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

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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

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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

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2: WATER BODIES

3. SAND 34. DEEP WATER

5. FALLOW (SOIL)(SOIL)

TEXTURE IS THE FREQUENCY OF TONAL CHANGES ON AN IMAGE

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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

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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

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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

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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)

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Suitability potential for High-rise building ( Dai et al 2001)

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Suitability potential for low-rise building ( Dai et al 2001)

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Suitability potential for the waste disposal ( Dai et al 2001)

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Suitability potential for the Natural conservation category ( Dai et al 2001)

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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

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Th k YThank You

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