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Über dieses Buch

For nearly three decades there has been a phenomenal growth in the field of Remote Sensing. The second edition of this widely acclaimed book has been fully revised and updated. The reader will find a wide range of information on various aspects of geological remote sensing, ranging from laboratory spectra of minerals and rocks, ground truth, to aerial and space-borne remote sensing.
This volume describes the integration of photogeology into remote sensing as well as how remote sensing is used as a tool of geo-exploration. It also covers a wide spectrum of geoscientific applications of remote sensing ranging from meso- to global scale.
The subject matter is presented at a basic level, serving students as an introductory text on remote sensing. The main part of the book will also be of great value to active researchers.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
Remote sensing, in the simplest words, means obtaining information about an object without touching the object itself. It has two facets: the technology of acquiring data through a device which is located at a distance from the object, and analysis of the data for interpreting the physical attributes of the object, both these aspects being intimately linked with each other.
Ravi Prakash Gupta

Chapter 2. Physical Principles

Abstract
As discussed in Chapter 1, in remote sensing, the electromagnetic (EM) radiation serves as the main communication link between the sensor and the object. Fraser and Curran (1976), Silva (1978) and Suits (1983), provide valuable reviews on the nature of EM radiation and physical principles. The properties of EM radiation can be classified into two main groups: (1) those showing a wave nature and (2) those showing particle characteristics.
Ravi Prakash Gupta

Chapter 3. Spectra of Minerals and Rocks

Abstract
Interactions of the EM radiation with matter at atomic—molecular scale result in selective absorption, emission and reflection, which are best explained by the particle nature of light. The relationship between the intensity of EM radiation and wavelength is called the spectral response curve, or broadly, spectral signature Fig. 1.1). A single feature or a group of features (pattern) in the curve could (g be diagnostic in identifying the object. In the context of remote sensing, objects can be marked by the following types of spectral behaviour.
Ravi Prakash Gupta

Chapter 4. Photography

Abstract
Photography was invented in 1839 by N. Niepce, W.H.F. Talbot and L.J.M. Daguerre, and since then photographic techniques have been used in applied sciences for various applications. Photographic pictures of the Earth were first acquired from balloons and kites, until the aeroplane was invented in 1903. World Wars I and II provided a new opportunities and challenges to apply photographic techniques from aerial platforms. Soon afterwards, man started exploring space and observing the Earth from space platforms, using improved photographic techniques. Photographic systems for remote sensing have been discussed by Smith and Anson (1968), Colwell (1976), Slater (1980, 1983), Eastman Kodak Company (1981, 1990, 1992), Curran (1985), Teng et al. (1997) and Lillesand and Kiefer (2000), and in numerous other publications.
Ravi Prakash Gupta

Chapter 5. Multispectral Imaging Systems

Abstract
In this chapter we discuss the non-photographic multispectral imaging systems operating in the optical range of the electromagnetic spectrum. The optical range has been defined as that range in which optical phenomena of reflection and refraction can be used to focus the radiation. It extends from X-rays (0.02-µm wavelength) through visible and infrared, reaching up to microwaves (<1-mm wavelength) (Fig. 2.3). However, as the useful region for remote sensing of the Earth lies between 0.35 µm and 14 µm, we largely focus our attention on this specific region. For valuable reviews on non-photographic sensors, see Lowe (1976), Silva (1978), Slater (1980, 1985), Joseph (1996) and Ryerson et al. (1997).
Ravi Prakash Gupta

Chapter 6. Geometric Aspects of Photographs and Images

Abstract
It is often pertinent to know not only what the object is, but also where it is; therefore, a universal task of remote sensing scientists is to deliver maps displaying spatial distribution of objects. Geometrically distorted image data provide incorrect spatial information. Geometric accuracy requirements in some applications may be quite high, so much so that the entire purpose of the investigation may be defeated if the remote sensing data are geometrically incorrect beyond a certain level. In brief, geometric fidelity of the remote sensing data is essential for producing scaled maps and for higher application prospects.
Ravi Prakash Gupta

Chapter 7. Image Quality and Principles of Interpretation

Without Abstract
Ravi Prakash Gupta

Chapter 8. Interpretation of Data in the Solar Reflection Region

Abstract
As stated earlier (Chapter 2), the EM spectral region extending from 0.3 µm to approximately 3 µm is the solar reflection (SOR) region in terrestrial remote sensing. The Sun is the only source of energy in this spectral range, and the solar radiation scattered by the Earth’s surface is studied for ground object discrimination and mapping.
Ravi Prakash Gupta

