Fundamentals of Remote
Sensing
Outline
Define RS
History of RS
Why RS
Basic concepts
Electromagnetic spectrum
Types of RS
Sensor
Platform
Types of resolutions
Multi spectral
FCC
Stages of RS
IRS - Different Satellite Images
Interpretation keys
Application of RS
Definition
Remote Sensing is the Science of making
inferences about objects from measurements,
made at a distance, without coming into physical
Contact with the objects under study.
Need of Remote Sensing
Remote Sensing provides timely and repetitive information on the
phenomenon happening on earth & its environment.
The highly competing and conflicting demands on natural resources
from the increasing population and the aspiration for improving the
quality of life, need management strategies to use our resources
optionally to meet the present day need while not endangering our
earth system so that the needs of the future generation can be met-
referred as Sustainable Development.
The world wide Economic and social development can be managed
efficiently & effectively by minimizing adverse impact on the Earth
Resources, Environment & Climate.
Electromagnetic spectrum
Light can be classified according to the
length of the wave
Wavelength
The Electromagnetic Spectrum
(EMS)
Gamma Rays X-Ray Ultraviolet Infrared Microwave TV/Radio
Visible
0.4 0.7
micrometers
The information needs a physical carrier
to travel from the objects to the sensors
through an intervening medium.
The ELECTROMAGNETIC RADIATION is
normally used as an information carrier
in remote sensing.
The output of a remote sensing system
is usually an IMAGE representing the
scene being observed.
Types of Remote Sensing
Passive Remote Sensing
Active Remote Sensing
PASSIVE REMOTE SENSING SYSTEM
The sensing system depends on an external
source of illumination.
e.g. The eyes passively senses the radiation
reflected or emitted from the object.
ACTIVE REMOTE SENSING SYSTEM
The sensing system provides its own
source of illumination.
Resolution
It refers to picture element or pixel
discernible on the image of the smallest
area resolvable or identifiable on
ground.
Spatial Resolution The smallest object that can be discerned
Spectral Resolution No. of bands
Temporal Resolution Periodicity of data collection
Radiometric Resolution Quantization levels of data
Geomatics Cell, NIRD
Resolutions
Spatial Spectral Temporal
GEOSTATIONARY ORBITS
These satellite appears stationary with
respect to the Earth's surface. Generally
placed above 36,000 km from the earth.
GEOSTATIONARY ORBITS
Communication Satellites are in GEOSYNCHRONOUS ORBIT
(Geo = Earth + synchronous = moving at the same rate).
This means that the satellite always stays over one spot on
Earth. The area on earth that it can SEE is called the
satellites FOOTPRINT
POLAR ORBITS
A near polar orbit is one with the orbital plane
inclined at a small angle with respect to the
earth's rotation axis.
A satellite following a properly designed near
polar orbit passes close to the poles and is able
to cover nearly the whole earth surface in a
repeat cycle.
Earth observation satellites usually follow
the sun synchronous orbits.
A Polar Orbit is a particular type of
Low Earth Orbit. The satellite
travels a North South Direction,
rather than more common East-
West Direction.
Why Use A Polar Orbit ?
As a Satellite orbits in a north-south direction, Earth
spins beneath it in an East-West direction. As a result,
a satellite in polar orbit can eventually scan the entire
surface.
It is like pealing an orange in
one piece. Around & around,
one strip at a time, and finally
youve got it all. For this
reason the Remote Sensing
Satellites are in this orbit to get
a thorough coverage of the
Earth.
SUN SYNCHRONOUS ORBIT
A sun synchronous orbit is a near polar
whose altitude is such that the satellite
will always pass over a location at a given
latitude at the same local solar time.
In this way, the same solar illumination
condition (except for seasonal variation)
can be achieved for the images of a given
location taken by the satellite.
polar sun synchronous orbit
ORBIT
GROUND TRACE & Repetivity
The Process of Remote Sensing
ENERGY
SOURCE
The Process of Remote Sensing
12
65
C 28
33
76
E
D
A
B
A. Radiation and the C. Energy recorded
atmosphere and converted by
B. Interaction with sensor E. Interpretation and
target D. Reception and analysis
processing Text by the Canadian Centre for Remote Sensing
What is an image?
