from an altitude of 600 m at El Biteira
village down to 390 m above mean sea level at the Nile. The highest massif in the
area is Jebel Gisia, which is 1285 m above mean sea level.
The topographic sheets on a scale 1: 250,000 that cover the area are (ND-
36-M) Jebel El Dair, (ND-36-N) El Jebelein, (NC-36-A) Rashad, and (NC-36-B)
3
Er Rank. Drainage patterns exhibit many streams flowing down the mountains in
different directions. The most well-defined watercourse flowing through the town
of Abu Jebeiha is known as Khor Tandik (Fig. 1).
The area has humid tropical summer and dry desert-type winter climate.
Average temperatures are about 40° C in April. The rainy season is from April to
October with the mean annual rainfall of approximately 800 mm in the south.
Figure 1: Location of the study area
(After the Geological Research Authority of Sudan GRAS, 2004)
4
1.4. Previous work
The area had received less attention in terms of geological research in the
past. However, some projects have been carried out, most of which aim at the
appraisal of surface and groundwater resources for agricultural development.
Among the researchers are Andrew and Karkanis (1945), Mansour and Samuel
(1957), Gabert et al. (1961), Rodis and Iskander (1963), and HUNTING
Technical services Ltd. (1964).
El Ageed (1974) worked with much emphasis on iron ore mineralization.
El Ageed and El Rabaa (1981) published an account of the geology and structural
evolution of the area. The German Federal Institute for Geosciences and Natural
Resources BGR, and the Sudanese Geological and Mineral Resources Department
GMRD (1981) jointly surveyed and discovered uranium and chromite in the area.
Concurrently, the University of Khartoum started conducting research in the
northeastern Nuba Mountains.
Vail (1973, 1978) produced a valuable geological map, including a
description of major stratigraphic units. The geology of the area was further
investigated by Shaddad et al. (1979), Badr El Din (1982), and Kropachev et al.
(1982). The first absolute age determination was done by Batyrmurzaev et al.
(1982) and Harris et al. (1984). The presence of ophiolite in the Nuba Mountains
was independently reported by both Vail (1983, 1984, and 1985) and Hirdes
(1983).
5
The Nuba Mountains have been covered by airborne magnetic and
radiometric surveys, in addition to ground geophysics, comprising spontaneous
potential, magnetic, electromagnetic, electrical resistivity, and radiometry
measurements. The area has also been mapped geochemically at some locations
of interest. These data are in unpublished reports of the Geological Research
Authority of Sudan GRAS. More recently, the GRAS carried out mineral
prospecting in the area as part of its campaign to encourage private companies to
invest in the mineral sector.
1.5. Objectives of the present study
The aim of this study is to use remote sensing for gold exploration in the
Sudan by establishing a relationship between known disseminated gold deposits
in the Red Sea Hills and similar rock occurrences in the Nuba Mountains. Remote
sensing images have been widely and successfully used for mineral exploration
for decades. Although gold can not be detected directly by any remote sensing
method, the presence of minerals such as iron oxides and clay minerals, whose
diagnostic spectral signatures, (in the visible/shortwave infrared portion of the
electromagnetic spectrum) could be used as indicators for identification of
hydrothermal alteration zones, which are associated with gold occurrences.
This research aims to answer the following question: how useful is
multispectral (LANDSAT TM and ASTER) datasets in identifying and mapping
hydrothermal alteration associated with gold mineralization in the Nuba
Mountains, Sudan?
6
CHAPTER TWO
GEOLOGLCAL SETTING
2.1. Regional geology
Sudan constitutes part of the East African orogenic belt, which comprises
the Arabian-Nubian Shield (ANS) in the north and the Mozambique belt in the
south (Stern, 1994). This Neoproterozoic crust resulted from the collision between
east and west Gondwana and extends to several countries (e.g., Ethiopia, Eritrea,
Egypt, Somalia, Saudi Arabia, and Yemen). The ANS is characterized by the
following:
i. Occurrence of arc assemblages associated with ophiolites and
granitoids.
ii. Rejuvenated older crustal terranes.
iii. Accumulation of sediments and /or volcanic rocks in aulacogens or
tectonic basins, which subsequently were metamorphosed and
deformed.
The final accretion of the different island arcs caused strong tectonic
deformation during what is known as the Pan-African orogenic period or
cratanization that occurred in the Precambrian. Most Pan-African structures
correspond to chains that either have a northeast-southwest (left-lateral
transpression) or north-south to northeast-southwest (left-lateral transpression)
strike. There were wide spread post-orogenic granitic intrusions following the
Pan-African orogeny (Kröner 1985).
7
2.2. Geology of the study area
Geology of the Nuba Mountains is a simplified model of the geology of
the Sudan because it contains almost all main rock units found in the whole
country. The basement complex as was revealed by drilling in the adjacent oil
fields consists of metamorphosed volcano-sedimentary series of rocks into which
dismembered ophiolites were emplaced. The basement rocks in the study area
(Fig. 2) have been subdivided into the following three units (BGR, 1981).
2.2.1. Volcano-sedimentary Series.
This series is composed of mafic to intermediate metavolcanic rocks
(mainly chlorite schist), intercalated with intermediate to felsic volcaniclastics and
ferruginous cherts and marbles, as well. Overlying the volcanic rocks is a varying
sequence of metasedimentary rocks (quartz-sericite schist, graphite schist,
ferruginous cherts, calcitic and dolomitic marbles, and rare meta-conglomerate
meta-arkose). The sequence is overlain by felsic metamorphic rocks.
2.2.2. Ophiolite Series
The ophiolites in this area form a NNE-SSW trending discontinuous belt,
which extends for over 70 km from the Balula area in the north to Kabus in the
south. These rocks were encountered at Jebel Fazari (talc), and Jebel Nugara
8
where glaucophane schist pebbles were found, suggesting their relation to
subduction (Hirdes et al.1983). These rock types are the westernmost of the
identified ophiolites on the Arabian-Nubian shield.
9
Figure 2: Geological Map of the study area
(After the German Federal Institute for Geosciences and Natural Resources BGR, 1986)
10
2.2.3. Non-Ophiolite Igneous Rocks
The metamorphic events of the crystalline gneiss were followed by a
significant mafic igneous phase, as evidenced by the exposure of several scattered
metagabbros, which are probably not related to the ophiolites because they differ
in fabric and geochemistry. The analyses conducted by the reconnaissance team
(BGR) indicated that ophiolitic gabbros contain an average of 47% SiO2, 16%
Al2O3, and 11% Fe2O3, where as the non-ophiolitic gabbros contain 52% SiO2, 8-
10% Al2O3, 7% Fe2O3. In both cases, MgO values range between 7 and 12%. The
Ni contents are similar, whereas Cr is three times higher in the Tafoni samples
(non ophiolite). There are granitic and syenitic intrusive masses penetrating the
basement gneisses and schist, some forming ring structures. Also, swarms of
dikes have been recognized in the study area consisting of basic and brecciated
dikes.
i. Basic dikes.
These are partly related to granitic-syenitic complex, usually thin, and they
persist for long distances (kilometers) and are mainly of fine-grained
feldspars.
ii. Brecciated felsic dikes.
These dikes are associated with quartz vein and gold mineralization in
some parts of the area. The dikes have been observed in the chains of
inselbergs between El Tarter and El Biteira. The quartz dikes are always
brecciated, with the fragments being cemented by limonite and manganese
11
oxides/hydroxides (Brinkmann, 1982). The dikes follow the strike of the
country rocks, commonly occurring at tectonic contacts and are closely
associated with meta-sediments and ferruginous meta-cherts.
Consequently, they are interpreted as being of hydrothermal origin caused
by tectonic fracturing and recrystallization (Steiner, 1985), and
assimilation of cherty sediments by intruding magma (Hirdes, 1983)
2.2.4. Post-Basement complex Formation
The tectonic events affecting the basement complex were followed by a
period of erosion and deposition of sedimentary cover, which include the Nawa
series, the Nubian Sandstone, and the Umm Ruwaba formations.
2.3. Geologic Structural setting
The structural setting of the Nuba Mountains has not yet been fully
resolved. Vail (1978) proposed two phases of folding whereas both El Ageed
(1974) and Khalil (1979) proposed three phases of deformation affecting the
basement rocks in this area.
The most recognizable feature in the study area is the domal-shaped
structure defined by the gneissic layering with rocks on its southern part
exhibiting lower metamorphic grade than those on the western part. The
longitudinal axis of the dome is an important tectonic divide in that all schist to
the west and northwest verge in the opposite direction. The dome is surrounded
by ferruginous quartzite on the flanks, which together with metasedimentary
rocks (mica-schist, sericite schist, amphibolite schist, and marble) form linear
12
zones aligned in NE-SW direction, between which belts of ophiolites are found
oriented in the same direction (Fig. 2).
