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MICRO-LEVEL PLANNING FOR THE DEVELOPMENT OF LAND AND WATER RESOURCES IN KALYANDURG AREA OF

ANANTAPUR DISTRICT, ANDHRA PRADESH, INDIA USING REMOTE SENSING AND GIS TECHNIQUES

Thesis submitted to the SRI KRISHNADEVARAYA UNIVERSITY

For the award of the degree of

DOCTOR OF PHILOSOPHY IN

GEOGRAPHY BY

K. RAGHUVEER NAIDU

Research supervisor

Prof. Y.V. RAMANAIAH &

Co-guide

Dr. NAGARAJA RAVOORI

DEPARTMENT OF GEOGRAPHY

SRI KRISHNADEVARAYA UNIVERSITY ANANTAPUR 515003 (A.P) INDIA

JUNE 2013

ii

Prof. Y. V. Ramanaiah

Department of Geography

S.K. University

Anantapur 515003

CERTIFICATE

This is certify that the thesis entitled Micro-Level Planning for the Development of Land and

Water Resources in Kalyandurg Area of Anantapur District, Andhra Pradesh, India Using

Remote Sensing and GIS Techniques submitted by K. Raghuveer Naidu for the degree of

Doctor of Philosophy in Geography to Sri Krishnadevaraya University, Anantapur, represents

the original work done by him under my guidance and supervision. The thesis did not form the

basis for the award to the candidate of any degree or diploma previously.

Prof. Y. V. Ramanaiah

(Research Supervisor)

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iv

DECLARATION

I hereby declare that the research work presented in this thesis entitled Micro-Level Planning

for the Development of Land and Water Resources in Kalyandurg area of Anantapur

District, Andhra Pradesh, India: Using Remote Sensing and GIS Techniques carried out by

me in the Department of Geography, Sri Krishnadevaraya University, Anantapur, is the original

work done by me under the supervision of Prof. Y.V. Ramanaiah, Department of Geography,

S.K. University, Anantapur and Dr. R. Nagaraja (co-guide) Group director, NDC, NRSC,

Balanagar, Hyderabad. The thesis was not submitted for any degree, diploma or other similar

titles earlier and no part of it has been published or sent for publication at the time of submission.

Place: Anantapur (K. RAGHUVEER NAIDU)

Date:

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ACKNOWLEDGEMENTS

I am grateful to Prof. Y.V. Ramanaiah, Dept. of Geography, S.K. University, Anantapur

for his guidance in carrying out this research work and for his help, assistance, cooperation

constant encouragement, and suggestions for the completion of the research work.

It is with immense pleasure, I wish to record my deep sense or gratitude to my Co-guide

Dr. R. Nagaraja, Group Director, NDC, NRSC, Balanagar, Hyderabad for his help, constant

encouragement, assistance, offering suggestions for preparing maps and spending his valuable

time to completion of the thesis.

I wish to place on records my thanks to Dr. G. Ravishankar, Head Land use Division,

NRSC, Dr. C.S. Murthi Head Agriculture Division, NRSC, Dr. K. Seshadri, Scientist,

Geoscience Division, NRSC, Mr. Elango, Scientist, NDC, NRSC for providing necessary data.

Thanks are due to P. Purushottam Reddy, Deputy Director, Groundwater department Anantapur

for providing data. I am thankful to M. Sudarshanam, Chief Planning Officer, Anantapur for

providing statistical data.

I am very much thanks to Dr .S.V.B. Krishna Bhagavan, Director (Tech), APSRAC,

Hyderabad for providing necessary data.

I express my sincere thanks to Prof. Krishna Kumari, Head of the department, S.K.

University, Anantapur for her inspiring suggestions and encouragement.

I am also thankful to Prof. M. Sambasiva Rao, Department of Geography, S.K.

University, Anantapur for his constant encouragement.

I sincere thanks to Prof. K. Krishnaiah, Department of Geography, S.V. University,

Tirupati for his help and constant encouragement.

I am very much thankful to Mr. J. Kesav Kumar for his help to finalize the thesis

I am ever thankful to my friends and colleagues Mr. V. Srinivasulu, Research Scholar,

Mrs. Suneela, Mr. J. Malleshwar Rao, Mr. M. Narayanaswami and Dr. Somasekar Reddy for

their help and encouragement.

Words are not sufficient to acknowledge the help and encouragement of my Pinni and

Babai K. Rupa Kala and Dr. K. Dasaratha Ramaiah for my education and development and I

dedicated my thesis to them.

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I take this opportunity to express my heartfelt gratitude to my Brother-in-law P. Amruth

kumar for his lot of help, support and constant encouragement in my education and life.

I am indebted to my beloved parents, wife, Sister, Brother-in-law, co-brother and sister-

in-law for their encouragements and timely help in carrying out this work.

Lastly I express my appreciation and thanks to all those who has extended their co-

operation either directly or indirectly in completing this thesis work.

Anantapur (K. RAGHUVEER NAIDU)

Date:

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DEDICATED

TO

Dr. K. DASARATHA RAMAIAH

AND

K. RUPAKALA

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CONTENTS

Page No

Certificates i, ii

Declaration iii

Acknowledgements iv to vi

List of Tables vii

List of Figures viii

List of Photographs ix

CHAPTER

I. A perspective on the research theme 1 - 26

II. Locational aspects of the study region 27 - 44

III. Dynamics of land use and land cover 45 - 70

IV. Soil, geology and land capability classification 71 - 99

V. Spatial distribution of Surface water resource 100 - 130

VI. Spatial pattern in the quality and quantity of sub-surface water resources 131 - 163

VII. Behavioral approach of Farming community towards land and water use 164 185

VIII. Summery and conclusions 186 - 191

References 192 - 195

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LIST OF TABLES

1.1 List of IRS Satellites

2.1. List of Villages in the Study Area

2.2 Mandal wise Population in the Study Area in 2001 and 2011 Census.

2.3. Population Density and Sex Ratio in 2001 and 2011

2.4. Mandal Wise Literacy Rate from 2001 to 2011

2.5. Village Wise Population details in the Study Area as per 2011 Census

3.1 Land Use and Land Cover Classes as Classified by NRSC

3.2 Area under different Land Use and Land Cover categories in the Study

Area-2000-2012

3.3 Level-1 Classification

3.6 Urban sprawl Map of the Kalyandurg town in the study area

4.1 Distribution of Soils in the study area

4.3 Distribution of soil degradation in the study area

4.4 Area under different rock formations in the study area

4.5 Area under different Land Capability classes in the Study area

5.1 Droughts and Famines in the study Region

5.2 Average annual Rainfall (in m.m)

5.3 Rainfall details in Kalyandurg station in the year 2012 (in m.m)

5.4 Rainfall details in Brahmasamudram station in the year 2012 (in mm)

5.5 Rainfall details in Settur station in the year 2012 (in mm)

5.6 BT project dam & reservoir information

5.7 year wise inflow and out flow information about the Project

5.8 Latest Irrigation Tank particulars in the Study Area

5.9 Land Use and Land Cover distribution in the Chapari watershed area.

5.10.Average runoff and rainfall for study catchments

6.1 Mandal wise Geological and Hydro geological Characteristics of the study area

6.2 Depth to Ground water (DTW) levels for 2000

6.3 Depth to Ground water levels for 2012

6.4 Mandal wise Ground water resource potential and stage of groundwater

development of the study area -2008-09

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6.5 Mandal-wise density of wells in the study area (2008-09)

6.6 Assessment of Dynamic Groundwater Resources in the Study area (2008-2009)

(ha.m).

6.7 Assessment of Ground water Recharge in three mandals of the study area (in

ha.m.) 2008-2009

7.1 Mandal-Wise Age of the Sample Farmers

7.2 Educational Standards of the Sample Farmers

7.3 Size of the Family of the Sample Farmers

7.4 Number of Family Members Engaged in Agriculture

7.5 Landholding Sizes of the Sample Farmers

7.6 Rainfed and Irigated Area under Different Crops of the Sample Farmers

7.7 Sources of Irrigation for Agriculture of Farmers

7.8 Area under Single Crop in of the Farmers in Sample Mandals

7.9 Problems Encountered In Agro-Biological Factors of Production of Farming Community

7.10 Awareness of the Farming Community on the Land Management Practices

7.11 Awareness of the Farming Community on Modern Irrigation Methods

7.12 Change of Cropping Pattern to use Less Water with More Irrigation

7.13 Awareness on the Benefit of Changing the Cropping Pattern

7.14 Awareness on Soil Conservation Practices

7.15 Awareness on Water Harvesting Methods

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LIST OF FIGURES

1.1 Study Area From Space

1.2. Electromagnetic spectrum

1.3 Part of Pennar Drainage Basin in the Study Area as vieweby LISS-III, LISS- IV, PAN and

Cartosat -1 image.

1.4 False colour composit of the Study Area

1.5 Procedure for processing and reconciling spatial data.

1.6. Selection calculate geometry in the attribute table

1.7. Selection of units square Kilometers (sq km) or Hectares in the Calculate

Geometry tool bar.

