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REMOTE SENSING
ITS APPLICATIONS IN CIVIL ENGINEERING
Dr. Anjana Vyas, CEPT University, Ahmedabad
anjanavyas@yahoo.com
Lect...
REMOTE SENSINGREMOTE SENSING
Remote Sensing refers to gathering
and processing of information about
earth’s environment an...
1903 - The Bavarian Pigeon Corps1903 - The Bavarian Pigeon Corps
Interactions with medium (atmospheric effect
Electromagnetic spectrumElectromagnetic spectrum
Measuring Light: BandsMeasuring Light: Bands
 Human eyes only ‘measure’ visible light
 Sensors can measure other portion...
Remote Sensing through instrumentRemote Sensing through instrument
Various
Platforms
Sensors:
LISS-III, WiFS,
PAN etc
Active and Passive RemoteActive and Passive Remote
SensingSensing
GEOSTATIONARY ORBITSGEOSTATIONARY ORBITS

These satellite appears stationary with
respect to the Earth's surface. General...
FOOTPRINTSFOOTPRINTS
Communication Satellites are in GEOSYNCHRONOUS
ORBIT
(Geo = Earth + synchronous = moving at the same ...
A Polar Orbit is a particular type of
Low Earth Orbit. The satellite
travels a North – South Direction,
rather than more c...
Panoramic View of Earth Station at Shadnagar
SWATH OF ADJACENT PATH
DESCENDING PATH
Latitude
Longitude
15
Orbit Number
1234567891011 121314
SWATH OF ADJACENT PATHSWATH OF ADJACENT PATH
cending ground traces...
GREEN BAND WITH BLUE
FILTER
STANDARD FALSE COLOUR
COMPOSITE
GENERATION OF FALSE
COLOUR COMPOSITE
RED BAND WITH GREEN FILTE...
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6
20
40
60
80
Spectral Reflectance curvesReflectance(%)
Wavelength (µm)
Vege...
• Spatial Resolution – The smallest
object that can be discerned
•Spectral Resolution – No. of bands
•Temporal Resolution ...
India’s Earth Observation Missions
INSAT-2E
VHRR, CCD (1 km)
1999
INSAT-1D
VHRR
INSAT-2A
VHRR
1992
1990
INSAT-2B
VHRR
1993...
IRS 1C Sensors overview
PAN
LISS III
WiFS
BANGKOK CITY, PAN DATA
PART OF ROME, LISS-III +PAN DATA
SAMPLE
IMAGES OF
IRS-1C/1D
SENSORS
0.6 m Resolution Space Image
1 m Resolution Space Image
ChinnaswamyChinnaswamy
StadiumStadium
MG RoadMG Road
FM CariappaFM Cariappa
Mem.ParkMem.Park
Cubbon RoadCubbon Road
Cubbon...
33
Vegetation/Forests/Agriculture
Kharif-1999 (Sep-Oct) Rabi-2000 (Feb-Mar)
A
pplications
Flood due to cyclone (29th
October 1999)
off Orissa coast
IRS LISS III
Pre-cyclone (11.10.99)
IRS LISS III
Post-cyclone (0...
• ROCK TYPES
• GEOLOGICAL STRUCTURES (LINEAMENT /FAULT/DYKE)
• VALLEY FILL WITH VEGETATION
• BLACK SOIL COVER
• SALT AFFEC...
• HILLY TERRAIN WITH FOREST
• AGRICULTURAL LANDS - DELTA
• RIVER COURSES
• COASTLINE
WHAT CAN BE SEEN FROM SATELLITE
IMAGE...
39
Mapping and monitoring mangroves, coastal
wetlands
PP
P
KRISHNA R.
IRS-1B LISS-I
IMAGE, 1992
KRISHNA R.
P = Prawn culti...
Gap Detection in Mango Orchards
High resolution satellite data 20 February 2000
Shadnagar, Mahbubnagar District, AP
(2.5 m...
