GIS AND REMOTE SENSING APPLICATIONS
7.1 Introduction Remote sensing (RS) is the science and it works as an art to some extent. The decade of the 1990 s was revolutionary in terms of the introduction of new technology and the acceptance of current investigations. It is acquiring information about the Earth's surface without any contact with object. This is done by sensing and recording reflected or emitted energy, processing, analyzing and applying that information. In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. This is represented by the use of imaging systems where the following seven elements are involved. However, the remote sensing also involves the sensing of emitted energy and the use of non-imaging sensors. The following seven elements comprise the remote sensing process from beginning to end (Fig. 7.1, Fig. 7.2 and Fig. 7.3). 7.1.1 Energy Source or Illumination (A): The first requirement for remote sensing is the energy source which illuminates or provides electromagnetic energy to the target of interest. 7.1.2 Radiation and the Atmosphere (B): The energy travels from source to the target area and it will come in a contact and interact with the atmosphere. This interaction may take place a second time as the energy travels from the target area to the sensor. 7.1.3 Interaction with the Target (C): Once the energy makes its way to the target through the atmosphere and it interacts with the target depending on the properties of both the target and the radiation. 7.1.4 Recording of Energy by the Sensor (D): After the energy has been scattered or emitted from the target point and then we require a sensor to collect and record the electromagnetic radiation. 7.1.5 Transmission, Reception, and Processing (E): The energy recorded by the sensor has to be transmitted into an electronic form. After receiving and processing data in station where the data has been processed into an image (hardcopy and/or digital). 153
7.1.6 Interpretation and Analysis (F): The processed image is interpreted, visually or digitally or electronically, to extract information about the target area which was illuminated. 7.1.7 Application (G): The final element of the remote sensing process is achieved then we need to extract information from the imagery about the target area to understand and reveal new information. Fig. 7.1 Remote sensing process from beginning to end. Fig. 7.2 Earth observation satellites 154
Fig. 7.3 Earth observation satellites current scenario 7.2 Optical Remote Sensing Systems Optical remote sensing systems make use of visible, near infrared and short-wave infrared sensors to form images of the earth's surface by detecting the solar radiation reflected from targets on the ground. Different materials reflect and absorb differently at different wavelengths. Therefore, the targets can be differentiated by their spectral reflectance signatures in the remotely sensed images. It is classified into the following types, depending on the number of spectral bands used in the imaging process. 7.2.1 Panchromatic imaging system It consists of single channel detector sensitive to radiation within a broad wavelength range. It usually displays images as a grey scale image and brightness of a particular pixel is proportional to the pixel digital number which is related to the intensity of solar radiation reflected by the targets in the pixel. Thus, a panchromatic image may be similarly interpreted as a black and white aerial photograph of the area. The radiometric information is the main information type utilized in the interpretation. These images extracted from a SPOT panchromatic scene at a ground resolution of 10 155
m. The ground coverage is about 6.5 km (width) by 5.5 km (height). The roads, river, buildings and vegetated area can be seen clearly. The following images are examples of panchromatic imaging systems. a) IKONOS PAN b) SPOT HRV PAN (High Resolution Visible) 7.2.2 Multispectral imaging system The sensor consists of multichannel detector with a few spectral bands. Each channel is sensitive to radiation within a narrow wavelength band. For visual display, each band of the image may be displayed one band at a time as a grey scale image, or in combination of three bands at a time as a colour composite image. Interpretation of a multispectral colour composite image will require the knowledge of the spectral reflectance signature of the targets in the scene. The following examples are the multispectral systems. a) LANDSAT MSS b) LANDSAT TM -7 spectral bands 30m spatial resolution 16 day repeat cycle c) SPOT HRV XS d) IKONOS MS - 4 spectral Bands 4m spatial resolution 5 day repeats cycle 7.2.3 Super-spectral imaging systems A super spectral imaging sensor has many more spectral channels (typically >10) than a multispectral sensor. The bands have narrower bandwidths, enabling the finer spectral characteristics of the targets to be captured by the sensor. These are the examples of super spectral systems. a) MODIS - Multi- spectral bands 250-1000m spatial resolution (band dependent) 1day repeat cycle b) MERIS 7.2.4 Hyperspectral imaging systems It is also known as an imaging spectrometer. It acquires images about a hundred or more contiguous spectral bands. The precise spectral information contained in a hyperspectral image enables better characterization and identification of targets. Hyperspectral images have potential applications in such fields as precision 156