2.1 Location Map of the Study Area

2.2 Village Administrative Boundaries of the Study Area

2.3 Base Map of the Study Area

2.4 Slope Map of the Study Area

2.5 Digital Elevation Model (DEM) in the Study Area.

2.6 Hydro geomorphology map

2.7 Anantapur Deviations in Annual Rainfall from the Long-Term Mean

2.8. Probability of Different Monthly Rainfall Amounts in Anantpur (CSWCRTI)

District

2.9. Mean Number of Rainydays for Anantpur

2.10. Population Distribution in the Study Area

2.11 Transportation Network in the Study Area

3.1 Land Use/Land Cover Map for April-2000

3.2 Land Use/Land Cover Map for Feb-2012

3.3 Distribution of Land use and Land cover Classes in April-2000

3.4 Distribution of Land use and Land cover Classes in Feb-2012

3.5. Built-up Land Urban (Kalyandurg town) in the Study Area

3.6 Urban sprawl Map of the Kalyandurg town in the study area.

3.7 Mine/Quarry in the study area

3.8. Paddy cultivation in the study area

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3.9. Agricultural Plantation in the study area

3.10 Scrub Forest in the Study Area

3.11. Barren rocky stony waste in the study area

3.12. Sandy area near in the study area.

4.1 NDVI (Normalized Differential Vegetation Index) Image

4.2 Soil classes and soil Depth of the study area

4.3 Distribution of the Soils in the study area

4.4 Soil slope map of the study area

4.5 Distribution of the Soil slope categories in the study area

4.6 Map showing soil degradation in the study area

4.7 Soil Degradation in the study area

4.8 Geology map of the study area

4.9 Distribution of different rock formation of the study area

4.10 Land Capability Map of the study area.

4.11 Distribution of Land Capability classes in the study area.

5.1 Kalyandurg monthly minimum and Maximum Temperature in 2012 Year

5.2 Rainfall Distribution in Kalyandurg Brahmasamudram and Setturu Mandals of

The study area

5.3 Pennar River Basin in the Study Area.

5.4 Krishna River Basin in the Study Area

5.5 Bhairavanithippa Project from Space

5.6 Micro-Watershed Boundaries of the Study Area

5.7 Location Map of the Chapari watershed in Kalyandurg mandal

5.8 Map shows Land Use and Land Cover of the Chapari watershed

5.9 False colour composite of the Chapari Watershed

5.10 Drainage network in the study area.

6.1 Groundwater Prospects Map in the Study Area.

6.2 Depth to Water Levels (DTW) for 2000

6.3 Depth to Groundwater Level Map for 2000

6.4 Depth to Ground water levels for 2012

6.5 Depth to Groundwater Level Map for 2012

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6.6 Stage of Groundwater Development in the Study Area.

6.7 Groundwater sample locations of the study area

6.8 pH levels in the study area

6.9 TDS levels in the study area

6.10 Chloride levels of the study area

6.11 Calcium levels in the study area

6.12 Magnesium levels in the study area

6.13 Total Hardness levels in the study area

6.14 Location of wells in Kalyandurg

6.15 Well construction timeline for Kalyandurg

6.16 Well failure rates in Kalyandurg

6.17 Use of wells in Kalyandurg

7.1 Mandal-Wise Age of the Sample Farmers

7.2 Educational Standards of the Sample Farmers

7.3 Size of the Family of the Sample Farmers

7.4 Number of Family members Engaged in Agriculture

7.5 Landholding Sizes of the Sample Farmers

7.6 Rainfed and Irigated Area under different Crops of the Sample Farmers

7.7 Sources of Irrigation for Agriculture among the Sample Farmers

7.8 Area under Single Crop in of the Farmers in Sample Mandals

7.9 Problems Encountered in Agro-Biological Factors of Production

7.10 Awareness of the Farming Community on the Land Management Practices

7.11 Awareness of the Farming Community on Modern Irrigation Methods

7.12 Change of Cropping Pattern to use Less Water with More Irrigation

7.13 Awareness on the Benefit of Change of Cropping Pattern

7.14 Awareness on Soil conservation Practices

7.15 Awareness on Water Harvesting methods

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LIST OF PHOTOGRAPHS

Plate 3.1 A view of the Banana Plantation in red soil belt near Chapari Village in Kalyandurg

Mandal

Plate.3.2 A view of the orange garden near Setturu which is one of the important fruite crop in

the study area

Plate 3.3 Jasmin Flower garden near East Kodipalli Village in Kalyandurg Mandal

Plate 3.4 A view of the Flowers cultivation under well irrigation in Pale Venkatapuram Village

in Brahmasamudram Mandal.

Plate 4.1 A view of the Red soil covers which is predominant soil type in all over the study area.

Plate.4.2 A view of the Red shallow loamy soils near Kalyandurg Town.

Plate 5.1 A view of the Drought situvation in the Study Area.

Plate 5.2 Formers over come the drought situvation and use in drip irrigation system to

cultivated Ground nut crop in the Study area

Plate 5.4 The Pennar River at Timmasamudram vllage, at present showing dry situation.

Plate.5.5 Dry condition Pulakunta Cheruvu in East Kodipalli Village in Kalyandurg Mandal

Plate 6.1 A view of the intensive Ground nut cultivation using Bore well in the Study Area

Plate 6.1 A view of the Dry Dug Well in the study area

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1

CHAPTER I

A PERSPECTIVE ON THE RESEARCH THEME

The needs of district or regional development demand a comprehensive approach to the

management of the natural resources. It is necessary to have accurate inventories of resources in

general and particular to land and water and updating the existing information periodically and

systematically. Timely availability of such multi-sectoral information will enable the planner to

plan the integrated land use planning with least damage to the ecological system.

The fast growing population and the aspiration of masses to have better standard of living

have resulted in increasing demands of food, fiber and fuel. There are also other co commitment

demands on land for housing, industry, communications and recreation. The scope for exploiting

hitherto unused land is rather limited. We must, therefore, make optimal use of our most

important natural endowments-viz. land and water. It is, therefore, necessary that a scientific land

use planning strategy should be adopted. As Dr. M.S. Swaminathan (1979) emphasized in the

seminar we should devise ways to produce more and more quantities of food from less and less

area. The importance of this statement can be judged easily if we realize that china has 20

percent less area under cultivation but produces twice as much food as India does. The land use

planning must strive not only to maximize productivity from the land ant also to improve, or at

least maintain, the quality of Land use planning phenomenon is of recent origin and

comparatively a new ramification in Economic Geography and is intimately associated with

special reference to agriculture. Land use concept falls broadly into two categories: The first

category includes climate, topography, geology, geohydrology, hydrology, soils and vegetation,

and the second category includes the products of past human activity such as roads, canals and

terraces. It includes the socio-economic infrastructure including the land put to use in the field of

industrial, mining, recreational and allied activities.

The concept of land has been defined in various ways by many authors. Prf. Vink, A.P.A

(1975) defines land use as any kind of permanent or cyclic human intervention to satisfy the

human needs either material or spiritual or both, from the complex of natural and artificial

resources, which together are called land. It is the application of human controls in a relatively

2

systematic manner to the key elements within any ecosystem in order to derive benefit from it.

The land use planning is in essence the determination of the optimum use of every acre of

the land and the optimum use must necessarily change from decade to decade with the changing

socio-economic conditions. Any land use planning must accordingly be dynamic and not static,

flexible and not rigid, capable of being adapting to changing condition (Stamp, L.D 1962).

Finally, it is apt to assert that the concept of agricultural land use implies not merely the rational

use of land put to cultivation, and bringing more land under plough and making it viable for

cultivation in every possible way, but also includes the conservation of land from erosion and

development of nutrients through multiple cropping system, increasing the fertility of land by

applying organic as well as inorganic manures, application of modern technology, and lastly but

not the least, the preclusion of over exploitation of land.

THE SCOPE AND SIGNIFICANCE OF THE PRESENT THEME

Agriculture is the backbone of Indian economy, providing livelihood to nearly two-thirds of

the population and contributing approximately 27 to 30 percent to the Gross National Product.

Foodgrain production has increased from 51 million tons in 1951 to 259.32 million tons in 2012-

13. On the other end, the Indian population increased to 1.21 billion with a decadal growth of

17.64 % but foodgrains production growth is not the same compare to population growth, and

calling for efficient agricultural management for better utilization of land and water resources on

a sustainable basis.

Because of growing population, economic development and industrialization, the

pressure is increasing on natural resources to meet the present demand. Community livelihood

options are also changing day by day. In this situation conservation and management of the

natural resources in a sustainable way and searching for the better livelihood options for people

to improve their socio-economic conditions are essential. Application of Information Technology

viz., Remote Sensing and Geographical Information System (GIS) in the areas of Planning and

Natural Resource Management will enable us to perform better and to take actions in time.

Both land and water are not bound by political borders and they are really spatially

assigned common properties of the Earth, they are the public goods to be shared and utilized

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with a social commitment and environmental engagement so that they are made available to all

and forever without endangering the ecological balance, and achieving this objective is a

stupendous task and really a challenging one.

The challenges to facilitate the livelihoods of any region to adopt a judicial and scientific

utilization of land and water resources for a sustainable well-being are huge, diversified, quite

complex and dynamics. The major problems like over-exploitation, under-utilization and miss-

utilization of these two resources are presented in one way or other as the significant regional

claims and conflicts and ultimately leading to jeopardizing the local environment from micro to

macro spatial levels. In view of these problems, spatial understanding and analysis of land and

water resource use for human welfare with environmental safeguards seems to be a paramount

need now.

