Presented by,
Nalla Anthony Kiranmai
15PAGR04
AGR 591 SEMINAR (0+1)
CHAIRMAN:
Dr. R. MOHAN
Professor, Dept. of Agronomy
MEMBERS:
Dr. R. POONGUZHALAN
Professor and Head, Dept. of Agronomy
Dr. S. NADARADJAN
Asst. Prof. (Crop physiology)
Dept. of Plant breeding and genetics.

Topics of discussion
• Introduction
• Principle of Geographic information system – Definition
• Components of GIS
• Information storage
• Spatial data representation
• Vector Vs Raster
• Spatial objects
• GIS functions
• Linkage between Remote sensing and GIS
• Remote sensing supported GIS operations
• Conceptual Model of GIS
• GIS – An Integrating Technology
• How a GIS holds data
• Map Scale
• Creating a GIS
• Data sources

• Building a GIS
• Advantages and Disadvantage of GIS
• Fields where GIS is applicable
• Weather, Soil and Agriculture applications of GIS
• GIS Applications
• GIS in Land suitability studies
• Integrated Assessment of Groundwater for Agricultural
Use
• Study on spatial variability of PAJANCOA East farm
soils using GIS
• GIS in yield forecasting
• GIS in drought assessment
• Advantages and disadvantages
• Conclusion
Contd.,

GIS = G + IS
= Geographic reference + Information system
Spatial coordinates on the surface of the
earth
Database
All data in GIS must be linked to a geographic reference
Introduction

It is an organized collection of computer hardware , software,
geographic data and the personnel designed to efficiently capture,
store, retrieve, update, manipulate, analyze and display all forms
of geographically referenced information according to the user
defined specifications.
Principle of Geographic information system
Definition

Tool for handling geographic data.
Geographic Information System
Spatial data Descriptive data
Location, shape
and relationship
among the
features
Characteristics of
the features.

Components of GIS
GIS software
ArcGIS, Geomedia, Idrisi, Grass

Information storage
Spatial data Attribute data:
Eg. Well locations or sampling points
River and road networks
Fields, soil delineations, or land use
classes.
Points, lines, polygons.
Eg. Characteristics of the spatial
feature
Soil map unit - predominant soil
series, soil drainage class, and
texture of the surface soil horizon
Color, symbol, patterns.

Spatial data Attribute data

Spatial data representation:
Represented by points , lines, polygons
Ability to visualize the geographic data by linking the geographic
data to the visual data elements (point, line, areas) which compose
the picture.
Visual data
Raster vector

Raster
• Raster data represent a point, a line or an area as a matrix of values.
• The size of the cell determines the resolution of the display.
• A raster database requires that all the values or entities be defined by
a single raster or group of raster

Vector
Points are usually represented by Cartesian coordinates(x, y), a line by a string
of coordinates and an area or polygon by a string of coordinates starting and
ending at the same point.
A vector model defines graphic elements using basic geometry, namely a
quantity which has magnitude and direction, represented by a directed line the
length representing the magnitude and whose orientation in space represent the
direction.

Vector Vs Raster
Fig source:
http://www.extension.umn.edu

14
Vector and Raster Formats
RASTER
VECTOR

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Most GIS software permit Raster-Vector format conversions:
Fig Source:
FAO
Vector to raster - easy
Raster to vector - hard

Spatial objects:

GIS functions
Data input functions:
Existing form Form that is suitable for use in the GIS
converts
Data management functions:
• Storage and retrieval of data from the GIS database
• Capability to read the data in a flexible and logical manner, to search and
identify specific items or attributes, and to display these information in a spatial
context.

Data manipulation and Analysis functions
Original spatial data sets geometry
Better manageable, accurate and consistent with the other data sets
already present or to be encoded in the system
Manipulation of spatial data
Map overlays
Creates new map layers with an existing one
Features of each coverage are intersected to create new output
features
Transform

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Map Layer Overlay
Overlay generates homogenous units – eg. agroecozones
All layers must be in
same projection and
scale
Fig Source: FAO

Map dissolve
• Deletes the boundaries between adjacent polygons having the
same attributes values for a specified feature.
• Clipping the unwanted polygons from the map.
Buffers
Polygons created around points, lines and polygons.

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Buffering
Buffering: forming
bands on either side of
lines or around of
points or polygons to
perform analysis within
the bands

Data output functions:
• The outputs (reports) may be in the form of CRT display, maps,
listings, data files or text in hard copy.
• CRT display during interactive data processing and map
development is an important operational requirement

Linkage between Remote sensing and GIS
• Noninvasive and nondestructive gathering of information
• Valuable source of input for GIS databases
• Quick collecting of data (often in a digital format) over large
areas.