agriculture (health, moisture status and maturity of crops), coastal management (monitoring of phytoplanktons, pollution, bathymetry changes). Hyperion on EO1 satellite is an example for hyperspectral system. 7.2.5 Solar Irradiation Optical remote sensing depends on the sun as the sole source of illumination. The solar irradiation spectrum above the atmosphere can be modeled by a black body radiation spectrum and its having a source temperature of 5900K with a peak irradiation located at about 500 nm wavelength. Physical measurement of the solar irradiance has also been performed using ground based and space borne sensors. After passing through the atmosphere, the solar irradiation spectrum at the ground is modulated by the atmospheric transmission windows. Significant energy remains only within the wavelength range from about 0.25 to 3 µm (Fig. 7.4). Fig. 7.4 Wavelength Vs Solar irradiance 7.2.6 Spectral Reflectance Signature When solar radiation hits a target surface, it may be transmitted, absorbed or reflected. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as 157
a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish. This premise provides the basis for multispectral remote sensing. The following graph shows the typical reflectance spectra of five materials like clear water, turbid water, bare soil and two types of vegetation. (Fig. 7.5). Fig. 7.5 Reflectance Spectrum of Five Types of Land-cover The reflectance of clear water is generally low. However, the reflectance is maximum at the blue end of the spectrum and decreases as wavelength increases. Hence, clear water appears dark bluish. Turbid water has some sediment suspension which increases the reflectance in the red end of the spectrum, accounting for its brownish appearance. The reflectance of bare soil generally depends on its composition. Hence, it should appear yellowish red to the eye. Hence, vegetation can be identified by the high near infrared (NIR) but generally low visible reflectance (Fig. 7.6). 158
Fig. 7.6 Typical Reflectance Spectrum of Vegetation. T (he labeled arrows indicate the common wavelength bands used in optical remote sensing of vegetation: A -blue band, B - green band, C - red band, D- near IR band, E shortwave IR band) 7.2.7 Colour Composite Images In displaying a colour composite image, three primary colors (red, green and blue) are used. When these three colors are combined in various proportions, they produce different colors in the visible spectrum. The each spectral band (not necessarily a visible band) to a separate primary colour results in a colour composite image. Many colors can be formed by combining the three primary colors (Red, Green and Blue) in various proportions (Fig. 7.7). Fig. 7.7 Three primary colors 159
7.2.8 True Colour Composite (TCC) If a multispectral image consists of the three visual primary colour bands (red, green, blue) and these three bands may be combined to produce a "true colour" image. For example, the bands 3 (red band), 2 (green band) and 1 (blue band) of a LANDSAT TM image or an IKONOS multispectral image can be assigned respectively to the R, G, and B colors for display. 7.2.9 False Colour Composite (FCC) The display colour assignment for any band of a multispectral image can be done in an entirely arbitrary manner. In this case, the colour of a target in the displayed image does not have any resemblance to its actual colour. The resulting product is known as a false colour composite image. There are many possible schemes of producing false colour composite images. However, some scheme may be more suitable for detecting certain objects in the image. Another common false colour composite scheme for displaying an optical image with a short-wave infrared (SWIR) band is shown below R = SWIR band (SPOT4 band 4, Landsat TM band 5) G = NIR band (SPOT4 band 3, Landsat TM band 4) B = Red band (SPOT4 band 2, Landsat TM band 3) 7.2.10 Natural Colour Composite (NCC) For optical images lacking one or more of the three visual primary colour bands (i.e. red, green and blue), the spectral bands (some of which may not be in the visible region) may be combined in such a way that the appearance of the displayed image resembles a visible colour photograph, i.e. vegetation in green, water in blue, soil in brown or grey, etc. Many people refer to this composite as a "true colour" composite. 160
