Lineament study in the Björkö area
using satellite radar and airborne VLF
data - possible tools for
groundwater prospecting
Clement Takyi
Master’s of Science Thesis in Geoinformatics
TRITA-GIT EX 08-006
School of Architecture and the Built Environment
Royal Institute of Technology (KTH)
100 44 Stockholm, Sweden
TRITA-GIT EX 08-006 ISSN 1653-5227
ABSTRACT
This thesis work mainly concerns lineament study, that is the extraction and analysis of lineaments from satellite radar and airborne VLF data using GIS to explore potential aquifers or groundwater locations associated in the study area (Björkö) situated west of Stockholm in the lake Mälaren. The other objective focuses on improving the general knowledge of hydrology, more importantly techniques and methods in lineament analysis.
The airborne VLF data for the Björkö area was received in the xyz-coordinate format. The data have been put into a 50 m grid (spatial resolution of 50 m) and was converted into Idrisi format using the analysis software Idrisi 32. The data was then analysed and classified into different categories based on the relative anomaly amplitude followed by on-line digitisation of suspected anomalies in each category. The anomalies from the different categories that were interpreted as lineaments were further added as a single overlay.
The radar data was originally received in the Ascii version (a Grass file format). The data have been put into a 50 m grid (spatial resolution of 50 m). The data was then converted into Idrisi format using the Idrisi 32-analysis software. A series of filtering operations in Idrisi were further carried out on the generated radar image from the conversion in order to enhance it for digitisation. Different directional images resulted from the filtering operation and were used for the digitisation of suspected anomalies interpreted as lineaments.
The result from the VLF operation shows different conductivity levels in anomalies that have been interpreted as lineaments depicting possible fracture zones in the bedrock from good conductors to weak conductors in the Björkö region. Lineaments derived from the radar data represent terrain escarpments that may be related to fracture zones. The obtained result from the maximum filtering represent terrain features at high angle to the radar incident signal. Also terrain features with a low angle to the radar incident signal were produced from the minimum filtering operation.
The methods employed in the study have been successful in identifying possible fracture zones in the study area and can subsequently be applied in groundwater prospecting analysis.
TABLE OF CONTENTS
ABSTRACT……….….I
TABLE OF CONTENTS……….II
1 INTRODUCTION………..1
1.1 Background………...1
1.2 Problem Statement and Aims………...2
2 AREA DESCRIPTION (BJÖRKÖ) AND GEOLOGY………..2
3 EXPLORATION METHODS………...3
3.1 Electromagnetic Surveying Methods………...4
3.1.1 VLF Method………...5
3.1.2 Radar………..6
3.2 Other Concepts of Geophysical techniques……….8
3.2.1 Remote Sensing……….8
3.2.2 Seismic Method………11
4 BASIC CONCEPTS IN GEOHYDROLOGY………...11
4.1 Classification of Groundwater………11
4.2 Water-bearing Formations………..12
4.3 Hydraulic Properties of Rocks………12
5 LINEAMENT STUDIES IN THE BJÖRKÖ AREA………13
5.1 Data Acquisition and Analysis………...15
5.1.1 VLF...15
5.1.2 Radar...21
6 RESULTS………..27
6.1 VLF………27
6.2 Radar………..28
7 DISCUSSION AND CONCLUSIONS………33
1 INTRODUCTION
1.1 Background
Water is an essential element for sustaining life of all living creatures on earth. This is
manifested in its greater percentage coverage of the earth’s surface (approximately 70 percent of the earth's surface is covered with water). Water exists in many different forms both on the surface and in the subsurface of the earth. It is easy to understand the significance water plays in our lives but it may be difficult to understand the water that exists below the earth's surface (subsurface) called groundwater.
Groundwater is often thought of as an underground river or lake. Only in caves or within lava channels does groundwater occur this way. Instead, groundwater is usually held in porous soil or rock materials, much the same way water is held in a sponge.
When rain falls to the ground, some of it flows along the surface in streams or lakes, some of it is used-up by plants, some evaporates and returns to the atmosphere, and some sinks into the ground. Groundwater is water that is found underground in cracks and spaces in soil, sand and rocks. The area where water fills these spaces is called the saturated zone. The top of this zone is called the water table.
Groundwater can be found almost everywhere. Groundwater is stored in, and moves slowly through, layers of soil, sand and rocks called aquifers. The speed at which groundwater flows depend on the size of the spaces in the soil or rock and how well the spaces are connected, that is, the hydraulic conductivity of geo-materials. Groundwater supplies are replenished, or recharged, by rain and snowmelt.
Aquifers (water-bearing strata) typically consist of gravel, sand, sandstone, or fractured rock. These materials are permeable for water because they have connected spaces that allow water to flow through. Water in aquifers is brought to the surface naturally through a spring or can be discharged into other water bodies. Groundwater can also be extracted through a well drilled into the aquifer.
1.2 Problem Statement and Aims
Successful identification of sites for groundwater prospecting in areas with crystalline rocks depends principally on the location of narrow fracture zones in the bedrock or the location of weathered zones with increased hydraulic conductivity.
Arid and semi-arid lands cover a large portion of the global land surface and are most often located in developing countries. Access to a safe and reliable water supply is an important factor of development. Several hundred million people in rural Africa are still without this basic need for a sustainable living. Hence, these regions suffer from deeply rooted problems of poverty, diseases and unsettled life. In my country Ghana, which is a developing country, access to safe and reliable water supply is on the whole a problem especially in the northern regions where the land cover is of the arid to semi-arid type. Latest development policies and resources allocations in developing countries like Ghana, will not totally alleviate the problem of water scarcity in the near future; this is not only due to the population growth, but also due to lack of basic knowledge of hydrology (dry land hydrology) as well as the lack of an
operational cost-effective prospecting materials and methods.
The main aim of this project is to extract, investigate and analyse lineaments from satellite radar and airborne VLF data using GIS to explore potential aquifers or groundwater locations associated in the study area.