Data that are organized in a grid of Columns and rows
Usually represents a geographical area
X-axis
Viewing images
Three bands are viewable simultaneously
Monitor
Part of
color guns
spectrum
Band
Blue 4
1
Green
Band
Red
3
5
2
NIR
SWIR Band
2
3
GENERATION OF FALSE
GREEN BAND WITH BLUE FILTER
COLOUR COMPOSITE IMAGE
RED BAND WITH GREEN FILTER
IR BAND WITH RED FILTER
STANDARD FALSE COLOUR COMPOSITE
Band Combinations
Features can become more obvious
4,5,3 (RGB) 2,3,1 (RGB)4,3,2 (RGB)
Urban
Vegetation
Shape
Size
Colour
Tone
Texture
Pattern
Association
Indias Earth Observation Missions
Sun Synchronous
Geo stationary
1988/91 IRS-1A & 1B
LISS-1&2 1990 INSAT-1D
(72/36m) VHRR
IRS-1C/1D
1995/1997
LISS-3 (23/70m); 1992
PAN (5.8m); INSAT-2A
WiFS (188m) VHRR
1999 IRS-P4
OCM
(360m), 1993 INSAT-2B
2001 MSMR VHRR
TES
Step& Stare
PAN (1m) 1999
INSAT-2E
IRS-P6: Resource Sat
LISS 3 (23m) VHRR, CCD (1 km)
LISS 4 (5.8m);
AWiFS (55m) 2002
KALPANA-1
VHRR
IRS-P5 PAN-2.5M,
2005
Carto-1, 30 km
2003 INSAT-3A
VHRR,CCD
Important Satellites Remote Sensing
LANDSAT 1-7 - USA
SPOT - Europe
MOS - Japan
JERS - Japan
RADARSAT - Canada
IRS - Indian
NOAA - USA
GMS - Japan
Remote Sensing Satellites of 21st Century
Satellite Date of launch Sensing System Spatial Resolution
Landsat-7 April 1999 ETM+ 15m PAN
30m MultiBand
Spot-5 May 2002 HRG, HRS 2.5 5m PAN
10m MultiBand
IKNOS Sep 1999 Visible camera 1m PAN
4m MultiBand
QuickBird Oct 2001 Visible camera 60 cm PAN
2.4 MultiBand
CartoSat-1 May 2005 PAN camera 2.5m PAN
CartoSat-2 Jan 2007 PAN camera 80cm PAN
CartoSat-2A
WorldView-1 Sep 2007 Visible camera 50cm PAN
GeoEye-1 Aug 2008 Visible camera 41cm PAN
1.65m MultiBand
Improvement in the Spatial Resolution
100
Spatial Resolution (in meters)
10
0.1
1970 1975 1980 1985 1990 1995 2000 2005 2010
0.01
Years
SENSORS ON-BOARD IRS 1C/1D
Sensors WiFS LISS-III LISS_IV
Spatial Resolution
(Pixel Size) 188 meters 23.5 meter 5.8 meter
Band 1 (Green) Band 1 (Green)
Spectral Bands Band 1 (Red) Band 2 (Red) Band 2 (Red)
(Micrometers) Band (NIR) Band 3 (NIR) Band 3 (NIR)
Band 4 (MIR)
Dynamic Range
7 Bit 7 Bit 7 Bit
Swath Width 810 km 141 km 14 km
PART OF ROME, LISS-III +PAN DATA SAMPLE
IMAGES OF
IRS-1C/1D
SENSORS
BANGKOK CITY, PAN DATA
Aerial
Photograph of
University of
Winconsin
Normal Color
Color Infrared
1 m Resolution Space Image
61 cm
Natural Color
Imaged by
Quick Bird
IKONOS Multispectral IMAGE
This image shows
part of Mecca.
Part of QATAR, captured by CARTOSAT-1
View of Palm Island- Dubai by Resourcesat-1 MX
BANGLORE 1m
Cubbon Road
Chinnaswamy
Stadium
FM Cariappa
Mem.Park
Cubbon
Park
MG Road
APPLICATIONS
SATELLITE REMOTE SENSING APPLICATIONS
AGRICULTURE
CROP ACREAGE AND PRODUCTION ESTIMATION
SOIL RESOURCES
SOIL MAPPING
LAND CAPABILITY, LAND IRRIGABILITY
SOIL MOISTURE ESTIMATION
MAPPING WATER-LOGGED AREAS
SALT-AFFECTED SOILS, ERODED LANDS, SHIFTING CULTIVATION
LANDUSE/LAND COVER
LAND USE/LAND COVER MAPPING
WASTELAND MAPPING
URBAN SPRAWL MAPPING
GEOSCIENCES
GEOLOGICAL / GEOMORPHOLOGICAL MAPPING
GROUND WATER POTENTIAL ZONE MAPPING
MINERAL TARGETTING
FORESTRY AND ENVIRONMENT
FOREST COVER MAPPING
FOREST MANAGEMENT PLAN - RS INPUTS
BIODIVERSITY CONSERVATION
ENVIRONMENTAL IMPACT ASSESSMENT
GRASSLAND MAPPING
SATELLITE REMOTE SENSING APPLICATIONS
WATER RESOURCES
SNOWMELT RUNOFF FORECASTING
RESERVOIR SEDIMENTATION
OCEAN APPLICATIONS
COASTAL ZONE MAPPING
POTENTIAL FISHING ZONE (PFZ)
MAPPING
CORAL REEF MAPPING
DISASTER ASSESSMENT
FLOOD / CYCLONE DAMAGE ASSESSMENT
AGRICULTURAL DROUGHT ASSESSMENT
VOLCANIC ERUPTION, UNDERGROUND
COAL FIRE
LANDSLIDE HAZARD ZONATION
FOREST FIRE AND RISK MAPPING
INTEGRATED MISSION FOR SUSTAINABLE
DEVELOPMENT
SUSTAINABLE WATERSHED
DEVELOPMENT
Mapping and monitoring mangroves, coastal
wetlands
KRISHNA R.
