{"id":30,"date":"2019-03-08T04:27:37","date_gmt":"2019-03-08T04:27:37","guid":{"rendered":"http:\/\/esp06.epgpbooks.inflibnet.ac.in\/?post_type=chapter&p=30"},"modified":"2019-03-08T04:28:10","modified_gmt":"2019-03-08T04:28:10","slug":"fundamentals-of-remote-sensing","status":"publish","type":"chapter","link":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/chapter\/fundamentals-of-remote-sensing\/","title":{"rendered":"Fundamentals of Remote Sensing"},"content":{"raw":"
\r\n\r\n \r\n\r\nCONTENTS<\/strong>\r\n\r\n \r\n\r\n1.\u00a0 Learning Objectives\r\n\r\n \r\n\r\n2.\u00a0 Introduction\r\n\r\n \r\n\r\n3.\u00a0 Energy Sources and Radiation Principles\r\n\r\n \r\n\r\n4.\u00a0 Energy Interactions in the Atmosphere\r\n\r\n \r\n\r\n5.\u00a0 Energy Interactions with Earth Surface Features\r\n\r\n \r\n\r\n6.\u00a0 Data Acquisition and Interpretation\r\n\r\n \r\n\r\n7.\u00a0 Reference Data\r\n\r\n \r\n\r\n8.\u00a0 Characteristics of Remote Sensing Systems\r\n\r\n \r\n\r\n9.\u00a0 Conclusions\r\n\r\n \r\n\r\n10.\u00a0 References\r\n\r\n \r\n\r\n \r\n\r\n1.\u00a0 <\/strong>Learning Objectives<\/strong>\r\n\r\n\u00a0<\/strong>\r\n
    \r\n \t
  • This module will help us understand the basic principle of remote sensing and the processes involved. For this purpose, we will begin with the fundamentals of the electromagnetic energy and then consider how the energy interacts with the atmosphere and Earth surface features.<\/li>\r\n \t
  • Next, we will summarize the process of acquiring and interpreting imagery in both digital and analog formats.<\/li>\r\n \t
  • \u00a0We would also discuss the role that reference data play in the data analysis procedure and describe how the spatial location of reference data observed in the field is often determined.<\/li>\r\n<\/ul>\r\n \r\n

    Thus, this module will deal with the electromagnetic energy sensors that are currently being operated from airborne and space-borne platforms; to assist in inventorying, mapping and monitoring earth\u2019s resources.<\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n \r\n\r\n2. Introduction<\/strong>\r\n\r\n \r\n

    Remote sensing is the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand and Kiefer, 1994). This implies that as we are reading the words that make up the sentences, our eyes act as remote sensor that respond to light reflected from this page. These responses based on reflected light help us distinguish between white areas of the paper and dark areas reflected from the letters of the page. These data are analyzed or interpreted in our brain to explain the dark areas on the page as a collection of letters forming words. This is continued further with words forming sentences and the information carried in the sentence is interpreted.<\/p>\r\n \r\n

    On extrapolating this entire process of reading to earth\u2019s surface features; remote sensing involves the use of various sensors to obtain information about the objects, area or phenomenon being investigated. Figure 1.1 schematically illustrates the generalized processes and elements involved in electromagnetic remote sensing of earth resources. The two basic processes involved are (i) data acquisition, and (ii) data analysis.<\/p>\r\n \r\n\r\n2.1 Data Acquisition<\/strong>: The elements of data acquisition process are:\r\n\r\n \r\n\r\na)\u00a0\u00a0\u00a0\u00a0\u00a0 Source of energy\r\n\r\n \r\n\r\nb)\u00a0\u00a0\u00a0\u00a0\u00a0 Propagation of energy through the atmosphere\r\n\r\n \r\n\r\nc)\u00a0\u00a0\u00a0\u00a0\u00a0 Energy interactions with earth surface features\r\n\r\n \r\n\r\nd)\u00a0\u00a0\u00a0\u00a0 Retransmission of energy through the atmosphere\r\n\r\n \r\n\r\ne)\u00a0\u00a0\u00a0\u00a0\u00a0 Airborne and\/or spaceborne sensors\r\n\r\n \r\n\r\nf)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Sensors- These interactions result in the generation of sensor data in pictorial and\/or digital form. Sensors are used to record variations in the way the Earth surface features reflect and emit electromagnetic energy.\r\n\r\n \r\n

    2.2 Data Analysis<\/strong>: The data analysis process (g) involves examining the data using various viewing and interpretation devices to analyze pictorial data and\/or a computer to analyze digital sensor data. This is further complemented by reference data that includes soil maps, crop statistics, or field-check\u00a0data to extracts information about the type, extent, location and condition of the various resources by the analyst. This information is then compiled (h), generally in the form of hardcopy maps and tables or as computer files that can be merged with other layers of information in a geographic information system (GIS). Finally, the information is presented to the users (i), who apply it to their decision-making process.<\/span><\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n\"\"\r\n\r\n \r\n

    Figure 1: Electromagnetic remote sensing of Earth resources<\/p>\r\n \r\n\r\n3. Energy Sources and Radiation Principles<\/strong>\r\n\r\n \r\n

    As we all know, electromagnetic energy travels in a harmonic, sinusoidal fashion at the velocity of light, (c = 3x108m\/s). It incorporates electric and magnetic waves, both perpendicular to each other and perpendicular to the direction of motion of wave. These electromagnetic radiations exhibit both particle and wave nature. The distance from one wave peak to the next is wavelength \u03bb, and the number of peaks passing a fixed point in space per unit time is the wave frequency v<\/em>.<\/p>\r\n \r\n\r\nFrom basic physics, waves obey the general equation\r\n\r\n \r\n\r\n= v<\/em> \u03bb------------------------(1.1)\r\n\r\n \r\n\r\nSince c is essentially a constant (3\u00d7108<\/sup> m\/sec), frequency v<\/em> and wavelength \u03bb for any given wave are related inversely.\r\n\r\n<\/div>\r\n

    \r\n\r\n\"\"\r\n\r\n \r\n

    Figure 2: Electromagnetic wave. Components include a sinusoidal electric wave (E) and a magnetic wave (M) perpendicular to each other as well as to the direction of propagation.<\/p>\r\n \r\n