The other objectives focuses on improving the general knowledge of hydrology, more importantly techniques and methods in lineament analysis in a study area in Stockholm (Sweden) and, if possible investigate in the near future, how such techniques and methods could be applicable to arid and semi-arid regions in certain parts of Ghana in an attempt to help in solving the problem of water scarcity that pertains now and the near future.
2 AREA DESCRIPTION (BJÖRKÖ) AND GEOLOGY
The Björkö structure is said to be a deep eroded impact crater formed some billion years ago in crystalline rocks. The structure is situated west of Stockholm in lake Mälaren. The crater is about 10 km in diameter and together with its porosity and a depth of approximately 5 km provides a potential for geothermal energy (Rodriguez 2002).
Studies based on airborne magnetic data provided by the Geological Survey of Sweden and combined digital elevation and bathymetric data compiled by Rodriguez (2002) reveals the presence of a large shear and fracture zones within the Björkö region (Rodriguez 2002). A look at the geological history, indicates strongly deformed crystalline gneisses of the older Proterozoic rocks, which were intruded by granites and dyke rocks and overlain by a mid to late Proterozoic non-deformed sandstone, remains of which are preserved locally. The southern part of the Björkö island is the site of the central uplift of the impact crater with approximately 10 km diameter as described earlier.
The lithology of the area form a transition from the highly metamorphic (almost granulite facies) sedimentary veined gneisses of central Sörmland in the south and the amphibolite facies micaschists and feldspar-quartzite of southern Uppland in the north. The transitional zone is wide and irregular with respect to the distribution of rock types.
In the southern part of the Björkö area, the steep east-west striking foliation typical of the Sörmland gneiss belt dominates. Different types of gneisses occurring juxtaposed with the lithological contacts dipping almost vertical.
The thickness of the sandstone, determined with refraction seismics, was interpreted to range from about 40 m at the northwest of Rasta to about 280 m at the south of Midsommar. The sandstone formation was thus found to encircle the southern part of Björkö island.
Lithological information from borehole Mid-01 at Midsommar island indicate the depth extend of the sandstone unit to more than 900 m, and the dip found to vary between 45o and 60o. Fracturing in the crystalline basement occurs down to centimetre spacing with several types of filled minerals, among others laumontite and graphite, which are electrically very conductive. The basement rocks along the edge of the structure are brecciated and indicate a present (after erosion) diameter of the structure of about 10 km.
3 EXPLORATION METHODS
In geophysical exploration methods (also referred to as geophysical surveying),
measurements within geographically restricted areas are used to determine the distributions of physical properties at depth that reflect the local subsurface geology (Kearey and Brooks 1984).
electromagnetic methods and the seismic refraction method are the most appropriate. If the goal is to find vertical structures like fracture zones, electromagnetic methods, remote sensing, resistivity methods may be suitable (Singhal and Gupta 1999).
3.1 Electromagnetic Surveying Methods
Electromagnetic (EM) surveying methods make use of the response of the ground to the propagation of electromagnetic fields, which are composed of an alternating electric intensity and magnetic field. Primary electromagnetic fields may be generated by passing alternating current through a small coil made up of many turns of wire or through a large loop of wire. The response of conductive features in the ground is the generation of secondary
electromagnetic fields and the resultant fields may be detected by the alternating currents that may induce a flow in a receiver coil by the process of electromagnetic induction (Kearey and Brooks 1984).
The primary electromagnetic field travels from the transmitter coil to the receiver coil via paths above and below the surface. Where the surface is homogeneous there is no difference between the fields propagated above the surface and through the ground other than a slight reduction in amplitude of the latter with respect to the former. However, in the presence of a conducting body the magnetic component of the electromagnetic field penetrating the ground induces alternating currents, or eddy currents, to flow in the conductor (Figure 1). The eddy currents generate their own secondary EM field that travel to the receiver. The receiver then responds to the resultant of the arriving primary and secondary fields so that the response differs in both phase and amplitude from the response to the primary field alone. These differences between the transmitted and received EM fields indicate the presence of the conductor and provide information on its geometry and electrical properties (Kearey and Brooks 1984).
One big advantage with the electromagnetic method is that it does not need direct contact with the ground, which eliminates problems associated with rocky surface, or bad contact due to dry conditions. If there is an electric conductive body in the ground and an alternating magnetic flux in the surrounding, it produces a current in the body, which will produce a secondary magnetic flux in the opposite direction of the primary field (Parasnis 1986). Powerful radio transmitters set up for the purpose of military communications with
submarines create such magnetic flux. In radio technology, the frequencies used are called very low frequencies (VLF) since the frequency of ordinary radio transmission is greater than ten times as high. For a long distance from the radio transmitters, the flux will be horizontal and if a vertical sheet-like conductor is located in the subsurface it creates a secondary electromagnetic field in the opposite direction to the primary flux. The vertical component compared to the horizontal is measured. The vertical field is divided into a real and imaginary component. Above a vertical conducting structure the secondary field will be horizontal and the vertical component is therefore zero (Parasnis 1986).
The electromagnetic data can be collected from airplanes or helicopters, which imply that big areas can be investigated faster at a lower cost compared to ground surveys.
Fracture zones are conductive due to the content of water and dissolved agents. In areas with shallow depth to the bedrock, fracture zones that are often vertical or slightly dipping, conducting sheets are most often the targets in water prospecting (Singhal and Gupta 1999).
3.1.1 VLF Method
The source utilised by the VLF method is electromagnetic radiation generated in the low frequency band of 15–25 kHz by the powerful radio transmitters used in long-range communications and navigational systems. Several stations using this frequency range are available around the world and transmit continuously either an unmodulated carrier wave or a wave with a superimposed morse code. Such signals may be used for surveying up to
The electric conductivity (expressed in Sm-1) or its inverse, the electric resistivity (expressed in Ωm) is the basic material property, which is responsible for the amplitude of the secondary electromagnetic field induced by the signals from distant VLF radio transmitters.
