SPATIAL DATA MANAGEMENT APPLICATION IN …

SPATIAL DATA MANAGEMENT APPLICATION IN AGRICULTURE RESEARCH

By

Prof.P.Jagadeeswara Rao

Head, Dept. of Geo-Engineering & Centre for Remote Sensing College of Engineering (A)

Andhra University Visakhapatnam-530 003

13-Dec-13 1

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

13-Dec-13 2

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

13-Dec-13 3

Remote Sensing

Definition

Science 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?

13-Dec-13 4

Remote Sensing Systems

Human eye

Camera

Radiometer

Radar

Sonar

Laser

• Passive

• Active

{ {

13-Dec-13 5

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 Spectrum Continuum 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).

13-Dec-13 8

Remote Sensing Process

13-Dec-13 9

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 long periods of time (such as seasonal or climate changes) or from inaccessible areas in order to understand the phenomena being studied well enough to take action or make decisions.

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

13-Dec-13 10

Cont…

1. Energy Source or Illumination - the first requirement for remote sensing is to have an energy source which illuminates or provides electromagnetic energy to the target of interest

2. Radiation and the Atmosphere - as the energy travels from its source to the target, it will come in contact with and interact with the atmosphere it passes through. This interaction may take place a second time as the energy travels from the target to the sensor

3. Interaction with the Target - once the energy makes its way to the target through the atmosphere, it interacts with the target depending on the properties of both the target and the radiation.

4. Recording of Energy by the Sensor

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Cont….

5. Transmission, Reception, and Processing - the energy recorded by the sensor has to be transmitted, often in electronic form, to a receiving and processing station where the data are processed into an image (hardcopy and/or digital

6. Interpretation and Analysis - the processed image is interpreted, visually and/or digitally or electronically, to extract information about the target which was illuminated

7. Application- Decision Making

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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. Cost 3. Repeatability

Advantages of remote sensing

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Remote Sensing Platforms

- Ground based - Aircraft - Space shuttle - Satellite

13-Dec-13 14

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 m 13-Dec-13 18

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 sensing wavelengths) adds information In this case by “sensing” RGB can combine to get full color rendition

0.4 m 0.7 m

Color

Images Blue 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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• Example - What do you believe the image would look like if you used near and middle infrared sensitive film?

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• Example (Con’t) - What do you believe the image would look like if you used a thermal infrared sensitive film?

13-Dec-13 24

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) 13-Dec-13 26

Satellite Orbit Determines...

• …what part of the globe can be viewed.

• …the size of the field of view.

• …how often the satellite can revisit the same place.

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

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Applications of Remote Sensing

• Images serve as base maps • Observe or measure properties or conditions of 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 distribution

patterns, alterations in vegetation phenological cycles and modifications in the plant physiology and morphology provide valuable insight into the climatic, edaphic, geologic and physiographic characteristics of an area.

Many of the remote sensing techniques are generic in nature and may

be 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 growth through photosynthesis and respiration, affects soil temperature, and controls available water in the soil. Farmers use soil temperatures and soil moisture to decide when to plant, what varieties of crops to choose, and to determine the likely development of key plant characteristics like flowering as well as emergence of insect pests and plant diseases. The occurrence of freezing temperatures in fall generally heralds the end of the growing season for most plants.

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

Green-0.54 um Yellow-Carotenes Pale yellow-Xanthophyll pigments

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GVMC as views on IRS-P6, 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

rd Type

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)

48 Highes

t 17.29 17.98 10.69

13 Lowest 0.04 0.06 0.02

13-Dec-13 39

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

• Quantifying the nature of relationships between precipitation and vegetation condition at a variety of temporal and spatial scales is fundamental for understanding and managing the environment. The data sets examined in this study, indicated that there are strong relationships between precipitation and NDVI, both spatially and temporally.

