IIRS - RS&GIS Complete Material
IIRS - RS&GIS Complete Material
IIRS - RS&GIS Complete Material
13/6/2020
1
Saturday
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Remotely
In situ
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Is it all…???
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
E G
B F
C
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
OUR FOCUS
B Properties of EMR
C Interaction of radiation with target
Interaction of radiation with
atmosphere
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Properties of EMR
-Wave Nature
-Particle nature
-Wave Particle duality
-EM spectrum
-Radiometry
-Black Body Radiation – Laws, Spectral Emissivity
Particle theory
Considers electromagnetic radiation as consisting of many discreet units
- photons
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Speed of light
c =λ ν
where λ is wavelength (m)
is frequency (cycles per second, Hz)
c is speed of light (3×108 m/s)
Light does not require a material medium for its propagation!!
Wave-particle duality
λ= h/p
h is Plank’s constant
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Higher wavelength
Lower wavelength
Visible range
Infra-Red range
Microwave range
RADIOMETRY
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Solid angle
It is the cone
angle subtended
by the portion of
a spherical
surface at the
center of the
sphere.
TIME
TIME
SOLID
ANGLE TIME
AREA
TIME
SOLID ANGLE
AREA
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Projected area
MANU MEHTA
प्रश्नोत्तरी / Quiz
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
सभी प्रतिभागियों से अनरु ोध है कि प्रश्नोत्तरी में भाि लेने हे िु वे ई-क्लास में लॉगिन िरें :
URL : https://eclass.iirs.gov.in/login
नोट: प्रतिभािी जो पहले से ही ई-क्लास में लॉिइन हैं , प्रश्नोत्तरी में भाि लेने हे िु िृपया अपने वेब पेज िो
ररफ्रेश िरें ।
URL : https://eclass.iirs.gov.in/login
Note : Participants who are already logged in, please refresh your Web Page to Participate in the quiz.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
QUIZ TIME
1. Is our eye a remote sensor?
A) YES B)NO
Black Body
M is spectral
exitance
C1 =3.74x10-16Wm2
C2 =1.44x10-2moK
is the wavelength
T is the absolute
temperature
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
INTEGRATE DIFFERENTIATE
Spectral Emissivity
The efficiency with which real materials emit thermal
radiation at different wavelengths is determined by
their emissivity ‘’
Interaction Processes
1. Reflection
Specular : Snell’s law
Diffused
Lambertian : Lambert Cosine law
2. Transmission
3. Absorption
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Reflection
Specular Diffused
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Spectral Signatures
Absorption
Scattering
Refraction
MANU MEHTA
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Atmospheric Windows
Mie
Rayleigh
Non-
selective
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Scattering
Scattering Wavelength Approximate Kinds
process dependency dependence of particles
on particle
size
Selective
Rayleigh -4 < 1 m Air molecules
Absorption
Only Atmospheric windows available !
Scattering
Modification of spatial/spectral distribution of incoming and
outgoing radiation !
Atmospheric turbulence limits resolution !
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
THANKS
For further queries and doubts :
manu@iirs.gov.in
Suggested readings
George J. (2005) : Fundamentals of remote Sensing;
Universities press (India)Pvt ltd, Hyderabad, india.
Lillesand T.M., Keifer R.W. and Chipman J. (2008) : remote
Sensng and Image Interpretation, 6th Edition, John Wiley.
Sabins F.F. (1996) : Remote Sensing and Interpretation ,
Waveland Pr. Inc.
Campbell J.B. (2002) : Introduction to Remote Sensing ,
Guilford Press
Remote Sensing III Edition : American Society of
photogrammetry and Remote Sensing.
Jenson, J. R., (2000) : Remote Sening of the Environment :
An Earth Resource Perspective, New Jersey : Prentice Hall.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Earth Observation
Sensors and Platforms
Vinay Kumar
Scientist, IIRS
vinaykumar@iirs.gov.in
17-Jun-20
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Application
•Land use
•Atmosphere
•Geology
•Hydrology
•Agriculture
•Forestry
•Disaster Management
Ground based
Airborne
Spaceborne
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Altitude Orbit of
Satellite
Inclination angle
Period Inclination
The satellite's orbit (North –South) and the rotation of the Earth (from west
to east) work together to allow complete coverage of the Earth's surface,
after it has completed one complete cycle of orbits
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Passive Active
Whiskbroom Pushbroom
79 77 45 38 59 77 84 86 85 85
80 82 69 44 32 45 72 86 82 78
88 79 86 87 65 40 41 75 79 78
Pixel
93 86 93 106 106 84 56 43 58 75
87 93 97 90 82 76 70 67 61 71
79 81 88 97 93 85 78 74 70 72
81 75 78 85 94 97 92 84 80 72
Resolution
Ability of the system to render the information at the smallest discretely separable quantity
in terms of distance (spatial), wavelength band of EMR (spectral), time (temporal) and
radiation (radiometric).
Spatial Resolution
The physical dimension on earth is recorded
It refers to the amount of detail that can be detected by Pixel = detector size
a sensor. CCD Linear Array
GRE = IFOV x H
IKONOS PAN 1m
Spatial Resolution
Smallest discernible detail in an image
I N D I A N I N S T I T U T E O F R E M OCloud
T E Spatterns,
E N S I N movement
G , D E H R A D U 1-2
N Kms.
Meteorology Water vapor Analysis 8 Kms.
Ocean Color Monitoring
(Chlorophyll, Sediment
Oceanography Map, Yellow Substance, 300-1100 m
Sea Surface Temp.
Mapping)
Crop monitoring,
Forest Mapping, 20-30 m
Hydrology etc.
Land use
Cartography, Urban 2-6 m
Planning
Military Surveillance 1m
• 1 Km to 1 m spatial Resolution
• 24 Days to every 30 mts. Repetitivity
• 1 Million scale to Cadastral Level
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Spectral Resolution
Spectral resolution describes the ability of a sensor to define fine wavelength intervals.
This refers to the number of bands in the spectrum in which the instrument can take
measurements.
Higher spectral resolution = better ability to exploit differences in spectral signatures
• panchromatic
• multispectral
• hyperspectral
Visible Infrared
1 2 3 4 5 7
3,2,1 4,3,2
True Color Composite False Color Composite
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
(0.45 to 0.52m) (0.52 to 0.60 m) (0.63 to 0.69 m) (0.75 to 0.90 m) (1.55 to 1.75 m) (2.09 to 2.35m)
Band 7-SWIR
IRS LISS-3 Both cloud and snow have higher reflectance in visible and hence
cannot be discriminated (except from shadow). In SWIR, low reflectance of Landsat 7 ETM+ 14. Feb. 2000,
© Space Imaging
Kilauea Volcano (Hawaii)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Radiometric Resolution
It describes the actual information content in an image.
Radiometric Resolution
2 (number of bits) = number of grey levels
256 colors
Temporal Resolution
Represents the frequency with which a satellite can re-visit an area of
interest and acquire a new image.
Depends on the instrument's field of vision and the satellite's orbit
Application demand
Meteorological hourly need to monitor clouds
Oceanographic 2-3 days of repetivity
Stereo viewing 0-1 days of repetivity
Vegetation monitoring 5 days of repetivity
सभी प्रतिभागियों से अनुरोध है कि प्रश्नोत्तरी में भाि लेने हे िु वे ई-क्लास में लॉगिन िरें :
URL : https://eclass.iirs.gov.in/login
नोट: प्रतिभािी जो पहले से ही ई-क्लास में लॉिइन हैं , प्रश्नोत्तरी में भाि लेने हे िु िृपया अपने वेब पेज िो
ररफ्रेश िरें ।
URL : https://eclass.iirs.gov.in/login
Note : Participants who are already logged in, please refresh your Web Page to Participate in the quiz.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Quiz time
Q. Which scanner has lesser dwell time ………….
a) Across Track Scanners b) Along Track Scanners c) None of these
*SPOT-6 (2012) & SPOT-7 (2014) form a constellation of Earth-imaging satellites designed
to provide continuity of high-resolution, wide-swath data up to 2024
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Band 1 (TIR)- 8 to 12
CIRC (Compact Infrared 96
628 200
Camera)
ALOS-2 2014
PALSAR(Phased Array 3 (100)
14
L-band Synthetic
25
Aperture Radar-2) Band 1 (1.257 GHz) (SAR-L)
(490)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
The Evolution RISAT-2BR1
Cartosat-3
HySIS 2019
2018
RISAT-2B
RESOURCESAT-
2016
Since IRS-1A, the First 2A: LISS 3, LISS 4,
2017 Cartosat-2 Series
AWiFS 2016
Operational Remote RISAT-1 : C-
2018
Sensing Satellite SAR
2012
CARTOSAT-2B : PAN
IMS-1: Mx, HySI 2011 RESOURCES MEGHA-
CARTOSAT-2A : PAN 2010 AT-2: LISS 3, TROPIQUES:
LISS 4, AWiFS SAPHIR, SCARAB
2008 & MADRAS
2009 RISAT-2 : X-SAR
CARTOSAT-1: 2005
PAN stereo
2007 OCEANSAT-2:
2001 OCM, SCAT, ROSA
TES : PAN 2003 CARTOSAT-2: PAN
RESOURCESAT-1 :
1999 LISS 3; LISS 4; AWiFS
1997
IRS-1D : LISS-3, OCEANSAT-1 :
PAN & WiFS OCM & MSMR
1996
IRS-P3 : WiFS,
1995 MOS, X-Ray
IRS-1C : LISS-3,
PAN & WiFS 1994
IRS-P2: LISS-
Hyperspectral Sensors
ISRO - NASA AVIRIS – NG
SAR Sensors
72 m 36 m
23 m 5.8 m
188 m 56 m
23 m 5.8 m
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Cartosat-Images
HYDERABAD BANGALORE
BANGALORE AUSTRALIA
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Cartosat-2Series Image Part of Kishangarh city Cartosat-2Series Image Part of Indore city
GeoEye-1 Image,
Nov. 15, 2009
IKONOS Image
March 17, 2011
IN D I A N I3N S T I T U T E O F R E M O T E S E N S I N G , D E H
KOMPSAT R A D UVIEW-3
WORLD N
KOMPSAT 3A
WORLD VIEW-4
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Meteorological Satellites
India has launched GSAT & INSAT series satellites, which are telecommunication,
and meteorological satellites.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
http://earthexplorer.usgs.gov/
http://www.nrsc.gov.in/
http://www.spaceimaging.com
http://www.digitalglobe.com
http://edcimswww.cr.usgs.gov/pub/imswelcome/
http://www.spotimage.fr/home
http://bhuvan-noeda.nrsc.gov.in/download/download/download.php
https://scihub.copernicus.eu/dhus/#/home
http://glcf.umiacs.umd.edu/data/
http://www.usgs.gov/pubprod/
https://cross.restec.or.jp/cross-ex/topControl.action?language=en-US
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Thank You
Thank You
IIRS
Email: vinaykumar@iirs.gov.in
The material for the presentation has been compiled from various sources-
books, tutorials, IRS satellite-datasets and several resources on the internet
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
A
D
E
B
F G
C E
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
IMAGE INTERPRETATION
Analysis of remote sensing imagery involves the
identification of various targets in an image.
Targets may be defined in terms of the way they
reflect or emit radiation.
Radiation is measured and recorded by a sensor, and
ultimately depicted as an image product.
• Act of examining images to identify objects and judge
their significance.
• Information extraction process from the images.
Involves a considerable amount of subjective
judgment.
• An interpreter is a specialist trained in study of
photography or imagery, in addition to his own
discipline.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Data Characteristics:
• Spectral resolution = part of the EM spectrum
measured.
• Radiometric resolution = smallest differences in energy
that can be measured.
• Spatial resolution = smallest unit area measured.
• Revisit time (temporal resolution) = time between two
successive image acquisitions over the same area.
Spectral Signature
Identity is whatever makes an entity recognizable.
A signature is that which gives an object or piece of
information its identity.
sorghum
cotton
Thick leaf
Thin leaf
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Mature plant
Healthy plants
100
80
Reflectance (%)
60
40
(21)
20 (24)
Infected plants
0
0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1
Wavelength (m)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Wet soil
Dry soil
Wavelength in nanometer
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Organic content:
A soil with 5% or more organic matter
usually appears black in colour.
Less decomposed organic materials have
higher reflectance in the near IR region.
Very high decomposed organic materials
show very low reflectance throughout the
reflective region of the solar spectrum
reflectance c
d
Reflectance in the green region e
d
a
decreases with increased iron d
Soil structure
A clay soil tends to have a strong structure, which leads to a
rough surface on ploughing; clay soils also tend to have high
moisture content and as a result have a fairly low diffuse
reflectance.
Sandy soils also tend to have a low moisture content and a
result have fairly high and often specular reflectance
properties.
CONTAMINANT PRESENT
Contaminations (soot, particles, etc.) Reduce snow
reflection in the visible region.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Band (.45 to .515m) Band (.525 to .605 m) Band (.63 to .690 m)
Band (.75 to .90 m) Band (1.55 to 1.75 m) Band (2.09 to 2.35m)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
True Color
Composite (3,2,1)
False Color
Composite (4,3,2)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
ELEMENTS OF IMAGE
INTERPRETATION
Recognizing targets is the key to interpretation
and information extraction.
Observing the differences between targets and
their backgrounds involves comparing different
targets based on any, or all, of the visual
elements of –
tone, shape, size, pattern, texture, shadow, and
association.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Tone
Tone refers to the
relative brightness or
colour of objects in an
image.
Generally, tone is the
fundamental element
for distinguishing
between different
targets or features.
Variations in tone also
allows the elements of
shape, texture, and
pattern of objects to be
distinguished.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Shape
Shape refers to the
general form, structure,
or outline of individual
objects.
Shape can be a very
distinctive clue for
interpretation.
Straight edge shapes
typically represent urban or
agricultural (field) targets,
while natural features, such
as forest edges, are
generally more irregular in
shape, except where man
has created a road or clear
cuts.
Farm or crop land irrigated by rotating sprinkler systems
would appear as circular shapes
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Size
Size of objects in an image is a
function of scale.
It is important to assess the size
of a target relative to other
objects in a scene, as well as the
absolute size, to aid in the
interpretation of that target.
A quick approximation of target
size can direct interpretation to
an appropriate result more
quickly.
For example, if an interpreter had to distinguish
zones of land use, and had identified an area with a
number of buildings in it, large buildings such as
factories or warehouses would suggest commercial
property, whereas small buildings would indicate
residential use.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Pattern
Pattern refers to the spatial
arrangement of visibly
discernible objects.
Typically an orderly repetition of
similar tones and textures will
produce a distinctive and
ultimately recognizable pattern.
Texture
Texture refers to the
arrangement and frequency of
tonal variation in particular
areas of an image.
Shadow
Shadow may provide an idea of
the profile and relative height of a
target or targets which could make
identification easier.
Association
Association takes into account
the relationship between other
recognizable objects or features
in proximity to the target of
interest.
The identification of features that
one would expect to associate
with other features may provide
information to facilitate
identification.
Commercial properties may be associated with proximity to
major transportation routes, whereas residential areas
would be associated with schools, playgrounds, and sports
fields.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Landsat image
Boston
Landsat image
Mid-Infrared Image of
Stromboli Island.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
1 2 1. In the image
identify the
features (1-5)
2. Discuss the
elements of
3 interpretation
used for
identification of
each feature.
5
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Manual vs Digital
Manual interpretation and Digital processing and analysis is
analysis dates back to the early more recent with the advent of
beginnings of remote sensing digital recording of remote
for air photo interpretation. sensing data and the
Manual interpretation requires development of computers.
little, if any, specialized Digital analysis requires
equipment. specialized, and often
Manual interpretation is often expensive, equipment.
limited to analyzing only a single The computer environment is
channel of data or a single more amenable to handling
image at a time.. complex images of several or
Manual interpretation is a many channels or from several
subjective process, meaning dates.
that the results will vary with Digital analysis is based on the
different interpreters. manipulation of digital numbers
in a computer and is thus more
objective, generally resulting in
more consistent results.
However, determining the validity and accuracy of the results from
digital processing can be difficult.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Email-
Tel-
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Presentation Outline
Digital Image
Digital Image Processing
Image Preprocessing
Radiometric Errors & Correction
Line /Column Dropout / Banding
Haze Correction
Sun angle Correction
Geometric Error & Correction
Rectification
Resampling
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
In the present context, the analysis of pictures that employ an overhead perspective, including the radiation not
visible to human eye are considered.
Pictorial Representation
Textual Description:
Ice cream is a sweetened frozen food typically eaten as a snack or dessert. It
is usually made from dairy products, such as milk and cream, and often
combined with fruits or other ingredients and flavours. It is typically sweetened
with sucrose, corn syrup, cane sugar, beet sugar, and/or other sweeteners.
Typically, flavourings and colourings are added in addition to stabilizers. The
mixture is stirred to incorporate air spaces and cooled below the freezing point
of water to prevent detectable ice crystals from forming. The result is a smooth,
semi-solid foam that is solid at very low temperatures (<35 °F / 2 °C). It
becomes more malleable as its temperature increases.
https://en.wikipedia.org/wiki/Ice_cream
Digital Image Processing Minakshi,PRSD,IIRS 3
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
What is an Image ?
An IMAGE is a Pictorial Representation of an object or a scene.
Forms of Images
Analog
Digital
Image Preprocessing
Remote sensing systems may not function perfectly all the
time.
The Earth’s atmosphere, land, and water are complex and
do not lend themselves well to being recorded by remote
sensing devices that have constraints such as spatial,
spectral, temporal, and radiometric resolution.