The VLF anomaly (A) has an inverse relation to the distance (d) between the conductor and the sensor. It has a directly proportional relation to the conductivity contrast (C), and the difference in strike direction (u) with respect to the direction to the transmitter, and a function f of the volume v and its orientation o:
A = d-4 C cos (u) f (v,o) (Henkel 2001).
The ground penetration depth of these signals decreases with increased conductivity (and vice versa) and is only a few metres in seawater. Conductors perpendicular to the direction to the transmitter will not have any secondary field induced, while those conductors, which are parallel to the direction of the transmitter, will generate a maximum secondary field. Man-made objects like power lines, metal fences and pipe lines, which are all good conductors, will show strong induced secondary electromagnetic fields (Henkel 2001). Measurements are made on the ground by profiling, whiles from airborne it is conducted along flight lines with a specified spacing.
3.1.2 Radar
Basic Concepts
Radar is an acronym for Radio Detection and Ranging. The very simple and ancient principle is that of detecting objects and determining their distances (range) from the echoes of sound they reflect.
The fundamental radar concept employs EM radiation emitted at frequencies between 108 -1010 Hz and the phenomenon of the Doppler effect in determining the relative speed or range rate of the reflecting object from the shift in the radio frequency of the reflected waves relative to that of the emitted waves. By sensing Doppler shifts, radar can not only measure range rates but also differentiate between echoes from moving targets and the clutter of echoes from the ground and objects on it, which are stationary. The radar echoes from the ground can be displayed on high-resolution maps of the terrain rather than rejecting them (Stimson 1998).
Radio Detection
energy is scattered in many directions, but a detectable portion of it is generally scattered back in the original emanating direction.
At the longer wavelengths (lower frequencies) used by many shipboard and ground based radars, the atmosphere is almost completely transparent, and it is nearly so even at the shorter wavelengths used by most airborne radars. By detecting the reflected radio waves, it is therefore possible to see objects not only at night, as well as in the daytime, but also through haze, fog, or clouds.
A radar system consists basically of five elements: a radio transmitter, a radio receiver tuned to the transmitter’s frequency, two antennas and a display.
To detect the presence of an object (hereby known as target), the transmitter generates radio waves, which are radiated by one of the antennas. The receiver, then, listens for the “echoes” of these waves, which are picked up by the other antenna. If a target is detected, a blip indicating its location appears on the display. In practice, the transmitter and receiver generally share a common antenna.
To avoid problems of the transmitter interfering with reception, the radio waves are usually transmitted in pulses, and the receiver is turned off (“blanked”) during transmission. The rate at which the pulses are transmitted is called the pulse repetition frequency (PRF). In order for the radar to differentiate between targets in different directions as well as detect targets at greater ranges, the antenna concentrates the radiated energy into a narrow beam.
To find a target, the beam is systematically swept through the region in which targets are expected to appear. The path of the beam is called the search scan pattern. The region covered by the scan is called the scan volume or frame, while the length of time the beam takes to scan the complete frame is known as the frame time.
In radar terms, “target” is broadly used to refer to almost anything one wishes to detect: an aircraft, a ship, a vehicle, a man-made structure on the ground, a specific point in the terrain, rain (weather radars), aerosols, even free electrons.
The strength of a target’s echoes is inversely proportional to the target’s range to the fourth power (1/R4). Therefore, as a distant target approaches, its echoes rapidly grow stronger (Figure 2), (Stimson 1998).
Figure 2: Range versus signal strength in target echoes detection (Stimson 1998).
The range at which targets become strong enough to be detected depends upon a number of factors. Some of the most important ones are:
- Power of the transmitted waves,
- Fraction of the time during which the power is transmitted, - Size of the antenna,
- Reflecting characteristics of the target,
- Length of time the target is in the antenna beam during each search scan, - Number of search scans in which the target appears,
- Wavelength of the radio waves,
- Strength of background noise or clutter.
3.2 Other Concepts of Geophysical Techniques
3.2.1 Remote Sensing
Remote Sensing is the science and technology of obtaining information about an object, area or phenomenon through the analysis of data acquired by the use of a device that is at a distance (remotely) from the object, area or phenomenon under investigation. The use of remote sensing in groundwater exploration is seen mainly in its cost effectiveness of well site selection (Lillesand and Kiefer 2000).
Basic Principle
surface, objects will reflect, absorb and transmit energy differently depending on their
physical properties. Within the visible spectrum, these variations result in different colours for different objects. The reflectance characteristics of earth surface features can be quantified by measuring the portion of incident energy that is reflected. This is measured as a function of wavelength and is called spectral reflectance, which can be graphed into a spectral reflectance curve and has a strong influence of the choice of wavelength regions, when data are acquired for a particular reason of investigation. With the use of information from one or more
wavelength ranges it is possible to determine different types of ground objects such as vegetation, wet soil, dry soil, water, snow (Lillesand and Kiefer 2000), see Figure 3 below.
Figure 3: Spectral reflectance curves of some selected objects (Singhal and Gupta 1999).
It is of importance to verify remote sensing data with actual ground truth in order to have a reference (Singhal and Gupta, 1999).
Groundwater Resource Applications
Groundwater can be located indirectly using remote sensing data in the visible and mid IR wavelengths. EM radiation only penetrates a few millimetres into the ground in the visible range, a few meters in the microwave range and up to several 100 m in the VLF range. These EM radiation principles can be applied on certain indicators, both on surface and sub-surface like different geological, hydrological, vegetation phenomena in locating and approximating the quantity of groundwater (Lillesand and Kiefer 2000).