The question/challenge of optimum utilization of land and water resource base has been

attracting planners, policy makers and academicians from local to global levels. The present

theme is really a spatial ones and so it is necessary a geographic and there by geographic tools

are essentially to be sought into address them. Geospatial technologies like Remote sensing, GIS,

and GPS, the current core methodologies of Geography, are the handy and powerful tools to

analyze the spatial locations and linkages towards working out the strategies for utilizing land

and water for a sustainable future. They help for processing, storing, visualizing and analyzing

geospatial data, and finally for building up of Spatial Data Infrastructure (SDI) which provides a

greater support to implementing institutions and also energizes the potentials for prediction and

monitoring.

The natural resource development calls for greater attention to be paid to the drought

prone areas as well as backward tracks. The potential for agricultural growth in these areas is

considerable if the available land and water resource base is effectively and scientifically

managed with a proper package of geospatial technologies and credit inputs. It is in this

perspective, the present study on Micro-level planning for the development of land and water

resources of Kalyandurg area of Anantapur District, Andhra Pradesh: Using Remote sensing and

GIS Techniques. is attempted to draw attention and to examine the distributional pattern and

utilization of these natural resources. It is scoped that this diagnostic study will immense help to

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evolve both prophylactic and curative measures ultimately to improve the land and water

resource base of this backward area.

The Kalyandurg area has been selected for this study because a) it falls in the backward

region of Rayalaseema, which has been experiencing droughts and famines for several decades,

at the same time Indias second lowest Rainfall was recorded area Hagiri Valley is located in this

region. b) it has diversified landforms. C) The easy availability of remotely sensed data and other

socio-economic data. d) The study area

STUDY AREA

The present Study area of Kalyandurg, consisting of Kalyandurg, Brahmasamudram and

Settur Mandals of Ananthapur district of Andhra Pradesh. Lies between 140 17' and 140 40' north

latitude and 760 50' and 770 24' east longitude. It is located in the middle of the peninsular region

and is confined to southwestern part of Andhra Pradesh. It is bounded by Gummagatta,

Beluguppa, Atmakur, Kanaganapalli and Kambadur Kundurphi mandals of the same district and

western side bounded by Karnataka state. The total geographical area of the study area is

1101.25 Sq Km., According to 2011 census the total population is 1, 76,297 of which urban

population is 32,335 (18 %), with literacy rate of 60.92 % and the sex ratio of total population is

964.

Kalyandurg area is the most chronicle drought prone part and also the most backward

area located on western side of Anantapr district. Annual temperatures vary between 21 and

42OC. In summer, temperatures will reach up to 42OC for three months from March to May.

Annual average rain fall varies between 370 m.m. and 760 m.m. Soil cover in the study area is

predominantly red loamy soils followed by black soils and alluvial soils. Natural vegetation is

very thin and scanty and mostly thorn scrub jungle type. The trrain is largely undulating and

closely disclosing the characteristic feature of plateau topography.

The study region is predominantly agricultural in its economy and hence the development

of agriculture is crucial and forms the basis of socio-economic development of the living

population. Undoubtedly, this region has vast land resource base which suitable for agriculture.

Groundnut is the mono-crop of this region cultivating on rain-fed conditions. Because there are

no perennial water resources base for stable and prosperous agriculture, tapping of sub-surface

water is the only major irrigation source for protective and productive agriculture. The major

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hindrance of this region is the frequent occurrences of drought, i.e. at every alternative year

which essentially has had a shattering effect on agricultural livelihoods.

It is indeed evident from the above facts that agriculture is the major stay of the economy

of the study region. In short, the present problems and prospects of agricultural development of

this region are intimately related to optimum utilization of land resources on one side and

judicious as well as conjunctive use of both surface and sub-surface water resources not only for

agriculture but also for domestic, industrial and other essential services. Hence, a critical

examination of the spatial- temporal scenario of land and water resource base of this backward

part of Anantapur district, will immensely helps to design, micro-level planning for effective

utilization of these two natural resources in order to bring stability and sustainability without

disturbing eco-system of the region.

OBJECTIVES OF THE STUDY

For providing a comprehensive micro-plan to develop land and water resource base of the

region, it is imperative to gain insights into various aspects of the research theme. To be more

explicit, the present theme includes.

(i) To classify the land use/ cover, for two points of time, namely 2000 and 2012 and

evaluate the stock of land resources base of the region and examine the spatial

patterns and differences.

(ii) To analyze the dynamics of land use/ cover during the period of 2000 and 2012.

(iii) To evaluate the soil group and geology and to bring out the land capability classes.

(iv) To understand the spatial distribution and extent of different slopes, a slope map

prepared using Arc GIS software, based on Satellite data (Carto DEM).

(v) To Understand the various land forms and geomorphic units existing in the area

(vi) To examine the different sources of surface water resources and their spatial

distribution and utility pattern.

(vii) To evaluate the ground water resource potentials, quality and quantity of its

distribution.

(viii) To know-how the behavioral approach of the farming community towards the

utilization of land and water resource.

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(ix) To prepare strategies for effective management of Land and Water Resources in the

study area.

DATA BASE AND METHODOLOGY

In the present study an application of geospatial technologies like remote sensing and GIS

techniques is vigorous for evaluation of land and water resources in the present study area

REMOTE SENSING

Remote sensing is the process of sensing and measuring objects from a distance without

directly coming physically into contact with them. This technique employs a sensor, positioned

on a platform, which detects and records data from one or more bands within the electromagnetic

spectrum. In this chapter, the basic parameters of remote sensing: electromagnetic spectrum, and

the past and present satellites available and its capabilities for earth resources observation were

discussed. The methodology followed in the present study is also described here.

ELECTRO MAGNETIC SPECTRIM

The sun and various artificial sources emits a wide range of electromagnetic energy. The

entire range of this electromagnetic is called the electromagnetic spectrum. The electromagnetic

energy travels in a regular sinusoidal oscillation called waves. The velocity of electromagnetic

energy in vacuum is constant and related to frequency and wavelength through the following

expression:

C=f

Where C is velocity of electromagnetic energy in vacuum

f is frequency

is wavelength

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Fig.1.2. Electromagnetic spectrum

a. Ultra violet (uv): The figure: indicates high level of atmospheric absorption of energy for

wavelength shorter than those of visible light, so that only a restricted range of gamma and

ultraviolet rays can be recorded. Because of absorption, ultra violet rays can only be recorded at

wavelengths greater than 0.27 m, and even these are affected by atmospheric scattering, which

produces a hazy image. Scattering is caused by reflection and refraction from particles in the

atmosphere. This region can be used to detect and monitor atmospheric pollution and oil spills on

water.

b. Visible light: This part of the spectrum (0.4to 0.7 m) is the maximum solar radiation region.

Most of the remote sensing investigations are concentrating in this region. Visible light can be

split into colour bands as shown in figure: .Most scattering occurs in short blue etc.

c. Infrared(IR):The infrared range is divided into three bands namely near, middle and far. The

near IR (0.7 to 3 m) is corresponds to reflective infrared, the middle, thermal or emissive IR

energy extents from 3 to 15 m with a maximum around 10, um and the far infrared extends

from 15 to 1000, um. Remote sensing is usually restricted to the near and middle IR bands. The

fig: shows that much of IR part of electromagnetic spectrum is observed by atmospheric gases

so that remote sensing is only effective in certain atmospheric windows.

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d. Microwave: This extents from1mm to 1m. Due to its ability to penetrate clouds and detect

surface and subsurface moisture. Much research has been done on microwave remote sensing in

the recent past. Wavelengths remote sensing detects different types of vegetation, soils, rocks,

crops.

PAST AND PRESENT SATELLITES IN REMOTE SENSING

LANDSAT: The technology of remote sensing gained momentum from 1972 onwards

with the launch of Earth Resource Technology Satellite-1 (ERTS-1) by NASA, USA, which was

later renamed as Landsat-1, Till now seven satellites of Land sat series have been launched Land

sat-II, III, IV, V, VI, VII.

SYSTEM PROBATOIR DOBSERVATION DE LA TERRA (SPOT)

French Centre National d Etudes Spatial (CNES) has launched third generation satellite

viz. SPOT in Feb.1986. The SPOT satellite is having two identical sensors viz HRV-1 & HRV-2

(High Resolution Visible) and observes in three spectral band (panchromatic 0.51-0.75 m) with

a ground resolution of the order of 10 meters.

Later SPOT 2, 3, 4, 5, series satellites has been launched; recently SPOT 6 was launched by

Indias PSLV-C21 mission on 9th September 2012, resolution for this, panchromatic-1.5 m,

multispectral-8 m.

REMOTE SENSING IN INDIA

Space activities in the country started during early 1960s with the scientific investigation

of upper atmosphere and ionosphere over the magnetic equator that passes over Thumba near

Thiruvananthapuram using small sounding rockets Realizing the immense potential of space

technology for national development, Dr. Vikram Sarabhai, the visionary leader envisioned that

this powerful technology could play a meaningful role in national development and solving the

problems of common man.