P
KRISHNA R.
IRS-1B LISS-I P P
IMAGE, 1992 IRS-1C LISS-III
IMAGE, 2000
P = Prawn cultivation
49
Flood due to cyclone (29th October 1999) off Orissa coast
RADARSAT
DATA of 2nd NOV
IRS LISS III IRS LISS III
Pre-cyclone (11.10.99) Post-cyclone (05.11.99)
Panchromatic image of QUICKBIRD satellite showing agro-ecosystem level tree resources
Lit Canopy
Canopy shadows
AERIAL DATA FOR DAMAGE ASSEMENTS
NEPAL BEFORE AND AFTER EARTHQUAKE -VISUAL
WHAT CAN BE SEEN FROM SATELLITE
IMAGES?
IRS-1C LISS-III & PAN merged FCC
M
s
B
CH
s
P w
T
Banana Maize Tobacco Salt affected
Chillies Cotton Paddy Water logged
WHAT CAN BE SEEN FROM SATELLITE
IMAGES?
HILLY TERRAIN WITH FOREST MANGROVE FOREST
AGRICULTURAL LANDS - DELTA WET LANDS
RIVER COURSES WATER TURBIDITY
COASTLINE
WHAT CAN BE SEEN FROM SATELLITE
IMAGES?
ROCK TYPES
GEOLOGICAL STRUCTURES (LINEAMENT /FAULT/DYKE)
VALLEY FILL WITH VEGETATION
BLACK SOIL COVER
SALT AFFECTED LAND
URBAN GROWTH IN MANILA, PHILLIPINES
Spatio-Temporal Analysis of Land Surface
Temperature and Land cover Dynamics
Using
Geospatial Technology-A case study of
Ahmedabad
STUDY AREA
Latitude: 23.03N
Longitude: 72.58E
Urban Heat Island Study DETAILED
METHODOLOGY
Satellite images of year 1999,
2009, 2011 year [Landsat TM5]
Normalized LU/LC Emissivity Land Surface
Difference Classification Temperature
Vegetation [LST]
Index Retrieval
Spatio-Temporal Analysis of
[NDVI] LULC, NDVI, &LST
LEVEL OF Comparing NDVI and
ANALYSIS: LULC with LST
AMC LIMIT
Spatial /Zonal Statistics
WARD LEVEL Correlation analysis to relate
2KM GRID LST with other parameters
LEVEL
Identification of zones
with higher Temperature
Ground Based Analysis using
Thermal IR Gun
Validation and Results
SPATIO-TEMPORAL ANALYSIS OF
Land Surface Temperature
Temporal Change in LST (C)
Year Minimum LST Maximum LST Mean LST
1999 19.6 34.0 28.5
2009 22.8 36.6 30.7
2011 23.0 39.1 31.6
* Considering Month of January and May
NW
N
W C
E
JAN 1999 JAN 2009 JAN 2011
NW
N
W C
E
MAY 1999 MAY 2009 MAY 2011
WARD WISE SURFACE TEMPERATURE CHANGES
WARD WISE SURFACE TEMPERATURE CHANGES
Urban Urban heat
Ecological
thermal field island
evaluation
variance phenomen
index
index on
<0 None Excellent
0.000 - 0.005 Weak good
0.005 - 0.010 Middle Normal
0.010 - 0.015 Strong Bad
0.015 - 0.020 Stronger Worse
> 0.020 Strongest Worst
Only few regions of the city
were experiencing the heat
island phenomena
Threshold of ecological evaluation index
Source: Urban Expansion in Wuxi City and Heat Island Response by RS Analysis
<0
0.000 -
0.005
0.005 -
0.010
0.010 -
0.015
0.015 -
0.020
> 0.020
The area extent under
heat island phenomena
increased.
<0
0.000 -
0.005
0.005 -
0.010
0.010 -
0.015
0.015 -
0.020
> 0.020
Now most of the city
was found to experience
the heat island
phenomena
NATIONAL SPACE SYSTEMS
COMMUNICATION REMOTE SENSING
INSAT SERIES OF SATELLITES IRS SERIES OF SATELLITES
METEOROLOGY, RADIO/TV BROADCAST, NATURAL RESOURCES MONITORING AND
DISASTER WARNING MANAGEMENT
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