1. Practical- 3rd Group
Satellite Images and its
interpretation
Paper Code- 17GEO24CL2
Neeraj Rani
Research Scholar
Department of Geography
M.D.U. Rohtak

2. Syllabus Practical

3. 1st Basic Introduction of Remote Sensing
Remote Sensing Definition and Meaning
Process; Stages.
EMR;
Energy interaction with Earth’s Surface ;
Interaction with Atmosphere.
Remote Sensing Sensor
Active Sensor
Passive Sensor
Platform
Space-Based; Ground Based
Resolution
Spatial; Temporal; Spectral, Radiometric

4. 1st Exercise
Kinds of Satellite Images:
True;
FCC;
Panchromatic ;
Thermal (Infrared) etc.
You have to take printouts of these types of satellite images
and paste them on the White sheets (Practical sheets- that are
given by dept. and purchased ourselves)

5. According to NASA “A satellite is a body that orbits around another body in space. There are two different types
of satellites – natural and man-made. Examples of natural satellites are the Earth and Moon. The Earth rotates
around the Sun and the Moon rotates around the Earth. A man-made satellite is a machine that is launched into
space and orbits around a body in space.
Source: NASA
What is a satellite? 1ST EXERCISE

6. NATURAL
SATELLITE
MOON
MAN-
MADE/ARTIFICIAL
SATELLITES
TYPES OF
SATELLITES
Communication
Satellites
Earth Observation
Satellites
Navigation Satellites
Experimental
Satellites
Small Satellites
Student
Satellites
Scientific
Spacecraft Source: ISRO

7. Communication Satellites
Supports telecommunication, television broadcasting,
satellite news gathering, societal applications, weather
forecasting, disaster warning and Search and Rescue
operation services.
Earth Observation Satellites
The largest civilian remote sensing satellite constellation
in the world - thematic series of satellites supporting
multitude of applications in the areas of land and water
resources; cartography; and ocean & atmosphere
Scientific Spacecraft
Spacecraft for research in areas like astronomy,
astrophysics, planetary and earth sciences, atmospheric
sciences and theoretical physics.
Navigation Satellites
Satellites for navigation services to meet the emerging
demands of the Civil Aviation requirements and to meet
the user requirements of the positioning, navigation and
timing based on the independent satellite navigation
system.
Experimental Satellites
A host of small satellites mainly for the experimental
purposes. These experiments include Remote Sensing,
Atmospheric Studies, Payload Development, Orbit
Controls, recovery technology etc.
Small Satellites
Sub 500 kg class satellites - a platform for stand-alone
payloads for earth imaging and science missions within a
quick turn around time.
Student Satellites
ISRO's Student Satellite programme is envisaged to
encourage various Universities and Institutions for the
development of Nano/Pico Satellites.
Source: ISRO
The Department of space has been developing
mainly satellites for communication, earth
observation, scientific, navigation, and
meteorological purposes.

8. The DSN is a collection of big radio
antennas in different parts of the world.
Spacecraft send information and pictures
back to Earth using the Deep Space Network
(DSN), a collection of big radio antennas. The
antennas also receive details about where the
spacecraft are and how they are doing. NASA
also uses the DSN to send lists of instructions
to the spacecraft.
How Does NASA Communicate With Spacecraft?
The DSN complex in Canberra, Australia. There are at least four antennas at each DSN
site. Image credit: NASA/CSIRO/Canberra Deep Space Communication Complex
How do Satellites Communicate?
Satellites communicate by using radio waves
to send signals to the antennas on the Earth.
The antennas then capture those signals and
process the information coming from those
signals. Information can include:
•scientific data (like the pictures the satellite
took),
•the health of the satellite, and
•where the satellite is currently located in
space
Source: NASA

9. In this video, the zigzag lines represent information passing between the spacecraft and
the DSN antennas. Image credit: Screenshot from DSN Now/NASA/JPL-Caltech
What happens once the DSN
antennas receive the signals?
Centers at each DSN site receive
incoming information. Then, they
send it to the Space Flight
Operations Facility.
Source: NASA

10. What is an orbit?
European Space Agency defined the orbit as “An orbit is a curved path that an object in
space (such as a star, planet, moon, asteroid or spacecraft) takes around another object due
to gravity”.
An orbit is a regular, repeating path that one object in space takes around another one. An
object in an orbit is called a satellite. A satellite can be natural, like Earth or the moon.
(NASA).
Source: European space Agency
& NASA
What Shape Is an Orbit?
Orbits come in different shapes. All orbits are elliptical, which means they are ellipses, similar to an oval.
For the planets, the orbits are almost circular. The orbits of comets have different shapes

11. There are many factors that decide which orbit
would be best for a satellite to use, depending on
what the satellite is designed to achieve.