INDIAN IMAGING CAPABILITY
• EVERY 30 MIN.
IMAGING
• 1M+ SCALES
• CLIMATE/WEATHER • EVERY 2 DAYS IMAGING
• 1:250 K SCALES
•...
• EVERY 22 DAYS
IMAGING
• 1:50 K SCALES
• DETAILED RESOURCES
SURVEY • EVERY 5 DAYS
IMAGING
• 1:12500 SCALES
• LARGE SCALE
...
Elements of Image Interpretation
•Primary
•Secondary
•Tertiary
• Higher
:
:
:
:
Tone / Colour
Size, Shape & Texture
Patter...
A
H
M
E
D
A
B
A
D
C
I
T
Y
Example:
Visual
interpretation
on screen
vector
tracing
Road
A
H
M
E
D
A
B
A
D
C
I
T
Y
On screen
Vector
tracing
Road
Builtup land
Vacant land
Waterbody
A
H
M
E
D
A
B
A
D
C
I
T
Y
ON SCREEN VISUAL INTERPRETATION
SATELLITE REMOTE SENSING APPLICATIONS
AGRICULTURE
• CROP ACREAGE AND PRODUCTION ESTIMATION
SOIL RESOURCES
• SOIL MAPPING
•...
SATELLITE REMOTE SENSING APPLICATIONS
WATER RESOURCES
• SNOWMELT RUNOFF FORECASTING
• RESERVOIR SEDIMENTATION
OCEAN APPLIC...
Creation of 3-D ViewCreation of 3-D View
Study Area
TPS : 19 (Memnagar)
Rasranjan Building
Corresponding
Attributes
3-D Visualization3-D Visualization
Walk ThroughWalk Through
PANORAMIC VIEWERPANORAMIC VIEWER
Fig. (L) :- Street View on the Golden
Gate Bridge on Google Earth
Fig. (R) :- Cylindrical...
Street-View in Google EarthStreet-View in Google Earth
An aerial view of a water
logged area in and
around Ahmedabad
Monday, July 04, 2005
SrSr
NoNo Name of catchmentName of catchment
Area ofArea of
catchmentcatchment
in Hain Ha
WatershedWatershed
runoffrunoff
...
 The maximum height is 57.5
meters and the minimum
height is 42 meters from mean
sea level.
 The study area is plain, dr...
DIGITAL ELEVATION MODEL (AMC)
 DEM is a digital representation of a
continuous variable over a two
dimensional surface by...
 Rational method has been used for computing surface runs off
Q = CIA/360
Where: Q = maximum rate of runoff (cum/sec)
C =...
ESTIMATING STORM WATER DRAINAGE
CARRYING CAPACITY BY MANNING’S METHOD
The Manning Formula is an empirical formula for flo...
 Area of catchments:- 80 Ha
 Total Built up Area:- 55 Ha
 Runoff Coefficient:- 0.8
 Main Storm water drain length in
t...
SOUTH NARANPURA CATCHMENT AREA
L_Section of existing SWD in Naranpura catchment area
0
10
20
30
40
50
60
0
180
363
541
721...
PALDI CATCHMENT AREA
L_Section of existing SWD in Ellisbrige catchment area
34
36
38
40
42
44
46
48
0
1
2
0
2
4
0
3
6
0
4
...
The areas of Vishwakunj char rasta, near shantivan pumping
stations, near Kochrab ashram, near jivraj hospital, near
yoge...
EXISTING STORM WATER DRAINAGE OF STUDY AREA
PROPOSED STORM WATER DRAIN OF STUDY AREA
FLOOD
VULNERABILITY
Vulnerability Total Area (Ha) Total Area (%)
Very Low Vulnerable
Zone
149 4%
Low Vulnerable Zone 422 11%
Moderate Vulnerab...
