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GEOINFORMATICS FOR GEOINFORMATICS FOR URBAN PLANNING AND
MANAGEMENT
Speaker:SUBASHISA DUTTA,
DEPARTMENT OF CIVIL ENGINEERING, IIT GUWAHATI
Geoinformatics
GeoinformaticsGeoinformatics
GeographicalInformation
Global Positioning Information
System (GIS)g
System (GPS)Satellite
Remote SensingRemote Sensing
2
STAGES OF URBAN PLANNING
1.Thematic map preparation from satellite data using visual interpretation techniques.
2.Generation of spatial framework in GIS environment for perspective and
development plans.
3.Integration of thematic maps using GIS techniques for urban sprawl analysis and
urban land use change analysis.
4.Area required for urbanization to be determined on the basis of population
projection of the city and its growth centers.
5 Calculation of land requirements for urban development based on the carrying5.Calculation of land requirements for urban development based on the carrying
capacity of the region.
6.Projection of urban land use suitability analysis.
7.Urban environmental sensitivity analysis based upon both physical as well as air
quality parameters.
8.Determination of composite functionality index to setup various amenities such as
educational, medical, recreational etc.
Outline of this Lecture
1. Introduction to Satellite Remote Sensingg
2. Use of high resolution satellite imagery for Base map preparation
3. Introduction to Geographical information System ( GIS)
4. Application of Remote Sensing and GIS for Urban Planning and Management
4
WHAT IS REMOTE SENSING?
`REMOTE SENSING IS THE SCIENCE OF MAKINGINFERENCES ABOUT OBJECTS FROM MEASUREMENTS,,MADE AT A DISTANCE, WITHOUT COMING INTO PHYSICALCONTACT WITH THE OBJECTS UNDER STUDY.’
`REMOTE SENSING MEANS SENSING OF THE EARTH’SSURFACE FROM SPACE BY MAKING USE OF THEPROPERTIES OF ELECTROMAGNETIC WAVE EMITTEDPROPERTIES OF ELECTROMAGNETIC WAVE EMITTED,REFLECTED OR DIFFRACTED BY THE SENSED OBJECTS,FOR THE PURPOSE OF IMPROVING NATURAL RESOURCEMANAGEMENT, LAND USE AND THE PROTECTION OF THEENVIRONMENT.’
SIGNATUREKEY TO FEATURE IDENTIFICATION FROM SPACE IMAGERYKEY TO FEATURE IDENTIFICATION FROM SPACE IMAGERY
DEPENDS ON THE CHARACTERISTIC CHANGES IN THE
PROPERTIES OF THE EM SPECTRUM REFLECTED/EMITTED
REFLE VEGETATION
SILTY CLAY SOIL60
80
SIGNATURES COULD BE INFERREDTHROUGH:
FROM THE TARGET SURFACE – REFERRED ‘SIGNATURE’
ECTANCE
(%) WATER (Shallow/Deep)
VEGETATION
MUCK SOIL20
40
• SPECTRAL VARIATION• POLARISATION CHANGE• THERMAL INERTIA
0.4 0.8 1.2 1.6 2.0 2.4
(%)
Wave length (µm)
WATER (Shallow/Deep)0
N• TEMPORAL VARIATION
6000
7000Y
SIGNATURES ARE NOTCOMPLETELY DETERMINISTIC;THEY ARE STATISTICAL INNATURE WITH A MEAN AND
2000
3000
4000
5000
FREQ
UEN
CY
NATURE WITH A MEAN ANDDISPERSION
RADIANCE (MW/CM2/ STR/µM)
0
1000
2.79 3.08 3.37 3.66 3.95 4.24 4.53 4.82
F
Platforms Used toSAcquire Remote Sensing Data
• Aircraft– Low, medium & high altitude– Higher level of spatial detail
• Satellite– Polar-orbiting, sun-synchronous
• 800-900 km altitude, 90-100 minutes/orbith– Geo-synchronous
• 35,900 km altitude, 24 hrs/orbit• stationary relative to Earth• stationary relative to Earth
Area arraySingle detector element
Area arrayLinear array
cross-track
along-track
(a) (b) (c)
cross track
Various modes in which an image can be generated.
Landsat-7 Satellite
• 705-km altitude• 16 day repeat cycle• 16-day repeat cycle• 185 km swath width
D di d t 10 00 15 i• Descending node at 10:00 - +15 min• Whisk-broom scanner• Radiometric resolution: 28
(256 levels)
RESOLUTION
Quality of information derived from RS images stronglyinfluenced by spatial, spectral, radiometric and temporalresolution of the sensorresolution of the sensor
• SPATIAL RESOLUTION instrument resolving power needed to spatially discriminate th ll t bj tthe smallest object
• SPECTRAL RESOLUTIONencompasses the width of bands used from the wavelengths of the EM spectrumof the EM spectrum.