2.4. Gold Mineralization
Gold exploration in the Sudan has been known since Pharaonic times
(3,000 years) as indicated by the existence of grinding mills, slag piles, tailings,
excavations, and village ruins. The ancient miners utilized visible gold in quartz
veins in areas, which are considered a precious heritage for modern prospecting.
In Sudan, known gold occurrences (Fig. 3) are located in six greenstone belts
namely:
1 The Red Sea Hills area in the north east.
2 The Nubian Desert east of the River Nile and south of the border with
Egypt.
3 The Hofrat En Nahas area in the west (Darfur region).
4 The Nuba Mountains area west of the White Nile.
5 The Blue Nile area in the south east.
6 The Equatoria region of south Sudan close to the border with Uganda.
The most important gold deposits of Sudan are located in Ariab Mineral
District (AMD), of the Red Sea Hills area. The lithological association of gold in
13
this area has been linked to presence of mafic-ultramafic sequences and
associated volcano-sedimentary rocks (GRAS, 1995). Gold mineralization in the
region occurs in different geological environments and at least three types are
present (Aloub and Elsamani, 1991):
(1) Pre-metamorphic mineralization associated with stratiform massive
sulphides deposits – This type of gold occurrence is syngenetic and predates the
regional metamorphism that affected the gneissic and volcano-sedimentary
formations throughout the Red Sea Hills.
(2) Gold mineralization related to the regional tectono-metamorphic
episodes – The occurrences of this type are lithologically controlled and assigned
to particular gold-bearing horizons. Two varieties have been categorized: (a) gold
collapse breccias, which occur in the form of auriferous units that are essentially
composed of silica, barite, and iron oxides. This type was first recognized in the
Ariab area and is referred to as silica barite rocks (Cottard et al., 1986b); (b) the
second category is that of gold in recrystallized host rocks close to sulfide
minerals.
(3) Gold mineralization related to shear zones – This type of
mineralization is dominant in the region and is exclusively in the form of
auriferous quartz veins which are always located along fracture zones and their
emplacement appears to be structurally controlled.
These types of gold mineralization mentioned above constitute the basis
for the exploration approach used in the present study.
14
Figure 3: Location of gold occurrences in the Sudan (after GRAS, 1990)
15
CHAPTER THREE
METHODOLOGIES
3.1 Remote sensing data and software
In this study, remotely sensed multispectral datasets were processed
comprising LANDSAT Enhanced Thematic Mapping (ETM+) and Advanced
Space borne Thermal Emission and Reflection Radiometer (ASTER) level 1 B
images. The capture dates are very important considering vegetation and cloud
cover when working in Sub Saharan areas like the Nuba Mountains (rainy season
is April to October), while it is of less importance in the northern arid other parts
of the Sudan. The LANDSAT TM image with the path 174, raw 52 was acquired
on November 27, 1999; and the ASTER image on June 23rd, 2006. Other datasets
include a geological map at a scale of 1:100,000 (BGR, 1986).
The ETM+ images are of LANDSAT 7 that contains a total of 8 bands; 6
in the visible and Near-Infrared (VNIR), 1 in the Thermal Infrared (TIR), region
of the electromagnetic spectrum, and 1 panchromatic channel (band 8). Spatial
resolution is 15 m for the panchromatic band, 30 m for VNIR bands, and 60 m for
the TIR bands.
The ASTER image has 14 bands; 3 bands in the visible/Near-Infrared
bands (VNIR), 6 bands in the Short Wave Infrared (SWIR), and 5 bands in the
Thermal Infrared (TIR), with a spatial resolution of 15 m for VNIR, 30 m for the
SWIR; and 90 m for TIR bands. Figure 5 shows distribution of LANDSAT TM
16
bands relative to ASTER bands (Kalinowski and Oliver, 2004). A complete
description of LANDSAT TM is illustrated in Table 1.
Digital processing of these multispectral images has been achieved by the
use of ER Mapper software version 7.0, which is a complete digital processing
program capable of carrying out preprocessing, enhancement, transformation, and
classification of remote sensing images in order to extract spatial and spectral
information that are related to geology, such as lithology, hydrothermal alteration,
and structure.
Figure 4: LANDSAT TM and ASTER bands (After Kalinowski and Oliver, 2004)
17
Table1: LANDSAT TM bands and their application
Bands Wavelength Application
TM1 0.45-0.52 (blue) Coastal water mapping/vegetation discrimination. Forest
classification, man-made feature identification
TM2 0.52-0.60 (green) Vegetation discrimination and health monitoring, man-made
feature identification
TM3 0.63-0.69 (red) Plant species identification, man-made feature identification
TM4 0.76-0.90 (near IR) Soil moisture monitoring, vegetation monitoring, water body
discrimination
TM5 1.55-1.75 (mid IR) Vegetation moisture content monitoring
TM6 10.4-12.5 (thermal IR) Surface temperature, vegetation stress monitoring, soil
moisture monitoring, cloud differentiation, volcanic
monitoring
TM7 2.08-2.35 (mid IR) Mineral and rock discrimination, vegetation moisture content
3.2 Processing techniques
Image processing methods are designed to transform multispectral image
data format into an image display that either increases contrast between
interesting targets and the background or yields information about the
composition of certain pixels in the image. The enhancements techniques, which
have been applied in this study, included color composite, band ratioing, principal
components analysis (PCA), and spatial filtering. The approach was to utilize
known targets in the study area and others in the Red Sea Hills as training sets on
the basis of which unknown targets in the study area of the Nuba Mountains could
be identified.
18
3.2.1 Color Composite Images
Satellite images for a given scene are captured in black and white bands.
The first three LANDSAT bands (1, 2, and 3) correspond to the blue, green, and
red portions of the visible spectrum, which are captured as gray-scale images,
together with ETM+ bands 4, 5, 6, and 7, also.
A composite image is generated by blending information from two or
more bands. When an image contains bands corresponding to the red, green, and
blue portion of the visible spectrum, then it is called a natural or true color
composite image (http://calview.casil.ucdavis.edu). When an image is created
from a combination of one or more non-visible EM spectra or non-true color
composite images, then it is known as a false color composite (FCC) image.
It is possible to create a color composite image by blending visible and
infrared bands or by using infrared bands only. Interpretation of FCC images
depends on the manner in which the bands are assigned to the three principal
colors used for the image display. The production of color composite images is
based on known spectral properties of rocks and alteration minerals in relation to
the selected spectral bands. For instance, LANDSAT TM band 7 is used primarily
for mineral and rock discrimination, whereas bands 4 and 5 are primarily used for
vegetation monitoring as shown in Table1 (http://LANDSAT.gsfc.nasa.gov).
Spectral analysis exploits spectral properties of rocks in order to interpret
lithological variations or rock alterations that are expressed as variations in color
intensity values within color composite images. A display of FCC images in a
19
color scheme that is well balanced among the display colors is known as contrast-
enhanced False Color Composite, which is meant to provide better overview of
the area of investigation. Contrast enhancement of images is a widely used
procedure by which brightness values of an image (0-255) are stretched such that
light-toned areas appear lighter and dark areas appear darker, making visual
interpretation easier.
Several FCC images of the study area have been generated by
combination of various bands displayed as red, green, and blue RGB, using both
LANDSAT TM and ASTER images.
i. LANDSAT TM Images:
LANDSAT TM band combinations have been used to extract lithology;
however, obtaining an optimum band combination that will give the best results is
difficult in most cases. Rent et al. (2001) introduced a statistical approach known
as Optimum Index Factor (OIF) that rank all possible 3-band combination based
on total variance and correlation coefficient between bands.
A color composite in RGB image with high standard deviation and less
redundancy is chosen based on OIF calculation. The best combination for
lithological mapping is to be selected from the highest ranking OIF calculated
values.
20
ii. ASTER Images
For Aster, there are a total of 364 color combinations derived from its 14
bands. The best 3-band combinations, based on top ranking OIF, which was used
for lithological discrimination, are selected by a process similar to that mentioned
above in the case of LANDSAT TM.
3.2.2 Band Ratios (BR)
Band ratioing is a technique used in remote sensing to effectively display
spectral variations (Vincent and Thompson, 1972; Goetz et al. 1975). Ratio
images enhance the contrast between materials by dividing the brightness values
(digital numbers, DN) at peaks/maxima and troughs/minima in a reflectance
curve, after additive atmospheric haze and additive sensor offset have been
removed. Spectral band rationing enhances compositional information while
suppressing other types of information about earth’s surface, such as terrain slope
and grain size differences (Vincent, 1997).
