Vegetation Change Detection in India Using MODIS...

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Master’s Thesis in Geo-Informatics

Vegetation Change Detection in India Using MODIS Satellite Images

By

Sashikiran Maranganti

Master of Science in Geo-Informatics

ISRN No-LIU-IDA/FFK-UP-A--09/005--SE

Supervisor: Mr. Chandan Roy

Examiner: Dr. Rita Kovordanyi

Department of Computer and Information Science,

Linköping Universitet SE-581 83 Linköping, Sweden

September, 2009

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Acknowledgement

Master thesis work was carried out at the Department of Computer and Information

Science, Linköping University, Linköping, Sweden. As a part of acknowledgement, I want to express my gratitude towards many people whose insightful suggestions, contributions enabled me to enhance my work at each stage. In addition to this, I wish to acknowledge the assistance provided by IDA of Linkoping University. I am deeply indebted to my supervisor Mr. Chandan Roy from Linköping University whose help, suggestions and encouragement at all time of research enabled me to successfully complete my master thesis. I specially thank Dr. Åke Sivertun and Dr. Rita Kovordányi for their guidance throughout the study. I would also like to thank all my friends from Linköping University, roommates Tirumal Kumar, Sujit, Karteek and Krishna Mohan for being surrogate family during the stay in Sweden. Especially, I am grateful to my parents whose love and affection has been great support for each and every success in my career. Without their cooperation and willingness to share data and assist in the field this research would not have been possible. Sashikiran Maranganti

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Dedicated to My Parents

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Abstract

Due to man made events and natural causes many regions are currently undergoing rapid and wide ranging changes in land cover globally including developing and developed countries. India is one of them where land use and land cover change are taking place at a rapid pace. Forests are the most valuable natural resources available to the mankind on planet earth. On the one hand, they are the essential source of livelihood for the poor and marginalized sections of the society; on the other hand they provide furniture and other items of desire for the rich. Forest land cover change is an important input for modeling ecological and environmental processes at various scales. Rapid delineation in naturally forested regions is one of the major environmental issues facing the world today. It has been estimated that vegetation change threatens about one sixth of the world's population and one quarter of global terrestrial land. Vegetation cover plays a key role in terrestrial biophysical process and is related to a number of ways to the dynamics of global climate. Monitoring seasonal changes in vegetation activity and crop phenology over wide areas is essential for many applications, such as estimation of net primary production, deciding time boundary conditions for crop yield modeling and supporting decisions about water supply. Vegetations are the major part of land cover and their changes have an important influence on the energy and mass biochemical cycles and are also a key indicator of regional ecological environment change. Urbanization, demand of land for agriculture and demand of timbers for industrial purposes are the main reasons of manmade natural forest destruction. Though we are planting trees through reforestation and afforestation programs but these new forests never can be the representative of natural forest. In order to understand and manage environment at large variety of temporal and spatial scales, up-to-date and reliable information is required all the time. Remote Sensing is a valuable data source which can provide us land-use/land-cover change information on a continuous basis with very high accuracy. Remotely sensed data like aerial photographs and satellite images are the only option that allows detecting land cover changes on a large scale. Satellite images have the potential of offering the most accurate and latest information compared to statistical, topographic or land use maps. In this study an attempt has been made in analyzing vegetation change detection that took place between 2000 and 2005 using Terra MODIS 32 day 500m time series data on a monthly basis. With the launch of National Aeronautics and Space Administration (NASA) onboard aqua and terra platform, a new generation of satellite sensor data is now available. Normalized Difference Vegetation Index method has been employed for accurate classification of images and has proved to be successful. Key words: MODIS, NDVI, Remote Sensing, Vegetation

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Table of Contents

Acknowledgements…………………………………………………………………........02

Dedication………………………………………………………………………………..03

Abstract ……………………………………………………………………………….....04

List of Figures……………………………………………………………………………10

List of Tables…………………………………………………………………………….11

List of Abbreviations…………………………………………………………………….12

1. Introduction……………………………………………………………………...14

1.1 Introduction………………………………………………………………….15

1.2 Aim…………………………………………………………………………..15

1.3 Objectives……………………………………………………………………15

1.4 Hypothesis…………………………………………………………………...15

1.5 Study Area…………………………………………………………………...16

1.5.1 Geographical Location……………………………………………….16

1.5.2 Area…………………………………………………………………..16

1.5.3 Topography…………………………………………………………..16

1.5.4 Climate……………………………………………………………….16

1.5.5 Temperature………………………………………………………….17

1.5.6 Rainfall………………………………………………………………17

1.5.7 Soils………………………………………………………………….17

1.5.8 Forests……………………………………………………………….17

1.5.9 Agriculture…………………………………………………………..19

1.5.10 Land use……………………………………………………………..19

1.5.11 Water Resources……………………………………………………..20

1.6 Data & Software’s…………………………………………………………...20

1.6.1 MODIS Imagery……………………………………………………..21

1.6.2 IDRISI Andes………………………………………………………..21

1.7 Background…………………………………………………………………..22

2. Applications of Remote Sensing…………………………………………………28

2.1 Remote Sensing……………………………………………………………...29

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2.2 Electro Magnetic Spectrum………………………………………………….30

2.3 Interaction of energy with earth surface features…………………………….30

2.4 Interaction with water, vegetation and soil…………………………………..31

2.5 Water…………………………………………………………………………32

2.6 Vegetation……………………………………………………………………32

2.7 Soil…………………………………………………………………………...32

2.8 Application of Remote Sensing……………………………………………...33

2.9 Observation of earth’s surface using satellites……………………………….33

2.10 Time series sensors suitable for land observations………………………33

2.11 Landsat…………………………………………………………………...34

2.12 Systeme Pour l’Observation de la Terre (SPOT)………………………...36

2.13 NOAA-AVHRR…………………………………………………………36

2.14 Moderate Range Imaging Spectro-radiometer (MODIS)………………..37

2.15 Indian Remote Sensing (IRS)……………………………………………39

2.16 Agriculture……………………………………………………………….40

2.17 Agriculture applications………………………………………………….40

2.18 Advantages………………………………………………………………41

2.19 Forestry…………………………………………………………………..41

2.19.1 Forestry applications…………………………………………………42

2.19.2 Clear cut mapping and Deforestation………………………………..42

2.19.3 Species identification and Typing……………………………………43

2.19.4 Burn Mapping………………………………………………………..43

3. Vegetation in India according to Physiographic Region………………………...45

3.1 Deccan Plateau……………………………………………………………….46


3.1.2 Area…………………………………………………………………..46

3.1.3 Topography…………………………………………………………..46

3.1.4 Climate……………………………………………………………….47

3.1.5 Rainfall……………………………………………………………….47

3.1.6 Temperature………………………………………………………….47

3.1.7 Soil…………………………………………………………………..47

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3.1.8 Vegetation……………………………………………………………47

3.1.9 Forests………………………………………………………………..47

3.1.10 Agriculture…………………………………………………………...49

3.2 Eastern Ghats………………………………………………………………...49


3.2.2 Area…………………………………………………………………..49

3.2.3 Topography…………………………………………………………..50

3.2.4 Climate……………………………………………………………….50

3.2.5 Rainfall……………………………………………………………….50

3.2.6 Temperature………………………………………………………….50

3.2.7 Soil…………………………………………………………………...51

3.2.8 Vegetation……………………………………………………………51

3.2.9 Forests………………………………………………………………..51

3.2.10 Agriculture…………………………………………………………...52

3.3 Northern Mountains/Himalayas……………………………………………...55


3.3.2 Area…………………………………………………………………..55

3.3.3 Topography…………………………………………………………..55

3.3.4 Climate……………………………………………………………….55

3.3.5 Rainfall……………………………………………………………….55

3.3.6 Temperature………………………………………………………….56

3.3.7 Soil…………………………………………………………………...56

3.3.8 Vegetation……………………………………………………………56

3.3.9 Forests………………………………………………………………..56

3.3.10 Agriculture…………………………………………………………..56

3.4 Indo-Gangetic Plains………………………………………………………...59

3.4.1 Geographical Location………………………………………………59

3.4.2 Area………………………………………………………………….59

3.4.3 Physical Environment………………………………………………..60

3.4.4 Topography…………………………………………………………..60

3.4.5 Climate……………………………………………………………….60

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3.4.6 Rainfall………………………………………………………………60

3.4.7 Temperature………………………………………………………….60

3.4.8 Soil…………………………………………………………………...60

3.4.9 Forests………………………………………………………………..61

3.4.10 Agriculture…………………………………………………………...61

3.5 Western Ghats………………………………………………………………..64


3.5.2 Area…………………………………………………………………..64

3.5.3 Topography…………………………………………………………..65

3.5.4 Climate……………………………………………………………….65

3.5.5 Rainfall……………………………………………………………….65

3.5.6 Temperature………………………………………………………….65

3.5.7 Soil…………………………………………………………………...65

3.5.8 Vegetation……………………………………………………………65

3.5.9 Forests………………………………………………………………..66

3.5.10 Agriculture…………………………………………………………...67

3.6 Problems of vegetation………………………………………………………70

4. Methodology & Results………………………………………………………….71

4.1 Remote Sensing Sensors……………………………………………………..72

4.2 Change Detection…………………………………………………………….72

4.3 Methodology…………………………………………………………………73

4.3.1 Vegetation Classification using NDVI………………………………73

4.3.2 Generation of NDVI…………………………………………………74

4.3.3 Image extraction & conversion in IDRISI…………………………..74

4.3.4 Goode’s Projection…………………………………………………..74

4.3.5 Image Pre-processing………………………………………………..74

4.3.6 Geometric correction………………………………………………..74

4.3.7 Atmospheric correction……………………………………………...75

4.3.8 Radiometric correction………………………………………………75

4.3.9 Windowing…………………………………………………………..75

4.3.10 Overlay………………………………………………………………75

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4.3.11 Image classification………………………………………………....75

4.4 Monitoring vegetation phenology using Remote Sensing………………….75

4.5 Results………………………………………………………………………76

4.6 Vegetation change detection & Comparison of Images…………………….77

4.7 Markov Model Prediction Algorithm……………………………………….82

4.8 Future change prediction……………………………………………………83

5. Conclusions……………………………………………………………………..89

6. References………………………………………………………………………90

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List of Figures Figure 1: Electro Magnetic Remote sensing of earth’s resources……………………….29

Figure 2: The Electro Magnetic Spectrum……………………………………………….30

Figure 3: Basic Interaction between electromagnetic energy and

earth surface features…………………………………………………………..31

Figure 4: Major factors influencing the spectral characteristics

of a water body…………………………………………………………………31

Figure 5: Reflectance spectra of different types of green vegetation

compared to spectral signature for senescent leaves…………………………...32

Figure 6: NDVI Time series graph………………………………………………………76

Figure 7: Vegetation change in November, 2000………………………………………..77

Figure 8: Vegetation change in February, 2001…………………………………………77

Figure 9: Vegetation change in November, 2001………………………………………..78

Figure 10: Vegetation change in March, 2002…………………………………………..78

Figure 11: Vegetation change in December, 2002…………………………………….....79

Figure 12: Vegetation change in January, 2003……………………………………….....79

Figure 13: Vegetation change in December, 2003……………………………………….80

Figure 14: Vegetation change in January, 2004………………………………………….80

Figure 15: Vegetation change in December, 2004……………………………………….81

Figure 16: Vegetation change in January, 2005………………………………………….81

Figure 17: Vegetation change in September, 2005………………………………………82

Figure 18: Markov prediction of Class 1, 2000………………………………………….84




Figure 22: Markov Transition Probability Matrix Graph………………………………..86

Figure 23: Markov Transition Area Graph………………………………………………87

Figure 24: SeaWiFs NDVI Time Series Graph for the year 2005-2007……..…………..87

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List of Tables

Table 1: Vegetation types of India………………………………………………………18

Table 2: Seasons best for identification of major vegetation types……………………...18

Table 3: Land use statistics in India……………………………………………………..19

Table 4: Water resources and potential in the river basins of India……………………..20

Table 5: The data acquisition and analysis process……………………………………...30

Table 6: Sensor characteristics of Thematic Mapper……………………………………35

Table 7: Sensor characteristics of Multi Spectral Scanner………………………………35

Table 8: Sensor characteristics of SPOT………………………………………………...36

Table 9: Sensor characteristics of AVHRR……………………………………………..37

Table 10: Data Formats of AVHRR…………………………………………………….37

Table 11: Sensor characteristics of MODIS……………………………………………..38

Table 12: Specifications of Present series of IRS Satellites……………………………..39

Table 13: Season wise crops of Andhra Pradesh………………………………………..47

Table 14: Season wise crops of Karnataka………………………………………………48

Table 15: Season wise crops of Maharashtra…………………………………………….48

Table 16: Season wise crops of Kerala…………………………………………………..49

Table 17: Geography and Topography…………………………………………………..50

Table 18: Season wise crops of Andhra Pradesh………………………………………..52



Table 21: Season wise crops of Chattisgarh……………………………………………..54

Table 22: Season wise crops of Orissa…………………………………………………..54

Table 23: Traditional calendar of Kullu valley, Western Himalayas……………………57

Table 24: Season wise crops of Jammu & Kashmir……………………………………..57

Table 25: Season wise crops of Himachal Pradesh……………………………………...57

Table 26: Season wise crops of Madhya Pradesh………………………………………..58

Table 27: Season wise crops of Assam…………………………………………………..58

Table 28: Season wise crops of West Bengal……………………………………………59

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Table 29: Season wise crops of Bihar……………………………………………………61

Table 30: Season wise crops of Haryana………………………………………………...62

Table 31: Season wise crops of Punjab…………………………………………………..63

Table 32: Season wise crops of Goa……………………………………………………..63

Table 33: Season wise crops of Gujarat………………………………………………….64

Table 34: Vegetation types of Western Ghats…………………………………………...66

Table 35: Physiographic and bioclimatic variation seen in different

hill ranges in the Western Ghats of Tamil Nadu, India……………………….66



Table 38: Season wise crops of Kerala…………………………………………………..68

Table 39: Season wise crops of Goa……………………………………………………..69

Table 40: Season wise crops of Gujarat……………………………………………….....70

Table 41: Markov Transition Probability Table…………………………………………86

Table 42: Markov Transition Area Table………………………………………………..87

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Abbreviations & Acronyms

AVHRR- Advanced Very High Resolution Radiometer EVI- Enhanced Vegetation Index ETM+- Enhanced Thematic Mapper Plus ENVISAT- Environmental Satellite EOS- Earth Observation Satellite GOES- Geostationary Operational Environmental Satellite HRV- High Resolution Visible HRVIR- High Resolution Visible and Infra-Red HRPT- High Resolution Picture Transmission HRG- High Resolution Geometry IRS- Indian Remote Sensing MERIS- Medium Resolution Imaging Spectrometer METEOSAT- Meteorological Satellite MSS- Multi Spectral Scanner MODIS- Moderate resolution Imaging Spectroradiometer NOAA- National Oceanic and Atmospheric Administration NDVI- Normalized Difference Vegetation Index NASA- National Aeronautics and Space Administration NIR- Near Infra-Red OrbView2- Satellite where SeaWiFs is mounted RBV- Radio Beam Vidicon SPOT- System Pour l’Observation de la Terre SeaWiFs- Sea view Wide Field Sensor SEVIRI- Spinning Enhanced Visible and Infra-Red Imager SWIR- Short Wave Infra-Red TM- Thematic Mapper VI- Vegetation Index VEGETATION- Vegetation monitoring instrument on board SPOT

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1. Introduction

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1.1 Introduction The land surface of the earth is the place where we live most of the time. Many regions are currently undergoing rapid and wide ranging changes globally. An important natural resource which provides basis to plant life is land. Land cover and its change is a key to many assorted applications such as environment, forestry, hydrology, agriculture and geology. The changes occurring are slow and natural and sometimes very quick and sudden due to anthropogenic activities. Land cover changes are taking place rapidly due to manmade events and natural phenomenon. Land-use changes are converting land-cover at a rapid pace worldwide and are observed more especially in tropics. Land cover composition and change are the two main important factors that affect ecosystem state and condition which help in assessing landscape condition and monitor status and trends over a specified period. “Some of the more common types of change detectable on remotely sensed data are associated with the clearing of natural vegetation, increased cultivation, urban expansion, the changing of surface levels in bodies of standing water, vegetation regeneration after wildfires and soil disturbances resulting from mining, landslides and overgrazing of animals”(Milne, A. K., 1988). In order to understand and manage environment at large variety of temporal and spatial scales, up-to-date and reliable information is required all the time. Land is a part of earth which is physically viewed as an area that is not covered by water. It sustains bio-diverse life-forms with humans playing a dominant part. Vegetation is a key component of eco-cycle and it helps in the exchange of energy between the land and the atmosphere. Climate plays an important role in distribution and controlling of natural vegetation and hence land cover will respond to changing. Human activities are the major cause behind the alteration of the climate system.