• RADIOMETRIC RESOLUTIONquantify No. of discernible signal levels in a band, {sensor’s ability to discriminate radiance differences (NE∆ρ)}ability to discriminate radiance differences (NE∆ρ)}
• TEMPORAL RESOLUTIONtime interval between imaging collections over the same
geographic locationgeographic location
FRESH SNOWGREEN VEGETATIONDARK TONED SOIL
LIGHT TONED SOILCLEAR WATERTURBID WATER
GREEN BAND
(0 52-0 59
RED BAND
(0 62-0 67
NEAR IR
(0 77-0 86
SHORTWAVE IR
(0.52 0.59 µm)
(0.62 0.67 µm)
(0.77 0.86 µm) (1.55-1.75 µM)
INDIAN IMAGING CAPABILITY
Current observational capabilities of Electro optical imaging instruments from Indian satellite platforms
Electro-optical Imaging Instrument
Spatial foot-print(Meters)
Swath(Kilometers)
Number of spectral channels
Number of instruments
No. of days required for global coverage
Minimum interval required for revisiting a target (days)
PAN 1 14. 5 1 1 1200 1 to 200
PAN 5.8 74 1 2 48 5
Liss3 23.5 148 3 2 24 24
Liss2 36 140 4 1 22 22Liss2 36 140 4 1 22 22
Liss3(SWIR) 70 148 1 2 24 24
Liss1 72 140 4 1 22 22
WiFS 188 810 2 3 5 2
WiFS(swir) 188 810 1 1 5 5
OCM 360 1420 8 1 2 2360 1420 8 1 2 2
CCD P/l 1000 300 3 2Continuous daytime monitoring of earth
disc
A region of 300km*6000 km can be covered every minute
VHRR 2000 Earth Disc 1 2 Continuous monitoring A region of 12000km*8000 km can be covered every 7VHRR 2000 Earth Disc 1 2 of earth disc km can be covered every 7
minutes
VHRR 8000 Earth Disc 2 2 Continuous monitoring of earth disc
A region of 12000km*8000 km can be covered every 7
minutes
VISUAL INTERPRETATION
COLOUR COMPOSITION
PRIME COLOUR BLUE GREEN AND RED PRIME COLOUR : BLUE, GREEN AND RED
GRAY COLOUR ( Black and White) : Single band
PSEUDOCOLOUR : SINGLE BAND ( COLOUR ASSIGNED)
TRUE COLOUR : THREE BAND COMPOSITIONRS BAND COLOUR
PRIME COLOUR
RS BAND COLOURBLUE BAND : BLUEGREEN BAND : GREEN RED BAND : RED
FALSE COLOUR COMPOSITE(FCC)NIR BAND : RED RED BAND : GREEN RED BAND : GREEN GREEN BAND : BLUE GRAY COLOUR
FALSE COLOUR COMPOSITE
NATURAL COLOUR
Natural colour is generated using the primary colours – Blue, Green andRed. The green vegetation appears green. The false colour compositeimage uses Green, Red and Near Infrared bands as blue, green and redimage uses Green, Red and Near Infrared bands as blue, green and redcolour respectively. Here vegetation appears in different hues of red.
ELEMENTS OF IMAGE INTERPRETATION
I) TONE (COLOUR),
iII) TEXTURE iII) TEXTURE,
iIII) SHAPE,
IV) SIZE, IV) SIZE,
V) SHADOWS,
VI) PATTERN,
VII) SITE AND
VIII) ASSOCIATION
Tone is a measure of the intensity of electromagnetic radiation reflected (emitted) by the objectsreflected (emitted) by the objects
1
24 1: 24 1:
VEGETATION(HVG)
2 WATER
35
2: WATER BODIES
3. SAND 34. DEEP WATER
5. FALLOW (SOIL)(SOIL)
TEXTURE IS THE FREQUENCY OF TONAL CHANGES ON AN IMAGE
11
3
2
1: SMOOTH ( EARLY PADDY)2 COARSE ( POTATO)2. COARSE ( POTATO)3. COARSE ( LATE PADDY)
SHAPE AND SIZE
1: MAJOR RESERVOIR 2: FISHING POND AND 3: SMALL CHECK DAMS
2
11
PATTERN : SPATIAL ARRANGEMENT OF OBJECTS 1: TOWNSHIP 2: FALLOW FIELD 3: VILLAGE 1: TOWNSHIP 2: FALLOW FIELD 3: VILLAGE
6
1
36
4
2
5
2
ASSOCIATIONASSOCIATION4: SAND (HIGH REFLECTANCE), 5: PADDY (GW) AND 6: PADDY (CW
33
Objective of study
Selection of RS dataCollateral data
SOI toposheet
FLOW CHART OF VISUAL INTERPRETATION
study SOI toposheet
Thematic maps
Aerial data Generation of enhanced product
Field data
Knowledge of the area
enhanced product
Preliminary interpretation
Base map
Identify doubtful
Photo interpretation key
Identify doubtful areasField verification
Interpretation modification
Final Mapping Evaluation
Accuracy Check
Final Mapping Evaluation
Area calculation
35
CARTOSAT-1 PANCHROMATIC PRODUCT ( RESOLUTION : 2.5 M)
COLOUR COMPOSITION OF RESOURCESAT (IRS P6) LISS-IV
ARC-NODE DATA STRUCTURE
BASIC GRAPHICAL FEATURPOINT LINE LINE POLYGON
ARC-NODE DATA STRUCTURE
NODES VERTICES
A rc N u m b er
S ta rt n o d e
V ertice s E n d n o d e
A rc N u m b er
S ta rt n o d e
V ertice s E n d n o d e
A rc N u m b erA rc N u m b er
