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True color combination using Resourcesat-1?

True color combination using Resourcesat-1?


I am working with an image from India. According to the metadata, I am working with an image acquired by the AWiFS sensor from Resourcesat-1:

Resourcesat-1 AWiFS


Which according to Wikipedia uses the following bands:

Each band was delivered in a separate image, so I have stacked them.

Now, my question is how to get a true color combination using those bands.

I remember that the following wavelengths correspond (more or less) to the following colors in the electromagnetic spectrum:

0.4-0.5 µm: B
0.5-0.6 µm: G
0.6-0.7 µm: R

Looking at the chart above, I realize that Blue is missing. So, is it even possible to get a true color image from these bands? If not, what would be the closest I can get?

My remote sensing skills are a little rusty as I am not working with imagery too much. I hope my question is clear. I am using QGIS 2.6.0 (what I used to stack the images), in case that makes any difference, but I am open to other options as well.


The short answer is that you can't.

The documentation says:

The Advanced Wide Field Sensor (AWiFS), on-board IRS-P6 operates in four spectral bands in green (0.52-0.59µm), red (0.62-0.68µm), near infrared (0.77-0.86µm) and short wave infrared (1.55-1.70µm)

Since the data does not have the regular 3 bands, you cannot get a True color composite of the data


Karen R Bradley

The increase in demand for electricity and the growing energy density in metropolitan cities have made it necessary to extend the existing high voltage network right up to the consumer. Stepping down the voltage from transmission to the distribution level at the substations located near the actual consumers not only yields economic advantages, but also ensures reliable power supply. Such substations are required to meet a number of severe requirements, including small installation size, effective protection against atmospheric pollution and moisture, noiseless operation, nonexplosive and flame resistant, reduced maintenance, minimal radio interference while providing excellent electric characteristics. Conventional substations using atmospheric air as the main dielectric cannot satisfy these requirements, but totally enclosed substations using sulphur hexafluoride (SF6) gas insulation that are also known as Gas Insulated Substations (GIS). GIS is now in widespread use in the electrical power industry, especially in metropolitan areas. This book will serve as a valuable reference for the novice as well as the expert who needs a wider and detailed scope of coverage within the area of GIS. Gas Insulated Substations provides a comprehensive coverage of a wide range of topics which include: | Introduction to GIS & Properties of SF6 | Layout, Design, Construction, Testing & Maintenance of GIS | Special Problems and Diagnostic Techniques | VFTO Phenomena and its Effects in GIS | Service Experience | Standards Specifications | Future Trends | Extensive References Gas Insulated Substations (GIS) is the first single source for authoritative information on the state of the art in GIS.

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Data Types

The type of data that we employ to help us understand a given entity is determined by (1) what we are examining, (2) what we want to know about that entity, and (3) our ability to measure that entity at a desired scale. The most common types of data available for use in a GIS are alphanumeric strings, numbers, Boolean values, dates, and binaries.

An alphanumeric string, or text, data type is any simple combination of letters and numbers that may or may not form coherent words. The number data type can be subcategorized as either floating-point or integer. A floating-point is any data value that contains decimal digits, while an integer is any data value that does not contain decimal digits. Integers can be short or long depending on the amount of significant digits in that number. Also, they are based on the concept of the &ldquobit&rdquo in a computer. As you may recall, a bit is the most basic unit of information in a computer and stores values in one of two states: 1 or 0. Therefore, an 8-bit attribute would consist of eight 1s or 0s in any combination (e.g., 10010011, 00011011, 11100111).

Short integers are 16-bit values and therefore can be used to characterize numbers ranging either from &minus32,768 to 32,767 or from 0 to 65,535 depending on whether the number is signed or unsigned (i.e., contains a + or &minus sign). Long integers, alternatively, are 32-bit values and therefore can characterize numbers ranging either from &minus2,147,483,648 to 2,147,483,647 or from 0 to 4,294,967,295.

A single precision floating-point value occupies 32 bits, like the long integer. However, this data type provides for a value of up to 7 bits to the left of the decimal (a maximum value of 128, or 127 if signed) and up to 23-bit values to the right of the decimal point (approximately 7 decimal digits). A double precision floating-point value essentially stores two 32-bit values as a single value. Double precision floats, then, can represent a value with up to 11 bits to the left of the decimal point and values with up to 52 bits to the right of the decimal (approximately 16 decimal digits) (Figure 5.1 "Double Precision Floating-Point (64-Bit Value), as Stored in a Computer").

Figure 5.1 Double Precision Floating-Point (64-Bit Value), as Stored in a Computer

Boolean, date, and binary values are less complex. Boolean values are simply those values that are deemed true or false based on the application of a Boolean operator such as AND, OR, and NOT. The date data type is presumably self-explanatory, while the binary data type represents attributes whose values are either 1 or 0.


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Remote Sensing Techniques and GIS Notes-Unit-2

Download Remote Sensing Techniques and GIS notes for Civil Engineering Sixth Semester Regulation 2013. Here you can download the notes for RS & GIS with good quality image explanation system with no watermark.

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Remote sensing is the art and science of making measurements of the earth using sensors on aeroplanes or satellites. These sensors collect data in the form of images and provide specialised capabilities for manipulating, analysing, and visualising those images. Remote sensed imagery is integrated within a GIS.

A geographic information system (GIS) is a computer-based tool for mapping and analysing feature events on earth.

UNIT II PLATFORMS AND SENSORS

Types of platforms – orbit types, Sun-synchronous and Geosynchronous – Passive and Active sensors – resolution concept – Pay load description of important Earth Resources and Meteorological satellites – Airborne and spaceborne TIR and microwave sensors.

  • Vehicle or carrier for remote sensors.
  • Typical platforms are satellites and
  • Platforms are broadly classified into three classes
    • Ground-based platforms
      • Remote sensing platforms that position the sensor at the earth’s surface.
      • Are systems fixed to the earth.
      • Sensors used are mostly to measure environmental conditions such as air-temperature , wind characteristics, earthquake intensity.
      • Are placed on tall structures such as towers, buildings.
      • Less expensive to operate and maintain but cannot be used for large scale studies.
      • Are mainly used for collecting the ground truth or for laboratory simulation studies.
      • Classified into balloon-borne and aircraft based platforms.
      • Balloons going upto 49 km altitude was used in the 1970s.
      • Now-a-days , mostly sensors mounted on aircraft used.
      • Aircrafts used in Remote sensing were used for obtaining photographs.
      • Other airborne platforms like rockets and helicopters can also be used.
      • Allow researchers to monitor very large areas of the surface, which would be impractical with ground-based sensors, or impossible or dangerous to visit.
      • Remote sensing from space is possible using satellites.
      • These platforms are less affected by atmosphere, their orbits are well defined.
      • Entire earth or any designated portion can be covered at specific intervals.
      • Most commonly used in Remote sensing application.
      • Currently being used to assist in scientific and socioeconomic activities like weather prediction, crop monitoring, mineral exploration, waste land mapping, cyclone warning, water resources management and pollution detection.
      • These missions are of much greater duration than airborne and the continuity of data provided is one great advantage.

      Disadvantage: Robustness of equipment carried by satellite and decay of orbit through atmospheric friction

      ORBITAL CHARACTERISTICS OF SATELLITES

      • The path followed by a satellite is its orbit.
      • Satellite orbits are matched to the capability and objective of the sensors they carry.
      • Orbit selection varies in terms of an altitude of the satellite and their orientation and rotation relative to the earth.
      • Based on that, the satellites used for remote sensing are generally of two types:

      ORBIT TYPES

      (1) Geostationary satellites

      (2) Sun-synchronous satellites

      • Geostationary satellites
        • At altitudes of 36,000 km from earth’s surface, revolve at speeds which match the rotation of earth and appear stationary with respect to the earth.
        • Allows the satellites to observe and collect information continuously over specific areas. They maintain a fixed location with respect to the earth’s surface.
        • Eg: Weather and communication satellite.
        • Due to its large altitude, used to monitor weather and cloud patterns.

        Eg: India’s INSAT (Indian National Satellite System) series

        • Sun-synchronous satellites
          • These are designed to follow an orbit, basically north-south, which in conjunction with the earth’s west-east rotation, allows them to cover most of the earth’s surface over a certain period of time.
          • These are near-polar orbits, so named for the inclination of the orbit relative to the line running between the North and South poles.
          • These satellites are located at much lower altitudes, generally a few hundred to a few thousand km’s.
          • The revisit interval of these satellites may be any integral number of days.
          • Therefore, useful for studying phenomena, vegetation vigor etc.
          • Another element that defines these satellites is the Swath width. As a satellite revolves around the earth, the sensor sees a certain portion of the earth’s surface. The area imaged on the surface is the swath width.
          • Therefore, most of the remote sensing satellites are defined by its Orbit, altitude, revisit period, the swath width, its spatial resolution and spectral resolution.

          Eg: Indian IRS (Indian Remote Sensing satellite) series

          • It is a device that record EMR reflected or emitted from Earth features.
          • Consists of mechanisms usually sophisticated lenses with filters. It is designed to operate specifically to study and produce outputs for a specific region of the EM spectrum, i.e, it is made sensitive to a particular region of the spectrum.
          • Different types of Sensors:
            • Passive sensors: Detect the reflected or emitted EMR from natural resources.
            • Active sensors: Detect the reflected responses from objects which are irradiated from artificially generated energy sources.

            Passive Sensor Active Sensor

            RESOLUTION CONCEPT

            • They include:
              • Spatial/Geometric Resolution
              • Spectral Resolution
              • Radiometric Resolution
              • Temporal Resolution

              Spatial Resolution

              • Indicates the physical size of the smallest feature or the closest separation of two features, which can be detected and distinguished by the imaging system.
              • What can be identified by this feature are the attributes like shape, size and texture of the objects.
              • It is determined by the instrumental parameters (like wavelength) and height of the satellite above the ground (H). It is proportional to H/λ.
              • Systems with shorter wavelengths like visible region produce a much better spatial resolution that those operating at longer wavelengths.
              • The picture shows different resolutions. It is observed that low values of resolution give better images.
              • Therefore, advantages of having a low resolution: Can cover the wider area.
              • For high resolution, though it gives more details, data would be bulky and a distinction between features may be slightly difficult.
              • The most frequently used measure, based upon the geometric properties of an imaging system is the instantaneous field of view (IFOV) of a sensor. It is the area on the surface that is viewed by the instrument from a given altitude at a given time.
              • IFOV is angular cone of visibility of the sensor (A)
              • Determines area is seen from a given altitude at a given time (B)
              • Area viewed is IFOV * altitude (C)
              • Known as ground resolution cell (GRC) or element (GRE)
              • The basis of the definition of spatial resolution can depend on the following:
              • Geometrical properties of the imaging system, i.e., the wavelength band used.
              • The ability to distinguish between point targets.
              • The ability to measure the periodicity of repetitive targets.
              • The ability to measure the spectral properties of small targets.