12. Geostationary orbit (GEO)
•It is also called Geosynchronous Equatorial Orbit.
• It is a low-inclination orbit.
• Satellites in geostationary orbit (GEO) circle Earth above
the equator from west to east following Earth’s rotation.
• It taking 23 hours 56 minutes and 4 seconds – by travelling
at exactly the same rate as Earth.
• This makes satellites in GEO appear to be ‘stationary’ over
a fixed position.
• In order to perfectly match Earth’s rotation, the speed of
GEO satellites should be about 3 km per second at an
altitude of 35 786 km.
• GEO is used by satellites that need to stay constantly
above one particular place over Earth, such as
telecommunication satellites.
• This way, an antenna on Earth can be fixed to always stay
pointed towards that satellite without moving.
•ISRO’s Indian National Satellite System [INSAT] is placed in GEO. [It is
one of the Asia-Pacific region's largest domestic communication satellite
systems.]
Geostationary orbit- INSAT-3D, Kalpana & INSAT 3A, INSAT
-3DR.
It can also be used by weather monitoring satellites
because they can continually observe specific areas to
see how weather trends emerge there.

14. Low Earth orbit (LEO)
• A low Earth orbit (LEO) is, as the name suggests, an orbit
that is relatively close to Earth’s surface.
• It is normally at an altitude of less than 1000 km but could
be as low as 160 km above Earth.
LEO’s close proximity to Earth makes it useful for several
reasons.
• It is the orbit most commonly used for satellite
imaging, as being near the surface allows it to take
images of higher resolution.
It is also the orbit used for the International Space Station
(ISS), as it is easier for astronauts to travel to and from it at a
shorter distance.
• Satellites in this orbit travel at a speed of around 7.8 km
per second; at this speed, a satellite takes approximately
90 minutes to circle Earth, meaning the ISS travels
around Earth about 16 times a day.
•The satellites placed in LEO can have a tilted plane.
•It is used by remote sensing satellites.

15. LEO ORBITS INDIAN SATELLITES
NAME⇅ Launch
Date
Launch
Mass
Launch
Vehicle
Orbit Type Application
EOS-01 Nov 07,
2020
PSLV-C49/
EOS-01
LEO (Low
Earth Orbit)
Disaster Management System,
Earth Observation
RISAT-2BR1 Dec 11,
2019
628 Kg PSLV-C48/
RISAT-2BR1
LEO (Low
Earth Orbit)
Disaster Management System,
Earth Observation
Bhaskara-II Nov 20,
1981
444 kg C-1
Intercosmo
s
LEO (Low
Earth Orbit)
Earth Observation,
Experimental
Rohini Satellite
RS-D1
May 31,
1981
38 kg SLV-3D1 LEO (Low
Earth Orbit)
Earth Observation
Source: Indian Space Research Organisation (ISRO)

16. Medium Earth orbit (MEO)
Medium Earth orbit comprises a wide range of orbits
anywhere between LEO and GEO. It is similar to LEO in
that it also does not need to take specific paths around
Earth, and it is used by a variety of satellites with many
different applications.
It is very commonly used by navigation satellite.
A medium-Earth orbit (MEO) is the region of space
between low-Earth and geostationary orbits.
•Altitude: 2000–36 000 km, most common is 20 000 km.
•Satellite period: 12 hours.
•Satellite examples: USA – Navstar 66, Russia –
GLONASS, China – Compass.
•The orbit, altitude of which is between LEO and GEO, is
known as Medium Earth Orbit.
•It is also known as Intermediate Circular Orbit.