Thumba Equatorial Rocket Launching Station (TERLS), a few meters from the coastline,

St Mary Magdalene Church. Thus, Indian Space programme born in the church beginning, space

activities in the country, concentrated on achieving self reliance and developing capability to

9

build and launch communication satellites for television broadcast, telecommunications and

meteorological applications; remote sensing satellites for management of natural resources.

The objective of ISRO is to develop space technology and its application to various

national tasks. Accordingly, Indian Space Research Organization (ISRO) has successfully

operationalised two major satellite systems namely Indian National Satellites (INSAT) for

communication services and Indian Remote Sensing (IRS) satellites for management of natural

resources; also, Polar Satellite Launch Vehicle (PSLV) for launching IRS type of satellites and

Geostationary Satellite Launch Vehicle (GSLV) for launching INSAT type of satellites.

From the beginning, space activities in the country, concentrated on achieving self

reliance and developing capability to build and launch communication satellites for television

broadcast, telecommunications and meteorological applications; remote sensing satellites for

management of natural resources.

Accordingly, Indian Space Research Organization (ISRO) has successfully

operationalised two major satellite systems namely Indian National Satellites (INSAT) for

communication services and Indian Remote Sensing (IRS) satellites for management of natural

resources; also, Polar Satellite Launch Vehicle (PSLV) for launching IRS type of satellites and

Geostationary Satellite Launch Vehicle for INSAT type of satellites.

Indian Remote Sensing (IRS) Satellite System

The Indian Remote Sensing (IRS) satellite system is one of the largest constellations of

remote sensing satellites in operation in the world today. The IRS programme commissioned

with the launch of IRS-1A in 1988 presently includes twelve satellites that continue to provide

imageries in a variety of spatial resolutions from better than one meter ranging up to 500 meters.

IRS data has been used for Natural Resources Management, Natural Resources

Information System. Ground Water potential zone mapping and mineral targeting tasks. The

ocean applications of IRS data include potential fishing zone identification and coastal zone

mapping Forest cover mapping, biodiversity characterization and monitoring of forest fire is now

10

carried out using IRS imagery. IRS spacecraft provide timely inputs to Flood and earthquake

damage assessment thereby providing the necessary supportive strength to disaster management.

Even in the field of Archaeological survey, the utility of IRS imagery has been well established.

The judicious combination of information derived from space based imagery with the

ground based socio economic data is leading to a holistic approach for resource monitoring and

its management.

EVOLUTION OF IRS PROGRAMME

Indian Space Research Organization (ISRO) realized the potential benefits of remote

sensing data for various societal applications in 70's and initiated remote sensing activities by

launching its first experimental RS Satellite, Bhaskara-1 in 1979 followed by Bhaskara-2

in1981. These missions carried optical (TV camera) and microwave (Radiometer) sensors and

provided valuable experience in the development and operation of spacecraft technology as well

as interpretation / utilization of RS Data. This experience paved the way for the definition of

Indian Remote Sensing (IRS) Programme under which the first operational satellite, IRS-1A

was launched on 17 March 1988. From then onwards, series of IRS spacecrafts were launched

with enhanced capabilities in payloads and satellite platforms (Table-1.1). The whole gamut of

the activities from the evolution of IRS missions by identifying the user requirements to

utilization of data from these missions by user agencies is monitored by National Natural

Resources Management System (NNRMS), which is the nodal agency for natural resources

management and infrastructure development using remote sensing data in the country.

Apart from meeting the general requirements, definition of IRS missions based on specific

thematic applications like natural resources monitoring, ocean and atmospheric studies and

cartographic applications resulted in the realization of theme based satellite series, namely,

Resourcesat, Ocean sat, Carto sat and RISAT.

11

12

Table 1.1 List of IRS Satellites

S. No Satellite Year of launch Orbit Sensor Resolution

in meter Swath

1 IRS-1A/ IB March1988/Aug 1991 904 Km LISS-1 72.5, LISS-2 36.25 148 Km/ 74*2

2 IRS-1C/ 1D Dec1995/Sept1997 817 Km PAN-5.8 m 70 km (3 x 23.33 km)

LISS-III-23.5 m-Visible and near IR region

141 km

LISS-III-70.5 m- Shortwave IR region 148 Km

WiFS 188m 810 Km

3

IRS-P3 March 1996 817 Km WiFS - 188 x 188 (B3 & B4) 188 x 246 (B5) 770 Km

MOS-A- 1565*1395 195 Km

MOS-B - 523*523 200 Km

MOS-C - 523 x 644 192 Km

4 Oceansat-1 May 1999 720 Km 360 m * 236 m 1420 Km

5 Resource sat-1 October 2003 817 Km LISS-III 23.5 m 140 Km

LISS-IV -5.8 m 23 Km

Awifs 70 m 740 Km

6 Cartosat-1 May 2005 618 Km 2.5 m 27 Km

7 Cartosat-2 January 2007 630 Km 0.8 to 1 m 9.6 Km

8 Cartosat-2 A April 2008 635 Km Better than 1 mt 9.6 Km

9 Cartosat-2B July 2010 637 km Better than 1 mt 9.6 Km

10 IMS-1 April 2008 626 Km 505.6 m and 37 m 151 Km

11 RISAT-2 April 2009 536.6 Km FRS - 3 mt 30 Km

FRS - 9 mt 30 Km

13

S. No Satellite Year of launch Orbit Sensor Resolution

in meter Swath

MRS -25 mt 115 Km

CRS -50 mt 230 Km12 Oceansat-2 September 2009 720 Km 50 Km * 50 Km 1400

k Cartosat-2B July 2010 630 Km Better than 1mt 9.6 Km

14 Resourcesat-2 April 2011 817 Km LISS-IV-5.8 m 23.9 Km

AWiFS-56m(nadir) 70 m (at field edge)

740 Km & 370 Km

15 RISAT-1 April 2012 536 Km HRS - 1 m 10 Km

FRS-1 3 m 25 Km

FRS-2 9 m 25 Km

MRS 21-23 m 115 Km

CRS 41-55 m 223 Km Source: www.isro.gov.in

GEOGRAPHIC INFORMATION SYSTEM (GIS)

Geographic information system tools are used for the processing of spatial data into

information, generally information explicitly used to make decisions. GIS can be defined as a

computer system for collecting, checking, integrating and analyzing information related to the

surface of the earth (Fundamentals of Geographic Information Systems by Michael N Demers

from John Wiley & sons Inc). Some of the major components of GIS are:

a). Hardware: It comprises the equipment needed to support the many activities of GIS ranging

from data collection to data analysis. The piece of equipment is the workstation, which runs the

GIS software and is the attachment point for ancillary equipment. Data collection efforts can also

require the use of a digitizer for conversion of the hard copy data to digital data and a GPS data

logger to collect data in the field.

14

b). Software: Different software packages are important for GIS. These software packages are

utilized for creating, editing and analyzing spatial and attribute data; therefore these packages

contain a myriad of GIS functions inherent to them. The extensions or add-on are software that

extends the capabilities of the GIS software package.

c). Data: The data is core of any GIS. The geodatabase is a collection of geographic datasets of

various types. A key geodatabase concept is the dataset. It is the primary mechanism used to

organize and use geographic information. The geo-database contains three primary dataset types:

i. Feature classes

ii. Raster datasets

iii. Tables

Creating a collection of these dataset types is the first step in designing and building a

geodatabase. Users typically start by building a number of these fundamental dataset types. Then

they add to or extend their geodatabase with more advanced capabilities (such as by adding

topologies, networks, or subtypes) to model GIS behavior, maintain data integrity and work with

an important set of spatial relationships. Components of GIS seek to build software applications

that meet a specific purpose and those are limited in their spatial analysis capabilities. Utilities

are standalone programs that perform a specific function.

METHODOLOGY

Material and Methods: Image interpretation can be carried out in two most popular ways

e.g. digital analysis and visual interpretation. During digital classification process training areas

for different classes are defined on to the satellite imagery on spectral response pattern in

different spectral bands is generated. Based on these training areas satellite imagery is classified

into different classes using parametric or nonparametric classifiers. Digital analysis is fast and

output image is raster, which simpler in structure but big in size. Masks are often used for

improving the classification of known areas. The chapter details the descriptions of different

steps need to be followed during analysis of Land Use Land Cover Satellite data of LISS III

and PAN. The methodology essentially is based on on-screen / heads up interpretation using

image interpretation keys. Semi automated approach can also be considered while analyzing few

categories at local level. In onscreen visual interpretation the imagery is displayed onto a

computer screen (normally as FCC) and intended classes are delineated based on image

15

interpretation elements, ancillary and legacy data. Resultant output from this will be vector

format, which supports complex GIS analysis and has smaller file size. Advantages for visual

interpretation approaches are as following:

Context / Texture / Pattern based classes can be delineated Various enhancement options are

possible to exploit the capability of multiband / multi season data.

Temporal assessment is time effective.

Adoptability and operational feasibility is high One of the advantages of visual interpretation

i.e, time effective temporal assessment has been illustrated herewith. Onscreen interpreted Land

use land cover details for the year 2000 and 2012 covering the study area. The example indicates

the versatility of the onscreen interpretation to change the vectors wherever there is a change in

the land use land cover pattern. This approach enables easy updating of the feature in the

subsequent cycles of interpretation.