There are many types of band ratios in use, depending on the purpose of
their application (e.g., lithology or alteration discrimination). Similarly, the choice
of bands depends on their spectral reflectance and positions of the absorption
bands of the mineral being mapped. For instance, to enhance a specific alteration
mineral that hosts a distinct absorption feature, the most unique spectral ratio for
that mineral is employed. An example is the LANDSAT ratio B5/B7 that is
needed to enhance AlOH minerals relative to the others (Fig.4). The greater
utility of the spectral bands radioing can be obtained when applying bands from
21
inside and outside a wavelength region of spectral reflectance maximum or
minimum of a particular target (Vincent, 1997). Spectral ratios of spectral bands
that are separated widely in wavelength can also be helpful when the mineral of
interest has a relatively unique overall shape of the reflectance spectrum,
involving both visible and shortwave infrared bands.
For iron minerals (e.g., gossans) the reflectance in LANDSAT TM band 1
(0.45-0.52 µm) is weaker in comparison to band 3 (0.63-0.69 µm) which is
stronger, so the band ratio 3/1 is suitable for discriminating iron oxides (Fig. 5).
Likewise, clay minerals such as kaolinite, montmorillonite, and alunite exhibit
low reflectance in band 7 (2.08-2.35 µm) and high reflectance in band 5 (1.55-
1.75 µm); hence, the band ratio 5/7 would have a characteristic bright signature
for clay alteration zones (Fig. 6 from Clark et al., 1993). The band ratio 4/5 has
much emphasis on silicate minerals in comparison to FeO-rich minerals (Abrams
et al. 1983).
22
Figure 5: Reflectance spectra of the iron oxide (hematite) and iron hydroxide (goethite)
(from Clark et al., 1993).
23
Figure 6: Reflectance spectra of kaolinite (1.5-6.0 μm plot)
i. LANDSAT TM Images:
A method developed by Vincent (1997) for detecting brightness codes and
ratio codes for the LANDSAT TM was applied in this study. The method helps in
finding the spectral ratios that display the most unique characteristics of a given
mineral by separating them into deciles of the ratio values and assigning them
codes of 9 for the highest decile (Perry and Vincent, 2009). The target mineral can
be made to appear red in a composite image by displaying its highest ratio code
(9) as red and other two low ratio codes of (0) as green and blue, respectively. A
set of brightness and ratio codes for ASTER data, (Perry and Vincent, 2009) are
displayed in the Appendix.
24
Using the six VNIR and SWIR LANDSAT bands, a total of 15 possible
non-reciprocal spectral ratios can be produced. The ratio of the highest (ratio code
of 9) spectral ratio will be among the highest 10% of all minerals in the reference
library, whereas those with ratio codes of (0) will be among the darkest 10% of
minerals in the reference Library (Vincent, 1997).
ii. ASTER Images:
For Aster, there are a total of 46 reciprocal spectral ratio of which 36 are
produced from the reflectance in the its nine VNIR and SWIR bands, in addition
to 10 spectral ratios that are produced from the emittance in the five TIR bands.
Thermal bands are usually not mixed with VNIR and SWIR bands in a single
ratio or composite bands when mapping chemical composition because the TIR
bands are measuring emission (temperature) of the Earth’s surface, whereas the
VNIR and SWIR bands are measuring reflectance of sunlight off the Earth’s
surface
3.2.3 Principal Components Analysis (PCA)
Principal components transformation is an image enhancement technique
for displaying the maximum contrast from several spectral bands with just three
primary display colors, (Vincent, 1997). Multispectral images often have similar
visual appearance for different bands, thus causing data redundancy (high
correlation of spectral bands). PCA is a multivariate statistical technique used to
reduce this data redundancy by transforming the original data onto new
orthogonal principal component axes producing an uncorrelated image, which has
much higher contrast than the original bands.
25
The produced number of output PC bands is the same as the number of the
input spectral bands. The first principal component (PC1) is a vector in the
direction of the maximum variance of pixels in the scene. PC1 contains most of
the data variability, dominated by brightness differences caused by variation of
surface topographic slope directions, with respect to the sun position, and often
displays important structural information. The second principal component (PC2)
contains the second most variability, and is orthogonal to PC1 in n directional
space. It emphasizes the spectral difference between the visible and the Infrared
spectra. The third PC contains the third most variability and is orthogonal to the
other two PCs, and so forth. The highest PC component contains all of the
remaining variance and separates the most spectrally unique pixels (objects) from
the rest of the pixels in the scene. Each subsequent PC removes the maximum
amount of variance, which becomes smaller as the order of the PC increases
(Vincent, 1997). The last PC bands contain less variance and so often appear
noisy.
Any three principal components can be blended into a color composite
image that contains information from all n bands, a characteristic that might help
in displaying boundaries between terrain units, which would not be revealed by a
color composite of any three single-band images. Also, PC color composite
images appear more colorful than the respective spectral color composite images,
due to their uncorrelated components.
This study utilized a technique described by Crosta and Moore (1987) that
is based on the examination of PCA eigenvector to determine which PC images
26
concentrate information directly related to the theoretical spectral signatures (such
as those in Fig.5) of specific targets. The relevant PC images could then show
targeted surface types (rock, soil, and vegetation) by highlighting them as bright
or dark pixels, depending on their respective Eigenvector magnitudes and signs
(positive/negative). The Crosta technique could be implemented to delineate
alteration zones (Loughlin, 1991).
3.3 Spatial Filtering
Spatial filtering is useful for extraction of oriented features, such as
geologic lineaments. These filters enhance visual interpretation of remotely
sensed lineament maps. Spatial filters are designed to highlight or suppress
features in images based on their spatial frequency, for instance rapid variations in
brightness level indicate high frequency “roughness”, whereas low spatial
frequency is expressed by “smoothness” which is characteristically a low
brightness level (www.gisdevelopment.net)
There are various filters in use depending on whether it is desirable to
retain low or high frequency features. In remote sensing, directional filters, which
are also known as edge direction filters, are usually utilized to enhance linear
features, such as faults, streams, roads, and other linear features that are oriented
in specific directions.
27
CHAPTER FOUR
RESULTS AND INTERPRETATIONS
4.1. General
The results, which have been derived from digital image processing of remotely
sensed datasets of the study area, are presented in this chapter with the aim of extracting
lithological, structural, and hydrothermal alteration information that might be utilized in
locating mineral deposits associated with gold occurrences in the area of investigation.
Several types of image processing have been employed in this research, which are each
described below in their respective subsections.
4.2 Single Band Combinations
The best combinations of single spectral bands for lithologic discrimination were
determined for both LANDSAT and ASTER datasets using the following algorithm for
calculating OIF.
OIF = Σ si / Σ I rj I (Chavez et al., 1982)
Where, si is the standard deviation for band k, rj is the correlation coefficient between any
two of the three bands being evaluated.
Any three band combination with high total variance within bands and low
correlation coefficient between bands will have high OIF value. In general, the largest
OIF will contain most of the image information with the least amount of duplication
(redundancy).
28
The OIF calculates the optimum 3-band combinations, but the assignment of the
colors (RGB) is the analyst discretion based on image enhancement favorable for
geologic mapping of a specific area of investigation. Arrangement of bands in any color
combination as RGB has no effect on the value of OIF. Table 2 shows OIF results
obtained from the statistics of LANDSAT TM bands.
Table 2: OIF values for LANDSAT TM band composites.
Rank BCC OIF Spectrum region Rank FCC OIF Spectrum region
1 1-5-7 42.05 VIS-SWIR-SWIR 11 3-5-7 14.95 VIS-SWIR-SWIR
2 1-4-5 34.72 VIS-NIR-SWIR 12 2-5-7 14.69 VIS-SWIR-SWIR
3 1-3-5 27.61 VIS-VIS-SWIR 13 3-4-5 12.94 VIS-NIR-SWIR
4 1-2-5 26.45 VIS-VIS-SWIR 14 3-4-7 12.81 VIS-NIR-SWIR
5 1-4-7 22.35 VIS-NIR-SWIR 15 2-4-5 12.70 VIS-NIR-SWIR
6 1-3-7 18.88 VIS-VIS-SWIR 16 2-4-7 12.32 VIS-NIR-SWIR
7 4-5-7 17.68 NIR-SWIR-SWIR 17 1-2-3 11.49 VIS-VIS-VIS
8 1-2-7 17.23 VIS-VIS-SWIR 18 2-3-7 10.04 VIS-VIS-SWIR
9 1-3-4 16.70 VIS-VIS-NIR 19 2-3-5 9.82 VIS-VIS-SWIR
10 1-2-4 15.35 VIS-VIS-NIR 20 2-3-4 8.83 VIS-VIS-NIR
The results (Table 2) show a total of 20 band combination comprising different
electromagnetic spectrum regions (e.g., visible (VIS), near infrared (NIR), and shortwave
infrared (SWIR). In Table 2, there are 6 band combinations with VIS+NIR+SWIR, 6
with VIS+VIS+SWIR, 3 with VIS+SWIR+SWIR, 3 with VIS+VIS+NIR, 1 with
NIR+SWIR+SWIR, and 1VIS+VIS+VIS (true composite).