1.2 Aim

The main aim is to study the rate of change in vegetation cover of the whole India that has taken place between the periods from 2000 to 2005 using MODIS 32 day composite time series satellite image data. 1.3 Objectives: The following specific objectives will be pursued in order to achieve the aim above:

• Identify, map and determine the spatial extent of vegetation cover through India using satellite data.

• Identify the socio economic change of the study area. 1.4 Hypothesis to be tested:

• Major Land use and Land cover changes has been occurred due to natural events and human activities.

• Phenological change is dominant in India because there is seasonal variation in agriculture and all the forests are not ever-green.

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• Area under natural vegetation has been reduced and this could be the result of climate change and / or large scale change land-use (from natural vegetation to agricultural lands and agricultural lands to forests and so on).

• Socio economic conditions of the people have been changed. 1.5 Study Area

India, officially the Republic of India, is a South Asian country with New Delhi as the capital city. It is seventh largest country in the world by geographical area and Asia’s second most populous country after china and most populous democracy in the world. For administrative purposes India is divided into 28 states and 7 union territories. 1.5.1 Geographical location: India is located on the Southern Asia bordering the Arabian Sea and Bay of Bengal between Burma and Pakistan. It has geographical coordinates of 20 00N, 77 00E where the mainland stretches from 8o 4' and 37o 6' N latitude and 68° 7' and 97° 25' E longitude. The Himalayas lie along the country’s north/western, northern and north/eastern border, Indian Ocean on the south, Bay of Bengal to the south east and Arabian Sea to the south west. The length of the frontier line is 15,200 Km long. 1.5.2 Area:

Having an area of 328.7 Mha covering 2.4% of the total geographical area India ranks seventh largest country in the world and Asia’s second largest country. 1.5.3 Topography:

It comprises of four broad geographical areas: (Prasad et al., 2004)

• The Northern mountains which include the great Himalayas • The Indo-Gangetic Plains • The Southern (Deccan) Peninsula bounded by the Western and Eastern Ghats • The Coastal plains and Islands.

1.5.4 Climate:

India has a very diverse climate with some regions being extremely hot or extremely cold, extremely arid or extremely humid, and drought-prone or flood-prone. The climate in India is divided into four seasons, which are summer or pre-monsoon (March-May), winter (December-February), post-monsoon (September-November) and the monsoon (June-August).

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1.5.5 Temperature: Temperatures in India differ from region to region, as the average temperature in the north is 17.5° C to 30° C, 12.5° C to 30° C in the northwest and in the south it is from 22.5° C to 30° C. 1.5.6 Rainfall:

The total annual rainfall over India is about 117cm of which 75% of the long term average annual rainfall comes down during the monsoon season. 100 to 150 cm of rainfall comes in most parts of the country, 15 to 30cm or even less in some parts of northwest and higher in northeast and Western Ghats ranging from 200 to 400cm.

1.5.7 Soils:

Soil is an important layer of the earth’s surface which act as basis for plant growth formed from rocks, minerals, organic matter, water and bacteria. Indian soils are classified into four types: (Sharad et al., 2007)

1. Indo-Gangetic Alluvium soils 2. Black cotton or Regur soils 3. Red soils lying on the metaphorphic rocks 4. Laterite soils

1.5.8 Forests:

India has diverse landforms and to name a few it includes mountain ranges like The Himalayas and The Vindhyas, The Thar desert, The Deccan plateau and various parts of hilly and jungle regions. It is also in rich in evergreen to semi-evergreen, dry/moist deciduous to mixed forests, Mangroves, Alpine and Sub-alpine forests. The forest cover is 67mh covering 20.6% of the total geographical area of the country of which thick forest covers an area of 54,569(1.66%) Sqkm, moderate-dense forest covers 332,647(10.12%) Sqkm and open forest covers 289,872 (8.82%) Sqkm. Forests in India are classified into six categories according to:(Champion and Seth, 1968)

1. The tropical wet evergreen forests 2. South montane wet temperate forests 3. Tropical semi-evergreen forests 4. Tropical moist deciduous forests 5. Tropical dry deciduous forests 6. Tropical thorn forest

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No Vegetation type General Composition Area SqKm (%)

1 Tropical wet Evergreen Dense tall forests entirely evergreen r nearly so 51,249(8.0) 2 Tropical semi Evergreen Dominants include deciduous species but evergreens pre-

dominants 26,424(4.1)

3 Tropical Moist Deciduous

Dominants mainly deciduous but subdominants and lower story largely evergreen top canopy even and dense but 25 m high

236,794(37.0)

4 Tropical Dry Deciduous Entirely deciduous or nearly so. Top canopy uneven, rarely over 25 m high

186,620(28.6)

5 Tropical Thorny/Scrub Deciduous with low thorny trees and xerophytes pre-dominants, top canopy more or less broken, less than 10 m high.

16,491(2.6)

6 Tropical Dry Evergreen Hard leaved evergreen trees predominate with some deciduous emergent often dense but usually under 20 m high.

1,404(0.2)

7 Littoral and Swampy Mainly evergreens of varying density and height but always associated predominantly with wetness

4,046(0.6)

8 Sub-tropical broad leaved

Broad-leaved largely evergreen high forests 2,781(0.4)

9 Sub-tropical pine Pine associated predominates 42,377(6.6) 10 Sub-tropical dry

evergreen Low xerophytic forest and scrubs 12,538(2.5)

11 Montane wet temperate Evergreen without coniferous species 23,365(3.6) 12 Himalayan wet/moist

temperate Evergreen forests mainly scleriphyllous oak and coniferous species 22,012(3.4)

13 Himalayan dry temperate

Coniferous forests with sparse xerophytic undergrowth 312

14 Sub-alpine Stunted deciduous or evergreen forests, usually close formation with or without confers

15 Moist alpine Low but often dense scrub of evergreen species 18,628(2.9) 16 Dry alpine Xerophytic scrub in open formation mostly of deciduous in nature.

Table 1: Vegetation types in India Source: (Champion and Seth, 1968)

S.No Vegetation Type Season for Forest Type

Season for Forest Density

1 Tropical wet/ dry Semi-Evergreen

Mar/Apr Nov/Dec

2 Tropical Moist Deciduous Feb/Mar Oct/Nov 3 Deciduous Late Feb Late Nov 4 Littoral and Swampy Mar/Apr Nov 5 Temperate Apr/May Late Nov 6 Alpine/Sub-alpine Oct/Nov Early Nov 7 Sal forest April Nov 8 Teak forest Late Feb Nov/Dec

Table 2: Seasons best for identification of major vegetation types. Source: (Champion and Seth, 1968)

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1.5.9 Agriculture: Agriculture is the main occupation and largest economic sector of India. 70% of the population depends on it contributing 22% of Gross Domestic Product of the country.

a) The Kharif Season: June to October falls under this season where the monsoon begins. Rice is the principal crop and the other crops include millets, maize, groundnuts, jute and cotton and also pulses.

b) The Rabi Season: November to April/May falls under this season following kharif. Wheat is the main principal crop and the other crops include gram, oilseeds like mustard and rapeseed.

c) The Zaid or Hot weather Season: May to July falls under this season. Crops grown are moong, urad, watermelon and cucumber. Non-food or cash crops like sugarcane, tea, rubber, coffee, cotton, tobacco, spices etc are also produced.

1.5.10 Land use: In India land is used for many purposes such as residential, industrial and agricultural purposes. Land use Area (Mha) Percentage

Total geographic area 328.73 Area for land utilization 306.05 100.0 Forests 69.02 22.6 Not available for cultivation 42.41 13.9 Permanent pasture and grazing land 11.04 3.6 Land under misc. tree crops and groves 3.62 1.2 Culturable waste land 13.83 4.5 Fallow land and other than current fallows

10.11 3.3

Current fallows 14.80 4.8 Net area sown 141.23 46.1 Table 3: Land use statistics in India Source: (Agricultural statistics at a glance, 2003)

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1.5.11 Water Resources: India is blessed with many rivers. Twelve of them are classified as major rivers whose total catchment area is 253Mha and another 48 of them are classified as medium rivers with a total catchment area of 24.9 Mha.

River Origin Catchment

area(Sqkm)

Avg. annual potential

in river (b.cu.m)/yr

Indus (upto border) Manasarovar(Tibet) 321289 73.31 a) Ganga b) Brahmaputra, c) Barak and other rivers

Gangotri (UttarKhasi) 861452 525.02 Kailash range (Tibet) 194413

585.60 41723 Sabarmati Aravalli hills (Rajasthan) 21674 3.81 Mahi Dhar(MadhyaPradesh, MP) 34842 11.02 Narmada Amarakantak (MP) 98796 45.64 Tapti Betul(MP) 65145 14.88 Brahmani Ranchi (Bihar) 39033 28.48 Mahanadi Nazri town (MP) 141589 66.88 Godavari Nasik (Maharashtra) 312812 110.54 Krishna Mahabhaleshwar (Maharashtra) 258948 78.12 Pennar Kolar (Karnataka) 55213 6.32 Cauvery Coorg (Karnataka) 81155 21.36 Total 2528084 1570.98 Other river basins 248505 298.02

Total 2776589 1869.00

Table 4: Water resources and potential in the river basins of India. Source: (Central Water Commission, 2005) 1.6 Data and Software’s:

For this study, MODIS Terra 32 day composite time series satellite images with a spatial resolution of 500m of India were acquired for five years on a monthly basis from 2000 to 2005 from the web based satellite data archives of the Global Land Cover Facility (GLCF) an Earth Science Data Interface and required information from the Forest Survey of India (FSI) and Ministry of Agriculture and Environment as well as from different published journals, articles and reports. 1.6.1 MODIS Imagery:

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is a coarse resolution scanning radiometer onboard the Terra (EOS AM) satellite launched December 18, 1999 to replace the Advanced Very High Resolution Radiometer (AVHRR) on the earlier NOAA ( National Oceanic and Atmospheric Administration) and TIROS ( Television and Infrared Observation Satellite) satellite platforms. With a

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temporal resolution of one or two days, the MODIS sensor has been acquiring usable data since February 24, 2000 across 36 spectral bands with spatial resolutions varying from 250m to 1000m across the entire globe.

1.6.2 Software’s used:

IDRISI32 Andes ArcGIS 9.2 Microsoft Word, Excel, Power point.

1.7 Background This chapter contains the information about previous research work done in the field of vegetation change detection and prediction using remotely sensed data. This study focuses on different methods and techniques that are already done on vegetation change detection and prediction. Sakamoto et al., 2005, developed a new method for determining the phonological stages of paddy rice using remotely sensed data and then compared with statistical data taken from 30 paddy rice fields to determine the validation. The methods used were Wavelet (Daubechies, Symlet and Coiflet) and Fourier transforms which were applied on MODIS time series data to determine planting, heading and harvesting dates and growing period. Of the above two methods, Wavelet transform using Coiflet filter generated good results in determining phonological stages and growing periods than Fourier transform. The comparison of estimated phonological stages against statistical data is: 12.1 days for planting date, 9.0 for heading date, 10.6 for harvesting date and 11.0 days for growing period. They proposed wavelet transform with Coiflet (order 4) for crop phenology detection as it determined regional characteristics of rice phenology. Xiao et al., 2005, used MODIS 8 day composite images (500m resolution) and developed latest paddy rice agriculture spatial database for 13 provinces in southern china. MODIS paddy rice algorithm which uses time series of three vegetation indices NDVI, EVI and LWSI has been applied from the day of flooding and transplanting (open canopy) to the rapid plant growth to the point of canopy exists by taking into account many factors like snow and cloud covers, water bodies and other vegetated land cover types that could affect the seasonal dynamics of vegetation indices and then analyzed further according to spatial distribution of rice fields and compared with the results collected derived from Land Sat ETM+ images (1999/2000) at provincial and county levels. MODIS derived map area estimates and Land Sat derived map estimates showed reasonable agreements and proved that MODIS based paddy rice algorithms can be used on large spatial scales. Xiao et al., 2006, used MODIS 8 day composite images (500m resolution) and developed latest paddy rice agriculture spatial database for 13 countries in south and south-east Asia. MODIS paddy rice algorithm which uses time series of three vegetation indices NDVI, EVI and LWSI has been applied from the day of flooding and transplanting to the rapid plant growth to the point of canopy exists by taking into account many factors like