Consequently, error may creep into the data acquisition
process and can degrade the quality of the remote sensor
data collected.
The two most common types of error encountered in
remotely sensed data are radiometric and geometric.
Radiometric and geometric correction of remotely sensed
data are normally referred to as preprocessing
operations because they are performed prior to information
extraction.
Digital Image Processing Minakshi,PRSD,IIRS 9
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Atmospheric errors
Digital Image Processing Minakshi,PRSD,IIRS 10
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Line dropout
If a detector fails to function this can result in
an entire line or column of data with no
spectral information.
Striping De-striped
y = a.x + b D (t ) D(t ) n 2 1
a = gain Measured
DN
b = offset
D (t m 2
)
x = input
y = output Input
with haze
without haze with haze
DN
DN '
SIN ( )
Difference in Geometry
• Shift
• Scale
• Rotation and
• Skew
Rectification
Is a process of geometrically correcting an image so that it can be
represented on a planar surface , conform to other images or
conform to a map.
That is it is the process by which geometry of an image is made
planimetric.
It is necessary when accurate area , distance and direction
measurements are required to be made from the imagery.
It is achieved by transforming the data from one grid system into
another grid system using a geometric transformation
Grid transformation is achieved by establishing mathematical
relationship between the addresses of pixels in an image with
corresponding coordinates of those pixels on another image or
map or ground.
Digital Image Processing Minakshi,PRSD,IIRS 23
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Mathematical Transformations
Linear Transformations/ Affine transformation/ first order
transformation
X = a0 + a1x + a2 y
Y = b0 + b 1 x + b 2 y
where
X , Y are the Rectified coordinates (output)
x, y are the source coordinates (input)
A first order transformation can change
Location in x and/or y
Scale in x and/or y
Skew in x and/or y
Rotation
Polynomial transformation
If the coefficients a0 ,a1,a2, b0, b1 and b2 are known then, the
above polynomial can be used to relate and point on map to its
corresponding point on image and vice versa. Hence six
coefficients are required for this transformation (three for X and
three for Y).
So it requires Minimum THREE GCP’s for solving the above
equation.
However the error cannot be estimated with three GCP’s alone.
Hence one additional GCP is taken
Before applying rectification to the entire set of the data, it is
important to determine how well the six coefficients derived from
the least square regression of the initial GCPs account for the
geometric distortion in the input image.
Digital Image Processing Minakshi,PRSD,IIRS 29
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Accuracy of transformation
In this method, we check how good do selected points fit between the map
and the Image?
To solve linear polynomials we first take four GCP’s to compute the six
coefficients. Its source coordinates in the original input image are say xi and
yi. The position of the same points in reference map in degrees, feet or
meters are say x,y
Now, if we input the map x,y values for the first GCP back into the linear
polynomial equation with all the coefficients in the place, we would get the
computed or retransformed xr and yr values , which are supposed to be
location of this point in input image
Ideally measured and computed values should be equal.
In reality this does not happen.
Where
xi and yi are the input
source coordinates and
xr and yr are the
retransformed coordinates
Nearest Neighbor
The nearest neighbor approach uses the value of the closest
input pixel for the output pixel value.
The pixel value occupying the closest image file coordinate to
the estimated coordinate will be used for the output pixel value
in the georeferenced image.
Nearest Neighbor
ADVANTAGES:
Output values are the original input values. Other methods of
resampling tend to average surrounding values. This may be
an important consideration when discriminating between
vegetation types or locating boundaries.
Since original data are retained, this method is recommended
before classification.
Easy to compute and therefore fastest to use.
DISADVANTAGES:
Produces a choppy, "stair-stepped" effect. The image has a
rough appearance relative to the original unrectified data.
Data values may be lost, while other values may be
duplicated.
Digital Image Processing Minakshi,PRSD,IIRS 35
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Bilinear Interpolation
The bilinear interpolation approach uses the weighted average of the nearest
four pixels to the output pixel.
4
Zk where Zk are the surrounding four data point values, and
2
k 1 Dk
D 2 are the distances squared from the point in question
k
BVwt 4 (x’, y’) to the these data points.
1
D
k 1
2
ADVANTAGES:
Stair-step effect caused by the nearest
k
neighbor approach is reduced. Image looks
smooth.
DISADVANTAGES:
Alters original data and reduces contrast by
averaging neighboring values together.
Is computationally more extensive than
nearest neighbor.
Cubic Convolution
The cubic convolution approach uses the weighted average of the nearest
sixteen pixels to the output pixel. The output is similar to bilinear
interpolation, but the smoothing effect caused by the averaging of
surrounding input pixel values is more dramatic.
16
Zk
2
k 1 Dk
where Zk are the surrounding four data
point values, and D2k are the distances
BVwt 16
1 squared from the point in question (x’, y’)
2
k 1 Dk
to the these data points.
ADVANTAGES:
Stair-step effect caused by the nearest neighbor
approach is reduced. Image looks smooth.
DISADVANTAGES:
Alters original data and reduces contrast by averaging
neighboring values together.
Is computationally more expensive than nearest
neighbor or bilinear interpolation.
Input Image
Rectified Image
Discussion / Query
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Thank You
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Outline
Image Enhancement
Modification of an image to alter its impact on viewer
Enhancements are used to make it easier for visual interpretation and
understanding of imagery.
Process of making an image more interpretable for a
particular application
Useful since many satellite images give inadequate
information for image interpretation.
In raw imagery, the useful data often populates only a small portion of
the available range of digital values
Attempted after image is corrected for distortions.
May be performed temporarily or permanently.
Image Enhancement Techniques Minakshi,PRSD, IIRS 3
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Enhancement Types
Point Operations
Modification of brightness values of each pixel in an image data set
independently. ( radiometric enhancement)
Brings out contrast in the image
Local operations
Modification of pixel values based on the values of surrounding pixels.
(spatial enhancement)
Image Transformations
enhancing images by transforming the values of each pixel on a
multiband basis (spectral enhancement )
CONTRAST
• Amount of difference between average gray level of an object
and that of surroundings
• Difference in illumination or grey level values in an image or
• Intuitively, how vivid or washed-out an image appears
• Ratio of Maximum Intensity to Minimum Intensity
• Larger the ratio more easy it is to interpret the image
Band2
Band4
FCC (4,2,1)
Band1
Contrast Enhancement
Expands the original input values to make use of the total
range of the sensitivity of the display device.
The density values in a scene are literally pulled farther apart, that
is, expanded over a greater range.
The effect is to increase the visual contrast between two areas of
different uniform densities.
This enables the analyst to discriminate easily between areas
initially having a small difference in density.
Types
Linear - Input and Output Data Values follow a linear relationship
Non Linear- Input and output are related via a transformation function
Y = ƒ(x)
Image Enhancement Techniques Minakshi,PRSD, IIRS 7
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Y = ax + b
grey
shade min DN max DN
0
0 DN 255
Transfer Function
Logarithmic Stretch
In this process the logarithmic values of the input
data are linearly stretched to get the desired
output values.
It is a two step process. In the first step we find
out the log values of the input DN values.
In the second step the log values are linearly
stretched to fill the complete range of DN no. (0-
255).
Logarithmic stretch has greatest impact on the
brightness values found in the darker part of the
histogram or on the low DN values.
Image Enhancement Techniques Minakshi,PRSD, IIRS 9
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Histogram Equalisation
In this technique, histogram of the
original image is redistributed to
produce a uniform population density.
This is obtained by grouping certain
adjacent gray values.
Thus the number of gray levels in
the enhance image is less than the
number of gray levels in the original
image.
Contrast is increased at the most
populated range of brightness values
of the histogram (or "peaks").
It automatically reduces the contrast
in very light or dark parts of the image
associated with the tails of a normally
distributed histogram
Image Enhancement Techniques Minakshi,PRSD, IIRS 10
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Local Operations
pixel value is modified based on the values surrounding it.
Spatial Filtering - is the process of dividing the image into its constituent
spatial frequencies, and selectively altering certain spatial frequencies to
emphasize some image features.
Process of suppressing (de-emphasizing) certain spatial frequencies &
passing (emphasizing) others.
• This technique increases
the analyst’s ability to
discriminate detail.
Low Frequency Details
• used for enhancing certain
features
High Frequency Details • removal of noise.
• Smoothening of image
Convolution Process
Input Image
10 15 20 22 11 18 14 10
c1 15
c2 20
c3 22 11 18 14
c1 c2 c3
15 12 12 20 12 21 12 c4 c5 c6 15
c4 12
c5 12
c6 20 12 21 12
c7 c8 c9
12 12 14 12 12 14 22 12
c7 12
c8 14
c9 12 12 14 22
12 15 12 12 20 15 12 Filter 12 15 12 12 20 15 12
15 20 14 12 12 14 20 15 20 14 12 12 14 20
12 12 12 15 12 12 12 12 12 12 15 12 12 12
12 15 12 14 12 15 12 12 15 12 14 12 15 12
Input Image
Step 1 : Window mask is placed over
part of Image
Convolution Process
Filter Types
Low Pass Filters
block high frequency details
has a smoothening effect on images.
Used for removal of noise
Removal of “salt & pepper” noise
Blurring of image especially at edges.
Mode filter 3x3 5x5
Original Image Mean filter 3x3 5x5 Median filter 3x3 5x5
Image Enhancement Techniques Minakshi,PRSD, IIRS 16
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Laplace Filter
Original Image
or from two or more images of resultant data are then rescaled to fill the
More Indices
Acronym Index Feature Highlighted
MNDWI Modified Normalized Difference Water Index water, shadow
(GREEN-SWIR) /(GREEN +SWIR)
NDWI Normalized Difference Water Index Water, built-up
((GREEN-NIR)/(GREEN+NIR)
NWI New Water Index Water, built-up ,shadow
(B1-(B5+B7+B4))/(B1+(B4+B5+B7))*C
NDPI Normalized Difference Pond Index Water and Shadow
(SWIR-GREEN)/(SWIR+GREEN
SI Shadow Index Shadow and water in lower threshold
(256- GREEN)(256- RED )^1/2
NDBI normalised difference built-up index Built-up and wetlands
(MIR-NIR)/(MIR+NIR)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Composite PC Image
Forest appears green,
river bed in blue, water
in Red – orange ,
vegetation appears in
varying shades of green
and fallow agriculture
field as pink to magenta
IMAGE FUSION
Most of the sensors operate in two modes: multispectral mode and the
panchromatic mode.
The panchromatic mode corresponds to the observation over a broad spectral
band (similar to a typical black and white photograph) and
the multispectral (color) mode corresponds to the observation in a number of
relatively narrower band.
Usually the multispectral mode has a better spectral resolution than the
panchromatic mode.
Most of the satellite sensors are such that the panchromatic mode has a better
spatial resolution than the multispectral mode,
Better is the spatial resolution, more detailed information about a landuse is
present in the imagery
To combine the advantages of spatial and spectral resolutions of two
different sensors, image fusion techniques are applied
Band substitution
Numerical
(Brovey Transform)
Multiplicative
Statistical (PCA)
Color space transformations (RGB, IHS)
THANK YOU
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
CLASSIFICATION
ACCURACY
ASSESEMENT and
Change detection
POONAM S. TIWARI
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Accuracy Assessment……
Accuracy Assessment
Because it is not practical to test every pixel in the classification image, a
representative sample of reference points in the image with known class
values is used
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Sources of Errors
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Ground Reference Test pixels
Locate ground reference test pixels (or polygons if the classification is based on human
visual interpretation) in the study area.
• These sites are not used to train the classification algorithm and therefore represent
unbiased reference information.
• It is possible to collect some ground reference test information prior to the
classification, perhaps at the same time as the training data.
• Most often collected after the classification using a random sample to collect the
appropriate number of unbiased observations per class.
Accuracy assessment “best practices”
30-50 reference points per class is ideal
Reference points should be derived from imagery or data acquired at or near the
same time as the classified image
If no other option is available, use the original image to visually evaluate the
reference points (effective for generalized classification schemes)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Sample Size
Sample size N to be used to assess the accuracy of a land-use classification map
for the binomial probability theory:
Z 2 ( p)( q)
N
P - expected percent accuracy, E2
q = 100 – p,
E - allowable error,
Z = 2 (from the standard normal deviate of 1.96 for the 95% two-sided confidence
level).
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Sample Size
For a sample for which the expected accuracy is 85% at an allowable
error of 5% (i.e., it is 95% accurate), the number of points necessary
for reliable results is:
2
2 (85)(15)
N 2
a minimum of 203 points.
5
With expected map accuracies of 85% and an acceptable
error of 10%, the sample size for a map would be 51:
2
2 (85 )(15 )
N 2
51 points
10
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Sample Design
There are basically five common sampling
designs used to collect ground reference test
data for assessing the accuracy of a remote
sensing–derived thematic map:
1. random sampling,
2. systematic sampling,
3. stratified random sampling,
4. stratified systematic unaligned
sampling, and
5. cluster sampling.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
ERROR MATRIX
Once a classification has been sampled a contingency table (also referred to as
an error matrix or confusion matrix) is developed.
This table is used to properly analyze the validity of each class as well as the
classification as a whole.
In this way the we can evaluate in more detail the efficacy of the
classification.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
One way to assess accuracy is to go out in the field and observe the actual land class at a sample
of locations, and compare to the land classification it was assigned on the thematic map.
There are a number of ways to quantitatively express the amount of agreement between the
ground truth classes and the remote sensing classes.
One way is to construct a confusion error matrix, alternatively called a error matrix
This is a row by column table, with as many rows as columns.
Each row of the table is reserved for one of the information, or remote sensing classes used by
the classification algorithm.
Each column displays the corresponding ground truth classes in an identical order.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
OVERALL ACCURACY
The diagonal elements tally the number of pixels classified correctly in each
class.
But just because 83% classifications were accurate overall, does not mean
that each category was successfully classified at that rate.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
USERS ACCURACY
A user of the imagery who is particularly interested in class A, say, might wish
to know what proportion of pixels assigned to class A were correctly assigned.
In this example 35 of the 39 pixels were correctly assigned to class A, and the
user accuracy in this category of 35/39 = 90%
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
PRODUCERS ACCURACY
Contrasted to user accuracy is producer accuracy, which has a slightly different
interpretation.
Producers accuracy is a measure of how much of the land in each category
was classified correctly.
It is found, for each class or category, as
Kappa Analysis
Khat Coefficient of Agreement:
• Kappa analysis yields a statistic, K̂ , which is an estimate of Kappa.
• It is a measure of agreement or accuracy between the remote sensing–
derived classification map and the reference data as indicated by a) the
major diagonal, and b) the chance agreement, which is indicated by the row
and column totals (referred to as marginals).
Kappa Coefficient
• Expresses the proportionate reduction in error generated by the
classification in comparison with a completely random process.
• A value of 0.82 implies that 82% of the errors of a random classification
are being avoided
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Kappa Coefficient
The Kappa coefficient is not as sensitive to differences in sample sizes
between classes and is therefore considered a more reliable measure of
accuracy; Kappa should always be reported
A Kappa of 0.8 or above is considered a good classification; 0.4 or below is
r r
considered poor
M n ij n n i j
ˆ i j 1 i j 1
K r
M2 n n i j
i j 1
Where:
r = number of rows in error matrix
nij = number of observations in row i, column j
ni = total number of observations in row i
nj = of observations in column j
total number
M = total number of observations in matrix
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Kappa Coefficient
classified image
i j 1
180,000 69,200 110,800
250,000 69,200 180,800
0.613
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
KAPPA COEFFICENT
For an error matrix with r rows, and hence the same number of
columns, let – A = the sum of r diagonal elements, which is the
numerator in the computation of overall accuracy. Let B = sum of
the r products (row total x column total).Then
Change Detection
Methods and Procedures
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
CHANGE DETECTION
• The process of identifying differences in the state of an object or
phenomenon by observing it at different times.
• Usually applied to Earth surface changes at two or more times.
• Land-use/land-cover change - a major component of global change with an
impact perhaps greater than that of climate change.
• It provides the foundation for better understanding relationships and
interactions between human and natural phenomena to better manage and use
resources.
• It involves the application of multi-temporal datasets for quantitative
analysis of the temporal effects of the phenomenon.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Note:
Digital change detection is affected by spatial, spectral, radiometric and temporal constraints.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• These methods (excluding CVA) are relatively simple, straightforward, easy to implement and interpret, but
these cannot provide complete matrices of change information.
• In this category, two aspects are critical for the change detection results: selecting suitable image bands,
selecting suitable thresholds.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Date 2
Advantages: - (difference) imagery
Simple and straightforward, easy to interpret results.
Efficient way to detect change
Requires only one classification
Disadvantages:
Cannot provide a detailed change matrix
(“From-to” change information is not available) Difference
images
Requires careful definition of “change - no change” threshold
(differences in DN values due to other factors such as phenology,
sun angle, atmosphere or sensors differences are
not “real” changes)
Image classification
Requires acquisition of comparable imagery and
careful radiometric calibration such that where there are no
changes in land cover the images are near identical (i.e., difference
equals zero)
“Change” map
Image differencing
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Image regression
Characteristics: Establishes relationships between bi-
temporal images, then estimates pixel values of the second-
date image by use of a regression function, subtracts the
regressed image from the first-date image.
Image ratioing
Characteristics: Calculates the ratio of registered images
of two dates, band by band.
Background subtraction
Characteristics:
Non-change areas have slowly varying background grey levels.
A low-pass filtered variant of the original image is used to approximate the variations to the background
image.
A new image is produced through subtracting the background image from the original image.
Advantages: Easy to implement.
Disadvantages: Low accuracy.