29
The most informative band combination should include one visible (1, 2, and 3),
one NIR (4), and one SWIR (5 and 7). Based on this concept of electromagnetic
spectrum regions, the best selected band combinations are: 1-4-5, 1-4-7, 3-4-5, 3-4-7, 2-
4-5, and 2-4-7.
-0.40
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1 2 3 4 5 6
LANDSAT TM Bands
Ref
lect
ance
Band 1Band 2Band 3Band 4Band 5Band 7
Figure 7: Correlation between LANDSAT TM bands, Nuba Mountains
Band correlation shown in Figure 7 indicates that bands 1 and 5 are less correlated
with the rest of the other LANDSAT TM bands; hence, any combination that includes
these bands will have higher OIF value, or less redundancy, and so contains most spectral
information about the mapped surfaces.
For the ASTER dataset, similar procedures have been applied to its 9 bands
(VNIR and SWIR) with the aim of obtaining OIF values, which will be used in ranking
the appropriate band color combinations as listed in Table 3.
30
Table 3: OIF values for ASTER band composites.
Rank BCC OIF Rank BCC OIF Rank BCC OIF Rank BCC OIF
1 1,2,7 22.92 22 2,5,7 21.27 43 1,6,9 20.27 64 4,6,7 18.75
2 2,4,8 22.57 23 1,6,7 21.18 44 2,3,9 20.15 65 3,6,9 18.70
3 1,2,8 22.38 24 2,5,6 21.17 45 1,3,6 20.12 66 4,5,8 18.62
4 2,4,7 22.22 25 1,4,5 21.16 46 3,7,8 20.02 67 1,2,3 18.43
5 1,4,8 22.20 26 2,3,7 21.07 47 3,6,8 19.93 68 4,5,6 18.27
6 2,4,6 21.93 27 1,5,8 21.05 48 3,4,7 19.91 69 4,5,7 18.27
7 2,7,8 21.87 28 1,4,9 20.93 49 1,3,4 19.89 70 4,8,9 18.25
8 1,2,4 21.86 29 1,3,8 20.83 50 1,3,5 19.87 71 3,5,9 18.22
9 2,6,8 21.84 30 2,7,9 20.79 51 1,5,9 19.78 72 4,6,9 18.03
10 1,4,7 21.84 31 2,8,9 20.79 52 3,4,6 19.60 73 6,7,8 17.99
11 1,2,6 21.81 32 1,5,7 20.78 53 3,6,7 19.59 74 4,7,9 17.97
12 1,2,5 21.68 33 2,6,9 20.78 54 1,3,9 19.53 75 5,6,8 17.69
13 2,4,5 21.64 34 1,5,6 20.67 55 3,5,8 19.53 76 5,7,8 17.51
14 2,6,7 21.57 35 2,3,6 20.63 56 3,4,5 19.21 77 4,5,9 17.48
15 1,4,6 21.55 36 1,3,7 20.55 57 3,5,7 19.19 78 5,6,7 17.42
16 2,5,8 21.54 37 2,3,5 20.48 58 4,6,8 19.11 79 6,8,9 17.21
17 1,7,8 21.47 38 1,8,9 20.47 59 4,7,8 19.05 80 6,7,9 17.00
18 1,6,8 21.45 39 2,3,4 20.39 60 3,8,9 19.04 81 7,8,9 16.97
19 2,4,9 21.42 40 2,5,9 20.38 61 3,4,9 19.04 82 5,8,9 16.68
20 2,3,8 21.34 41 3,4,8 20.33 62 3,5,6 19.04 83 5,6,9 16.65
21 1,2,9 21.28 42 1,7,9 20.28 63 3,7,9 18.78 84 5,7,9 16.47
31
Comparing bands matrix plots in Figure 8 it is obvious that the VNIR bands (1, 2,
and 3) are correlated as well as the SWIR bands (5, 6, 7, 8, and 9) with exception of band
4 which shows less redundancy. Consequently, band 4 constitutes the best band
combination for mapping target surface types.
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1 2 3 4 5 6 7 8 9
ASTER Bands
Refle
ctanc
e
Band 1Band 2Band 3Band 4Band 5Band 6Band 7Band 8Band 9
Figure 8: Correlation between ASTER bands, Nuba Mountains
4.3 Principal Component Analysis (PCA)
In this study, the Crosta technique (Crosta et al., 1993) has been applied for PCA,
whose important aspect is the prediction of whether the target surface type will be
highlighted by dark or bright pixels in the relevant PC image. After application of PCA
to both LANDSAT TM and ASTER datasets of the study area, the next step was
examination of eigenvector matrix that was used in calculating these PCA with the aim of
32
identifying which PC contains useful spectral information of the target. The more
spectrally unique pixels on the ground will be highlighted by the higher numbered PC’s.
4.3.1 Feature-oriented Principal Component Selection (FPCS)
This method accounts only for the bands that exhibit spectral signatures caused by
Fe and OH bearing minerals. PC transformation was applied to both LANDSAT TM and
ASTER images of the study area whose statistical analysis, eigenvalues, and eigenvector
loadings were tabulated below.
For LANDSAT TM, the bands for mapping iron oxides and clay-bearing mineral
are: (TM1, TM3, TM4, and TM5) and (TM1, TM4, TM5, and TM7), respectively. It is
possible to use only one band from the short-wave infrared (TM5 or TM7) in case of iron
oxides, whereas only one visible band (TM1, TM2, and TM3) can be selected in case of
clay-bearing minerals. FPCS for iron oxides and clay minerals have been determined as
shown in Tables 4 and 5, respectively.
To obtain spectral information about iron-oxides and hydroxyl-bearing minerals,
the remaining components have been examined in relation to the theoretical signatures
(absorption and reflectance bands) of those specific targets. As illustrated (Table 4) by
the loadings of bands TM1 and TM3 on PC3, which are positive (reflectance) and
negative (absorption) respectively, anomalous iron-oxides minerals will be represented
by dark pixels in PC3. On the other hand, hydroxyl-bearing minerals will be highlighted
as dark pixels in both PC2 and PC3 images because the eigenvector loadings of TM5 and
TM7 are negative (absorption) and positive (reflectance) respectively. The enhancement
33
to both iron oxides and the hydroxyl-bearing minerals were obtained after their respective
PCs were made negative (DN multiplied by -1), such that these alteration minerals would
be mapped in brighter tones (Figs. 9 and 10).
Table 4: FPCS for iron oxide minerals using LANDSAT TM bands (1, 3, 4, and 7).
Input Band Band 1 Band 3 Band 4 Band 7
Band mean 173.28 47.49 58.10 53.63
SD of Band 6.78 7.15 13.13 14.97
Eigenvector Matrix
Eigenvalues (%)
PC 1 0.122 0.331 0.616 0.704 86.61
PC 2 0.992 -0.042 -0.057 -0.103 8.14
PC 3 0.022 -0.317 -0.637 0.702 4.75
PC 4 -0.010 -0.888 0.460 0.016 0.50
34
Figure 9: LANDSAT TM image showing FPCS for ferric oxides minerals (PC3) as bright pixels.
35
Table 5: FPCS for clay minerals using LANDSAT TM bands (1, 4, 5, and 7).
Input Band Band 1 Band 4 Band 5 Band 7
Band mean 173.28 58.10 67.23 53.63
SD of Band 6.78 13.13 10.64 14.97
Eigenvector Matrix
Eigenvalues (%)
PC 1 0.095 0.610 0.381 0.688 78.41
PC 2 0.584 0.048 -0.754 0.295 14.14
PC 3 -0.668 -0.351 -0.314 0.576 5.04
PC 4 -0.451 0.709 -0.433 -0.327 2.41
36
Figure 10: LANDSAT TM image showing FPCS for clay minerals (PC3) as bright pixels.