S ta rt n o d eS ta rt n o d e
V ertice sV ertice s E n d n o d eE n d n o d e Polygon Arc ListPolygon Arc ListPolygonPolygon Arc ListArc List
ARC-NODE STRUCTUREPOLYGON STRUCTUNODES, VERTICES
1 2 0 d ,c ,b .a 1 0
2 1 0 e 2 0
1 2 0 d ,c ,b .a 1 0
2 1 0 e 2 0
11 2 02 0 d ,c ,b .ad ,c ,b .a 1 01 0
22 1 01 0 ee 2 02 0
A 1,2
2 3
A 1,2
2 3
AA 1,21,2
2 32 33 1 0 f,g ,h ,i,j 2 03 1 0 f,g ,h ,i,j 2 033 1 01 0 f,g ,h ,i,jf,g ,h ,i,j 2 02 0
B 2,3B 2,3BB 2,32,3
TOPOLOGY : DEFINING SPATIAL RELATIONSHIPS
three major topological concepts:three major topological concepts:
Connectivity: Arcs connect to each other at nodes.A d fi i i A th t t t d d fiArea definition: Arcs that connect to surround an area define a
polygon Contiguity: Arcs have direction and left and right sides
CONNECTIVITY
AREA DEFINITION
CONTIGUITY : ADJACENCY
REGIONS, ROUTES AND EVENTS
POLYGONS VS REGIONS
ROUTES
spatial target theme selector the
TOPOLOGY spatial
relationship purpose target theme feature types
selector thefeature typ
are completely withinselects target theme features that are completely within
pointline polygon
selector theme features polygon
completely containselects target theme features that completely contain
l h fpolygon
pointline
lselector theme features polygon
have their center inselects target theme features whose centers fall inside selector theme features
pointlinepolygon
polygonselector theme features polygon
contain the center ofselects target theme features which contain the centers of selector theme features
polygonpointlinepolygonselector theme features polygon
intersectselects target theme features that intersect selector theme features
pointlinepolygon
linepolygonp yg
are within distance ofselects target theme features that are within a given distance of selector theme features
pointlinepolygon
pointlinepolygon
SPATIAL DATA MODELLING
Two Types
1) A model of Spatial form: Representing the structure andRepresenting the structure and
distribution of features in geographical space
2) A model of Spatial processes:the processes-based interaction between the f tfeatures
BASIC FUNCTIONS
MEASUREMENTS IN GIS: LENGTH LENGTH PERIMETERS AREA AREA
QUERIESTWO TYPES : SPATIAL AND ASPATIAL
SET OF QUERIES : BOOLEAN OPERATORS
A OR B A AND B A OR B
A NOT B A XOR B
QUERY : WHERE ARE THE ENGLISH MEDIUM SCHOOLS IN GUWAHATI CITY THAT HAVEMORE THAN 200 STUDENTS?
Making queriesSelects records from tables/features from themes
QUERY BUILDER BUTTON
Displaying selected setsSelected records from tables also select features from themes
Graphical representation of tabular data
NUMBERS ARE DIFFICULT TO INTERP
CHARTS ARE EASY TO INTERPRET
RECLASSIFICATION : DATA SIMPLIFICATION
Two ways: 1) Attribute-based2) Graphical-based
Note: 1) common boundaries between polygons removed2) rebuilding the new topological relationships
BUFFERING
Quantifying a spatial entity to influence its neighboursOr the neighbours to influence the character of agSpatial entity
POINT
LINE
POLYGONON
POINT BUFFER: Zone of influence
LINE BUFFER
OVERLAY ANALYSIS
Overlay operators
PROXIMITY ANALYSIS
Selecting adjacent features
SELECTION OF LINE SEGMENT BASED ON POLYGON
POINTS WITHIN POLYGON
APPLICATION OF GEOINFORMATICSAPPLICATION OF GEOINFORMATICS FOR URBAN PLANNING AND MANAGEMENTMANAGEMENT
70
URBAN SPRAWL
CASE STUDY : suitability potential for different land use ( Dai et al 2001)
72Study area : Lanzhou city, Northwest China
Methodology ( Dai et al 2001)
73
Suitability potential for High-rise building ( Dai et al 2001)
74
Suitability potential for low-rise building ( Dai et al 2001)
75
Suitability potential for the waste disposal ( Dai et al 2001)
76
Suitability potential for the Natural conservation category ( Dai et al 2001)
77
Conclusion
Geoinformatics helps
1. Development of Urban Information System ( UIS)
2 Understanding Urban growth processes and its2. Understanding Urban growth processes and its environmental impact
3 i di i l k l d h i l d3. Incorporating traditional knowledge on the spatial and non-spatial data
78
Th k YThank You
79