    In remote sensing, it is a common practice to categorize electromagnetic waves by their wavelength location in the electromagnetic spectrum. The electromagnetic spectrum comprises of an array of wavelength or frequency of electromagnetic radiations in ascending or descending order. These radiations exist in a continuum and there is no distinct demarcation between these radiations in a spectrum.<\/p>\r\n \r\n\r\nThe different wavelength bands of electromagnetic spectrum are as follows:\r\n\r\n \r\n\r\n3.1 Cosmic rays: These are very high frequency waves that originate from sun.\r\n\r\n \r\n\r\n3.2 Gamma rays: These follow cosmic rays with a wavelength less than 0.01nm.\r\n\r\n \r\n\r\n3.3 X-rays: These waves range from 0.01 to 10nm.\r\n\r\n \r\n

    3.4 Ultraviolet (UV): This energy adjoins the blue end of the visible portion of the spectrum and has wavelengths of 10 \u2013 310 nm.<\/p>\r\n \r\n

    3.5 Visible: This corresponds to the spectral sensitivity of the human eye and extends from approximately 0.4 \u00b5m to 0.7 \u00b5m. The color blue has the range of 0.4 to 0.5 \u00b5m, green from 0.5 to 0.6 \u00b5m and red from 0.6 to 0.7 \u00b5m.<\/p>\r\n \r\n

    3.6 Infrared: Adjoining the red end of visible region are three different categories of infrared (IR) waves: near IR (from 0.7 to 1.3 \u00b5m), middle or short-wave IR (from 1.3 to 3 \u00b5m) and thermal IR (3 to 14 \u00b5m).<\/span><\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n \r\n

    3.7 Microwave: These wavelengths follow infrared region of the spectrum and lie in the wavelength of 1mm to 1m.<\/p>\r\n \r\n

    3.8 TV and Radio waves: These waves extend beyond 1mm of the microwave region.<\/p>\r\n\"\"\r\n

    Figure 3: Electromagnetic spectrum<\/p>\r\n \r\n

    Although many characteristics of electromagnetic radiation are most easily described by wave theory, the particle theory suggests that electromagnetic radiation is composed of many discrete units called photons or quanta. The energy of a quantum is given as:<\/p>\r\n \r\n\r\nQ=hv------------------(1.2)<\/em>\r\n\r\n \r\n\r\nwhere,\u00a0 Q= energy of a quantum, joules (J)\r\n\r\n \r\n\r\nh= Planck\u2019s constant, 6.626\u00d7 10-34 J sec\r\n\r\n \r\n\r\nv<\/em>= frequency\r\n\r\n \r\n

    We can relate the wave and quantum models of the electromagnetic radiation behavior by solving Eq. 1.1 for v<\/em> and substituting into Eq. 1.2 to obtain<\/p>\r\n \r\n\r\n=\u210ec\/\u03bb-------------------(1.3)\r\n\r\n<\/div>\r\n

    \r\n\r\n \r\n

    Thus, an inverse relationship exists between energy and wavelength. This has important implications in remote sensing since naturally emitted long wavelength radiations, such as microwave emission from terrain features, is more difficult to sense than radiation of shorter wavelengths, such as emitted thermal IR energy. The low energy content of long wavelength radiation means that, in general, systems operating at long wavelengths must view large areas of the Earth at any given time in order to obtain a detectable energy signal.<\/p>\r\n \r\n

    Based on the source of energy, remote sensors are classified as passive and active sensor. Passive sensors use sun as a source of energy; examples include thermal scanners that sense thermal infrared energy. Some sensors use their own source of energy to observe the features of the earth. Examples include RADAR (Radio Detection and Ranging) and LIDAR (Light Detection and Ranging). Photographic camera can act as a passive sensor in sunlight while it becomes an active sensor while utilizing a flash.<\/p>\r\n \r\n\r\n4. Energy Interactions in the Atmosphere<\/strong>\r\n\r\n \r\n

    All radiations detected by remote sensors passes through some distance, or path length of atmosphere, irrespective of its source. The net effect of the atmosphere varies with these differences in the path length and also varies with the magnitude of energy signal being sensed, the atmospheric conditions present and the wavelengths involved. So, atmosphere can have a profound effect on the intensity and spectral composition of radiation available to any sensing system. These effects are caused principally through the mechanisms of atmospheric scattering and absorption.<\/p>\r\n \r\n\r\n4.1 Scattering<\/strong>\r\n\r\n \r\n\r\nAtmospheric scattering is the unpredictable diffusion of radiation by particles in the atmosphere. There are three types of scattering:\r\n\r\n \r\n\r\n4.1 Rayleigh scattering\r\n\r\n \r\n\r\n4.2 Mie scattering\r\n\r\n \r\n\r\n4.3 Non-selective scattering\r\n\r\n<\/div>\r\n

    \r\n\r\n\"\"\r\n

    Figure 4: Types of scattering<\/p>\r\n \r\n

    4.1 Rayleigh scattering: This occurs when radiation interacts with atmospheric molecules and other tiny particles that are much smaller in diameter than the wavelength of the radiation. The effect of Rayleigh scatter is inversely proportional to the fourth power of wavelength. Hence, there is much stronger tendency for short wavelengths to be scattered by mechanism than long wavelengths. This is the reason why sky appears blue during daytime; while black during night-time. At sunrise and sunset, however, the sun\u2019s rays travel through a longer atmospheric path length than during midday. With the longer path, the scatter of short wavelengths is so complete that we see only the less scattered, longer wavelengths of orange and red. Rayleigh scatter is responsible for causing haze in imagery that reduces the contrast of the image.<\/p>\r\n \r\n

    4.2 Mie scattering: It occurs when the diameter of atmospheric particles is almost equal to the wavelengths of the energy being sensed. Water vapor and dust are major causes of Mie scatter. This type of scattering tends to influence longer wavelengths compared to Rayleigh scatter; and is significant in slightly overcast skies.<\/p>\r\n \r\n

    4.3 Non-selective scattering: This type of scattering happens when the diameters of the particles causing scatter are much larger than the wavelengths of the energy being sensed. Example includes scattering by water droplets. They commonly have a diameter in the range 5 to 100 \u00b5m and scatter all\u00a0visible and near to mid-IR wavelengths about equally, that\u2019s why it is said to be non-selective. This implies that equal quantities of blue, green and red light are scattered; hence fog and clouds appear white.<\/span><\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n \r\n\r\n4.2 Absorption<\/strong>\r\n\r\n \r\n

    The atmosphere prevents, or strongly attenuates, transmission of radiation through the atmosphere. Atmospheric absorption results in the effective loss of energy to atmospheric constituents. Water\u00a0vapor, carbon dioxide and ozone are the most efficient absorbers of solar radiation.<\/p>\r\n \r\n