GIS BASED EMERGENCY
RESPONSE SYSTEM
Facilities in High Vulnerable Zone Slum Locations in High Vulnerable Zone
Facilities in Low Vulnerable Zone
NOAA’s LIDAR Image of Ground Zero of World Trade
Center in New York City
COLOR
Value
(meters)
Value (feet)
Dark Green -9.2...
GROUND PENETRATING RADAR
(GPR)
Planning Scenario for a Major Earthquake
in Ahmedabad City
Anup Karanth [EP 0101]
M 6
M 6.5
M 7
damage area
Location of bu...
Planning Scenario for a Major Earthquake
in Ahmedabad City
Anup Karanth [EP 0101]
Overlap showing the damage buildings for...
Planning Scenario for a Major Earthquake in Ahmedabad City
Anup Karanth [EP 0101]
Urban Sprawl
Urban Sprawl
JAN 1999 JAN 2009 JAN 2011
MAY 1999 MAY 2009 MAY 2011
SPATIO-TEMPORAL
ANALYSIS OF LULC
Non builtup
Built up
Vegetation Wat...
JAN 1999 JAN 2011
MAY 1999 MAY 2009 MAY 2011
SPATIO-TEMPORAL ANALYSIS OF NDVI
(Normalized Difference Vegetation Index)NDVI...
JAN 1999 JAN 2009 JAN 2011
MAY 1999 MAY 2009 MAY 2011
SPATIO-TEMPORAL ANALYSIS OF LAND
SURFACE TEMPERATURE
GRID LEVEL ANALYSIS OF LST WITH LULC DURING JANUARY 1999GRID LEVEL ANALYSIS OF LST WITH LULC DURING JANUARY 1999
AND 2011A...
•For a comfortable, normally dressed adult, the weighted average
temperature of the bare skin and clothed surfaces is abou...
120
Hands on with GPSHands on with GPS
Recording The Tree’s Position
123
““Mobile Mapping”Mobile Mapping”
Integrates GPS technology
and GIS software
Makes GIS data directly
accessible in the ...
124
Solutions need vision…Solutions need vision…
125
But may be easier than we think…But may be easier than we think…
to use Technologyto use Technology
THANK YOU
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
Application of Remote Sensing in Civil Engineering
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Application of Remote Sensing in Civil Engineering

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Presentation cum talk delivered by Dr Anjana Vyas, Dean CEPT University, Ahmedabad during 31st National Convention of Civil Engineering organized by The Institution of Engineers (India) Gujarat State Center, Ahmedabad

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Application of Remote Sensing in Civil Engineering

  1. 1. REMOTE SENSING ITS APPLICATIONS IN CIVIL ENGINEERING Dr. Anjana Vyas, CEPT University, Ahmedabad anjanavyas@yahoo.com Lecture delivered at 31st National Convention of Civil Engineers, Ahmedabad on 20th September 2015
  2. 2. REMOTE SENSINGREMOTE SENSING Remote Sensing refers to gathering and processing of information about earth’s environment and its Natural & Cultural Resources through Aerial photography and Satellite scanning.
  3. 3. 1903 - The Bavarian Pigeon Corps1903 - The Bavarian Pigeon Corps
  4. 4. Interactions with medium (atmospheric effect
  5. 5. Electromagnetic spectrumElectromagnetic spectrum
  6. 6. Measuring Light: BandsMeasuring Light: Bands  Human eyes only ‘measure’ visible light  Sensors can measure other portions of EMS Bands
  7. 7. Remote Sensing through instrumentRemote Sensing through instrument Various Platforms
  8. 8. Sensors: LISS-III, WiFS, PAN etc
  9. 9. Active and Passive RemoteActive and Passive Remote SensingSensing
  10. 10. GEOSTATIONARY ORBITSGEOSTATIONARY ORBITS  These satellite appears stationary with respect to the Earth's surface. Generally placed above 36,000 km from the earth.
