SPATIAL DATA MANAGEMENT APPLICATION IN CLEAN DEVELOPMENT MECHANISM

By

Prof.P.Jagadeeswara Rao, Former Scientist, CGWBDept. of Geo-Engineering &Centre for Remote SensingCollege of Engineering (A)

Andhra UniversityVisakhapatnam-530 003

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Structure of talk

1. Introduction to Remote Sensing

2. Role of Remote Sensing in Vegetation

3. Define GIS and related terms

4. Case study

5. Integrating data with GIS

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Today many remote sensing satellites orbit the Earth and provide extensive data concerning the composition of our planet

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Remote Sensing

DefinitionScience and art of obtaining information about an object, area or

phenomenon through an analysis of data acquired by a device

that is not in direct contact with the area, object or phenomenon

under investigation.

Lillesand, Thomas M. and Ralph W. Kiefer, “Remote Sensing and Image

Interpretation” John Wiley and Sons, Inc, 1979, p. 1

What are some common examples of remote sensors?

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Remote Sensing Systems

Human eye

Camera

Radiometer

Radar

Sonar

Laser

• Passive

• Active

{{

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Wave Theory

Electromagnetic radiation consists of:

Electrical Field (E) which varies in magnitude in a direction perpendicular to the direction in which the radiation is traveling, and a

Magnetic Field (M) oriented at right angles to the electrical field. Both these fields travel at the speed of light (c).

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Electromagnetic Spectrum

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Electromagnetic SpectrumContinuum of EM Wave arranged according to wavelength or frequency

The Electromagnetic Spectrum ranges from the shorter wavelengths (including gamma and x-rays) to the longer wavelengths (including microwaves and broadcast radio waves).

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Remote Sensing Process

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Why Remote Sensing from Space?

The answer to this question has two primary parts:

Many phenomena of interest are best observed with a synoptic or global view -- atmosphere and ocean dynamics, geologic applications where large-scale structures are being investigated, and some biologic phenomena

Some of these require data extending over longperiods of time (such as seasonal or climate changes)or from inaccessible areas in order to understand thephenomena being studied well enough to take action ormake decisions.

Because of their orbit and unique viewing position, satellites canacquire data covering the entire globe within a relatively shorttime, and once in orbit, a satellite can remain there for extendedperiods of time, repeating the measurements as the data changes.

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Why Is Remote Sensing Useful?

Large regions can be observed over time

Sensors can measure energy at wavelengths beyond range of human vision

Records information in “real time”

1. Global coverage 2. Synoptic view 4. Cost3. Repeatability

Advantages of remote sensing

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Remote Sensing Platforms

- Ground based

- Aircraft

- Space shuttle

- Satellite

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Remote Sensing

Four Fundamental Properties For Design

• Image depends on the wavelength response of the sensing instrument (radiometric and spectral resolution) and the emission or reflection spectra of the target (the signal).

- Radiometric resolution

- Spectral resolution

• Image depends on the size of objects (spatial resolution) that can be discerned

- Spatial resolution

• Knowledge of the changes in the target depends on how often (temporal resolution) the target is observed

- Temporal resolution

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Radiometric Resolution

• Number of Shades or brightness levels at a given wavelength

• Smallest change in intensity level that can be detected by the sensing system

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Spectral Response Differences

TM Band 3 (Red) TM Band 4 (NIR)

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Band 1 0.45 - 0.52 m Band 2 0.52 – 0.59 m

Band 3 0.62- 0.68 m Band 4 0.77 – 0.86 m7/20/2017 16

Pixels

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• Example: Black and white image

- Single sensing device

- Intensity is sum of intensity of all visible wavelengths

Spectral Resolution

0.4 m 0.7 m

Black &

White

Images

Blue + Green + Red

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Spectral Resolution (Con’t)

• Example: Color image

- Color images need least three sensing devices, e.g., red, green, and blue; RGB

Using increased spectral resolution (three sensingwavelengths) adds information

In this case by “sensing” RGB can combine toget full color rendition

0.4 m 0.7 m

Color

ImagesBlue Green Red

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Spectral Resolution (Con’t)

• Example

- Blue only sensitive film

- Green only sensitive film

- Red only sensitive film

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Spectral Resolution (Con’t)Example of sampling wavelengths

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Data Acquisition - Satellite Orbits

Satellites:

•Sun-synchronous (Landsat, SPOT, IRS)

•Geostationary (TIROS,INSAT)7/20/2017 22

Satellite Orbit Determines...

• …what part of the globe can be viewed.