              Spectral Resolution

              • This represents the width of the spectral band and the number of spectral bands in which the image is taken.
              • For example, a true colour photography will consist of 3 spectral bands, each sensitive to the blue, green and red region of the EM spectrum.
              • For studying vegetation, we would go for a combination of 4 bands, i.e., bands of the visible light and IR band.
              • Thus, spectral resolution describes the ability of a sensor to define fine wavelengths intervals. The finer the spectral resolution, the narrower the wavelengths range for a particular band.
              • To improve the better potential of the system to discriminate between features, it is better to increase the spectral resolution or increase the number of bands. This would lead to more narrower wavelength bands and finer the spectral resolution.
              • Features, which may have a reflectance over a broadband, may differ in detail if the spectral interval of sensing is narrowed.
              • The use of several bands of the spectrum is referred to as multispectral sensing.
              • Present-day sensor systems can detect hundreds of very narrow spectral bands throughout the different regions of the EM spectrum.
              • Their very high spectral resolution facilitates fine discrimination between different targets.
              • Advantage of narrow band over broadband
              • Narrow bands give more spectral detail
              • More bands = more information to store, transmit and process
              • BUT more bands enables discrimination of more spectral detail

              Radiometric Resolution

              • Describe the actual information content in the image.
              • It is the capability to differentiate the spectral reflectance or emittance between various targets.
              • It is the smallest change in intensity level that can be detected by the sensing system.
              • It is the ability of the system to discriminate very slight differences in energy.
              • Every time an image is acquired by a sensor, its sensitivity to the magnitude of the EME determines the radiometric resolution.
              • It is expressed as the number of binary digits, i.e, bits, recorded as exponents of power 2.
              • If a sensor used 8 bits to record the data, there would be 256 digital values available ranging from 0 – 255, representing different colours.
              • Likewise, if only 4 bits were used, then there are 16 values ranging from 0 – 15.
              • The finer the radiometric resolution of a sensor, the more sensitive it is for detecting small differences in reflected or emitted energy.

              Temporal Resolution

              • Indicates the time interval between successive overpasses of the sensor when the imaging is repeated, i.e., the satellite revisits the same area (referred to as revisit period and it is usually several days)
              • During each successive overpass, changes or variations in reflectivity or emissivity of objects are expected, and this can be detected.
              • The use of repeat coverage becomes necessary when the phenomena of interest undergo significant changes with the passage of time.
              • Very useful in identification of agricultural crops.
              • This is important when studying
                • Short-lived phenomena need to be imaged (Floods, oil slicks)
                • Spread of a forest disease from one year to the next.
                • Changing appearance of a feature over time can be used to distinguish it from near-similar features (Wheat/Maize)

                Pay Load Description of Earth resource (or) Observation Satellites

                (Indian Remote Sensing Satellites)

                1. Aryabhata
                • First Indian Experimental Satellite
                • Launch Date: April 19, 1975
                • Objectives: The objectives of this project were to design and fabricate a space-worthy satellite system and evaluate its performance in orbits.
                1. Bhaskara I
                • First Indian low orbit Earth Observation Satellite
                • Launch Date: June 7, 1979
                • Television Cameras operating in visible (0.6 microns) and near-infrared (0.8 microns) to collect data related to hydrology, forestry and geology.
                1. IRS-1A
                • First operational remote sensing satellite
                • Launched on: March 17, 1988.
                • Out of service: 1995.
                • Repeat cycle: 22 days
                • Orbit Height: 904 km
                • Orbit type: Sun Synchronous
                1. IRS- 1B
                • Launched on: August 29, 1991.
                • Out of service: 1996.
                • Repeat cycle: 22 days
                • Orbit Height: 904 km
                • Orbit type: Sun Synchronous
                • Swath Width: 148 km.
                • Resolution: 72.5 m
                • Visible/Near IR bands (4 spectral bands)
                • 6 bits radiometric resolution
                1. IRS-1C and IRS-1D
                • Launched on December 1995 and September 1997.
                • Same as IRS 1A/1B
                1. IRS-P2
                  • Launched on: October 15, 1994
                  • Repeat cycle: 24 days
                  • Orbit Height: 817 km
                  • Orbit type: Sun Synchronous
                  • Swath Width: 740 km.
                  • Resolution: 25 m
                  • Multispectral
                  • Excellent for large-area vegetation monitoring
                2. IRS-P4 (Oceansat-1)
                  • First Indian satellite dedicated fully for the study of oceans.
                  • Launched on May 26, 1999.
                  • Helpful in study of oceanographic phenomena such as sea temperature, sea surface height, rain over oceans and useful in measuring various ocean parameters.
                  • Swath: 1360 km for MSMR and 1420 km for OCM
                  • Radiometric resolution: 12 bits
                  • Spatial resolution: 40 km
                  • IRS-P6 (Resourcesat-1)
                  • Launched on October 17, 2003.
                  • Altitude: 817 km
                  • Sun-synchronous satellite.
                  • Carries 3 sensors that deliver an array of spectral bands, with resolutions from 5.8 – 60 m.
                  • Advanced applications in vegetation dynamics, crop yield estimates, disaster management.
                  • Swath varies from 25 km – 1400 km depending on the sensor.
                3. IRS-P5 (Cartosat-1)
                  • Launched on May 5, 2005
                  • Altitude: 618 km
                  • Sun-synchronous
                  • Revisit: 5 days
                  • Sensors: 2 cameras
                  • Resolution: 2.5 m
                  • Swath – 30 km
                  • Spectral Band: Visible and Near IR bands.
                  • Data available useful for cartographic applications, cadastral mapping and updating, land use and other GIS applications.
                4. Cartosat-2
                  • Launched on January 10, 2007
                  • Altitude: 630 km
                  • Revisit: 4 days
                  • One Sensor: Camera
                  • Spatial resolution: < 1 m
                  • Swath: 9.6 km

                Same application as Cartosat-1.

                1. RESOURCESAT 2A
                  • Launched on December 7, 2016.
                  • Sun-synchronous
                  • Revisit: 5 days
                  • Sensors: LISS IV (Visible and Near Infrared Region)
                  • Resolution: 5.8 m
                  • Swath – 30 km
                  • Orbit type: Sun-synchronous polar orbit (SSPO)
                  • Spectral Band: Visible and Near IR bands.

                Pay Load Description of Metrological Satellites

                1. INSAT3DR
                  • Launched on Sep 08, 2016
                  • Geostationary orbit
                  • Mass – 2000 kg
                  • longitude 82° East
                  • Altitude: 35791 km
                  • Periods: 23.93 hours
                  • inclination -0.08°

                The significant improvements incorporated in INSAT-3DR are:

                -Imaging in Middle Infrared band to provide night time pictures of low clouds and fog

                -Imaging in two Thermal Infrared bands for estimation of Sea Surface Temperature (SST) with better accuracy

                -Higher Spatial Resolution in the Visible and Thermal Infrared bands

                1. INSAT3D
                  • Launched on July 26, 2013
                  • Geostationary orbit
                  • Mass – 2000 kg
                  • longitude 82° East
                  • Altitude: 35791 km
                  • Periods: 23.93 hours
                  • Band – C band
                  • inclination -0.08°
                  • 6 channel multi-spectral Imager,19 channel Sounder, Data Relay Transponder (DRT),Search and Rescue Transponder
                2. SARAL
                  • Launched on Feb 25, 2013
                  • Sun-synchronous
                  • Mass – 407 kg
                  • Orbit inclination -98.538°
                  • Altitude: 781 km
                  • Revisit: 36 days
                  • 4 PI sun sensors
                  • Swath: 11 km
                1. METSAT 1/ Kalpana 1
                  • Launched on Sep 12, 2002
                  • Geostationary orbit
                  • Mass – 1060 kg
                  • longitude 74° East
                  • Altitude: 35779 km
                  • Periods: 24 hours
                  • Band – visible, infrared, thermal infrared
                  • inclination -0.0°
                  • Its mission is to collect data in layer of clouds, water vapor, and temperature of the atmosphere.
                2. INSAT 2E
                  • Launched on April 2, 1999
                  • Geostationary orbit
                  • Mass – 2550 kg
                  • longitude 83° East
                  • Altitude: 35806 km
                  • Periods: 23.93 hours
                  • Transponders G/H Band
                  • inclination -0.08°
                  • It also carries two meteorological instruments the Very High Resolution Radiometer, and a CCD camera capable of returning images with a resolution of one kilometre.

                MICROWAVE SENSORS

                Principles of Microwave Remote Sensing

                Microwave remote sensing, using microwave radiation using wavelengths from about one centimeter to a few tens of centimeters enables observation in all weather conditions without any restriction by cloud or rain. This is an advantage that is not possible with the visible and/or infrared remote sensing. In addition, microwave remote sensing provides unique information on for example, sea wind and wave direction, which are derived from frequency characteristics, Doppler effect, polarization, back scattering etc. that cannot be observed by visible and infrared sensors. However, the need for sophisticated data analysis is the disadvantage in using microwave remote sensing.

                There are two types of microwave remote sensing active and passive.

                The active type receives the backscattering which is reflected from the transmitted microwave which is incident on the ground surface.

                Synthetic aperture radar (SAR), microwave scatterometers, radar altimeters etc. are active microwave sensors. The passive type receives the microwave radiation emitted from objects on the ground. The microwave radiometer is one of the passive microwave sensors.

                Types of Microwave Sensor

                There are two types of microwave sensors, passive and active. Many of the earth observation satellites to be launched after 1992 are planned to have microwave sensors onboard. Active sensors will be classified into more types in terms of the target with respect to horizontal or vertical polarisation.

                The table shows the typical microwave sensors and targets to be measured.

                Table shows the frequency of passive microwave sensor for monitoring major targets.