    4.2.1 Ozone (O3): absorbs ultraviolet radiation high in atmosphere<\/p>\r\n \r\n

    4.2.2 Carbon-dioxide (CO2): absorbs mid and far-infrared (13-17.5microm) in lower atmosphere<\/p>\r\n \r\n\r\n4.2.3 Water vapor (H2O): absorbs mid-far infrared (5.5-7.0, >27microm) in lower atmosphere.\r\n\r\n \r\n

    \u00a0Therefore, the concept of Atmospheric Windows comes into picture, which are those wavelengths that are relatively easily transmitted through the atmosphere. Thus, the wavelength ranges in which the atmosphere is particularly transmissive of energy are referred to as atmospheric windows.<\/p>\r\n \r\n\r\n\"\"\r\n\r\n \r\n

    Figure 5: Atmospheric Window (http:\/\/www.crisp.nus.edu.sg\/~research\/tutorial\/atmoseff.htm#windows<\/em>)<\/p>\r\n \r\n\r\n<\/div>\r\n

    \r\n\r\n\"\"\r\n

    Figure 6: Spectral characteristics of (a) energy sources, (b) atmospheric transmittance, and (c) common remote sensing systems<\/p>\r\n \r\n

    The interrelationship between the energy sources and atmospheric absorption characteristics is shown in Figure 1.5. Figure 1.5a shows the spectral distribution of the energy emitted by the sun and (b) the earth features. In figure 1.5b, spectral regions in which the atmosphere blocks energy are shaded. Remote sensing data acquisition is limited to the non-blocked spectral regions, the atmospheric windows.<\/p>\r\n \r\n\r\nUV & visible: 0.30-0.75 \u03bcm\r\n\r\n \r\n\r\nNear infrared:\u00a0 0.77-0.91 \u03bcm\r\n\r\n \r\n\r\nMid infrared:\u00a0\u00a0 1.55-1.75 \u03bcm, 2.05-2.4 \u03bcm\r\n\r\n \r\n\r\nFar infrared:\u00a0\u00a0\u00a0 3.50-4.10 \u03bcm, 8.00- 9.20 \u03bcm, 10.2-12.4 \u03bcm\r\n\r\n \r\n\r\nMicrowave:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1mm-1m\r\n\r\n \r\n\r\nThe atmospheric windows are important for RS sensor design; which is based on the following criteria:\r\n\r\n \r\n\r\n(1) the spectral sensitivity of the sensors available\r\n\r\n \r\n\r\n(2)\u00a0\u00a0 the presence or absence of the atmospheric windows in the spectral range in which one wishes to sense and<\/span>\r\n\r\n \r\n\r\n(3)\u00a0\u00a0 the source, magnitude and spectral composition of the energy available in these ranges.<\/span>\r\n\r\n<\/div>\r\n

    \r\n\r\n5. Energy Interactions with Earth Surface Features<\/strong>\r\n\r\n \r\n

    When electromagnetic energy is incident on any given Earth surface feature, three fundamental interactions with the feature are possible. These are illustrated in Figure 1.6 for an element of the volume of a water body. Various fractions of the energy incident on the element are reflected, absorbed and\/or transmitted. Applying this principle of conservation of energy, we can state the inter-relationship among these three energy interactions as<\/p>\r\n \r\n\r\nE1 (\u03bb) =ER (\u03bb)+EA (\u03bb)+ET (\u03bb)-----------------(1.6)\r\n\r\n \r\n\r\nWhere\r\n\r\n \r\n\r\nE1 = incident energy\r\n\r\n \r\n\r\nER = reflected energy\r\n\r\n \r\n\r\nEA = absorbed energy\r\n\r\n \r\n\r\nET = transmitted energy\r\n\r\nwith all energy components being a function of wavelength \u03bb.\r\n\r\n\"\"\r\n\r\n \r\n

    Figure 7: Basic interactions between electromagnetic energy and an Earth surface feature<\/p>\r\n \r\n

    From Equation 1.6, following two points become evident:<\/span><\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n \r\n

    (1)The proportions of energy reflected, absorbed and transmitted will vary for different Earth features, depending on their material type and condition. These differences permit us to distinguish different features on an image.<\/p>\r\n \r\n

    (2) The wavelength dependency means that, even within a given feature type, the proportion of reflected, absorbed and transmitted energy will vary at different wavelengths.<\/p>\r\n \r\n

    Thus, two features may be indistinguishable in one spectral range and be very different in another wavelength band. Within the visible portion of the spectrum, these spectral variations result in visual effect called color. Because many remote sensing systems operate in the wavelength regions in which reflected energy predominates, the reflectance properties of the Earth features are very important. Hence, it is often useful to think of the energy balance relationship expressed by Equation 1.6 in the form<\/p>\r\n \r\n

    ER(\u03bb)=E1(\u03bb)-[EA (\u03bb)+ET (\u03bb)]--------------------(1.7)<\/span><\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n \r\n

    That is, the reflected energy is equal to the energy incident on a given feature reduced by the energy that is either absorbed or transmitted by that feature.<\/p>\r\n \r\n

    The reflectance characteristics of the Earth surface features may 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; \u03c1\u03bb .It is mathematically defined as<\/p>\r\n \r\n\r\n\u03c1\u03bb = ER (\u03bb)\/E1 (\u03bb)\r\n\r\n=?????? ?? ?????????\u210e \u03bb reflected from the object\/?????? ?? ?????????\u210e \u03bb incident upon the object\u00d7 100---(1.8)\r\n\r\n \r\n\r\nWhere, \u03c1\u03bb is expressed as a percentage.\r\n\r\n \r\n

    A graph of the spectral reflectance of an object as a function of wavelength is termed as spectral reflectance curve. The configuration of spectral reflectance curves gives us insight into the spectral characteristics of an object and has a strong influence on the choice of wavelength regions in which remote sensing data are acquired for a particular application.<\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n6.\u00a0\u00a0<\/strong>Data Acquisition and Interpretation<\/strong>\r\n\r\n \r\n

    The detection of electromagnetic energy can be performed either photographically or electronically. The process of photography uses chemical reactions on the surface of a light sensitive film to detect energy variations within a scene.<\/p>\r\n \r\n

    Electronic sources generate an electric signal that corresponds to the energy variations in the original scene; and offer broader spectral sensitivity. An example is a video camera.<\/p>\r\n \r\n

    In remote sensing, term photograph is reserved exclusively for images that were detected as well recorded on the film. The more generic term image is used for any pictorial representation of image data. As the term image relates to any pictorial product, all photographs are images. Not all images however, are photographs.<\/p>\r\n \r\n