  11. 11. FOOTPRINTSFOOTPRINTS Communication Satellites are in GEOSYNCHRONOUS ORBIT (Geo = Earth + synchronous = moving at the same rate). This means that the satellite always stays over one spot on Earth. The area on earth that it can “SEE” is called the satellite’s “FOOTPRINT”
  12. 12. A Polar Orbit is a particular type of Low Earth Orbit. The satellite travels a North – South Direction, rather than more common East-West Direction.
  13. 13. Panoramic View of Earth Station at Shadnagar
  14. 14. SWATH OF ADJACENT PATH DESCENDING PATH
  15. 15. Latitude Longitude 15 Orbit Number 1234567891011 121314 SWATH OF ADJACENT PATHSWATH OF ADJACENT PATH cending ground traces of IRS-1A/1B for one day. In 24hrs satellite makes 13.9545 revolutions around the earth. The orbit on the second day (15th orbit) is shifted westward from orbit No.1 by about 130 km. The ground traces repeat after every 307 orbits in 22 days.
  16. 16. GREEN BAND WITH BLUE FILTER STANDARD FALSE COLOUR COMPOSITE GENERATION OF FALSE COLOUR COMPOSITE RED BAND WITH GREEN FILTER IR BAND WITH RED FILTER
  17. 17. 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 20 40 60 80 Spectral Reflectance curvesReflectance(%) Wavelength (µm) Vegetation Soil Water Snow
  18. 18. • Spatial Resolution – The smallest object that can be discerned •Spectral Resolution – No. of bands •Temporal Resolution – Periodicity of data collection •Radiometric Resolution – Quantization levels of data Resolutions
  19. 19. India’s Earth Observation Missions INSAT-2E VHRR, CCD (1 km) 1999 INSAT-1D VHRR INSAT-2A VHRR 1992 1990 INSAT-2B VHRR 1993 KALPANA-1 VHRR INSAT-3A VHRR,CCD 2003 2002 Geo stationary IRS-1A & 1B LISS-1&2 (72/36m) 1988/91 IRS-1C/1D LISS-3 (23/70m); PAN (5.8m); WiFS (188m) 1995/1997 IRS-P4 OCM (360m), MSMR 1999 2001 TES Step& Stare PAN (1m) IRS-P6: Resource Sat LISS 3 (23m) LISS 4 (5.8m); AWiFS (55m) 2003 Sun Synchronous IRS-P5 PAN-2.5M, Carto-1, 30 km 2005 Carto-2 PAN-0.8M, 11 km 2007
  20. 20. IRS 1C Sensors overview PAN LISS III WiFS
  21. 21. BANGKOK CITY, PAN DATA PART OF ROME, LISS-III +PAN DATA SAMPLE IMAGES OF IRS-1C/1D SENSORS
  22. 22. 0.6 m Resolution Space Image
  23. 23. 1 m Resolution Space Image
  24. 24. ChinnaswamyChinnaswamy StadiumStadium MG RoadMG Road FM CariappaFM Cariappa Mem.ParkMem.Park Cubbon RoadCubbon Road CubbonCubbon ParkPark 1m1m
  25. 25. 33 Vegetation/Forests/Agriculture Kharif-1999 (Sep-Oct) Rabi-2000 (Feb-Mar) A pplications
  26. 26. Flood due to cyclone (29th October 1999) off Orissa coast IRS LISS III Pre-cyclone (11.10.99) IRS LISS III Post-cyclone (05.11.99) RADARSAT DATA of 2nd NOV
  27. 27. • ROCK TYPES • GEOLOGICAL STRUCTURES (LINEAMENT /FAULT/DYKE) • VALLEY FILL WITH VEGETATION • BLACK SOIL COVER • SALT AFFECTED LAND WHAT CAN BE SEEN FROM SATELLITE IMAGES?