Features Associated with Recharge and Discharge Zones
Surface water bodies such as ponds, rivers, lakes etc are indicators of recharge/discharge zones. Near infrared, thermal infrared and microwave EM radiation is highly sensitive to surface moisture. Recharge/discharge zones can be separated due to the fact that discharge zones have shallow water tables and lower reflectance than the recharge zones. Even the shape of a water body can determine whether it is a recharge or a discharge zone. Depending on whether a river gets thinner or thicker downstream, it is evidently loosing or getting richer in water and can therefore be classified as either a recharge or discharge zone (Singhal and Gupta 1999).
Soil Moisture
The intensity of soil moisture can be detected throughout the EM spectrum. On the panchromatic band (i.e. visible wavelength) high moisture content shows as darker photo-tones. Moisture can also be detected in the near infrared and the thermal-infrared part of the EM spectrum where wet areas appears cooler because of the evaporation, i.e. darker than the dryer areas (Singhal and Gupta 1999). Except for moisture, surface roughness and organic matter reduce soil reflectance (Lillesand and Kiefer 2000).
Vegetation
Vegetation can be a direct indication of groundwater. Lush vegetation associated with lineaments could be a sign of possible bedrock fractures, with higher porosity and permeability that allows easier development of root systems and water storage. Some vegetation is a better indicator than others. Plants, which are used for agricultural purposes, could be irrigated and are therefore bad indicators (Taylor et al. 1999). In general,
phreatophytic vegetation refers to a deep-rooted plant that obtains its water from the water table or the layer of soil just above it whereas xerophytic plants would show dry arid conditions (Singhal and Gupta 1999). Vegetation strongly absorbs energy in the red wavelength band 0,6-0,7 μm, which is the chlorophyll absorption band. What our eyes consider as healthy vegetation is a strong green colour, which is an indication of a strong absorbance of blue and red and a relatively higher reflectance of green. If a plant is exposed to some kind of stress that interrupts its natural growth and productivity, it results in a decrease in chlorophyll production and hence plants turn yellow (Lillesand and Kiefer 2000).
3.2.2 Seismic Method
In seismic surveying, seismic (sound) waves are propagated through the earth’s interior and the travel times are measured of waves returning to the surface after refraction or reflection at geological boundaries within the ground. These travel times may be converted into depth values and, hence, the distribution of subsurface interfaces of geological interest may be systematically mapped.
Seismic methods are widely applied to exploration problems involving the detection and mapping of subsurface boundaries of simple geometry. The methods are particularly well suited for mapping of layered sedimentary sequences and are therefore widely used in the search for oil or gas. The methods are also well suited, on a small scale, to the mapping of near-surface sediment layers, the location of the water table and, in an engineering context, site investigation of foundation conditions including the determination of depth to bedrock and the width of fractured zones (Kearey and Brooks 1984).
There are several types of seismic waves, the most important the S-wave, the P-waves and the surface waves. If a P-wave hits a body with a different seismic velocity it will reflect both an S-wave and a P-wave. The velocity of the P-wave is faster in water-saturated soils than dry soils (Singhal and Gupta 1999).
4 BASIC CONCEPTS IN GEOHYDROLOGY
4.1 Classification of Groundwater
Subsurface water can be classified into different groups depending on its physical occurrence in the soil, as shown in Figure 4. Two main zones are widely identified, saturated and
Figure 4: Water-carrying zones in soils (Sen 1995).
4.2 Water-bearing Formations
The occurrence and movement of groundwater depends on subsurface characteristics such as lithology (rock formation), texture and structure. The different formations are classified into the following types depending on their relative permeability (Singhal and Grupta 1999):
Aquifer
An aquifer is a naturally occurring formation that contains sufficient amount of water and permeable materials to yield significant amount of water to wells and springs. Geo materials that serve as aquifers are gravels, sand, fractured crystalline and other fractured rocks. Aquifers can also be grouped into confined-, unconfined-, perched- and leaky aquifers (Sen 1995).
Aquiclude
This formation is capable of absorbing water slowly, but not capable of transmitting it fast enough to yield reasonable quantity of water to a well. These are confining formations like crystalline rocks, clays and shales (Sen 1995).
Aquitard
Aquitards have insufficient permeability to act as an aquifer, but can still serve as source or interchange medium between neighbouring aquifers. These contain usually silt or shale (Singhal and Gupta 1999).
4.3 Hydraulic Properties of Rocks
Porosity
Porosity is a measure of voids (pore spaces) in the rock formation. It is defined as the ratio of pore volume to total volume in percent. Porosity is of two types; Primary porosity which is inherent from the rock formation and secondary porosity, developed through weathering (Sen 1995).
Hydraulic Conductivity and Permeability
Hydraulic conductivity is a measure of the ability of a rock formation to transmit water. It depends both on the properties of the medium and the fluid, which makes this concept rather complicated to use. A more rational concept is permeability, which does not take the fluid properties into account (Singhal and Gupta 1999).
Storativity
The ability of an aquifer to store water is called storativity. It is defined as the volume of water that a vertical column of the aquifer of unit cross-sectional area releases from storage as the average head within this column declines by unit distance. It is measured in mass
(volume) per time. (Singhal and Gupta 1999).
Transmissivity
This demonstrates the ability of the aquifer to transmit water. It is defined as the rate of flow of water at unit hydraulic gradient through a cross-section of unit width over the whole saturated thickness of the aquifer (Sen 1995).
4.4 Fractures and Discontinuities
From geohydrological point of view, fractures and discontinuities are two of the most
important geological structures. Most rocks contain fractures and discontinuities within which fluids can be trapped or move through (Singhal and Gupta 1999). Fractures develop due to pressure and temperature differences during or after the rock formation. If an evident displacement occurs, it is called a fault. If there is no appreciable displacement, it is called a joint whereas a fissure is a fracture whose faces have moved apart (Sen 1995).