17. Polar Orbit & Sun-Synchronous Orbit (SSO)
•The SSO satellites travel past earth from north to south instead of west
to east.
•These pass roughly over the earth’s poles
•Polar orbits are a type of low Earth orbit, as they are at low
altitudes between 200 to 1000 km.
•Sun-synchronous orbit (SSO) is a particular kind of polar
orbit. Satellites in SSO, traveling over the polar regions, are
synchronous with the Sun.
operational satellites are in Sun-synchronous orbit –
RESOURCESAT-1, 2, 2A CARTOSAT-1, 2, 2A, 2B,
RISAT-1 and 2, OCEANSAT-2, Megha-Tropiques,
SARAL and SCATSAT-1,

19. Source: ISRO

20. Geostationary Transfer Orbit (GTO)
•The orbits are used by the satellites to travel from one orbit to
another. It is a Hohmann Transfer Orbit between LEO and GSO.
•GTO provides satellites a halt [intermediate step] before they
can be placed in their destination orbit. This way, it uses
relatively less energy from built-in motors.
•The launchers do not have to directly place a satellite into GEO.
Instead, it can first make use of GTO.
•It is a highly eccentric orbit. [Meaning – The path is elliptical
When satellites are launched from Earth and carried to space
with launch vehicles such as Ariane 5, the satellites are not
always placed directly on their final orbit. Often, the satellites
are instead placed on a transfer orbit: an orbit where, by
using relatively little energy from built-in motors, the satellite
or spacecraft can move from one orbit to another.

21. REMOTE SENSING SENSORS
PASSIVE AND ACTIVE SENSORS
Remote sensors are the instruments which detect various objects on the earth’s surface by
measuring electromagnetic energy reflected or emitted from them. The sensors are
mounted on the platforms discussed above. Different sensors record different wavelengths
bands of electromagnetic energy coming fromthe earth’s surface. As for example, an
ordinary camera is the most familiar type of remote sensor which uses visible portion of
electromagnetic radiation.

22. SENSORS
A sensor is a device that gathers
electromagnetic radiation, converts
it into a signal, and presents it in a
form suitable for obtaining
information about the objects under
investigation. The sensors are
classified into-
• A photographic sensor (camera) records the
images of the objects at an instance of
exposure.
Photographic (analogue)
• A non–photographic sensor obtains the
images of the objects in bit-by-bit form.
These sensors are known as scanners.
Non– photographic (digital) sensors
The non–photographic
sensors that are used in
satellite remote sensing.

23. INDIAN REMOTE
SENSING SATELLITES
2nd EXERCISE

24. About ISRO
•Indian space research organization (ISRO).
•Established by India's first PM, Jawaharlal Nehru, and his close friend and
scientist Vikram Sarabhai in1969.
•ISRO’s mission is to bring space to the service of the common man and to
the service of the Nation.
•One of the Six largest Space agencies in the world.
•ISRO maintains one of the largest fleet of remote sensing (IRS)satellites.

25. Indian Remote Sensing Satellites (IRS) are a series of Earth
observation satellites, built, launched and maintained by the Indian
Space Research Organization(ISRO).
•The IRS provides many remote sensing services to India.
•The IRS system is the largest constellation of remote sensing satellites for civilian use in
The operation today in the world.
•Starting with IRS-1A in 1988, ISRO has launched many operational remote sensing
satellites.
•Currently, 13 operational satellites are in Sun-synchronous orbit and 4 in Geostationary
orbit.

28. IRS
• India's remote sensing program was developed with the idea of applying
space technologies for the benefit of humankind and the development of the
country.
• The program involved the development of three principal capabilities.
• The first was to design, build and launch satellites to a Sun-synchronous
orbit.
• The second was to establish and operate ground stations for spacecraft
control, data transfer along with data processing and archival.
• The third was to use the data obtained for various applications on the
ground.

29. India's remote sensing program under the Indian Space Research
Organization (ISRO) started off in 1988 with the IRS-1A, the first of the series
of indigenous state-of-art operating remote sensing satellites, which was
successfully launched into a polar Sun-synchronous orbit on March 17, 1988,
from the Soviet Cosmodrome at Baikonur.
Following the successful demonstration flights of Bhaskara-1 and Bhaskara-
2 satellites launched in 1979 and 1981, respectively, India began to develop
the indigenous Indian Remote Sensing (IRS) satellite program to support the
national economy in the areas of agriculture, water resources, forestry and
ecology, geology, water sheds, marine fisheries and coastal management.