MATERIALS

Material requirement for mapping land use/land cover are detailed below. Basic

Requirements a). Hardware requirement for interpretation for robust handling and timely

accomplishment of the steps involved in image analysis, the following minimum standard

hardware configuration is required. i. Processor: Minimum of 2.0 GHz P IV make or equivalent

processor ii. Disk space: Minimum of 80 Gigabyte iii. RAM: Minimum of 512 Mb iv. Display

size: At least 17 inch monitor Note: These are the minimum requirements and any additional

capacity available in above configuration will improve the functionality. b). Ancillary data : To

achieve optimum land use land cover classification reference to the following data is required:

i.Topographical maps ii .Land use land cover AWiFS data base iii. Biodiversity data base iv.

Wasteland data base v. District profiles vi. Any other published relevant material c). Field work:

Optimal ground data collection in terms of precision and content needs to be carried out by

involving following instruments i.GPS ii. Good quality photographic camera (Digital camera is

preferred) iii. Hardcopies of images for ground data collection.

16

SATELLITE DATA:

For delineation and mapping of land use land cover, Resolution merged data of IRS LISS

III and PAN during the period of 2000 and LISS-IV and Cartosat-1 in the period of 2012 data

has been used With a band combination of 3, 2 & 1 on 1:10,000 scale as shown in Fig.1.4

TOPOGRAPHIC MAPS:

Survey of India Topographic maps on 1: 50000 scale has been used for preparation of base

features such as Settlements, Transportation, Forest boundaries Drainage features and other

Resource maps. The study area falls under Survey of India topo sheet No D43 K14, K15, L2, L3,

L6 and L7 of 1: 50,000 scale (latest series).

GROUND DATA: Ground data form an important source of information for mapping and

accuracy estimation. Procedural steps for collecting the ground data are discussed in later

section.

SECONDARY DATA: Collected various departments like Groundwater department, Irrigation

department, Statistical department, Agricultural department, Soil testing laboratory and other

department for preparing the Depth to Groundwater levels maps and quality figures, cropping

details, soil types and fertility maps, population details etc.,

17

18

METHODOLOGY

Preparing the data is a primary requirement before undertaking interpretation and

subsequent analysis. Preparations of datasets for use in the current study are described below.

ORTHO RECTIFICATION: Satellite data is which is available in a raster form need to be

geo-referenced to a map coordinate system so as to generate spatial information and subsequent

use in a GIS environment. The processes of Ortho rectification involves assigning a coordinate

system and transform the raster to the input coordinate system that enable viewing, querying and

analyzing the geographic data. Hence they need to be referenced to a common projection system

before they are transformed to the original images. Different methods are available for

rectification of images in commercially available software packages. Fast Fourier Transform

(FFT) is an automated method of referencing satellite data. This method basically considers shift

or rotation parameters while rectification. The other method of Ortho rectification is using the

camera model with TM Ortho rectified data and SRTM 90 meter DEM. Attempts were also

made using Ortho-rectified TM data with SRTM90 meter DEM overlay on it as a reference data.

Evaluation of the datasets generated by both the methods indicated that the RMS error in both

the methods is around 500 meters. It is proposed to use TM Ortho-rectified data with SRTM 90

meter DEM overlay on it as a reference image for the Ortho-rectification process in the project.

The following steps are involved in the process._ Preparation of ETM data as reference image_

Preparation of SRTM data as DEM image_ GCP selection between ETM and LISSIII_ GCP

selection in overlap regions of adjacent scenes._ LISS-III Ortho product generation

(Registration) _ Evaluation of registered image with ETM data. Reference Image Creation ETM

data is available in UTM projection with WGS84 datum. This data has been re-projected to LCC

(Lambert Conformal Conical) projection with WGS84 datum before the Ortho-rectification

process. The image is subsequently cut to match with LISS-III and LISS-IV path/row area.

Image to image registration option has to be used for selection of ground control points.

Registration involves selection of ground control points between reference image and input L3

image. The following are the steps involved during the process.

Steps

1. Open Reference Image in viewer (ETM image)

19

2. Open Input Image in another viewer (LISS-III or LISS-IV image)

3. Activating Registration process by pressing Geometric correction from raster menu of Input

Image viewer.

4. Select Projective transform in Set Geometric model dialog

Set the following parameters in Projective transform model dialog

a. Select Elevation source as File

b. Select DEM file in Elevation File.

c. Give min Z and max Z (obtained from Image Info of DEM image)

d. Press Apply.

1. Windows will automatically arrange for GCP selection

2. Select accurate (precise) GCPs between ETM and LISS-III or LISS-IV image

3. Press sigma (S) for model calculation Transformation of the raster with a geometric model

result in warping the raster to map coordinates specified. This essentially involves use of a

mathematical transformation to determine the correct map coordinate location for each cell in the

raster.

Basically two geometric models are under consideration in the present study. For images

acquired in hilly terrain and also for the areas with large geometric distortions, ortho rectification

method is used to minimize the distortions due to the relief and viewing angle.

The process considers the conditions of data acquisition. However, simple polynomial

models can be considered for flat terrain. In order to maintain seamlessness of the data, it is

recommended to use the former process. Minimum of three points are required for computing the

first order polynomial. However, it is essential to collect more than 20 points evenly distributed

in the image, especially at all the corners so as to increase the accuracy of the transformation and

subsequent matching with the adjacent scenes. The Root Mean Square Error should be less than

one pixel. The following steps detail the procedure for resampling process to get the final output.

Press Display Resample dialog in Geo correction tools dialog Give output image file name Select Cubic convolution as Resample Method. Select output resolution as 24mx24m

20

The output file may be overlaid on the reference data used for Ortho-rectification for

evaluating the accuracy. For this the option of swipe or flicker available in the image tools may

be used for identifying the distortions if any and the measuring tool to quantify the mismatch.

The exercise needs to be repeated if the distortion is exceeding the tolerance limits.

For better interpretation, high resolution capability of PAN data and multi-spectral

advantage of LISS-III for 2000 and Cartosat-1 and LISS-IV data for 2012 were fused using

ERDAS imagine and Bilinear Interpolation resampling algorithm to generate the merged image.

This merged image was used for interpretation/ analysis. The steps taken are presented

schematically in Figure 1.2 (Flow chart)

IMAGE PREPARATION: Consistency in the image handling requires a thorough pre-processing

of satellite data for inter and intra image alignments in terms of geometry and radiometry. Image

covering the study areas, as available in specific scene-id is to be identified. In general it is

intended to keep the image data in Geo Tiff format and it need be it may be imported using

suitable format converters .While changing the raster image format care should be taken to

maintain the geo referencing scheme and an informal check on projection parameters may be

done after the conversion.

IMAGE ENHANCEMENTS: Image enhancement is essential for improving the image contrast

which allows the best possible separation of land cover classes by tuning the contrast. Image

radiometry characteristically varies from one scene to another. Hence standardization of

enhancement has to be achieved depending upon the major earth surface elements and the land

cover class being delineated. The type of enhancement varies depending upon the scene

coverage, feature type to be extracted etc. For instance major part of a scene may be covered

with sea surface or snow which will have bearing on the overall contrast of the scene, which

needs to be balanced. Subsequently, targeted land cover class (e.g. built-up areas or barren rocky

etc.) requires specific enhancement apart from the above mentioned aspect

21

Fig.1.5. Procedure for processing and reconciling spatial data.

DataBase

Satellitedata Collateraldata

IRSPAN/CARTOSAT

IRSIC/IDLISSIII/LISSIV

GeometricCorrection

Enhancement

Datamerging(PAN+LISSIII)and

(CARTOSAT+LISSIV)

Imageinterpretation

LandUse/LandCoverMaps

LandCapabilityMap

HydroGeomorphologyMap

DEMMap

Topomaps,otherresourceMaps

Groundwaterdata,Fielddata,Census,andotherstatisticaldata

Digitization,editing,labeling,mosaicinganddatalinkingetc

BaseMap

DrainageMap

WatershedMap

BasinMap Geology

SoilMap

Ground DepthtowaterlevelMap

StageofGWdevelopmentMap

VillageMap

MandalMap

WellsMap PopulationMap

NDVIMap

22

Image Interpretation: Image interpretation is defined as the the art of examining images

for the purpose of identifying objects or surface features and judging their significance.

Interpreter studies the remotely sensed data and attempts through logical processes in detecting

and identifying, classifying, measuring and evaluating the significance of physical and cultural

significance of spatial relationship (Manual of Remote Sensing, Vol. 1p. 369).

Image Interpretation key: The image interpretation key provides a critical reference base

for advanced interpretation. It helps the interpreter in evaluating the information in an organized

and consistent manner. Ideally an interpretation key consists of two components viz. 1.

Collection of annotated / captioned images illustrating features, 2. A graphic or textual

description of the systematically recognizes image features (Lillesand & Kiefer, 2000).