7.3 ASTER Image Applications Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) which is aboard the Earth observing system (EOS) TERRA platform. It is records solar radiation in 14 spectral bands (Fig. 7.8). It measures reflected radiation in 3 bands between 0.52 and 0.86 μm (VNIR), in 6 bands from 1.6 to 2.43 μm (SWIR), and emitted radiation in 5 bands in the 8.125 to 11.65μm wavelength region (TIR). The resolution of VNIR, SWIR, and TIR is 15m, 30m and 90m respectively (Fujisada, 1995). The spectral resolution provided by ASTER, identification of specific alteration assemblages becomes feasible. The VNIR, SWIR, and TIR wavelength regions provide complementary data for lithologic mapping and exploration through alteration mapping. Already ASTER data have been extensively used for these purposes (Rowan et al, 2005). The ASTER image obtains highresolution (15 to 90 square meters per pixel) images of the Earth in 14 different wavelengths of the electromagnetic spectrum, ranging from visible to thermal infrared light (Table 7.1 and Fig. 7.9). Table 7.1 ASTER image applications Subsystem Band No. Spectral Range (µm) Spatial Resolution (m) Quantization Levels VNIR SWIR TIR 1 0.52-0.60 2 0.63-0.69 3N 0.78-0.86 3B 0.78-0.86 4 1.6-1.70 5 2.145-2.185 6 2.185-2.225 7 2.235-2.285 8 2.295-2.365 9 2.360-2.430 10 8.125-8.475 11 8.475-8.825 12 8.925-9.275 13 10.25-10.95 14 10.95-11.65 15 8 bits 30 8 bits 90 12 bits 161
Fig. 7.8 ASTER image applications Fig. 7.9 Instrument Characteristics 60 Characteristics 60 km swath; <16 day repeat cycle stereo 7.4 Landsat Image applications Landsat multispectral scanner (MSS) data is using by geoscientists because it is the only earth resource satellite data available. Geoscientists found that MSS data was found to be useful for mapping topographic and landforms. MSS spectral data, while limited in the comparison to Landsat thematic mapper data can be processed effectively display the abundance of iron oxide minerals often associated with 162
mineralised areas. MSS data was successfully processed to display vegetation anomalies associated with nickel laterite deposits in Indonesia (Taranik, et. al. 1978). The Landsat thematic data are now routinely used by explorationists in the most of the remote, unexplored areas of the world. Landsat TM data have been the data set of choice because nine SPOT scene are required to cover the same area as one TM full scene (185 km by 185 km) Taranik (1990). 7.5 Minimum Noise Fraction (MNF) It is used to show the variation between MNF between bands in an image. This is a statistical method which works out differences in an image based on pixel DNs DNsin various bands. MNF determines the inherent in dimensionality of image data, to segregate noise in the data, and dimensionality and to reduce d the computational requirements for subsequent processing. 7.6 Satellite Imagery Applications The high resolution satellite imageries from satellite sensors such as GeoEye-1, WorldView-2 Worldview-1, Quick-Bird, IKONOS, SPOT-5 and other remote sensing products for analysis and mapping applications such as Geographic Information System (GIS). Geologists and Geoscientists are using these satellite images to serve as databases like Pick out lithology, geomorphology, structural setting (folds, faults and unconformities), evaluate dynamic changes from natural events (e.g., floods and volcanic eruptions), seek surface clues (such as hydrothermal alterations, associated with signs of gold mineralization) to subsurface deposits of ore minerals, oil and gas, and groundwater. This data could be utilized to interpret actual surface lithology to identify clays, oxides and soils from satellite imagery. 7.7 Orbits and Swaths The remote sensing instruments can be placed on a variety of platforms to view and image targets. Although ground based and aircraft platforms may be used, satellites provide a great contract of the remote sensing imagery. Satellites have several unique characteristics which make them particularly useful for remote sensing of the Earth's surface. The path followed by a satellite is referred to as its orbit. Satellite orbits are matched to the capability and objective of the sensor(s) which they carry. 163
7.8 Resolution The resolution is the minimum distance between two objects that can be distinguished in the image. Objects closer than the resolution appear as a single object in the image. However, in remote sensing the term resolution is used to represent the resolving power, which includes not only the capability to identify the presence of two objects, but also their properties. In qualitative terms resolution is the amount of details that can be observed in an image. Thus an image that shows finer details is said to be of finer resolution compared to the image that shows coarser details. Four types of resolutions are defined for the remote sensing systems. a) Spatial resolution b) Spectral resolution c) Temporal resolution d) Radiometric resolution 7.8.1 Spatial Resolution A digital image consists of an array of pixels. Each pixel contains information about a small area on the land surface, which is considered as a single object. Spatial resolution is a measure of the area or size of the smallest dimension on the Earth s surface and it can be made independent measurement by the sensor. It is expressed by the size of the pixel on the ground in meters. 