30. Serial
No.
Satellite Date of Launch Launch Vehicle Status
1 IRS-1A 17 March 1988 Vostok, USSR Mission Completed
2 IRS-1B 29 August 1991 Vostok, USSR Mission Completed
3 IRS-P1 (also IE) 20 September 1993 PSLV-D1 Crashed, due to launch failure of PSLV
4 IRS-P2 15 October 1994 PSLV-D2 Mission Completed
5 IRS-1C 28 December 1995 Molniya, Russia Mission Completed
6 IRS-P3 21 March 1996 PSLV-D3 Mission Completed
7 IRS 1D 29 September 1997 PSLV-C1 Mission Completed
8 IRS-P4 (Oceansat-1) 27 May 1999 PSLV-C2 Mission Completed
9 Technology Experiment Satellite (TES) 22 October 2001 PSLV-C3 Mission Completed
10 IRS P6 (Resourcesat-1) 17 October 2003 PSLV-C5 Mission Completed
The initial versions are
composed of the 1 (A,B,C,D).
The later versions are named
based on their area of
application, including OceanSat,
CartoSat, ResourceSat. Some of
the satellites have alternate
designations based on the
launch number and vehicle (P
series for PSLV). From 2020, the
naming criteria was returned to
the generic EOS, which stands
for Earth Observation Satellite.
Jones, Andrew (2020-11-07). "India back in action with
launch of Earth observation satellite, nine rideshare small
sats". SpaceNews. Retrieved 2021-01-06.

31. 11 IRS P5 (Cartosat 1) 5 May 2005 PSLV-C6 Mission Completed
12 IRS P7 (Cartosat 2)
10 January
2007
PSLV-C7 Mission Completed
13 Cartosat 2A 28 April 2008 PSLV-C9 In Service
14 IMS 1 28 April 2008 PSLV-C9 Mission Completed
15 RISAT-2 20 April 2009 PSLV-C12 In Service
16 Oceansat-2
23 September
2009
PSLV-C14 In Service
17 Cartosat-2B 12 July 2010 PSLV-C15 In Service
18 Resourcesat-2 20 April 2011 PSLV-C16 In Service
19 Megha-Tropiques
12 October
2011
PSLV-C18 Mission Completed
20 RISAT-1 26 April 2012 PSLV-C19 Mission Completed

32. 21 SARAL 25 Feb 2013 PSLV-C20 In Service
22 Cartosat-2C 22 June 2016 PSLV-C34 In Service
23 ScatSat-1 26 September 2016 PSLV-C35 In Service
24 RESOURCESAT-2A 07 Dec 2016 PSLV-C36 In Service
25 Cartosat-2D 15 Feb 2017 PSLV-C37 In Service
26 Cartosat-2E 23 June 2017 PSLV-C38 In Service
27 Cartosat-2F 12 Jan 2018 PSLV-C40 In Service
28 RISAT-2B 22 May 2019 PSLV-C46 In Service
29 Cartosat-3 27 Nov 2019 PSLV-C47 In Service
30 RISAT-2BR1 11 Dec 2019 PSLV-C48 In Service
31 EOS-1 (RISAT-2BR2) 07 Nov 2020 PSLV-C49 In Service
32 EOS-3 (GISAT-1) 12 Aug 2021 GSLV-F10
Crashed, due to
launch failure
of GSLV
33 EOS-4 (RISAT-1A) 14 Feb 2022 PSLV-C52 In Service

33. A.K.S. Gopalan in 1997 discussed the application of IRS
The IRS data has been used for a variety of operational and national-
level applications. Notable applications are:
• Groundwater targeting, where in remote sensing data has been used to map the hydrogeomorphology
for the whole country on a district-wise basis.
• Forest mapping, where the extent and density of forests have been mapped for the whole country.
• Flood-affected area mapping, where the flood-affected areas are being mapped on a near-real-time
basis for the Gangetic and Brahmaputra basins.
• Crop acreage and Production Estimation for various crops-wheat, paddy, groundnut, sorghum, and
cotton are estimated using digital IRS data and image analysis methods. This is being done for a large
part of the country’s cropped area.
• Coastal environment mapping for the country’s entire coastline where wetlands, coastal landforms,
coastal processes, shorelines, mangroves, etc. are mapped using IRS data.
• Environmental impact of mining; watershed characterization for soil conservation; mineral targeting; land
use mapping etc. are some of the other significant applications of IRS data

34. Applications in Agriculture and Soil
The agricultural applications of IRS satellite series are following: - (i)
Cropping pattern mapping; (ii) Pre- harvest crop area, production and
yield estimation; (iii) Condition assessment; (iii) Monitoring command
areas; (iv) Compliance monitoring (farming practices) e.g. crop
stubble burning; (v) Identification of suitable sites for different
agricultural practices; (vi) Mapping of soil characteristics; (vii)
Mapping of soil management practices; (viii) Mapping of saline soils
and monitoring of land reclamation; (ix) Inventorying and
categorization of wastelands; and (x) Identification of fishery
prospects.