  28. 28. • HILLY TERRAIN WITH FOREST • AGRICULTURAL LANDS - DELTA • RIVER COURSES • COASTLINE WHAT CAN BE SEEN FROM SATELLITE IMAGES? • MANGROVE FOREST • WET LANDS • WATER TURBIDITY
  29. 29. 39 Mapping and monitoring mangroves, coastal wetlands PP P KRISHNA R. IRS-1B LISS-I IMAGE, 1992 KRISHNA R. P = Prawn cultivation IRS-1C LISS-III IMAGE, 2000
  30. 30. Gap Detection in Mango Orchards High resolution satellite data 20 February 2000 Shadnagar, Mahbubnagar District, AP (2.5 m) Natural Resources Inventory Farm level information in Hirakud Irrigation Command Area High resolution satellite data (0.60 m)
  31. 31. INDIAN IMAGING CAPABILITY • EVERY 30 MIN. IMAGING • 1M+ SCALES • CLIMATE/WEATHER • EVERY 2 DAYS IMAGING • 1:250 K SCALES • OCEAN APPLICATIONS • EVERY 5 DAYS IMAGING • 1:250 K SCALES • NATIONAL SURVEYS
  32. 32. • EVERY 22 DAYS IMAGING • 1:50 K SCALES • DETAILED RESOURCES SURVEY • EVERY 5 DAYS IMAGING • 1:12500 SCALES • LARGE SCALE MAPPING • STEREO CAPABILITY • LOCAL AREA IMAGING • 1:2000 / 4000 / 8000 SCALES • STEREO CAPABILITY INDIAN IMAGING CAPABILITY 0.8m
  33. 33. Elements of Image Interpretation •Primary •Secondary •Tertiary • Higher : : : : Tone / Colour Size, Shape & Texture Pattern, Height & Shadow Site & Association
  34. 34. A H M E D A B A D C I T Y Example: Visual interpretation on screen vector tracing Road
  35. 35. A H M E D A B A D C I T Y On screen Vector tracing Road
  36. 36. Builtup land Vacant land Waterbody A H M E D A B A D C I T Y ON SCREEN VISUAL INTERPRETATION
  37. 37. SATELLITE REMOTE SENSING APPLICATIONS AGRICULTURE • CROP ACREAGE AND PRODUCTION ESTIMATION SOIL RESOURCES • SOIL MAPPING • LAND CAPABILITY, LAND IRRIGABILITY • SOIL MOISTURE ESTIMATION • MAPPING WATER-LOGGED AREAS • SALT-AFFECTED SOILS, ERODED LANDS, SHIFTING CULTIVATION LANDUSE/LAND COVER • LAND USE/LAND COVER MAPPING • WASTELAND MAPPING • URBAN SPRAWL MAPPING GEOSCIENCES • GEOLOGICAL / GEOMORPHOLOGICAL MAPPING • GROUND WATER POTENTIAL ZONE MAPPING • MINERAL TARGETTING FORESTRY AND ENVIRONMENT • FOREST COVER MAPPING • FOREST MANAGEMENT PLAN - RS INPUTS • BIODIVERSITY CONSERVATION • ENVIRONMENTAL IMPACT ASSESSMENT • GRASSLAND MAPPING Natural Resources
  38. 38. SATELLITE REMOTE SENSING APPLICATIONS WATER RESOURCES • SNOWMELT RUNOFF FORECASTING • RESERVOIR SEDIMENTATION OCEAN APPLICATIONS • COASTAL ZONE MAPPING • POTENTIAL FISHING ZONE (PFZ) MAPPING • CORAL REEF MAPPING DISASTER ASSESSMENT • FLOOD / CYCLONE DAMAGE ASSESSMENT • AGRICULTURAL DROUGHT ASSESSMENT • VOLCANIC ERUPTION, UNDERGROUND COAL FIRE • LANDSLIDE HAZARD ZONATION • FOREST FIRE AND RISK MAPPING INTEGRATED MISSION FOR SUSTAINABLE DEVELOPMENT • SUSTAINABLE WATERSHED DEVELOPMENT URBAN APPLICATION ENGINEERING APPLICATIONS Infrastructure
  39. 39. Creation of 3-D ViewCreation of 3-D View
  40. 40. Study Area TPS : 19 (Memnagar)
  41. 41. Rasranjan Building Corresponding Attributes
  42. 42. 3-D Visualization3-D Visualization
  43. 43. Walk ThroughWalk Through