7.8.2 Spectral Resolution Spectral resolution represents the spectral band width of the filter and the sensitiveness of the detector. Many remote sensing systems are multispectral, that record energy over separate wavelength ranges at various spectral resolutions like IRS LISS-III uses 4 bands: 0.52-0.59 (green), 0.62-0.68 (red), 0.77-0.86 (near IR) and 1.55-1.70 (mid-ir). 7.8.3 Temporal Resolution Temporal resolution is a measure of the repeat cycle or frequency with which a sensor revisits the same part of the Earth s surface. The frequency will vary from several times per day, for a typical weather satellite, to 8 20 times a year for a moderate ground resolution satellite, such as Landsat TM. 164
7.9 Digital Image Processing Digital image processing of the multispectral remotely sensed data is carried out in order to improve the appearance of the images, enhance information in the geological context. In this view the various techniques have adopted here to improve the visual geological interpretation and automated spectral signatures extraction related to the altered halos of the gold mineralized zones. 7.9.1 Image enhancement techniques Image enhancement techniques adopted in this study compromised different spectral techniques. Spectral enhancement can be carried by simple contrast stretching technique to enhance certain spectral characteristic features in the digital images. Contrast stretching is one of the most simple, important and widely used methods. It deals with rescaling of grey levels to the entire digital number (DN) range so that features of interest are better shown in the image (Gupta, 2003). 7.9.2 Mineral exploration Mineral deposits are usually associated with alteration zones, especially those related to sulphide deposits. These alteration successive zones constitute one of the most important controllers for mineral exploration. They are rich in alteration minerals such as, the iron oxides, the hydroxyl-bearing minerals, carbonates and quartz-feldspar minerals. The spectral features of the hydroxyl-bearing and iron oxide minerals are the main indicators for prospecting and delineating mineral deposits in multispectral remote sensing context and are widely applicable in the prospecting projects. 7.9.3 Band ratioing images The band ratio is a technique that has been used for many years in remote sensing to display spectral variations effectively (Goetz et al. 1983). A band ratio image is created by dividing brightness values, pixel by pixel, of one band by another. The primary purpose of such ratios is to enhance the contrast between objects by dividing brightness values at peaks and troughs in a spectral reflectance curve. In mineral exploration band rationing is widely used to enhance the spectral feature of the alteration zones depending on the absorption bands of their 165
altered minerals. For example iron oxides (ferrous and ferric oxides) minerals are illuminated by band ratio 3/1, 3/5 and 5/4, while band ratio 5/7 is used for detecting high values of the hydroxyl-bearing minerals (kaolinite, illunite, muscovite, epidotes, chlorites, amphiboles) (Gupta et al., 2003). The obtained image portrays gossans and alteration zones in crimson reddish hues due to high contents of their iron oxides and clay minerals (Sabins, 1997). In addition, ratio operation may also provide unique information that is not available in any single band which is very useful for disintegrating the surface materials (Jensen 1996). The band ratios images are known for enhancement of spectral contrasts among the bands considered in the ratio operation and have successfully been used in mapping of alteration zones (Segal 1983). 7.9.4 Principal Component Analysis (PCA) The principal components analysis (PCA) method used in the principal components transformation technique for reducing dimensionality of correlated multispectral data. The selected bands are believed to exhibit spectral information over an intended target (Crosta and Mc. Moore, 1989). This method developed by Loughlin (1991) to map alteration zones and it is well known by Crosta image. The analysis is based on multivariate statistical technique that selects uncorrelated linear combinations (eigenvector loadings) of variables in such a way that each successively extracted linear combination, or principal component (PC), has a smaller variance (Singh and Harrison 1985). The statistical variance in multispectral images is related to the spectral response of various surficial materials such as rocks, soils, and vegetation, and it is also influenced by the statistical dimensionality of the image data (Loughlin 1991). 7.9.5 GIS Geospatial Analysis The obtained classified maps from vectorized to polygon feature class and reclassified, whereby alterations layers from both maps are extracted to two new layers. Geospatial overly analysis by intersect processing give new alteration map. This final GIS layer directly depicts alteration zones from both original images. 166