    A common exception to the above terminology is use of the term digital photography. Digital cameras use electronic detectors rather than film for image detection.<\/p>\r\n \r\n\r\n\"\"\r\n

    Figure 8: Digital Photography<\/p>\r\n \r\n

    \u00a0Though the image shown in Figure 8(a) appears to be a continuous tone photograph, it is actually composed of two-dimensional array of discrete picture elements or pixels. The intensity of each pixel corresponds to the average brightness or radiance, measured electronically over the ground area corresponding to each pixel. Whereas the individual pixels are virtually impossible to discern in (a), they are readily observable in the enlargements shown in (b) and (c). Typically, the DNs constituting a digital image are recorded over numerical ranges as 0 to 255 (8-bit data), 0 to 511 (9-bit), 0 to 1023 (10-bit) or higher. 7. <\/strong><\/p>\r\n \r\n

    Reference Data<\/strong><\/p>\r\n \r\n

    The acquisition of reference data is referred by the term \u2018ground truth\u2019, and involves collecting measurements or observations about the objects, areas or phenomena that are being remotely sensed. Reference data involves field measurements of temperature and other physical\/chemical properties of various features.<\/span><\/p>\r\n\r\n<\/div>\r\n

    \r\n\r\n \r\n\r\nReference data might be used to serve any or all of the following purposes:\r\n\r\n \r\n
      \r\n \t
    • To aid in the analysis and interpretation of remotely sensed data<\/li>\r\n \t
    • To calibrate a sensor<\/li>\r\n \t
    • To verify information extracted from remote sensing data<\/li>\r\n<\/ul>\r\n

      Ground based measurement of the reflectance\/emittance of surface materials to determine their spectral response pattern is one form of reference data collection. An example is spectro-radiometer that measures electromagnetic spectrum by recording data in very narrow bands simultaneously.<\/p>\r\n \r\n\r\n8. Applications of Remote Sensing<\/strong>\r\n\r\n \r\n\r\nAny design of successful remote sensing efforts involve,\r\n\r\n \r\n\r\n(1)\u00a0\u00a0 Problem defining\r\n\r\n \r\n\r\n(2)\u00a0\u00a0 Evaluating the potential for solving the problem through remote sensing techniques\r\n\r\n \r\n\r\n(3)\u00a0\u00a0 Data acquisition related to the problem\r\n\r\n \r\n\r\n(4)\u00a0\u00a0 Data interpretation procedures\r\n\r\n \r\n\r\n(5)\u00a0\u00a0 Assessment of accuracy of the information collected\r\n\r\n<\/div>\r\n

      \r\n\r\n\"\"\r\n

      Figure 9: Multistage remote sensing concept<\/p>\r\n \r\n\r\nThe success of many applications of remote sensing is improved considerably by making a multiple-view approach to data collection. This may involve:\r\n\r\n \r\n\r\n1.\u00a0\u00a0\u00a0\u00a0\u00a0 Multistage sensing: Data about a site is collected from multiple altitudes.\r\n\r\n \r\n\r\n2.\u00a0\u00a0\u00a0\u00a0\u00a0 Multispectral sensing: Data is acquired simultaneously in several spectral bands.\r\n\r\n \r\n\r\n3.\u00a0\u00a0\u00a0\u00a0\u00a0 Multitemporal sensing: Data about a site is collected on more than one occasion.\r\n\r\n \r\n

      In the multistage approach, satellite data maybe analyzed in conjunction with high altitude data, low altitude data and ground observations. Thus, more information is obtained by analyzing multiple views of the terrain than by analysis of any single view. Further, it is pertinent to mention that any successful application of remote sensing requires appropriate data acquisition and data interpretation techniques besides conventional methods. Remote sensing data are currently being used in conjunction with GIS to acquire best possible solutions to problems.<\/p>\r\n \r\n\r\n \r\n\r\nWorks Cited<\/strong>\r\n\r\n \r\n

        \r\n \t
      • Avery, T.E., and G.L. Berlin, Fundamentals of Remote Sensing and Airphoto Interpretation, Macmillan, New York, 1992.<\/li>\r\n \t
      • Bauer, M.E., et al., \u201cField Spectroscopy of Agricultural Crops,\u201d IEEE Transactions on Geosciences and Remote Sensing, vol. GE-24, no. 1, 1986, pp. 65-75.<\/li>\r\n \t
      • Bowker, D.E., et al., Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies, National Aeronautics and Space Administration, Washington, 1985.<\/li>\r\n \t
      • Campbell, G.S., and J.M. Norman, An Introduction to Environmental Biophysics, 2nd ed., Springer, New York, 1997.<\/li>\r\n \t
      • Campbell, J.B., Introduction to Remote Sensing, 3rd ed., Guilford Press, New York, 2002. Dozier, J., and T.H. Painter, \u201cMultispectral and Hyperspectral Remote Sensing of Alpine Snow Properties,\u201d Annual Review of Earth and Planetary Sciences, vol. 32, 2004, pp. 465-494. Elachi, C., Introduction to the Physics and Techniques of Remote Sensing, Wiley, New York, 1987.<\/li>\r\n \t
      • Jet Propulsion Laboratory, ASTER Spectral Library, California Institute of Technology, Pasadena, 1999.<\/li>\r\n \t
      • Joseph G. (2003). Fundamentals of Remote Sensing. University Press, Hyderabad.<\/li>\r\n \t
      • Jupp, D.L.B., and A.H. Strahler, \u201cA Hotspot Model for Leaf Canopies, \u201cRemote Sensing of Environment, vol.38, 1991, pp. 193-210.<\/li>\r\n \t
      • Kalensky, Z., and D.A. Wilson, \u201cSpectral Signatures of Forest Trees,\u201dProceedings: Third Canadian Symposium on Remote Sensing, 1975, pp. 155-171.<\/li>\r\n \t
      • Lillesand T.M. and Kiefer R.W. (2007). Remote Sensing and Image Interpretation. John Wiley and Sons, USA.<\/li>\r\n \t
      • Schott, J.R., Remote Sensing: The Image Chain Approach, Oxford University Press, New York, 1997.<\/li>\r\n<\/ul>\r\n<\/div>","rendered":"
        \n