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snow and cloud covers, water bodies and other vegetated land cover types that could affect the seasonal dynamics of vegetation indices and then analyzed further according to spatial distribution of rice fields and compared with the results collected from National agriculture statistical data (NAS) at national and Sub-national levels. MODIS derived area estimates at national level are highly correlated and positively correlated at sub-national level with statistical results obtained from NAS. Kang et al., 2003, demonstrated the utility of MODIS land products and developed a simple one step phonological model by incorporating latest 2001 MODIS land data, Digital Elevation Model (DEM), Leaf Area Index (LAI) and climatic data from weather monitoring stations for determining the onset of vegetation greenness in temperate mixed forests in Korea. Thermal summation obtained from MODIS based timing of onset is related with 30 year mean air temperature. Using iterative cross validation technique two unknown parameters and regression models have been determined and validated with ground measured onset of greenness data which include length of the bud and subsequent leaf development. A mean absolute error (MAE = 3.0 days) and bias (+ 1.6 days) were found between predicted and MODIS based timings of onset. The predicted onsets were in correlation with ground measured onset of greenness (MAE =2.5 days and bias = 2.5 days). Civco et al., 2002, has compared both qualitative and quantitative results of different land use and land cover change detection techniques and mapping methods. The methods that were used are 1) Traditional Post-Classification 2) Cross Correlation Analysis, 3) Neural networks 4) Image segmentation and Object-Oriented Classification. He used Thematic Mapper (TM) data for March 27 and September 3 1989 represent conditions at T1, and Enhanced Thematic Mapper (ETM) data for September 23, 1999 and May 4, 2000 represent conditions at T2. A combination of both T1 to T2 change detection and post classification technique were employed. Each method has its own merits and cannot be able to solve the land use change detection problem individually. Petit, C. C and Lambin, E. F., 2001, explained the methodology of integration of different land cover maps from different sources for identifying the land cover change detection (i.e.) combining different maps and images acquired from different sources by equaling their thematic contents and spatial details. Based on the hypothesis that map generalization can improve change detection and spatial structure, which is a good indicator level of generalization of that map, they have combined different land cover maps of aerial photographs into one and then compared with target map (SPOT XS) and identified change detection. Results showed that the percentage agreement can be increased from 42 to 93% and the optimum level of generalization of two land cove maps was found at a resolution of 41m. Milne, A. K., 1988, reviewed a general methodology of change detection and explained the factors affecting change detection in case of Land Sat Imagery. The importance of remote sensing data used for analyzing the changes in land use and land cover and the factors that limit the type and nature of changes to be observed are explained. General

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steps to be employed for processing of the image and the necessary precautions to be taken for change detection analysis are explained. Lu et al., 2004, explained the importance of change detection and its role in making better decisions if there is an up to date and reliable information on a regular basis on existing land use patterns and changes. Changes may be due to man made and natural phenomena and it is very important to understand the land use dynamics which is a major determinant of land cover changes. He reviewed different methods from previous literatures that involve change detection techniques and suggested for advanced data from satellite and airborne sensors for effective results. Yang, X and Lo, C. P., 2002, as a part of project over the past 25 years used time series data of Land Sat MSS and TM to identify Land use and Land cover change detection in the Atlanta, Georgia metropolitan area. Their objective is to model the impacts of LU/LC change on temperature and air quality. The data has been analyzed on different techniques like radiometric normalization to generate a common radiometric response, unsupervised classification using image clustering and cluster labeling, GIS based image spatial reclassification to deal with classification errors like spectral confusion and Post classification comparison with GIS overlay to identify spatial dynamics which can generate results of high accuracy and compatibility. Most of the change detection revealed loss of forests and urban sprawl. Muttitanon, W and Tripathi, N. K, 2005, demonstrated the Land Use (LU) and Land Cover (LC) changes as the main concern and necessary measures need to be taken for proper control and management of global environment and natural resources. He used Land Sat 5 TM color composite images for the years 1990, 1993, 1996 and 1999 using NDVI data to detect LU/LC changes in coastal areas of Thailand. Different change detection techniques like Overlaying in which all the 4year images were overlaid to detect change, Image differencing in which two consecutive images were subtracted to identify the change, NDVI differencing to detect vegetation change of two image differenced images, NDVI composite classification in which same classes can be shown in one category. Image differencing and NDVI differencing showed similar changes, where as overlay technique showed different changes for different years like vegetation to shrimp farm and Agricultural to shrimp farm. Helmschrot, J and Flugel, W. A., 2002, demonstrated the impacts of afforestation on water resources where the availability of water is limited. Their objective is to determine updated land use patterns and assess the spatial dynamics over time which helps in saving water resources. Landsat TM images from 1995-1999 have been analyzed using classification techniques and LAI determination using NDVI data in semi arid Umzimvibu catchment, South Africa. The results showed an overall accuracy of 86% has been obtained with a significant change in land use patterns and has been validated with ground forest data. Sedano, F et al., 2005, demonstrated the potential of MODIS land cover products in assessing land cover in tropical regions of miombo woodlands in the province of Zambezia, Mozambique. Land cover mapping techniques such as single date supervised

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classification; principal component analysis (PCA) of band pair image differencing and multi temporal NDVI analysis of different dates were performed for assessment and then compared with surveyed land cover maps and test sites for accuracy estimation. PCA performed well in assessing the changes related to agriculture, wetlands and grasslands, thick and open forests with an accuracy of 90%. The multi temporal NDVI provided better results for dense forest cover. Song et al., 2001, explained the significance of atmospheric correction in remote sensing data and its role in classification and change detection techniques. An attempt has been made to compare between atmospheric corrected data and uncorrected atmospheric data. He mentioned that atmospheric data is not always needed as long as the training data and the data to be classified are on the same relative scale. They used multi-temporal 7 Land Sat 5 TM images from 1988 to 1996 for comparison with seven absolute and one relative atmospheric corrected algorithm with uncorrected raw data. In order to assess surface reflectance over time bringing them to same radiometric scale only correction is needed. The best results were obtained when using Rayleigh scattering effect to conventional dark object subtraction. Finally they recommended simple dark object subtraction with or without relative atmospheric correction for classifications and change detection applications. Reeves et al., 2001, explained the importance of rangelands and the necessary measures to be taken to monitor rangelands on a timely basis for effective management. He mentioned conventional methods like ground-based inventory method and point based sampling schemes and their drawbacks and compared with the potential of satellite derived weekly data capable of assessing the inaccessible rangeland growth stages and spatial extent of vegetation response. MODIS Leaf Area Index (LAI) algorithms on a weekly basis (8 days with 1 km2 resolution) have been applied and every 8day data has been added to annual estimate of net primary productivity (NPP) 1990-1994. Less productivity is observed in 1991 and more in 1993 than five year average productivity. Jackson, T. J et al., 2004, explained the role of Vegetation Water Content (VWC) in agriculture, forests and hydrology applications and in retrieving soil moisture. The potential of remote sensing techniques for mapping and monitoring VWC for corn and Soya beans canopies has been explained. Hermite cubic interpolation method on a pixel by pixel basis has been performed on Land Sat TM and ETM+ (30m resolution) data using vegetation indices NDVI and NDWI and compared with ground based VWC estimates. The overall results suggested that NDWI is of great value than NDVI based on quantitative analysis of bais and standard error. The daily map of VWC for the watershed can be extended to a larger domain. For obtaining daily coverage there is a need to develop operational and robust methods using MODIS instruments on the Terra and Aqua platforms. Tutubalina, O. B and Rees, W. G., 2001, identified the reasons for degradation of vegetation in Norlisk’s region which is a permafrost region due to contamination of huge amount of sulphur dioxide and heavy metals, water pollution and heat generation from industrial and residential activities and demonstrated the scope of using remote sensing

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techniques for identification of vegetation. He used two kinds of approaches to find vegetation degradation. One approach is taking panchromatic spy satellite image in 1961 and the other is TM image acquired in 1995, performed classification methods on local scale and then compared. The results showed that 80 SqKm of vegetation has been degraded on a local scale. Another approach is that he used MSS of 1972 and Land Sat TM of 1995 using NDVI to identify the vegetation degradation and both were compared. The results showed that 186 Sqkm of vegetation has been degraded. Simic, A et al., 2004, highlighted the importance of snow cover products for developing hydrological models and coupled hydrological and atmospheric models. Three kinds of snow cover products algorithms namely SPOT 4 VGT S1(1km) validated between January and June 2000, Terra MODIS (500 m resolution) MOD10A1 and NOAA GOES +SSM/I (4km) validated between January and June 2001 has been analyzed and then compared independently with daily snow depth measurements taken from 2000 meteorological stations. The results showed that MODIS and NOAA products showed good similar levels of agreement of which NOAA is consistent among land cover types and has the highest percentage of cloud free pixels. SPOT4 VGT has been omitted due to biased errors and low agreement with the ground data. Fung, T, 1990, evaluated the information content and accuracy of Land sat TM digital images in detecting land cover changes in his Kitchener- Waterloo, Ontario study area. Change detection techniques like image differencing, principal component analysis and tasseled-cap transformation has been performed on two Land Sat images (1985, 1986) to yield 12 images. They were thresholded according to the highest kappa coefficient of agreement and checked with producer’s accuracies of specific land cover changes. The kappa coefficient of 12 images range from 32.2 -63.2. The results showed that image differencing is able to detect rural to urban land conversion and not agricultural changes. Tasseled cap transformation is able to detect all crop type changes with good accuracies and principal component analysis is able to detect some crop type changes and urban changes with low accuracies. Fung, T and Siu, W., 2000, used NDVI derived SPOT HRV multispectral data for the years 1987, 1991, 1993, 1995 in detecting vegetation change and distribution which could help in assessing the environmental quality and management in Hong Kong. Conventional techniques like Image differencing and Principal Component Analysis (PCA) techniques were indicated change in loss of vegetation or restoration but failed to identify subtle changes in particular amount and quality of vegetation. The mean NDVI values revealed that increase in NDVI is observed in old urban districts and decrease is observed in large parts of rural new territories areas indicating urban expansion. Aplin, P and Atkinson, P. M., 2001, have developed a method in order to transform soft land cover classification into hard land cover classification. An attempt to identify sub pixel scale based on individual pixel segmentation has been analyzed using vector field boundaries. Each pixel segments is named with a land cover class (by area). Pre-processing, Per-pixel classification, Per-field classification and Accuracy assessment have been employed. Overall the Hard per pixel is least accurate due to mixed pixel

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problem and per field classification based on soft per pixel classification is more accurate and per field classification based on hard per pixel classified image is more accurate than hard per pixel classification. Ferreira, L. G et al., 2001, assessed the response of MODIS NDVI and EVI indices in monitoring and estimating the distinct vegetation content types present in cerrado region in Brazil. Both the vegetation indices performed well in identifying the distinct vegetation types and proved to be satisfactory. Overall the EVI is superior to NDVI in detecting seasonal changes in vegetation cover. Anderson, et al, 2005, presented a methodology for estimating deforestation in near real-time over the Amazon region in Brazil which will support Natural Institute of Space Research (INPE) to take decisions to manage forest resources. They used soil fraction images derived from MODIS data (250m spatial resolution) and linear spectral mixing model and validated with Land Sat ETM+ derived deforestation monitoring. An attempt has been made to evaluate the possibility of using MODIS data as a basis for designing a deforestation alert project and proved to be successful. Both MODIS and ETM+ data results showed reasonable agreement to each other with r2 = 0.73. Kaufman, J. Y and Remer, L. A., 1994, proposed a new method for detecting dark, dense vegetation in forests using mid-IR channels instead of using Red (0.64µm) and near-IR (0.84µm) channels which are sensitive to aerosol loadings. Aerosols present in the atmosphere degrade the apparent reflectance in the red and near-IR bands. AVHRR (3.75µm) and MODIS (3.95µm) are best suitable in using mid-IR channels. In their study they used AVHRR images with 3.75µm reflectance part and found to be correlated with 0.64µm reflectance and were exceptionally better than 0.64µm and NDVI. They finally

proposed ρ

3, 75<0.025 for aerosol studies in vegetation. Lambin, E. F., 1997, generally reviewed recent approaches that were used for modeling and monitoring of deforestation and dry land degradation in the tropical regions and highlighted the need to investigate on a particular area of interest. Different studies like empirical models which are explained by land cover changes, descriptive models explained by projection of future land cover changes, spatial statistical models explained by projection of changes in spatial patterns, dynamic eco-system models explained by testing scenarios on future land cove changes and economic models by design of policy interventions have been discussed and explained. Palmer, A. R and Van Rooyen, A. F., 1998, examined the possibility of identifying the magnitude and direction of change in reflectance with the help of Land sat TM derived data in the southern Kalahari Desert area which comprises three land use types nature conservation, commercial game ranching and commercial pastoralism from 1989 to 1994. These land use types help to make policy decisions on vegetation pattern, cover and species composition. Taking into consideration semi arid savannas where provision of surface water and grass land has been shifted to low grass cover shrub land and high annual grass cover due to cattle grazing, Change Vector analysis (CVA) has been performed on Land sat TM and extracted three bands namely red, visible, near-IR which

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showed the magnitude and direction of change between two anniversary data images. Near-IR activity showed a good response on active dunes near water points on commercial farms since 1989 and no change of magnitude and direction in reflectance is observed in conservation area from1989 to 1994. Fraser, et al., 2000, assessed the capability of using satellite data images in estimating the burned areas caused by fires in boreal forest in Canada that helps to estimate the amount of emissions at regional and global scales and then compared with statistical and conventional fire surveys. They tested new automated technique for burned area mapping called HANDS or hotspots and NDVI differencing on Canada for the 1995 and 1996 fire seasons. A total burned forest area of 6.8 mha in 1995 and 2 mha in 1996 were computed and it accurately depicted the outline boundaries of burned areas and identified those areas that were missed in conventional mapping methods. When HANDS was applied with NOAA-AVHRR imagery it showed a reasonable agreement in mapping large burns (>10 km2) which are characteristics of boreal forests. He suggested the use of new generation advanced sensors like SPOT vegetation, Terra MODIS for wider range of environment applications. Beck, P. S. A et al., 2005, mentioned that previous models which incorporated NDVI time series have performed very poor in environmental issues due to short growing seasons, long periods of darkness in winter, existence of snow cover and high amount of vegetation and suggested for the use of monitoring vegetation dynamics at high latitudes using MODIS NDVI data from 2000 to 2004. The area taken in their study is northern Scandinavia (35 x 162 km2, 68-N 23-E) and performed NDVI and then compared with the existing methods based on Fourier series and asymmetric Gaussian functions. It performed well in handling outliers well and estimated the parameters related to phonological events such as timing of spring and autumn. Mas, J. F., 1999, compared different change detection techniques using Land sat MSS images acquired for detecting change in areas in the region of Terminus Lagoon which is a coastal zone in Mexico. The techniques used are Image differencing, vegetation index differencing, selective principal component analysis (SPCA), direct multi date unsupervised classification, post classification change differencing and a combination of image enhancement and post classification comparison. The accuracy of the results of these techniques was compared with aerial photographs through kappa coefficient calculation. Result indicate that post classification technique is accurate in detecting the changes. Image enhancement method performed very poor and remaining methods were less sensitive and robust in dealing data acquired at different times of the year.