7.9.6Typical image processing routines include a) Restoring line dropouts b) Restoring periodic line striping c) Restoring line offsets d) Filtering random noise e) Correcting for atmospheric scattering f) Correcting geometric distortions g) Contrast enhancement h) Density slicing i) Edge enhancement Image processing and analysis. j) Making digital mosaics k) Intensity, hue, and saturation transformations l) Merging data sets m) Synthetic stereo images n) Principal-component images o) Ratio images p) Multispectral classification q) Change-detection images. 7.10 Global Positioning System The Global Positioning System (GPS) is a satellite based navigation system made up of a network of 24 satellites which are placed into orbit by the U.S. Department of Defense. GPS was originally intended for military applications, but in the 1980s, the government made the system available for civilian use. It works in any weather conditions, anywhere in the world, 24 hours a day. There are no subscription fees or setup charges to use GPS. GPS satellites circle the earth twice a day in a very precise orbit and transmit signal information to earth. It receivers take this information and use triangulation to calculate the user's exact location. Basically, the GPS receiver compares the time a signal was transmitted by a satellite with the time it was received. Today's GPS receivers are extremely accurate and error is approximately 3 to 5m depending upon the weather condition and in dense forest area. In differential GPS, one receiver is mounted in a stationary position, usually at the farm office, while the 167
other is on the tractor or harvesting equipment. The stationary receiver calculates the error and transmits the necessary correction to the mobile receiver. 7.11 Datum and GIS Having a standard accurate datum set becomes increasingly important as multiple layers of information about the same area are collected and analyzed. The layers are developed into geographic information systems (GIS), which enable the relationships between layers of data to be examined. In order to function effectively, a GIS must possess one essential attribute. It must have the ability to geographically relate data within and across layers. For example, if a dataset about vegetation is being examined against the data sets for topography and soils, the accurate spatial compatibility of the two datasets is critical. 7.12 Geographic Information System (GIS) Applications High resolution remote sensing data and Geographic Information Systems (GIS) are important tools to map subtle anomalies associated with unknown gold deposits (Asadi, 2000). GIS is the key tool for resource management, planning and monitoring programs requiring on accurate information about the land cover in a region. Mineral exploration project starts with regional level studies to identify and prioritize target area for detailed exploration, survey and drilling. Such studies are an essential prerequisite to develop detailed exploration plan for cost optimization and reduction of business risk which includes following study. In Gadag schist belt, Nagavi area is one of the potential gold mineralisation zones. The auriferous shear quartz vein zones were identified in the contact of banded iron formations (BIFs) and metabasalt near Nagavi. The gold mineralisation mainly occurs in hydrothermal alterations and associated with sulphide mineralization, clay-sericite alterations, silicification and carbonatization. Due to the unsystematic gold exploration and lack of modern exploration techniques, there are still several unexplored areas of gold mineralisation in the Nagavi area. The high resolution remote sensing data and GIS techniques are important tools to identify the good anomalies associated with unknown gold mineralisation (Asadi, 2000). 168