5 LINEAMENT STUDIES IN THE BJÖRKÖ AREA
power lines, contrast-emphasised contacts between natural or man-made geographic features (e.g. fence lines), or vague “false alarms” caused by unspecified factors. The human eye tends to single out both genuine and deceitful linear features, so that some, thought to be geological origin may, in fact, be of other origins. A fracture (or joint) is just a crack or break in the rock in which the rock on either side springs apart some small distance. A fault is a break in which the rock on one side slides or slips against the rock on the other side so that each side is displaced some distance from the other. As seen from the air or space, in a photo/image, a fracture is just a linear mark in which the tone of the rocks is the same on both sides. Most faults cause enough movement for individual layers or even formations to be displaced, so that there may be a sharp discontinuity in tonal pattern, where one type of rock is brought against another (rst.gsfc.nasa.gov).
The common assumption in lineament mapping is that these features are closely related to fractured zones and lineaments studies have therefore been one of the most important and common approaches in groundwater explorations in hard rock terrain. It has been shown that wells located on or close to fractured zones are often more productive than wells positioned farther away from the fracture zone (Singhal and Gupta 1999).
Lineaments are often visual on remote sensing data as topographic, drainage, vegetation, or soil tonal anomalies. Determining the hydrological importance of different fractures is however very difficult. The most common criteria are lineaments associated with prominent vegetation, straight segments of drainage and topographic relief. Other criteria are length, width, continuity along strike and parallelism with other lineaments (Sander et al 1997). Since faults usually have similar directions, an important means to determine the significance of the lineament is to check if the direction of the lineament is systematic and similar to known fault zones (Drury 1993).
To determine the statistical distribution of lineaments, the most common approach is to: Count the number of lineament per unit area,
Measure the total length of lineaments per unit area, or Count the number of lineament intersections per unit area.
The third approach is most commonly used. The intersections are plotted as points and a high density of points corresponds to a more fractured zone (Singhal and Gupta 1999). However, this technique may fail when the lineaments in the intersecting areas are undefined which often is the case.
Geographical Information System (GIS)
A geographical information system (GIS) is a tool for storing manipulating, retrieving and presenting both spatial and non-spatial data in a quick, efficient and organised manner. Since most land information elements have a geographic connotation, geographically referenced data with GIS techniques and application comes to the fore. The term “geographic” in GIS refers to the location attributes, which define the spatial positioning of the piece of
information on the face of the earth. Preparation and maintenance of data in the form of maps and referenced tabular files itself can be considered as a primitive form of GIS. However, with the advent of digital computers with high data processing speed and the development of analytical tools thereon to handle geographically referenced data with ease and flexibility, computer aided GIS has become a reality. Such systems generally deal with data classified into spatial type (locationally referenced), attribute type (non- locational connotation) and the time variant or repetitive types of data. The three components – location, attributes and time represents the contents of most GIS.
With regards to this work, the computer analytical software idrisi 32 was employed for the lineament study of the Björkö structure. Idrisi is a raster analytical functionality covering the full spectrum of GIS and Remote Sensing needs from database query, to spatial modelling, to image enhancement and classification.
5.1 Data acquisition and Analysis
The Björkö project covers an area of 25km x 25km. The data used with respect to this analysis are airborne VLF and satellite radar data.
5.1.1 VLF
The airborne VLF data for the Björkö area was received in the xyz-coordinate format. The source transmitter is the GBR transmitter in England directed towards southwest of the Björkö area. The data have originally been collected airborne along flight lines in the N-S direction with 200 m spacing between flight lines and 40 m between measurements along the lines. The data have been put into a 50 m grid (spatial resolution of 50 m) and was converted into idrisi format using the analysis software Idrisi 32.
The conversion was carried out using the data paths as follows: Import ⇒ General Conversion ⇒ XYZIDRIS
The resulting vector file was interpolated using the INTERPOL operator to achieve a raster image Figure 5. INTERPOL interpolates a full surface from point data.
The input parameters for the INTERPOL operation was as follows: VLF window coordinates (in Swedish National System):
Min X: 1 584 925 Min Y: 6 564 925 Max X: 1 610 075 Max Y: 6 590 075 503 Columns 503 rows
M D
Figure 5: VLF image of the Björkö area. Units: per mille (1/1000) of the secondary field with respect to the primary field.
As seen in the image, areas between points M and D upwards are areas with missing data, which can be due to transmission errors resulting in erratic flight line striping.
Power lines in the southeastern corner have generated a Y-shape anomaly feature in the image with high intensity values.
The remaining features are thought to be of the naturally occurring anomalies from
conductors parallel to the direction of the transmitter and showing varying strength according to their conductivity.
The elongated anomalies could be lineaments. The large centrally located blue area is a water body. The image depicts suspected anomalies on land only.
Figure 6: Histogram of the VLF image of the Björkö area.
The image Figure 5 was reclassified into five categories based on the relative anomaly amplitude and with the help of the histogram above:
The high resistivity areas (0 – 1 020) are the most frequent in the VLF image. They are the high frequency values in the histogram.
The power lines show extreme high conductivity values, but are of no interest, as they do not represent conductive values from the subsurface bedrock material.
Values from 1020 to 1450 are conductors with varying strength classified from weak to good conductors. These are naturally occurring conductors of interest to this work.
Figure 7: Reclassified vlf-image. 1 - High resistivity (low conductivity) areas, 2 - Weak conductors, 3 - Medium conductors, 4 - Good conductors, 5 - Power lines.
Categories 2, 3 and 4 contain the anomalies of interest with respect to this analysis, which are the naturally occurring conductors.
• Zoom into preferred areas in the original image to enhance the visible spatial resolution.
• Length of lineament (considered ≥ 150m). • Follow and mark maxima values.
• Follow consistent direction of lineament as long as it is above the background level. • The classification of anomalies into a particular layer or category depended on the
anomaly level that dominates (i.e. considering ≥50% of the anomaly length). Generalize the anomaly with a straight line.
Areas not digitised were those that contained missing data and highest conductivity regions as a result of artificial conductors such as power lines.