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2. Application of Remote Sensing

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Remote Sensing and its use in Vegetation Mapping

2.1 Remote Sensing: Remote sensing is nothing but acquiring the information of the target object or data on the earth’ surface from space through satellites sensors with or without contact with the object being observed. The objects can be seen with the help of some light energy present in the atmosphere called Electro Magnetic Radiation. In 1950, a scientist named Ms. Evelyn Pruitt from USA working for US Office of Naval Research was the first person to use the name of Remote Sensing. The atmosphere acts as a barrier between the earth and sensors which caused degradation of images. The remote sensing images obtained are in the digital form. Image pre-processing techniques will be employed in order to extract useful information which will enhance the image for visual interpretation and can be restored or corrected if they are subjected to geometric distortion, atmospheric, noise, blurring. Data acquisition and analysis are the two basic processes involved in remote sensing.

Fig 1: Electro Magnetic Remote sensing of earth’s resources Source: (Lillesand, T. M and Keifer, 2002)

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Data acquisition Data analysis

Energy sources Propagation of energy through atmospheres Energy interactions with the earth surface features Retransmission of energy through the atmosphere Airborne/Space borne sensors Generation of data in graphic/digital format

Examining the data using various viewing and interpretation devices to analyze pictorial data and or a computer to analyze digital sensor data. Compilation of the information in the form of hard copy, maps and tables or as computer files that can be used for further interpretation Presentation of information to the users so that they can use it for decision making

Table 5: The data acquisition and analysis process Source: (Lillesand, T. M and Keifer, 2002) 2.2 Electro Magnetic Spectrum:

“The electro-magnetic spectrum is a continuum of all electro-magnetic waves arranged systematically according to frequencies and wavelengths” (Schneider, D. J., 1993).

Fig 2: The Electro Magnetic Spectrum Source: (Lillesand, T. M and Keifer, 2002) 2.3 Interaction of energy with earth surface features: Electro-magnetic energy is a form of energy that transforms from one place to another in the form of waves when incident on any given earth’s surface. The energy is absorbed, reflected or transmitted due to atmosphere present in the air. Applying the energy conservation principle to it defined by

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E1 (λ) = ER (λ) + EA (λ) + ET (λ), where E1 = Incident energy, ER= Reflected energy, EA= Absorbed energy, ET= Transmitted energy

Fig 3: Basic Interaction between electromagnetic energy and earth surface features. Source: (Lillesand, T. M and Keifer, 2002) 2.4 Interaction with Water, Vegetation and Soil:

Fig 4: Major factors influencing the spectral characteristics of a water body Source: (Campbell, J. B., 1996)

E1 (λ) = ER (λ) + EA (λ) + ET (λ)

ER (λ)= Reflected E1 (λ) = Incident Energy

EA(λ)= Absorbed Energy ET(λ)= Transmitted Energy

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2.5 Water:

When radiation is incident on water surface, it is either absorbed or transmitted but not reflected. Visible wavelengths absorb less water radiation and more at near infrared and longer visible wavelengths. Three factors that affect the variability in reflectance of a water body are depth of water, materials within water and surface roughness of water. 2.6 Vegetation:

The spectral characteristics of vegetation vary with wavelength. A green pigment called chlorophyll is present in the leaves that strongly absorbs radiation in the red and blue wavelengths but reflects green wavelength in order to perform photosynthesis.

Fig 5: Reflectance spectra of different types of green vegetation compared to spectral signature for senescent leaves. Source: (Smith, R. B., 1999) 2.7 Soil:

When light is incident on a soil surface, it is either reflected or absorbed and a little is transmitted. “The characteristics of soil that determine its reflectance properties are its moisture content, organic matter content, texture, structure and iron oxide content.

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2.8 Applications of Remote Sensing: Some of the important applications of remote sensing are: (Schowengerdt, R. A., 2007)

• Environmental assessment and monitoring (urban growth, hazardous waste) • Global change detection and monitoring (atmospheric ozone depletion,

deforestation, global warming) • Agriculture (crop condition, yield prediction, soil erosion) • Non-renewable resource exploration (minerals, oil, natural gas) • Renewable natural resources (wetlands, soils, forests, oceans) • Meteorology (atmosphere dynamics, weather prediction) • Mapping (topography, land use, civil engineering) • Military surveillance and reconnaissance (strategic policy, tactical assessment) • News media (illustrations, analysis)

2.9 Observation of earth surface using satellites: There are different kinds of satellite sensors that monitor earth’s surface round the clock. Specially designed latest satellite sensors are used for observing land cover and land cover change. “Earth observation satellites provide data covering different portions of the electromagnetic spectrum at different spatial, temporal and spectral resolutions” (Pohl, et al., 1998). Satellites can cover large areas in a very less time and proved to be cost effective. The selection of the sensor depends on satellite and sensor characteristics: (Pohl & Genderen, 1993) 1) Orbit 2) Platform 3) Imaging geometry of optical and radar satellites 4) Spectral, spatial and temporal resolution 2.10 Time series sensors suitable for land observations:

“A time series is defined as a collection of observations made sequentially through time” (Chatfield, C., 2004). In order to monitor time series vegetation cover changes due to phenology the sensors require high temporal resolution which is acquired by geostationary satellites and some polar orbiting systems. Weather satellite instruments on geostationary orbits such as SEVIRI on METEOSAT or Imagers on GOES and Polar orbiting systems such as TM or ETM+ on Land Sat and HRV or HRVIR on SPOT. Wide field view instruments such as AVHRR (1981) on the NOAA platforms, SeaWiFs (1997) on OrbView2, VEGETATION (1998) on SPOT, MERIS (2002) on ENVISAT, or MODIS (1998 & 2002) onboard TERRA and AQUA will have full global coverage within few days. SeaWiFs and MERIS are suitable for oceanographic applications with bands limited to visible and near Infra-red wavelengths.

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2.11 Land sat:

The first earth observing satellite used to monitor and study earth’s surface was the Land sat 1 launched on July 23, 1972 carrying onboard two instruments, Radio Beam Vidicon (RBV) built by Radio Corporation of America (RCA) and Multispectral Scanner System (MSS) built by general electric. On Jan 22, 1975, another satellite named Land sat 2 carrying onboard two instruments, RBV (for engineering purposes) and MSS (for collection of images) was launched and later decommissioned in Feb, 1982. On March 5, 1978, third satellite named Land sat 3 carrying onboard two instruments, MSS and RBV (30m resolution) having two RCA cameras using only one broad spectral band (green to near-IR; 0.505 – 0.750 µm) instead of three separate bands was launched On March, 1983 the satellite was decommissioned. On July 16, 1982, fourth satellite named Land sat 4 carrying onboard only MSS but not RBV was launched carrying MSS but not RBV. An improved high resolution sensor named as Thematic Mapper (TM) has been carried additionally along with MSS. In the year 2001 the satellite was decommissioned. On March 1, 1984, fifth satellite named Land sat 5 carrying MSS and TM instruments on board Tracking and Data Relay Satellite System (TDRSS) was launched. The normal functionality of TDRSS band was stopped in the year 1987 and MSS was turned off in August 1995. The temporary suspension of TM operations due to solar array problems was done in Nov, 2005 and resumed on Jan 30, 2006. The launch of Land sat 6 on Oct 5, 1993 carrying onboard Enhanced Thematic Mapper (ETM) with seven bands was a failure since the satellite wasn’t been able to be placed in the orbit. Along with seven bands, an additional band called sharpening or panchromatic band with a spatial resolution of 15m is also included. Finally the satellite named Land sat 7 which is owned by the government was launched on April 15, 1999 equipped with an earth observing system named Enhanced Thematic Mapper Plus (ETM+). The new features of ETM+ are: (NASA, 2008) a) A panchromatic band with 15m spatial resolution b) On board, full aperture, 5% absolute radiometric calibration c) A thermal IR channel with 60m spatial resolution d) On board data recorder

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Channel Wavelength range (µm) Applications

TM 1 0.45 - 0.52 (blue) soil/vegetation discrimination; bathymetry/coastal mapping; cultural/urban feature identification

TM 2 0.52-0.60 (green) green vegetation mapping (measures reflectance peak); cultural/urban feature identification

TM 3 0.63 - 0.69 (red) vegetated vs. non-vegetated and plant species discrimination (plant chlorophyll absorption); cultural/urban feature identification

TM 4 0.76 - 0.90 (near IR) identification of plant/vegetation types, health, and biomass content; water body delineation; soil moisture

TM 5 1.55 - 1.75 (short wave IR) sensitive to moisture in soil and vegetation; discriminating snow and cloud-covered areas

TM 6 10.4-12.5 (thermal IR) vegetation stress and soil moisture discrimination related to thermal radiation; thermal mapping (urban, water)

TM 7 2.08 - 2.35 (short wave IR) discrimination of mineral and rock types; sensitive to vegetation moisture content

Table 6: Sensor characteristics of Thematic Mapper Source: (Canada Center for Remote Sensing tutorial)

2.12 Multi Spectral Scanner (MSS):

Channel Wavelength

range(µm)

Applications

Land sat-1,2,3

Land sat-4,5

MSS 4 MSS 1 0.5-0.6 (green) Vegetation assessment, useful for the measurement of sediment concentrations in water

MSS 5 MSS 2 0.6-0.7 (red) Strongly absorbed by chlorophyll; an important band for vegetation discrimination

MSS 6 MSS 3 0.7-0.8 (near IR) Very strong vegetation reflectance; useful for determining biomass

MSS 7 MSS 4 0.8-1.1 (near IR) Useful for determining biomass. High land water contrast so good for determining water bodies and coast lines

Table 7: Sensor characteristics of Multi Spectral Scanner Source: (Canada Center for Remote Sensing tutorial)

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2.13 Systeme Pour l’Observation de la Terre (SPOT):

SPOT-1,2,3,4,5 were launched on 21 st Feb, 1986, 21st Jan, 1990, 25th Sep, 1993, 24th March, 1998, 4th May 2002 respectively. SPOT-1, 2, 3 are equipped with two High Resolution Visible sensors (HRV). SPOT 4 and 5 are equipped with High Resolution Visible and Infrared (HRVIR) and High Resolution Geometry (HRG) respectively that can scan either in panchromatic or multispectral modes. Both of them will have Vegetation (VGT) sensor in common. “SPOT satellites are capable of obtaining an image of any place on earth everyday and having an advantage of mapping vegetation at flexible scales (regional, national, continental or global)” (Xie et al., 2008). Mode/Band Wavelength range (µm) Applications

Panchromatic (PLA) 0.51 - 0.73 (blue-green-red)

Multispectral (HRV)

Band 1 0.50 - 0.59 (green) Water and urban studies

Band 2 0.61 - 0.68 (red) Water and vegetation studies

Band 3 0.79 - 0.89 (near infrared) Vegetation and topography

Table 8: Sensor characteristics of SPOT Source: (Canada Center for Remote Sensing tutorial) 2.14 NOAA-AVHRR:

In the recent years monitoring of vegetation cover and change in climate globally using polar orbiting satellites such as Advanced Very High Resolution Radiometer (AVHRR) data of the National Oceanic Atmospheric Administration (NOAA) with a high temporal frequency has been widely used. AVHRR, which is a multispectral scanner with medium spatial resolution is the main important instrument carried out by the satellites. Transmission of the data to the ground from all NOAA satellites is done by the help of High Resolution Picture Transmission (HRPT) broadcaster.

Mode/

Band Resolution

at nadir

Wavelength Range

(µm) Applications

1 1.09 Km 0.58 - 0.68 (red) Global vegetation monitoring, forest fire activity, canopy gaps,

2 1.09 Km 0.725-1.1 (near IR) Water, vegetation, and agriculture surveys 3A 1.09 Km 1.58-1.64 (mid IR) Snow and ice detection 3B 1.09 Km 3.55-3.93 (mid IR) Night cloud mapping, sea surface temperature,

volcanoes, canopy gaps and forest fire activity

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4 1.09 Km 10.3-11.3 (thermal IR) Night cloud mapping, sea surface temperature, volcanoes, forest fire activity, soil moisture

5 1.09 Km 11.5-12.5(thermal IR) Sea surface temperature, urban studies, soil moisture

Table 9: Sensor characteristics of AVHRR Source: (Kubo, M and Muramoto, K., 2007)

2.15 AVHRR Data Formats:

Format Spatial

Resolution

Transmission and Processing

Automatic Picture Transmission (APT)

4 Km Low resolution direct transmission and display

High Resolution Picture Transmission (HRPT)

1.1 Km Full resolution direct transmission and display

Global Area Coverage (GAC) 4 Km Low-resolution coverage from recorded data

Local Area Coverage (LAC) 1.1 Km Selected full resolution local area data from recorded data

Table 10: Data formats of AVHRR Source: (Canada Center for Remote Sensing tutorial) 2.16 Moderate Range Imaging Spectro-radiometer (MODIS):

“Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and aqua satellites provides multispectral, temporal and angular data for medium and coarse resolution land cover characterization” (Justice et al., 2002). It has a total of 36 spectral bands ranging from the visible region to the Thermal Infrared region with each band having specific wavelengths. Of the 36 spectral bands present, the first seven bands are designed only for observing earths land surface features. Three bands are available in the visible region, one in the Near Infrared (NIR) and the remaining three in the Short wave Infrared regions (SWIR).