Remote sensing supported GIS operations
• Display backdrop: Visual aid or for the interactive creation and updating of
maps. Desired base is a photograph, then scanning is needed to convert it to
digital form.
• For the generation of thematic maps: Utilized as a data layer for GIS
functions.
• For the derivation of input variables for models.
• Real-time link

What a GIS can do:
Location :What exists at a particular location ?
Conditions : Where do certain conditions apply ?
Trends : What changes have occurred over time ?
Spatial Patterns :What spatial patterns exist ?
What if …: What will be the consequences of decisions (GIS +
Models)-Spatial Decision Support Systems.

Conceptual Model of GIS
GIS
“themes,”
“layers,” or
“coverages”
The real
world

GIS – An Integrating Technology
Fig Source: FAO

How a GIS holds data
• GIS holds spatial information in independent map layers – single
phenomenon mapped across space
• Integrates layers by registering them to a common locational
reference (lat/long grid).
• Thematic layers can all be made visible at the same time or
selectively and linked by common location
• Allows overlaying to get homogenous land units and other types
of information
• Allows collating data from several layers for any location
• Allows spatial analysis

New data can also be entered into a GIS in many different ways,
including:
– Digitizing from a digitizer
– GPS
– Surveys, via COGO (computer geometry operations)
– Scanned images and Digitization
– Acquisition from remote sensing instrumentation

Map Scale
Scale: Ratio of distances on map to distances
on earth’s surface
Representation:
Graphical: km
Verbal: 1 cm = 2.5 km
Numeric: 1:250000
Preferred representation: graphical

Map scale determines the size and shape of features
• Large scale
• Small scale
city
1:500 1:24000
1:24000
city
1:250000
Source: ESRI

Standard Scales:
1:1000,000 Country level
1: 250,000 State level
1: 50,000 District level
1: 12,500 Micro level
Survey of India Maps (topographic maps) are
available at all scales except 1:12,500

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Creating a GIS
Data Input
 Getting spatial data into GIS
 Getting attribute data into GIS
Data Storage
 Making data usable
Data analysis
 Geographic analysis
Data Output
 Presenting results

35
Fig Source: (Murai and
Murai, 1999))
Spatial data
Attribute data
Data sources

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Building a GIS
 Data base design Area boundaries
Co-ordinate system (datum/projection/GCPs)
data layers
features for each layer
attributes for each feature type
coding and organizing attribute data
(external database design)
 Entering spatial data from maps for each layer (digitizing layers)
 Entering attribute data
 Managing the data base
 Presenting maps in customized form

Fields where GIS is applicable
Astronomy/Planetary Disaster management Healthy Mapping
Agriculture Ecology Mining
Archaeology Economics Ocean / Marine
Architecture Energy Politics/Government
Aquatics Engineering Soils
Aviation Environment Sports & Recreation
Automobile Integration Education Surveying
Banking Forestry Telecommunications
Business & Commerce Geostatistics Tourism
Consumer Science and
Behavior
Groundwater Transmission Planning and
Routing
Climate Change Geology Utilities
Crime Hydrology Volunteer GIS and Open
Technology
Defense/Military Municipality/Urban Weather

Weather, Soil and Agriculture applications of GIS:
Weather Soil Agriculture
Rainfall Soil Types Precision Farming
Weather Stations Soil Grid Disease Control
Doppler Radar Texture Classification Crop Assimilation Model
Cirrus Clouds Soil Moisture Water Stress
Weather Warnings Soil Survey Geographic
Database
Crop Resilience to Climate
Change
Ocean Surface Current
Analysis Real-Time
(OSCAR)
Water Retention Capacity Crop Productivity
Real-time Lightning Erosion Reduction Strategy Irrigation
Global Wind Vectors Slope Parameters Drought
Albedo Soil Loss Equation Crop Forecasting
Solar Irradiance Salinity Organic Farming
Snowfall Vegetation Erosion Drainage Ditches
3D Atmospheric Data Normalized Difference Soil
Index (NDSI)
Length of Growing Period

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GIS Applications
Resource Inventory
(Data Base Management)
Integration of Different Layers
(Overlay)
Interfacing with simulation
models – GIS based DSS
Level-1
Level-2
Level-3

APPLICATIONS OF GIS IN AGRICULTURE

Ronald and Moses, 2016
Land suitability study for rice growing in Kisumu county(Ronald and Moses,2016)

Criteria relationship:
Ronald and Moses, 2016

Different parameters considered:
Land cover map Interpolated rainfall map

Interpolated temperature map Slope map
Contd.,

Contd.,
Soil depth map Soil drainage map

Soil texture map
Rice suitability map
Ronald and Moses, 2016

Rice crop suitability classes in Kisumu county
Ronald and Moses, 2016

Integrated Assessment of Groundwater for Agricultural Use in Mewat District
of Haryana, India Using Geographical Information System (GIS)
Mamta et al., 2016

Groundwater quality map Groundwater potential map
Mamta et al., 2016

Groundwater vulnerability map
Integrated groundwater assessment map
Mamta et al., 2016