An image interpretation key for the study area has to be designed accordingly, prior to

interpretation, which can be further refined in course of interpretation. a. Collection of annotated

/ captioned images, Complete scene has to be studied thoroughly for distinction of features in

different possible band combinations across different seasons to understand the spectral response

patterns. Image subsets for each land cover need to be prepared and annotations/captions for

each are to be provided.

VISUAL INTERPRETATION TECHNIQUES

Visual interpretation techniques based on image characteristics such as colour, texture,

pattern, size, shape, location and association enable to identify and delineate different categories

of resources. The following are the brief description of the image characteristics.

i. Tone or Colour: Different surface objects reflect and emit different amounts of radiant energy.

These differences are recorded as tonal/colour or density variations on the imagery. In black and

white images, appear in different grey tones.

ii. Shape: It refers to physical form of an object and is also a function of scale of the image or

photo. Size and shape are inter-related in the image. The shape refers to palm or top view of the

object, as seen by the satellite. Shape can be irregular e.g. salt affected patches; boundaries of

geomorphic units; regular urban patterns or plantations.

23

iii. Shadow: They are cast due to suns illumination angle, size and shape of the object or sensor

viewing angle. The shape and profile of shadows help in identifying different surface objects

example clouds, nature of hill slopes aspect, apparent relief, etc.

iv. Location: The geographical location of the object often provide clue for identifying objects and

understanding their genesis eg. Salt affected land, eroded land, jhum or forest blanks, mountain

peaks etc.

v. Association: It refers to the situation of the object with respect to their surface features and

neighbouring eg. Canals with agricultural fields: marsh or swamps with flood plains and tidal

flats; gullies or ravines with severely eroded lands.

vi. Resolution: It is of two types spatial and spectral. The former refers to picture element or pixel

discernible on the image or smallest area resolvable or identifiable on the ground. Spatial

resolution allows the interpreter to detect and distinguish the smallest object on the ground.

LAND USE LAND COVER INTERPRETATION

Satellite remote sensing techniques are used to map the structure and dynamics of land

use /land cover on 1:10,000 scale.

1. Using the interpretation key prepared, land use land cover classes have to be delineated by

using onscreen interpretation procedure.

2. Relevant satellite image(s) has to be displayed on the computer screen at 1:10,000 scales.

3. Shape file has been generated and used for a continuous interpretation.

4. Onscreen interpretation has to be carried out in a separate layer (in shape file format) after

opening the grid tiles onto the image. Conventional method of interpretation in vector format

(line format) requires rigorous and time taking editing to eliminate dangles, label errors etc.

Shape files are easier in use for interpretation and it eliminates the post interpretation editing

work. Further this can be used with most GIS / image processing suites available and it also

complies to OGIS (white paper on shape file) specifications.

5. Land Use / Land Cover class codes has been used for labeling using the specific codes (Table

1 ) Textual errors while manually entering in label would require additional but avoidable effort

to rectify them. Preferably, a tool may be a customized GUI having drop down list of the labels

24

which may appear along with text. Such tool may be incorporated as add-in in to commercial

suites available.

6. Integration of layers such as base, village and watershed has to be carried out.

GROUND TRUTHING: Ground truth/ field verification is an important component in mapping

and its validation exercise. Utmost care and planning is required for collecting ground data and

verification. To facilitate a good ground truthing exercise the following steps need to be

followed:

a. Identify and list all the doubtful areas for the ground verification and refer all such areas with

respect to the top sheet to know their geographical location and accessibility on the ground.

b. Prepare field traverse plan to cover maximum doubtful areas in the field. Ensure that each

traverse covers, as many land use / land cover classes as possible, apart from the doubtful areas.

Surface water bodies map, ground water potential map, soil map, land use /cover maps

were prepared based on SOI topo sheet and Resolution merged satellite Image. The procedure

for identification, classification and mapping on 1:10,000 scale using satellite data is broadly

grouped under five phases. They are:

1. Based on image characteristics a preliminary visual interpretation key was developed for

each theme.

2. Identification and delineation of different categories based on interpretation key on the

image.

3. Doubtful areas were identified and marked on the preliminary interpreted maps.

4. Ground data collection and verification:

All the doubtful areas were identified and listed for ground verification. Field traverse

programme were planned in such a way to cover maximum doubtful areas.

Field details were entered on the preliminary interpreted map or on the toposheet for post ground

truth corrections and modifications.

Other field details like well yields in different geomorphic units, climatic data, cropping pattern

and other demographic units.

1. Final Interpretation and Modification:

25

2. After the ground truth data collection, the field details were incorporated in the final maps.

All the preliminary maps were updated in all respects before finalizing the maps. Recheck

and cross checks were carried where ever possible to ensure higher accuracy and reliability

of information.

All the resource maps including base map, drainage map were interpreted on. 1:50,000 scale and

presented in the corresponding chapters.

AREA CALCULATION: Using Arc GIS software area calculation procedure is as

followed.Open Arc map, open the Land use/cover shape file or coverage, go to layers and open

attribute table add a separate field namely area_sqkm and right click that field and click calculate

geometry then ok and select the use coordinate system of the data source and choose sq km in the

unit bar then ok.

Fig.1.6. Selection Calculate Geometry in the Attribute Table

26

Fig.1.7. Selection of units square Kilometers (sq km) or Hectares in the Calculate Geometry tool

bar.

Base Map and other Resource Maps: Land Capability, Soil, Slope, Hydro geomorphology, NDVI

(Normalized Differential Vegetation), DEM, Hill shade Maps, Base map, Drainage Map

prepared based on Resolution merged Satellite Imagery of LISS-IV and Cartosat-1 and SOI

Toposheet and other soil maps, using on screen interpretation on 1: 50,000 scale and presented

corresponding chapters. To linked the non spatial data of the Groundwater levels and : quality, to

the spatial database using Spatial analyst tool in Arc toolbox prepared Groundwater level maps

for pre and post monsoon period of 2000 and 2012 and Groundwater quality graphs prepared

using MS Word and presented respective Chapter.

27

CHAPTER-II

LOCATIONAL ASPECTS OF THE STUDY REGION

LOCATION

Study area of Kalyandurg, Brahmasamudram and Settur Mandals of Ananthapur district

lies between 140 17' and 140 40' north latitude and 760 50' and 770 24' east longitude is selected

as study area. It is located in the middle of the peninsular region and is confined to southwestern

part of Andhra Pradesh. It is bounded by Gummagatta, Beluguppa, Atmakur, Kanaganapalli and

Kambadur Kundurphi mandals of the same district and western side bounded by Karnataka state.

The total geographical area of the study area is 1101.25 Sq Km and its account 5.76 per cent of

the total geographical area of the Anantapur district. Anantapur district was formed, in the year

1882, after separation of Bellary district. In terms of geographical area, Anantapur is now the

largest district (19,125 Sq Km) accounting 6.9 per cent of the total geographical area of Andhra

Pradesh State. Administratively, Study area is divided in to 3 mandals and 39 Villages as

mentioned in Table 2.1 and as shown in Fig.2.2.

Table 2.1 List of Villages in the Study Area

S.No. Name of the Village S.No. Name of the Village

Brahmasamudram Mandal

1 Gundiganihalli 2 Bhyravanithippa

3 West Kodipalle 4 Eradikera

5 Vepalaparthy 6 Chelimenahalli

7 Brahmasamudram 8 Yerrakondapuram

9 Bhairasamudram 10 Kannepalle

11 Theetakal 12 Pillalapalle

13 Santhekondapuram

Kalyandurg Mandal

14 Chapiri 15 Kalyandurg (Rural)

16 Kalyandurg (Urban) 17 Garudapuram

18 Kurubarahalli 19 Bedrahalli

28

S.No. Name of the Village S.No. Name of the Village

20 Duradakunta 21 Palavoy

22 Mudigal 23 Golla

24 East Kodipalle 25 Muddinayanapalle

26 Varli 27 Thimmasamudram

28 Manirevu 29 Hulikul

Setturu Mandal

30 Settur 31 Adivigollapalle

32 Yatakal 33 Bachchupalle

34 Kamthanahalli 35 Ayyagarlapalle

36 Mulakaledu 37 Chintarlapalle

38 Lakshmanpalle 39 Khairevu

29

30

31

32

PHYSIOGRAPHY

Based on the topography, the study area can be divided in to three broad regions (i) the

fluvial plain of Penneru or Pennar in the eastern part and Hagiri or Vedavathi in the north-west

part. (ii) The hilly track in the north and middle and

(iii) The Mysore Plateau land towards south.

The study area forms the northern extension of the Mysore Plateau and slopes from about

700 m above MSL in the northern and southern part. Consisting of a series of disconnected

peaks and hills rising above 900 m MSL near Rangasamudra in pavugada taluk of Tumkur

district of Karnataka state. The northern extention of this occurs as scattered hills, rocky sounds

in the plains of Kalyandurg Mandal. Most of the study area forms the pedeplain underlain by

granite gneisses, Schists and granites ranging in elevation from 600 m to 300 m above MSL with

a gentle slope pennar river as shown in the Slope and Digital Elevation Model (DEM) maps

(Fig.2.4 and 2.5).