Key factors: Develops the background image.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
(1) Put two or more dates of images into a single file, then perform PCA and analyse the minor
component images for change information.
(2) Perform PCA separately, then subtract the second-date PC image from the corresponding PC image of
the first date.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Disadvantages:
PCA is scene dependent, thus the change detection results between different dates are often difficult to
interpret and label.
It cannot provide a complete matrix of change class information and requires determining thresholds to
identify the changed areas.
Key factors: Analyst’s skill in identifying which component best represents the change and selecting
thresholds.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Advantages: The association of transformed components with scene characteristics allows the
extraction of information that would not be accessible using other change detection
techniques.
Disadvantages:
It is difficult to extract more than one single component related to a given type of change.
The GS process relies on selection of spectral vectors from multi-date image typical of
the type of change being examined.
Key factors: Initial identification of the stable subspace of the multi-date data is required.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Chi-square
Y: digital value of change image
X: vector of the difference of the six digital values between the two dates
M: vector of the mean residuals of each band
T: transverse of the matrix,
Σ−1 : inverse covariance matrix of the six bands
Disadvantages: The assumption that a value of Y=0 represents a pixel of no change is not
true when a large portion of the image is changed.
Also the change related to specific spectral direction is not readily identified.
1.Post-Classification Comparison
2.Spectral-Temporal Combined Analysis
3.EM Transformation
4.Unsupervised Change Detection
5.Hybrid Change Detection
6.Artificial Neural Networks (ANN)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Post-classification
• Post-classification (delta classification)
– Classify Date 1 and Date 2 separately, compare
class values on pixel by pixel basis between dates
• Advantages:
– Avoids need for strict radiometric calibration
– Favours classification scheme of user
– Designates type of change occurring
• Disadvantages:
– Error is multiplicative from two parent maps
– Changes within classes may be interesting
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Comparison of Classifications
Date 1 imagery
Classification
of Date 2
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Classifications
Two dates are Classification
of Date 1
classified separately
Date 2 imagery
“Change” map
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Comparison of Classifications
Advantages
provides “from - to” change class information
next base year is already completed
Disadvantages
accuracy of change map depends on the accuracy of the
individual classifications
requires two classifications
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Unsupervised techniques
► Objective
Produce a change detection map in which changed
areas are separated from unchanged ones.
Supervised techniques
Objective:
Generate a change detection map where changed areas
are identified and the land-cover transition type can be
identified.
Main techniques:
• Post-classification comparison
• Multi-date direct classification
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Post-classification comparison
► Standard supervised classifiers are used to classify the
two images independently.
► Advantage
▪ Common and intuitive.
▪ Provides change matrix.
► Drawback
▪ Critically depends on the accuracy of the classification
maps. Accuracy close to the product of the two results.
▪ Does not exploit the dependence between the information
from the two points in time.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Post-classification comparison
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
► Advantage
▪ Exploits the multi-temporal information.
▪ Error rate not cumulative.
▪ Provides change matrix.
► Drawback
▪ Ground truth required also for transitions
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• Then, on the basis of such estimates, an automatic method for the unsupervised
analysis of the difference image is described.
• The method makes use of Markov random fields (MRFs) for modelling the
spatial-contextual information included in the neighbourhood of each pixel.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
•Markov model is capable of accurate measurement of the magnitude of change but fails in
predicting the direction of change
• Cellular Automata (CA) incorporates the spatial component and thus includes direction into
modeling process.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• This method first performs multi-scale decomposition for the difference image by the
DT-CWT and extracts the change characteristics in high-frequency regions by using a
MRF-based segmentation algorithm.
• Then this method estimates the final maximum a posterior (MAP) according to the
segmentation algorithm of iterative condition model (ICM) based on fuzzy c-
means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of
each layer respectively.
• Finally, the method fuses the above segmentation results of each layer by using the
fusion rule proposed to obtain the mask of the final change detection result.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• Algorithms for constructing the change rules, then use these rules to
process the data.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Analyzing Nature of Change
•Nature of change can be assessed by analyzing the change trajectories in land cover classes over
time.
•These trajectories are described as trends over time which is responsible for changes in earth
resource dynamics in a particular area .
•Trajectories of land-cover change are generated which refer to successive land-cover types for a
given pixel over period of observation.
Analyzing Spatial Pattern of Change
•Knowledge of
•Changes occurred at particular location
•the reason for such changes
•the rate at which the changes have occurred
•the future scenario if driving forces operate at same pace
सभी प्रतिभागियों से अनुरोध है कि प्रश्नोत्तरी में भाि लेने हे िु वे ई-क्लास में लॉगिन िरें :
URL : https://eclass.iirs.gov.in/login
नोट: प्रतिभािी जो पहले से ही ई-क्लास में लॉिइन हैं , प्रश्नोत्तरी में भाि लेने हे िु िृपया अपने वेब पेज िो
ररफ्रेश िरें ।
URL : https://eclass.iirs.gov.in/login
Note : Participants who are already logged in, please refresh your Web Page to Participate in the quiz.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Quiz Time…….
THANKYOU
Faculty Profile : Dr. Ashutosh Bhardwaj
He is Scientist/Engineer-‘SF’ at Photogrammetry and Remote
Sensing Department, Indian Institute of Remote Sensing,
Dehradun. He is a graduate in Civil Engg. from M.R.E.C. (present
MNIT), Jaipur; M.Tech. (Remote Sensing) from BIT, Ranchi & PhD
from Civil Engg. Dept., IIT-Roorkee. He joined ISRO in 2001 after
a brief career as faculty at Dept. of Civil Engg., BITS, Pilani.
• He has been engaged in industry, teaching and research in Surveying,
Photogrammetry, GNSS, Cartography, Remote Sensing and topographic
modelling for the past 20 years. He has published over 50 research papers
in Journals and conferences. He has widely contributed to various
departmental projects on remote sensing and Mapping at NRSC and IIRS.
• He has received appreciation from the Ministry of Planning and National
Development (MPND), Republic of Maldives for rendering expert services
for GPS ground control survey in the National Mapping Project (Maldives).
ORCID iD: https://orcid.org/0000-0002-9241-5427
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Satellite Navigation
Global Navigation Satellite System
&
GPS receivers, surveying
techniques, processing methods,
errors and accuracy
Dr. Ashutosh Bhardwaj
Scientist/Engineer-’SF’, PRSD (GT&OP Group)
ashutosh@iirs.gov.in
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Xiaomi Mi8
Source: https://rntfnd.org/wp-content/uploads/eLoran-Definition-Document-0-1-Released.pdf
Source: https://www.geospatialworld.net/blogs/the-redmi-note-9-pro-with-navic-support-is-
here/#:~:text=The%20Redmi%20Note%209%20Pro%20with%20a%20Qualcomm%C2%AE%20Snapdragon,175)%20on%20the%20Mi%20Store.&text=The%20Navigation%20and%20positioning%20features,%2F%20Galileo%2F%20GLONASS%20%2F%20Beidou.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
SATELLITE NAVIGATION
• A satellite navigation system is a system that uses satellites to
provide autonomous geo-spatial positioning. Example:
• GLOBAL
• NAVSTAR GPS
• GLONASS
• BEIDOU
Transition: As a direct result of the tragedy killing all 269
• GALILEO aboard Korean Air Lines Flight 007 which strayed into
Russian airspace accidentally & shot down by Soviet air-
to-air missiles on September 1, 1983, near Sakhalin
• REGIONAL Island, Russia, President Ronald Reagan announced on
September 16, 1983 that the GPS system that had
• IRNSS previously been intended for U.S. military use only
• QZSS would now be made available for everyone to use.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Position
• 1010-20m
- 20 m 10-20m ~10-20m ~10-20m
• Velocity ~ 10cm/s
Velocity 10cm/s 10cm/s currently)
20cm/s 20/40cm/s
• Time ~ 0.1 µs (15ns
• 6/3 0.1
Time orbits
µs (15ns) 20ns 20ns 20/50ns
1 •
st Worldwide
Satellite PRN 4 Coverage
Kosmos1413 *GIOVE-A Beidou-1A
• 24 hour access 12Oct.1982 28Dec,2005 30Oct.2000
22Feb.1978
Ellipsoid WGS84 PZ - 90.11 GTRF CGCS2000* *
• Common Coordinate System
# ##
Space Segment
(Initial Operational Capability(IOC)-1993)
(Full Operational Capability(FOC)-1995)
Block I Block II/IIA
Two Launches: Dec. 23, 2018 and 22 August, 2019
Total IIIA Series: 10 planned; IIIF Series: 11th onwards
Entirely new design
new ground control system (known as OCX $500 million/ $6
billion : FOC by 2021 by Raytheon). OCX passed
cybersecurity tests.
first GPS sent aboard on a SpaceX rocket
M-Code: more powerful GPS 3 signal for military users &
First Launch: 22 Feb 78(78-85)# First Launch: 14 Apr 89(89-97) more secure against jamming or spoofing.
On-Orbit: None, Total=11 Total II Series: 27 (1+14+12+8) S-Band serial telemetry link
Block IIIA/IIIF
GLONASS satellites
Source: https://www.glonass-iac.ru/en/guide/
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
55̊
Equator
Colorado
Springs
Ascension Kwajalein
Hawaii
Islands
Diego
Garcia
GLONASS signals
• The GLONASS satellite signal identifies the satellite and provides:
position, velocity and acceleration vectors at a reference epoch to compute
satellite locations
synchronization bits, data age and satellite health
offset of GLONASS time from UTC (SU) (formerly Soviet Union and now Russia)
almanacs of all other GLONASS satellites
Unlike GPS, all GLONASS satellites transmit the same code at different
frequencies. They derive signal timing and frequencies from one of three on-board
cesium atomic clocks operating at 5 MHz:
For example, L1 = 1602 MHz + (n x 0.5625) MHz where n = the frequency
channel number (n = 0, 1, 2 and so on)
L2= 1246 MHz + n × 0.4375 MHz
• The frequency ratio f2 /f1 is constant for all GLONASS satellites and amounts to
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
The GNSS constellation system’s potential civil applications are many and mirror those of GPS.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Why GPS?
SA: Selective
availability
Xll
Acc.: 10-100m
Vl
Multi-Satellite Ranging
Timing
• Accuracy of position is only as good as your clock
• To know where you are, you must know when you receive.
• Receiver clock must match SV clock to compute delta-T
• Each Galileo satellite has two master passive hydrogen maser atomic clocks and
two secondary rubidium atomic clocks which are independent of one other
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Interactive Session
• Which is the counterpart system of LORAN: • Which is the counterpart system of Alpha:
• Chyka • Chyka
• GLONASS • Omega
• GPS • GPS
• Alpha • GLONASS
• Galileo is from:
• Beidou is from:
• Russia
• Russia
• European Union
• European Union
• USA
• USA
• Japan
• China
Position Equations
P1 ( X X 1 ) 2 (Y Y1 ) 2 ( Z Z 1 ) 2 b
P2 ( X X 2 ) 2 (Y Y2 ) 2 ( Z Z 2 ) 2 b
P3 ( X X 3 ) 2 (Y Y3 ) 2 ( Z Z 3 ) 2 b
P4 ( X X 4 ) 2 (Y Y4 ) 2 ( Z Z 4 ) 2 b
Where: Pi = Measured PseudoRange (Biased ranges) to the ith SV
Xi , Yi , Zi = Position of the ith SV, Cartesian Coordinates
X , Y , Z = User position, Cartesian Coordinates, to be solved-for
b = User clock bias (in distance units), to be solved-for
• The above nonlinear equations are solved iteratively using an initial estimate of the user
position, XYZ, and b- same for all satellites.
• To solve the user position equations, one must know where the SV is:
• The navigation and time code provides this
• 50 Hz signal modulated on L1 and L2
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
GPS Time
• GPS time is referenced to 6 January 1980, 00:00:00
• Jan 6 = First Sunday in 1980
• GPS satellite clocks are essentially synched to International Atomic Time (TAI)
(and therefore to UTC/zulu time since Jan. 1, 1972 for global civil time)
• TAI, maintained at Lab., France, is the basis for Coordinated Universal Time (UTC), used for
most civil timekeeping
• GPS time = TAI + 13s
• Since 13 leapseconds existed on 1/6/1980
L3(1381.05MHz); L4 (1379.913MHz): used only for a atomic flash detection; Nudet (Nuclear Detection) System
(NDS). L5: 1176.45 MHz (25.5 cm, In-door apps., anti-jamming). L2C: 1227.60MHz (in pre-operational testing
and available on 24 satellites since May, 2017). L1C:1575.42MHz (III series onwards)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Navigation Message
n In order to solve the user position equations, one must know where the SV is:
n The navigation and time code provides this
u 50 Hz signal modulated on L1 and L2
n The SV’s own position information is transmitted in a 1500-bit data frame
u Pseudo-Keplerian orbital elements
F Determined by control center via ground tracking
u Receiver implements orbit-to-position algorithm
n Also includes clock data and satellite status
n And ionospheric / tropospheric corrections
n International Telecommunication Union (ITU) has reserved 1559-1610MHz band for satellite
based navigation through World Radio Communication (WRC) conferences, held every three
year.
n GPS bands (US Federal Communication Commission): (1215-1240MHz, 1559-1610 MHz, L5-
1164-1188MHz)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Navigation Message
The Almanac
• In addition to its own nav data, each
SV also broadcasts info about ALL the
other SV’s
• In a reduced-accuracy format
• Known as the Almanac
• Permits receiver to predict, from a
cold start, “where to look” for SV’s
when powered up
• GPS orbits are so predictable, an
almanac may be valid for months
• Almanac data is large
• 12.5 minutes to transfer in entirety
Source:www.glonass-iac.ru/en/GLONASS/ephemeris.php
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Phase Observations
Received Satellite
Wavelength of the signal is 19 cm on L1 and 24 cm
on L2 Phase
Receiver compares self-generated phase with
received phase Generated
Number of wavelengths is not known at the time Phase from
the receiver is switched on (carrier phase ambiguity) Receiver
As long as you track the satellite, the change in
dT
distance can be observed (the carrier phase
ambiguity remains constant)
D = c .dT + N λ
If longer PRN code is used, receiver becomes more resistive to Jamming signal. But, signal
processing is more complex
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Satellite Geometry
Satellite geometry can affect the quality of signals and accuracy of receiver
trilateration.
Positional Dilution of Precision (PDOP) reflects each satellite’s position relative to
the other satellites being accessed by a receiver.
PDOP can be used as an indicator of the quality of a receiver’s triangulated
position.
It’s usually up to the GPS receiver to pick satellites which provide the best
position trilateration.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Satellite Geometries
Ideal Satellite Geometry Good Satellite Geometry
GNSS
Systems
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Advantages of GNSS
• The use of GLONASS in addition to GPS provides very significant
advantages:
increased availability of satellites & signals.
markedly increased spatial distribution of visible satellites
reduced HDOP and VDOP (DOP) factors
Better atmospheric correction
decreased occupation times means faster RTK results
A larger satellite constellation also improves real-time carrier phase differential
positioning performance.
accurate, robust & reliable services even in bad conditions
Less expensive high-end services
Source: https://www.novatel.com/assets/Documents/Papers/GLONASSOverview.pdf
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
STEPS IN PHOTOGRAMMETRY
Aerial Photo
/ satellite image Input
Aero/Sat. Triangulation/
Control extension and Block
Adjustment
Digital Topographic
maps
Ortho Image
/ Mosaic Generation
Assessment of the MERIT DEM and EarthEnv DEM90 DEMs using GCPs
Table 1: Vertical accuracy measures computed for the experimental sites
Experimetnal ME MAE RMSE St. Dev. LE90
Sites (m) (m) (m) (m) (m)
Jaipur Site 1.37 4.03 3.27 2.97 4.91
Kendrapara -3.33 3.33 4.00 2.21 3.65
site
Dehradun Site 3.17 4.79 7.82 7.15 11.79
Chandigarh 0.27 4.79 5.59 5.58 9.20
Site
Kalka Site -0.19 12.82 16.61 16.58 27.36
Delhi Site -11.19 10.07 13.14 6.90 11.38
FUSED DEMs
Elevation extraction at
GCP locations
Datum Conversion
Figure 1: Location map of the MERIT experimental sites with DEMs
GCPs
Table 2: Comparison of vertical accuracy for the MERIT DEM & EarthEnv DEM90
Experimetnal Sites ME (m) MAE (m) RMSE (m) Statistics generation
DEMs-90m MERIT EarthEnv MERIT EarthEnv MERIT EarthEnv
Jaipur Site 1.37 0.70 4.03 2.28 3.27 3.05
Kendrapara site -3.33 -3.64 3.33 3.64 4.00 4.22 Quality Assessment
Dehradun Site 3.17 0.57 4.79 5.42 7.82 6.55
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
https://www.gim-international.com/files/0812346fc5116deee99fe6f235d260dc.doc
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Services
NAVSTAR GPS:
• Standard Positioning Service(SPS)
• Precise Positioning Service(PPS)
GLONASS:
• Standard Precision Service(SPS)
• High Precision Service(HPS)
Galileo:
• The Open source (OS) ECEF
• The Safety of life (SoL)
• The Commercial Service (CS) by Galileo Operating Concessionaire (GOC)
• The Public Regulated services(PRS): European Police/Antifraud offices.
• The Search & Rescue(SAR-L6) Service:+ to COSPAS-SARSAT system
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Receivers
Single Frequency Dual Frequency
• Baseline Accuracy 1cm/5mm + 2/1ppm (rms) • The “high end” of the GPS Market
• Uses Post process L1 carrier phase • Baseline Accuracy ranging from 5/3/3.5mm +
1/0.4ppm (rms)
• Used for all Surveying tasks with baselines up
to 15Km • Used in all GPS Surveying tasks :-
• Network Densification, Detail Surveys • Geodetic Control Networks, Tectonic Plate
• Real Time Monitoring, Photogrammetric Control,
• Smaller Occupation time Network Densification, Detail Surveys, etc.