Characteristics of integrated groundwater map
Mamta et al., 2016

Study on spatial variability of PAJANCOA east farm soils using GIS:
East Farm map of PAJANCOA & RI, Karaikal with sampling points
Aruna et al., 2016

• Parameters considered for the study:
• pH and EC,
• organic carbon,
• Available Nitrogen
• Available Phosphorus
• Available Potassium

Soil pH variability in east farm of PAJANCOA and RI, Karaikal.
Aruna et al., 2016

Electrical conductivity variability in East farm soils of PAJANCOA &
RI, Karaikal
Aruna et al., 2016

Organic carbon variability
Aruna et al., 2016

Aruna et al., 2016
Available Nitrogen variability

Available Phosphorus variability
Aruna et al., 2016

Available Potassium variability
Aruna et al., 2016

Remote Sensing and GIS Based Spectro-Agrometeorological Maize Yield
Forecast Model for South Tigray Zone, Ethiopia
Abiy et al., 2016

• Normalized Difference Vegetation Index
• Rainfall Estimate
• Water Requirement Satisfaction Index
WRSI = (ETa /WR) x 100
ETa = Seasonal actual evapotranspiration
WR = Seasonal crop water requirement
Where.,
WR= PET x Kc
PET = potential evapotranspiration
Kc = crop coefficient
Where.,

Correlation between NDVI Variables and Maize Yield:
Maize yield as a function of NDVIa Maize yield as a function of NDVIc
NDVIa = actual NDVI NDVIc = cumulative NDVI

Maize yield as a function of NDVIx
Correlation between RFE and Maize Yield
Maize yield as a function of RFE

Correlation between WRSI and Maize Yield:
Maize yield as a function of WRSI

Correlation between ETa Variables and Maize Yield:
Maize yield as a function of Eta Maize yield as a function of Eta total.

Multiple Linear Regression Model for Yield Forecasting:
This multiple regression generated the following equation.
Predicted Maize Yield (q ha-1) = -1.06 + (21.99 x NDVIa) + (0.24 x REF)
Actual yield from spectro-agrometerological model as a function of predicted yield.

ANOVA of maize yield forecast model.
Parameters estimates of the maize forecast model.
Abiy et al., 2016

Evaluation of Conventional Crop Yield Forecast Using the Developed Model:
Evaluation of conventional crop yield (q.ha-1) forecast using developed model.
Abiy et al., 2016

Maize yield forecast map of South Tigary zone in Ethiopia for the year 2013.
Abiy et al., 2016

Drought Assessment Using GIS and Remote Sensing in
Amman-Zarqa Basin, Jordan
Rainfall data satellite images
Standardized Precipitation Index
(SPI)
Normalized Difference
Vegetation Index (NDVI
• Spatial digital database
• Generate thematic layers
• Delineate areas with high drought risk
• Compare the results of both models
GIS software

Normal Difference Vegetation Index (NDVI) (Tucker, 1979)
NDVI = (λNIR - λRED) / (λNIR + λRED)
where,
λNIR = Reflectance in the near infrared (NIR)
λRED = Reflectance in the Red bands
It varies in the range of -1 to + 1.
DEVNDVI = NDVIi- NDVImean, m
Where,
DEVNDVI = NDVI deviation
NDVIi = NDVI value for month i
NDVImean,m = long-term mean NDVI for the same month, m

NDVI drought Index Map for selected years and selected
months.
Nezar Hammouri and Ali El-Naqa, 2007

Standardized Precipitation Index (McKee et al., 1993)
Quantify the precipitation scarcity for multiple time scales
Long-term record is fitted to a probability distribution, which is
transformed into a normal distribution where the mean SPI for the
location and desired time period is zero.
Classification of SPI Values.
(McKee et al., 1993)

The SPI values for 6 and 12 months for selected stations.
(McKee et al., 1993)

The SPI values for 6 and 12 months for selected stations.
(McKee et al., 1993)

Advantages and Disadvantage of GIS
Advantages:
 Data are stored in a physically compact format and can be retrieved
quickly.
 Spatial analysis is conducted by computer algorithms that, from a
practical perspective, are not performed on analog map data, such as
multi parameter spatial modeling and change analysis.
 Spatial and attribute data are integrated into a single system.
 It is cost effective for certain complex spatial modeling tasks.
 Data collection, spatial analysis, and decision making are integrated into
a single system.

Disadvantages
 The cost can be prohibitively high to convert existing maps and attribute.
 Purchase and maintenance costs of computer software and hardware are
high for complex modeling tasks or sustaining large databases.
 A relatively high level of technical expertise is required for successful GIS.
 The primary cost when establishing a working GIS involves database
development; this accounts for over 90% of the total system cost in some
cases. After a digital database is established, however, it can be easily
updated and used for numerous applications.

Conclusion:
The agronomic community, including farmers, land managers,
fellow scientists, policymakers, and the general public should
benefit from this evolving and expanding field.