• …the size of the field of view.

• …how often the satellite can revisit thesame place.

• …the length of time the satellite is on the sunny side of the planet.

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Types of Orbits

• Lower Earth Orbit (LEO)

- Orbit at 500 - 3,000 km above the Earth (definition varies)

- Used for reconnaissance, localized weather and imaging of natural resources.

- Space shuttle can launch and retrieve satellites in this orbit

- Now coming into use for personal voice and data communications

- Weather satellites

> Polar orbit - Note, as the satellite orbits, the Earth is turning underneath. Current NOAA satellites orbit about 700 - 850 km above Earth’s surface

> Orbital period about every 98 - 102 min

Satellite Observations

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Types of Orbits (Con’t)

• Medium Earth Orbit (MEO)

- Orbit at 3,000 - 30,000 km (definition varies)

- Typically in polar or inclined orbit

- Used for navigation, remote sensing, weather monitoring, and sometimes communications

> GPS (Global Position System) satellites‡ 24-27 GPS satellites (21+ active, 3+

spare) are in orbit at 20,000 km (about 10,600 miles) above the Earth; placed into six different orbital planes, with four satellites in each plane

‡ One pass about every 12 h

Satellite Observations

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Types of Orbits (Con’t)

• Highly Elliptical Orbits (HEO)

- Typically pass low (1,000 km) over the southern regions, then loop high

over the northern regions

- One pass every 4 to 12 h

- Used in communications to provide coverage of the higher latitudes and the polar regions

Satellite Observations

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Types of Orbits (Con’t)

• Geosynchronous

- Orbital period of 1 day, i.e., satellite stays over the same spot on the Earth

- Orbital radius is 42,164 km or 35,786 km above the Earth’s surfaceat the Equator where the Earth’s radius is 6.378 * 106 m

- Used for many communication satellites;

> Cover a country like Australia

> Don’t require complex tracking dishes to receive the signals;Note: satellite stay stationary relative to Earth

Satellite Observations

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Types of Orbits (Con’t)

• Geosynchronous (Con’t)

- Weather satellites

> GOES (Geosynchronous Operational Environmental Satellites)Satellite

Satellite Observations

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Applications of Remote Sensing

• Images serve as base maps

• Observe or measure properties or conditionsof the land, oceans, and atmosphere

• Map spatial distribution of “features”

• Record spatial changes

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Agriculture/Vegetation

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Approx. 70% of the land surface covered with vegetation.

Vegetation is one of the most important components of ecosystems.

Knowledge about vegetation species and community distributionpatterns, alterations in vegetation phenological cycles andmodifications in the plant physiology and morphology providevaluable insight into the climatic, edaphic, geologic andphysiographic characteristics of an area.

Many of the remote sensing techniques are generic in nature and maybe applied to a variety of vegetated landscape, include

1. Agriculture 2. Forests 3. Wet lands 4. Urban vegetation

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Radiation - Target Interactions

• Spectral response depends on target

• Leaves reflect green and near IR

• Water reflects at lower end of visible

range

Spectral characteristics of vegetation

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Leaf cross-section

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Air temperature is important to agriculture because it influences plant growththrough photosynthesis and respiration, affects soil temperature, and controlsavailable water in the soil. Farmers use soil temperatures and soil moisture todecide when to plant, what varieties of crops to choose, and to determine thelikely development of key plant characteristics like flowering as well asemergence of insect pests and plant diseases. The occurrence of freezingtemperatures in fall generally heralds the end of the growing season for mostplants.

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Chlorophyll a at 0.43-0.66 umChlorophyll b at 0.45-0.65 um

Green-0.54 umYellow-CarotenesPale yellow-Xanthophyll pigments

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GVMC as views on IRS-ID, January, 2011

Case study-1

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NDVI in GVMC

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Avenue plantation near Akkayyapalem highway

Degraded Forest due to over grazing near Hanumantawaka junction

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Vegetation characterized map of GVMC

Feature Area

(Sq.km)

Percentage of area

with respect to total

GVMC area

Deciduous forest 107 20

Scrub land 46 9

Avenue 09 2

Degraded 07 1

Builtup land 371 68

Total 540 100

Deciduous forest at Rushikonda

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Ward wise vegetation characterized map

Wa

rdType

vegetation

in the ward

in IRS 1D,

LISS III in

Sq.km

vegetation in

the ward in

IRS 1C , PAN

in sq.km

Area as a

percentage of

total

vegetation

cover in the

study area

(162 Sq.Km)

48Highes

t17.29 17.98 10.69

13 Lowest 0.04 0.06 0.02

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• Most plants have a range of temperature at which growth occurs. Some plants aremore adaptable (such as grass) and can grow throughout the range, while otherplants have more specific temperature requirements. When the temperaturereaches the upper end of the spectrum, in general, plant photosynthesisdeclines. Optimal temperatures are different from plant to plant, and can even bedifferent within one species.

