SPATIAL DATA MANAGEMENT APPLICATION IN CLEAN DEVELOPMENT MECHANISM …APHRDI/2017/7_July/GE… ·...
Transcript of SPATIAL DATA MANAGEMENT APPLICATION IN CLEAN DEVELOPMENT MECHANISM …APHRDI/2017/7_July/GE… ·...
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
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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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