Chapter 9. Interpretation of Data in the Thermal Infrared Region

Abstract
The EM wavelength region of 3–35 µm is popularly called the thermal-infrared region in terrestrial remote sensing. This is because of the fact that, in this wavelength region, radiation emitted by the Earth due to its thermal state is far more intense than solar reflected radiation (Fig. 9.1), and therefore any sensor operating in this region would primarily detect the thermal radiative properties of ground materials.
Ravi Prakash Gupta

Chapter 10. Digital Image Processing of Multispectral Data

Abstract
In the most general terms, a digital image is an array of numbers depicting spatial distribution of a certain field or parameter. It is a digital representation in the form of rows and columns, where each number in the array represents the relative value of the parameter at that point/over the unit area (Fig. 10.1). The parameter could be reflectivity of EM radiation, emissivity, temperature, or a parameter such as topographical elevation, geomagnetic field or even any other computed parameter. In this chapter, we deal with remote sensing multispectral images.
Ravi Prakash Gupta

Chapter 11. Hyperspectral Sensing

Abstract
Hyperspectral sensing, also called imaging spectrometry, is a relatively new field that has rapidly grown during the last two decades. The term hyperspectral is used here to indicate a very large number of narrow spectral channels, e.g. 64 to about 200 channels at 10–20-nm interval, in comparison to the multispectral sensing in which we have typically 4–10 spectral channels at approximately 100–200nm interval. The main aim in hyperspectral sensing is to image a scene in a large number of discrete contiguous narrow spectral bands. An almost continuous spectrum can be generated for each pixel, and the data are in image format, hence the name imaging spectrometry (Fig.11.1). A wealth of review information on imaging spectrometry technique and its geological applications can be found in Clark (1999), Kruse (1999), Mustard and Shine (1999) and van der Meer (1999).
Ravi Prakash Gupta

Chapter 12. Microwave Sensors

Abstract
The EM spectrum range 1 mm to 1.0 m is designated as microwave. In the context of terrestrial remote sensing, this spectral region is marked by an excellent atmospheric window, i.e. the radiation traverses the atmosphere with minimal absorption and attenuation (Fig. 12.1). Therefore, this spectral range has aroused much interest for remote sensing applications.
Ravi Prakash Gupta

Chapter 13. Interpretation of SLAR Imagery

Abstract
The technique of SLAR imaging and the various aerial and space-borne SLAR sensors were discussed in the preceding chapter. Briefly, the radar mounted on the base of the sensor platform looks sideways down on the Earth, transverse to the flight direction. It emits pulses of microwave energy, which illuminate long narrow stripes on the ground. The back-scattered signal, sensed by the antenna, is recorded in order of arrival time. This yields a radar image.
Ravi Prakash Gupta

Chapter 14. SAR Interferometry

Abstract
Synthetic Aperture Radar (SAR) interferometry, also called Interferometric SAR (InSAR, also IFSAR or ISAR), is a relatively new technique for producing digital elevation models (DEMs). It has made rapid strides during the last decade. As it provides a higher order of accuracy than the conventional stereo radargrammetry, the technique is on the verge of becoming operational as an alternative to the latter. The InSAR method combines complex images recorded by SAR antennas at different locations and/or at different times to generate interferograms. This permits determination of differences in the 3-D location of objects, i.e. generation of DEMs. The DEMs have wide application for geoscientific studies, e.g. for topographic mapping, geomorphological studies, detecting surface movements, earthquake and volcanic hazard studies, and several other applications.
Ravi Prakash Gupta

Chapter 15. Integrating Remote Sensing Data with Other Geodata (GIS Approach)

Abstract
The purpose of integrated multidisciplinary investigations is to study a system or phenomenon using several approaches and as many attributes as possible/required, in order to obtain a more comprehensive and clearer picture. The main advantage of such an approach is that ambiguities, which may arise from the use of only one type of data, can often be resolved by combining several data sets.
Ravi Prakash Gupta

Chapter 16. Geological Applications

Abstract
Multispectral remote sensing data have shown tremendous potential for applications in various branches of geology — in geomorphology, structure, lithological mapping, mineral and oil exploration, stratigraphic delineation, geotechnical, ground water and geo-environmental studies etc. The purpose of this chapter is to review briefly the parameters involved in various thematic applications and present a few illustrative examples using mainly satellite data.
Ravi Prakash Gupta

Backmatter

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