The three vector anomaly layers were reformatted into raster using the LINERAS module from the raster/vector conversion mode. A blank image was created in each case from the initial image and then updated during the LINERAS operation.
Flow Chart showing how analysis of the Airborne VLF data proceeded: VLF DATA SOURCE CONVERSION TO IDRISI FORMAT RECLASSIFICATION INTO 5 CATEGORIES DIGITIZE ANOMALIES INTO COVERAGES
VECTOR TO RASTER CONVERSION OF ANOMALY COVERAGES
OVERLAY OF COVERAGES
5.1.2 Radar
The radar data covering the Björkö project area was originally received in the Ascii version (a grass file format). The data have been put into a 50m grid (spatial resolution of 50m) (The Björkö project. Integrated project results 2001). The data was then converted into Idrisi format using the Idrisi 32-analysis software.
The conversion proceeded as follows:
Import ⇒ General Conversion ⇒ GRASSIDR
The radar window has coordinates (in Swedish National System) as follows: Min X: 1 584 925 Min Y: 6 564 925 Max X: 1 610 075 Max Y: 6 590 075 503 Columns, 503 Rows.
Figure 9: Stretched Radar image of Björkö. Green- sloping areas facing the radar, Yellow- water areas with wavy surfaces (wind effect), Red- water areas close to the leeward side of west facing slopes, Black- shadows.
The image was stretched to create a new image by linearly scaling between a specified minimum and maximum limit using the STRETCH operation:
The linear stretched image, with a choice to leave out zero as background values and a minimum value of 0 and a maximum value 255 is shown in Figure 9.
Only a portion of the legend window can be shown at a time, with the possibility of a scroll-up/scroll-down, to view different values when necessary, as seen in Figure 9.
The radar transmitter and receiver position is to the east of the Björkö area. That means steep slopes towards the west will be shadow areas (i.e. the black areas in the image).
Sloping areas facing the radar exhibits maximum or high reflectance (green areas in the image). The water areas in general yield intermediate reflectance. However, water areas farther from the west-facing slope areas (yellow areas) show relatively higher reflectance than water areas close to this leeward side (red areas). This may be due to possible wavy-surface characteristics of the water from wind effect.
FILTER creates a new image in which each pixel value is based on its value and those of its immediate neighbours in an input image. The nature of this operation is determined by the values stored in a 3 x 3, 5 x 5, 7 x 7 or variable-sized template that is centered over each pixel as it is processed. For all filters the pixel and its neighbours are multiplied by the values stored in the corresponding positions of the template, and the resulting values are summed to arrive at a new value for the pixel.
A 3 x 3 kernel (filter template values are usually set by the user) was employed in the series of operation carried out. The following user defined 3 x 3 coefficient matrices were used to enhance the specified directions:
2 0 -2 2 2 2 2 0 -2 0 0 0 2 0 -2 -2 -2 -2
N-S (north - south direction) W-E (west - east direction)
0 2 0 0 -2 0 2 0 -2 2 0 -2 0 -2 0 0 2 0
SW_NE (southwest-northeast) NW_SE (northwest-southeast)
The different symmetrical arrangement of coefficients as shown above was carried out to enhance features in the respective directions. However, direction filtering does not totally exclude features in other directions, but the features in the direction under operation will be more prominent.
in the selected window. Maximum value features are thus enhanced with specified direction; although other directional features may still be present, they will not be prominent. The minimum does the inverse.
The filtering with maximum and minimum operator produced some multiple pixel size areas as shown in Figure 10. The effect occurs as a result of the operator assigning the maximum or minimum value in a selected window to the central pixel. This is a limitation to the maximum – minimum filter operation. This problem alters or changes the spatial resolution, and the problem could magnify in the situation where larger filter windows like 7 x 7 or 9 x 9 are used.
Figure 10: Multiple pixel size areas resulting from maximum and minimum filtering effect. The resulting images from the entire operation were used for the digitisation of suspected lineaments were eight images (with maximum and minimum each having four directional images).
On-screen digitising was applied in digitising the suspected lineaments in the eight different directional images to obtain eight different vector layers.
• Zoom into preferred areas in the image to enhance the visible spatial resolution. • Follow and mark consistent direction of lineament as long as it is above the
background level.
• The classification of suspected lineaments in each direction depended on the extent or length of the lineament (considered lengths ≥ 150m) as well as the values across the length.
• Generalize the lineament with a straight line.
The resulting eight vector layers were reformatted to raster using LINERAS from the raster/vector conversion mode. A blank image was created in each case from the initial stretched image and then updated during the LINERAS operation.
Flow Chart showing how analysis of the satellite radar data proceeded: RADAR DATA SOURCE CONVERSION TO IDRISI FORMAT
STRETCH RASTER IMAGE
USER-DEFINED FILTER WITH 3x3 CO-EFFICIENT MATRIX
MEDIAN FILTER
MAXIMUM FILTER MINIMUM FILTER
DIGITIZE ANOMALIES INTO FOUR SEPARATE DIRECTIONAL
VECTOR COVERAGES
6 RESULTS
6.1 VLF
The final result of the VLF operation is the image Figure 11 showing different conductivity levels in anomalies that have been interpreted as lineaments.
The result depicts possible fracture zones based on the conductivity strength of the suspected anomaly areas from good conductors to weak conductors in the Björkö region.
Greater parts of the plain areas are those with missing values as earlier mentioned.
Conductors parallel to the direction of the transmitter receive maximum secondary field while those that are perpendicular to the transmitter’s direction get no induced secondary field. This is evident in the SW-NE direction of almost all extracted lineaments seen in Figure 11. The few lineaments crossing in different direction (example NW-SE direction) may be from man-made conductive materials.
The strong anomalies from the power lines as a result of strong induced secondary field are omitted, as they are man made objects.
Figure 11: Addition of anomalies suspected to be lineaments from the VLF image.0- Background, 1 – Good conductors, 2 – Medium conductors, 3 – Weak conductors.