  44. 44. PANORAMIC VIEWERPANORAMIC VIEWER Fig. (L) :- Street View on the Golden Gate Bridge on Google Earth Fig. (R) :- Cylindrical panoramic image in ArcSoft Panoramic Viewer
  45. 45. Street-View in Google EarthStreet-View in Google Earth
  46. 46. An aerial view of a water logged area in and around Ahmedabad Monday, July 04, 2005
  47. 47. SrSr NoNo Name of catchmentName of catchment Area ofArea of catchmentcatchment in Hain Ha WatershedWatershed runoffrunoff (cum/sec)(cum/sec) Total pipeTotal pipe carryingcarrying capacity (usingcapacity (using Manning’sManning’s hydraulichydraulic table)table) (cum/sec)(cum/sec) VulnerabiliVulnerabili tyty 11 Vasna catchment areaVasna catchment area 280280 23.4323.43 16.4716.47 HighHigh 22 Paldi catchment areaPaldi catchment area 238238 18.918.9 12.6612.66 HighHigh 33 Ellisbrige catchment areaEllisbrige catchment area 210210 20.3320.33 14.8914.89 HighHigh 44 Navrangpura catchmentNavrangpura catchment areaarea 142142 12.1212.12 12.6012.60 LowLow 55 Gandhigram catchmentGandhigram catchment areaarea 179179 15.1415.14 17.8517.85 MediumMedium 66 Stadium catchment areaStadium catchment area 155155 12.8512.85 11.7511.75 LowLow 77 Naranpura catchment areaNaranpura catchment area 301301 23.7523.75 22.6022.60 MediumMedium 88 New wadaj catchment areaNew wadaj catchment area 425425 21.0921.09 24.9024.90 MediumMedium 99 Near old wadaj catchmentNear old wadaj catchment areaarea 111111 23.6423.64 22.8022.80 LowLow 1010 Sabarmati catchment areaSabarmati catchment area 286286 22.3922.39 21.8021.80 MediumMedium FLOOD VULNERABILITY OF CATCHMENTS
  48. 48.  The maximum height is 57.5 meters and the minimum height is 42 meters from mean sea level.  The study area is plain, dry and sandy. It covers an area of 3844 Ha. CONTOUR MAP OF STUDY AREA (AMC)
  49. 49. DIGITAL ELEVATION MODEL (AMC)  DEM is a digital representation of a continuous variable over a two dimensional surface by a regular array of ‘Z’ value represented to a common datum  less than 5 percent slope
  50. 50.  Rational method has been used for computing surface runs off Q = CIA/360 Where: Q = maximum rate of runoff (cum/sec) C = runoff coefficient representing a ratio of runoff to rainfall I = average rainfall intensity for a duration equal to the tc (mm/hr) A = drainage area contributing to the design location (ha) Percentage coefficients of runoff for the Catchments characteristics:  Densely built up area of cities with metalled road—0.80  Residential areas not densely built , with metalled road—0.60  Ditto, with unmetalled roads --- 0.20 – 0.50  Lightly covered --- 0.50  largely cultivated--- 0.30  Suburbs with gardens, lawns and macadamized roads—0.30  Sandy soil, light growth—0.20 CONSTANTS ESTIMATION OF SURFACE RUNOFF USING RATIONAL METHOD