                Table shows the frequency of active microwave sensor for monitoring major targets.

                ACTIVE MICROWAVE SENSORS

                Unlike optical and infrared imaging sensors which are inherently passive, meaning they rely on reflected or radiated energy, radar is an active sensor–providing its own illumination in the form of microwaves. Microwaves are electromagnetic (EM) waves in approximately the 1-1000 GHz region of the EM spectrum

                Imaging radar as shown in Table 4.1.1 is classified further into Real Aperture Radar (RAR) and Synthetic Aperture Radar (SAR).

                Real Aperture Radar

                The two main types of radar images are the circularly scanning plan-position indicator (PPI) images and the side-looking images. The PPI applications are limited to the monitoring of air and naval traffic.

                RAR transmits a narrow-angle beam of pulse radio wave in the range direction at right angles to the flight direction (called the azimuth direction) and receives the backscattering from the targets which will be transformed into a radar image from the received signals, as shown in Figure

                Usually, the reflected pulse will be arranged in the order of return time from the targets, which corresponds to the range direction scanning.

                Synthetic Aperture Radar

                A Synthetic Aperture Radar (SAR), or SAR, is a coherent mostly airborne or spaceborne side looking radar system which utilises the flight path of the platform to simulate an extremely large antenna or aperture electronically, and that generates high-resolution remote sensing imagery. Compared to real aperture radar, Synthetic Aperture Radar (SAR) synthetically increases the antenna’s size or aperture to increase the azimuth resolution though the same pulse compression technique as adapted for range direction. Synthetic aperture processing is a complicated data processing of received signals and phases from moving targets with a small antenna, the effect of which is to should be theoretically converted to the effect of a large antenna, that is a synthetic aperture length, as shown in Figure.

                The synthetic aperture length is the beam width by the range which a real aperture radar of the same length, can project in the azimuth direction.

                PASSIVE MICROWAVE SENSORS

                Microwave Radiometer

                A part of the microwave is also radiated by thermal radiation from the objects on the earth. Microwave radiometers or passive type microwave sensors are used to measure the thermal radiation of the ground surface and/or atmospheric condition.

                Brightness temperature measured by a microwave radiometer is expressed by Rayleigh-Jean’s law, which is the resultant energy of thermal radiation from the ground surface and the atmospheric media. Multi-channel radiometers with multi- polarisation are used to avoid the influences of unnecessary factors to measure the specific physical parameter.

                The figure shows two typical microwave scanning radiometers the conical scanning type and the cross-track scanning type. The former is used for the microwave channel which is influenced by the ground surface, while the latter is used for the channel which can be neglected by the influence of the ground surface.

                The most simple radiometer is the total power radiometer, as shown in Figure. This system has a mixer to enable it to mix high frequency of a local oscillator in order to amplify the high signal after transforming to a low frequency. However, the influence of system gain variation cannot be neglected in this system.

                All-Weather Imaging

                Due to the cloud-penetrating property of microwave, SAR is able to acquire “cloud-free” images in all weather. This is especially useful in the tropical regions which are frequently under cloud covers throughout the year. Being an active remote sensing device, it is also capable of night-time operation.

                THERMAL INFRARED SENSOR

                The Thermal Infrared Sensor (TIRS) will measure land surface temperature in two thermal bands with a new technology that applies quantum physics to detect heat.

                TIRS was added to the satellite mission when it became clear that state water resource managers rely on the highly accurate measurements of Earth’s thermal energy obtained by LDCM’s predecessors, Landsat 5 and Landsat 7, to track how land and water are being used. With nearly 80 percent of the fresh water in the Western U.S. being used to irrigate crops, TIRS will become an invaluable tool for managing water consumption.

                TIRS uses Quantum Well Infrared Photodetectors (QWIPs) to detect long wavelengths of light emitted by the Earth whose intensity depends on surface temperature. These wavelengths, called thermal infrared, are well beyond the range of human vision. QWIPs are a new, lower-cost alternative to conventional infrared technology and were developed at NASA’s Goddard Space Flight Center in Greenbelt, Md.

                The QWIPs TIRS uses are sensitive to two thermal infrared wavelength bands, helping it separate the temperature of the Earth’s surface from that of the atmosphere. Their design operates on the complex principles of quantum mechanics. Gallium arsenide semiconductor chips trap electrons in an energy state ‘well’ until the electrons are elevated to a higher state by thermal infrared light of a certain wavelength. The elevated electrons create an electrical signal that can be read out and recorded to create a digital image.

                Wavelength / Spectral Range

                The infrared portion of the electromagnetic spectrum is usually considered to be from 0.7 to 1,000 μm. Within this infrared portion, there are various nomenclatures and little consensus among various groups to define the sub-boundaries. In terrestrial remote sensing, the region of 3 to 35 μm is popularly called thermal infrared. As in all other remote sensing missions, data acquisitions are made only in regions of least spectral absorption known as the atmospheric windows. Within the thermal infrared, an excellent atmospheric window lies between 8-14 μm wavelength.

                Poorer windows lie in 3-5 μm and 17-25 μm. Interpretation of the data in 3-5 μm is complicated due to overlap with solar reflection in day imagery and 17-25 μm region is still not well investigated. Thus 8-14 μm region has been of greatest interest for thermal remote sensing.

                APPLICATIONS

                Thermal property of a material is representative of upper several centimetres of the surface. As in thermal remote sensing, we measure the emitted radiations, it proves to be complementary to other remote sensing data and even unique in helping to identify surface materials and features such as rock types, soil moisture, geothermal anomalies etc. The ability to record variations in infrared radiation has the advantage in extending our observation of many types of phenomena in which minor temperature variations may be significant in understanding our environment. Thermal remote sensing reserves immense potential for various applications. The following is a list of some of the areas in which thermal data is put to use


                As one of CRC’s Essential titles, this book and stands out as one of the best in its field and is a must-have for researchers, academics, students, and professionals involved in the field of environmental science, as well as for libraries .

                Author: Emilio Chuvieco

                Fundamentals of Satellite Remote Sensing: An Environmental Approach, Third Edition, is a definitive guide to remote sensing systems that focuses on satellite-based remote sensing tools and methods for space-based Earth observation (EO). It presents the advantages of using remote sensing data for studying and monitoring the planet, and emphasizes concepts that make the best use of satellite data. The book begins with an introduction to the basic processes that ensure the acquisition of space-borne imagery, and provides an overview of the main satellite observation systems. It then describes visual and digital image analysis, highlights various interpretation techniques, and outlines their applications to science and management. The latter part of the book covers the integration of remote sensing with Geographic Information System (GIS) for environmental analysis. This latest edition has been written to reflect a global audience and covers the most recent advances incorporated since the publication of the previous book, relating to the acquisition and interpretation of remotely sensed data. New in the Third Edition: Includes additional illustrations in full color. Uses sample images acquired from different ecosystems at different spatial resolutions to illustrate different interpretation techniques. Includes updated EO missions, such as the third generations of geostationary meteorological satellites, the new polar orbiting platforms (Suomi), the ESA Sentinels program, and high-resolution commercial systems. Includes extended coverage of radar and LIDAR processing methods. Includes all new information on near-ground missions, including unmanned aerial vehicles (UAVs). Covers new ground sensors, as well as machine-learning approaches to classification. Adds more focus on land surface characterization, time series, change detection, and ecosystem processes. Extends the interactions of EO data and GIS that cover different environmental problems, with particular relevance to global observation. Fundamentals of Satellite Remote Sensing: An Environmental Approach, Third Edition, details the tools that provide global, recurrent, and comprehensive views of the processes affecting the Earth. As one of CRC’s Essential titles, this book and stands out as one of the best in its field and is a must-have for researchers, academics, students, and professionals involved in the field of environmental science, as well as for libraries developing collections on the forefront of this industry.


                GEOGRAPHIC INFORMATION SYSTEM (GIS) AND REMOTE SENSING - PowerPoint PPT Presentation

                REMOTE SENSING Lecture 4 Zakaria . the most distinctive characteristic is the energy . spectral responses measured by the remote sensors are what permit us to . &ndash PowerPoint PPT presentation

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                presentations for free. Or use it to find and download high-quality how-to PowerPoint ppt presentations with illustrated or animated slides that will teach you how to do something new, also for free. Or use it to upload your own PowerPoint slides so you can share them with your teachers, class, students, bosses, employees, customers, potential investors or the world. Or use it to create really cool photo slideshows - with 2D and 3D transitions, animation, and your choice of music - that you can share with your Facebook friends or Google+ circles. That's all free as well!


                Table of Contents

                1. Thematic Cartography and Geovisualization
                1.1 What is a Thematic Map?
                1.2 How are Thematic Maps Used?
                1.3 Basic Steps for Communicating Map Information
                1.4 Consequences of Technological Change in Cartography
                1.5 Geovisualization
                1.6 Related Techniques
                1.7 Cognitive Issues in Cartography
                1.8 Social and Ethical Issues in Cartography

                2. A Historical Perspective on Thematic Cartography
                2.1 A Brief History of Cartography
                2.2 History of Thematic Cartography
                2.3 History of U.S. Academic Cartography
                2.4 The Paradigms of American Cartography

                3. Statistical and Graphical Foundation
                3.1 Population and Sample
                3.2 Descriptive Versus Inferential Statistics
                3.3 Methods for Analyzing Spatial Data, Ignoring Location
                3.4 Numerical Summaries in Which Location Is an Integral Component

                PART II
                Principles of Cartography

                4. Data Classification
                4.1 Common Methods of Data Classification
                4.2 Using Spatial Context to Simplify Choropleth Maps
                4.3 Using Multiple Criteria to Determine Class Intervals

                5. Principles of Symbolization
                5.1 Nature of Geographic Phenomena
                5.2 Levels of Measurement
                5.3 Visual Variables
                5.4 Comparison of Choropleth, Proportional Symbol, Isopleth, and Dot Mapping
                5.5 Selecting Visual Variables for Choropleth Maps

                6. Scale and Generalization
                6.1 Geographic and Cartographic Scale
                6.2 Definitions of Generalization
                6.3 Models of Generalization
                6.4 The Fundamental Operations of Generalization
                6.5 An Example of Generalization
                6.6 MapShaper: A Free Web-Based Generalization Service