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Hyperspectral remote sensing

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The images below show how hyperspectral imaging (in this case data obtained from the Hyperion spaced based sensor) can be used to image burn scars and hot spots (seen as orange and bright orange spots on the right image) through smoke resulting from wildfires. The smoke is more transparent in the SWIR bands than in the VNIR bands. Using a contrast ratio of two different SWIR bands, a Burn Index (BI) can be created to measure the severity of burn scars.

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GEOGRAPHIC INFORMATION SYSTEM

GEOGRAPHIC INFORMATION

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

13-Dec-13 45

Definition

• A powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world

» 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. 13-Dec-13 46

What makes data spatial?

Place name Grid co-ordinate

Post code

Distance & bearing Description

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 accuracy 13-Dec-13 54

a raster view of the world...

Tessellation

Raster Features

Sampling

raster model

The entity information is explicitly recorded for a basic data unit (cell, grid or pixel)

vector model

• In a vector-based GIS data are handled as:

– Points X,Y coordinate pair + label

– Lines series of points

– Areas line(s) forming their boundary (series of polygons)

line feature

area feature point feature

vector model

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 from different sources can easily be integrated using location.

• This can be used to build up complex models of the real world from widely disparate sources.

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Linking polygons to tables

advantages and implications of using a DBMS

• store and manipulate very large data sets

• the same data set can be used by many users at the same time (data sharing)

• define integrity constraints to achieve data correctness

• query language for flexible retrieval of data (indexing)

• backup and recovery functions to avoid loss of data

• avoid unnecessary duplication of data (redundancy)

• self-description of the database (data dictionary)

• security restrictions: user privileges and authorisation

• expert knowledge, high capital investment, overhead

ATTRIBUTE DATA MODELS

– Flat file attribute model

– Hierarchical model

– Network model

– Relational model

– Object Orientated model

Hierarchical Model

The data is organized in tree structure. The relations among the different entities are defined by the organization of the hierarchy.

University

Department

Students Professors

Courses

Organization of the Hierarchy of Entities

The top of the hierarchy is termed the root. It is comprised of one entity, in this case a University, the University of Roorkee. The root may be represented containing many fields. Except for the root, every element has one higher level element related to it, termed its parent, and one or more subordinate elements, termed children. In the model every relation is a many-to-one relation or a one-to-one relation. The many departments belong to one university, there are many students in each department.

Organization of the data records in Hierarchical Data Model

University Name Andhra Univer

University Record Field Name

Data Record

No. of Prof.

17

No. of Support Staff

No. of Grad. Students

7 23 Physics

Dept Name

Last Name

Saran

First Name Marks

Sameer 23 692214

Student No. Last Name

Ram

First Name Teaching yrs

Prakash 5 700

Emp. ID

Course Name

Electronics

Hrs/week

7 12-247A

Course-id

Department Record

Professor Record Student Record

Course Record


University Name Andhra Universi

University Record

Field Name

Data Record

No. of Prof.

17



7 23 Physics

Dept Name

Last Name

Saran

First Name Marks

Sameer 23 692214


Ram


Prakash 5 700

Emp. ID

Course Name

Electronics

Hrs/week

7 12-247A

Course-id

Department Record


Course Record

Query: In order to final all the courses offered by a specific department? Solution : It requires a two stage search, first the records for all the professors teaching in that department would be retrieved and then the courses that each of those professors taught would be retrieved. This is a less efficient type of retrieval. This search would be more efficient if a course be directly related to a department as well as to a professor. Therefore, in hierarchial model an entity can have only one parent, so the Course entity is not permitted to have both the Depart. And Prof. Entities as parent.


Another limitation of the hierarchical model is that searches cannot be done on the attribute fields. In this example, the retrieval of all the second year students could not be done because the year data fields is not a key. For this search to be possible, the database would have to be restructured or special linkages, such as pointers, would have to be used to modify the data base organization. Query: Then you will say what is pointer? Sol: Pointer is a code that indicates a location in a file, such as the location in a file where the attributes of a geographic features are stored. Because the relations between entities are encoded in the database, it is difficult to modify

University Name

Andhra University

University Record

Field Name

Data Record

No. of Prof.