The procedure for analyzing the principal components for both LANDSAT and
ASTER are the same with the exception of the greater availability of spectral information
in the shortwave infrared (SWIR) region of the electromagnetic spectrum in case of
ASTER due to the increase in its SWIR bands in comparison to those of LANDSAT.
This advantage enables ASTER to characterize surface materials in detail.
PCA was also applied to ASTER bands that enhance iron alteration (bands 1, 3, 4,
and 7) as shown in Table 6. It can be observed from computed statistics of the ASTER
37
dataset that PC1 has the highest eigenvalue (87.90%) most of which is contributed by
band 4.
PC3 enhances ferric oxide minerals because there is reflectance in band 3 (0.619)
and absorption in band 1 (-0.522). Also the highest eigenvector value (0.633) in PC4 is
shown by band 4, which is the ASTER band that covers the spectral portion of strong
hydrothermal alteration reflectance.
Table 6: FPCS for iron oxide minerals using ASTER bands (1, 3, 4, and 7).
Input Band Band 1 Band 3 Band 4 Band 7
Band mean 132.53 92.74 100.31 88.21
SD of Band 17.21 14.65 19.26 17.47
Eigenvector Matrix
Eigenvalues (%)
PC 1 0.480 0.423 0.579 0.505 87.90
PC 2 -0.663 -0.376 0.352 0.543 9.03
PC 3 -0.522 0.619 0.374 -0.452 2.29
PC 4 0.238 -0.544 0.633 -0.497 0.70
38
Figure 11: ASTER image showing FPCS for ferric oxides minerals (PC3) as bright pixels.
For mapping clay minerals with FPCS method, it is appropriate to use the
diagnostic features of AlOH-bearing minerals (kaolinite, montmorillonite, and illite) in
addition to buddingtonite and alunite. The major absorption by these minerals occurs
within bands 5 and 6. The statistics listed in Table 7 indicates high loading of eigenvector
on PC4 is contributed by band 6. Since the value is negative (-0.796) then it means
absorption in bands 6 and reflectance in band 7; hence, PC4 enhances rocks containing
AlOH (clay) minerals as observed in Figure 12.
39
Table 7: FPCS for clay minerals using ASTER bands (1, 4, 6, and 7).
Input Band Band 1 Band 4 Band 6 Band 7
Band mean 132.53 100.31 93.95 88.21
SD of Band 17.21 19.26 17.84 17.47
Eigenvector Matrix
Eigenvalues (%)
PC 1 0.428 0.554 0.514 0.497 90.60
PC 2 -0.894 0.146 0.277 0.321 7.96
PC 3 -0.134 0.781 -0.161 -0.588 1.31
PC 4 -0.008 0.250 -0.796 0.551 0.13
40
Figure 12: ASTER image showing FPCS for clay minerals (PC4) as bright pixels.
4.4 Band Ratioing
Ratio images used in this study were prepared by applying the ratio codes and
spectral ratioing method introduced by Vincent (1997). The method enabled extraction of
spectral information from LANDSAT TM and ASTER datasets. The tables in Appendix
were utilized in producing images of hydrothermal alteration minerals (hematite,
goethite, kaolinite, alunite, buddingtonite, muscovite, and chlorite) representing surface
expression for auriferous sulfide deposits in the Arabian-Nubian shield, in which the
study area is located.
41
4.5 Mapping lithologic units
Remote sensing techniques for mapping lithologies are based on differences in
spectral reflectance of dominant rock types in the area of investigation. Methods used
include band combination, band ratioing, and principal component analysis. Color
composite images displayed as red, green, and blue (RGB), respectively, show rocks of
similar composition in colors that tend to have same resemblance.
In this research, before lithologic discrimination was performed, the contributions
to the area scene by vegetation cover were identified first using LANDSAT TM band
combinations 4-3-2 and ASTER bands 3-2-1 in RGB colors, which revealed vegetation
as red features mostly along streams (e.g. Khor Tandik in the southern part of the study
area trending NW) (Figs. 13 and 14). These images were dated as November 27th, 1999
and June 23rd, 2006 for both LANDSAT TM and ASTER, respectively.
Figure 13: Vegetation cover appears red in LANDSAT TM image 4-3-2 (RGB).
42
Figure 14: ASTER bands 3-2-1 (RGB) showing vegetation cover as red, specially the Mango orchards along Khor Tandik.
Examination of the true color composite image of the area (Fig. 15) displays
geologic features as it would be visualized by the human eye.
43
Figure 15: LANDSAT TM bands 3-2-1 (RGB) which is a true color composite image, Nuba Mountains.
A better contrast in images of the study area has been obtained by application of
OIF as shown in the LANDSAT TM color composite image of bands 6-4-1 displayed as
RGB in Figure 16.
44
Figure 16: LANDSAT TM 6 - 4 -1 (RGB) False Color Composite FCC image of the study area.
Visual analysis of Figure 16 shows vegetation in light green, granitoids in
variations of red/pink, ultra-mafic rocks in purple, quartz-chlorite schist in dark green and
graphitic schist in greenish-blue color with its soils in a deep blue color, seen along
drainage patterns and around Kurun Mountain in the SE, representing weathered and
eroded graphitic rocks being scattered by run-off. Also some geologic features, such as
the domal shaped structure and folds in the north eastern part of the image, appear clearer
than in the true composite image (Fig. 15).
45
The LANDSAT TM 4-5-1 FCC image (Fig. 17) clearly discriminates lithologies
and defines drainage patterns as well as linear structure e.g. in the northwestern part of
the area, where numerous faults nearly oriented in E-W directions exist.
Figure 17: LANDSAT TM 4-5-1 (RGB) FCC image clearly discriminates lithologies and defines drainage patterns as well.
In Figure 18, LANDSAT TM band 4 which is sensitive to vegetation reveals
plants as red. The image shows ultramafic rocks in dark green, granitoids as brown,
46
weathered graphitic schist soils as blue, quartz-chlorite and graphitic schist as variations
of brown colors, quartzite and marbles are very small in sizes to be represented at this
scale, except the folded feature at El Biteira south of the domal feature in the north east.
The ASTER FCC image of bands 7-3-2 RGB (Fig.18) clearly maps rock units of the area
with a better resolution than the corresponding LANDSAT TM image.
Figure 18: ASTER FCC image for bands 7-3-2 (RGB), Nuba Mountains. The image reveals vegetation in light green, granitoids in pink, the ultramafic rocks in a variety of brown colors, the graphitic schist in grayish or navy blue, and the quartz-mica-chlorite schist in a variety of dark green colors.
47
Figure 19: PC3 of ASTER bands 1-9, showing Iron oxides rocks as brighter pixels.
The PC image (Fig.19) represents the third component PC3 of ASTER 6 bands
(1-9) in which band 3 has a higher (positive) eigenvector loading while band 1 has a
lower (negative) eigenvector value; hence, this image highlights iron oxides rocks as
bright pixels.
48
Band ratios have been utilized in lithologic mapping of the study area, since their
images are characteristically stable with respect to illumination differences. Figure 20
comprises a combination of bands ratios R (3, 1), R (5, 4), and R (5, 7) in RGB. It can be
observed that ultramafic rocks are shown in light green color, ferric-iron-bearing rocks as
reddish brown, and rocks containing clay minerals appear in variations of yellowish
green colors.
Figure 20: LANDSAT TM image of band ratios R (3, 1), R (5, 4), and R (5, 7) as RGB.
49
In the case of ASTER band ratios, the image in Figure 21 indicates mapping of
ultra-mafic rocks in yellowish-green color, the schist group in dark green with their
weathered soils in blue, granitoids in purple, and vegetation in bright red colors. The
cyan color spotted in different parts of the image indicates mixed spectral features
contributed by rocks (e.g., granites) with minerals such as biotite and feldspars, which
can be altered into iron and clay bearing products.
Figure 21: ASTER image of band ratios R (4, 7), R (4, 3), and R (2, 1) as RGB
50
4.6 Mapping hydrothermal alteration
The conceptual exploration model developed for the Arabian-Nubian shield
(ANS) geologic region, such as the study area indicates association of gold deposits with
gossans (ferric-oxides) and volcanogenic massive sulphides (VMS). Alteration zones are
mostly broader than the targeted ore body, and so are useful in determining deposit
locations. The main alteration types which are related to gold occurrences are comprised
of, among others sericitization, oxidation, silicification, and carbonitization, and
ammoniation. Alteration products of these processes include mineral species, such as
muscovite, kaolinite, chlorite, alunite, buddingtonite, and ferric oxides (gossans) and
hydroxides. Mapping of these alteration minerals has been facilitated by the application
of the “ratio codes technique” (Vincent, 1997) and the common ASTER bands ratios
(Kalinowski et al., 2004).