                7. The Earth and Its Coordinate System
                7.1 Basic Characteristics of the Earth&rsquos Graticule
                7.2 A Brief History of Latitude and Longitude
                7.3 Determining the Earth&rsquos Size and Shape

                8. Elements of Map Projections
                8.1 The Map Projection Concept
                8.2 The Reference Globe and Developable Surfaces
                8.3 The Mathematics of Map Projections
                8.4 Map Projection Characteristics
                8.5 Distortion on Map Projections
                8.6 Projection Properties

                9. Selecting an Appropriate Map Projection
                9.1 Potential Selection Guidelines
                9.2 Examples of Selecting Projections

                10. Principles of Color
                10.1 How Color Is Processed by the Human Visual System
                10.2 Hardware Considerations in Producing Color Maps for Graphics Displays
                10.3 Models for Specifying Color

                11. Map Elements and Typography
                11.1 Alignment and Centering
                11.2 Map Elements
                11.3 Typography

                12. Cartographic Design
                12.1 Cartographic Design
                12.2 Case Study: Real Estate Site Suitability Map

                13. Map Reproduction
                13.1 Reproduction Versus Dissemination
                13.2 Planning Ahead
                13.3 Map Editing
                13.4 Raster Image Processing for Print Reproduction
                13.5 Screening for Print Reproduction
                13.6 Aspects of Color Printing
                13.7 High-Volume Print Reproduction
                13.8 Nonprint Reproduction and Dissemination

                PART III
                Mapping Techniques

                14. Choropleth Mapping
                14.1 Selecting Appropriate Data
                14.2 Data Classification
                14.3 Factors for Selecting a Color Scheme
                14.4 Details of Color Specification
                14.5 Legend Design
                14.6 Classed Versus Unclassed Mapping

                15. Dasymetric Mapping
                15.1 Selecting Appropriate Data and Ancillary Information
                15.2 Eicher and Brewer&rsquos Work
                15.3 Mennis and Hultgren&rsquos Intelligent Dasymetric Mapping (IDM)
                15.4 LandScan
                15.5 Langford and Unwin&rsquos Generalized Dasymetric Approach

                16. Isarithmic Mapping
                16.1 Selecting Appropriate Data
                16.2 Manual Interpolation
                16.3 Automated Interpolation for True Point Data
                16.4 Criteria for Selecting an Interpolation Method for True Point Data
                16.5 Limitations of Automated Interpolation Approaches
                16.6 Tobler&rsquos Pycnophylactic Approach: An Interpolation Method for Conceptual Point Data
                16.7 Symbolization

                17. Proportional Symbol and Dot Mapping
                17.1 Selecting Appropriate Data For Proportional Symbol Maps
                17.2 Kinds of Proportional Symbols
                17.3 Scaling Proportional Symbols
                17.4 Legend Design for Proportional Symbol Maps
                17.5 Handling Overlap on Proportional Symbol Maps
                17.6 Redundant Symbols
                17.7 Selecting Appropriate Data for Dot Maps
                17.8 Creating a Dot Map

                18. Multivariate Mapping
                18.1 Bivariate Mapping
                18.2 Multivariate Mapping Involving Three or More Attributes
                18.3 Cluster Analysis

                19. Cartograms and Flow Maps
                19.1 Cartograms
                19.2 Flow Mapping

                Part IV
                Geovisualization

                20. Visualizing Terrain
                20.1 Nature of the Data
                20.2 Vertical Views
                20.3 Oblique Views
                20.4 Physical Models

                21. Map Animation
                21.1 Early Developments
                21.2 Visual Variables and Categories of Animation
                21.3 Examples of Animations
                21.4 Using 3-D Space to Display Temporal Data
                21.5 Does Animation Work?

                22. Data Exploration
                22.1 Goals of Data Exploration
                22.2 Methods of Data Exploration
                22.3 Examples of Data Exploration Software

                23. Visualizing Uncertainty
                23.1 Basic Elements of Uncertainty
                23.2 General Methods for Depicting Uncertainty
                23.3 Visual Variables for Depicting Uncertainty
                23.4 Applications of Visualizing Uncertainty
                23.5 Studies of the Effectiveness of Methods for Visualizing Uncertainty

                24. Web Mapping
                24.1 A Brief History of Web Mapping
                24.2 Cartographic Web Sites: A Classification
                24.3 Tying Together the Five Continua

                25. Virtual Environments
                25.1 Defining Virtual and Mixed Environments
                25.2 Technologies for Creating Virtual Environments
                25.3 The Four &ldquoI&rdquo Factors of Virtual Environments
                25.4 Applications of Geospatial Virtual Environments
                25.5 Research Issues in Geospatial Virtual Environments
                25.6 Developments in Mixed Environments
                25.7 Health, Safety, and Social Issues

                26. Trends in Research and Development
                26.1 Linked Micromap Plots and Conditioned Choropleth Maps
                26.2 Using Senses Other Than Vision to Interpret Spatial Patterns
                26.3 Collaborative Geovisualization
                26.4 Multimodal Interfaces
                26.5 Information Visualization and Spatialization
                26.6 Spatial Data Mining
                26.7 Visual Analytics
                26.8 Mobile Mapping and Location-Based Services
                26.9 Keeping Pace with Recent Developments

                Appendix: Lengths of One Degree Latitude and Longitude
                Glossary
                References
                Index


                True color combination using Resourcesat-1? - Geographic Information Systems

                Digital Karst Density Layer and Compilation of Mapped Karst Features in Pennsylvania

                By Stuart O. Reese, P.G. and William E. Kochanov, P.G.

                Pennsylvania Geological Survey, 3240 Schoolhouse Road, Middletown, PA 17057
                Telephone (717) 702-2017 fax (717) 702-2065 e-mail: [email protected], [email protected]

                In 1985, the Pennsylvania Geological Survey began a series of investigations to map karst features of carbonate rocks in Pennsylvania (fig. 1). Locations of surface depressions, sinkholes, surface mines, and caves were compiled from municipal questionnaires, field surveys, published literature, and unpublished data sources as well as through an extensive aerial photograph review. Results of these investigations were released in a series of county-based open-file reports (Kochanov, 1987&ndash1995). This information has been available since 1998 in paper maps and through an online database, but no digital layer files had been developed for geographic information system (GIS) tools.

                Figure 1. Carbonate rocks in Pennsylvania (modified after Pennsylvania Geological Survey, 2000).

                Over the course of several months, a digital compilation of karst data points was completed. In total, the compilation included over 111,000 data points from 14 counties and 107 7.5-minute quadrangles (fig. 2). Digital data allowed for GIS mapping and for computer analysis of karst features. A colorized density surface was created from these merged files using ArcView 3.2 software. Mapping the density of karst points is useful for assessment of potential structural and environmental problems associated with karst geology. High-density areas of karst points where land subsidence may be a problem are noted, or where karst features can serve as direct recharge zones to the groundwater. These areas are highly vulnerable to groundwater contamination.

                Figure 2. Location of quadrangles with mapped karst features in south-central and southeastern Pennsylvania.

                Previously, karst feature locations from the open-file reports were plotted on a mylar stable base over the corresponding topographic map. The data points were digitized from the mylars using GSMAP v.8 (Selner and Taylor, 1992) and their coordinates were entered into a relational database management system. Karst data points were recorded in latitude/longitude(decimal degrees) by quadrangle. ArcView 3.2 was used to compile the karst point data from the database. ArcView shapefiles were created and merged to form a regional dataset. The shapefiles were placed over base maps of digital raster graphic images of 7.5-minute quadrangles in Universe Traverse Mercator (UTM), North American 1927 datum, Clarke 1866 spheroid, in UTM Zones 17 or 18.

                The digital database was cross-checked against the original locations. As the data were reviewed, it became apparent that for many of the quadrangles, a systematic digitizing error had been introduced into the latitude and longitude data. These data points were corrected using the ArcView extension ShapeWarp (Version 2.2). In addition, on-screen digitizing procedures were used to create new files where previous digitizing had not been done. Compiled karst data points were identified by feature type (surface depression, sinkhole, surface mine, or cave), quadrangle, and county. County name was assigned by a spatial join command (ArcView geoprocessing, assign data by location).

                Density Surface Preparation

                The ArcView extension Spatial Analyst (version 2.0) was used to develop the digital density layer of the combined data points. A density surface is based on the division of the study area into square cells, which can be sized as appropriate. ArcView software calculates a density value for each cell by counting the number of points within a defined search radius from the center of each cell (fig. 3) and dividing by the search area. The density value (features per square mile) is assigned to the cell. The search circle is then shifted to the next cell and the floating process is repeated until all of the cells have been assigned a density value. This process smoothes the density layer over the study area.

                Figure 3. Procedures used in density calculation.

                ArcView has two options for density calculations &mdash a simple density formula (described above) and a weighted &ldquokernel&rdquo procedure. A weighted method called a kernel (a &ldquoquartic approximation to a Gaussian kernel&rdquo) can be used which assigns more value to points located closer to the center of the cell. For this project, the simple density function was used. The kernel method further smoothes the data but in this case caused a more bi-modal appearance of the density surface. For this reason, the simple density surface was retained.

                Density calculations must be done in a map projection that minimizes error for area, distance, and direction. For example, a density calculation using a decimal degrees (geographic) framework would result in severe errors. The projection used for the density calculations was the Albers equal area conic projection with standard parallels at 40°N and 42°N, and a central meridian of -78°W. This projection maintains true area and shape with negligible distortion at the scale used, which was less than or equal to 1:24,000.

                When doing density calculations, the recommended number of cells is between 10 and 100 cells per density unit (Mitchell, 1999). The density unit of features per square mile was used here, which, using this recommendation, would equal approximately 100 cells (with dimensions of 160 × 160 meters) per square mile. This results in a 5 MB file size with over 1.3 million cells. At a scale of 1:24,000 (the scale at which the features were mapped), the density surface has a noticeably blocky appearance. A smaller cell size will smooth the surface, but require more computer processing time and file storage space. A cell size of 25 meters was chosen to smooth the data to the lower limit of mappability at the 1:24,000 scale. This produced a 213 MB file with over 55 million cells. Despite the larger file size, the resulting density surface portrays a smooth gradation of cells and allows more local variation to be seen at the 1:24,000 scale.