35. Applications in Bio-resources and Environment
The applications of the IRS satellite series in forestry, biodiversity and
ecosystem sustainability are the following: - (i) Mapping of forest cover,
types, density, and species inventory; (ii) Measurement of biophysical
conditions of forest strands; (iii) Social forestry and agroforestry mapping;
(iv) Biomass estimate on; (v) Afforestation and deforestation assessment;
(vi) Forest fire surveillance; (vii) Forest health and vigor monitoring; (viii)
Detailed survey and inventory of the existing bio-resources; (ix)
Environmental impact assessment including pollution (land, water, and air);
(x) Mapping and monitoring of tiger reserves, elephant corridors, biosphere
reserves, mangroves and coral reefs; (xi) Assessment of fuel wood and
timber resources; and (xii) Environmental hazard related studies like
zonation and damage assessment (floods, drought, cyclone, landslide,
volcano, earthquake etc.).

36. Applications in Oceanography
The applications of IRS series satellites, especially Oceansat-1 and
Oceansat-2, include the following: - (i) Identification of potential fishery zones;
(ii) Phytoplankton abundance and habitat assessment; (iii) Observation of
marine pollution and sedimentation and its impact; and (iv) Assessment of
sediment dynamics, tidal fluctuations, sea level changes and coastal
circulations. Applications in Water Resources The applications of IRS series
satellite data products in water resource include the following: - (i) Mapping of
surface water bodies; (ii) Identification of potential ground water resources; (iii)
Wetland mapping and monitoring; (iv) Snow pack and glacial monitoring; (v)
Ice thickness measurements; (vi) Rivers, watersheds and ice lake monitoring
and modelling; (vii) Flood mapping and monitoring; (viii) Monitoring reservoir
extends over seasons and irrigation scheduling and flood management; and
(ix) Snowmelt runoff forecasting.

37. Elements of Visual Interpretation of a satellite image
Exercise No.3

38. INTERPRETATION OF SATELLITE IMAGERIES
The data obtained from the sensors are used for information extraction related to the forms,
and patterns of the objects and phenomena of the earth’s surface. We have seen that different
sensors obtain photographic and digital data products. Hence, the qualitative and quantitative
properties of such features could be extracted using either visual interpretation methods or
digital image processing techniques. Visual interpretation is a manual exercise. It involves the
reading of the images of objects for the purpose of their identification. On the other hand,
digital images require a combination of hardware and software to extract the desired
information. It would not be possible to deliberate upon the digital image processing
techniques under time, equipment, and accessories constraints. Hence, only visual
interpretation methods would be discussed.

39. Elements of Visual Interpretation
Whether we are conscious of it or not we use the form, size, location of the objects and their relationships with the
surrounding objects to identify them in our day-to-day life. These characteristics of objects are termed as elements of
visual interpretation. We can further group the characteristics of the objects into two broad categories, i.e. image
characteristics and terrain characteristics. The image characteristics include tone or colour in which objects appear, their
shape, size, pattern, texture and the shadow they cast. On the other hand, location and the association of different
objects with their surrounding objects constitute the terrain characteristics.
1. Tone or Colour:
We know that all objects receive energy in all regions of spectrum. The interaction of EMR with the object surface leads
to the absorption, transmittance and reflection of energy. It is the reflected amount of the energy that is received and
recorded by the sensor in tones of grey, or hues of colour in black and white, and colour images respectively. The
variations in the tone or the colour depend upon the orientation of incoming radiations, surface properties and the
composition of the objects.

41. In other words, smooth and dry object surfaces reflect more energy in comparison to the rough and
moist surfaces. Besides, the response of the objects also varies in different regions of the spectrum
(Refer para ‘C – Stages in remote sensing data acquisition’). For example, healthy vegetation reflects
strongly in the infrared region because of the multiple-layered leaf structure and appears in a light tone
or bright red colour in standard false colour composite and the scrubs appear in greyish red colour).
Similarly, a fresh water body absorbs much of the radiations received by it and appears in dark tone or
black colour, whereas the turbid water body appears in light tone or light bluish colour in FCC due to
mixed response shown by the water molecules as well as suspended sand particles (Figures 7.13 a and
b).

42. The colours in
which different
features of the
earth’s surfaces
are recorded in
remote sensing
images are given
in Table 7.2.