HYDROGEOMORPHOLOGY :

hydrogeomorphology or ground water prospects in relation to other natural resources like

lithology, geologic structure, Land use land cover, soils, rainfall and slope to know their

relationship with one another. Hydro geomorphological mapping is carried out using fussed data

of LISS-IV and Cartosat-1. Landforms broadly divided into four categories namely Inselberg

occupied about 1506.58 ha (1.37%), Moderately weathered pediplain occupied 14164.62 ha

(12.86%), Pediment Inselberg complex occupy about 24407.14 ha (22.16 %), Shallow

weathered Pediplain occupy major area in the study area about 61152.81 ha (55.53%) and

Shallow dissected Pediplain occupy about 8895.09 ha (8.08%) as shown in the figure.2.6., in the

chapter V discussed elaborately about groundwater prospects.

33

34

35

36

DRAINAGE

The study area forms part of Pennar and Krishna basins. The study area is mainly drained

by Pennar or Penneru and Hagiri or Vedavati rivers. The Pennar river enters the southern part of

the Anantapur district from Karanaka State near chavlum village near Hindupur town, and its

enter the study area near Mangalakunta Village, Kalyandurg Mandal. The Vedavati river enter

the study area at Bhairavanithippa village, and its drains predominantly in the Northern part

of study area, which is a tributary to Tungabhadra. In the water resource chapter elaborately

discussed about drainage. Eloborately discussed about Drainage pattern In the Chapter V.

AGRO-CLIMATE

The climate prevailing in the three mandals is semi-arid to arid with hot summer and

mild winter. Study area Rainfall and Temperature is similar to the district and it consider as a

whole. As per Koppens classification it comes under the Aw and Bshw type of climate. The

dominant aspect of the climate of the region is generally dry but for the rainy season during the

monsoon for a short period of the year. The onsets of monsoons are highly variable. Rainfed

agricultural production in south-western Andhra Pradesh is a far from easy as monsoon rains are

often unevenly distributed and droughts are common. Fig 2.7 indicates, In fact, Anantapur has

been listed as the second most drought-prone areas in India in terms of rainfall (Hill, 2001 ). Far

from the east-coast, this part of Andhra Pradesh does not receive the full benefits of the north-

east monsoon (October to December); and being cut off by the Western Ghats. The south- west

monsoon (June to September) is also prevented from fully reaching the district. The south-west

monsoon and north-east monsoon rainfall contribute about 57% and 27% of the total rainfall for

the year, respectively (Hill, 2001).

37

Fig.2.7 Anantapur Deviations in Annual Rainfall from the Long-Term Mean

Source: aprlp

Using data from Anantapur, Figure 2.7 shows that there is considerable variation in

annual rainfall around the average value. In some years during the period 1901-2001, rainfall

was as much as 475 mm higher than the average and in others it was more than 400 mm less the

average.

Extreme inter- and intra-annual rainfall variability is an important characteristic of the

agro-climate of the study mandals. A major challenge facing farmers in this area is the adoption

of farming systems that both cope with periods of low rainfall, bearing in mind the fact that

meteorological drought is a natural and recurring phenomenon, and capitalize on years of above

average rainfall. The general perception is that in every ten year period, there will be five

droughts of different intensities. Two of these droughts will be moderate, two will be severe and

one will be catastrophic.

Although a widely-held view is that annual average rainfall has been declining in dry

areas of south-western Andhra Pradesh, statistical analysis of 100 years data from 13 stations in

Anantapur, reveals that, if anything, average annual rainfall has been increasing, albeit by

around 25 mm, throughout the district since the mid-1970s (Hill, 2001).The only station to show

38

a decline during recent years being Kalyandurg (Hill, 2001). Comparison of two periods (1901-

51 and 1951-2001) revealed a slight decreasing trend in variability over the whole Anantapur

District, with seven of the thirteen stations witnessing decreasing variability. However, decadal

analysis demonstrated that during the most recent decades of 1991-2001 and 1981-91, nine and

eleven stations, respectively, faced increasing variability (Hill, 2001).

Fig. 2.8 Probability of Different Monthly Rainfall Amounts in Anantapur (CSWCRTI) District.

Source: aprlp

From the above figure shows the probability of monthly rainfall exceeding 20, 40, 60 or

100 mm at Anantapur. It can be seen that the highest probability of monthly rain exceeding 60

mm is during September-October. Hence, this is the period during which large volumes of

runoff are most likely to be generated.

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Fig.2.9. Mean Number of Rainy days for Anantpur

Source: aprlp

Figure presents the mean number of rain days per month for Anantapur. As the number

of rain days is not well correlated with the rainfall, this also indicates that rainfall events tend to

be smaller during the early and late parts of the south-west and north-east monsoons

respectively.

VEGETATION

The Forest cover in the study area is thin and scanty and not rich in forest wealth. It is

evident from the fact that only 6.49 percent (7152.28 hectares) of the total geographical area of

the study area is under forest cover which is far less than required proportion of forest to keep up

the ecological balance. And also most of the forest is of dry deciduous and open scrub type.

AGRICULTURE

Agriculture is the backbone of Indian economy, providing livelihood to about seventy

percent of the population and contributing approximately forty percent to the Gross National

40

Product. Food grain production has increased from 51 million tons in 1951 to 259.32 million

tons in 2011-12. On the other end, the Indian population crossed the billion and needs around

300 million tons of food grains and calling for efficient agricultural management for better

utilization of land and water resources on a sustainable basis.

Groundnut, millets and pulses which together account for 85 per cent of the gross cropped

area of the study area. Groundnut is the primary crop in the study area.

IRRIGATION

The chief sources of Irrigation in the district are tanks, wells and canals. The major

irrigation project in the district is Tungabhadra High level canal project Stage-I&II with an

ayacut of 51771 ha and six medium projects. Apart from these projects, there are 5353 irrigation

tanks and about 87,000 wells the gross irrigated area is 1, 54,000 ha and the net irrigated area is

1, 25,000 ha in the district. Out of net area irrigated, 31 per cent is from surface water irrigation

and 69 percent is from ground water irrigation.

At the same time in the study area of kalyandurg and other two mandals are also well-

irrigation is the major source of irrigation which accounts for 60 per cent of the total irrigated

area, Canals, Tanks and Other irrigated sources is 40 percent. There is a medium project namely

Bhairavanithippa minor Irrigation project constructed across the Vedavathi or Hagiri River and

providing Canal irrigation facilities in some part of the Brahmasamudram mandal of the study

area.

POPULATION

Table.2.2. Mandal wise Population in the Study Area in 2001 and 2011 Census.

Mandal Total Population in 2001 Total population in 2011 Growth

Persons Male Female Persons Male Female %

Kalyandurg 81086 41292 39794 89925 45391 44534 10.90

Brahmasamudram 39518 20120 19398 43162 21533 21629 9.22

Setturu 38281 19493 18788 43210 22078 21132 12.87

Total 158885 80905 77980 176297 89002 87295 10.95

Source: Census Department, Hyderabad.

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From the above Table shows that total population of the study area as per the 2011

census is 176297 and in 2001 census 158885 and growth rate is 9.87 per cent. And its

percentage in the district population 3.89 %, Anantapur district population in 2011 is 40, 83,315.

Table indicates that Kalyandurg mandal has highest population 89925 (as per 2011 census) in

the study area; lowest population mandals are setturu 41162 and Brahmasamudram 43210.

Highest population growth rate 12.87 % is recorded in Setturu mandal. Lowest population

growth rate 9.22 % is recorded in Brahmasamudram mandal, Overall study area growth rate is

recorded 10.95 % from 2001 to 2011 period. Female population growth rate is high compare to

male population in all mandals except Setturu. In Kalyandurg Male and Female population

growth rate is 9.92 % and 11.91 %, Braamudram is 7.02 % and 1.50 %, Setturu 13.26 % and

12.47 % and in the overall study area is 10.00 % and 11.94 % Male and Female population

growth rate recorded Respectively.

Table.2.3. Population Density and Sex Ratio in 2001 and 2011

S.No Mandal Sex Ratio In 2001 Sex Ratio In 2011

Population Density

(Per sq.km) 2001

Population Density

(Per sq.km) 2011

1 Kalyandurg 964 981 183 193

2 Brahmasamudram 964 1004 139 140

3 Setturu 964 957 124 124

Source: Census Department, Hyderabad.

Above Table shows that Sex ratio has been increased in the study area from 964 in 2001 to

980.6 females for 1000 males in 2011. At the same time Kalyandurg 964 to 981 and

Brahmasamudram 964 to 1004 also has been increased, but in Setteru sex ratio has been

decreased from 964 in 2001 to 957 in 2011. At the same time population density also has been

increased in the study area from 148.6 in 2001 to 152.3 in 2011.

Figure 2.6 indicates that highest population is concentrated in Kalyandurg town and

surrounding villages, it shows more peoples lives in urban areas because study area is belongs

42

to drought prone area and uneven distribution of Rainfall, these impacts on Agricultural

productivity, results people migrate from rural to urban for employment in secondary or Tertiary

sectors.

Rural and urban population in the study area: In the study area only one urban town is there

that is Kalyandurg, population 29266 and 32335 as per 2001 and 2011 census, at the same time

rural populations in the study area is 129619, 143962 persons as per 2001 and 2011 census.

Urban population growth rate is 10.48 % and Rural is 11.06 % recorded from 2001 to 2011 in

the study area.