6.2 Radar
Terrain surfaces to some extent may give an insight to sub-surface formation.
a i r c d e f b Steep vertical, fracture zones
Figure 12: Terrain surface, with incident and reflected radar signals i and r respectively of the radar.
From Figure 12 above, the radar is sending and receiving signals from its position to the east of the study area. Maximum reflectance is likely to occur at slopes “d” and “f” and minimum reflectance and potential shadow along sloping area “e”. A fracture is likely to be located at the base of the slope. Areas “a”, “b” and “c” all show intermediate reflectance.
(NS + WE) max
Figure 14: Addition of suspected lineaments in the N-S, W-E, NE-SW and NW-SE directions
(NS + WE) min
Figure 16: Addition of suspected lineaments in the NS, WE, NE-SW and NW-SE directions
7 DISCUSSION AND CONCLUSIONS
VLF operation, which is a subsurface analysis, has a direct link to groundwater prospecting. VLF will penetrate the surface and for that matter give information beneath the base at “b” in Figure 12. It will therefore be able to pick up possible fractures within the subsurface.
The inability of satellite radar to differentiate “a” from “b” in Figure 12 may require the use of DTM for clarification.
The satellite radar operation, which is a surface analysis, does not have direct link to groundwater prospecting. However, it could be of considerable help (example from the maximum and minimum operation in lineament extraction) to support the VLF analysis. Looking at the lineament image from the VLF operation (Figure 11), it could be said that there might be substantial number of fractures within the subsurface. This could yield reasonable quantities of water, as these fractures on the average show good conductivity due to dissolved chemical compositions.
To obtain as much as possible information about fracture related and linear structures from the radar image MAXIMUM and MINIMUM filtering with 3 x 3 filter size was used. The obtained result from the maximum filtering represent terrain features at high angle to the radar incident signal. Also terrain features with a low angle to the radar incident signal were
produced from the minimum filtering operation.
The lineament images, Figures 13-16 from the radar operation show more lineaments in directions other than just the almost one-directional (SW-NE) from the VLF operation. Should there be a possible VLF transmitter in another direction (such as in the SE-NW direction), lineaments from its operation could be used in conjunction with lineaments in that direction from the radar, to identify more possible fractures in the area.
It can be concluded that:
Lineaments derived from the analysis of the VLF data represent more conductive zones in the bedrock, most likely fractured zones.
Lineaments derived from the radar data represent terrain escarpments that may be related to fracture zones.
As opposed to the VLF results, the results from the radar analysis are less direction dependent.
The radar analysis was the most hectic and time consuming task in the entire analysis as it has been the first time satellite radar image is employed in subsurface lineament extraction. GIS technique provides useful tools for analysing complex spatial data.
The methods employed in the study have been successful in identifying possible fracture zones in the study area.
8 REFERENCES
Drury, S.A. (1993). Image Interpretation in Geology (2nd Edition). London: Chapman & Hall. ISBN 0-412-48880-9.
Henkel, H. (2001). Remote Sensing and Image Analysis. TRITA-GEOFOTO 2001:1, ISSN 1400-3155. 38pp.
Kearey P. and Brooks M. (1984). An Introduction to Geophysical Exploration (Volume 4). London: Blackwell Scientific Publications. ISBN 0-632-01049-5.
Lillesand, T.M. and Kiefer, R.W. (2000). Remote Sensing and Image Interpretation (4th Edition). New York: Wiley. ISBN 0-471-25515-7.
Parasnis D.S. (1986). Principles of Applied Geophysics (4th Edition). ISBN 0-412-28330-1. Sander, P., Minor, T.B. and Chesley, M. (1997). Groundwater exploration based on lineament analysis. Groundwater, 35. ISSN: 0017467X.
Sen, Z. (1995). Applied Hydrogeology for Scientist and Engineers. ISBN 1-56670-091-4. Singhal, B.B.S. and Gupta, R.P. (1999). Applied Hydrogeology of Fractured Rocks. ISBN 0-412-75830-X.
Stimson, G.W. (1998). Introduction to Airborne Radar (2nd Edition). U.K. ISBN 0-85296-942-2.
Taylor, K.C., Minor, T.B., Chesley, M. and Matanawi, K. (1999). Cost effectiveness of well site selection methods in a fractured aquifer. Groundwater, 37. ISSN: 0017467X.
Ekneligoda, T.C. and Henkel, H., (2006). The spacing calculator software. Computer and Geosciences, 32: 542-553.
Bergman, M., Caliz, J.J., Ernerfeldt, H., Hautanen, U., Härdin, S., Larsson, J., Larsson, P. and Nilsson, A-C (2001). The Björkö Project. Course 1E1450 Integrated Project Results. KTH, 57pp.
Rodriguez, S. (2002). Integration of digital elevation data and bathymetric data of the Björkö structure. TRITA-INFRA EX 02-046, ISSN 1651-0194.
Reports in Geographic Information Technology 2006-2008
The TRITA-GIT Series - ISSN 1653-52272006
06-001 Uliana Danila. Corrective surface for GPS-levelling in Moldova. Master of Science thesis in geodesy No. 3089. Supervisor: Lars Sjöberg. TRITA-GIT EX 06-001. January 2006.
06-002 Ingemar Lewén. Use of gyrotheodolite in underground control network. Master of Science thesis in geodesy No. 3090. Supervisor: Erick Asenjo. TRITA-GIT EX 06-002. January 2006.
06-003 Johan Tornberg. Felfortplantningsanalys i GIS-projekt genom Monte Carlo-simulering. Master of Science thesis in geoinformatics. Supervisor: Mats Dunkars. TRITA-GIT EX 06-003. February 2006.
06-004 Constantin-Octavian Andrei. 3D affine coordinate transformations. Master of Science thesis in geodesy No. 3091. Supervisor: Huaan Fan. TRITA-GIT EX 06-004. March 2006.