• Quantifying the nature of relationships between precipitation and vegetationcondition at a variety of temporal and spatial scales is fundamental forunderstanding and managing the environment. The data sets examined in thisstudy, indicated that there are strong relationships between precipitation andNDVI, both spatially and temporally.

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Representing the World: Projections

• 3-D to 2-D (at first)

– Projections change a round world into a flat one.

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GEOGRAPHIC INFORMATION SYSTEM

GEOGRAPHICINFORMATION

SYSTEM

ID Name Pop_90 MMR

1 … 1897 4.5

2 … 2345 5.6

3 … 1293 1.2

4 … 560 6.7

…

0,0 100,0

1

2 3

4

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Definition

• A powerful set of tools for collecting, storing, retrieving atwill, transforming and displaying spatial data from the realworld

» Burrough, 1987

A GIS is defined as follows (Arnoff, 1989):-

• A GIS is a computer-based system that provides the following four sets of capabilities to handle geo-referenced data:-– Input.

– Data management (data storage and retrieval).

– Manipulation and analysis.

– Output. 7/20/2017 44

What makes data spatial?

Place nameGrid co-ordinate

Post code

Distance & bearingDescription

Latitude / Longitude

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GIS components

Specific applications /

decision making objectives

?G I S

Spatial

data

Computer hardware /

software tools

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Need of GIS

• Microscope is to Biology, similarly GIS is to Geographic Analysis.

• Geography is apart of everyday world.

• Provides insight to issues.

• Managing recourses.

• Decisions constrained, influenced or dictated by Geography.

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Why use GIS?

• Dynamic digital map Vs Paper map.

• Rapid data processing.

• Low cost of data per unit.

• Complex spatial analyses.

• Repetitive processing of data.

• Dynamic visualisation.

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The Space on Earth

• The Earth is finite!

– If not now, within our lifetimes there may be no natural ecosystems.

– Land managers, natural resource workers, and politicians are and will continue to make decisions about biological systems.

– Good information and tools are needed to do this.

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Map Concepts

• What is a map?

– What are some properties of maps?

– Vector vs. raster: two digital mapping methods

• Maps reflect the databases we create

• Mapping the third dimension: examples of 3-D maps

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Types of data– Two types of data are stored for each item in the

database

• 1. Attribute data:– Says what a feature is

• Eg. statistics, text, images, sound, etc.

• 2. Spatial data:– Says where the feature is– Co-ordinate based– Vector data – discrete features:

• Points• Lines• Polygons (zones or areas)

– Raster data:• A continuous surface

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Raster vs. Vector: types of GIS map representation

• Vector vs. Raster

• Two basic ways that spatial data can be represented

• Raster:

– Data represented by pixels with values, creating a grid

– Allows certain types of operations not possible with vector data

– Map algebra is possible with multiple data layers – creating index maps

• Vector:

– Data stored as points, lines, and polygons

– Uses less memory than raster format

– Does not loose positional accuracy7/20/2017 52

Geo-referencing data

• Capturing data– Scanning: all of map converted into raster data

– Digitising: individual features selected from map as points, lines or polygons

• Geo-referencing– Initial scanning digitising gives co-ordinates in inches from

bottom left corner of digitiser/scanner

– Real-world co-ordinates are found for four registration points on the captured data

– These are used to convert the entire map onto a real-world co-ordinate system

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Example of geo-referencing

Source: ESRI (1997)

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Layers

• Data on different themes are stored in separate “layers”.

• As each layer is geo-referenced layers fromdifferent sources can easilybe integrated using location.

• This can be used to build upcomplex models of the realworld from widely disparatesources.

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Raster data

Scale: 1:100,000

Grid cell size: 50 m.

Minimum altitude: 0 m.

Maximum altitude: 174 m.

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Vector data

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How is all this done?