  51. 51. ESTIMATING STORM WATER DRAINAGE CARRYING CAPACITY BY MANNING’S METHOD The Manning Formula is an empirical formula for flow driven by gravity. It was developed by the Irish engineer Robert Manning. The available head in the storm water drain is utilized in overcoming internal resistance. The Manning Formula given below is commonly used for such design. The Manning’s Formula states: V = 1/n (3.968 * 10-3 ) D2/3 * S1/2 Q = 1/n (3.118 * 10-6 ) D8/3 * S ½ where: V= velocity in mt per second Q = Discharge S = slope of hydraulic gradient (generally slope in SWD) D = Internal diameter of pipeline in mm n = Manning’s coefficient of roughness
  52. 52.  Area of catchments:- 80 Ha  Total Built up Area:- 55 Ha  Runoff Coefficient:- 0.8  Main Storm water drain length in the catchments area:- 274 mt  Average size of SWD drain:- 600 mm  Storm Water carrying capacity of existing SWD line:- 2.65 (cum/sec)  Runoff of catchments:- 7.11 (cum/sec) CATCHMENT NO 1 Runoff = CIA/360 = 80*0.8*40*1/360 = 7.11 cum/sec
  53. 53. SOUTH NARANPURA CATCHMENT AREA L_Section of existing SWD in Naranpura catchment area 0 10 20 30 40 50 60 0 180 363 541 721 911 1091 1274 1454 1754 1924 2104 2664 2848 Chainage in mt. Ground level Invert level North Naranpura Catchments area  Area of catchments:- 1400Ha  Runoff of catchments:-23.22 (cum/sec)  Main Storm water drain length in the catchments area:-3100mt  G.L at start point:-60.46mt  G.L at end point:-60.38mt  I.L at start point:-58.48.26mt  I.L at end point:-50.50mt  Average size of SWD drain:-900mm  Storm Water carrying capacity of existing SWD line:- 22.60(cum/sec) land use Area in hectares Area in % road footpath 206.57 15.16 COMMERCIAL 213.99 15.70 RESIDENTIAL 886.45 65.06 open plot 55.36 4.06 Total 1,362.37 Runoff=886.45*0.8*40*1 /360+213.99*0.85*0.4*1/ 360+55.36*0.4*40*1/360 +206.57*0.9*40*1/360= 23.22 cum/sec
  54. 54. PALDI CATCHMENT AREA L_Section of existing SWD in Ellisbrige catchment area 34 36 38 40 42 44 46 48 0 1 2 0 2 4 0 3 6 0 4 9 0 6 1 0 7 4 0 8 6 0 9 9 0 1 1 1 0 1 2 3 0 1 3 8 0 1 5 2 0 1 6 7 0 1 7 9 0 1 8 5 0Chainage in mt. Ground level Invert level  Area of catchments:- 168 Ha  Runoff of catchments:-15.03(cum/sec)  Main Storm water drain length in the catchments area:-1850 mt  G.L at start point:-44.66mt  G.L at end point:-43.87mt  I.L at start point:-43.50mt  I.L at end point:-39.42mt  Average size of SWD drain:-450mm  Storm Water carrying capacity of existing SWD line:-14.89(cum/sec) Land Use Area in Ha Area in % Roads 5.0 2.96 Commercial 8.27 4.90 Residential 154.05 91.39 open plot/Vegetati on/lake 1.24 0.74 Total 168.56 Total runoff = 154.05*0.80*40*1/360 + 8.27*0.85*40*1/360 + 5*0.90*40*1/360 + 1.24*0.40*40*1/360 = 15.03 cum/sec
  55. 55. The areas of Vishwakunj char rasta, near shantivan pumping stations, near Kochrab ashram, near jivraj hospital, near yogeshwarnagar, which include many of the important business FLOOD VUNERABLE ZONE AMC
  56. 56. EXISTING STORM WATER DRAINAGE OF STUDY AREA
  57. 57. PROPOSED STORM WATER DRAIN OF STUDY AREA
  58. 58. FLOOD VULNERABILITY
  59. 59. Vulnerability Total Area (Ha) Total Area (%) Very Low Vulnerable Zone 149 4% Low Vulnerable Zone 422 11% Moderate Vulnerable Zone 1112 29% High Vulnerable Zone 1453 38% Very High Vulnerable Zone 707 18%