• Less expensive alternative to Dual frequency • Real Time
• Most unsophisticated receivers track only L1 • Smaller Occupation time
and use a simplified correction model • New applications are found on a daily basis
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
• Mapping grade
• capable of <3 meters accuracy
• portable, less expensive
• accurate mapping for integration with GIS
• Navigation
• capable of 10 meters accuracy
• light weight, cheap
• basic navigation, limited data storage
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Sources of error
1. [Selective availability]
2. Clock errors
3. Ephemeris errors
4. Atmospheric delays
5. Multipath effects
6. Receiver errors
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Error Budget
User Range Errors (URE) consists of System Errors (Ephemeris Data, & Satellite
Clocks) and atmospheric Errors (Ionospheric/Tropospheric: 90% Dry delay due
to dry gases; 10% Wet delay due to WV & condensed clouds).
User Equipment Errors (UEE) consist of Receiver Noise and Multipath error.
PDOP of 2 means that in the worst case, a 1m URE will result in 2m positional
error.
Typical Error in Meters (per satellite)
Standard GPS Differential GPS
Satellite Clocks 1.5 0
Orbit Errors 2.5 0
Ionosphere 5 0.4
Troposphere 0.5 0.2
Receiver Noise 0.3 0.3
Multipath 0.6 0.6
SA 30 0
30m
• Accomplished by:
• “Dithering” the clock data
• Results in erroneous pseudoranges
• Truncating the nav message data
P
• Erroneous SV positions used to compute user pos.
• Degrades SPS solution by a factor of 4 or more
• Long-term averaging is only effective SA compensator
• Positional accuracy 50m(1sigma), 100m (95%) +/- 100m (95%)
Atmospheric Delay
GPS signals are delayed as they
pass through the atmosphere (a.
layer of charged ions and free
electrons known as ionosphere and
b. the troposphere)
< 10 km > 10 km
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Tropospheric Refraction
• Troposphere is the lowest layer of atmosphere varying from (ground to) 7 to
14kms.
• Pseudo range errors varies from 2m if the satellite is at zenith to 30m for a
5° elevation satellite.
• Hydrostatic delay occurs due to presence of dry gases: about 2 meters in the
zenith direction to 10 meters for lower elevations.
• Wet delay occurs due to water vapor and condensed clouds: only some tens
of centimetres.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Atmospheric Refraction
Ionsospheric Refraction Depends on:
•Sun’s activity (11 years sunspot cycle),
•Ionospheric thickness & proportions/concentration of ionized particles,
•Season,
•Actual path (i.e. relative position of satellite & receiver) of the wave i.e. signal
•Pseudo range errors vary from 0 to 15m at zenithal incidence to as much as 45m
for low incidence signals.
•delay is proportional to the integral no. of free electrons along the transmission
path and inversely proportional to the square of the transmission frequency.
Multipath Error
SV1
Choke Ring
antenna
SV2
Caused by local reflections of the GPS/GNSS signals that mix with the desired signals
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Signal Obstruction
SV1 SV2
SV3
When something blocks the GPS signal.
Areas of Great Elevation Differences
Canyons
Mountain Obstruction
Urban Environments
Indoors
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Human Error
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Quiz Time
• DGNSS requires minimum…. GNSS receivers: • Beidou use following reference coordinate system:
• 2 • CGCS
• 3 • GTRF
• 4 • ITRF
• 5 • WGS 84
Use
Differential GPS/GNSS
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
High Accuracy
(Receiver position, satellite position, frequency-ionospheric
corrections, time-ambiguity of carrier phase measurements)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Differential Positioning
• It is possible to determine the position of Rover ‘B’ in relation to
Reference ‘A’ provided
– The coordinates of the Reference
Station (A) are known
DGPS Method
Method to remove errors from GPS measurements
Uses a GPS receiver at a fixed, surveyed location i.e. base station, to measure
error in pseudo range
Base station receives the same GPS signals as the roving receiver and instead
of using timing signal to calculate its position, it uses its known position to
calculate timing.
Pseudorange error for each satellite is subtracted from rover before calculating
position during RTK mode or post processing
Post - Processed
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Real - Time
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
R1 R2
R8
R3
R7
Base
R4
R6
R5
Case1: Survey with Two GNSS Geodetic Receivers
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
R1/3
R1/7
Base
R2/4
R2/6
R1/5
Atmospheric partial
Multipath no
Receiver noise no
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Point
Line Area
Calendar Contacts
Today Messaging
screen
Directional
MIO Power/USB socket button
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
References
http://www.glonass-ianc.rsa.ru/pls/htmldb/f?p=202:1:15000421459964108253
http://igscb.jpl.nasa.gov/
http://www.navcen.uscg.gov/gps/precise/default.htm
https://rntfnd.org/wp-content/uploads/eLoran-Definition-Document-0-1-Released.pdf
Interface Control Documents:
http://www.navcen.uscg.gov
http://www.Glonass-ianc.ras.ru
http://www.Galileoju.com
Links:
UNAVCO http://archive.unavco.ucar.edu/cgi-bin/dmg/pss
CDDIS http://cddisa.gsfc.nasa.gov/cddis.html
NGS/CORS http://www.ngs.noaa.gov/CORS/ Contact Details of the Faculty:
SOPAC http://sopac.ucsd.edu/
Email- ashutosh@iirs.gov.in
Tel- 0135-2524117
Faculty Profile : Dr. Ashutosh Bhardwaj
He is Scientist/Engineer-‘SF’ at Photogrammetry and Remote
Sensing Department, Indian Institute of Remote Sensing,
Dehradun. He is a graduate in Civil Engg. from M.R.E.C. (present
MNIT), Jaipur; M.Tech. (Remote Sensing) from BIT, Ranchi & PhD
from Civil Engg. Dept., IIT, Roorkee. He joined ISRO in 2001 after
a brief career as faculty at Dept. of Civil Engg., BITS, Pilani.
• He has been engaged in industry, teaching and research in Surveying,
Photogrammetry, GNSS, Cartography, Remote Sensing and topographic
modelling for the past 20 years. He has published 50 research papers in
Journals and conferences. He has guided more than 50 graduate and post
graduate students on projects. He has widely contributed to various
projects of GOI on remote sensing and Mapping.
• He has received appreciation from the Ministry of Planning and National
Development (MPND), Republic of Maldives for rendering expert services
for GPS ground control survey in the National Mapping Project (Maldives).
ORCID iD: https://orcid.org/0000-0002-9241-5427
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Satellite Navigation
Satellite Based Augmentation System
(SBAS)
and
GPS Aided Geo Augmentation
Navigation (GAGAN)
A multi-constellation and multi-frequency GNSS environment
Outline
Receivers
Single Frequency Dual Frequency
• Baseline Accuracy 1cm/5mm + 2/1ppm (rms) • The “high end” of the GPS Market
• Uses Post process L1 carrier phase • Baseline Accuracy ranging from 5/3/3.5mm +
1/0.4ppm (rms)
• Used for all Surveying tasks with baselines up
to 15Km • Used in all GPS Surveying tasks :-
• Network Densification, Detail Surveys • Geodetic Control Networks, Tectonic Plate
• Real Time Monitoring, Photogrammetric Control,
• Smaller Occupation time Network Densification, Detail Surveys, etc.
• Less expensive alternative to Dual frequency • Real Time
• Most unsophisticated receivers track only L1 • Smaller Occupation time
and use a simplified correction model • New applications are found on a daily basis
3 Classes of GPS receivers
• Geodetic class: capable of sub-centimeter accuracy, high-precision mapping
• Mapping grade: capable of <3 meters accuracy, portable, less expensive
• Navigation: capable of 10 meters accuracy, light weight, cheap
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Error Budget
User Range Errors (URE) consists of System Errors (Ephemeris Data, & Satellite
Clocks) and atmospheric Errors (Ionospheric/Tropospheric: 90% Dry delay due
to dry gases; 10% Wet delay due to WV & condensed clouds).
User Equipment Errors (UEE) consist of Receiver Noise and Multipath error.
PDOP of 2 means that in the worst case, a 1m URE will result in 2m positional
error.
Typical Error in Meters (per satellite)
Standard GPS Differential GPS
Satellite Clocks 1.5 0
Orbit Errors 2.5 0
Ionosphere 5 0.4
Troposphere 0.5 0.2
Receiver Noise 0.3 0.3
Multipath 0.6 0.6
SA 30 0
Stereovision/Stereoplotting Systems
Stereovision/Stereoplotting
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Experimental Site EGM96 (360 deg.) EGM 2008 (2160 deg.) EGM84 (180 deg.)
Mean (m) St.Dev. (m) Mean(m) St.Dev. (m) Mean(m) St.Dev. (m)
Kendrapara -62.923 .0.275 -62.249 0.243 -61.691 0.234
Jaipur -50.243 0.467 -50.601 0.466 -50.765 0.391
Dehradun -44.021 1.223 -43.019 1.203 -37.765 12.338
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Assessment of the MERIT DEM and EarthEnv DEM90 DEMs using GCPs
Table 1: Vertical accuracy measures computed for the experimental sites
Experimetnal ME MAE RMSE St. Dev. LE90
Sites (m) (m) (m) (m) (m)
Jaipur Site 1.37 4.03 3.27 2.97 4.91
Kendrapara -3.33 3.33 4.00 2.21 3.65
site
Dehradun Site 3.17 4.79 7.82 7.15 11.79
Chandigarh 0.27 4.79 5.59 5.58 9.20
Site
Kalka Site -0.19 12.82 16.61 16.58 27.36
Delhi Site -11.19 10.07 13.14 6.90 11.38
FUSED DEMs
Elevation extraction at
GCP locations(1) Ahmedabad,
(2) Alwar,
Figure 1: Location map of the MERIT experimental sites with DEMs Datum Conversion (3) Bhopal,
GCPs
(4) Chamoli,
Table 2: Comparison of vertical accuracy for the MERIT DEM & EarthEnv DEM90 (5) Dehradun,
Experimetnal Sites ME (m) MAE (m) RMSE (m) (6) Hyderabad,
Statistics generation
DEMs-90m MERIT EarthEnv MERIT EarthEnv MERIT EarthEnv (7) Jaipur, and
Jaipur Site 1.37 0.70 4.03 2.28 3.27 3.05
Kendrapara site -3.33 -3.64 3.33 3.64 4.00 4.22
(8) Shimla
Quality Assessment
Dehradun Site 3.17 0.57 4.79 5.42 7.82 6.55
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
https://www.gim-international.com/files/0812346fc5116deee99fe6f235d260dc.doc
GENERATION OF RADARGRAMMETRY DEM USING RISAT-1
STEREO SAR PAIR AND VERTICAL ACCURACY
ASSESSMENT
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Uplink Corrections
Repeat, Broadcast
Corrections
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Integrity is the trust that can be placed in the correctness of information supplied by a navigation
system.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Vector
-Free
Streamlined Arrivals
Departures All-Weather
Approaches
Benefits:
• Enhanced Safety • Increased Flight Efficiencies
• Increased Capacity • Increased Schedule Predictability
• Reduced Delays • Environmentally Beneficial Procedures
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Communication
L band
Navigation
message
C band
System (WAAS)
Navigation message:
Use/Don't use
Orbit corrections
Iono corections
Clock correction
Ranging signal
User
Ground Earth
Station
WAAS reference
stations network
Collect satellite data
(GPS signal errors) Master Station
Process data;
WAAS
Enhanced Navigation for All Phases of Flight
Enroute, Arrival, and Departure: Provides Navigation Services
to Users Not Currently Served by Land Based Navigation Aids
Increases Availability of GNSS
Vertically Guided Aircraft Approach:
Enhances Safety by Providing Vertical Guidance
No Ground Hardware Required at Airport
Allows Instrument Operations to All Capable Airports
Supports Every Runway in Coverage Area
Source: http://insidegnss.com/wp-content/uploads/2018/01/janfeb16-GAGAN.pdf
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
GAGAN
Certified for required Navigation Performance
(RNP) services including Lateral Precision with
Vertical Guidance (LPV)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Satellite Type:
SBAS Satellite ID: GAGAN GSAT-8 (127)/ GSAT-10 (128)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Navigation
Reference
Signal Navigation
(C-band) Signals
(L-band)
EUROCONTROL (http://www.eurocontrol.int/)
the European organization for the Safety of Air navigation. Operating using
INMARSAT GEOs and ESA ARTEMIS
A civil and military organisation which currently numbers 38 Member States.
Primary objective is to ensure safety in the development of a seamless,
pan-European Air Traffic Management (ATM) system to cope with capacity needs
and environmental aspects.
#Source: https://egnos-user-support.essp-sas.eu/new_egnos_ops/services/about-edas
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Source: https://www.gsa.europa.eu/european-gnss/egnos/egnos-system
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Ranging Signals
Augmentation Signals
Sapporo
Users GMS
Kobe MCS
(and GMS)
Fukuoka GMS
Hitachi-Ota MCS
Tokyo (and GMS)
GMS
Ground
Network
Naha GMS
Source: https://app.qzss.go.jp/GNSSView/gnssview.html?t=1568606845861
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
NCS NCS
International
ACC Area Control Centre Kobe MCS Ibaraki MCS Network
MCS Master Control Station (backup)
NES Navigation Ground Earth Station
CPF Central Processing Facility
NCS Network Communication System QZSS Control Center
MRS Monitoring and Ranging Station Hitachi-Ota (Primary)
GMS Ground Monitor Station
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Master
Stations
सभी प्रतिभागियों से अनुरोध है कि प्रश्नोत्तरी में भाि लेने हे िु वे ई-क्लास में लॉगिन िरें :
URL : https://eclass.iirs.gov.in/login
नोट: प्रतिभािी जो पहले से ही ई-क्लास में लॉिइन हैं , प्रश्नोत्तरी में भाि लेने हे िु िृपया अपने वेब पेज िो
ररफ्रेश िरें ।
URL : https://eclass.iirs.gov.in/login
Note : Participants who are already logged in, please refresh your Web Page to Participate in the quiz.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Quiz Time
• SDCM SBAS system belongs to: • Satellite control facility for IRNSS is situated as:
• China • Bhopal & Hasan
• Russia • Jodhpur & Hyderabad
• Japan • Dehradun & Hasan
• India • Banglore and Mahendragiri
• The SDCM services are: Accuracy of 1 to 1.5 meters in the horizontal plane and of 2 to 3 meters in
vertical. In addition, it is expected to offer a cm-level positioning service for users at a range of 200
kilometers of the reference stations.
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
SDCM Space Segment: includes 3 operating geostationary satellites of multifunctional Space System Luch,
broadcasting SDCM data to users by means of SBAS radiosignals
Source: http://www.sdcm.ru/smglo/ICD_SDCM_1dot0_Eng.pdf
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Source: http://www.csno-tarc.cn/system/constellation&ce=english
Source: https://www.glonass-iac.ru/en/BEIDOU/
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
SACCSA
The SBAS initiative in South/Central America and the Caribbean is called
SACCSA (Soluciόn de Aumentaciόn para Caribe, Centro y Sudamérica).
http://www.igs.org/network
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Email- Ashutosh@iirs.gov.in
Tel- 0135-2524117
Geographic Phenomena -
Concepts and Examples
Prasun Kumar Gupta
Geoinformatics Department
Indian Institute Of Remote Sensing
Online Certificate Course on “Remote Sensing & GIS Technology and Applications”
for University Teachers & Government Officials (June 13 -July 01, 2020)
June 20, 2020
Slides adapted from several sources including ITC, The Netherlands, Books (Burrough, Chang, Maidment) and Internet.
Introduction
Geographic phenomena are
the study objects of a GIS.
• Example: Building
• Characteristics:
• Crisp boundaries
• Inside the boundary only one value
Source: http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/Cell_size_of_raster_data/009t00000004000000/
Raster
• A raster is a set of
regularly spaced (and
contiguous) cells with
associated (field)
values.
• The associated values
represent cell values,
not point values.
• This means that the
value for a cell is
assumed to be valid for
all locations within the
cell.
Tessellation (Irregular)
• Irregular tessellations are again partitions of
space into mutually exclusive cells, but now the
cells vary in size and shape, allowing them to
adapt to the spatial phenomena they represent.
• Example: Quadtrees, Run length encoding
Source: https://www.e-education.psu.edu/geog486/book/export/html/1758
Vector based - Point (0-D)
• Points are defined as single coordinate pairs
(x, y) when we work in 2D or coordinate triplets
(x, y, z) when we work in 3D
• Points are used to represent objects, that are
shape- and size less (zero-dimensional)
• Examples:
• Cities on India Map
• Schools on Chandigarh Map
Vector - Line (1-D)
• Used to represent one-
dimensional objects (roads,
railroads, canals, rivers…)
• Line is defined by 2 end
nodes and 0-n internal
nodes.
• An internal node or vertex
is like a point that only
serves to define the line
• Many GISs store a line as a
sequence of coordinates of
its end nodes and vertices,
assuming that all its line
segments are straight.
Line (2)
• By increasing the
number of internal
vertices, we can
improve the shape
• Number of vertices
determines the
precision.
• Scale is related to the
spatial accuracy - lower
number of internal
vertices - coarse scale
- generalization.
Vector - Area (Polygon) (2-D)
• When area objects are
stored using a vector
approach, the usual
technique is to apply a
boundary model.