Discrimination of gossans in this area was performed using ASTER band
combination 6-2-1 (RGB), representing host rock, alteration, and gossans, respectively,
(Kalinowski et al., 2004) as illustrated in Figure 22. Goethite which is shown in red color
(Fig. 23) was mapped using the ratio codes 9, 4, and 4 (RGB) whose band ratios were R (9, 1),
R (8, 6), and R (8, 5) respectively.
51
Figure 22: ASTER band combination 6-2-1 (RGB) illustrates gossans in reddish-brown color.
52
Figure 23: Goethite mineral mapped in red color by ASTER band ratio codes 9, 4, and 4 (RGB).
In case of kaolinite, the ASTER ratio code was 8, 1, and 1 (RGB) corresponding to the
band ratios R (7, 5), R (5, 1), and R (6, 1), respectively. The kaolinite image (Fig. 24) shows
widespread distribution of the mineral in the study area due to weathering of granitic rocks,
specifically the feldspar minerals contained therein.
53
Figure 24: ASTER band ratio codes 8, 1, 1 (RGB) showing kaolinite in red color.
The ratio code for alunite was 9, 0, and 0 (RGB) comprising band ratios R (7, 6), R (4, 3),
and R (6, 3), respectively. Alunite has less spatial coverage in comparison to kaolinite as
observed in Figure 25.
54
Figure 25: Alunite is mapped in a brighter red color by ASTER band ratio codes 9, 0, and 0 (RGB).
Figure 26 shows muscovite in red color, located at the NE corner (Jebel Tertera) and
extending along the eastern periphery of the domal feature, in addition to some scattered along
the major structurally controlled stream channel (e.g., Khor Tandik) in the southern part of the
area.
55
Figure 26: Muscovite (red) mapped using ASTER band ratio R (8, 6), R (6, 4), and R (6, 5), respectively.
Chlorite which is formed due to biotite change in a process that releases Fe +2 has been
mapped (Fig. 27) utilizing the ratios code 9, 0, and 0 (RGB), for band ratios R (9, 4), R(7, 5), and
R (7, 6), respectively.
56
Figure 27: Chlorite (red) occurrence in the study area.
The ammoniated hydroxyl (buddingtonite) exhibits a strong absorption in bands 5 and 6;
hence, it has been mapped by the band ratio R (8, 6) assigned to a red color and the other two
ratios R (5, 4), and R (9, 3) are assigned to the green and blue colors, respectively (Figure 28).
The occurrence of this mineral in the area is limited (e.g., Khor Tandik) compared to the other
clay alterations.
57
Figure 28: Buddingtonite shown in red color was mapped by the ratios R (8, 6) assigned to a red, where as R (5, 4), and R (9, 3) are assigned to the green and blue colors, respectively.
4.7 Mapping geologic structures
Satellite images of the study area were processed, using edge filters for the
purpose of extracting geologic (faults, folds, and fracture zones) and topographic (ridges
and drainage segments) lineaments. Mapping geologic lineaments is important for
mineral exploration because of their potentials for harboring ore bodies that are carried
and deposited by ascending hydrothermal fluids.
In this study, a Digital Elevation Model (DEM) image with elevation given on 90
m postings was acquired and used for delineating linear geologic and morphologic
58
features. The process was performed by an image shading algorithm in ER Mapper,
where by an azimuth increment of 15 degrees (from 0 to 180 degrees) and an inclination
angle of 30 degrees were applied in delineation of lineaments. The angle of incidence of
the illumination source is important, because it makes structures oriented perpendicular to
the source appears as recognizable linear dark tonal features. Figure 29 displays a
combination of all detected lineaments at various azimuths, overlain on an image
illuminated at a single azimuth angle of 120 degrees.
Figure 29: Prominent geologic lineaments extracted from a DEM image with an Image illumination of120 degrees azimuth and inclination at 30 degrees.
59
The False Color Composite (FCC) image in Figure 31 illustrates geologic
structures in the research area. The most prominent is the dome-shaped feature in the
NE, dominated by leucocratic gneisses. The long axis of this structure is an important
tectonic divide to the west of which all schist rock units exhibits opposite vergence
directions (GRAS, 2000). This phenomenon could be observed by the brighter
appearance of the western portion of the ASTER contrast stretched gray scale image (Fig.
32) in comparison to its eastern part.
Figure 30: ASTER FCC 1-4-5 (RGB) showing geologic structures in the vicinity of the domal structure.
60
Figure 31: Image showing area A in Figure 30 above, where folds, foliations, and shearing are clearly observed in the vicinity of the domal structure.
This domal structure (anticline) is bounded at some locations by ferruginous
quartzite rock units on the flanks, which form identifiable geologic structures, such as the
principal fold located to the south of it at El Biteira Village whose axial plane trends
NNE. Other structures are defined by narrow streaks of thinly layered volcano-
sedimentary rocks (Fig. 31).
61
Figure 32: ASTER contrast stretched gray scale image of the study area showing brighter and
darker parts of the image corresponding to opposite verging orientations.
The lineament image in Figure 30 shows prominent features trending in two
major directions NE and NW. There are other less prominent features with randomly
oriented strikes. Also, there are signs of shearing with prominent NNE trending folds and
foliations clearly observed on quartzite rocks in the vicinity of the domal (anticline)
structure shown in Figure 31.
62
The structural analysis of a total of 179 lineaments with a total length of 1487.33
km is shown in Table (8). The dominant trends of these lineaments are oriented NE-SW
(46.06 %), NW-SE (41.65 %), E-W (7.87 %), and N-S (4.42 %), respectively.
Table 8: Structural analysis of lineaments identified in the study area
Azimuth (degrees) Number of sets (%) Length (%)
0-15 5.59 4.41
15-30 48.38 8.47
30-45 17.32 17.65
45-60 9.50 9.25
60-75 5.59 4.09
75-90 9.50 6.88
90-105 7.26 7.87
105-120 4.47 3.11
120-135 15.64 20.54
135-150 3.91 5.26
150-165 6.15 4.27
165-180 6.70 8.19
The principal component image (Fig. 33) reveals a north-trending feature
truncating the known NW-SE oriented fracture zone along which the major stream in the
area (Khor Tandik) flows through Abu Jubeiha. This identified feature continues from
south to north and might be related to the regional tectonism in the area, which is
63
considered a part of the western boundary of the Arabian Nubian shield, ANS
(Abdelsalam, 1996).
Figure 33: Delineated structural features in the study area identified by PC6 of ASTER bands 4-9.
A remarkable observation is that the hydrothermal minerals sites, which have been
mapped and illustrated in the aforementioned figures, are mostly located in the vicinity of Jebel
Kurun in the SE, Jebel Tumluk , Jebel Umm Takatik, Jebel Uru, and along the flanks of the
domal feature (Fig. 2). The N-S- oriented structural feature revealed by the Principal Component
64
Analysis (Fig. 33) also constitutes an important zone, in addition to some locations in the central
part near El Biteira Village, extending in a NE-SW direction. The validity of the applied
techniques and the obtained results will be discussed later.
4.8 Validation of results
The validity of the applied remote sensing techniques can be determined by comparison
between the applied methods and effectiveness of their results with spectral and non-spectral
ground data (ground truth) in the exploration area. In the present study, there were no ground
spectral data, and the validation was performed on a geological map as well as the mineral
inventory conducted in the Nuba Mountains by BGR.
The results indicated matching of hydrothermal alteration zones with the location of
known base metals (Cu, Zn, and Ni) in the area shown in the inventory map (Fig. 34). Almost all
of the known sulfide deposits in the study area have been identified and delineated by ASTER
ratio image (Fig. 35) comprising the band ratios R(4/1), R(3/1), and R(3/5) corresponding to the
red, green, and blue colors , respectively.
65
Figure 34: Mineral inventory map, Nuba Mountains (After BGR, 1986)
66
Figure 35: Variability map showing mineral inventory locations superimposed on ASTER ratio image R (4, 1), R (3, 1), and R (3, 5) as RGB, respectively. Ferric oxides rocks (gossans) are in reddish brown color.
The same locations of alteration zones coincide well with geophysical anomalies
(magnetic, EM, and radiometric) obtained during reconnaissance surveys conducted by various
teams (BGR, 19879-1984; GRAS, 1999-2003) in the Nuba Mountains area.
67
Spatial association between known mineral deposits in the Red Sea Hills and certain
geologic features that control their occurrence helps in predicting further targets in similar
geologic environments. An example is shown by the image in Figure 36 below.