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Band Spect.Range(nm) Spat.Res(m) Applications

1 620-670 250 Vegetation chlorophyll absorption/Land cover transformations/Cloud/ edge detection/masks

2 841-876 250 Land cover transformations/masks Cloud/ Vegetation/Water/edge detection

3 459-479 500 Soil and Vegetation differences 4 545-565 500 Green Vegetation 5 1230-1250 500 Leaf and Canopy properties 6 1628-1652 500 Snow and Cloud differences/masks 7 2105-2155 500 Land and cloud properties 8 405-420 1000 Water colour (chlorophyll/pigments/sediments)

Atmospheric correction/cloud mask 9 438-448 1000 Water colour 10 483-493 1000 Water colour 11 526-536 1000 Water colour 12 546-556 1000 Sediments 13 662-672 1000 Sediments, atmosphere 14 673-683 1000 Chlorophyll fluorescence 15 743-753 1000 Aerosol properties 16 862-877 1000 Aerosol and Atmospheric properties 17 890-920 1000 Water vapour/atmospheric properties 18 931-941 1000 Water vapour/atmospheric properties 19 915-965 1000 Water vapour/atmospheric properties 26 1360-1390 1000 Cirrus cloud/cloud masks 20 3660-3840 1000 Sea surface temperature 21 3931-3987 1000 Forest fires/Volcanoes 22 3929-3989 1000 Cloud 23 4020-4080 1000 Cloud /surface temperature/cloud mask 24 4433-4498 1000 Cloud /surface temperature/cloud mask 25 4482-4549 1000 Tropical temperature/cloud fraction 27 6535-6895 1000 Tropical temperature/cloud fraction 28 7175-7475 1000 Mid-tropical humidity 29 8400-8700 1000 Upper-tropical humidity 30 8980-9880 1000 Surface temperature/cloud mask 31 10780-11280 1000 Total ozone 32 11770-12270 1000 Cloud /surface temperature 33 13185-13485 1000 Cloud /surface temperature 34 13485-13785 1000 Cloud and height fraction 35 13785-14085 1000 Cloud and height fraction 36 14085-14385 1000 Cloud and height fraction Table 11: Sensor Characteristics of MODIS instrument. Source: (Mathew, C. R et al., 2001)

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2.17 Indian Remote Sensing (IRS):

Satellites (year) Sensor Bands (µm) Spat.Res

(m)

Swath

width (Km)

Rad. Res

(bits)

Rpt. cycle

(days)

IRS-1A/1B (1988, 1991)

LISS-I 0.45-0.52(B) 72.5 148 7 22

0.52-0.59(G) 0.62-0.68(R) 0.77-0.86(NIR) LISS-II Same as LISS-I 36.25 74 7 22 IRS-P2(1994) LISS II Same as LISS-I 36.25 74 7 24 IRS-1C/1D (1995, 1997)

LISS III 0.52-0.59(G) 23.5 141

0.62-0.68(R) 0.77-0.86(NIR) 7 24 1.55-1.70(SWIR) 70.5 148 WiFs 0.62-0.68(R) 188 810 7 24(5) 0.77-0.86(NIR) PAN 0.50-0.75 5.8 70 6 24(5) IRS -P3(1996) MOS-A 0.755-0.768(4bands) 1570 * 1400 195 16 24 MOS-B 0.408-1.010(13ban) 520 * 520 200 16 24 MOS-C 1.6(1 band) 520 * 640 192 16 24 WiFs 0.62-0.68(R) 188 810 7 5 0.77-0.86(NIR) 1.55-1.70(SWIR) IRS -P4(1999) OCM 0.402-0.885(4bands) 360 * 236 1420 12 2 MSMR 6.6,10.65,18,21 Ghz

(V & H) 150, 75, 50, 50 Km resp.

1360 2

IRS- P6(2003) LISS IV 0.52-0.59(G) 0.62-0.68(R) 0.77-0.86(NIR) 5.8 70 10(7) 24(5) LISS III 0.52-0.59(G) 0.62-0.68(R) 23.5 141 7 24 0.77-0.86(NIR) 1.55-1.70(SWIR) AWiFs 0.52-0.59(G) 0.62-0.68(R) 56 737 10 24(5) 0.77-0.86(NIR) 1.55-1.70(SWIR) IRS-P5(CartoSat-1) 2005

PAN(Fore (+26), Aft(-5)

0.50-0.85 2.5 30 10 5

CartoSat-2(2007) PAN 0.50-0.85 0.8 9.6 10 5

Table 12: Specifications of Present series of IRS Satellites Source: (Navalgund, R. R et al., 2007)

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Satellite remotely sensed data offers several additional advantages over conventional data sources such as aerial photography or field methods, because it provides the capability (Franklin, 2001; Jensen, 2005):

• Include extensive geographic regions in their entirety • Evaluate dynamic landscape patterns (synoptic) • Observe changes and trends across large scale patterns through time • Provide spatially and temporally comprehensive data • Be objective, repeatable and consistent

2.18 Agriculture:

Agriculture plays a dominant role in economies of both developed and undeveloped countries. Whether agriculture represents a substantial trading industry for an economically strong country or simply sustenance for a hungry, overpopulated one, it plays a significant role in almost every nation. The production of food is important to everyone and producing food in a cost-effective manner is the goal of every farmer, large-scale farm manager and regional agricultural agency. A farmer needs to be informed to be efficient, and that includes having the knowledge and information products to forge a viable strategy for farming operations. These tools will help him understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions.

2.19 Agricultural applications: (CCRS)

• Classification of different crops • Assessment of crop condition • Estimation of crop yield • Mapping of soil characteristics • Mapping of soil management practices • Compliance monitoring (farming practices)

a) Crop type mapping:

Reliable information is needed for the mapping of crop type and acreage and this can be obtained with the help of remote sensing. Along with the detailed image, this can also provide the information of the structure and the vegetation health. The spectrum shown on the remote sensing sensor would change during the course of time as there would be the change in the growth of the crop, the stage the crop is in and its health and this is possible using multi-spectral sensors which monitors and measures all these changes.

b) Crop monitoring and damage assessment:

Large scare area monitoring of crops and early detection of crop damage plays an important role for an efficient agricultural productivity. The assessment of the possible

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destruction in advance caused due to pest, deficiency of nutrients, wind and hail damage, weed and fungal infestation could give the farmer an opportunity to protect the yield. The crops usually have deficiencies in various factors (nutrients, sun light, water stagnation etc) that vary across the field and hence the productivity varies. Remote sensing could help the farmer to correct the deficiencies and have good productivity. This could prevent environmental impacts by not using excessive pesticides or fertilizers that are not needed for an area allowing farmer to enjoy good yield with minimal input costs. It can identify the crops that are suitable for a certain area by giving an insight of too wet or too dry regions, the common infestation in that area and primarily the damage due to weather. With the help of remotely sensed images farmer can identify the problem remotely before visually identified and can take timely decisions about managing the crops. Plants contain a dark green pigment called chlorophyll present in leaves that absorb all wavelengths in the visible spectrum except green and near infrared. It absorbs red and blue light which cannot be seen by us and reflects light in the green and near infrared regions. That is why the leaves appear brighter at the red and blue regions indicating high vegetation and dark indicating low or sparse vegetation. Dark vegetation indicates crops stress which can be easily detected by remote sensing and can take precautionary measures. Examining variations in crop growth within one field is possible. Areas of consistently healthy and vigorous crop would appear uniformly bright. Stressed vegetation would appear dark amongst the brighter, healthier crop areas. 2.20 Advantages: (Balaselvakumar, S & Saravanan., 2002)

• Vantage point • Coverage • Permanent record • Cost saving • Time saving • Real time capability

2.21 Forestry: The balance in the earth’s CO2 content is maintained by the forests. They play a vital role in the carbon cycle by being a medium in transferring the carbon from the atmosphere and then to the geo-sphere and hydrosphere. Forests are being wiped off from the face of the earth at an alarming rate, be it a natural cause (wild fires) or manmade causes. The forests and climate are interdependent on each other. Along with its effect of climate, forests also influence the soil conservation, hydro-cycles and the biodiversity of a region. Forestry can be divided into two categories, one being Commercial and the other being Non- commercial. Commercial forestry is the use of forestry for timber etc. Non- commercial use of forestry is its use for agriculture, residential and industrial purposes, which makes permanent change to the ecology of that area which was previously covered by forest.

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2.21.1Forestry applications:

a) Reconnaissance mapping:

Objectives to be met by national forest/environment agencies include forest cover updating, depletion monitoring, and measuring biophysical properties of forest stands.

• Forest cover type discrimination • Agro-forestry mapping

b) Commercial forestry:

Of importance to commercial forestry companies and to resource management agencies are inventory and mapping applications: collecting harvest information, updating of inventory information for timber supply, broad forest type, vegetation density, and biomass measurements.

• Clear cut mapping / regeneration assessment • Burn delineation • Infrastructure mapping / operations support • Forest inventory • Biomass estimation • Species inventory

c) Environmental monitoring:

Conservation authorities are concerned with monitoring the quantity, health and diversity of the Earth's forests.

• Deforestation (rainforest, mangrove colonies) • Species inventory • Watershed protection (riparian strips) • Coastal protection (mangrove forests) • Forest health and vigour

2.21.2 Clear cut mapping and Deforestation: Deforestation is increasing at an alarming rate in the developed and developing countries. It is caused by natural causes (forest fires, cyclones) and human activities (agriculture, burning, clear-cutting). Humans cut trees for building houses, cooking food which leads to irregular cutting. Without the existence of remote sensing devices, illegal cutting or loss of forest cover continues to remain unnoticed for longer periods of time. It is the only source to identify and monitor clear cutting and deforestation problems by acquiring multi-temporal images of the same area and then compared with the previous year’s images to find out the differences in the sizes and extents of clear cuts or loss of forests.

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2.21.3 Species identification and typing:

It is because of remote sensing that we are able to identify and outline the area covered by certain forest types, which would be very difficult to precisely map from the ground. We are able to extract huge amounts of data locally and globally which would enable the governments, NGO’s and universities to use it for their respective purposes effectively and efficiently. Using various spectral and radar equipment, large-scale and small scale identification of data is possible which will enable to know more about different species of plants and forest types in a more detailed manner. This data could be fed into the GIS along with the boundaries of ownership, roads and many other ancillary data. Fine radiometric data can be extracted from very high spatial resolution using hyper-spectral imagery which will generate spectral signatures of different vegetation species and provides an insight on the health of the forest by having a check on the infestations that grows on the trees. This is all made possible and much easier, and we owe thanks to the remote sensing technology. 2.21.4 Burn mapping:

Burn mapping can be easily explained as the tracing and interpretation of forest fires can be done using a remote sensing approach. Forest fires are seen as a natural phenomenon in which the nature replenishes itself and keeping a balance in the nutrient content of an ecosystem. It has been this way for millions of years, but it is not the same case now. They became a major threat to forests in the present day world. These fires once considered as a natural process is now considered destructive as it creates a loss to the human settlement which are directly dependent on these forest. They have an influence on vegetation cover, plants, soils, animals and climate. It destroys the very few areas that are left for the wildlife and puts many species in the endangered list. Reliable data on how to control fire and estimate the damage is essential. This is where the burn mapping part of the remote sensing application would play its role. Remote sensing will help in reducing the risk caused by fire and minimize the damage. It provides up-to date information on weather data like temperature, humidity and precipitation allowing foresters to calculate risk assessments and isolate the vulnerable areas. It provides information on wind speed and direction which help in predicting the direction and speed at which fire spreads so that rescue operations by firefighters could be done most effectively and safely thereby preventing the major damage. Re-growth of forests from fires can be easily monitored and detected by remote sensing. In the form of single image, the health condition and re-growth of an area can be obtained and combination of multi-temporal images provides the progress of vegetation.

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3. Vegetation in India

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3. Vegetation in India according to Physiographic Region Wise The vegetation in India spans over major regions with the Western Ghats and Eastern Ghats extending from the south to the southwest and southeast, the Vindhyans in the central part and the Himalayas in the north. Tropical scrub and deciduous forests are the most common in the Indian subcontinent. Small patches of tropical evergreen and rain forests can be seen along the coasts and islands. They are prominent in the northeastern part of the Himalayas. Indian Cropping seasons are classified into two main categories, Kharif which is in between June and Rabi precedes kharif starting from October and lasting till May. The major crops grown in the season of kharif are rice, soybeans, sugarcane, sorghum, maize, pearl-millet, cotton etc. Wheat, gram, barley, chickpea, mustard, linseed are some of the major crops grown during the Rabi season. Fruits like Mango, banana, grapes, pineapple, jackfruit etc, Spices such as pepper, cardamom, cinnamon, nutmeg, clove, turmeric, ginger etc, and Vegetables such as brinjal, bitter-gourd, tomato, ash-gourd, lady finger, okra, cauliflower etc are some crops grown in India. The Indian sub-continent comprises three major physiographic regions (Velayutham et al., 1999):

• Mountains and Hill regions of the Himalayas • Indo-Gangetic Plains • Peninsular Plateau, including the Coastal plain and a group of islands

3.1 DECCAN PLATEAU

3.1.1 Geographical location:

The Central and Southern part of India constitutes the Deccan plateau covering most of the southern states and some parts of Maharashtra. 3.1.2 Area:

The Deccan plateau is bounded by the Western Ghats, Eastern Ghats, Nilgiris , Vindhyas and the Satpura ranges covering 1,421,1000 SqKm accounting to 43.2% of the area of India 3.1.3 Topography:

The elevation ranges from 1500-2500 ft (450-750) with a steep slope or cliff on the tip of the Western Ghats and incline downwards to the east.

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3.1.4 Climate: The climate varies from very hot to moderate cold keeping the region dry for most of the time usually 6-9 months in a year. 3.1.5 Rainfall:

Rainfall is very low due to obstruction of moisture from Western Ghats varying from 400-2000 mm in a year. Less rainfall is received on the western side and more on the eastern side. 3.1.6 Temperature:

The temperatures are very hot in summer and cold in winter. The maximum temperature is 400 C and minimum is 150 C. 3.1.7 Soil: Soil acts as a basis for the growth of the plants. Red and Black soils are usually found in this region. Black cotton soils are the most famous ones having a tendency of retaining the moisture any time favoring the crops that require artificial irrigation.