• GIS stores data in a relational database structure (‘3-D spreadsheets’)– e.g. employee names linked to

store number, store number linked to shipment arrival

– any data can be linked by a common attribute to any other data

• Example shown here is a list of counties (geographic data) by income code (demographic data)

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Querying GIS data

• Attribute query– Select features using attribute data (e.g. using SQL)

– Results can be mapped or presented in conventional database form

– Can be used to produce maps of subsets of the data or choropleth maps

• Spatial query– Clicking on features on the map to find out their attribute

values

• Used in combination these are a powerful way of exploring spatial patterns in your data

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DigitalMapping

Photo-grammetry

Computer

AidedDesign

Surveying

RemoteSensing

Databases

GIS

Cross-disciplinary nature of GIS

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High End 3-D Representation

• Surfaces are made from Triangular Irregular Networks (TIN) that interpolate 3-D surfaces from 2-D contour values.

• Uses: – Hydrology: surface and

underground flows

– Line-of-Sight analysis

– Pollution Plume tracking

– Customer analysis

– Soil erosion potential7/20/2017 61

Role of Geo-Spatial Technologies in Clean Development Mechanism-A case study of Bhamini

Mandal, Srikakulam district, Andhra Pradesh

Case study-2

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1. To identify the waste/fallow lands for CDM Project development using geospatial technologies.

2. To find out eligible land parcel owners to be part in CDM project

Aim of the project

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Location Map of the Study Area

Study area as viewed on IRS-P6, January, 20117/20/2017 64

Data used

Survey of India Toposheets

LANDSAT,1990 satellite data

IRS-P6,2011 satellite data

Village/mandal Cadastral map

Google map

GPS coordinates measure using GARMIN handheld equipment

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Procedure adopted

Identification of waste/fallow lands in different Villages/Mandals/Districts.

Geometric Rectification of toposheet, satellite imagery and cadastral map.

Projection of the satellite images converted into Google Map projection

Geographic(Lat/Long) WGS 84

GPS readings measured of the eligible land parcels through field survey.

Importing the GPS coordinates on to the Google Map.

Superimposing the vector data from the Google Earth on to the Satellite images considered for eligibility and area calculations.

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satellite data

IRSP6,2011 LANDSAT,19907/20/2017 67

Geometric rectification(GCS,LAT/LONGS)-Bhamini mandal

Toposheet LANDSAT-1990 IRSP6-2011

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Geometric rectification(WGS84)-Bhamini mandal

ToposheetIRSP6-2011LANDSAT-1990

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Methodology 1

LANDSAT-

TM(1990)

SOI

ToposheetsGeometric

Rectification

Mandal

Toposheets

IRS P6-LISS-

III (2011)Waste/Fallow Land

Afforestation/

Reforestration under

CDM

Mandal Satellite

ImagesGPS GARMIN

Standard Visual

Interpretation

Geometric

Rectification

Enhance livelihood for

the rural poor

Cadastral map

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WASTE/FALLOW LANDS IDENTIFIED IN DIFFERENT TIME PERIODS7/20/2017 71

Constraints of Methodology 1:

Shifting problem occurring between satellite imagery, toposheet.

District/Mandal/Village boundaries are not accurate.

Error in GPS readings

Individual ownership area can’t be accurately interpreted.

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Cadastral

/District

Mandal Map

LANDSATM-

(1990)TM

(1990)

SOI

ToposheetsGeometric

Rectification

Mandal

Toposheets

IRS P6-LISS-

III (2011)Waste/Fallow Land

Afforestation/Refores-

tration under CDM

Mandal Satellite

ImagesGPS GARMIN

Standard Visual

Interpretation

Geometric

Rectification

Enhance livelihood for

the rural poor

METHODOLOGY 2

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CADESTRAL MAP

CADASTRALMAP SUPERIMPOSED ON TOPOSHEETRECTIFIED

SUPERIMPOSED OF WASTE/FALLOW LANDS

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Constraints of Methodology 2:

A. Shifting problem occurs between satellite imagery ,toposheet and cadastral maps.

B. District/Mandal/Village boundaries are not accurate.

A. Error in GPS readings

B. Survey no. land parcels identified on satellite images but Individual ownership land area extension can’t be delineated.