         <\/p>\n

        CONTENTS<\/strong><\/p>\n

         <\/p>\n

        1.\u00a0 Learning Objectives<\/p>\n

         <\/p>\n

        2.\u00a0 Introduction<\/p>\n

         <\/p>\n

        3.\u00a0 Energy Sources and Radiation Principles<\/p>\n

         <\/p>\n

        4.\u00a0 Energy Interactions in the Atmosphere<\/p>\n

         <\/p>\n

        5.\u00a0 Energy Interactions with Earth Surface Features<\/p>\n

         <\/p>\n

        6.\u00a0 Data Acquisition and Interpretation<\/p>\n

         <\/p>\n

        7.\u00a0 Reference Data<\/p>\n

         <\/p>\n

        8.\u00a0 Characteristics of Remote Sensing Systems<\/p>\n

         <\/p>\n

        9.\u00a0 Conclusions<\/p>\n

         <\/p>\n

        10.\u00a0 References<\/p>\n

         <\/p>\n

         <\/p>\n

        1.\u00a0 <\/strong>Learning Objectives<\/strong><\/p>\n

        \u00a0<\/strong><\/p>\n

          \n
        • This module will help us understand the basic principle of remote sensing and the processes involved. For this purpose, we will begin with the fundamentals of the electromagnetic energy and then consider how the energy interacts with the atmosphere and Earth surface features.<\/li>\n
        • Next, we will summarize the process of acquiring and interpreting imagery in both digital and analog formats.<\/li>\n
        • \u00a0We would also discuss the role that reference data play in the data analysis procedure and describe how the spatial location of reference data observed in the field is often determined.<\/li>\n<\/ul>\n

           <\/p>\n

          Thus, this module will deal with the electromagnetic energy sensors that are currently being operated from airborne and space-borne platforms; to assist in inventorying, mapping and monitoring earth\u2019s resources.<\/p>\n<\/div>\n

          \n

           <\/p>\n

          2. Introduction<\/strong><\/p>\n

           <\/p>\n

          Remote sensing is the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand and Kiefer, 1994). This implies that as we are reading the words that make up the sentences, our eyes act as remote sensor that respond to light reflected from this page. These responses based on reflected light help us distinguish between white areas of the paper and dark areas reflected from the letters of the page. These data are analyzed or interpreted in our brain to explain the dark areas on the page as a collection of letters forming words. This is continued further with words forming sentences and the information carried in the sentence is interpreted.<\/p>\n

           <\/p>\n

          On extrapolating this entire process of reading to earth\u2019s surface features; remote sensing involves the use of various sensors to obtain information about the objects, area or phenomenon being investigated. Figure 1.1 schematically illustrates the generalized processes and elements involved in electromagnetic remote sensing of earth resources. The two basic processes involved are (i) data acquisition, and (ii) data analysis.<\/p>\n

           <\/p>\n

          2.1 Data Acquisition<\/strong>: The elements of data acquisition process are:<\/p>\n

           <\/p>\n

          a)\u00a0\u00a0\u00a0\u00a0\u00a0 Source of energy<\/p>\n

           <\/p>\n

          b)\u00a0\u00a0\u00a0\u00a0\u00a0 Propagation of energy through the atmosphere<\/p>\n

           <\/p>\n

          c)\u00a0\u00a0\u00a0\u00a0\u00a0 Energy interactions with earth surface features<\/p>\n

           <\/p>\n

          d)\u00a0\u00a0\u00a0\u00a0 Retransmission of energy through the atmosphere<\/p>\n

           <\/p>\n

          e)\u00a0\u00a0\u00a0\u00a0\u00a0 Airborne and\/or spaceborne sensors<\/p>\n

           <\/p>\n

          f)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Sensors- These interactions result in the generation of sensor data in pictorial and\/or digital form. Sensors are used to record variations in the way the Earth surface features reflect and emit electromagnetic energy.<\/p>\n

           <\/p>\n

          2.2 Data Analysis<\/strong>: The data analysis process (g) involves examining the data using various viewing and interpretation devices to analyze pictorial data and\/or a computer to analyze digital sensor data. This is further complemented by reference data that includes soil maps, crop statistics, or field-check\u00a0data to extracts information about the type, extent, location and condition of the various resources by the analyst. This information is then compiled (h), generally in the form of hardcopy maps and tables or as computer files that can be merged with other layers of information in a geographic information system (GIS). Finally, the information is presented to the users (i), who apply it to their decision-making process.<\/span><\/p>\n<\/div>\n

          \n

          \"\"<\/p>\n

           <\/p>\n

          Figure 1: Electromagnetic remote sensing of Earth resources<\/p>\n

           <\/p>\n

          3. Energy Sources and Radiation Principles<\/strong><\/p>\n

           <\/p>\n

          As we all know, electromagnetic energy travels in a harmonic, sinusoidal fashion at the velocity of light, (c = 3x108m\/s). It incorporates electric and magnetic waves, both perpendicular to each other and perpendicular to the direction of motion of wave. These electromagnetic radiations exhibit both particle and wave nature. The distance from one wave peak to the next is wavelength \u03bb, and the number of peaks passing a fixed point in space per unit time is the wave frequency v<\/em>.<\/p>\n

           <\/p>\n

          From basic physics, waves obey the general equation<\/p>\n

           <\/p>\n

          = v<\/em> \u03bb————————(1.1)<\/p>\n

           <\/p>\n

          Since c is essentially a constant (3\u00d7108<\/sup> m\/sec), frequency v<\/em> and wavelength \u03bb for any given wave are related inversely.<\/p>\n<\/div>\n

          \n

          \"\"<\/p>\n

           <\/p>\n

          Figure 2: Electromagnetic wave. Components include a sinusoidal electric wave (E) and a magnetic wave (M) perpendicular to each other as well as to the direction of propagation.<\/p>\n

           <\/p>\n

          In remote sensing, it is a common practice to categorize electromagnetic waves by their wavelength location in the electromagnetic spectrum. The electromagnetic spectrum comprises of an array of wavelength or frequency of electromagnetic radiations in ascending or descending order. These radiations exist in a continuum and there is no distinct demarcation between these radiations in a spectrum.<\/p>\n

           <\/p>\n

          The different wavelength bands of electromagnetic spectrum are as follows:<\/p>\n

           <\/p>\n

          3.1 Cosmic rays: These are very high frequency waves that originate from sun.<\/p>\n

           <\/p>\n

          3.2 Gamma rays: These follow cosmic rays with a wavelength less than 0.01nm.<\/p>\n

           <\/p>\n

          3.3 X-rays: These waves range from 0.01 to 10nm.<\/p>\n

           <\/p>\n

          3.4 Ultraviolet (UV): This energy adjoins the blue end of the visible portion of the spectrum and has wavelengths of 10 \u2013 310 nm.<\/p>\n