44. 2. Texture:
The texture refers to the minor variations in tones of grey or hues of colour. These variations are primarily
caused by an aggregation of smaller unit features that fail to be discerned individually such as high density
and low density residential areas; slums and squatter settlements; garbage and other forms of solid waste;
and different types of crops and plants. The textural differences in the images of certain objects vary from
smooth to coarse textures (Fig. 7.14 a and b). For example, dense residential areas in a large city form fine
texture due to the concentration of the houses in a smaller area and the low-density residential areas
produce a coarse texture. Similarly, in high resolution images the sugarcane or millet plants produce coarse
texture in comparison to the fine texture of rice or wheat plants. One can also notice the coarse texture in
the images of scrubbed lands if compared with the fine texture of lush green evergreen forests.

46. 3. Size:
The size of an object as discerned from the resolution or scale of an image is another important
characteristic of individual objects. It helps in distinctively identifying the industrial and industrial
complexes with residential dwellings stadium in the heart of the city with the brick kilns at an urban
fringe, size and hierarchy of the settlements, etc.

47. Association:
The association refers to the relationship between the objects and their surroundings along with
their geographical location. For example, an educational institution always finds its association with
its location in or near a residential area as well as the location of a playground within the same
premises. Similarly, stadium, race course and golf course holds good for a large city, industrial sites
along highway at the periphery of a growing city, and slums along drains and railway lines.
Internet sources for more information: • www.isro.gov.in • www.nrsc.gov.in • www.iirs.gov.in

48. Pattern:
The spatial arrangements of many natural and man–made features show
repetitive appearance of forms and relationships. The arrangements can easily be
identified from the images through the utilization of the pattern they form. For
example, planned residential areas with the same size and layout plan of the
dwelling units in an urban area can easily be identified if their pattern is followed
(Figure 7.17). Similarly, orchards and plantations produce arrangements of the
same type of plants with uniform inter – plant distances. A distinction can also be
made between various types of drainage or settlements if their pattern is properly
studied and recognised.

50. Active sensors
When you take a picture with the flash turned on, the camera sends its
own source of light. After it illuminates the target, the camera captures
the reflected light back to the camera lens.
So, cameras are active sensors when the photographer uses flash. It
illuminates its target and measures the reflected energy back to the
camera.

52. Spectral signatures are the combination of reflected, absorbed and transmitted or emitted EMR by objects at varying
wavelengths, which can uniquely identify an object.
Spectral signatures
Different surface types such as water, bare
ground, and vegetation reflect radiation
differently in various channels. The radiation
reflected as a function of the wavelength is
called the spectral signature of the surface.
Source: ESA
The spectral signature of an object is a
function of the incident EMR and that part of
electromagnetic (EM) spectrum in which they
are interacting. The energy reflected back from
the object is measured by instruments such as
task-specific spectrometers.
All spectral reflectance data are unique to the
material and the environment in which they are
measured as shown in Fig.1. Mineral/rock
signatures, for example, will vary from sample
to sample. Vegetation is even more variable,
being dependent on growth stage, plant health,
and moisture content. Fig.1.
Spectral signatures of common Earth materials in NIR – near infrared, SWIR –
shortwave infrared regions of the electromagnetic energy

53. SPECTRAL SIGNATURE OF VEGETATION
• As you see, vegetation appears green. We know that an object appears green when it reflects green light (light in
green region within the visible range of the EM spectrum).
• In case of vegetation, reflection of green light is due to the presence of the chlorophyll pigment in plant leaves.
• The reflectance of vegetation is low in both the blue and red regions of the EM spectrum, due to the absorption of
blue and red wavelengths by chlorophyll for photosynthesis.
• It has a peak reflectance at the green region that gives green colour to vegetation.
• In the near infrared (NIR) region, the reflectance is much higher than that in the visible band due to the cellular
structure in the leaves.

54. Resolution
Resolution plays a role in how data from a sensor can be used. Resolution can vary depending on the satellite’s orbit and
sensor design. There are four types of resolution to consider for any dataset—radiometric, spatial, spectral, and temporal.
Radiometric resolution is the amount of information in each pixel, that is, the number of bits representing
the energy recorded. Each bit records an exponent of power 2. For example, an 8 bit resolution is 28, which
indicates that the sensor has 256 potential digital values (0-255) to store information. Thus, the higher the
radiometric resolution, the more values are available to store information, providing better discrimination
between even the slightest differences in energy..