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SOI TOPOSHEETS

GOOGLE EARTH

IRS-P6, 2011.SATELLITE

IMAGE

LANDSAT IMAGE, 1990

GPSFARMER’S LAND PARCEL

BOUNDARY

RECTIFICATION AREA OF

INTEREST,TOPOSHEETS

ELIGIBLE LANDS AS PER UNFCCC

COMPARE

AREA OF INTEREST,IRS

P6,2011

AREA OF INTEREST,

LANDSAT, 1990

METHODOLOGY 3

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LONGITUTE LATTITUDE

18.98333 83.03805556

18.9825 83.04277778

18.97972 83.0425

18.98 83.03833333

18.98111 83.04083333

LONGITUTE LATTITUDE

18 47 720 83 51 677

18 47 717 83 51 694

18 47 707 83 51 693

18 47 708 83 51 678

18 47 712 83 51 687

GPS COORDINATES

IN DECIMAL DEGREES IN DECIMALS MINUTES AND SECONDS

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ELIGIBLE LAND IDENTIFICATION THROUGH GPS

LANDSAT-1990 IRSP6-2011GOOGLE MAP

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LANDSAT-1990 GOOGLE MAP IRSP6-2011

ELIGIBLE LAND IDENTIFICATION THROUGH GPS

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Id names area_acres eligibility1Vatarautu Ramana 1.81251000000 eligibie2Sasupalli Tavudu 0.43189000000 eligibie3sasupalli Ramesh 0.19965400000 eligibie4Punnana Ammayamma 0.28326500000 not eligibie5Punnana Rangarao 0.37929100000 not eligibie6Gummada Ramarao 0.12243500000 eligibie7Munjeti Tirupatirao 0.71652600000 eligibie8Gummada Shekar 0.16779800000 eligibie9Pinnanti Dasunaidu 3.21914000000 eligibie

10Borra Lakshmanarao 0.93141900000 eligibie11LOtugedda Ugadi 0.78971700000 eligibie12Pinnanti Rajarami naidu 0.14393800000 not eligibie13Balaga Sanjeevarao 0.16178900000 eligibie14Pisini Anjayya 0.19296300000 eligibie15Gummada Goshagirirao 1.37384000000 eligibie16Pisini Narayana 0.14686600000 eligibie17Tompala Radhamma 0.22941900000 eligibie18Sasupalli Trinathrao 0.40383500000 eligibie19Palavalasa Mahalakshmi 1.23893000000 eligibie20Tompala Lakshmi 0.51125500000 eligibie21Sasupalli Rajarao 0.02190620000 eligibie22Tompala Sangayya 1.26037000000 not eligibie23Gedela Jayalakshmi 1.80634000000 eligibie24Gummada Apparao 0.09873690000 eligibie25Runku Krishnarao 0.24708900000 eligibie26Gorle Jagan 0.52513100000 not eligibie27Pasharla Timanna 0.46468200000 eligibie28Pasarla Vasu 0.34749900000 eligibie29Pasharla Buchibabu 0.89990200000 eligibie30Vatrautu Aadi 0.35750000000 eligibie

Total 19.48563610000

Dimili Village land parcels data

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Id names area_acres eligibility

1 Vatarautu Ramana 1.81251000000 eligibie

2 Sasupalli Tavudu 0.43189000000 eligibie

3 sasupalli Ramesh 0.19965400000 eligibie

6 Gummada Ramarao 0.12243500000 eligibie

7 Munjeti Tirupatirao 0.71652600000 eligibie

8 Gummada Shekar 0.16779800000 eligibie

9 Pinnanti Dasunaidu 3.21914000000 eligibie

10 Borra Lakshmanarao 0.93141900000 eligibie

11 LOtugedda Ugadi 0.78971700000 eligibie

13 Balaga Sanjeevarao 0.16178900000 eligibie

14 Pisini Anjayya 0.19296300000 eligibie

15 Gummada Goshagirirao 1.37384000000 eligibie

16 Pisini Narayana 0.14686600000 eligibie

17 Tompala Radhamma 0.22941900000 eligibie

18 Sasupalli Trinathrao 0.40383500000 eligibie

19 Palavalasa Mahalakshmi 1.23893000000 eligibie

20 Tompala Lakshmi 0.51125500000 eligibie

21 Sasupalli Rajarao 0.02190620000 eligibie

23 Gedela Jayalakshmi 1.80634000000 eligibie

24 Gummada Apparao 0.09873690000 eligibie

25 Runku Krishnarao 0.24708900000 eligibie

27 Pasharla Timanna 0.46468200000 eligibie

28 Pasarla Vasu 0.34749900000 eligibie

29 Pasharla Buchibabu 0.89990200000 eligibie

30 Vatrautu Aadi 0.35750000000 eligibie

TOTAL 16.89364110000

Eligible Farmer’s list for CDM Project

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Constraints:

1. Shifting problem between satellite imagery ,toposheet and GOOGLE maps.

2. District/Mandal/Village boundaries are not accurate.

3. Error in GPS readings

4. Projection Differences

5. Google map having 2 time period data

6. Individual parcels/ownerships are also not accurate.

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