                The chosen search radius influences the appearance of the density surface. The larger the radius, the more generalized the patterns will be. The smaller the radius, the more local variation is portrayed, to the extreme of remapping the point data. The 250-meter search radius was selected in a trial and error process to show enough local variation without over-generalizing the density. Karst feature data points were overlaid upon the density surface to evaluate different parameter values. The 250-meter radius parameter provides for a smooth simple calculated density surface.

                Further preparation of the density surface was accomplished using ArcGis 8.3. The cells were color coded using a graduated color scheme for the density values. The density color scale that was developed represents an ESRI ArcMap 8.3 &ldquoquantile gradation&rdquo of the density values. Quantile values are useful in comparing the density over an area, and from map to map. The large number of classes (30 quantiles) allows the color gradation to be displayed as a range and it allows the value of &ldquo0&rdquo to be given a transparent definition on the map. The mapped areas of higher density of karst features are portrayed in orange and red colors. Values in the red approach 640 karst features per square mile (one feature per acre) or more. The lowest density value, represented by thedarkest green color, indicates at least one karst feature within the 250-meter search radius of the cell (approximately 48 acres or 0.07 mi2).

                The units of measurement were selected to consider the proposed audience of the product. Although units of meters were used to develop the density grid, density was calculated for each grid cell in units of karst features per square mile (1 mi 2 = 2.59 km 2 ). A value in acres equivalent to the square mile value was added to the karst map. Units of square miles (and acres) allow the non-scientist to more easily relate to the map data. In the maps generated, yellow, orange and red colors approach 640 features per mile, or about one per acre.

                The main products include digital coverages of karst points by county and by quadrangle, and a regional map showing the density of the karst features. Figure 4 shows an example of the density layer. County maps in a mapbook style were completed. Such easy-to-interpret maps will help homeowners, municipal planners and others understand the intensity of mapped karst features per square mile or acre. Because of potential misinterpretation, efforts were made to qualify the digital products.

                Figure 4. Density of mapped karst features in the Lititz 7.5-minute quadrangle, Lancaster County, Pa.

                Beyond the accompaniment of metadata with a GIS coverage, explicit caveat statements are very important because of the multiple meanings that could be interpreted from the karst data. On the regional karst density map, the bright colors are quickly noticed as areas of concern. However, the proper response needs to be guided. Banning all activity in the high-density zones is not a rational response, but neither should caution be thrown to the wind. The recommended uses of the data should be made obvious. Here, the karst data are useful for regional planning and preliminary site studies, but they are not a substitute for site-specific subsurface investigations.

                The occurrence of a sinkhole, a subsidence feature that breaks the land surface, depends on numerous factors including rock type, geologic structure (the presence of fractures, joints and faults), soil cover, surface hydrology, and land use. Areas of subsidence are not necessarily restricted to the high-density areas shown in dark shades on the map. Surface depressions, which by definition do not show a land-surface break, were the dominant type of mapped karst feature (96 percent). However, subsidence can occur in areas where there are no discernible surface depressions, or where sinkholes are not observed.

                In addition, there may be instances where subsidence features are shown outside the mapped limits of carbonate bedrock. Surficial material such as colluvium typically conceals the actual contact between non-carbonate and carbonate bedrock formations. Undetected faults also can displace carbonate bedrock and account for subsidence features outside the current mapped formational contacts of carbonate rocks.

                Land use can bias the detection of the karst feature. Urban land cover often masks karst features, making them difficult to detect. Sinkholes, though highly visible and often disruptive when they occur, are typically quickly filled or covered. Therefore, mapped karst features are most often under-represented in the urban setting. Urban land cover often accelerates the formation of sinkholes through changes to surface water drainage. Land thought to be free of sinkholes may suddenly develop karst subsidence features, especially where the surface hydrology has been altered. In more rural areas, karst features can be difficult to discern in wooded areas, whereas karst features in fields are more easily detected.

                All of the potential biases must be considered when using the karst density maps and the digital data of the karst features. Caveat statements are needed to provide proper direction on the use and interpretation of the digital products. Because the mapped features are based on interpretation, cautionary statements are extremely important to direct the use of such digital products, especially when there are known limits to the mapping process. An understanding of the limits of the data is crucial to the responsible use of the data.

                We wish to thank staff members of the Pennsylvania Geological Survey Dr. Jon Inners and Michael Moore for critically reviewing the manuscript.

                Kochanov, W. E., 1987a, Sinkholes and karst-related features of Lehigh County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 87&ndash01, scale 1:24,000, 19 p, 6maps.

                Kochanov, W. E., 1987b, Sinkholes and karst-related features of Northampton County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 87&ndash02, scale 1:24,000, 24 p, 10 maps.

                Kochanov, W. E., 1988a, Sinkholes and karst-related features of Berks County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 88&ndash01, scale 1:24,000, 13 p., 16 maps.

                Kochanov, W. E., 1988b, Sinkholes and karst-related features of Lebanon County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 88&ndash02, scale 1:24,000, 11 p., 4 maps.

                Kochanov, W. E., 1989a, Sinkholes and karst-related features of Cumberland County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 89&ndash02, scale 1:24,000, 16 p., 9 maps.

                Kochanov, W. E., 1989b, Sinkholes and karst-related features of Dauphin County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 89&ndash01, scale 1:24,000, 8 p., 5 maps.

                Kochanov, W. E., 1989c, Sinkholes and karst-related features of Franklin County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 89&ndash03, scale 1:24,000, 15p., 20 maps.

                Kochanov, W. E., 1990, Sinkholes and karst-related features of Lancaster County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 90&ndash01, scale 1:24,000, 12 p., 18 maps.

                Kochanov, W. E., 1993a, Sinkholes and karst-related features of Bucks County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 93&ndash03, scale 1:24,000, 9 p., 4 maps.

                Kochanov, W. E., 1993b, Sinkholes and karst-related features of Montgomery County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 93&ndash02, scale 1:24,000, 7 p., 5 maps.

                Kochanov, W. E., Lichtinger, J. F., and Becker, Mona, 1993, Sinkholes and karst-related features of Chester County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 93&ndash01, scale 1:24,000, 9 p., 10 maps.

                Kochanov, W. E., 1995, Sinkholes and karst-related features of York County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 95&ndash06, scale 1:24,000, 9 p., 10 maps.

                Kochanov, W. E., and Miller, Rachel, 1995, Sinkholes and karst-related features of Adams County, Pennsylvania: Pennsylvania Geological Survey, 4th ser., Open-File Report 95&ndash05, scale 1:24,000, 9 p., 6 maps.

                Mitchell, A., 1999, The ESRI Guide to GIS Analysis, Volume 1: Geographic Patterns & Relationships: Redlands, Ca., Environmental Systems Research Institute, Inc.


                True color combination using Resourcesat-1? - Geographic Information Systems

                Geographic Information System (GIS) market worldwide is projected to grow by US$9. 3 Billion, driven by a compounded growth of 9. 9%. Hardware, one of the segments analyzed and sized in this study, displays the potential to grow at over 9%.

                New York, Jan. 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Geographic Information System (GIS) Industry" - https://www.reportlinker.com/p05798672/?utm_source=GNW
                The shifting dynamics supporting this growth makes it critical for businesses in this space to keep abreast of the changing pulse of the market. Poised to reach over US$3.2 Billion by the year 2025, Hardware will bring in healthy gains adding significant momentum to global growth.

                - Representing the developed world, the United States will maintain a 8.7% growth momentum. Within Europe, which continues to remain an important element in the world economy, Germany will add over US$372 Million to the region’s size and clout in the next 5 to 6 years. Over US$313.1 Million worth of projected demand in the region will come from Rest of Europe markets. In Japan, Hardware will reach a market size of US$81.2 Million by the close of the analysis period. As the world’s second largest economy and the new game changer in global markets, China exhibits the potential to grow at 12.6% over the next couple of years and add approximately US$2.1 Billion in terms of addressable opportunity for the picking by aspiring businesses and their astute leaders. Presented in visually rich graphics are these and many more need-to-know quantitative data important in ensuring quality of strategy decisions, be it entry into new markets or allocation of resources within a portfolio. Several macroeconomic factors and internal market forces will shape growth and development of demand patterns in emerging countries in Asia-Pacific, Latin America and the Middle East. All research viewpoints presented are based on validated engagements from influencers in the market, whose opinions supersede all other research methodologies.

                - Competitors identified in this market include, among others, Autodesk, Inc. Bentley Systems, Inc. Caliper Corporation Computer Aided Development Corporation Limited (Cadcorp) Environmental Systems Research Institute, Inc. (ESRI) General Electric Company Hexagon AB Hi-Target Surveying Instrument Co., Ltd. MacDonald, Dettwiler and Associates Ltd. Pitney Bowes, Inc. Topcon Corporation Trimble, Inc.