• This means that each area
feature is represented by
some arc/node structure
that determines a polygon
as the area’s boundary.
• Area features of the same
type are stored in a single
data layer, represented by
mutually non-overlapping
polygons.
Triangulated Irregular
Networks
• A vector-based representation of
a surface
• Commonly used in applications
that involve terrain
• Composed of a series of
contiguous, non-overlapping
triangles that are known as faces
• Built from a series of points using
a technique called Delaunay
triangulation
• Advantages: More efficient at
storing data
• Also used to construct Thiessen
polygons, which form the basis for
interpolating to areas.
Source: https://www.e-education.psu.edu/geog486/book/export/html/1758
TIN (Examples)
SLOPE ASPECT
REPRESENTATIONS OF
GEOGRAPHIC FIELDS
(CONTINUOUS)
• Continuous fields (like
elevation) can be
represented as:
• Tessellation
• Isolines
• TIN
• Continuous fields when
represented as a
tessellation will lead to
floating point cell values
• Both tessellation and TIN
can be regarded as
surfaces
REPRESENTATIONS OF
GEOGRAPHIC FIELDS
(DISCRETE)
• Discrete fields (like
landuse or soil type)
can be represented
as:
• Tessellation
• Polygons
• Discrete raster
representations will
lead to integer cell
values
Representation of Objects
• Line and point objects
are more awkward to
represent using
rasters, as rasters are
area-based
• Objects are more
naturally represented
in vector
Representation of Objects -
Points as Cells
Representation of Objects -
Line as a Sequence of Cells
Representation of Objects -
Polygon as a Zone of Cells
Comparison
Property Raster Data Model Vector Data Model
Data Structure Simple Complex
Overlaying Easy and Efficient Difficult to perform
Compatible to RS imagery Yes No
Spatial Variability Efficient Insufficient
Programmability Simple Difficult
Storage Inefficient Compact
Geometric Properties Erroneous Correct
Network Analysis Difficult Easy
Map Visual Appeal Less High
Information Variation Based on resolution Based on scale
Topology Not implemented Efficient encoding
Choice of representation
• From the suitable digital representations the
choice is generally based on two issues:
Canal (1:50,000)
River (1:5000)
Ward boundary
Quiz time! - Answers
• Fill a option in the correct box
Field Object
(Continuous or (Point, Line or
Discrete) Polygon)
Elevation Points Point
SYSTEM
“A GIS is a computer-based system that provides the
following four sets of capabilities to handle geo-
referenced data:
- Input
- Data management (storage and retrieval)
- Manipulation and analysis
- Output.”
(Aronoff, 1989)
GIS Functional Modules
Data Input
Database
Database definitions
- a database system in which most of the data are spatially indexed,
and upon which a set of procedures are operated in order to
answer queries about spatial entities in the database.
GIS
Sources of Input Data
Remote Photo-
Sensing grammetry
ALTM GPS
GIS Database
Hard
Total
Copy
Station
Maps
spatial data models
raster model
vector model
raster model
line
feature
area
point feature
feature
vector model
layers in an vector-based
model (2)
Topology
Topology is a branch of mathematics that
deals with properties of space that remain
invariant under certain transformations.
disjoint covered by
meet contains
equal covers
inside overlap
Overlay Operation
After Bonham-Carter
Overlay Operation: Raster Layers
Map A Map C
5 5 2 2 15 15 12 12
5 5 5 2 MapC= MapA + 10 15 15 15 12
- Arithmatic Operations 6 2 2 2 16 12 12 12 Map C1
- Relational and Logical Operators 6 6 6 6 16 16 16 16 9 9 10 10
9 9 9 10
- Conditional Statements Map B MapC1= MapA + MapB 7 3 3 10 Map C2
- Any Combination 4 4 8 8 7 7 14 14 11 11 60 60
4 4 4 8 11 11 11 60
1 1 1 8 71 33 33 60
1 1 8 8 MapC2= ((MapA - MapB)/(MapA + MapB)) *100 71 71 14 14
Why? Methods?
Answer geographic questions • SIMPLE QUERY
• Where is the nearest school to my
home?
• SPATIAL QUERY
• NETWORK ANALYSIS
1. Qualitative Data
(i) Nominal/ Categorical Data – describe data of different
categories (e.g. soil data)
(ii) Ordinal Data – differentiate data by a ranking relationship
(e.g. soil erosion, road network)
2. Numeric Data
(iii) Interval Data – data having known interval between values
(e.g. temperature)
(iv) Ratio Data – data having absolute values
(e.g. population density)
Different kinds of Data & Data Values
Page Number: 42
COVID-19 Dashboard by the John Hopkins University (JHU)
Different kinds of data values
Page Number: 42
Different kinds of data values
Page Number: 42
Recent Trends in Geoinformatics and its
Applications
Decision Support Systems
33
Source: Hu, Y. & Li, W. (2017). "Spatial Data Infrastructures", The Geographic Information Science & Technology Body of Knowledge, John P. Wilson (ed.).
http://dx.doi.org/10.22224/gistbok/2017.2.1
SDI as Common Spatial Data Infrastructure
Organizations and individuals
cooperating
Using electronic technology to
Collection of Traffic Data and LoD0 (Level of Detail) Model Storage (PostGIS, 3D City DB) &
Noise Level Samples Reconstruction (Satellite Image conversion to CityGML for Semantic
Processing) analysis and integration
Traffic Noise Analysis using LoD2 Model Reconstruction Spatial query at semantic level,
Empirical Relationship (Terrestrial Laser Scanner) rendering & visualisation
Stage 1
Stage 2
Stage 3
Konde, A, Sameer Saran and A. Senthil Kumar, 2015. “Web enabled Spatio-temporal semantic
analysis of Traffic Noise using CityGML” Geocarto International (under review).
Data Warehouse and OLAP
A data cube supports viewing/modelling of a variable of interest.
Warehouse is a Specialized DB
Measures are used to report the values of the particular variable with
Global attempts
Public sector:
NASA Earth Exchange (NEX)
ESA Cloud Toolbox
Australian Geoscience Data Cube (AGDC)
Private sector:
Google Earth Engine (GEE)
Amazon Web Services (AWS)
Traffic monitoring using wireless sensors
Traffic Data
Data Pre-processing Store
• Data Cleaning
• Lanewise Filtering
Data Aggregation
(1 m int. lanewise)
42
CITIZEN SCIENCE
What is Citizen Science? iirs
Public involvement in inquiry and
discovery of new scientific knowledge
Who are Citizen Scientists?
Volunteers who contributes their
time, effort, and resources toward
scientific research
Why Citizen Science?
Bridging Gaps, Scope and Policy
Citizen science are diverse in: ecology,
medicine, computer science, statistics,
psychology, genetics, engineering and
many more.
CROWDSOURCE BASED APPROACH
https://www.covid19india.org/
• Uses crowdsource data from twitter and other sources which is Data Downloads :
validated by a group of volunteers and published into a Google sheet Data can be downloaded in different
formats (JSON/CSV) using API service
and an API. provided for crowdsourced data.
• Data is updated based on state press bulletins, official (CM, Health M)
handles, PBI, Press Trust of India, ANI reports. These are generally
more recent.
Citizen Science Based Software Solution for Swachh Bharat Abhiyaan (SBA)
Salient Features
http://sb.iirs.gov.in
Geospatial Solutions for Governance
Citizen Science
approach in IBIN
Release of Geospatial Solution for Forest Fire Reporting Mobile
App and Dashboard
By
Hon. Advisor, J & K
On Oct 3, 2018
Mobile App
Dashboard
CLUSTER Geographic
ANALYSIS Epidemiology
CORRELATION
ACCESSIBILITY Spatial ENVIRONMENT
People
Data
ANALYSIS
Analysis Software
Mapping
Health Care DIFFUSION
Geography
FACILITY
PLANNING
GEO-HEALTH: APPLICATION AREAS
ENVIRONMENTAL
HEALTH HAZARDS
PUBLIC
RISK OF VECTOR-
PARTICIPATION &
BORNE DISEASE
HEALTH OBSERVE
PEOPLE HEALTH
ANALYZE
MODEL SPREAD OF
HEALTH
INFECTIOUS
DISPARITIES
VISUALIZE DISEASE
SCENARIO
LOCATION TIME
DEVELOPMENT
SPATIAL
LOCATING HEALTH
CLUSTERING OF
SERVICES
HEALTH EVENTS
ACCESS TO
“Health Care Geography” “Geographic Epidemiology”
HEALTH SERVICES
Advanced Visualisation
https://coronavirus.jhu.edu/map.html
5
3
Introduction- BIG DATA
Sagiroglu, S., & Sinanc, D.
(2013)
54
Big data is basically defined as a huge collection of myriad datasets which is difficult to
process by using the traditional data processing platforms or state of the art approaches
for data processing (Chen & Zhang, 2014)
“… a blanket term for any collection of data sets so large and complex that it
becomes difficult to process using on-hand database management tools or
traditional data processing applications.” (Wiki, 2014)
Source: http://www.ibmbigdatahub.com/infographic/four-vs-big-data
Twitter Analytics- A Use Case
USA vs Portugal
World Cup
Football Match
June, 2014
Analysis of billions
of tweets (White
= USA Fans; Red
= Portugal Fans)
Source: https://www.washingtonpost.com/news/morning-mix/wp/2014/06/24/tweets-lit-up-the-map-through-u-s-portugal-world-cup-game/
Spatial Data Science
• Data Science:
– is an inter-disciplinary field that uses scientific methods,
processes, algorithms and systems to extract knowledge
and insights from many structural and unstructured data.
– is related to data mining and big data.
• Spatial Data Science (SDS):
– allows analysts to extract deeper insight from data using a
comprehensive set of analytical methods and spatial
algorithms,
– including machine learning and deep learning techniques.
https://earthengine.google.com/
Spatial Analysis
Spatial 5% Analysis
Past Present/Future
The application of GIS is limited only by the
imagination of those who use it
Jack Dangermond
Thank you..
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Faculty Profile
Shiva Reddy Koti,
Geoinformatics Department,
Geospatial Technology and Outreach Group,
IIRS, ISRO
Shiva Reddy Koti holds M.Tech in Geomatics Engg. from IIT, Roorkee and B.E
in Information Technology from Govt. Engg. College , Bilaspur (C.G).
His area of expertise is in the field of geospatial software development , and
Health GIS. He has been actively involved in the teaching and R&D activities
in GIS, Health GIS, Web GIS, Programming , Data Mining and Databases.
He is QGIS 3 contributor and the author of popular QGIS plugin “QRealTime”.
His FOSS4G contributions can be followed at
https://github.com/shivareddyiirs/
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
2
22-Jun-20
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• legal records
• coordinate lists with associated tabular data
•Aerial photographs
Field coordinate measurement
• Coordinate Surveying
• GPS
Image data
• Manual or automated classification
• direct raster data entry
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Based on
coordinate
surveys
Plotted and
printed
carefully
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Field Measurement
GPS
Coordinate Surveying
(courtesy NGS)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Scanner
Drum Scanner
Flat Bed Design
Scanner Quality (dpi):
dpi: Dot per Inch
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Quiz
a) 254
b)25.4
c)2.54
d) None of the above
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Scanning
Manual Digitizing
•nodes at line
endpoints
•vertices
define line
shape
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Manual Digitizing
common errors that require editing
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Correcting errors
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Editing
Line snapping:
When a vertex or node is “close” to a line or
end point, the lines are “snapped” together
Point snapping:
Points which fall within a specified distance of
each other are snapped (typically, on point
eliminated).
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Snapping
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Automated Digitisation
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Summary
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Digitisation Overview
Email- shivareddy@iirs.gov.in
Tel-01352524126
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
1
22-Jun-20
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Faculty Profile
Ashutosh Kumar Jha is scientist SE in Geoinformatics department.
He holds M.Tech. in remote sensing and B.E. in Computer
Engineering.
His area of expertise is Geospatial modeling and processing
optimization of raster/vector Data using High performance
distributed computing.
Currently he is working on BigGIS, Machine Learning and 3D
Modeling.
He has been actively involved in Weather Forecast and air quality
application development. He has built a open source LULC dynamics
modeling framework called OpenLDM.
https://github.com/ashutoshkumarjha/OpenLDM
He has been awarded Best Innovation Award in ACRS-2017 for the
development of mobile application for Municipals.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Contents
A to B
?
User Requirement
Or
Questions
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Human
Machine
GIS
GIS Requirement GIS Questions Solutions Answered
Process/Models
Cant Road
B
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Machine
TIN
3D globe to 2D
Map Data Structure
(Spherical to Cartesian)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
i
h
g
• No duplication
• No topology
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
L 0 1 2 3 4 5 6 7 8 9
i 1
n 2
e 3
4 Pixel (8,-4) Center
n
5
u
m 6 46 f(8,-6)=46
b 7
e 8
r 9
(
Y
• Spatial Arrangement is called Grid • Raster resolution to be chosen:
)
#10
(5,9) 1
PL1
(3,6) #9
(2,4) 2 2
#8 #6 #2
(3,5) #7
#3 (4,5) 2 2 2
#5 #4 #4
(4,5) 3 3
(3,3)
#l1 #3 3
#2 3 3
(1,1) (3,1)
#1
Point Polygon
Line
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Rasterization Effect
Smoothness of geometry is lost
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Vector Vs Raster
Vector Raster
Based on object model .e.g each Cell based Modal : Full area field
feature is a bounded cover
Position of points can be in fraction X,Y location will always be number
Takes less space Takes more space
Object based either point, line or Basic object cell
polygon
Geometry based analysis Algebra based analysis
All Points are referenced Grids Origin is referenced. Cell
location are computed relative to
origin
File Type: arcInfo (.e00), File Type: Geotiff(.tiff), Erdas
shape(.shp), KML, KMZ, Imagin (*.img), Scientic Format
OpenSteetFormat(.osm) , Autocad (.hdf,.nc) etc
(.dwg and .dxf) , Bently Microsation
(*.dgn)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
TIN Model
3D Data model
Triangle based
Delaunay Triangle
based
On triangular height
can be computed
using interpolation
Node height are
stored
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
ST-Object model
Src: http://loi.sscc.ru/gis/data_model/may.html
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Spatial Relationships
disjoint covered by
meet contains
equal covers
inside overlap
coverage
Personal Geodatabase
Raster
shapefile
Table
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Geodatabase (gdb)
Feature (vector) datasets Anatomy of a Geodatabase
Spatial Reference Geodatabases may contain: feature datasets,
Object classes and subtypes raster datasets, TIN datasets, locators
Feature Classes and subtypes Feature datasets contain vector data
Relationship classes All data in a single feature dataset share a
Network Topology common spatial reference system
Planar topology Similar Objects (e.g. Jane Blow, land owner) are
instances of object classes (e.g. land owners)
and have no spatial form.
Domains
Features and feature classes are spatial objects
(e.g. land parcels) which are similar and have
Validation Rules same spatial form (e.g. polygon)
Object (or feature) classes are the tables, and
Raster Datasets objects (or features) are the rows of the table
rasters Attributes are in the columns of the table
Subtypes are an alternative to multiple object (or
TIN (3-D) datasets feature) classes (e.g. ‘concrete’, ‘asphalt’,
‘gravel’ road subtypes): think of subtype as
nodes, edges, faces the most significant classification variable
(attribute) in the class table
Locators Domains define permitted data values.
addresses x,y locations Topology is saved as a relationship between the
Zip codes place names feature classes in the feature dataset.
route locations
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Geojson
FeatureCollection
Features Feature Object
Tuples
Relation Attribute
Table Based
One-One,One-Many,Many-One Relatioship
SQL(Strucure based query for tuples to build
new relatioship or filter relationship
Relational Alzebra based data organisation for
consistency using normalisation
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Email- akjha@iirs.gov.in
Tel- 0135-2524134
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Question
Vector Data has georeferenced information for each point (Y/N)
Which of these data structure is based on tessellation?
1. Simple Vector Point
2. Raster
3. TIN
4. Polygon
Which of these geometrical models have more than one features?
1. Multipoint
2. Multiline String
3. Multipolygon
4. Geometry Collection
Which of these is used for raster data format?
1. Run length encoding Model
2. Spaghetti Model
3. Dictionary Model
4.Dime Model
How many topological neighbours are there of a vector polygon?
1. 8
2. 4
3. n-numbers
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
1
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• Once the Model of the Earth is decided, we need to locate the positions
of the features.
• Everything is Relative
Circle Ellipse
𝑥2 𝑦2
+ =1
𝑎2 𝑏2
𝑥 2 + 𝑦 2 = 𝑎2
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Reference Ellipsoid
• An Ellipsoid is the mathematical surface which can be generated by rotating
an ellipse about its minor axis. Such ellipsoid is called an Oblate Ellipsoid.
Earth
Surface
Ellipsoid
b
Semi Minor
Axis
a
Semi Major
Axis
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• Geocentric Ellipsoid
Geocentric
Ellipsoid
Best Fit
Centre of
Ellipsoid
Best Fit
Earth Centre of Ellipsoid
Surface Geocentric
Ellipsoid
Indian Datum
• The Indian geodetic datum used till few years back, was realized during 19th century.
• It was a result of Great Trigonometric Survey (GTS) carried out from 1802 to 1839.
The conventional triangulation method was used as it was the best available technique
at that time. The series of connected triangles from southernmost point of India up to
the Himalayan foothills was used to compute the arc distance what was named the
‘Great Arc’.