Figure 36: Discrimination mapping using ASTER band ratios R (4, 1), R (3, 1), and R (3, 5) as RGB, respectively. The red color represents gossans and the quadrangles represent gold mines in the Red Sea Hill.
The mines are situated on shear / fault zones (Fig. 37), which indicate the control of
geologic features on mineralization.
68
Figure 37: Location of gold mines in the Red Sea Hills of Sudan in relation to fault / shear zones. Ariab Mineral District, AMD is defined by red quadrangles (after AMC, 2002).
69
This study proved the effectiveness of remote sensing in mapping of geological
lithologies in the area; the results are consistent with the published geological map of the study
area shown in Figure 3.
4.9 Mapping ophiolites with ASTER (TIR) bands.
An attempt to map zones of silicification using ASTER thermal infrared bands resulted in
an image covered by stripes and no clear recognition of any rocks could be inferred. This error
was attributed to some defects in the TIR sensor seen on ASTER images acquired after 2003
including the images used in this study which were captured in 2006. It was observed that the TIR
bands exhibit saturation, which is the maximum cell values (255). Consequently, the ASTER
TIR bands were not processed further and alternatively a different approach was attempted using
cell value profile. Cell values are utilized to calculate spectral ratio codes, which are then
matched with known candidates from the USGS Library of minerals (Perry & Vincent, 2009) to
predict or identify the unknown rock unit from the ASTER image.
The ophiolitic rocks delineated in the geological map of the study are as well as the
locations of rocks identified in the mineral inventory map were verified using this approach. Cell
values for VNIR and SWIR ASTER bands were collected for verification of ultramafic rocks
from J, Togla, where sampling location are marked as XXX in Figure 38.
70
Figure 38: ASTER 1-4-5 (RGB) image showing cell values sampling sites (Marked as X X X and ZZZ) at Jebel Togla and Jebel Umm Sanagir, respectively.
The values were averaged and later applied in obtaining ratio codes for the unknown
mineral candidate that has been correlated with other known ultramafic minerals, such as talc for
better identification. The results are shown in Figures 39 and 40 below.
71
0
1
2
3
4
5
6
7
8
9
R21 R41 R61 R81 R32 R52 R72 R92 R53 R73 R93 R64 R84 R65 R85 R76 R96 R97
Band Ratio
Rat
io C
odes
Candidate (Ophiolites)Talc
Figure 39: Comparison of ratio codes for unknown rock candidate (ophiolites) versus talc.
72
0
1
2
3
4
5
6
7
8
9
10
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Band Ratio
Rat
io C
odes
Candidate (Ophiolites)Smithonite
Figure 40: Comparison of ratio codes for unknown rock candidate (ophiolites) vs. smithsonite
The prediction indicated possible occurrence of the following mineral candidates:
1) Smithsonite
2) Talc
3) Anthophylite
4) Serpentinites
5) Magnesiochromite
Based on origin, composition, and geologic environment of the predicted mineral
candidates, it is likely that those specific cell values represent pixels occupied by ophiolitic rocks,
and so may be applied in updating the old geological map.
73
4.10 Comparison between the applied image enhancement methods
Data sets (LANDSAT TM and ASTER) have been processed using three
enhancement techniques, each with its advantages and disadvantages. Below are the
comparisons among the three methods.
1. Optimum Index Factor (OIF):
The OIF simplifies the complex and tedious process of selecting three appropriate
bands to be combined in colors that are viewable to the human eye. Although this method
narrows the possible band combinations, its disadvantages include:
(a) Non uniformity of images due to assignment of colors based on the analyst consent.
(b) The highest ranking OIF value, which is the prime combination, may not necessarily
represent the best combination or image.
(c) The method is scene dependent in that different OIF rankings are obtained for
different image subsets (OIF for a subset is different than for the whole scene).
2. Feature-oriented Principal Component Selection (FPCS):
This technique utilizes the generalized reflectance curve of the component of
interest, such as hydrothermal alteration in which band ratios are considered in the choice
of the best Principal Component, based on the ratio of their respective eigenvector values.
For instance, to determine which PC best represent iron-bearing minerals depends on the
eigenvector values of bands 3 and 1 in a LANDSAT TM dataset. Likewise, the clay
minerals are controlled by the eigenvector values of bands 5 and 7 reference to their
generalized reflectance spectra curve of the USGS Library of minerals.
The signs (+/-) of the eigenvector value loadings are being considered in the
ratioing process, because they determine which component of interest (Fe oxide or clay)
74
would be represented as bright or dark pixels in the image. In selecting the optimum
principal component, the two loadings should always be different in signs.
Consequently, when the numerator is positive implies bright pixels, where as when it is
negative implies dark pixels will represent the feature of interest.
Although this technique helps in identifying iron and clay minerals, it is not
definitive in discriminating or naming the various possible minerals which may constitute
the brighter color exhibited by a particular group of pixels at a specific location on the
image.
3. Band ratioing (BR) method:
Band ratioing involves division of reflection and absorption peaks and troughs of
relevant bands resulting in an improvement of images regardless of illumination positions
or terrain. Despite the improvement in image contrast by band ratioing its weakness is
demonstrated by the reduction in the reflection intensity of objects on the images. Also,
there could be more than one mineral candidate for the same band ratio (e.g., the
LANDSAT TM ratio R (3, 1) and R (5, 7)) and are generally used to discriminate iron-
and clay-bearing minerals, respectively. That is ambiguous due to the presence of many
iron and clay minerals within each category.
The remedy to this method is by the application of ratio codes which specifically
define which mineral is being mapped by each code. In brightness and ratio codes
technique, the mineral library spectra is divided into deciles, with each decile of a
spectral band or ratio is labeled from 9 for the highest decile, down to 0 for the lowest
decile. A triplet combination of any three codes of 9, 0, 0 may be displayed in the
primary colors of red, green, and blue (RGB), respectively, thus makes the mineral of
interest to appear as red in the resulting image (Perry & Vincent, 2009).
75
The utilization of brightness and ratio codes (Vincent, 1997) facilitated
identification of hydrothermal alteration minerals (e.g., alunite, buddingtonite, chlorite,
hematite, kaolinite, and muscovite) with greater ease than it would be with the other
enhancement methods, which identify alteration zones generally, but not specifically in
terms of particular diagnostic minerals or hydrothermal alteration indicators.
Consequently, band ratioing is the best of all the methods applied in this research work.
4.11 Updating geological map of the study area.
The need for an accurate and updated geological map is essential for exploration and
development of mineral resources. The existing geological map was published in 1986, utilizing
conventional ground surveys in acquiring field data that was collected along traverses at some
intervals. Such processes are vulnerable to errors during plotting and extrapolation.
Consequently, the final geological map will likely be of low accuracy and precision.
In the present study, an update to the above mentioned map has been executed utilizing
LANDSAT TM and ASTER images with the aim of delineating major structural features and the
main lithological units. The procedure involves integration of geological information derived
from processed multispectral imagery in addition to ground truth. Overlaying of various images
was performed for the extraction of lithologic boundaries between major units, and the
delineation of linear structures and hydrothermal alteration zones.
False color composite images, band ratio and ratio codes, in addition to principal
component analysis images were converted into vector format that are later overlaid on the
existing geological map for comparison and an updated map was produced. The new map
76
incorporates all the digitized features from the various enhanced images and so differs from the
old map in the following:
The core of the domal structure in the old map was shown to be formed predominantly of
gneissic rocks, but the new map has indicated the presence of other rock units comprising ferric
oxides (gossans) as shown in Figure 42. Also, hydrothermally altered rocks have been revealed
within the domal feature by the band ratio method using R (3/1) for ferric oxides and R (5/7) for
the clay mineral alterations. These were mapped as composed mainly of chlorite schist in the old
map. The ultramafic rocks identified at Jebel Togla in the old map were subdivided into two
different rocks based on the ferric oxide alteration zones derived from the Principal Component
Analysis image in Figure 42. The granites mapped at the northeastern part of the old map have
not been detected in any of the enhanced images. The surficial deposits remain the same on both
maps. The two maps (old and updated) are shown in Figures 41 and 42, respectively.
77
Figure 41: Simplified geological map of the study area (after BGR, 1986). (Original map is shown in Figure 2)
78
Figure 42: Updated geological map of the study area (After BGR, 1986)
4.12 Mineral potential map.
Production of a mineral potential map for the study area requires integration of
various spatial features associated with the occurrence of sulfide mineralization in the
region, which might be derived from lithology, structure, geochemistry, geophysics, and
remote sensing.