           <\/p>\n

          3.5 Visible: This corresponds to the spectral sensitivity of the human eye and extends from approximately 0.4 \u00b5m to 0.7 \u00b5m. The color blue has the range of 0.4 to 0.5 \u00b5m, green from 0.5 to 0.6 \u00b5m and red from 0.6 to 0.7 \u00b5m.<\/p>\n

           <\/p>\n

          3.6 Infrared: Adjoining the red end of visible region are three different categories of infrared (IR) waves: near IR (from 0.7 to 1.3 \u00b5m), middle or short-wave IR (from 1.3 to 3 \u00b5m) and thermal IR (3 to 14 \u00b5m).<\/span><\/p>\n<\/div>\n

          \n

           <\/p>\n

          3.7 Microwave: These wavelengths follow infrared region of the spectrum and lie in the wavelength of 1mm to 1m.<\/p>\n

           <\/p>\n

          3.8 TV and Radio waves: These waves extend beyond 1mm of the microwave region.<\/p>\n

          \"\"<\/p>\n

          Figure 3: Electromagnetic spectrum<\/p>\n

           <\/p>\n

          Although many characteristics of electromagnetic radiation are most easily described by wave theory, the particle theory suggests that electromagnetic radiation is composed of many discrete units called photons or quanta. The energy of a quantum is given as:<\/p>\n

           <\/p>\n

          Q=hv——————(1.2)<\/em><\/p>\n

           <\/p>\n

          where,\u00a0 Q= energy of a quantum, joules (J)<\/p>\n

           <\/p>\n

          h= Planck\u2019s constant, 6.626\u00d7 10-34 J sec<\/p>\n

           <\/p>\n

          v<\/em>= frequency<\/p>\n

           <\/p>\n

          We can relate the wave and quantum models of the electromagnetic radiation behavior by solving Eq. 1.1 for v<\/em> and substituting into Eq. 1.2 to obtain<\/p>\n

           <\/p>\n

          =\u210ec\/\u03bb——————-(1.3)<\/p>\n<\/div>\n

          \n

           <\/p>\n

          Thus, an inverse relationship exists between energy and wavelength. This has important implications in remote sensing since naturally emitted long wavelength radiations, such as microwave emission from terrain features, is more difficult to sense than radiation of shorter wavelengths, such as emitted thermal IR energy. The low energy content of long wavelength radiation means that, in general, systems operating at long wavelengths must view large areas of the Earth at any given time in order to obtain a detectable energy signal.<\/p>\n

           <\/p>\n

          Based on the source of energy, remote sensors are classified as passive and active sensor. Passive sensors use sun as a source of energy; examples include thermal scanners that sense thermal infrared energy. Some sensors use their own source of energy to observe the features of the earth. Examples include RADAR (Radio Detection and Ranging) and LIDAR (Light Detection and Ranging). Photographic camera can act as a passive sensor in sunlight while it becomes an active sensor while utilizing a flash.<\/p>\n

           <\/p>\n

          4. Energy Interactions in the Atmosphere<\/strong><\/p>\n

           <\/p>\n

          All radiations detected by remote sensors passes through some distance, or path length of atmosphere, irrespective of its source. The net effect of the atmosphere varies with these differences in the path length and also varies with the magnitude of energy signal being sensed, the atmospheric conditions present and the wavelengths involved. So, atmosphere can have a profound effect on the intensity and spectral composition of radiation available to any sensing system. These effects are caused principally through the mechanisms of atmospheric scattering and absorption.<\/p>\n

           <\/p>\n

          4.1 Scattering<\/strong><\/p>\n

           <\/p>\n

          Atmospheric scattering is the unpredictable diffusion of radiation by particles in the atmosphere. There are three types of scattering:<\/p>\n

           <\/p>\n

          4.1 Rayleigh scattering<\/p>\n

           <\/p>\n

          4.2 Mie scattering<\/p>\n

           <\/p>\n

          4.3 Non-selective scattering<\/p>\n<\/div>\n

          \n

          \"\"<\/p>\n

          Figure 4: Types of scattering<\/p>\n

           <\/p>\n

          4.1 Rayleigh scattering: This occurs when radiation interacts with atmospheric molecules and other tiny particles that are much smaller in diameter than the wavelength of the radiation. The effect of Rayleigh scatter is inversely proportional to the fourth power of wavelength. Hence, there is much stronger tendency for short wavelengths to be scattered by mechanism than long wavelengths. This is the reason why sky appears blue during daytime; while black during night-time. At sunrise and sunset, however, the sun\u2019s rays travel through a longer atmospheric path length than during midday. With the longer path, the scatter of short wavelengths is so complete that we see only the less scattered, longer wavelengths of orange and red. Rayleigh scatter is responsible for causing haze in imagery that reduces the contrast of the image.<\/p>\n

           <\/p>\n

          4.2 Mie scattering: It occurs when the diameter of atmospheric particles is almost equal to the wavelengths of the energy being sensed. Water vapor and dust are major causes of Mie scatter. This type of scattering tends to influence longer wavelengths compared to Rayleigh scatter; and is significant in slightly overcast skies.<\/p>\n

           <\/p>\n

          4.3 Non-selective scattering: This type of scattering happens when the diameters of the particles causing scatter are much larger than the wavelengths of the energy being sensed. Example includes scattering by water droplets. They commonly have a diameter in the range 5 to 100 \u00b5m and scatter all\u00a0visible and near to mid-IR wavelengths about equally, that\u2019s why it is said to be non-selective. This implies that equal quantities of blue, green and red light are scattered; hence fog and clouds appear white.<\/span><\/p>\n<\/div>\n

          \n

           <\/p>\n

          4.2 Absorption<\/strong><\/p>\n

           <\/p>\n

          The atmosphere prevents, or strongly attenuates, transmission of radiation through the atmosphere. Atmospheric absorption results in the effective loss of energy to atmospheric constituents. Water\u00a0vapor, carbon dioxide and ozone are the most efficient absorbers of solar radiation.<\/p>\n

           <\/p>\n

          4.2.1 Ozone (O3): absorbs ultraviolet radiation high in atmosphere<\/p>\n

           <\/p>\n

          4.2.2 Carbon-dioxide (CO2): absorbs mid and far-infrared (13-17.5microm) in lower atmosphere<\/p>\n

           <\/p>\n

          4.2.3 Water vapor (H2O): absorbs mid-far infrared (5.5-7.0, >27microm) in lower atmosphere.<\/p>\n

           <\/p>\n

          \u00a0Therefore, the concept of Atmospheric Windows comes into picture, which are those wavelengths that are relatively easily transmitted through the atmosphere. Thus, the wavelength ranges in which the atmosphere is particularly transmissive of energy are referred to as atmospheric windows.<\/p>\n