  60. 60. GIS BASED EMERGENCY RESPONSE SYSTEM
  61. 61. Facilities in High Vulnerable Zone Slum Locations in High Vulnerable Zone
  62. 62. Facilities in Low Vulnerable Zone
  63. 63. NOAA’s LIDAR Image of Ground Zero of World Trade Center in New York City COLOR Value (meters) Value (feet) Dark Green -9.272 to 0 -30.42 to 0 Green 0 to 30 0 to 98.43 Yellow 30 to 100 98.43 to 328.08 Magenta 100 to 150 328.08 to 492.12 Red 150 to 201.19 492.12 to 764.59
  64. 64. GROUND PENETRATING RADAR (GPR)
  65. 65. Planning Scenario for a Major Earthquake in Ahmedabad City Anup Karanth [EP 0101] M 6 M 6.5 M 7 damage area Location of buildings in groups where there is possibility of maximum damage to buildings from the scenario earthquake. EARTH QUAKE
  66. 66. Planning Scenario for a Major Earthquake in Ahmedabad City Anup Karanth [EP 0101] Overlap showing the damage buildings for magnitude M7 with the existing land use
  67. 67. Planning Scenario for a Major Earthquake in Ahmedabad City Anup Karanth [EP 0101]
  68. 68. Urban Sprawl
  69. 69. Urban Sprawl
  70. 70. JAN 1999 JAN 2009 JAN 2011 MAY 1999 MAY 2009 MAY 2011 SPATIO-TEMPORAL ANALYSIS OF LULC Non builtup Built up Vegetation Waterbody AMC Zones * C – Central, E – East, S – South, N – North, W – U R B A N H E A T I S L A N D S
  71. 71. JAN 1999 JAN 2011 MAY 1999 MAY 2009 MAY 2011 SPATIO-TEMPORAL ANALYSIS OF NDVI (Normalized Difference Vegetation Index)NDVI: (NIR - RED)/(NIR + RED) 0.2 – 0.4-0.5 – 0.2 0.4 – 0.6 0.6 – 0.75 JAN 2009 Grass land Dense vegetationBarren/rock sand/ScrubBuilt up
  72. 72. JAN 1999 JAN 2009 JAN 2011 MAY 1999 MAY 2009 MAY 2011 SPATIO-TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE
  73. 73. GRID LEVEL ANALYSIS OF LST WITH LULC DURING JANUARY 1999GRID LEVEL ANALYSIS OF LST WITH LULC DURING JANUARY 1999 AND 2011AND 2011 JAN 1999 JAN 2011 Vegetation Non built up Built up Waterbody AMC Zones 2 Km Grid
  74. 74. •For a comfortable, normally dressed adult, the weighted average temperature of the bare skin and clothed surfaces is about 80°F (27°C). Source: Human comfort & Health requirements, Radiation,Pg:10 < 26◦C: Lower risk to UHI impact (8.6 km2 ) 26◦C - 28 ◦C : Moderate risk to UHI impact (208.2 km2 ) > 28◦C : Higher risk to UHI impact (233.2 km2 ) (Considering an area of 450 km2 ) Weighted Sum Overlay Analysis For LST( 1999,2009, 2011)Weighted Sum Overlay Analysis For LST( 1999,2009, 2011)
  75. 75. 120 Hands on with GPSHands on with GPS
  76. 76. Recording The Tree’s Position
  77. 77. 123 ““Mobile Mapping”Mobile Mapping” Integrates GPS technology and GIS software Makes GIS data directly accessible in the field Can be augmented with wireless technology
  78. 78. 124 Solutions need vision…Solutions need vision…
  79. 79. 125 But may be easier than we think…But may be easier than we think… to use Technologyto use Technology
  80. 80. THANK YOU

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