3.1.8 Vegetation: Since this region is covered with hills and plains of unequal size the natural vegetation is very sparse in this region. Due to poor climatic conditions the soil is not fertile in this region. 3.1.9 Forests: The plateau itself supports dry deciduous vegetation or thorn scrub formations. There are two main vegetation types on the Deccan: (Sladanha, C. J., 2000)

a) Moist forests:

• Southern tropical moist deciduous forests • Southern tropical deciduous riverine forests

b) Dry forests:

• Southern dry deciduous forests • Thorn scrub forests

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3.1.10 Agriculture:

States Crops Cropping Seasons

A N D H R A

P R A D E S H

Early Kharif Kharif Rabi Summer Paddy S: Jun-Aug; H:Oct-Feb S: Nov-Feb; H: Mar-May Wheat S: Nov-Dec; H: Mar-Apr Bajra S: Jun-Jul; H: Aug-Sep S: Jan-Feb;

H: Apr-May Arhar/Tur S: Jun-Jul; H: Jan-Feb Red Gram S: Jun-Jul; H: Mar-Apr S: Feb-Mar;

H: May-May Blackgram/Urd S: Jun-Jul; H: Sep-Sep S: Nov-Jan; H: Feb-Apr Green Gram S: Jun-Jul; H: Aug-Sep S: Nov-Dec; H: Feb-May Horsegram S: Feb-Apr;

H: Apr-Jun

Gram S: Oct-Nov; H: Mar-Mar Masur S: Sep-Nov; H: Feb-Apr Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S: Jun-Jul; H: Sep-Nov S: Nov-Jan; H: Feb-May S: Dec-Jan;

H: Mar-Jun Linseed S: Nov-Dec; H: Feb-Mar S: Sep-Oct; H: Jan-Mar Castor S: Jun-Sep; H: Oct-Mar Sesamum/Til S: Apr-Aug;

H: Oct-Nov S: May-Jun; H: Aug-Sep S: Oct-Feb; H: Feb-Mar S: Dec-Feb;

H: Mar-May Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Aug; H: Sep-Nov S: Dec-Jan; H: Mar-Apr Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Onion S: Jul-Nov; H: Mar-May S: Jul-Nov; H: Mar-May Potato S: Oct-Nov; H: Jan-Mar S: Oct-Nov; H: Jan-Mar S: Jan-Mar;

H: Jun-Nov Cotton S: Jun-Jul; H: Dec-Mar S: Oct-Jan; H: Feb-May Maize S: Jun-Jul; H: Sep-Oct S: Sep-Jan; H: Jan-May Sugarcane S: Dec-Jun; H: Dec-May S: Jun-Oct; H: Oct-Jan Jute S: Feb-Jun; H: Aug-Oct Table 13: Season wise crops of Andhra Pradesh Source: (Agricultural Statistics at a Glance, 2008)

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States Crops Cropping Seasons K

A R N A T A K A

Kharif Rabi Summer

Paddy S: May-Jul; H:Oct-Dec S: Aug-Sep; H: Jan-Feb Wheat S: Oct-Dec; H: Jan-Feb Bajra S: Jul-Sep; H: Oct-Nov S: Jan-Feb; H:Apr-May Arhar/Tur S: Jun-Jul; H: Nov-Feb S: Sep-Octl; H: Dec-Jan Blackgram/Urd S: Jun-Jul; H: Sep-Oct Green Gram S: Jun-Jul; H: Sep-Oct S: Sep-Oct; H: Dec-Jan

Groundnut S: May-Jul; H: Sep-Dec S: Dec-Jan; H: Mar-Apr Linseed S: Sep-Oct; H: Jan-Mar Castor S: Jun-Jul; H: Oct-Feb Sesamum/Til S: Apr-Jul; H: Jul-Aug Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Jul; H: Sep-Oct S: Sep-Oct; H: Dec-Jan Onion S: Mar-Aug; H: Aug-Dec S: Mar-Aug; H: Aug-Dec Potato S: Mar-Aug; H: Aug-Nov S: Mar-Aug; H: Aug-Dec Maize S: May-Jun; H: Sep-Oct S: Sep-Oct; H: Jan-Mar Sugarcane S: Dec-Mar; H:Dec-May Table 14: Season wise crops of Karnatakaa Source: (Agricultural Statistics at a Glance, 2008)


M A H A R A S T R A

Kharif Rabi Summer Paddy S:Jul-Aug; H:Oct-Dec Wheat S:Oct-Dec; H :Feb-Apr Bajra S:Jun-Jul; H:Sep-Oct S:Jan-Feb; H:Apr-May Arhar/Tur S:Jul-Jul; H:Dec-Jan Blackgram/Urd S:Jul-Aug; H: Oct-Oct Gram S:Sep-Oct; H:Feb-Mar Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S:Jul-Aug; H:Oct-Nov S:Jan-Feb; H:Apr-May Linseed S: Sep-Oct; H: Jan-Mar Sesamum/Til S:Oct-Oct; H:Jan-Feb S:Dec-Feb; H: Mar-May Sunflower S:Jun-Aug; H:Sep-Nov S:Oct-Nov; H:Jan-Feb Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Onion S:Nov-Dec; H:Mar-May S:Nov-Dec; H:Mar-May Potato S:Nov-Dec; H:Feb-May S:Nov-Dec; H:Feb-May Maize S:Jul-Aug; H:Oct-Nov Sugarcane S:Jul-Aug; H:Oct-Nov

Table 15: Season wise crops of Maharashtra Source: (Agricultural Statistics at a Glance, 2008)

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K E R A R L A

Early Kharif Kharif Rabi Summer Paddy S:Aug-Nov; H:Nov-Jan Wheat S: Oct-Dec; H: Feb-Jun Bajra S:Jun-Jul; H:Sep-Oct Arhar/Tur S:May-Aug;

H: Aug-Oct S:Aug-Nov; H:Oct-Nov

Blackgram/Urd S:Mar-Jun; H: Jun-Sep S:Sep-Oct; H:Nov-Dec

Green Gram S:Oct-Nov;H:Nov-Nov S:Sep-Oct; H:Nov-Dec

Gram S: Oct-Dec; H: Mar-Apr Masur S: Sep-Nov; H: Feb-Apr Pulses/Lentil S:Jun-Oct; H: Nov-Dec S: Oct-Nov; H:Mar-May Groundnut S: Jun-Jul; H: Oct-Nov S: Nov-Jan; H: Feb-May Linseed S: Sep-Oct; H: Jan-Mar Sesamum/Til S:Aug-Oct; H:Dec-Apr S:Dec-Feb; H:Mar-May S: Apr-Aug;

H: Aug-Oct Soyabean S:Jun-Aug; H:Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Cotton S:Jun-Jul; H: Nov-Dec Sugarcane S:Oct-Feb; H:Oct-Dec Table 16: Season wise crops of Kerala Source: (Agricultural Statistics at a Glance, 2008) 3.2 EASTERN GHATS 3.2.1 Geographical Location:

The Eastern Ghats extend along the east cost of India in parallel to the Bay of Bengal. They are bounded between 110 31’ and 220 N latitude and 760 50’ and 860 30’ E longitude which extends over 1750 km. 3.2.2 Area:

It begins from West Bengal in the north and ends up in South of Tamilnadu. In between it passes through Orissa and Andhra Pradesh states covering an area of 75,000 Sqkm.

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3.2.3 Topography: A series of disconnected mountains and hill ranges along the east coast with an average elevation of 750m reaching to a maximum of 1750m above msl.

Region Name of the hills/District Altitude Northern Eastern Ghats

Districts of sambalpur and Bolangir (Gandhamardan hills), Mayurbhanj and Kalahandi (Khondmal hills), Phulbhani and Koraput Orissa.Srikakulam (Palakonda- Antikonda-Burra Konda ranges), Vizianagaram, Vishakhapatnam (Madgole hills-Anantagiri-Chintalapalli-Sapparla-Gudem-Marripakalau hill ranges), East Godavari (Gurtedu-Addateegala-Rampachodavaram Maredumilli ranges) and West Godavari (Polavaram-Papikonda ranges) - Andhra Pradesh districts.

Altitudes of above 400 m, few peaks with above 1100 m, Mahendragiri-1501 m, Debmali parbat-1672 m, Koraput-1515 m, Singaraju parbat-1516 m, Devagiri-1381.2m, Turiakonda-1598 m, Hatimali-1391 m, Chandragiri- 1269 m, Armakonda- 1680 m, Dharakonda- 1365 m, Sambarikonda near Gudem village-1527m, Gali konda-1643 m.

Middle Eastern Ghats

Districts of Krishna (Kondapalli ranges), Kurnool, Mahaboob Nagar, Prakasam (Nallamali ranges), Anantapur, Cuddapah, Chittoor, Prakasam (Palakonga-Seshachalam-Lankamala, Nagari hills and Nellore (Veligonda range)-Andhra Pradesh

Average elevation 850 m Nallamalais 800 m, Seshachalam hills-850 m

Southern Eastern Ghats

North Arcot-Javadi hills, South Arcot- Gingee hills, Salem (Hills of Shervaroy, Kollimalai, Kalrayan and Bodamalai), Dharmapuri (Melahiri hills) and Tiruchirapalli(Pachamalai hills)

Javadi hills upto 1375 m, Pachamalai hills upto 1000 m, Shervaroy hills 400-1600 m, Kolli hills 1000-1500 m

Table 17: Geography and Topography Source: (Sandhyarani et al, 2007)

3.2.4 Climate: It has a tropical climate receiving most of the rain during the monsoon period. This region has climate varying from hot and humid at higher altitudes to semi-arid at the foothills. 3.2.5 Rainfall: This region falls under tropical monsoon climate. Due to mountains and hill ranges it has variable rainfall in northern, central and southern parts. The annual rainfall ranges from 1150-1660 mm in northern parts, 600 cm on southern side and 1000 mm on the central side. 3.2.6 Temperature: The temperatures during the summer are very hot reaching to maximum of 410 C and cold during the winter seasons falling to 20 C during the night time.

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3.2.7 Soil: Many varieties of soils such as loamy soils, black soils, red soils, laterite and lateritic soils, peaty and marshy soils are found in Eastern Ghats. 3.2.8 Vegetation:

“The Eastern Ghats are endowed with rich flora and fauna with various types of coastal ecosystem such as estuaries, mangroves, lagoons and coral reefs” (Rajendran et al., 2004). 3.2.9 Forests:

Vegetation varies considerably with altitudes and shows a distinct zonation of forest types in all these Eastern Ghats. Practically all these forests are classified under four types as below: (Rao, D. M & Pullaiah, T., 2007).

• Scrub Jungles - to 400 m (Foot hills) • Deciduous forests - 300 to 900 m (Slopes) • Evergreen forests - 800 to 1300 m (Plateau) • Sholas - 1200 to 1600 m

Forests are broadly divided into different types: (Sandhyarani et al., 2007) a. Tropical Evergreen b. Tropical semi evergreen (Moist deciduous forests and mixed with evergreen elements) c. Tropical Moist deciduous

• Northern Sub Tropical Deciduous forests (Sal forests) • Southern Indian Tropical Moist Deciduous forests (Non-Sal forests) • Southern Tropical Moist Deciduous riverine forests • Tropical Dry Deciduous forests • Teak Bearing forests • Non-Teak Bearing forests

Mixed Dry Deciduous forests

a. Northern Mixed Dry Deciduous forests b. Southern Mixed Dry Deciduous forests

• Dry Savannah forests • Scrub forests • Tropical Dry Evergreen forests • Tropical Dry Evergreen Scrub

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3.2.10 Agriculture:


A N D H R A

P R A D E S H

Early Kharif Kharif Rabi Summer Paddy S: Jun-Aug; H:Oct-Feb S: Nov-Feb; H:Mar-May Wheat S: Nov-Dec; H: Mar-Apr Bajra S: Jun-Jul; H: Aug-Sep S: Jan-Feb;

H: Apr-May Arhar/Tur S: Jun-Jul; H: Jan-Feb Red Gram S: Jun-Jul; H: Mar-Apr S: Feb-Mar;

H: May-May Blackgram/Urd S: Jun-Jul; H: Sep-Sep S: Nov-Jan; H: Feb-Apr Green Gram S: Jun-Jul; H: Aug-Sep S: Nov-Dec; H: Feb-May Horsegram S: Feb-Apr;

H: Apr-Jun

Gram S: Oct-Nov; H: Mar-Mar Masur S: Sep-Nov; H: Feb-Apr Pulses/Lentil S:Jun-Oct; H: Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S: Jun-Jul; H: Sep-Nov S: Nov-Jan; H: Feb-May S: Dec-Jan;

H: Mar-Jun Linseed S:Nov-Dec;H: Feb-Mar S: Sep-Oct; H: Jan-Mar Castor S: Jun-Sep; H: Oct-Mar Sesamum/Til S: Apr-Aug;

H: Oct-Nov S:May-Jun; H:Aug-Sep S: Oct-Feb; H: Feb-Mar S: Dec-Feb;

H: Mar-May Soyabean S:Jun-Aug;H: Sep-Nov Sunflower S:Jun-Aug;H: Sep-Nov S: Dec-Jan; H: Mar-Apr Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Onion S:Jul-Nov;H: Mar-May S: Jul-Nov; H: Mar-May Potato S:Oct-Nov;H: Jan-Mar S: Oct-Nov; H: Jan-Mar S: Jan-Mar;

H: Jun-Nov Cotton S: Jun-Jul; H: Dec-Mar S: Oct-Jan; H: Feb-May Maize S: Jun-Jul; H: Sep-Oct S: Sep-Jan; H: Jan-May Sugarcane S:Dec-Jun;H: Dec-May S: Jun-Oct; H: Oct-Jan Jute S: Feb-Jun; H: Aug-Oct

Table 18: Season wise crops of Andhra Pradesh Source: (Agricultural Statistics at a Glance, 2008)


Kharif Rabi Summer Paddy S: May-Jul; H:Oct-Dec S: Aug-Sep; H: Jan-Feb Wheat S: Oct-Dec; H: Jan-Feb Bajra S: Jul-Sep; H: Oct-Nov S:Jan-Feb;H:Apr-May

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K A

R N A T A K A

Arhar/Tur S: Jun-Jul; H: Nov-Feb S:Sep-Oct; H: Dec-Jan Blackgram/Urd S: Jun-Jul; H: Sep-Oct Green Gram S: Jun-Jul; H: Sep-Oct S: Sep-Oct; H: Dec-Jan Gram S:Oct-Nov; H: Jan-Mar Masur S:Sep-Nov; H: Feb-Apr Groundnut S:May-Jul; H: Sep-Dec S:Dec-Jan;H: Mar-Apr Linseed S: Sep-Oct; H: Jan-Mar Castor S: Jun-Jul; H: Oct-Feb Sesamum/Til S: Apr-Jul; H: Jul-Aug Soyabean S:Jun-Aug;H: Sep-Nov Sunflower S: Jun-Jul; H: Sep-Oct S: Sep-Oct; H: Dec-Jan Onion S:Mar-Aug;H:Aug-Dec S:Mar-Aug;H:Aug-Dec Potato S:Mar-Aug;H:Aug-Nov S:Mar-Aug;H:Aug-Dec Maize S:May-Jun; H: Sep-Oct S: Sep-Oct; H: Jan-Mar Sugarcane S:Dec-Mar;H:Dec-May Table 19: Season wise crops of Karnataka Source: (Agricultural Statistics at a Glance, 2008)


M A H A R A S T R A

Kharif Rabi Summer Paddy S:Jul-Aug; H:Oct-Dec Wheat S:Oct-Dec; H:Feb-Apr Bajra S:Jun-Jul; H:Sep-Oct S:Jan-Feb; H:Apr-May Arhar/Tur S:Jul-Jul; H:Dec-Jan Blackgram/Urd S:Jul-Aug; H: Oct-Oct Green Gram S: Jul-Jul; H:Aug-Sep Gram S:Sep-Oct; H:Feb-Mar Masur S: Sep-Nov; H: Feb-Apr Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S:Jul-Aug; H:Oct-Nov S:Jan-Feb; H:Apr-May Linseed S: Sep-Oct; H: Jan-Mar Sesamum/Til S:Oct-Oct; H:Jan-Feb S:Dec-Feb;H:Mar-May Soyabean S:Jul-Aug; H:Sep-Nov Sunflower S:Jun-Aug; H:Sep-Nov S:Oct-Nov; H:Jan-Feb Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Onion S:Nov-Dec;H:Mar-May S:Nov-Dec; H:Mar-May Potato S:Nov-Dec; H:Feb-May S:Nov-Dec; H:Feb-May Maize S:Jul-Aug; H:Oct-Nov Sugarcane S:Jul-Aug; H:Oct-Nov