           <\/p>\n

          \"\"<\/p>\n

           <\/p>\n

          Figure 5: Atmospheric Window (http:\/\/www.crisp.nus.edu.sg\/~research\/tutorial\/atmoseff.htm#windows<\/em>)<\/p>\n

           <\/p>\n<\/div>\n

          \n

          \"\"<\/p>\n

          Figure 6: Spectral characteristics of (a) energy sources, (b) atmospheric transmittance, and (c) common remote sensing systems<\/p>\n

           <\/p>\n

          The interrelationship between the energy sources and atmospheric absorption characteristics is shown in Figure 1.5. Figure 1.5a shows the spectral distribution of the energy emitted by the sun and (b) the earth features. In figure 1.5b, spectral regions in which the atmosphere blocks energy are shaded. Remote sensing data acquisition is limited to the non-blocked spectral regions, the atmospheric windows.<\/p>\n

           <\/p>\n

          UV & visible: 0.30-0.75 \u03bcm<\/p>\n

           <\/p>\n

          Near infrared:\u00a0 0.77-0.91 \u03bcm<\/p>\n

           <\/p>\n

          Mid infrared:\u00a0\u00a0 1.55-1.75 \u03bcm, 2.05-2.4 \u03bcm<\/p>\n

           <\/p>\n

          Far infrared:\u00a0\u00a0\u00a0 3.50-4.10 \u03bcm, 8.00- 9.20 \u03bcm, 10.2-12.4 \u03bcm<\/p>\n

           <\/p>\n

          Microwave:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1mm-1m<\/p>\n

           <\/p>\n

          The atmospheric windows are important for RS sensor design; which is based on the following criteria:<\/p>\n

           <\/p>\n

          (1) the spectral sensitivity of the sensors available<\/p>\n

           <\/p>\n

          (2)\u00a0\u00a0 the presence or absence of the atmospheric windows in the spectral range in which one wishes to sense and<\/span><\/p>\n

           <\/p>\n

          (3)\u00a0\u00a0 the source, magnitude and spectral composition of the energy available in these ranges.<\/span><\/p>\n<\/div>\n

          \n

          5. Energy Interactions with Earth Surface Features<\/strong><\/p>\n

           <\/p>\n

          When electromagnetic energy is incident on any given Earth surface feature, three fundamental interactions with the feature are possible. These are illustrated in Figure 1.6 for an element of the volume of a water body. Various fractions of the energy incident on the element are reflected, absorbed and\/or transmitted. Applying this principle of conservation of energy, we can state the inter-relationship among these three energy interactions as<\/p>\n

           <\/p>\n

          E1 (\u03bb) =ER (\u03bb)+EA (\u03bb)+ET (\u03bb)—————–(1.6)<\/p>\n

           <\/p>\n

          Where<\/p>\n

           <\/p>\n

          E1 = incident energy<\/p>\n

           <\/p>\n

          ER = reflected energy<\/p>\n

           <\/p>\n

          EA = absorbed energy<\/p>\n

           <\/p>\n

          ET = transmitted energy<\/p>\n

          with all energy components being a function of wavelength \u03bb.<\/p>\n

          \"\"<\/p>\n

           <\/p>\n

          Figure 7: Basic interactions between electromagnetic energy and an Earth surface feature<\/p>\n

           <\/p>\n

          From Equation 1.6, following two points become evident:<\/span><\/p>\n<\/div>\n

          \n

           <\/p>\n

          (1)The proportions of energy reflected, absorbed and transmitted will vary for different Earth features, depending on their material type and condition. These differences permit us to distinguish different features on an image.<\/p>\n

           <\/p>\n

          (2) The wavelength dependency means that, even within a given feature type, the proportion of reflected, absorbed and transmitted energy will vary at different wavelengths.<\/p>\n

           <\/p>\n

          Thus, two features may be indistinguishable in one spectral range and be very different in another wavelength band. Within the visible portion of the spectrum, these spectral variations result in visual effect called color. Because many remote sensing systems operate in the wavelength regions in which reflected energy predominates, the reflectance properties of the Earth features are very important. Hence, it is often useful to think of the energy balance relationship expressed by Equation 1.6 in the form<\/p>\n

           <\/p>\n

          ER(\u03bb)=E1(\u03bb)-[EA (\u03bb)+ET (\u03bb)]——————–(1.7)<\/span><\/p>\n<\/div>\n

          \n

           <\/p>\n

          That is, the reflected energy is equal to the energy incident on a given feature reduced by the energy that is either absorbed or transmitted by that feature.<\/p>\n

           <\/p>\n

          The reflectance characteristics of the Earth surface features may 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; \u03c1\u03bb .It is mathematically defined as<\/p>\n

           <\/p>\n

          \u03c1\u03bb = ER (\u03bb)\/E1 (\u03bb)<\/p>\n

          =?????? ?? ?????????\u210e \u03bb reflected from the object\/?????? ?? ?????????\u210e \u03bb incident upon the object\u00d7 100—(1.8)<\/p>\n

           <\/p>\n

          Where, \u03c1\u03bb is expressed as a percentage.<\/p>\n

           <\/p>\n

          A graph of the spectral reflectance of an object as a function of wavelength is termed as spectral reflectance curve. The configuration of spectral reflectance curves gives us insight into the spectral characteristics of an object and has a strong influence on the choice of wavelength regions in which remote sensing data are acquired for a particular application.<\/p>\n<\/div>\n

          \n

          6.\u00a0\u00a0<\/strong>Data Acquisition and Interpretation<\/strong><\/p>\n

           <\/p>\n

          The detection of electromagnetic energy can be performed either photographically or electronically. The process of photography uses chemical reactions on the surface of a light sensitive film to detect energy variations within a scene.<\/p>\n

           <\/p>\n

          Electronic sources generate an electric signal that corresponds to the energy variations in the original scene; and offer broader spectral sensitivity. An example is a video camera.<\/p>\n

           <\/p>\n

          In remote sensing, term photograph is reserved exclusively for images that were detected as well recorded on the film. The more generic term image is used for any pictorial representation of image data. As the term image relates to any pictorial product, all photographs are images. Not all images however, are photographs.<\/p>\n

           <\/p>\n

          A common exception to the above terminology is use of the term digital photography. Digital cameras use electronic detectors rather than film for image detection.<\/p>\n