• The Great Arc measurements were used to determine the geometrical parameters
(semi major axis and semi minor axis) to define the reference ellipsoid which was
named after Sir George Everest as Everest Ellipsoid. This is a locally fit ellipsoid with
following parameters:
Everest GRS 80
Representation of Point
Horizontal : Ellipsoid
Vertical : Geoid
h
Earth Surface H
H = Orthometric Height
h = Ellipsoidal Height
N = Geoidal Undulation Geoid
N
Ellipsoid
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
h P Ellipsoidal
Greenwich Normal
Meridian
Observer’s
Meridian
Latitude : ø Equatorial
Plane
Longitude : λ
Ellipsoidal Height : h
Map Projection
EARTH :
MAP : 2D
3D
Projection
Map Projection
Transformation of Three Dimensional Space onto a two dimensional map
Types of Projections
Equal Area: Maintain equal relative sizes. Used for maps that show distributions or
other phenomena where showing area accurately is important. Examples: Lambart
Azimuthal Equal Area, The Albert Equal Area Conic.
Conformal: Maintain angular relationships and accurate shapes over small areas. Used
where angular relationships are important such as for navigation and meteorological
charts. Examples: Mercator, Lambert Conformal Conic
Equidistant: Maintains accurate distance from center of the projection or along given
lines. Used for radio and seismic mapping, and for navigation. Examples: Equidistant
conic, Equirectangular.
Classification
• Map projections can be classified based on their method of construction.
• There are many surfaces which are not plane but which can be created
by rolling a plane surface.
Classification
A) Based on Extrinsic property
• Nature:
Plane, Cone, Cylinder
• Coincidence:
Tangent, Secant, Polysuperficial
• Position:
Normal, Transverse, Oblique
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Azimuthal Projections
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Cylindrical Projections
Transverse
Tangent Secant
Oblique
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Conic Projections
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Intrinsic Property
Many properties can be measured on the earth's surface. Some of these
properties are:
The degree and kinds of distortion vary with the projection used. Some
projections are suited for mapping large areas that are mainly north-south in
extent , others for large areas that are mainly east-west in extent.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Classification
• Mercator
• Transverse Mercator
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Mercator Projection
Cylindrical, Conformal
Meridians are equally spaced straight lines
Parallels are unequally spaced straight lines
Scale is true along the equator
Great distortion of area in polar region
Used for navigation
Conic Projections
For a conic projection, the projection surface is
cone shaped
Scale is true along two standard parallels, normally, or along just one.
Used for maps of countries and regions with predominant east west expanse
Standard Parallels
1 Standard 2 Standard
Parallel Parallels
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
LCC PROJECTION
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Polyconic Projection
Central meridian and equator are straight lines; all other meridians are curves.
Used in India for all topographical mapping on 1:250,000 and larger scales.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Three partial equidistant conic maps, each based on a different standard parallel,
therefore wrapped on a different tangent cone (shown on the right with a quarter
removed plus tangency parallels). When the number of cones increases to infinity,
each strip infinitesimally narrow, the result is a continuous polyconic projection
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Azimuthal Projections
For an azimuthal, or planar projection, locations are projected forward onto a flat plane.
The normal aspect for these projections is the North or South Pole.
Conformal
Grids
Rectangular grids have been developed for the use of Surveyors.
Grid systems are normally divided into zones so that distortion and variation of scale
within any one zone are kept small.
Central Meridian
Natural Origin
Central Parallel
Coordinate
System Origin
Universal
Transverse
Mercator (UTM)
• Particular case of
Transverse Mercator
Projection.
The location
Shape
Projection Parameters
The units
Conclusions
Contact Details:
Email- asrivastava@iirs.gov.in
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Spatial Analysis –
Introductory Concepts and
Overview
Prabhakar Alok Verma
Scientist/ Engineer – ‘SD’
Geoinformatics Department
prabhakar@iirs.gov.in
1
6/23/2020
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Learning Objective
Learning Outcome
Brain Storming
Distance of nearest hospital?
Route to the nearest hospital?
Nearest school which is 100 meters away from major road?
How to make landslide hazard zones?
Suitable places to establish a textile industry?
Find out the suitable sites to construct a High School in Dehradun City
based on the following criteria.
At a distance of more than 1.5 Kilometres from existing schools
At a distance between 500 and 1000 metres of the main roads (to
minimize traffic noise pollution, but still to have proper access)
Not in use by
Businesses (business areas)
Forest (forest areas)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Spatial data
Computer with GIS capabilities
Requirement
Algorithm/ tool able to perform desired analysis
Visualization
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Measurement
Continued…
Measurement - Raster
Raster measurements
include: location, distance
and area size
Location of an individual cell
derived from anchor point
and resolution
Area size number of cells *
cell size
Cell size: 30 m X 30 m
Distance standard distance
900 * 5 = 4500 m2
function applied to the
locations of their mid-points
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Continued…
Note: Resolution=20x20 m
X=?; Y=?
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Retrieval
Interactive
Spatial Selection by Attribute conditions
Relational operators
Logical operators
Combining attribute conditions
Spatial selection using topological relationships
Selecting features that are inside selection objects
Selecting features that intersect
Selecting features adjacent to selection objects
Selecting features based on their distance
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Interactive
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Negate Condition
Like Operator
Spatial Relationships
Disjoint
Meet
Equal
Inside
Covered by
Contains
Covers
Overlap
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Disjoint
Meet
Poly-poly
Line-line
Poly-line
Poly-point
Line-point
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Questions
Questions
Questions
Overlap
Intersecting boundaries
Poly-poly
Poly-line
Line-line
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Equal
Poly-poly
Line-line
Point-point
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Inside
Poly-poly
Poly-line
Poly-point
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Contains
Poly-poly
Poly-line
Poly-point
Line-point
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Covers
Poly-poly
Poly-line
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Covered by
1 3
2
Select all cities that are located in the state Georgia. (INSIDE RELATIONSHIP)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
1 3
2
Select the highways that run (partly) through the state Georgia. First select the state Georgia, then select
all the highways that intersect the selected state. (OVERLAP)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Select all the states that are neighbors of the state Georgia. (MEET RELATIONSHIP)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Select hospitals in the Select all roads that are within a From the selected roads,
Building layer distance of 200 meters from the
Select the major roads
selection in the Building layer
Question: Select all major roads that are located within a distance of 200 meter from a hospital.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
(Re) Classification
(Re) Classification
Remove detail from an input dataset to reveal
important spatial patterns.
Reduce the number of classes and eliminate
details.
If the input dataset itself is the result of a
classification we call it a reclassification.
Reclassify data in different systems or for
different purposes.
Assign codes based on specific attributes.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
(Re)Classification - procedure
Example:
soil types reclassified into soil suitability for
agricultural purpose.
House hold income classification:
low
below average
average
above average
high.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Classification
Classification - Reclassification
Vector Classification with post processing
User controlled classification
Classification table
Automatic classification
Equal interval technique
Equal frequency technique
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
User controlled
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Automatic
(Re)Classification - merge
Five classes of house hold
Five classes of house hold income with original
income with original polygons in the same
polygons intact categories were merged
(boundary dissolved)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Vector Raster
Geometric or No. Because only polygon (line, No. Because only pixel
topological change point) attributes are changed attributes are changed.
Post processing: Yes. For example, neighbour No.
Spatial merging, polygons with the same category
aggregation or are merged into one bigger
dissolve feature.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Email - prabhakar@iirs.gov.in
Tel - +91 1352524129
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Basic concepts
Error
It encompasses both the imprecision of data and its
inaccuracies.
Accuracy
It is the degree to which information on a map or in a
digital database matches true or accepted values.
Precision
refers to the level of measurement and exactness of
description in a GIS database.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Basic concepts
Data
A collection of facts from which conclusions may be drawn
Quality
Data quality refers to the state of qualitative or quantitative pieces of
information.
data is generally considered as high quality if it is "fit for its intended uses
in operations, decision making and planning“.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Data Quality
13
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Data analysis
Interpolation of point data into lines / surfaces e.g. TIN / contours.
Overlay of layers, digitized separately from different sources or scales, e.g.
soils and vegetation.
They have common borders, but slight differences cause 'slivers'.
The compounding effects of processing and analysis of multiple layers: for
example, if two layers each have correctness of 90%, the accuracy of the
resulting overlay is around 81%.
Inappropriate or inadequate inputs for models
Dubious classifications
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Dubious analysis
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
GIS operations that can introduce errors include the classification of data,
aggregation or disaggregation of area data and the integration of data using
overlay technique.
Measuring error
Typos/drawing errors
Incorrect implementation error
Planning/coordination error
Incorrect use of devices error
Erroneous methodology error
Other human errors
21
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Rounding errors
Processing errors
Geometric coordinate transformation
Map scanning, geometric approximations
Vector to fine raster errors
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Output Error
Lecture Outline
Review GIS Data Models
What is Spatial Data Analysis?
Broad Classification of analytical GIS capabilities
Spatial Data Analysis: Vector Based Operations
Spatial Data Analysis: Raster Based Operations
Comparison of Vector and Raster-based Spatial Data Analysis
Example- Site Suitability
2
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Real World
600
1 2 3 4 5 6 7 8 9 10
1 B G Trees
500
2 B G G
3 B
400
4 B G G Trees
Y-AXIS
5 B G G
300
6 B G BK House
7 B G 200
8 B B River
9 B 100
Spatial Data
Analysis
Vector Raster
Based Based
4
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Soho
+ Cholera death
Water pump
5
Source: http://www.ph.ucla.edu/epi/snow/snowcricketarticle.html
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
7
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Overlay Operation
Standard overlay operators
take two input data layers;
assume they are georeferenced in the same
system;
overlap in study area.
If either condition is not met, the use of an
overlay operator is senseless.
The principle is to:
compare the characteristics of the same
location in both data layers, and
to produce a new output value for each
location. 8
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Overlay Operations
Vector (point, lines, polygons)
Intersection
Clip
Union ….
Raster
Arithmetic operators
Comparison and logical operators
Conditional
9
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10
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Line-in-polygon overlay
11
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
12
Source: ArcGIS Desktop Documentation (ESRI)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
13
Source: ArcGIS Desktop Documentation (ESRI)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
14
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
15
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
CLIP VS ERASE
Clip extracts features inside the boundary
Erase keeps features outside the boundary
16
Source: https://learngis.org/textbook/section-two-overlay-analysis
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Dissolve
17
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
18
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
20
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Raster Operations
21
Source: ArcGIS Desktop Documentation (ESRI)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
22
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
23
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
24
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
25
Source: ArcGIS Desktop Documentation (ESRI)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
• Reclassification of continuous
data involves replacing a
range of values with a new
values.
26
Source: http://www.geography.hunter.cuny.edu/~jochen/GTECH361/lectures/lecture11/concepts/Reclassifying%20raster%20data.htm
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
• Reclassification of categorical
data involves replacing
individual values with new
values.
• For example, land use values
can be reclassified into
preference values of low (1),
medium (2), and high (3).
Input discrete raster Reclassified raster • Land use values not desired in
the analysis are given values
of NoData.
27
Source: http://www.geography.hunter.cuny.edu/~jochen/GTECH361/lectures/lecture11/concepts/Reclassifying%20raster%20data.htm
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
29
Source: ArcGIS Desktop Documentation (ESRI)
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
• Raster layer
• All the cells within a zone have the
same value on the output raster
layer
• Table
• Each row in the table contains the
statistics for a zone.
• The first column is the value (or
ID) of each zone.
• The table can be joined back to the
zone layer.
31
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Quiz
1) Dissolve operations requires two input layers d) Global
a) True
b) False 4) Euclidean distance is an example of ________
operation in GIS
2)Re-classification is an example of_______
operation in GIS a) Local
a) Local b) Focal
b) Focal c) Zonal
c) Zonal d) Global
d) Global
3) Computing NDVI is an example of _________ 5) Erase Operation keeps features outside the
operation in GIS. boundary
a) Local a) True
b) Focal b) False
c) Zonal 32
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Quiz
1) Dissolve operations requires two input layers d) Global
a) True
b) False 4) Euclidean distance is an example of ________
operation in GIS
2)Re-classification is an example of_______
operation in GIS a) Local
a) Local b) Focal
b) Focal c) Zonal
c) Zonal d) Global
d) Global
3) Computing NDVI is an example of _________ 5) Erase Operation keeps features outside the
operation in GIS. boundary
a) Local a) True
b) Focal b) False
c) Zonal 33
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Terrain Analysis
35
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36
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Site Selection/Suitability
40
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41
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42
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1. The school should be 1000 meters away from existing Inter colleges.
43
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
44
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
3.To find out the area which satisfies third criteria of residential density,
we need to select only medium density areas which are highly suitable
45
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
46
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
To select the sites which satisfy 1st and 3rd criteria, the Inter College buffer
area need to be removed from selected residential area (medium density)
47
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Finally to select the areas which also satisfy road (second) criteria, overlay
operation is used
48
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Final Result
49
INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN
Email- Kapil@iirs.gov.in
Tel-01352524128
50
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Internet Technology
The origins of the Internet reach back to research of the 1960s,
commissioned by the United States government to build robust,
fault-tolerant, and distributed computer networks.
Internet users
As of March 2020, more than ~4.5 billion people—over a
third of the world's human population—have used the
services of the Internet
(http://www.internetworldstats.com/stats.htm)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
External and
Internal data
Data Management; Model Management; Major Components
Spatial & Non- Spatial model & Non-
spatial data spatial model • Database Server-
Attribute
Database
data Knowledge Management
Management
• Application server-
Application
Dialog Management; Attribute software
base query and report; Spatial
Spatial query and spatial output • Hardware
data
Decision maker
Information system
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
User 1
User 3
User 4
Response User n
Client end
Network Based Geo-Information Services
Data Server
Application Server
B
GIS/Map Server
Response
E
Thick client
R
V
E
r
Standard Web
• Standards-Based
Open Services • Cross-Platform
Platform • IT Focused
GIS Server
Geo-RDBMS
XML Standards
Distributed Computing
Web 2.0
Service Provider
Service Consumer
Interact
Service
Client
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Web Services
Websites
NOT websites
Provide HTML pages and Operations that can be
forms for human users to called to return
navigate and perform information
functions Invoked automatically
Searching, Shopping, through a program
Interaction
Publicly available and
Front end user interfaces standardized for use by
through the browser all programmers
Example: www.google.com Example: ?
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Mission:
A world in which everyone
benefits from geographic
information and services
made available across any
network, application, or
platform
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
There are several tools available which allow users to create and
edit web content, such as tagging tools, wiki software
(Wikipedia), and web-based spatial data editors (e.g., Google Map
Maker, OpenLayers, Bhuvan Collaborative tool etc).
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
users
State Govt. organization
Universities/ Academia
NGOs
Others
Web user interface One gateway using standard Metadata from Organizations
protocols
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
• 19000 Unique users / month Data Download (CartoDEM, AWiFS , LISS III,
• > 80000 image downloads since Sep 2011 Geophy. Prod.)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
AOI based search, metadata display and data download Tile based search and download for AWiFS data
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Thematic Services
Bhuvan-Thematic Services facilitate the users to select,
browse and query the Thematic Datasets from this portal.
Users can also consume these Thematic Datasets and
integrate into their systems as ‘OGC Web Services’.
Land Use Land Cover, NUIS, SIS-DP
Flood (Annual and Hazard Layer) http://bhuvan-noeda.nrsc.gov.in/theme
Land Degradation “OGC Web Services (WMS, WMTS) towards interoperabil
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Bhuvan Thematic Services
Bhuvan thematic services Loading of state level thematic data
India-Wris
India-Wris
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Demonstration on QGIS
- Basic GIS operations
and analysis
FOSS4G
Prasun Kumar Gupta
Indian Institute of Remote Sensing (ISRO)
prasun@iirs.gov.in
Visualize
• Bar Graph
• Pie Chart
• Map
IIRS app:
QRealTime
Other apps:
GeoODK
InputApp.io
Possible Application Areas
• Completing surveys about households
• Crisis mapper tasked to capture images and
locations of damaged areas
• Collecting multimedia data - audio, video
• Baseline surveys and project evaluations for
collecting both quantitative and qualitative
data
Success Stories
• MAp the Neighbourhood in Uttarakhand
(MANU), for damage assessment post
Uttarakhand disaster’ 2013
• Geotagging the post offices of India
• Monitoring of watershed under IWMP for
change detection
• Digitisation of assets of NYKS under Ministry
of Youth Affairs and Sports, Govt. of India
Crowdsourced data visualization
prasun@iirs.gov.in
27-06-2020
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Governance and
Organizational Coordination and
Cooperation
SENDAI FRAMEWORK
Expected Outcome
Goals
Prevent new and reduce existing
disaster risk through the
implementation of integrated and
inclusive economic, structural,
legal, social, health, cultural,
educational, environmental,
technological, political and
institutional measures that prevent
and reduce hazard exposure and
vulnerability to disaster, increase
preparedness for response and
recovery, and thus strengthen
resilience
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Observation for
Information Satellite based location
service
Satellite
Communication
KALPANA
RISAT- 2B
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Space Observations
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Monitoring of Disaster
Role of Geospatial Technology
F
F F F
F F
F SF
F Fr
Fr
Geological Disaster
Earthquake
Observation and Monitoring fore warning and risk analysis
Global Plate
Motions
responsible for
major
InSAR for detection of subsidence earthquakes in
the world
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Geological Disaster
Landslide
RISK ASSESMENT
Hydro-meteorological Disaster
Flood
Observation and Monitoring Early warning and risk analysis
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Hydro-meteorological Disaster
Extreme Weather Events and Flash Flood
Cloud Burst/EREs Flash Flood
Hydro-meteorological Disaster
Drought
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Coastal Disaster
Tsunami
Trinkat
Island
A&N
Coastal Disaster
Sea Level Rise and Storm Surge
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Coastal Disaster
Cyclones
COVID 19 status
GI Cases
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Environmental Disaster
Heat Wave
Environmental Disaster
Forest Fire
https://bis.iirs.gov.in:8443/fire/composer
Burnt Area Estimation
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Environmental Disaster
Atmospheric Pollution and Dust Storm
Black Carbon Emissions
Oil Spills
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1. What are the aspects which can be addressed through Remote Sensing
in NHDRM?