                1. MARKET OVERVIEW
                Global Competitor Market Shares
                Geographic Information System (GIS) Competitor Market Share
                Scenario Worldwide (in %): 2019 & 2025
                2. FOCUS ON SELECT PLAYERS
                3. MARKET TRENDS & DRIVERS
                4. GLOBAL MARKET PERSPECTIVE
                Table 1: Geographic Information System (GIS) Global Market
                Estimates and Forecasts in US$ Million by Region/Country:
                2018-2025
                Table 2: Geographic Information System (GIS) Global
                Retrospective Market Scenario in US$ Million by Region/Country:
                2009-2017
                Table 3: Geographic Information System (GIS) Market Share
                Shift across Key Geographies Worldwide: 2009 VS 2019 VS 2025
                Table 4: Hardware (Segment) World Market by Region/Country in
                US$ Million: 2018 to 2025
                Table 5: Hardware (Segment) Historic Market Analysis by
                Region/Country in US$ Million: 2009 to 2017
                Table 6: Hardware (Segment) Market Share Breakdown of Worldwide
                Sales by Region/Country: 2009 VS 2019 VS 2025
                Table 7: Software (Segment) Potential Growth Markets Worldwide
                in US$ Million: 2018 to 2025
                Table 8: Software (Segment) Historic Market Perspective by
                Region/Country in US$ Million: 2009 to 2017
                Table 9: Software (Segment) Market Sales Breakdown by
                Region/Country in Percentage: 2009 VS 2019 VS 2025
                Table 10: Data (Segment) Geographic Market Spread Worldwide in
                US$ Million: 2018 to 2025
                Table 11: Data (Segment) Region Wise Breakdown of Global
                Historic Demand in US$ Million: 2009 to 2017
                Table 12: Data (Segment) Market Share Distribution in
                Percentage by Region/Country: 2009 VS 2019 VS 2025
                Table 13: Government (End-Use) Demand Potential Worldwide in
                US$ Million by Region/Country: 2018-2025
                Table 14: Government (End-Use) Historic Sales Analysis in US$
                Million by Region/Country: 2009-2017
                Table 15: Government (End-Use) Share Breakdown Review by
                Region/Country: 2009 VS 2019 VS 2025
                Table 16: Water & Wastewater (End-Use) Worldwide Latent Demand
                Forecasts in US$ Million by Region/Country: 2018-2025
                Table 17: Water & Wastewater (End-Use) Global Historic Analysis
                in US$ Million by Region/Country: 2009-2017
                Table 18: Water & Wastewater (End-Use) Distribution of Global
                Sales by Region/Country: 2009 VS 2019 VS 2025
                Table 19: Telecommunications (End-Use) Sales Estimates and
                Forecasts in US$ Million by Region/Country for the Years 2018
                through 2025
                Table 20: Telecommunications (End-Use) Analysis of Historic
                Sales in US$ Million by Region/Country for the Years 2009 to
                2017
                Table 21: Telecommunications (End-Use) Global Market Share
                Distribution by Region/Country for 2009, 2019, and 2025
                Table 22: Engineering & Business Services (End-Use) Global
                Opportunity Assessment in US$ Million by Region/Country:
                2018-2025
                Table 23: Engineering & Business Services (End-Use) Historic
                Sales Analysis in US$ Million by Region/Country: 2009-2017
                Table 24: Engineering & Business Services (End-Use) Percentage
                Share Breakdown of Global Sales by Region/Country: 2009 VS 2019
                VS 2025
                Table 25: Aerospace & Defense (End-Use) Worldwide Sales in US$
                Million by Region/Country: 2018-2025
                Table 26: Aerospace & Defense (End-Use) Historic Demand
                Patterns in US$ Million by Region/Country: 2009-2017
                Table 27: Aerospace & Defense (End-Use) Market Share Shift
                across Key Geographies: 2009 VS 2019 VS 2025
                Table 28: Oil & Gas Refining (End-Use) Global Market Estimates
                & Forecasts in US$ Million by Region/Country: 2018-2025
                Table 29: Oil & Gas Refining (End-Use) Retrospective Demand
                Analysis in US$ Million by Region/Country: 2009-2017
                Table 30: Oil & Gas Refining (End-Use) Market Share Breakdown
                by Region/Country: 2009 VS 2019 VS 2025
                Table 31: Oil & Gas Exploration (End-Use) Demand Potential
                Worldwide in US$ Million by Region/Country: 2018-2025
                Table 32: Oil & Gas Exploration (End-Use) Historic Sales
                Analysis in US$ Million by Region/Country: 2009-2017
                Table 33: Oil & Gas Exploration (End-Use) Share Breakdown
                Review by Region/Country: 2009 VS 2019 VS 2025
                Table 34: Transportation & Logistics (End-Use) Worldwide Latent
                Demand Forecasts in US$ Million by Region/Country: 2018-2025
                Table 35: Transportation & Logistics (End-Use) Global Historic
                Analysis in US$ Million by Region/Country: 2009-2017
                Table 36: Transportation & Logistics (End-Use) Distribution of
                Global Sales by Region/Country: 2009 VS 2019 VS 2025
                Table 37: Healthcare (End-Use) Sales Estimates and Forecasts in
                US$ Million by Region/Country for the Years 2018 through 2025
                Table 38: Healthcare (End-Use) Analysis of Historic Sales in
                US$ Million by Region/Country for the Years 2009 to 2017
                Table 39: Healthcare (End-Use) Global Market Share Distribution
                by Region/Country for 2009, 2019, and 2025
                Table 40: Other End-Uses (End-Use) Global Opportunity
                Assessment in US$ Million by Region/Country: 2018-2025
                Table 41: Other End-Uses (End-Use) Historic Sales Analysis in
                US$ Million by Region/Country: 2009-2017
                Table 42: Other End-Uses (End-Use) Percentage Share Breakdown
                of Global Sales by Region/Country: 2009 VS 2019 VS 2025