06-005 Helena von Malmborg. Jämförelse av Epos och nätverks-DGPS. Master of Science thesis in geodesy No. 3092. Supervisor: Milan Horemuz. TRITA-GIT EX 06-005. March 2006.
06-006 Lina Ståhl. Uppskattning av kloridhalt i brunnar - modellering och visualisering med hjälp av SAS-Bridge. Master of Science thesis in geoinformatics. Supervisor: Hans Hauska. TRITA-GIT EX 06-006. May 2006.
06-007 Dimitrios Chrysafinos. VRS network design considerations applicable to the topology of the Hellenic Positioning System (HEPOS) stations. Master of Science thesis in geodesy No.3093. Supervisor: Lars Sjöberg. TRITA-GIT EX 06-007. May 2006.
06-008 Tao Zhang. Application of GIS and CARE-W systems on water distribution networks. Master of Science thesis in geoinformatics. Supervisor: Mats Dunkars. TRITA-GIT EX 06-008. May 2006.
06-009 Krishnasamy Satish Kumar. Usability engineering for Utö tourism information system. Master of Science thesis in geoinformatics. Supervisor: Mats Dunkars. TRITA-GIT EX 06-009. May 2006.
06-010 Irene Rangle. High resolution satellite data for mapping Landuse/land-cover in the rural-urban fringe of the Greater Toronto area. Supervisor: Yifang Ban. TRITA-GIT EX 06-010. May 2006.
06-011 Kazi Ishtiak Ahmed. ENVISAT ASAR for land-cover mapping and change detection. Supervisor: Yifang Ban. TRITA-GIT EX 06-011. May 2006.
06-012 Jian Liang. Synergy of ENVISAT ASAR and MERIS data for landuse/land-cover classification. Supervisor: Yifang Ban. TRITA-GIT EX 06-012. May 2006.
06-013 Assad Shah. Systematiska effecter inom Riksavvägningen. Master of Science thesis in geodesy No.3094. Supervisor: Tomas Egeltoft. TRITA-GIT EX 06-013. August 2006.
2007
07-001 Carl Schedlich. Turn at the roundabout: A practical assessment of spatial representations in two
different GPS interfaces from a pedestrian’s perspective. Bachelor of Science thesis in geoinformatics.
Supervisor: Michael Le Duc. January 2007.
07-002 Staffan Bengtsson. Förändringsanalys i ortofoton. Master of Science thesis in geoinformatics. Supervisor: Jonas Nelson and Patric Jansson. TRITA-GIT EX 07-002. March 2007.
07-003 Joseph Addai. Qantification of temporal changes in metal loads – Moss data over 20 years. Master of Science thesis in geoinformatics. Supervisor: Katrin Grunfeld. March 2007.
07-004 Stephen Rosewarne. Deformation study of the Vasa Ship. Bachelor of Science thesis in geodesy No.3097. Suppervisor: Milan Horemuz. March 2007.
07-005 Naeim Dastgir. Processing SAR Data Using Range Doppler and Chirp Scaling Algorithms. Master of Science thesis in geodesy No.3096. Supervisor: Lars Sjöberg. April 2007.
07-006 Torgny Israelsson and Youssef Shoumar. Motion Detection with GPS. Master of Science thesis in geodesy No.3098. Supervisor: Milan Horemuz. April 2007.
07-007 Akjol Djenaliev. Multicriteria decision making and GIS for railroad planning in Kyrgyzstan. Master of Science thesis in geoinformatics. Supervisor: Hans Hauska. May 2007.
07-008 Anna Hammar. Quality comparison of automatic 3D city house modelling methods from laser data. Master of Science thesis in geodesy No.3099. Supervisor: Milan Horemuz. May 2007.
07-009 Md Ubydul Haque. Mapping malaria vector habitats in the dry season in Bangladesh using Spot
imagery. Master of Science thesis in geoinformatics. Supervisor: Hans Hauska. May 2007.
07-010 Jing Jiang. Analysis of the Suitable and Low-Cost Sites for Industrial Land Using Multi Criteria
Evaluation: A Case of Panzhihua, China. Master of Science thesis in geoinformatics. Supervisor:
Yifang Ban. June 2007.
07-011 Raghavendra Jayamangal. Quantification of coastal erosion along Spey Bay and the Spey River using photogrammetry and LiDAR imagery-derived DTMs. Master of Science thesis in geoinformatics. Supervisor: Yifang Ban and Jim Hansom. June 2007.
07-012 Alicia E. Porcar Lahoz. An analysis of how geographical factors affect real estate prices. Master of Science thesis in geoinformatics. Supervisor: Yifang Ban. October 2007.
07-013 Ebenezer Kwakye Bentum. Detection of land use change in the Accra Metropolitan Area from 1990 to 2000. Master of Science thesis in geoinformatics. Supervisor: Hans Hauska. November 2007.
2008
08-001 Wan Wen. Road Roughness Detection by analyzing IMU Data. Master of Science thesis in geodesy No.3101. Supervisor: Milan Horemuz. Janaury 2008.
08-002 Ilias Daras. Determination of a gravimetric geoid model of Greece using the method of KTH. Master of Science thesis in geodesy No.3102. Supervisor: Huaan Fan and Kalliopi Papazissi. Janaury 2008. 08-003 Anwar Negash Surur. Surveying, modelling and visualisation of geological structures in the
Tunberget tunnel. Master of Science thesis in geoinformatics. Supervisor: Hans Hauska. February 2008. 08-004 Helena Swanh. Noggrannhetskontroll av data 3D-modell från pulsskanner och fasmätningsskanner.
Master of Science thesis in geodesy No.3103. Supervisor: Milan Horemuz. March 2008. 08-005 Henrik Löfqvist. Inpassning av mätdata i inhomogena nät. Master of Science thesis in geodesy
No.3104. Supervisor: Milan Horemuz and Anders Viden. April 2008.