17



7 23 Physics

Dept Name

Last Name

Saran

First Name Marks

Sameer 23 692214


Ram


Prakash 5 700

Emp. ID

Course Name

Electronics

Hrs/week

7 12-247A

Course-id

Department Record


Course Record

Yr.

2


Advantages:

• Easy to understand • They are easy to update • They can provide high speed access to large data sets, • They work well when the structures of the hierarchy is optimized for the searches to be performed. However, this requires that the complete range of queries be known in advance ex. Airline reservation system, the types of searches are very predictable, and so they can be tightly specified.

Disadvantages: • Data relationships are difficult to modify • Queries are restricted to traversing the existing hierarchy • For applications like environmental assessment or geographic information analysis, the data searches are often exploratory and cannot be predicted in advance. The inflexibility of this model makes it too restrictive for this type of application. There are many applications where an element needs to be represented as a member of multiple groups. Networks model addresses some of these restrictions

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

13-Dec-13 75

Digital Mapping

Photo- grammetry

Computer

Aided Design

Surveying

Remote Sensing

Databases

GIS

Cross-disciplinary nature of GIS

13-Dec-13 76

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 potential 13-Dec-13 77

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

Mandal, Srikakulam district, Andhra Pradesh

Case study-2

13-Dec-13 78

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

13-Dec-13 79

Location Map of the Study Area

Study area as viewed on IRS-P6, January, 2011 13-Dec-13 80

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.

13-Dec-13 82

satellite data

IRSP6,2011 LANDSAT,1990 13-Dec-13 83

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

Toposheet LANDSAT-1990 IRSP6-2011

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Geometric rectification(WGS84)-Bhamini mandal

Toposheet IRSP6-2011 LANDSAT-1990

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Methodology 1

LANDSAT-

TM(1990)

SOI

Toposheets Geometric

Rectification

Mandal

Toposheets

IRS P6-LISS-

III (2011) Waste/Fallow Land

Afforestation/

Reforestration under

CDM

Mandal Satellite

Images GPS GARMIN

Standard Visual

Interpretation

Geometric

Rectification

Enhance livelihood for

the rural poor

Cadastral map

13-Dec-13 86

WASTE/FALLOW LANDS IDENTIFIED IN DIFFERENT TIME PERIODS 13-Dec-13 87

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.

13-Dec-13 88

Cadastral

/District

Mandal Map

LANDSATM-

(1990)TM

(1990)

SOI

Toposheets Geometric

Rectification

Mandal

Toposheets

IRS P6-LISS-

III (2011) Waste/Fallow Land

Afforestation/Refores-

tration under CDM

Mandal Satellite

Images GPS GARMIN

Standard Visual

Interpretation

Geometric

Rectification

Enhance livelihood for

the rural poor

METHODOLOGY 2

13-Dec-13 89

CADESTRAL MAP

CADASTRALMAP SUPERIMPOSED ON TOPOSHEET RECTIFIED

SUPERIMPOSED OF WASTE/FALLOW LANDS

13-Dec-13 90

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.

13-Dec-13 91

SOI TOPOSHEETS

GOOGLE EARTH

IRS-P6, 2011.SATELLITE IMAGE

LANDSAT IMAGE, 1990

GPS FARMER’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 IRSP6-2011 GOOGLE MAP

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LANDSAT-1990 GOOGLE MAP IRSP6-2011

ELIGIBLE LAND IDENTIFICATION THROUGH GPS

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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 4 Punnana Ammayamma 0.28326500000 not eligibie 5 Punnana Rangarao 0.37929100000 not 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 12 Pinnanti Rajarami naidu 0.14393800000 not 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 22 Tompala Sangayya 1.26037000000 not eligibie 23 Gedela Jayalakshmi 1.80634000000 eligibie 24 Gummada Apparao 0.09873690000 eligibie 25 Runku Krishnarao 0.24708900000 eligibie 26 Gorle Jagan 0.52513100000 not 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 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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