2. In case of floods how can you take care of the cloud cover?
Satellite Navigation
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Satellite Navigation
• Consists of one hub station at MHA, New Delhi, primary nodes and user nodes spread all
over the country.
• Capable of providing voice, data and video traffic between any two nodes.
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Add user specific data Tool: The tool allows users Multi Layer Analysis Tool: Enables the user to add
to add specific custom data in GIS format. multiple layers for analyzing the features for effective
decision making.
Uploaded layer
Thank you
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Satellite Communication
Telemedicine
In a major effort to improve emergency medical support to soldiers posted in high-
altitude areas, especially Siachen, the Integrated Defence Staff of the Defence Ministry
and the Indian Space Research Organisation (ISRO) signed a memorandum of
understanding to set up telemedicine nodes in critical places across the country.
17
RS and GIS Applications
in Forest Resources & Ecosystem Analysis
• Biennial report
• At 1:50,000 scale
• 15 cycle competed
• Forest loss/gain
• Estimate of TOFs
Porosity,
Fragmentation Interspersion,
Juxtaposition
SPLAM Terrain Complexity
(Spatial Landscape Modeling)
Disturbance Index
Biological Richness
Forest Biomass/Carbon Stock Mapping
Medium resolution biomass/carbon stock mapping High resolution biomass/carbon stock mapping
Vilayati Tulsi
In flowering Phenology during monsoon (left) and early winter (right)
Invasion hotspots
Susceptibility to Invasion
< 25% (<5 species)
25-50% (6-25 species)
50-75% (46-65 species)
75-100% (>65 species)
Vilayati Tulsi’s fractional cover (colored pixels)
mapped in Doon Valley using ALI satellite data Non-forest
(Superimposed on grey scale ALI red band) Biogeographic Zone
Forest Fire Prevention & Management
Forest fire risk assessment & danger rating
Active fire detection and monitoring
Burnt area assessment
Recovery assessment
Quantification of emissions from forest fires
Ecological Impacts of forest fires
2500 2282 2236 2259
No. of fire incidences
2000 VIIRS
1500
MODIS
Resourcesat-1 LISS-III Image Resourcesat-1 LISS-III Image
5th April 2012 Rajaji National Park 5th April 2012 Rajaji National Park
1000 907
761
594
500 348 351 279
115 128 95
Smoke plume
63
9
0
Active flame
Burnt area
(severe)
Burnt area
SFCC (NIR,Red,Green) FCC (SWIR,NIR,RED) (moderate)
VIIRS/MODIS
Forest Carbon Sequestration Studies
527.11 gCm2yr-1
702.73 gCm2yr-1
Geospatial Technologies in Agriculture & Soils
Email: suresh_kumar@iirs.gov.in
Remote
Sensing GIS
Agricultural Resources
Food Security
Sustainable Environment
POTENTIAL APPLICATIONS OF REMOTE SENSING AND GIS
vegetation
B2
B3
Spectral Index : Normalized Difference Vegetation Index (NDVI)
It is a function of Incident and reflected light
Fallow land
Sugarcane Ratoon
Poplar plantation
Poplar plantation
Spatial Resolution
10s ..
Resourcesat-2,/2A, Carto2S,
RISAT, SARAL
5.8m 23m
Resourcesat-1 , Cartosat-
00s .. 1,2/2A/2B SUB- METER
PAN : 2.5 m, 1m LISS-
3/4/AWiFS: 23 m/5.8m/56m
80s .. (IRS-1A/1B)
LISS-I/II: 72.5m/ 36.5m
188m 360m 1km
BHASKARA 1 /2
70s .. TV Camera, Microwave
Radiometer
Temporal Resolution
17 Jan 13 Feb 17 Mar 02 Apr 05 May 25 May
17 Jan. 13 Feb 17 Mar 02 Apr 05 May 25 May
1995
1995 1996
1996 1997 1997 1998
1998 1999
1999
1972 2002
• Temporal resolution:
- Weekly, fortnightly or monthly data to study crop growth analysis
- Seasonal data : Crop at optimum growth stages (Rabi, Kharif and Zaid)
Winter (Rabi)
Agro-Horticulture Agro-ecosystem (High Resolution Data)
Pixel-based classification
Tree/crop
Hedge
Orchard
CHAMAN (Coordinated Horticulture Assessment & Management using
geoinformatics)
Ministry of Agriculture & Farmers Welfare launched a programme, called CHAMAN
(Coordinated Horticulture Assessment & Management using geoinformatics) during
September, 2014.
Crop yields basically depends on many more factors than chlorophyll presence
35
30
.40 .50 .60 .70 .80 .90 60
NDVI
55
( c)
Predicted Yield (Q/ha)
50
45
40
35
30
30 35 40 45 50 55 60
- Use of high resolution remote sensing data from satellites and UAVs for optimum
crop cutting experiment planning and improving yield estimation.
- Currently, The study was conducted in 4 districts in the 4 states (one in each
state) during Kharif season for Rice and cotton crop.
- In each district, CCE (Crop Cutting Experiment) sites were generated based on
various remote sensing data (both optical and microwave) derived parameters,
such as sowing /transplanting date, Biomass, NDVI (Normalized Difference
Vegetation Index), Leaf Area Index (LAI), LSWI.
- Approximately 250 CCEs were conducted in each district. The CCE data were
analyzed to understand the minimum number of CCEs required for getting
block level yield with defined accuracy level.
Microwave remote sensing: RISAT-1, first Indian microwave RS was
launched on 26 April 2012 by ISRO. It has opened up new vistas for operational
utilisation of microwave data for management of natural resources and Disaster
management.
RISAT-1 carries a multi-mode C-band (5.35 GHz)
Spatial resolution : 3 to 50 m depending on the type of mode.
2 3
2 Sentinel-1A EU C-band
1 & 1B (2014/2016) SAR
1 2
Orchard
Wheat
Wasteland
Village
Sensitivity analysis
C
Soil Resource Management
• National Organizations:
S = f (cl, o, r, p, t, .......)
where, cl - Climate, o - Organism, r - Relief,
p - Parent material, t - Time
Piedmont
- Gently sloping
Lower Piedmont -Cropland
-Gently sloping
-cultivated
Piedmont
- Gently sloping
- Forest
Alluvial plain : • Satellite data (FCC) when there is no crop (Fallow period)
nearly level • RS data of appropriate scale (1:50,000 scale)
• Visual interpretation of RS data to interpret soil forming factors
1. Preliminary Visual Interpretation :
C
SIDE SLOPES OF DENUDATIONAL HILLS
SOIL PROFILE
VALLEY FILLS
Ap SOIL PROFILE IN
A2 VALLEY FILLS
B1
Satellite Imagery
Crop
Suitability
Agricultural
Land-use
planning
policies
& plans
Land Capability Classification
Land Capability
classes
(i). Land Capability Classification :
LEGEND
Suitable for Crops with Mod Lim
Suitable for Crops with Mod Lim
Suitable for Crops with Mod Lim
Suitable for Crops with Severe Lim
Suitable for Forestry/Plantations
Suitable for Forestry – Mod Lim
Suitable for Forestry – Mod Lim
Suitable for Forestry - Severe Lim
Not Suitable for Vegetation
LAND DEGRADATION
Erosion Processes
E2
E1
- Significant change in surface
characteristics of soil
Waterlogged
area
1975 – 46,029 ha
1999 – 28,749 ha
Hyperspectral Remote Sensing Data
• 200+ channels at 10 to 12 bits
• examines very detailed spectra of the earth's surface
• Provide images in many narrow, contiguous, spectral bands
- Hyperion (EO-1) satellite data with 220 spectral bands
- Warfighter-1 with 200 channels onboard ORBVIEW-4
Applications
- Crop stress mitigation and site specific management
(sustainability with precision agriculture)
- Crop yield prediction
- Narrow band NDVI related to leaf chlorophyll (link to nitrogen)
- Leaf area index, Biomass
EC distribution
p540
!( !( 39
!(
•SALINITY INDEX
hs3
hw321!(!(
38 √ Band 9 (436.99 nm) * Band 28 (630.32 nm)
(!!( p6
!(
hw136
(!!(
!(
37 •BRIGHTNESS INDEX
3334 35
!( !( !(
!( 32
p4
!( 23 !( 31 30
!(24 !(
√ ((Band 9 2 (436.99 nm) + Band 20 2 (548.93nm)
25 !( !(28!( 29
!(26
!( !(
!(
27 + Band 28 2 (630.32 nm))/3)
Salt-affected soils
High
Medium
Low Legend
Normal Normal Soil (0.16 - 3.07)
Slightly Salt-Affected Soil (4.28 - 7.70)
Moderately Salt-Affected Soil (8.10 - 10)
Highly Salt-Affected Soil (10.21 - 30.41)
Correlation Coeff.
Spectral EC pH ECe ESP SAR
Indices The RMSE between observed and predicted EC, SAR, ESP maps for
Salinity 0.81 0.52 0.78 0.80 0.80 Salinity index was the least i.e. 7.48%, 18.14% and 7.85% followed
index by Brightness index i.e. 7.7%, 33.36% and 9.60% respectively.
Brightness 0.77 0.52 0.73 0.79 0.77
index
Hyperspectral modelling for mapping of soil properties using AVIRIS-NG
Objective: Spatial prediction and mapping of soil properties using PLSR modelling
Study Area & Data Used
• ICRISAT and Shadnagar sites
• AVIRIS-NG image
• Field collected soil samples analysis data
Brief Methodology Adopted
Pre-processing and
atmospheric correction of Field collected
AVIRIS -NG soil Samples
R – factor : 931
DEM
(MJmmha-1h-1 yr -1) V. Slight
Slight
1406 Moderate
Severe
1313 Very Severe
Settlement
1219 Unmettled road
1126
1032
939
96. 9500
Crop land (maize+tree) 15.87
77. 5600 Mod. Dense Forest 9.65
58. 1700
Dense Scrub 15.75
38. 7800
19. 3900 Open scrub 128.23
0. 0000
Terrain characterization
• Elevation
• Slope and aspect
• Terrain wetness Index
• Stream Power Index
• Sediment Transport Index
• Specific catchment Area.
Digital Soil Mapping
There is need of large scale soil information for soil health and quality assessment as well as for watershed management.
Soil Sampling
N
•Among the terrain indices, TWI showed highest correlation coefficient for TC (r 2= 0.71), N
(r2= 0.67) and P (r2= 0.66) followed by SPI and STI.
•TWI alone as co-variable has improved the performance by 55.81, 21.78 and 4.50 % for
TC, N and P, respectively relative to ordinary kriging.
Soil Carbon Stock Assessment
NCP- SCP- Soil Carbon Pool Assessment- Phase I (April,2008- March, 2012)
Reliable estimate of soil organic and inorganic density mapping and estimation of their pool sizes are
important for accounting global warming, climate change impact and for policy perspectives.
• SOC and SIC pool sizes of India was estimated 22.7 Pg an 12.8 Pg, respectively.
• Higher SOC density was observed in soils in plantation (23.5 Kg m-2) followed
by forest (13.99 Kg m-2) and agricultural land (5.85 – 6.74 Kg m-2).
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
2015
Time (Year)
Input parameters: 10000
Organic carbon (g m-2)
• Monthly rainfall & temp., soil type, crop 8000 Simulation of SOC change : 2011 - 2099
management practices. 6000
4000
• SDSM model used to downscale GCM
2000
the climatic variables (temp. and
0
rainfall) under scenarios A2periods:
2020 (2011-2040), 2050 (2041-2070),
2080 (2071-2099).
Sites (Vilage name)
• Management practices constant Obs. Pred.
Predicted OC from 2011 to 2099 and compared with the base year.
SOC in Bhaitan, Kanatal, Kotdwar, Malas, Pata and Thangdhar soil series may
decrease by 11.6, 15.8, 17.19, 13.54, 19.2 and 12.7 percent, respectively for A2
scenario.
A Nation that destroy its soils destroy itself.
- Franklin Roosevelt, US President, 1937
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Rajat S. Chatterjee
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Remote Sensing Image Interpretation & Analysis
An image of the earth surface represents a STACK of many
thematic layers:
We do it by -
Visual Image Interpretation
Digital Image Enhancement and Feature Extraction
iirs R.S. Chatterjee/GSD/IIRS(ISRO)
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
IGNEOUS Rocks
• Homogeneous, Massive, Hard, Compact and
Resistant. Lacks Bedding. Course textures.
Characteristic Joint Patterns and Landforms.
SEDIMENTARY Rocks
• Soft and Bedded. Vulnerable to more
dissection. Fine Texture. Differential resistance
to weathering and erosion yield Ridge-and-
Valley Topography. Characteristic Landforms.
METAMORPHIC Rocks
• Regional Foliation gives striated appearance in
the rock mass due to differential weathering
along the weak planes. Presence of
deformational structures.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Metamorphic Rocks
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Drainage
Significance
Pattern
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Visual Image Interpretation Elements: Terrain/Geotechnical Elements …
Drainage Density and Significance …
Drainage density can be estimated as the ratio of total drainage length divided
by area.
It describes the hardness and infiltration capacity of surface materials. It can be
used to differentiate rock types based on hardness and/or infiltration capacity.
Drainage Density depends on the following factors:
• Resistance of rock formation: Harder rocks – Low; Softer rocks – High
• Permeability of rocks: Permeable rocks – Low; Impervious rocks – High
• Topographic slope: Gentle slope – Low; Steep slope – High
• Climate (Rainfall & Temperature)
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Remote Sensing based Information on geological structures and their analysis provide
important input for Groundwater Prospecting, Geological Hazard Assessment,
Engineering Geological Projects, Mineral Exploration etc.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
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Atmospheric Transmittance
iirs R.S. Chatterjee/GSD/IIRS(ISRO)
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
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ERS at 11H25 A.M. Landsat TM at 10H43 A.M. Aerial Photograph SIR-C RADAR Image
(Scene Size: 50 km x 50 km)
Waterford, Ireland on August 9, 1991 by ERS and Landsat TM An erupting volcano in the Kamchatka Peninsula
(Russia) on November 28, 1994.
Penetrates through vegetation and ground.
Band-X Band-L Band-S Band-P
Underground Pipeline
Vegetation parcels
Ground Penetration of Radar Wave
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Radar Remote Sensing – Surficial Bedrock Mapping
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Radar Remote Sensing – Surficial Bedrock Mapping
Lithological discrimination is
clear from optical multispectral
data.
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
HH σo Final Product
HV σo Final Product
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
HH σo Final Product
HV σo Final Product
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Monochromatic satellite image of Kosi Fan showing Multi-spectral satellite image showing alluvial
Progressive westward migration of Kosi River (Kosi river fan of Ghaggar River modified by human
draining over an area of very high relief and monsoonal climate activity (Panchkula, near Chandigarh City)
generate high sediment load)
iirs R.S. Chatterjee/GSD/IIRS(ISRO)
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Alluvial plain
Ox-bow lake
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• Recharge estimation.
• Numerical groundwater flow model
• Quality Assessment
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Groundwater prospects in
alluvial aquifers based on the
occurrences of effluent/
influent streams, soil
moisture and vegetation
differences.
Khondalite
Laterite/ Vegetation
Bauxite
Mud water
Red soil
(i)
End member selection by
spectral unmixing (i) and FCC of
Laterite/Bauxite (red), red soil (ii)
(blue) and vegetation (green) end
members (ii)
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Radar Remote Sensing – Mineral Exploration
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Radar Remote Sensing – Mineral Exploration
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
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B. Based on activity
Active and Non-active (old)
Non-active: (i) Dormant, (ii) Stabilized
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
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ERS1 (12 April, 1996): Master ERS2 (13 April, 1996): Slave
295 m.
120 m.
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40C
53C 38C
50C
55C
45C
50C
48C
55C 42C
48C 48C
LEGEND
1.0
0.76
Emissivity modelling
iirs R.S. Chatterjee/GSD/IIRS(ISRO)
3
4
W
1
2
W W
W
6 W
5
JHARIA
7
1 Loyabad 6 Mahuda
8 W
2 Sijua 7 Bhowra
3 Katrasgarh 8 Pathardhi
9
W 4 Baghmara 9 Chasnala
5 Jamadoba
w Coal washery and
adjacent coal dumps
Spatial distribution of mining-related indicators in Jharia Coalfield, Jharkhand, India extracted from
optical, night-time thermal and interferometric SAR data overlying IRS LISS-III single band image.
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Land Subsidence in Jharia Coalfield: Multi-Freq. Observation …
Spatial Coverage: 24 sq
km.
Mining induced subsidence
Coal fire & fire induced subsidence
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3.04 cm
Indonesia
Husein Sastranegara
West Java Province airport
Bandung
Land Subsidence
F1
F3
Dayeuh Kolot
0
25.0
10.0
5.0
0.0
Ground water extraction in Bandung D-InSAR, GPS and GW-induced Potential Subsidence
iirs R.S. Chatterjee/GSD/IIRS(ISRO)
60.00
40.00
20.00
0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Groundwater level decline in some
Study Area wells (in cm/yr) during 2002-2008
Bahaman
Zarand
Rafsanjan
C-band DInSAR of ENVISAT ASAR data of 2005 and the resulting subsidence map Field Photographs of Land Subsidence
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I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
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26
29-06-2020
E-mail: rschatterjee@iirs.gov.in
27
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
D. Mitra
Group Head, Marine and Atmospheric Sciences Department
mitra@iirs.gov.in
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
The coastal zone is the transitional area between land and sea. It is a band
rather than a line. The width of the band varies from place to place and is
determined by the interaction of marine and terrestrial processes.