Fig.2.10. Population Distribution in the Study Area

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LITERACY

Table.2.4. Mandal Wise Literacy Rate from 2001 to 2011

S.No. Mandal Literacy Rate -2001 Literacy Rate -2011

Persons %

Male %

Female%

Persons %

Male %

Female%

1 Kalyandurg 57.51 68.26 46.34 65.86 74.60 57.02

2 Brahmasamudram 45.42 58.09 32.27 57.63 67.36 48.04

3 Settur 51.10 63.42 38.35 59.29 68.26 49.89

Source: Census Department, Hyderabad.

The literacy rate in the study area is 60.92 % as per 2011 census as against 64.28 % in the

district and 67.66 % in the state as a whole. The percentage of literacy in the study area

increased from 51.35 per cent in 2001 to 60.92 in 2011. In the study area Kalyandurg mandal is

having highest literacy (65.86 %) and lowest is Brahmasmudram mandal (57.63 %). Highest

female literacy growth rate is recorded in Brahmasamudram mandal as shown in Table 2.4.

Table.2.5. Village Wise Population details in the Study Area as per 2011 Census

Name of the Village/ town

Total/ Rural/ Urban

No. House holds

Total Population

Male Female

Child pop (0-6 age)

Child pop-M

(0-6 age)

Child pop-F (0-6 age)

Brahmasamudram (m) Total 9363 43162 21520 21642 5055 2610 2445 Gundiganihalli Rural 325 1510 742 768 175 78 97 Bhyravanithippa Rural 862 4243 2183 2060 533 283 250 West Kodipalle Rural 576 2536 1295 1241 272 136 136 Eradikera Rural 519 2508 1235 1273 288 152 136 Vepalaparthy Rural 825 3962 2054 1908 459 253 206

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Name of the Village/ town

Total/ Rural/ Urban

No. House holds

Total Population

Male Female

Child pop (0-6 age)

Child pop-M

(0-6 age)

Child pop-F (0-6 age)

Chelimenahalli Rural 271 1179 605 574 141 75 66 Brahmasamudram Rural 700 3686 1544 2142 344 172 172 Yerrakondapuram Rural 111 504 244 260 66 29 37 Bhairasamudram Rural 752 3528 1790 1738 438 235 203 Kannepalle Rural 1139 5078 2554 2524 612 327 285 Theetakal Rural 539 2229 1128 1101 237 121 116 Pillalapalle Rural 1331 6028 3066 2962 769 397 372 Santhekondapuram Rural 1413 6171 3080 3091 721 352 369 Kalyandurg(m) Total 20621 89879 45307 44572 9973 5192 4781 Kalyandurg Urban 7220 32328 16036 16292 3404 1760 1644 Hulikal Rural 791 3262 1674 1588 351 186 165 Chapiri Rural 637 2751 1417 1334 286 159 127 Kalyandurg (Rural)

Rural 1661 7527 3940 3587 771 412 359

Garudapuram Rural 1426 6253 3146 3107 739 373 366 Kurubarahalli Rural 244 1295 677 618 141 70 71 Bedrahalli Rural 508 2202 1087 1115 289 148 141 Duradakunta Rural 553 2345 1170 1175 273 135 138 Palavoy Rural 1315 5517 2812 2705 619 331 288 Mudigal Rural 1194 5104 2534 2570 587 298 289 Golla Rural 753 3356 1693 1663 381 192 189 East Kodipalle Rural 984 4259 2147 2112 514 276 238 Varli Rural 186 754 384 370 86 44 42 Muddinayanapalle Rural 1060 4387 2205 2182 504 280 224 Manirevu Rural 957 3882 1988 1894 484 256 228 Thimmasamudram Rural 1132 4657 2397 2260 544 272 272 Settur (m) Total 9476 43172 21955 21217 4971 2515 2456 Settur Rural 1014 4751 2516 2235 464 238 226 Adivigollapalle Rural 190 808 420 388 112 62 50 Yatakal Rural 1469 6597 3340 3257 730 373 357 Bachchupalle Rural 561 2454 1231 1223 293 156 137

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Name of the Village/ town

Total/ Rural/ Urban

No. House holds

Total Population

Male Female

Child pop (0-6 age)

Child pop-M

(0-6 age)

Child pop-F (0-6 age)

Ayyagarlapalle Rural 412 1875 946 929 266 135 131 Mulakaledu Rural 2917 13518 6881 6637 1553 770 783 Chintarlapalle Rural 624 2688 1352 1336 304 157 147 Khairevu Rural 1457 6596 3294 3302 787 377 410 Lakshmanpalle Rural 832 3885 1975 1910 462 247 215 Total Study area Total 39460 176297 89002 87295 19999 10317 9682

Source: Census Department, Hyderabad.

From the above table it is found that there is only one urban settlement, namely,

Kalyandurg registered with a total urban population of 32328 and households of 7220 recently, it

was made as municipal town. The significant feature of this municipal town is females are

outnumbered (16292) to male population (16036). The remaining settlements in all the three

mandals are villages having population ranging betweena minimum of 190 population in

Adivigollapalli in setturu mandal to a maximum of 13518 in Mulakaledu village located in

Settutu mandal.

Highest Child population 9973 is registered in Kalyandurg mandal and lowest 4971 in

Setturu mandal. At the same time Highest 1553 child population is registered in Mulakaledu

Village in Setturu Mandal, lowest 66 child population is registered in Yerrakondapuram Village

in Brahmasamudram mandal, and also Highest 770, 783 male and female child population is

registered in Mulakaledu village in Setturu mandal and lowest 29, 37 male and female child

population is registered in Yerrakondapuram village in Brahmasamudram mandal in the study

area.

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TRANSPORTATION

The study area is well connected by road, but there is no Railways and Airways in the

study area. Roadways: State Highways (S.H.): Direction-Rayadurg to Madakasira, and

Kalyandurg to Anantapur and Dharmavaram. District roads: Kalyandurgam to Brahmasamudram

and Settur, Metal roads: connected Villagess to Manda Head quarters showing in the Fig 3.7.

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48

CHAPTER III

DYNAMICS OF LAND USE AND LAND COVER

Land is the most important natural endowment on which all the human activities are

based. Water and other resources culminate in the development of land use. A sequential

development of land use with time results in different land utilization patterns and trends.

Among the major resources available in the country, the most important is land comprising soil,

water and associated plant and animals involving the total eco-system. The communitys demand

for food, energy and many other needs has to depend on the preservation and improvement of the

productivity of this natural resource.

Growing population, and increased human activates are exerting pressure on limited land

resources. This is evident the per capita availability of this resource is, in fact, declining. The per

capita availability of land declined from 0.9 ha. In 1951 to 0.3 ha. In 2009-10. The availability of

cultivable land is even worse. The per capita availability of cultivable land has declined from

0.48 ha. In 1951 to 0.13 ha. In 2009-10. The changing trends in land use pattern also indicates

that by the turn of the century, the net area sown was increased from the 118.75 million ha. in

1950-51 to 140.02 m. ha. in 2009-10 and total cropped area 131.89 m. ha to 192.20 m.

ha.(source: Directorate of Economics & statistics, Department of Agriculture & cooperation).

And at the same time millions of productive arable land will be lost due to urban and industrial

activity. Therefore, this unprecedented demand on land for agriculture, urban and industrial,

mining, besides for forests and pastures (apart from land degradation and erosion) call for an

optimum utilization of land. This requires timely and up to date information about the spatial

distribution, location, extent, type of different land use and its spatial pattern of changes over a

period of time for scientific land use planning and management.

CONCEPT OF LAND USE AND LAND COVER

The concept of land use has been defined in various ways by many geographers. Sauer (1919,

p.47) defined land use as the use to which the entire land surface is put. Stamp (1962,p.426)

stated that the land as a whole must be so used as to satisfy as many as possible of the needs and

legitimate desires of the people, the nation as a whole. Vink (1975, p.1) explained land use as

any kind of permanent or cyclic human intervention to satisfy human needs, either material or

49

spiritual or both, from the complex of natural or artificial resources which together are called

land.its use is the result of a continuous field of tension created between available resources

and human needs and acted upon by human efforts. The form and function of land use is a

human enterprise, while the development of landscape is the continuous efforts of man for his

needs and sustenance under every possible combinations of climatic, vegetative and soil

conditions.

The terms both land use and land utilization are generally used synonymously although there

is some fine difference according to Burley (1961,p.18).

Land use data are needed in the analysis of environmental processes and problems that must be

understood if living conditions and standards are to be improved or maintained at current levels

( Anderson et al 1971)

Clawson has given nine major ideas or concepts about land. These are

1. Location or the relation of a specific parcel of land to the poles, the equator, and the

major oceans and land masses. There is also relationship between various tracts of land,

as well as a political location.

2. Activity on the land, for what purpose this piece of land or tract is used.

3. Natural qualities of the land, including its surface and subsurface characteristics and its

vegetative cover.

4. Improvements to and on the land. This is closely related to activity.

5. Intensity of land use or amount of activity per unit of area.

6. Land tenure, i.e who owns the land, which uses it.

7. Land prices, land market activity and credit as applied to land.

8. Interrelations betwe