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Table 21: Season wise crops of Chattisgarh Source: (Agricultural Statistics at a Glance, 2008)

Table 22: Season wise crops of Orissa Source: (Agricultural Statistics at a Glance, 2008)


C H A T T I S G A R H

Kharif Rabi Paddy S: May-Aug; H: Sep-Jan Wheat S: Oct-Dec; H: Feb-Jun Redgram/Arhar S: Jun-Jul; H: Dec-Feb Blackgram/Urd S: Jun-Jul; H: Sep-Nov S:Apr-Dec;H: Sep-Nov Greengram/Moong S: Jun-Aug; H: Sep-Dec S:Aug-Oct; H: Sep-Oct Groundnut S: Jun-Jul; H: Oct-Nov S:Nov-Jan;H: Feb-May Sesamum/Til S: May-Jul; H: Aug-Oct S: Oct-Oct; H: Jan-Feb Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct S: Sep-Jun; H: Jan-Mar Gram S:Oct-Dec; H: Mar-Apr Maize S: Mar-Jul; H: Sep-Dec S: Sep-Jan; H: Jan-May Lentil S:Oct-Nov;H:Mar-May Mustard S:Sep-Nov; H:Feb-Mar Linseed S: Nov-Dec; H:Feb-Mar S: Sep-Oct; H: Jan-Mar Sugarcane S: Feb-Aug; H:Aug-Nov S: Jun-Oct; H: Oct-Jan


O R I S S A

Kharif Rabi Paddy S: Jun-Aug;H:Dec-Jan Wheat S:Oct-Nov; H:Mar-Apr Bajra S:Jun-Jul; H:Sep-Oct Red Gram S: Nov-Dec;H:Mar-Apr Castor S:Jun-Jul; H:Dec-Jan Rape&Mustard S:Nov-Dec; H:Feb-Mar Cotton S:Jun-Jul; H: Nov-Dec Maize S:Jun-Jul; H:Sep-Oct Sugarcane S:Feb-May; H: Nov-Dec Jute S:May-Jun; H:Aug-Sep

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3.3 The Northern Mountains or Himalayas 3.3.1 Geographical location:

The Himalayas are the longest mountain range in India covering twelve states of Republic of India. The ranges are bounded between 210 57’ to 3705’ N latitude and 72040’ to 97025’ E longitude which includes very high diverse regions in India. The Himalayas are mostly covered under snow and vast glaciers and this provides a natural habitat to almost fifty percent of the forest cover enabling forty percent of the endemic species to this part of the subcontinent. 3.3.2 Area: This region stretches all along the northern frontiers of the country comprising of five states namely West Bengal, Himachal Pradesh, Arunachal Pradesh, Jammu & Kashmir and Sikkim covering 55.3 Mha accounting to 17% of the total area of India. 3.3.3 Topography:

The main Himalayas are divided into three major fold axes: (Joshi, 2004)

• Himadri called as Greater Himalayas having average height of 3000-7000m above mean sea level.

• Himachal called as Lesser Himalayas having average height of 1200-3000m. • Siwaliks called as Outer Himalayas having average height of 600-1200m.

3.3.4 Climate: Climate plays an important role in India. It varies from place to place depending upon elevation and geographical location. At the base it has tropical climate and permanent ice and snow at the high altitudes. It has four seasons namely: (Basistha et al., 2008)

• Cold winter season- December to February • Hot summer season- March to May • Monsoon season- June to September • Post-monsoon season- October and November

3.3.5 Rainfall: This region receives very less rainfall annually and this occurs only during the monsoon period. Throughout the rest of the year it is mostly covered in snow.

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3.3.6 Temperature: The temperature varies from lesser Himalayas to greater Himalayas and is dependent on both altitude and local climatic conditions. Maximum temperatures during summer reach to 380 C and minimum of 50 C in winter. At the top of foothills the temperature falls below freezing point. 3.3.7 Soil:

Soil can be considered as a major natural resource which acts as a basic medium for the growth of plants. Soil varies from sandy loam to alluvial in the Himalayan region. 3.3.8 Vegetation: Different types of forest vegetation are present in Himalayas that varies according to the both altitude and climatic conditions. 3.3.9 Forests:

Different types of vegetation are found in Northern Himalayas and are defined by (Negi, S. S, 1993) as: Tropical vegetation extends up to an elevation of 1500m Sub-tropical vegetation extends up to an elevation of about 1700 m in the outer and lower Himalayan ranges and their offshoots. Temperate vegetation extends from an elevation of about 1500 to 3500 m. It may extend to a relatively higher elevation on the main slopes of the Himalayas. Sub-alpine vegetation extends from about 3500 m to the tree line. Alpine vegetation lies just below the snowline, usually above an altitude of 4200 m. Tree growth is sparse and stunted. No trees are found above the tree or timberline. Alpine vegetation is further divided into moist alpine pasture or scrub and dry alpine pasture or scrub. 3.3.10 Agriculture:

“Paddy (mainly sown in irrigated land which is less than 10% of the total cultivated area) wheat, local coarse grains, maize and potato are the main agricultural crops of the region” (Tiwari, P. C., 2000).

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Local term Period Description (ideal) Description (present) Magh Jan 15-Feb 15 Cold with snowfall Some rain and snow Falgun Feb 15-Mar 15 Less cold with snow Rain Chaitra Mar 15-Apr 15 Rain, snow rare Some rain and snow Baisakh Apr 15-May 15 No rain, Clear skies (Paddy,dal and corn sown) Dry with some rain Jeth May15-Jun15 Hot(Paddy,dal&corn sown) Dry Asadh Jun 15-Jul 15 Hot (Until June 30) pre monsoon rain Hot and dry Sawn Jul15-Aug 15 Rain Hot and rainy Bhadra Aug 15-Sep 15 Rain(until August 30, apple harvest), Dry Predominantly rainy Ashwin Sep15-Oct15 Clear(corn, dal harvest; wheat, barley sown) 1st half rainy, 2nd half dry Karthik Oct15-Nov 15 Mostly clear (paddy harvest), shorter days Same as ideal Mangsir Nov15-Dec 15 Snowfall, drying fir leaves and wood for fuel Same as ideal Paush Dec15-Jan15 Maximum cold with snow Very little snow

Table 23: Traditional calendar of Kullu valley, Western Himalayas. Source: (Vedwan, N and Rhodes, R. E., 2001)

States Crops Cropping Seasons J K A A M & S S H M M U I R

Kharif Rabi Summer Paddy S:May-Jun H: Oct-Nov Wheat S:Oct-Dec; H:May-May Green Gram S: Jun-Jun; H: Aug-Sep Groundnut S:Dec-Jan;H: Mar-Jun Sesamum/Til S:Dec-Feb;H:MarMay Oilseeds S:Oct-Nov; H:Apr-Apr

Table 24: Season wise crops of Jammu & Kashmir Source: (Agricultural Statistics at a Glance, 2008)

States Crops Cropping Seasons H P I R M A A D C E H S A H

L

Kharif Rabi Summer Paddy S:May-Jul;H:Sep-Oct Wheat S:Oct-Nov; H:Apr-Jun Bajra S:Jan-Feb; H:Apr-May Groundnut S:Dec-Jan; H:Mar-Apr Maize S:May-Jun;H:Sep-Oct

Table 25: Season wise crops of Himachal Pradesh Source: (Agricultural Statistics at a Glance, 2008)

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M A D H Y A

P R A D E S H

Kharif Rabi Summer Paddy S: Jun-Jul;H: Sep-Dec Wheat S:Oct-Dec;H:Feb-Apr Bajra S: Jun-Jul;H: Sep-Dec Arhar/Tur S: Jun-Jul;H: Dec-Feb Red Gram S:Feb-Mar;H:May-May Blackgram/Urd S:Jun-Aug;H:Sep-Nov S:Oct-Nov;H:Feb-May Green Gram S:Jun-Aug;H:Sep-Dec S:Oct-Dec;H:Feb-May Gram S:Oct-Dec;H:Feb-Apr Masur S:Sep-Nov;H:Feb-Apr Pulses/Lentil S:Oct-Nov;H:Mar-May Groundnut S: Jun-Jul;H: Sep-Dec Linseed S:Oct-Nov;H:Feb-Apr Sesamum/Til S: Jun-Jul;H: Sep-Dec Soyabean S:Jun-Jul;H:Oct-Nov Sunflower S: Jun-Aug;H: Sep-Oct Rape&Mustard S:Sep-Nov;H: Feb-Mar Oilseeds S:Sep-Nov;H:Dec-Mar Onion S:Oct-Dec;H:Apr-Jul S:Oct-Dec; H:Apr-Jul Potato S:Oct-Dec;H:Feb-Jun S:Oct-Dec; H:Feb-Jun Maize S:Jun-Jul;H:Aug-Dec Sugarcane S:Oct-Apr;H:Oct-Mar Table 26: Season wise crops of Madhya Pradesh Source: (Agricultural Statistics at a Glance, 2008)


A S S A M

Kharif Rabi Paddy S: Jun-Sep;H:Oct-Feb Blackgram/Urd S:Aug-Sep; H:Nov-Dec Green Gram S:Aug-Sep; H:Nov-Dec Pulses/Lentil S:Oct-Nov; H:Mar-Apr Groundnut S: Jul-Aug;H:Nov-Dec Sesamum/Til S: Jul-Aug; H:Oct-Nov Rape&Mustard S:Oct-Nov;H:Feb-Mar Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov;H: Feb-Mar Sugarcane S:Mar-Apr; H:Dec-Jan

Table 27: Season wise crops of Assam Source: (Agricultural Statistics at a Glance, 2008)

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W E S T

B E N G A L

Early Kharif Kharif Rabi Summer Paddy S:Jul-Aug;H:Nov-Jan Wheat S:Nov-Dec; H:Mar-Apr Arhar/Tur S:Jun-Jun;H:Feb-Mar Red Gram S:Feb-Mar;

H:May-May Blackgram/Urd S: Jun-Jul; H: Sep-Nov Green Gram S:Feb-Mar

H:Apr-May S:Jul-Aug;H:Oct-Nov

Horsegram S: Feb-Apr; H: Apr-Jun

Gram S:Nov-Dec;H:Mar-Mar Pulses/Lentil S:Nov-Nov;H:Feb-Mar Groundnut S:Nov-Dec;H:Feb-Mar S:Feb-Mar;

H:May-Jun Linseed S:Nov-Dec;H:Feb-Mar Castor S:Jun-Jul; H:Dec-Jan Sunflower S:Dec-Jan;H:Apr-Apr Rape&Mustard S:Oct-Nov;H:Jan-Mar Potato S:Oct-Dec; H:Jan-Mar S:Oct-Dec;H:Jan-Mar Cotton S:Oct-Nov; H:Sep-Sep Maize S:Mar-May;H:Jun-Aug S:Nov-Nov;H:Mar-Mar Jute S:Mar-May; H:Jul-Aug Table 28: Season wise crops of West Bengal Source: (Agricultural Statistics at a Glance, 2008) 3.4 INDO-GANGETIC PLAINS (IGP)

3.4.1 Geographical Location:

The IGP is the largest plain land in the Indian subcontinent, which accounts for about 21% of the total area of India. It is bounded between from 21.750 N to 31.00 N latitudes and 74.250 E to 91.50 E longitudes.

3.4.2 Area:

Having an area of 6, 00000 Sqkm passing through Punjab, Haryana, Bihar, West Bengal and Uttar Pradesh covering 20% of the total area of India. It is 1700 Km long and 200-300 km wide.

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3.4.3 Physical Environment: IGP has four Agro-Climatic Regions (ACRs): (Narang & Virmani, 2001).

• TGP- Trans-Gangetic Plains (Punjab, Haryana and parts of Uttar Pradesh) • UGP- Upper-Gangetic Plains (Uttar Pradesh) • MGP- Middle-Gangetic Plains (Most of Bihar and parts of Uttar Pradesh) • LGP- Lower-Gangetic Plains (Most of West Bengal)

3.4.4 Topography: This region has varied topography from east to west ranging from 0-50 and 150-300m respectively. 3.4.5 Climate:

Climate varies from semi-arid in the west to humid in the east dominated by Asian summer monsoon. The year is broadly divided into four seasons: (Singh, N & Sontakke, N. A., 2002)

• Winter- January- February • Summer- March- May • Summer Monsoon- June to September • Post-monsoon or north-east monsoon- October to December

3.4.6 Rainfall:

In the IGP, there is an average rain fall of 300mm to 1600mm with a variation of 0.6mm/km towards east. There is a mean rainfall of 500 to 800mm in the western part and 1500 to 3200mm in the eastern part of the IGP. 3.4.7 Temperature: In summer the temperatures are very hot reaching to 450 C and moderate cold falling to 40 C. The mean annual temperature will be in the range of 22 to 270 C. 3.4.8 Soil:

There are two types of alluvial soils (Sibhu et al., 2007)

• Older alluvium: fine textured, dark colored, calcareous with pronounced pedogenic activity

• Younger alluvium: coarse structured, light colored, with little or no pedogenic activity.

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Alkali soils are mostly found in the states of Punjab, Haryana and Uttar Pradesh, though they extensively found all over parts of the world (Chhabra, R & Thakur, N. P., 2000).

3.4.9 Forests:

Forests are classified according to (Sandhyarani et al., 2007; Daniels, R. J. R., 2001; Champiom & Seth., 1968)

• Tropical Dry and Moist deciduous forests • Teak and Non teak bearing forests • Swamp forests • Wooded grasslands • Delta forests • Scrub and Thorn forests

3.4.10 Agriculture:

Kharif (June/July-September/October) and Rabi (October/November-March/April) are the two main cropping seasons of this region.