7.13 LISS III image processing with auriferous shear zones In the Nagavi study area the BIF bands were identified in the field and the same data were plotted in GIS software and superimposed on LISS III image by using ArcGIS software (Fig. 7.10). The BIF bands are trending in NW-SE direction. The major lineaments were identified in the field during fieldwork and plotted and super imposed on LISS III. The auriferous shear zone gold values have been plotting on LISS III imageries by using ArcGIS (Fig. 7.11). Sulphide mineralization in Nagavi area occurs in BIF hosted deformed-altered shear zones. The auriferous shear zones were identified in the study area near Nagavi and Mallasamudra area. These shear zones are clearly indicating the gold mineralisation. The field data has been integrated and superimposed on LISS III satellite imagery with gold values. The main types of hydrothermal alterations, associated with gold mineralization are clay-sericite alterations, silicification and carbonatization. In this research, a spectral analysis was performed on the ETM satellite imagery data of the Nagavi auriferous shear zone associated with the gold mineralization. 7.14 Lineament In the Nagavi study area the lineaments were identified during the field work and superimposed on Cartosat DEM by using ArcGIS. The gold analysis assay samples were plotted on Cartosat DEM imageries and the auriferous shear zones also superimposed on Cartosat DEM. The auriferous shear zone gold values have been plotting on LISS III imageries by using ArcGIS (Fig. 7.12). Lineaments are visible at the earth s surface, which are the representations of geological and geomorphological phenomena (Clark and Wilson, 1994). In geomorphometric analysis, a linear feature may have geometric origin only and represent a change in terrain elevation, such as a valley or ridgeline, slope-break. In terms of digital modelling, a lineament is a continuous series of pixels having similar terrain values (Koike et al., 1998). The most of the lineament trends are NW-SW direction parallel to BIF and some of the lineaments trends are in NE-SW direction. 169
Fig. 7.10 BIF bands were superimposed on LISS III image by using ArcGIS 170
Fig. 7.11 Auriferous sheared zones with gold values data integrated with LISS III image 171
7.15. Landuse / Land cover The Landuse / Land cover map prepared by using ERDAS software. In the majority of the land cover is Agriculture land followed by landwithcrub. The remaining area covered with built up, vegetable and plantation (Fig. 7.13). 7.16 Contour and Slope map The contour and slope map was generated through ArcGIS software for the study area. The contour map was providing an opportunity to measure land surface geometry in terms of elevation and its derivatives (Fig. 7.14 and Fig. 15). The basic geometric properties that characterize the terrain surface at a point are: (1) elevation; (2) properties of the gradient vector its magnitude defining slope. The relationship between geometric point attributes and tectonic structures such as slope-breaks and fractures is often straightforward (Siegal and Gillespie, 1980; Drury, 1987; Prost, 1994; Salvi, 1995). 7.17 Image Processing of Landsat ETM data The Nagavi BIF bands and lineaments were superimposed on Landsat ETM imageries by using ArcGIS. This will help us to identify the shear zones as well as lineaments in the same area (Fig. 7.16). The Enhanced Thematic mapper (ETM) plus sensor was launched on April 15, 1999 on the Landsat 7 spacecraft. The sensor uses the whiskbroom scanner, common to the Thematic Mapper (TM) sensor family that was flown on Landsat 4 and 5. The sensor improved with several evolutionary refinements including the addition of a 15- m resolution panchromatic band and a higher resolution (60 m) thermal band (Markham et al. 2008, Mather 1987 and Lillesand et al. 2004). There are many studies around the world related to hydrothermal alteration mapping using multispectral satellite images especially, Landsat and ASTER Abdelsalam et al. 2000; Ramadan et al. 2001; Madani et al. 2003; Ramadan and Kontny 2004). Thematic mapping multispectral images from Landsat satellites cover the visible and infrared spectrum of hydrothermal alterations (Hunt 1979; Hunt and Ashley 1979). Iron oxide is quite a common constituent of alteration zones associated with hydrothermal sulphide deposits (Poormirzaee and Oskouei 2009). The major iron oxide species goethite, jarosite, and hematite that are formed from the weathering of 172
sulfides absorb energy at different frequencies in the VNIR/SWIR (Rowan 1983), providing a means of discrimination using hyperspectral scanners (Taranik et al. 1991). The hydrothermal alteration is the reflection of response of pre-existing, rock-forming minerals to physical and chemical conditions different than those, under which they originally formed, especially by the action of hydrothermal fluids (Beane 1982). They appear concentrically around a core which has the highest grade alteration and greatest economic interest. The matched filtering (MF) finds the abundance of targets using a partial un-mixing algorithm (Jin et al. 2009). The importance of the recognition of such spatial patterns of alteration makes the remote sensing technique one of the standard procedures in exploration geology, due to its high efficiency and low cost (Yetkin 2003). 173
Fig. 7.12 Lineaments with Cartosat DEM by using ArcGIS 174
Fig. 7.13 Land use land cover of Nagavi area by using ERDAS software 175
Fig. 7.14 Contour map of Nagavi area 176
Fig. 7.15 Slope map of Nagavi area using Arc GIS 177
Fig. 7.16 Landsat ETM image with BIF bands using ArcGIS 178