56. Spatial resolution is defined by the size of each pixel within a digital image and the area
on Earth’s surface represented by that pixel. For example, the majority of the bands
observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) have a spatial
represents a 1 km x 1km area on the ground. MODIS also includes bands with a spatial

58. Spectral resolution is the ability of a sensor to discern finer wavelengths, that is, having more and
narrower bands. Many sensors are considered to be multispectral, meaning they have 3-10 bands.
even thousands of bands and are considered to be hyperspectral.
Temporal resolution is the time it takes for a satellite to complete an orbit and revisit the
same observation area. This resolution depends on the orbit, the sensor’s characteristics,
and the swath width. Because geostationary satellites match the rate at which Earth is
rotating, the temporal resolution is much finer. Polar orbiting satellites have a temporal
resolution that can vary from 1 day to 16 days.

59. satellite sensors store
information about objects as a
grid. Digital data is collected
from the area covered in the
form of individual image points,
so-called pixels. A pixel is the
smallest area unit in a digital
image.
Pixels
A digital image comprises of a two dimensional array of individual picture elements called pixels arranged in columns
and rows. Each pixel represents an area on the Earth's surface. A pixel has an intensity value and a location address in
the two dimensional image.

60. How does the computer know which parts of the image should be dark and which
one should be bright?
Computers understand the numeric language of binary numbers, which are sets of numbers
consisting of 0s and 1s that act as an "on-off" switch.
Converting from our decimal system to binary numbers, 00 = 0, 01 = 1, 10 = 2, 11 = 3.
Each pixel represents a square area on an image that is a measure of the sensor's ability to resolve objects of
different sizes.
For example, the Enhanced Thematic Mapper (ETM+) on the Landsat 7 satellite has a maximum resolution of 15
meters; therefore, each pixel represents an area 15 m x 15 m, or 225 m2 . Higher resolution (smaller pixel area)
means that the sensor is able to discern smaller objects.

61. For example, consider an image that is made
up of 8 columns by 5 rows of pixels. In this
figure, four shades are present: black, dark gray,
light gray and white. The darkest point is
assigned the binary number 00, dark gray as
01, light gray as 10, and the brightest part the
binary number 11. We therefore have four
pixels (B5, C4, D7 and E2) that the spacecraft
says are 00. There are three dark gray pixels
(B3, C2, C6 and E6) assigned the binary number
01, three light gray pixels (D3, D6 and E5) that
are binary number 10, and 29 white pixels are
assigned the binary number 11.
Four shades between white and black would produce images
with too much contrast, so instead of using binary numbers
between 00 and 11, spacecraft use a string of 8 binary numbers
(called "8-bit data"), which can range from 00000000 to
11111111. These numbers correspond from 0 to 255 in the
decimal system. With 8-bit data, we can assign the darkest point
in an image to the number 00000000, and the brightest point in
the image to 11111111. This produces 256 shades of gray
between black and white. It is these binary numbers between 0
and 255 that the spacecraft sends back for each pixel in every
row and column—and it takes a computer to keep track of every
number for every pixel!
http://earthobservatory.nasa.gov/Features/RemoteSensing/remote_06.php

62. • Remote sensing images are composed of a matrix of picture elements, or pixels,
• Which are the smallest units of an image.
• Image pixels are normally square and represent a certain area on an image.
• It is important to distinguish between pixel size and spatial resolution - they are not interchangeable.
• If a sensor has a spatial resolution of 20 metres and an image from that sensor is displayed at
full resolution, each pixel represents an area of 20m x 20m on the ground.
• In this case the pixel size and resolution are the same.
•

63. Sensors on earth observing satellites measure the amount of electromagnetic radiation (EMR) that is
reflected or emitted from the Earth’s surface.
These sensors, known as multispectral sensors, measure data in multiple regions of the electromagnetic
spectrum, including visible light, near and short wave infrared.
The range of wavelengths measured by a sensor is known as a band and is commonly described by the
wavelength of the energy.
Bands can represent any portion of the electromagnetic spectrum, including ranges not visible to the eye,
such as the infrared or ultraviolet sections.
Each band of a multispectral image can be displayed one band at a time as a grey scale image, or in a
combination of three bands at a time as a color composite image.
The three primary colors of light are red, green, and blue.
Computer screens can display an image in three different bands at a time, by using a different primary color
for each band.
When we combine these three images we get a color composite image.