The zone occupies less than 18% of the Earth's land surface. Only 40% of
the one million km of coast-line is accessible and temperate enough to be
habitable. Yet it accommodates more than 60% of the world's population.
UNCED 1992, Agenda 21, Chapter 17 (Oceans).
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
The Coast
Occupies 18% of the surface of the Globe
60% of the world population lives
2/3rd of the major cities in the world
Population density six times higher than non-
coastal areas
90% of the world fish catch
8% of the ocean surface
75-80% of the global sink of suspended river
load
90% of the global sedimentary mineralization
(IOCCG Report No. 3)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
The Coast
Of vital importance to humanity
Essential, fragile element of the global
ecosystem
Zone of rapid transitions, gradients and
variations
Very difficult to put boundaries around
Highly dynamic
Subject to multiple uses
HEP
Industry Forestry
Flood
protection
Sand extraction
Urban areas Quarrying Irrigation
Agriculture
Land reclamation
Tourism
Beaches
Diving
Outfall Dredging
Erosion control
Oil tanker
Inshore movements
fishing
Wrecks Ferries
MANGROVE
DEGRADATION
due to
AQUACULTURE
Along
KAKINADA
COAST, INDIA
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
KAKINADA
AND
ENVIRONS
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Storm Tide: The actual level of sea water resulting from the astronomic tide
combined with the storm surge.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Hurricanes / Cyclones
Typhoons or hurricanes are tropical revolving storms.
The are called cyclones, when they occur in the Indian
Ocean area.
It is low-pressure systems or depressions around which
the air circulates in an anti-clockwise direction in the
northern hemisphere, but in a clockwise direction in
the southern hemisphere.
The speed of the circulating air may exceed 33 metres
per second near the earth’s surface.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Tropical Cyclone
Damage to Flooding
Structures
Loss of Life
Destruction of Crops
28
29
30Oct-9gmt
Oct-3gmt
Oct-6gmt
Oct-3gmt
Oct-6gmt
Oct-9gmt
SUPER CYCLONE
OVER ORISSA
COAST
INSAT IMAGES
SHOWING THE
CYCLONE MOVEMENT
DURING 28 OCT TO
30 OCT, 1999
Access to data
Cannot always be gathered because of
Costissues
Security/ sensitivity issues
Time series data
If data are available, are they in a suitable form?
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Well locations data pertaining to parameters like Total Depth (TD), Static Water Level (SWL), Total Dissolved
Solids (TDS), Carbonate (C03), Bi-Carbonate (HC03) and Chloride (Cl) around Bhavnagar district have been
collected from Gujarat Water Resources Development Corporation (GWRDC), Gandhinagar, Gujarat for the
year 1983 to 2003 in every five years interval for pre-monsoon (May) and post-monsoon (October) period.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Thermal
fronts
indicating
fishing zones
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Tsunami waves
Tsunami Japanese word for ‘ Harbor wave’
A series of waves of extreme length and period triggered by a
sudden displacement of the sea floor: seismic activity or
volcanic eruption
The wave travels outwards in all directions from the source area
with speeds of over 500 km/hr
Still it can have a velocity of over 50 km/hr and a height of 30 m
at the coast
Several waves may follow each other at intervals of 15 - 45
minutes
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Satellite remote sensing data is also very helpful for finding out inundations
due to tsunami. Penetration of water body because of tsunami towards land
can be easily traced from suitable remote sensing data.
The inundation line derived from ground survey can be superimposed over
the taluk or village map using GIS to have an idea about the affected
agricultural areas, human population and infrastructure.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Email-mitra@iirs.gov.in
Tel-0135-2524181
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Geospatial Technology
for
Urban & Regional Planning
Pramod Kumar
Group Head, URSD
Indian Institute of Remote Sensing
Dehradun
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
CONTENTS
Some definitions
Importance of Urban Planning
Innovative Technologies for Urban Planning
Data required for Urban Planning
Some aspects of Geospatial Technology
Datasets available for Urban & Regional Planning
Upcoming Geospatial Technology for Urban Planning
Geospatial Applications in Urban Planning
Geospatial Appl. in Urban Development Programs of GoI
Spatial Data Cube of Urban Environs
Geospatial data needs for Future Urban Planning
2
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
2011 2030
31% population 40% population
63% of GDP 75% of GDP
80% of Urban India of 2030 yet to be built
A new city of Chicago dimensions to be built every year
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
PHYSICAL PARAMETERS
∙ Land use/ land cover
∙ Road Infrastructure….
LEGAL FRAMEWORK
∙ Master Plan
∙ Govt. policies…..
(a) (b) (c)
FISCAL MANAGEMENT
∙ Taxation: Unit area method…
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Reflectance Curve
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
10
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
SPATIAL RESOLUTION
Source: Yang, C., Wong, D., Miao, Q. and Yang, R. eds., 2010.
Advanced Geo-Information Science. CRC Press.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
IRS – OCM 360 meters IRS – WIFS 188 meters IRS – LISS-I 76 meters IRS – LISS-II 36 meters
Sources:
1. http://www.eurosense.com/documents/our-expertise/spaceborne/satellite-data-sources/very-high-
resolution-optical-imagery.xml?lang=en-gb
2. https://dg-cms-uploads-
production.s3.amazonaws.com/uploads/document/file/196/DG2017_WorldView-4_DS.pdf
3. …
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
17
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Toronto, Canada
0.6
0.4
0.2
1050 1550 2050
Concrete roof
Bare soil
Dataset used: LANDSAT-7 ETM+, Terra ASTER (Aster level-1B, Sand
LANDSAT-7 ETM+ level-1G) 19
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
21
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
22
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
23
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
24
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
26
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
29
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
BUILT-UP DENSITY
GRID PLAN
Grid plan, grid street plan, or gridiron plan is a type of city plan in which streets
run at right angles to each other, forming a grid. Infrastructure cost for regular grid
patterns is generally higher than for patterns with discontinuous streets.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
RADIAL PLAN
Washington
Area Applications
Urban Planning Large scale base maps, Map updation, Infrastructure mapping, Land use, Zonal
Plans, City Development Plans, Space use maps, Urban growth, Urban sprawl,
Suitability analysis, Urban growth modeling…
Inclusive Planning Vulnerability analysis and mapping of informal settlements…
Urban Governance/ Property Taxation, Municipal GIS, Monitoring of urban encroachments,
Municipal Reforms Demographic studies, Urban cadastral studies…
Urban Environment Urban landscape, Urban green spaces, Solid waste disposal management &
Sites selection, Urban Heat island, Urban pollution, Micro-climate, Solar
potential, Health GIS…
Heritage Applications of Close Range photogrammetry & Terrestrial Laser Scanner,
Conservation Analysis and mapping of Heritage zones…
Urban Design 3D city visualisation using LiDAR, Regeneration and redevelopment analysis,
Rainwater harvesting…
Urban Utility Surface/ subsurface utility mapping, Transportation, Hydro-geom. mapping
Urban Hazard Hazard mapping/analysis, Micro-seismic studies, Urban fires, Urban flood…
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
URBAN SPRAWL
48.93
23.05 31.71
0.00 7.97
1987-1992 1992-1998 1998-2003 2003-2008
300.00 2003-2008
1998-2003
200.00
1992-1998
100.00 1987-1992
till 1987
0.00
A B C D E F G H
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Simulated land cover for 2021 under BS, CGS and HGS using ANN based predictive model
Cultivated and Urban built- Non-urban Overall
vegetation PCM up PCM built-up PCM
PCM
Model calibration 98% 95.52% 93.07% 98% Percent Correct Match
(2005) (PCM)= N1*100/N2
Model validation 85.43% 81.3 % 62.85 %. 81% Ratio between correctly
simulated cells and total cells
(2009)
Conclusions
₋ Base line scenario (BS) predicts urban growth under “business as usual” scenario. No development
is allowed within a user defined buffer zone of river channels.
₋ Compact growth scenario (CGS) simulates urban growth if a policy of high density nucleated growth
is pursued. Future development will occur in the west and south direction of existing
development.
₋ Hierarchical growth scenario (HGS) simulates urban growth process if a multi-nucleated growth is
promoted, this will result in a hierarchy of urban settlements with the most prominent urban
centre coming up in west of the study area. 41
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
SPACE USE
Cities are expanding in vertical
dimensions.
Information essential for planners
‐ character of Space use and extent
Space Use: applies to Multistoried
buildings
Different activities on different
storey/ floors
3D models
‐ Aerial Photographs
‐ High Resolution Stereo Pair
‐ LiDAR
Ground survey Space Use Survey Map
Commercial 3D SPACE USE MAP
% of No. of
60
Houses
Residential
40
Floor Area Ratio 20
FAR = [TBA / TA ] 0
Where:
G G+1 G+2
FAR = Floor Area Ratio
TBA = Total Built-up Area
Space use
TA = Total Area
% of Residential
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
PROPERTY TAXATION
SOCIAL SCENARIO
Unit Area Method (UAM) has been used for
property tax assessment.
Unit Area Value
CATEGORY A B C D E F G H
Unit Area Value 630 500 400 320 270 230 200 100
(Rs/sq.meter)
(b) (c) LIG & squatter Annual Tax Value = Covered Area x Unit
development
Interpretation Key: Shape, Size, Pattern, Tone, Texture, Area Value x SF x OF x UF x AF
Association Category
A B C D E F G H
Plotted Flats, group Plotted colonies Institutions, hotels, Farm Religious Govt. Others
colonies, housing, flats, up to 100 sq. m business, towers, houses institutions properties
houses and shops etc. hoardings and industry
shops
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
0.75 km2
Building Pipeline
Typology Distribution
House type
Population Moderat
Settlement Very low e
type Low High
Density, etc.
Very
Catchment boundary Planning boundary high Catchment boundary
Most vulnerable Municipal Wards: No. 4, 15, 20, 21, 26, 27, 46 48, 67 52
40% area prone to urban flood risk for 20 year return period of extreme rainfall event (297.46 mm/day)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
57
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
27.02.2000
Transit Building
24.02.2019
Proposed Redevelopment
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
1984 2000
2019
59
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
2003 2012
2019
60
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
https://bhuvan-app1.nrsc.gov.in/state/AP_housing/
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
GIS Applications
Site Identification, Selection , evaluation, etc.
Planning, Design & Visualization of cities.
Construction & Project Management .
GIS Facility Management (FM) information system
62
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Thrust Areas
Water supply,
Sewerage facilities and septage management, Storm water drains to reduce flooding,
Pedestrian, non-motorised and public transport facilities, parking spaces and
Enhancing amenity value of cities by creating and upgrading green spaces, parks and
recreation centers, especially for children, etc.
Generation of Base Map & Thematic Maps and Urban Database at 1:4,000 scale
Enabling the formulation of Master Plan
Capacity Building
Tier-I (Decision Making level) : 3 days duration
Tier-II ( Middle level Officials): 2 weeks duration.
Tier-III (Operators level Officials): 4 weeks duration.
63
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
HRIDAY:
Heritage City Development and Augmentation Yojana
To improve Services:
Use mobile phone technology
Information and Communications
Technology (ICT)
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Development of Apps.
66
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
https://ghsl.jrc.ec.europa.eu/HPI.php
Four epochs since 1975 and at fine spatial resolution (38 m)
Areas of higher development intensity are very accurately classified and highly reliable.
Rural areas show low deg. of accuracy, which could be affected
68 by misalignment
between reference data & data under test where built-up land is scattered and rare.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Urban
Growth
Modeling
Night
Time
Light
Data
Night time Light Data of Bihar Night time Light Area and Sum of Lights GSDP and Consumption of Electricity
REFERENCES
1. Bengt Paulsson, 1992. Urban Applications of Satellite Remote Sensing and GIS
Analysis, The World Bank, Washington, D.C.
(http://ww2.unhabitat.org/programmes/ump/documents/ump9.pdf)
2. Qihao Weng (Editor) and Dale A. Quattrochi (Editor) (2006). Urban Remote
Sensing, CRC Press, ISBN-13: 978-0849391996.
3. Xiaojun Yang (Editor), 2011. Urban Remote Sensing: Monitoring, Synthesis and
Modeling in the Urban Environment. John Wiley & Sons, Ltd., Print ISBN:
9780470749586, Online ISBN: 9780470979563, DOI: 10.1002/9780470979563.
4. URPFI Guidelines (http://moud.gov.in/URDPFI)
Acknowledgement: some case studies have been borrowed from URSD/ IIRS faculty. Their efforts are duly acknowledged.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
By
Dr. S.P. Aggarwal, FIE
Group Head, Water Resources Department
spa@iirs.gov.in
Issues
17% of World’s Population Declining per
capita availability
4% of World’s Water Resources 1947: 5200 M3
2017: 1500 M3/ year
Agriculture Use : Around 80-85% • Over Exploitation
Efficiency is poor : India 38% Of Ground Water
• Multidimensional data
Cartosat- 1/2
IKONOS/QB 2010 Interlinking of
• Modelling High
Resourcesat – 1,2 Rivers
• GIS
Resolution 2008
Irrigation Infrastructure
IRS P3 Mapping and
WiFS monitoring
Moderate OCM-1 2005
Resolution MODIS
Flood mapping and
IRS IC/ID monitoring
Coarse PAN
IRS P2 1996
Resolution Ground Water Snowmelt runoff
IRS IA/IB Prospecting and Glaciers
Landsat
1995
Command Area Monitoring,
Reservoir Sedimentation
1985
Snow melt Run off; Forecast
Glacier Inventory
1980
Hydrological Parameters
Precipitation:
Period: 1.5 hours
TRMM:
Launch date: 27 November 1997
TRMM observes global tropics between 35° S
to 35°N latitudes
One active and two passive rain sensors
• Precipitation Radar (PR)
• TRMM Microwave Imager (TMI)
• Visible and Infrared Scanner (VIRS)
http://www.ntsg.umt.edu/projct/modis
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Hydrological Parameters
Precipitation:
GPM:
https://pmm.nasa.gov/multimedia/
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Hydrological Parameters
Precipitation:
IMD :
• Daily Merged Satellite based Rainfall (GPM)
data ( 0.25 x 0.25 degree) Binary File
• Time Scale: Real-time
• GPM data along with Gauge data is utilized
in generating this gridded dataset
• The dataset is outcome of joint work of IMD
and NCMRWF.
Krishna
Reservoir
Velugodu
Reservoir
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Satellites : Resourcesat
1,2
swath: 150 km
I N D I A/Spatial
Sensor N I N S T I T U T E O F R E M O T E S E N S I N G , Suitability
DEHRADU N Mapping
for
Resolution
LISS-IV Main, Branch.
(5.8m × 5.8 m) Distributary, Minor
Satellites : Resourcesat canal having width
1,2 around 5 m or more
Swath: 23.9 km Field level mapping
IRS 1D Pan(5.8)
Branching of canals
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Superpassage
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
from 18 sates
from 14 sates
WATER BODIES
IRRIGATED
AGRICULTURE
IRRIGATED
AGRICULTURE
Courtesy : IGBP
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Flood Inundation
Mapping and Damage
Assessment
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
28
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Integration in
GIS
Marooned Villages
July - III
Snow Cover Depletion Curves
July - I
zone 10
June - II
May - III zone 9
May - I
April - II
March - III
zone 8
March - I
February - II
zone 7
January - III
January - I
zone 6
December - II
November - III
November - I
90 October - II
80
70
60
50
40
30
20
10
0
100
Snow cover [%]
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
SRM calculation
The basic equation of SRM model is
Qn+1 = [CSn an (Tn + Tn) Sn+ CRn Pn] A·10000 (1-kn+1)+ Qn kn+1
86400
T, S and P are variables to be measured or determined each day. CR, CS, lapse
rate to determine T, TCRIT, k are parameters which are characteristic for a given
basin.
[cSCn · aCn (Tn + TCn ) SCn + cRCn · PCn ]A+ (1-kn+1 )} + Qn ·kn+1
C* 10000
86400
In this project all the model parameters are derived as 10 daily average values and
used to compute the 10 daily average runoff.
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Martinec-Rango SRM
240
220
200
180 • Temperature
discharge [m /s]
160 • Precipitation
140 • Snow Cover Area
120 Dv(%)=1.8
estimated
100 real
80
60
40
20
0
June II
July II
August II
September II
October II
November II
December II
January II
February II
March II
April II
May II
June II
July II
August II
September II
October II
November II
December II
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Inputs:
Vegetation
Soil,
Topography
Forcing:
Min. Max. Temp,
Rainfall etc.
VIC Model
• Runoff can be
estimated on daily
basis
http://dms.iirs.gov.in/
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Flood inundation d/s of Cheruthoni dam as simulated using MIKE 11
Simulated flood
During Flood
After Flood
Source:http://www.newindianexpress.com/states/kerala/2018/aug/11/kerala-floods- Source:https://www.mathrubhumi.com/environment/specials/
53500-people-in-relief-camps-water-level-at-idukki-dam-recedes-1856128.html kerala-floods-2018/news/post-flood-cheruthoni-bus-stand-
I N D I A N I N S T I T U T E O F R E M O T E S E N S I N G, D E H R A D U N
Convergence of Technologies
GIS
RS Field
Data
GPS
LBS
Internet
Photo-
grammetry
Altimetry