           <\/p>\n

          \"\"<\/p>\n

          Figure 8: Digital Photography<\/p>\n

           <\/p>\n

          \u00a0Though the image shown in Figure 8(a) appears to be a continuous tone photograph, it is actually composed of two-dimensional array of discrete picture elements or pixels. The intensity of each pixel corresponds to the average brightness or radiance, measured electronically over the ground area corresponding to each pixel. Whereas the individual pixels are virtually impossible to discern in (a), they are readily observable in the enlargements shown in (b) and (c). Typically, the DNs constituting a digital image are recorded over numerical ranges as 0 to 255 (8-bit data), 0 to 511 (9-bit), 0 to 1023 (10-bit) or higher. 7. <\/strong><\/p>\n

           <\/p>\n

          Reference Data<\/strong><\/p>\n

           <\/p>\n

          The acquisition of reference data is referred by the term \u2018ground truth\u2019, and involves collecting measurements or observations about the objects, areas or phenomena that are being remotely sensed. Reference data involves field measurements of temperature and other physical\/chemical properties of various features.<\/span><\/p>\n<\/div>\n

          \n

           <\/p>\n

          Reference data might be used to serve any or all of the following purposes:<\/p>\n

           <\/p>\n

            \n
          • To aid in the analysis and interpretation of remotely sensed data<\/li>\n
          • To calibrate a sensor<\/li>\n
          • To verify information extracted from remote sensing data<\/li>\n<\/ul>\n

            Ground based measurement of the reflectance\/emittance of surface materials to determine their spectral response pattern is one form of reference data collection. An example is spectro-radiometer that measures electromagnetic spectrum by recording data in very narrow bands simultaneously.<\/p>\n

             <\/p>\n

            8. Applications of Remote Sensing<\/strong><\/p>\n

             <\/p>\n

            Any design of successful remote sensing efforts involve,<\/p>\n

             <\/p>\n

            (1)\u00a0\u00a0 Problem defining<\/p>\n

             <\/p>\n

            (2)\u00a0\u00a0 Evaluating the potential for solving the problem through remote sensing techniques<\/p>\n

             <\/p>\n

            (3)\u00a0\u00a0 Data acquisition related to the problem<\/p>\n

             <\/p>\n

            (4)\u00a0\u00a0 Data interpretation procedures<\/p>\n

             <\/p>\n

            (5)\u00a0\u00a0 Assessment of accuracy of the information collected<\/p>\n<\/div>\n

            \n

            \"\"<\/p>\n

            Figure 9: Multistage remote sensing concept<\/p>\n

             <\/p>\n

            The success of many applications of remote sensing is improved considerably by making a multiple-view approach to data collection. This may involve:<\/p>\n

             <\/p>\n

            1.\u00a0\u00a0\u00a0\u00a0\u00a0 Multistage sensing: Data about a site is collected from multiple altitudes.<\/p>\n

             <\/p>\n

            2.\u00a0\u00a0\u00a0\u00a0\u00a0 Multispectral sensing: Data is acquired simultaneously in several spectral bands.<\/p>\n

             <\/p>\n

            3.\u00a0\u00a0\u00a0\u00a0\u00a0 Multitemporal sensing: Data about a site is collected on more than one occasion.<\/p>\n

             <\/p>\n

            In the multistage approach, satellite data maybe analyzed in conjunction with high altitude data, low altitude data and ground observations. Thus, more information is obtained by analyzing multiple views of the terrain than by analysis of any single view. Further, it is pertinent to mention that any successful application of remote sensing requires appropriate data acquisition and data interpretation techniques besides conventional methods. Remote sensing data are currently being used in conjunction with GIS to acquire best possible solutions to problems.<\/p>\n

             <\/p>\n

             <\/p>\n

            Works Cited<\/strong><\/p>\n

             <\/p>\n

              \n
            • Avery, T.E., and G.L. Berlin, Fundamentals of Remote Sensing and Airphoto Interpretation, Macmillan, New York, 1992.<\/li>\n
            • Bauer, M.E., et al., \u201cField Spectroscopy of Agricultural Crops,\u201d IEEE Transactions on Geosciences and Remote Sensing, vol. GE-24, no. 1, 1986, pp. 65-75.<\/li>\n
            • Bowker, D.E., et al., Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies, National Aeronautics and Space Administration, Washington, 1985.<\/li>\n
            • Campbell, G.S., and J.M. Norman, An Introduction to Environmental Biophysics, 2nd ed., Springer, New York, 1997.<\/li>\n
            • Campbell, J.B., Introduction to Remote Sensing, 3rd ed., Guilford Press, New York, 2002. Dozier, J., and T.H. Painter, \u201cMultispectral and Hyperspectral Remote Sensing of Alpine Snow Properties,\u201d Annual Review of Earth and Planetary Sciences, vol. 32, 2004, pp. 465-494. Elachi, C., Introduction to the Physics and Techniques of Remote Sensing, Wiley, New York, 1987.<\/li>\n
            • Jet Propulsion Laboratory, ASTER Spectral Library, California Institute of Technology, Pasadena, 1999.<\/li>\n
            • Joseph G. (2003). Fundamentals of Remote Sensing. University Press, Hyderabad.<\/li>\n
            • Jupp, D.L.B., and A.H. Strahler, \u201cA Hotspot Model for Leaf Canopies, \u201cRemote Sensing of Environment, vol.38, 1991, pp. 193-210.<\/li>\n
            • Kalensky, Z., and D.A. Wilson, \u201cSpectral Signatures of Forest Trees,\u201dProceedings: Third Canadian Symposium on Remote Sensing, 1975, pp. 155-171.<\/li>\n
            • Lillesand T.M. and Kiefer R.W. (2007). Remote Sensing and Image Interpretation. John Wiley and Sons, USA.<\/li>\n
            • Schott, J.R., Remote Sensing: The Image Chain Approach, Oxford University Press, New York, 1997.<\/li>\n<\/ul>\n<\/div>\n","protected":false},"author":3,"menu_order":7,"template":"","meta":{"_acf_changed":false,"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":["dr-puneeta-pandey"],"pb_section_license":""},"chapter-type":[],"contributor":[58],"license":[],"class_list":["post-30","chapter","type-chapter","status-publish","hentry","contributor-dr-puneeta-pandey"],"part":3,"_links":{"self":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/30","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/users\/3"}],"version-history":[{"count":2,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/30\/revisions"}],"predecessor-version":[{"id":32,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/30\/revisions\/32"}],"part":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/parts\/3"}],"metadata":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/30\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/media?parent=30"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapter-type?post=30"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/contributor?post=30"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/license?post=30"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}