                GEOGRAPHIC MARKET ANALYSIS
                UNITED STATES
                Market Facts & Figures
                US Geographic Information System (GIS) Market Share (in %) by
                Company: 2019 & 2025
                Table 43: United States Geographic Information System (GIS)
                Market Estimates and Projections in US$ Million by Segment:
                2018 to 2025
                Table 44: Geographic Information System (GIS) Market in the
                United States by Segment: A Historic Review in US$ Million for
                2009-2017
                Table 45: United States Geographic Information System (GIS)
                Market Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 46: United States Geographic Information System (GIS)
                Latent Demand Forecasts in US$ Million by End-Use: 2018 to 2025
                Table 47: Geographic Information System (GIS) Historic Demand
                Patterns in the United States by End-Use in US$ Million for
                2009-2017
                Table 48: Geographic Information System (GIS) Market Share
                Breakdown in the United States by End-Use: 2009 VS 2019 VS 2025
                CANADA
                Table 49: Canadian Geographic Information System (GIS) Market
                Estimates and Forecasts in US$ Million by Segment: 2018 to 2025
                Table 50: Canadian Geographic Information System (GIS)
                Historic Market Review by Segment in US$ Million: 2009-2017
                Table 51: Geographic Information System (GIS) Market in
                Canada: Percentage Share Breakdown of Sales by Segment for
                2009, 2019, and 2025
                Table 52: Canadian Geographic Information System (GIS) Market
                Quantitative Demand Analysis in US$ Million by End-Use: 2018 to
                2025
                Table 53: Geographic Information System (GIS) Market in
                Canada: Summarization of Historic Demand Patterns in US$
                Million by End-Use for 2009-2017
                Table 54: Canadian Geographic Information System (GIS) Market
                Share Analysis by End-Use: 2009 VS 2019 VS 2025
                JAPAN
                Table 55: Japanese Market for Geographic Information System
                (GIS) : Annual Sales Estimates and Projections in US$ Million
                by Segment for the Period 2018-2025
                Table 56: Geographic Information System (GIS) Market in Japan:
                Historic Sales Analysis in US$ Million by Segment for the
                Period 2009-2017
                Table 57: Japanese Geographic Information System (GIS) Market
                Share Analysis by Segment: 2009 VS 2019 VS 2025
                Table 58: Japanese Demand Estimates and Forecasts for
                Geographic Information System (GIS) in US$ Million by End-Use:
                2018 to 2025
                Table 59: Japanese Geographic Information System (GIS) Market
                in US$ Million by End-Use: 2009-2017
                Table 60: Geographic Information System (GIS) Market Share
                Shift in Japan by End-Use: 2009 VS 2019 VS 2025
                CHINA
                Table 61: Chinese Geographic Information System (GIS) Market
                Growth Prospects in US$ Million by Segment for the Period
                2018-2025
                Table 62: Geographic Information System (GIS) Historic Market
                Analysis in China in US$ Million by Segment: 2009-2017
                Table 63: Chinese Geographic Information System (GIS) Market
                by Segment: Percentage Breakdown of Sales for 2009, 2019, and
                2025
                Table 64: Chinese Demand for Geographic Information System
                (GIS) in US$ Million by End-Use: 2018 to 2025
                Table 65: Geographic Information System (GIS) Market Review in
                China in US$ Million by End-Use: 2009-2017
                Table 66: Chinese Geographic Information System (GIS) Market
                Share Breakdown by End-Use: 2009 VS 2019 VS 2025
                EUROPE
                Market Facts & Figures
                European Geographic Information System (GIS) Market: Competitor
                Market Share Scenario (in %) for 2019 & 2025
                Table 67: European Geographic Information System (GIS) Market
                Demand Scenario in US$ Million by Region/Country: 2018-2025
                Table 68: Geographic Information System (GIS) Market in
                Europe: A Historic Market Perspective in US$ Million by
                Region/Country for the Period 2009-2017
                Table 69: European Geographic Information System (GIS) Market
                Share Shift by Region/Country: 2009 VS 2019 VS 2025
                Table 70: European Geographic Information System (GIS) Market
                Estimates and Forecasts in US$ Million by Segment: 2018-2025
                Table 71: Geographic Information System (GIS) Market in Europe
                in US$ Million by Segment: A Historic Review for the Period
                2009-2017
                Table 72: European Geographic Information System (GIS) Market
                Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 73: European Geographic Information System (GIS)
                Addressable Market Opportunity in US$ Million by End-Use:
                2018-2025
                Table 74: Geographic Information System (GIS) Market in
                Europe: Summarization of Historic Demand in US$ Million by
                End-Use for the Period 2009-2017
                Table 75: European Geographic Information System (GIS) Market
                Share Analysis by End-Use: 2009 VS 2019 VS 2025
                FRANCE
                Table 76: Geographic Information System (GIS) Market in France
                by Segment: Estimates and Projections in US$ Million for the
                Period 2018-2025
                Table 77: French Geographic Information System (GIS) Historic
                Market Scenario in US$ Million by Segment: 2009-2017
                Table 78: French Geographic Information System (GIS) Market
                Share Analysis by Segment: 2009 VS 2019 VS 2025
                Table 79: Geographic Information System (GIS) Quantitative
                Demand Analysis in France in US$ Million by End-Use: 2018-2025
                Table 80: French Geographic Information System (GIS) Historic
                Market Review in US$ Million by End-Use: 2009-2017
                Table 81: French Geographic Information System (GIS) Market
                Share Analysis: A 17-Year Perspective by End-Use for 2009,
                2019, and 2025
                GERMANY
                Table 82: Geographic Information System (GIS) Market in
                Germany: Recent Past, Current and Future Analysis in US$
                Million by Segment for the Period 2018-2025
                Table 83: German Geographic Information System (GIS) Historic
                Market Analysis in US$ Million by Segment: 2009-2017
                Table 84: German Geographic Information System (GIS) Market
                Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 85: Geographic Information System (GIS) Market in
                Germany: Annual Sales Estimates and Forecasts in US$ Million by
                End-Use for the Period 2018-2025
                Table 86: German Geographic Information System (GIS) Market in
                Retrospect in US$ Million by End-Use: 2009-2017
                Table 87: Geographic Information System (GIS) Market Share
                Distribution in Germany by End-Use: 2009 VS 2019 VS 2025
                ITALY
                Table 88: Italian Geographic Information System (GIS) Market
                Growth Prospects in US$ Million by Segment for the Period
                2018-2025
                Table 89: Geographic Information System (GIS) Historic Market
                Analysis in Italy in US$ Million by Segment: 2009-2017
                Table 90: Italian Geographic Information System (GIS) Market
                by Segment: Percentage Breakdown of Sales for 2009, 2019, and
                2025
                Table 91: Italian Demand for Geographic Information System
                (GIS) in US$ Million by End-Use: 2018 to 2025
                Table 92: Geographic Information System (GIS) Market Review in
                Italy in US$ Million by End-Use: 2009-2017
                Table 93: Italian Geographic Information System (GIS) Market
                Share Breakdown by End-Use: 2009 VS 2019 VS 2025
                UNITED KINGDOM
                Table 94: United Kingdom Market for Geographic Information
                System (GIS) : Annual Sales Estimates and Projections in US$
                Million by Segment for the Period 2018-2025
                Table 95: Geographic Information System (GIS) Market in the
                United Kingdom: Historic Sales Analysis in US$ Million by
                Segment for the Period 2009-2017
                Table 96: United Kingdom Geographic Information System (GIS)
                Market Share Analysis by Segment: 2009 VS 2019 VS 2025
                Table 97: United Kingdom Demand Estimates and Forecasts for
                Geographic Information System (GIS) in US$ Million by End-Use:
                2018 to 2025
                Table 98: United Kingdom Geographic Information System (GIS)
                Market in US$ Million by End-Use: 2009-2017
                Table 99: Geographic Information System (GIS) Market Share
                Shift in the United Kingdom by End-Use: 2009 VS 2019 VS 2025
                SPAIN
                Table 100: Spanish Geographic Information System (GIS) Market
                Estimates and Forecasts in US$ Million by Segment: 2018 to 2025
                Table 101: Spanish Geographic Information System (GIS)
                Historic Market Review by Segment in US$ Million: 2009-2017
                Table 102: Geographic Information System (GIS) Market in
                Spain: Percentage Share Breakdown of Sales by Segment for 2009,
                2019, and 2025
                Table 103: Spanish Geographic Information System (GIS) Market
                Quantitative Demand Analysis in US$ Million by End-Use: 2018 to
                2025
                Table 104: Geographic Information System (GIS) Market in
                Spain: Summarization of Historic Demand Patterns in US$ Million
                by End-Use for 2009-2017
                Table 105: Spanish Geographic Information System (GIS) Market
                Share Analysis by End-Use: 2009 VS 2019 VS 2025
                RUSSIA
                Table 106: Russian Geographic Information System (GIS) Market
                Estimates and Projections in US$ Million by Segment: 2018 to
                2025
                Table 107: Geographic Information System (GIS) Market in
                Russia by Segment: A Historic Review in US$ Million for
                2009-2017
                Table 108: Russian Geographic Information System (GIS) Market
                Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 109: Russian Geographic Information System (GIS) Latent
                Demand Forecasts in US$ Million by End-Use: 2018 to 2025
                Table 110: Geographic Information System (GIS) Historic Demand
                Patterns in Russia by End-Use in US$ Million for 2009-2017
                Table 111: Geographic Information System (GIS) Market Share
                Breakdown in Russia by End-Use: 2009 VS 2019 VS 2025
                REST OF EUROPE
                Table 112: Rest of Europe Geographic Information System (GIS)
                Market Estimates and Forecasts in US$ Million by Segment:
                2018-2025
                Table 113: Geographic Information System (GIS) Market in Rest
                of Europe in US$ Million by Segment: A Historic Review for the
                Period 2009-2017
                Table 114: Rest of Europe Geographic Information System (GIS)
                Market Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 115: Rest of Europe Geographic Information System (GIS)
                Addressable Market Opportunity in US$ Million by End-Use:
                2018-2025
                Table 116: Geographic Information System (GIS) Market in Rest
                of Europe: Summarization of Historic Demand in US$ Million by
                End-Use for the Period 2009-2017
                Table 117: Rest of Europe Geographic Information System (GIS)
                Market Share Analysis by End-Use: 2009 VS 2019 VS 2025
                ASIA-PACIFIC
                Table 118: Asia-Pacific Geographic Information System (GIS)
                Market Estimates and Forecasts in US$ Million by
                Region/Country: 2018-2025
                Table 119: Geographic Information System (GIS) Market in
                Asia-Pacific: Historic Market Analysis in US$ Million by
                Region/Country for the Period 2009-2017
                Table 120: Asia-Pacific Geographic Information System (GIS)
                Market Share Analysis by Region/Country: 2009 VS 2019 VS 2025
                Table 121: Geographic Information System (GIS) Market in
                Asia-Pacific by Segment: Estimates and Projections in US$
                Million for the Period 2018-2025
                Table 122: Asia-Pacific Geographic Information System (GIS)
                Historic Market Scenario in US$ Million by Segment: 2009-2017
                Table 123: Asia-Pacific Geographic Information System (GIS)
                Market Share Analysis by Segment: 2009 VS 2019 VS 2025
                Table 124: Geographic Information System (GIS) Quantitative
                Demand Analysis in Asia-Pacific in US$ Million by End-Use:
                2018-2025
                Table 125: Asia-Pacific Geographic Information System (GIS)
                Historic Market Review in US$ Million by End-Use: 2009-2017
                Table 126: Asia-Pacific Geographic Information System (GIS)
                Market Share Analysis: A 17-Year Perspective by End-Use for
                2009, 2019, and 2025
                AUSTRALIA
                Table 127: Geographic Information System (GIS) Market in
                Australia: Recent Past, Current and Future Analysis in US$
                Million by Segment for the Period 2018-2025
                Table 128: Australian Geographic Information System (GIS)
                Historic Market Analysis in US$ Million by Segment: 2009-2017
                Table 129: Australian Geographic Information System (GIS)
                Market Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 130: Geographic Information System (GIS) Market in
                Australia: Annual Sales Estimates and Forecasts in US$ Million
                by End-Use for the Period 2018-2025
                Table 131: Australian Geographic Information System (GIS)
                Market in Retrospect in US$ Million by End-Use: 2009-2017
                Table 132: Geographic Information System (GIS) Market Share
                Distribution in Australia by End-Use: 2009 VS 2019 VS 2025
                INDIA
                Table 133: Indian Geographic Information System (GIS) Market
                Estimates and Forecasts in US$ Million by Segment: 2018 to 2025
                Table 134: Indian Geographic Information System (GIS) Historic
                Market Review by Segment in US$ Million: 2009-2017
                Table 135: Geographic Information System (GIS) Market in
                India: Percentage Share Breakdown of Sales by Segment for 2009,
                2019, and 2025
                Table 136: Indian Geographic Information System (GIS) Market
                Quantitative Demand Analysis in US$ Million by End-Use: 2018 to
                2025
                Table 137: Geographic Information System (GIS) Market in
                India: Summarization of Historic Demand Patterns in US$ Million
                by End-Use for 2009-2017