65. .
A natural or true color composite is an image displaying a
combination of visible red, green and blue bands to the
corresponding red, green and blue channels on the computer.
The resulting composite resembles what would be observed
naturally by the human eye, vegetation appears green, water
dark is blue to black and bare ground and impervious surfaces
appear light grey and brown. Many people prefer true color
composites, as colors appear natural to our eyes, but often
subtle differences in features are difficult to recognize. Natural
color images can be low in contrast and somewhat hazy due the
scattering of blue light by the atmosphere.
Natural or True Color Composites

66. False color images are a representation of a multi-spectral image produced using bands other than
visible red, green and blue as the red, green and blue components of an image display. False color
composites allow us to visualize wavelengths that the human eye can not see (i.e. near-infrared). Using
bands such as near infra-red increases the spectral separation and often increases the interpretability
of the data. There are many different false colored composites which can highlight many different
features. See the heading below for more information about common band combinations for false color
composites
False Color Composites

67. This false-color composite scheme allows vegetation to be detected readily in the image. vegetation appears
in different shades of red depending on the types and conditions of the vegetation, since it has a high
reflectance in the NIR band
FCC Band Combination (RGB) Description
3,2,1
Standard “false-colour”
combinaton. Vegetaton shows in
red.
4,3,2
The “nature colour” combinaton.
It provides the most water
penetraton.

69. The four most common false-color band combinations are:
1.Near-infrared (red), green (blue), red (green). This is a traditional and popular band combination
useful in seeing changes in plant health.
2.Shortwave infrared (red), near-infrared (green), and green (blue), are often used to show floods
or newly burned land.
3.Blue (red), two different shortwave infrared bands (green and blue). We use this to differentiate
between snow, ice, and clouds.
4.Thermal infrared is usually shown in tones of gray to illustrate temperature.

70. Colour Signatures on Standard False Colour Composite of Surface Features
Features Colour In Standard FCC
Evergreen Red to magenta
Deciduous Brown to red
Scrubs Light brown with red patches
Cropped land Pink to Bright red
Fallow land Light blue to white
Wetland vegetaton Blue to grey
Healthy Vegetation and Cultivated Areas:

71. Features Colour In Standard FCC
Clear water Dark blue to black
Turbidity waterbody Light blue
Waterbody:
Features Colour In Standard FCC
High density Dark blue to bluish green
Low density Light blue
Built up Area:

72. Features Colour In Standard FCC
Rock outcrops Light brown
Sandy deserts/River sand/ Light blue to white
Salt afected Deep ravines Dark green
Shallow ravines Light green
Water logged/Wet lands Motelled black
Waste lands/Rock outcrops

73. Panchromatic imagery uses a single band
image. This imagery is extremely useful, as it
is generally of a much higher spatial
resolution than the multispectral
imagery from the same satellite. For
example, the QuickBird satellite produces
pan imagery having a pixel equivalent to an
area of 0.6 m × 0.6 m (2 ft × 2 ft), while the
multispectral pixels represent an area of
2.4 m × 2.4 m (8 ft × 8 ft).

74. QuickBird
Quick Bird was a high-resolution commercial Earth observation satellite owned by DigitalGlobe,
launched in 2001 and reentered after orbit decay in 2015.
QuickBird used Ball Aerospace's Global Imaging System 2000 (BGIS 2000).
The satellite collected panchromatic (black and white) imagery at 61 centimeter resolution
and multispectral imagery at 2.44- (at 450 km) to 1.63-meter (at 300 km) resolution, as orbit altitude is
lowered during the end of mission life.
What is the purpose of QuickBird satellite?
• QuickBird offered sub-meter resolution imagery with high geolocational accuracy. With global
collection of panchromatic and multispectral imagery.
• QuickBird was designed to support a wide range of geospatial applications:
 Acquire high quality satellite imagery for map creation,
 change detection,
 and image analysis.

75. Launch Date October 18, 2001
Launch Vehicle Boeing Delta II
Launch Location
Vandenberg Air Force Base,
California, USA
Orbit Altitude 450 Km / 482 Km – (Early 2013)
Orbit Inclination 97.2°, sun-synchronous
Speed 7.1 Km/sec (25,560 Km/hour)
Equator Crossing Time 10:30 AM
Orbit Time 93.5 minutes
Revisit Time
1-3.5 days, depending on latitude (30°
off-nadir)
Swath Width (Nadir) 16.8 Km / 18 Km – (Early 2013)
Metric Accuracy 23 meter horizontal (CE90)
Digitization 11 bits
Quickbird Satellite Specifications

76. Sensor Resolution
Panchromatic 65 cm
Multispectral 2.62 m
•4-Band Multispectral (BLUE, GREEN, RED, NIR1)
•4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1)
•4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1)
•3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED)
•3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1)
•Natural Colour / Coloured Infrared (3-Band pan-sharpened)