79
The importance of mapping gossans is due to their formation from mineral
assemblages containing predominantly sulfide mineralization (pentlandite, pyrrhotite,
pyrite, and chalcopyrite), which are associated with mafic and ultramafic rocks. The
presence of pyrite as primary or secondary mineral contributes much in the chemical
reactions that generate gossans above sulfide ore deposits (Burns, 1988). Likewise,
delineation of structural features in the study area aids in locating sites that could be
favorable for mineral accumulation, which might have been transported away from
weathering locations of primary sulfides mentioned above.
Combining all the available information from lithology, structure, and alteration
zones have been utilized to create a probabilistic map of sulfide mineral potential, based
on the conceptual model for gold occurrence in the AMD. The geological features of
prime interest for modeling gold potentials include:
a) Proximity to fold axis, fault/ shear zones.
b) Proximity to alteration zones
c) Proximity to contact metamorphism (e.g. granite-greenstone contacts)
The maps shown in Figure 43 and 44 delineate favorable zones for gold occurrence;
hence, would serve as guidance for further exploration in the study area.
80
Figure 43: Mineral Potential Map showing Locations of iron (blue) and clay (red) alteration zones, overlain on LANDSAT TM (Kernel edge) filtered image, aimed at identifying spatial relation between structural lineaments and alteration zones, as favorable sites for gold exploration in the study area.
81
Figure 44: Mineral Potential Map showing Locations of iron (blue line) and clay (red line) alterations derived from PCA, overlain on ASTER 6-2-1 FCC image representing gossans (red), hydrothermal alteration (green), and host rock (blue), as means of establishing a conceptual model for gold occurrences in the area.
82
CHAPTER FIVE
CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
Multispectral remote sensing (LANDSAT+ and ASTER) image enhancement and
interpretation proved to be useful in identification, detection, and delineation of
lithological rock units, hydrothermal alterations, and geologic structures associated with
auriferous sulphides deposits in the research area of the Nuba Mountains, Sudan.
The selection of optimum 3-band color combination for lithological and
hydrothermal alteration discrimination was performed by the Optimum Index Factor
(OIF) which was developed by Chavez (1982). The method is best in ranking 3-band
combination, but their assignment to the principal colors (RGB) solely depends on the
selection by the analyst.
The use of ratio codes and spectral ratio techniques (Vincent, 1997) enabled
lithologic and hydrothermal alteration mapping based on diagnostic spectral signatures of
iron and hydroxyl minerals. Generally, the band ratios R (3, 1), R (5, 4), and R (5, 7)
were utilized for mapping ferric, ferrous, and clay minerals, respectively.
The application of Feature Oriented Principal Component Selection (FPCS),
which is based on the Crosta Technique of the Principal Component Analysis (PCA), was
effective in identifying hydrothermal alteration zones in the area. Together with the
directional filters, the FPCS were useful for lineament extraction in the study area.
83
Geologic mapping is important in evaluating mineral resources of a country like
the Sudan, in which exploration of its prospecting regions has always been a challenge
due to inaccessibility, unavailability of technology and of financial resources. To
facilitate and reduce exploration expenses, it is best utilizing remote sensing capabilities
for such tasks in order to obtain better coverage and accuracy with significantly reduced
time and cost.
5.2 Recommendations
The application of remote sensing for gold exploration in the Nuba Mountains has
been successful in mapping hydrothermal alteration zones at which association of gold
and sulphides is highly likely. Consequently, the technology is worth applying in the
other areas of the Arabian Nubian shield (ANS) that are characterized by highly
favorable granitoids-greenstone belt geology with mature arc development, in addition to
the atmospheric conditions of arid terrain, which provide clear bedrock exposure.
Integration of other exploration methods, such as geochemistry and geophysics, will
improve the results of the present exploration approach.
Further research in this respect at this study area, as well as in the other parts of
the Sudan which had been under civil war before the Comprehensive Peace Agreement
(CPA) of 2005 will lead to better understanding of the conceptual model of
mineralization, especially the association of alteration minerals with gold and with major
structural pattern in the region known for the occurrence of volcanogenic massive
sulfides (VMS) deposits that constitute the main source of economically significant
quantities of gold produced from the Red Sea Hills of the Sudan.
84
REFERENCES
Abdel-Rahman, M., 1993. Geochemical and geotectonic controls of the metallogenic evolution of selected ophiolite complexes from the Sudan. Berliner geowissenschaftliche Abhandlungen 145,175.
Abdelsalam, M.G., Stern, R.J., 1993. Tectonic evolution of the Nakasib suture, Red Sea Hills, Sudan: Evidence for a late Precambrian Wilson cycle. Journal of the Geological Society of London 150, 393–404.
Abdelsalam,M.G., Stern, R.J., 1996. Sutures and Shear Zones in the Arabian-Nubian Shield. Journal of African Earth Sciences 23, 289–310.
Ahmed, F.,1975. The geology of J. Qeili igneous complex, central Sudan. Geol. Rdsch., 64, (3), p.835-846.
Almond D. C. 1984. The concept of “Pan-African Episode” and “Mozambique Belt” in relation to the geology of East and North-East Africa. Bull. Fac. Earth Sci. King abdulaziz Univ., Jeddah. 6 (1983), p. 71-87.
Aloub, O. A. and Elsamani, Y. 1991. The geology of gold deposits in the Red Sea Hills of the Sudan: Pan-African tectono-metamorphic models. Bull. GRAS, Sudan, 39, 30p.
Burns R. G., 1988. Gossans on Mars; Proc. Lunar Planet Sci. Conf. 18, Cambridge Univ. Press, P 713-721.
Cottard, F. Deschamps, Y. Bernadet, G. and El Samani, Y. 1986b. Gold deposits of Ariab area. BRGM Rep. No. 86 SDN 110, Khartoum, 55p.
Crosta, A. P., and Rabelo, A., 1993. Assessing of Landsat TM for hydrothermal alteration mapping in central western Brazil . Proceedings of Ninth Thematic conference geologic remote sensing Pasadinea, p. 1053-61, California, USA.
GRAS, 1989. Base metal in the Sudan, GRAS Rep. Sudan
GRAS, 1995. Gold in Sudan. GRAS Rep. Sudan
GRAS, 2004. Geological map of the Sudan. GRAS, Sudan.
Kenea, N. H. 1997. Improved geological mapping using Lansat TM data, Southern Red Sea Hills, Sudan: PC and HIS decorrelation stretching. Intern. Jour. of Remote sensing, 18, (12), p. 1233-1244.
Kröner, A. et al. 1987. Pan-African crustal evolution in the Nubian segment of northeastern Africa. Am. Geophys. Union Geodynamic Series, 17, p. 235-257.
Kusky, T.M., Abdelsalam, M., Tucker, R., Stern, R., 2003. Evolution of the East African and Related
85
Orogens, and the Assembly of Gondwana. Special Issue of Precambrian Research 123, 81–344.
Omer, M. K., 1983. The geology of the Nubian Sandstones formationin Sudan, stratigraphy, sedimentation dynamics, and diagenesis. Geol. Miner. Resour. Dep., Khartoum, Sudan, 227p.
Perry, J., 2003, An investigation into the use of ASTER (Advance Space-borne Thermal Emission and Reflection Radiometer) data for geologic mapping in the Powder River basin, Wyoming: Unpublished MS thesis, Bowling Green, Ohio, Bowling Green State University, 121p.
Perry, J., and Vincent, R. K., 2009, ASTER Brightness and Ratio Codes for Minerals: Application to Lithologic Mapping in the West-Central Power River Basin, Wyoming. Reviews in Economic Geology, v. 16, p. 143-168.
Qui, F., Abdelsalam, M., and Thakkar, P., 2006, Spectral analysis of ASTER data covering part of the Neoproterozoic Allaqi-Heiani suture, southern Egypt: Journal of African Earth Sciences, v. 44, p. 169-180.
Tombe, M. A. 2006 Image processing techniques for geological mapping of Kurmuk-Qeissan area. GRAS, 25p.
Vail, J. R. 1978. Outline of the geology and mineral deposits of the Democratic Republic of the Sudan and adjacent areas. Overseas Geol. Miner. Resour., London, 49, p. 1-67.
Vincent, R. K., and F. J. Thomson, 1972, Rock-Type Discrimination from Ratioed Infrared Scanner Images of Pisgah Crater, California, Science, Vol. 175, pp. 986-988.
Vincent, R. K. 1997. Fundamentals of Geological and Environmental Remote Sensing. Prentice Hall, 370p.
Whiteman, A. J. 1970. The geology of the Sudan Republic. Clarendon press: Oxford, 290p.
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APPENDIX
ASTER Brightness and Ratio Codes (After Perry and Vincent, 2009)
87
88
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91
92
93
94
95
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97
98
99