                Table 138: Indian Geographic Information System (GIS) Market
                Share Analysis by End-Use: 2009 VS 2019 VS 2025
                SOUTH KOREA
                Table 139: Geographic Information System (GIS) Market in South
                Korea: Recent Past, Current and Future Analysis in US$ Million
                by Segment for the Period 2018-2025
                Table 140: South Korean Geographic Information System (GIS)
                Historic Market Analysis in US$ Million by Segment: 2009-2017
                Table 141: Geographic Information System (GIS) Market Share
                Distribution in South Korea by Segment: 2009 VS 2019 VS 2025
                Table 142: Geographic Information System (GIS) Market in South
                Korea: Recent Past, Current and Future Analysis in US$ Million
                by End-Use for the Period 2018-2025
                Table 143: South Korean Geographic Information System (GIS)
                Historic Market Analysis in US$ Million by End-Use: 2009-2017
                Table 144: Geographic Information System (GIS) Market Share
                Distribution in South Korea by End-Use: 2009 VS 2019 VS 2025
                REST OF ASIA-PACIFIC
                Table 145: Rest of Asia-Pacific Market for Geographic
                Information System (GIS) : Annual Sales Estimates and
                Projections in US$ Million by Segment for the Period 2018-2025
                Table 146: Geographic Information System (GIS) Market in Rest
                of Asia-Pacific: Historic Sales Analysis in US$ Million by
                Segment for the Period 2009-2017
                Table 147: Rest of Asia-Pacific Geographic Information System
                (GIS) Market Share Analysis by Segment: 2009 VS 2019 VS 2025
                Table 148: Rest of Asia-Pacific Demand Estimates and Forecasts
                for Geographic Information System (GIS) in US$ Million by
                End-Use: 2018 to 2025
                Table 149: Rest of Asia-Pacific Geographic Information System
                (GIS) Market in US$ Million by End-Use: 2009-2017
                Table 150: Geographic Information System (GIS) Market Share
                Shift in Rest of Asia-Pacific by End-Use: 2009 VS 2019 VS 2025
                LATIN AMERICA
                Table 151: Latin American Geographic Information System (GIS)
                Market Trends by Region/Country in US$ Million: 2018-2025
                Table 152: Geographic Information System (GIS) Market in Latin
                America in US$ Million by Region/Country: A Historic
                Perspective for the Period 2009-2017
                Table 153: Latin American Geographic Information System (GIS)
                Market Percentage Breakdown of Sales by Region/Country: 2009,
                2019, and 2025
                Table 154: Latin American Geographic Information System (GIS)
                Market Growth Prospects in US$ Million by Segment for the
                Period 2018-2025
                Table 155: Geographic Information System (GIS) Historic Market
                Analysis in Latin America in US$ Million by Segment: 2009-2017
                Table 156: Latin American Geographic Information System (GIS)
                Market by Segment: Percentage Breakdown of Sales for 2009,
                2019, and 2025
                Table 157: Latin American Demand for Geographic Information
                System (GIS) in US$ Million by End-Use: 2018 to 2025
                Table 158: Geographic Information System (GIS) Market Review
                in Latin America in US$ Million by End-Use: 2009-2017
                Table 159: Latin American Geographic Information System (GIS)
                Market Share Breakdown by End-Use: 2009 VS 2019 VS 2025
                ARGENTINA
                Table 160: Argentinean Geographic Information System (GIS)
                Market Estimates and Forecasts in US$ Million by Segment:
                2018-2025
                Table 161: Geographic Information System (GIS) Market in
                Argentina in US$ Million by Segment: A Historic Review for the
                Period 2009-2017
                Table 162: Argentinean Geographic Information System (GIS)
                Market Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 163: Argentinean Geographic Information System (GIS)
                Addressable Market Opportunity in US$ Million by End-Use:
                2018-2025
                Table 164: Geographic Information System (GIS) Market in
                Argentina: Summarization of Historic Demand in US$ Million by
                End-Use for the Period 2009-2017
                Table 165: Argentinean Geographic Information System (GIS)
                Market Share Analysis by End-Use: 2009 VS 2019 VS 2025
                BRAZIL
                Table 166: Geographic Information System (GIS) Market in
                Brazil by Segment: Estimates and Projections in US$ Million for
                the Period 2018-2025
                Table 167: Brazilian Geographic Information System (GIS)
                Historic Market Scenario in US$ Million by Segment: 2009-2017
                Table 168: Brazilian Geographic Information System (GIS)
                Market Share Analysis by Segment: 2009 VS 2019 VS 2025
                Table 169: Geographic Information System (GIS) Quantitative
                Demand Analysis in Brazil in US$ Million by End-Use: 2018-2025
                Table 170: Brazilian Geographic Information System (GIS)
                Historic Market Review in US$ Million by End-Use: 2009-2017
                Table 171: Brazilian Geographic Information System (GIS)
                Market Share Analysis: A 17-Year Perspective by End-Use for
                2009, 2019, and 2025
                MEXICO
                Table 172: Geographic Information System (GIS) Market in
                Mexico: Recent Past, Current and Future Analysis in US$ Million
                by Segment for the Period 2018-2025
                Table 173: Mexican Geographic Information System (GIS)
                Historic Market Analysis in US$ Million by Segment: 2009-2017
                Table 174: Mexican Geographic Information System (GIS) Market
                Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 175: Geographic Information System (GIS) Market in
                Mexico: Annual Sales Estimates and Forecasts in US$ Million by
                End-Use for the Period 2018-2025
                Table 176: Mexican Geographic Information System (GIS) Market
                in Retrospect in US$ Million by End-Use: 2009-2017
                Table 177: Geographic Information System (GIS) Market Share
                Distribution in Mexico by End-Use: 2009 VS 2019 VS 2025
                REST OF LATIN AMERICA
                Table 178: Rest of Latin America Geographic Information System
                (GIS) Market Estimates and Projections in US$ Million by
                Segment: 2018 to 2025
                Table 179: Geographic Information System (GIS) Market in Rest
                of Latin America by Segment: A Historic Review in US$ Million
                for 2009-2017
                Table 180: Rest of Latin America Geographic Information System
                (GIS) Market Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 181: Rest of Latin America Geographic Information System
                (GIS) Latent Demand Forecasts in US$ Million by End-Use: 2018
                to 2025
                Table 182: Geographic Information System (GIS) Historic Demand
                Patterns in Rest of Latin America by End-Use in US$ Million for
                2009-2017
                Table 183: Geographic Information System (GIS) Market Share
                Breakdown in Rest of Latin America by End-Use: 2009 VS 2019 VS
                2025
                MIDDLE EAST
                Table 184: The Middle East Geographic Information System (GIS)
                Market Estimates and Forecasts in US$ Million by
                Region/Country: 2018-2025
                Table 185: Geographic Information System (GIS) Market in the
                Middle East by Region/Country in US$ Million: 2009-2017
                Table 186: The Middle East Geographic Information System (GIS)
                Market Share Breakdown by Region/Country: 2009, 2019, and 2025
                Table 187: The Middle East Geographic Information System (GIS)
                Market Estimates and Forecasts in US$ Million by Segment: 2018
                to 2025
                Table 188: The Middle East Geographic Information System (GIS)
                Historic Market by Segment in US$ Million: 2009-2017
                Table 189: Geographic Information System (GIS) Market in the
                Middle East: Percentage Share Breakdown of Sales by Segment
                for 2009, 2019, and 2025
                Table 190: The Middle East Geographic Information System (GIS)
                Market Quantitative Demand Analysis in US$ Million by End-Use:
                2018 to 2025
                Table 191: Geographic Information System (GIS) Market in the
                Middle East: Summarization of Historic Demand Patterns in US$
                Million by End-Use for 2009-2017
                Table 192: The Middle East Geographic Information System (GIS)
                Market Share Analysis by End-Use: 2009 VS 2019 VS 2025
                IRAN
                Table 193: Iranian Market for Geographic Information System
                (GIS) : Annual Sales Estimates and Projections in US$ Million
                by Segment for the Period 2018-2025
                Table 194: Geographic Information System (GIS) Market in Iran:
                Historic Sales Analysis in US$ Million by Segment for the
                Period 2009-2017
                Table 195: Iranian Geographic Information System (GIS) Market
                Share Analysis by Segment: 2009 VS 2019 VS 2025
                Table 196: Iranian Demand Estimates and Forecasts for
                Geographic Information System (GIS) in US$ Million by End-Use:
                2018 to 2025
                Table 197: Iranian Geographic Information System (GIS) Market
                in US$ Million by End-Use: 2009-2017
                Table 198: Geographic Information System (GIS) Market Share
                Shift in Iran by End-Use: 2009 VS 2019 VS 2025
                ISRAEL
                Table 199: Israeli Geographic Information System (GIS) Market
                Estimates and Forecasts in US$ Million by Segment: 2018-2025
                Table 200: Geographic Information System (GIS) Market in
                Israel in US$ Million by Segment: A Historic Review for the
                Period 2009-2017
                Table 201: Israeli Geographic Information System (GIS) Market
                Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 202: Israeli Geographic Information System (GIS)
                Addressable Market Opportunity in US$ Million by End-Use:
                2018-2025
                Table 203: Geographic Information System (GIS) Market in
                Israel: Summarization of Historic Demand in US$ Million by
                End-Use for the Period 2009-2017
                Table 204: Israeli Geographic Information System (GIS) Market
                Share Analysis by End-Use: 2009 VS 2019 VS 2025
                SAUDI ARABIA
                Table 205: Saudi Arabian Geographic Information System (GIS)
                Market Growth Prospects in US$ Million by Segment for the
                Period 2018-2025
                Table 206: Geographic Information System (GIS) Historic Market
                Analysis in Saudi Arabia in US$ Million by Segment: 2009-2017
                Table 207: Saudi Arabian Geographic Information System (GIS)
                Market by Segment: Percentage Breakdown of Sales for 2009,
                2019, and 2025
                Table 208: Saudi Arabian Demand for Geographic Information
                System (GIS) in US$ Million by End-Use: 2018 to 2025
                Table 209: Geographic Information System (GIS) Market Review
                in Saudi Arabia in US$ Million by End-Use: 2009-2017
                Table 210: Saudi Arabian Geographic Information System (GIS)
                Market Share Breakdown by End-Use: 2009 VS 2019 VS 2025
                UNITED ARAB EMIRATES
                Table 211: Geographic Information System (GIS) Market in the
                United Arab Emirates: Recent Past, Current and Future Analysis
                in US$ Million by Segment for the Period 2018-2025
                Table 212: United Arab Emirates Geographic Information System
                (GIS) Historic Market Analysis in US$ Million by Segment:
                2009-2017
                Table 213: Geographic Information System (GIS) Market Share
                Distribution in United Arab Emirates by Segment: 2009 VS 2019
                VS 2025
                Table 214: Geographic Information System (GIS) Market in the
                United Arab Emirates: Recent Past, Current and Future Analysis
                in US$ Million by End-Use for the Period 2018-2025
                Table 215: United Arab Emirates Geographic Information System
                (GIS) Historic Market Analysis in US$ Million by End-Use:
                2009-2017
                Table 216: Geographic Information System (GIS) Market Share
                Distribution in United Arab Emirates by End-Use: 2009 VS 2019
                VS 2025
                REST OF MIDDLE EAST
                Table 217: Geographic Information System (GIS) Market in Rest
                of Middle East: Recent Past, Current and Future Analysis in US$
                Million by Segment for the Period 2018-2025
                Table 218: Rest of Middle East Geographic Information System
                (GIS) Historic Market Analysis in US$ Million by Segment:
                2009-2017
                Table 219: Rest of Middle East Geographic Information System
                (GIS) Market Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 220: Geographic Information System (GIS) Market in Rest
                of Middle East: Annual Sales Estimates and Forecasts in US$
                Million by End-Use for the Period 2018-2025
                Table 221: Rest of Middle East Geographic Information System
                (GIS) Market in Retrospect in US$ Million by End-Use:
                2009-2017
                Table 222: Geographic Information System (GIS) Market Share
                Distribution in Rest of Middle East by End-Use: 2009 VS 2019 VS
                2025
                AFRICA
                Table 223: African Geographic Information System (GIS) Market
                Estimates and Projections in US$ Million by Segment: 2018 to
                2025
                Table 224: Geographic Information System (GIS) Market in
                Africa by Segment: A Historic Review in US$ Million for
                2009-2017
                Table 225: African Geographic Information System (GIS) Market
                Share Breakdown by Segment: 2009 VS 2019 VS 2025
                Table 226: African Geographic Information System (GIS) Latent
                Demand Forecasts in US$ Million by End-Use: 2018 to 2025
                Table 227: Geographic Information System (GIS) Historic Demand
                Patterns in Africa by End-Use in US$ Million for 2009-2017
                Table 228: Geographic Information System (GIS) Market Share
                Breakdown in Africa by End-Use: 2009 VS 2019 VS 2025

                AUTODESK
                BENTLEY SYSTEMS, INCORPORATED
                CALIPER CORPORATION
                COMPUTER AIDED DEVELOPMENT CORPORATION LIMITED (CADCORP)
                ENVIRONMENTAL SYSTEMS RESEARCH INSTITUTE (ESRI®)
                GENERAL ELECTRIC COMPANY
                HEXAGON AB
                HI-TARGET SURVEYING INSTRUMENT CO.LTD
                MACDONALD, DETTWILER AND ASSOCIATES
                PITNEY BOWES
                TOPCON CORPORATION
                TRIMBLE

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