B I H A R

Kharif Rabi Paddy S: Jun-Jul; H:Nov-Dec Wheat S: Nov-Dec; H: Mar-Apr Bajra S: Jun-Jul; H: Sep-Nov Arhar/Tur S: Jun-Jul; H: Dec-Feb Blackgram/Urd S: Jun-Jul; H: Sep-Nov Green Gram S: Jun-Aug; H: Sep-Dec S: Aug-Oct; H: Sep-Oct Gram S: Jun-Jul; H:Nov-Dec S: Oct-Nov; H:Mar-Mar Masur S: Sep-Nov; H: Feb-Apr Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S: Jun-Jul; H: Oct-Nov S: Nov-Jan; H: Feb-May Linseed S: Sep-Oct; H: Jan-Mar Castor S: Apr-Jun; H: Oct-Feb Sesamum/Til S: May-Jul; H: Aug-Oct S: Oct-Oct; H: Jan-Feb Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct S: Sep-Jun; H: Jan-Mar Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec Onion S: Oct-Jan;H: Feb-Apr S: Oct-Jan; H:Feb-Apr Potato S: Oct-Feb; H: Dec-Mar S: Oct-Feb; H:Dec-Mar Cotton S: Apr-Jul; H: Sep-Dec Maize S: Jun-Jul; H: Nov-Dec S:Oct-Nov; H:Feb-Mar

Table 29: Season wise crops of Bihar Source: (Agricultural Statistics at a Glance, 2008)

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H A R Y A N A

Early Kharif

Kharif Rabi Summer

Paddy S:Jul-Jul; H:Oct-Nov Wheat S:Oct-Dec; H:Apr-Apr Bajra S: Jun-Jul; H: Sep-Nov Blackgram/Urd S: Jun-Jul; H: Sep-Nov Green Gram S:Jun-Aug;

H:Aug-Sep S:Jun-Aug; H: Sep-Dec

Horsegram S:Feb-Apr; H: Apr-Jun

Gram S:Oct-Oct; H:Mar-Mar Pulses/Lentil S: Jun-Oct; H:Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S: Jun-Jul; H: Oct-Nov S: Dec-Jan;

H: Mar-Jun Linseed S: Sep-Oct; H: Jan-Mar Sesamum/Til S: May-Jul;H:Aug-Oct S: Oct-Oct; H: Jan-Feb S: Dec-Feb;

H: Mar-May Soyabean S:Jun-Aug; H:Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct S: Sep-Jun; H: Jan-Mar Rape&Mustard S:Sep-Nov; H:Feb-Mar Oilseeds S:Sep-Oct; H:Mar-Mar Onion S: Apr-Apr; H: Jun-Jun S: Apr-Apr; H: Jun-Jun Potato S: Jul-Dec; H: Dec-Feb S: Jul-Dec; H: Dec-Feb Table 30: Season wise crops of Haryana Source: (Agricultural Statistics at a Glance, 2008)


P U N J A B

Early Kharif Kharif Rabi Summer Paddy S: May-Jul;H:Sep-Nov S: Aug-Sep; H: Jan-May Wheat S: Oct-Dec; H:Apr-May Bajra S:Jun-Jul; H:Sep-Oct Arhar/Tur S:May-Aug;

H: Aug-Oct S:Aug-Nov; H:Oct-Nov

Red Gram S: Jun-Jul; H: Mar-Apr S: Feb-Mar; H: May-May

Blackgram/Urd S: Mar-Jun; H: Jun-Sep S:Sep-Oct; H:Nov-Dec

Green Gram S:Oct-Nov; H:Nov-Nov S:Sep-Oct; H:Nov-Dec

Horsegram S: Feb-Apr; H: Apr-Jun

Gram S: Oct-Dec;H: Mar-Apr Masur S: Sep-Nov; H: Feb-Apr

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Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S: Jun-Jul; H: Oct-Nov S: Nov-Jan; H: Feb-May Sesamum/Til S:Aug-Oct; H:Dec-Apr S:Dec-Feb; H:Mar-May S: Apr-Aug;

H: Aug-Oct Soyabean S: Jun-Aug; H:Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct Rape&Mustard S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Onion S: Apr-Apr; H: Jun-Jun S: Apr-Apr; H: Jun-Jun Potato S: Jul-Dec; H: Dec-Feb S: Jul-Dec; H: Dec-Feb S: Jan-Mar;

H: Jun-Nov Cotton S: Jun-Jul; H: Nov-Dec Maize S: Sep-Jan; H: Jan-May Sugarcane S:Oct-Feb; H:Oct-Dec Table 31: Season wise crops of Punjab Source: (Agricultural Statistics at a Glance, 2008)


G O A

Kharif Rabi Paddy S:May-Jul; H:Sep-Dec S:Nov-Jan; H:Mar-Apr Wheat S: Oct-Dec; H: Feb-Jun Bajra S: Jun-Jul; H: Sep-Nov Arhar/Tur S: Jun-Jul; H: Dec-Feb Blackgram/Urd S: Jun-Jul; H: Sep-Nov Green Gram S: Jun-Aug; H: Sep-Dec Gram S: Oct-Dec; H: Mar-Apr Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H:Mar-May Groundnut S: Jun-Jul; H: Oct-Nov S: Nov-Jan; H: Feb-May Linseed S: Sep-Oct; H: Jan-Mar Sesamum/Til S: Oct-Oct; H: Jan-Feb Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct S: Sep-Jun; H: Jan-Mar Rape&Mustard S:Aug-Sep; H: Nov-Dec S:Dec-Jan: H:Mar-May Oilseeds S: Jun-Jul; H: Nov-Dec Potato S: Jul-Dec; H: Dec-Feb Maize S: Sep-Jan; H: Jan-May

Table 32: Season wise crops of Goa Source: (Agricultural Statistics at a Glance, 2008)

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States Crops Cropping Seasons G

U J A R A T

Early Kharif Kharif Rabi Summer Paddy S:Jun-Aug; H:Oct-Dec S:Nov-Feb;H:Mar-May Wheat S:Oct-Nov; H:Feb-Mar Bajra S: Jun-Jul; H:Sep-Nov S:Feb-Feb;

H:May-May Arhar/Tur S: Jun-Jul; H: Dec-Feb Green Gram S:Jun-Aug;

H: Aug-Sep S:Jun-Aug; H: Sep-Dec

Gram S:Oct-Nov; H:Feb-Mar Masur S:Sep-Nov; H: Feb-Apr Pulses/Lentil S:Jun-Oct; H: Nov-Dec S:Oct-Nov;H:Mar-May Groundnut S: Jun-Jul; H: Sep-Nov S:Jan-Jan;

H:May-Jun Linseed S: Sep-Oct; H: Jan-Mar Castor S: Apr-Jun; H: Oct-Feb Sesamum/Til S:May-Jul; H: Aug-Oct S: Oct-Oct; H: Jan-Feb Soyabean S:Jun-Aug; H:Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct S: Sep-Jun; H: Jan-Mar Rape&Mustard S:Sep-Nov; H:Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S:Sep-Nov; H:Feb-Mar Potato S: Jul-Dec; H: Dec-Feb S: Jul-Dec; H: Dec-Feb Cotton S:May-May; H:Oct-Apr Maize S: Jun-Jul; H:Sep-Nov Table 33: Season wise crops of Gujarat Source: (Agricultural Statistics at a Glance, 2008) 3.5 WESTERN GHATS

3.5.1 Geographical Location: The Western Ghats has boundaries with the Tapti River in the north to the southernmost tip of India with a total range of approximately 1600km. “It runs from 80 N, all the way up the western coast to the mouth of the river Tapti, 210 N and lie between 730 -770 E” (Nagendra, H & Utkarsh, G., 2003). 3.5.2 Area:

It passes through the states of Gujarat, Goa, Maharastra, Karnataka, Kerala and Tamil Nadu covering a total area of 170,000 Sqkm. It runs in parallel to the west coast adjacent to the Arabian Sea.

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3.5.3 Topography:

The mountains in these ranges rise to a maximum height of 2800m and have a width of about 100 km. The average height of the Western Ghats ranges between 600 to 1400m with Anaimudi being the highest peak. It has three zones namely (Tewari 1995):

1. Surat to Goa (Northern Western Ghats) 2. Goa to Palghat Gap (Central Western Ghats) 3. South of Palghat Gap (Southern Western Ghats)

3.5.4 Climate: The climate in the Western Ghats depends on the altitude and the distance from the sea as well as the distance from the equator. It has a tropical climate and that’s the reason why it is warm and humid throughout the year and as the altitude increases it would mostly be a subtropical and moderately cool occasionally. 3.5.5 Rainfall:

The Western Ghats get most of its rainfall during the months of June and October, which is the period of the south west monsoon. It receives an average of 2500mm of rainfall annually. It has a short dry season in the south which lasts for two to three months, unlike in the north where the dry season usually last for five to eight months.

3.5.6 Temperature:

This region experiences a maximum temperature of 300 C in the summer and a minimum of 00 C in the winter. The average temperature ranges from 200C to the 240C in the northern part of the Ghats. 3.5.7 Soil: The three main types of soils that are usually found in this region are Laterite, Red and Black humid. 3.5.8 Vegetation:

It covers one third of the total area of India being one of the best biodiversity hotspots in the world. It is also rich in biodiversity and ranks 25th in the world.

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3.5.9 Forests:

Veg Type Distribution Rainfall Dominant Flora Tropical evergreen

200-1500m ASL 2500-5000mm Emergents up to 60m; Acrocarpus, Aglaia, Artocarpus, Calophyllum, Canarium, Cullenia, Dipterocarpus, Holigama, Knema, Myristica etc.

Moist deciduous

500-900m ASL 2500-3500mm Bridelia, Pterocarpus, Sterculia, Pterospermum, Lagerstroemia, Tectona, Terminalia etc.

Dry deciduous 300-900m ASL 1000-2000mm Albizia, Anogeissus, Bauhinia, Buchnania, Butea, Dillenia, Emblica etc.

Scrub jungles 200-500m ASL 300-600mm Acasia, Carissa, Capparis, Flacourtia, Gardenia etc. Sholas Above 1500m ASL Med to high Short trees 15-20m high; Actinodaphne,

Elaeocarpus, Eunymus, Michelia, Rhodomyrtus, Schefllera, Symplocos etc.

Savannas 1700-1900m ASL Med to high Grass, Chrysopogon, Arundinella, Eulalia, Heteropogon etc.

High rainfall savannas

Montane Extremely high

Herbaceous to shrubby cover; Rhododendron, Anaphalis, Strobilanthes etc.

Peat bogs Above 2000m ASL High Grasses, sedges and mosses; carex, Cyanotis, Cyperus, Eriocaulon etc.

Myristica swamps

Sea level to around 600m ASL

Med to high Myristica, Knema, Hydnocarpus, Lophopetalum etc.

Table 34: Vegetation types of Western Ghats. Source: (Daniels, R. J. R., 2001) Hill range Altitude (m) Annual avg.

Rainfall (RF mm)

Mean temp. of the coldest month (T0

C)

Dry season (DS)

No. of rainy days (RD)

Min. Max. Min. Max. Min. Max. Nilgiri 700 2490 900 >5000 <13 23 2-5 85-92 Anaimalai 690 1997 900 5000 16 >23 3-4 66-102 Palni 869 2396 900 1600 <13 <23 1-5 44-92 Tirunalveli 500 1280 1200 5000 13.5 23 3-5 89-92 Table 35: Physiographic and bioclimatic variation seen in different hill ranges in the Western Ghats of Tamil Nadu, India. Source: (Amarnath et al., 2003)

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3.5.10 Agriculture:

States Crops Cropping Seasons K

A R N A T A K A

Kharif Rabi Summer Paddy S: May-Jul; H:Oct-Dec S: Aug-Sep; H: Jan-Feb Wheat S: Oct-Dec; H: Jan-Feb Bajra S: Jul-Sep; H: Oct-Nov S: Jan-Feb; H: Apr-May Arhar/Tur S: Jun-Jul; H: Nov-Feb S: Sep-Oct; H: Dec-Jan Blackgram/Urd S: Jun-Jul; H: Sep-Oct Green Gram S: Jun-Jul; H: Sep-Oct S:Sep-Oct; H: Dec-Jan Horsegram Gram S: Oct-Nov; H: Jan-Mar Masur S:Sep-Nov; H: Feb-Apr Pulses/Lentil S: Jun-Oct; H: Nov-Dec S:Oct-Nov;H:Mar-May Groundnut S: May-Jul; H: Sep-Dec S: Dec-Jan;H: Mar-Apr Linseed S: Sep-Oct; H: Jan-Mar Castor S: Jun-Jul; H: Oct-Feb Sesamum/Til S: Apr-Jul; H: Jul-Aug Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Jul; H: Sep-Oct S: Sep-Oct; H: Dec-Jan Rape&Mustard S:Sep-Nov;H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S:Sep-Nov;H: Feb-Mar Onion S: Mar-Aug; H: Aug-Dec S:Mar-Aug;H:Aug-Dec Potato S: Mar-Aug; H: Aug-Nov S:Mar-Aug;H:Aug-Dec Maize S: May-Jun; H: Sep-Oct S: Sep-Oct; H: Jan-Mar Sugarcane S: Dec-Mar; H:Dec-May

Table 36: Season wise crops of Karnataka Source: (Agricultural Statistics at a Glance, 2008)


M A H A R A S T R A

Kharif Rabi Summer Paddy S:Jul-Aug; H:Oct-Dec Wheat S:Oct-Dec; H:Feb-Apr Bajra S:Jun-Jul; H:Sep-Oct S:Jan-Feb; H:Apr-May Arhar/Tur S:Jul-Jul; H:Dec-Jan Blackgram/Urd S:Jul-Aug; H: Oct-Oct Green Gram S: Jul-Jul; H:Aug-Sep Gram S:Sep-Oct; H:Feb-Mar Masur S: Sep-Nov; H: Feb-Apr Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H: Mar-May Groundnut S:Jul-Aug; H:Oct-Nov S:Jan-Feb; H:Apr-May Linseed S: Sep-Oct; H: Jan-Mar Sesamum/Til S:Oct-Oct; H:Jan-Feb S: Dec-Feb; H: Mar-May

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Soyabean S:Jul-Aug; H:Sep-Nov Sunflower S:Jun-Aug; H:Sep-Nov S:Oct-Nov; H:Jan-Feb Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Onion S:Nov-Dec; H:Mar-May S:Nov-Dec; H:Mar-May Potato S:Nov-Dec; H:Feb-May S:Nov-Dec; H:Feb-May Maize S:Jul-Aug; H:Oct-Nov Sugarcane S:Jul-Aug; H:Oct-Nov



K E R A R L A

Early Kharif Kharif Rabi Summer Paddy S:Aug-Nov;H: Nov-Jan Wheat S: Oct-Dec; H: Feb-Jun Bajra S:Jun-Jul; H:Sep-Oct Arhar/Tur S:May-Aug;

H: Aug-Oct S:Aug-Nov;H:Oct-Nov

Blackgram/Urd S: Mar-Jun; H: Jun-Sep S:Sep-Oct; H:Nov-Dec

Green Gram S:Oct-Nov;H:Nov-Nov S:Sep-Oct; H:Nov-Dec

Gram S: Oct-Dec; H: Mar-Apr Masur S: Sep-Nov; H: Feb-Apr Pulses/Lentil S: Jun-Oct;H: Nov-Dec S: Oct-Nov; H:Mar-May Groundnut S: Jun-Jul; H: Oct-Nov S: Nov-Jan; H: Feb-May Linseed S: Sep-Oct; H: Jan-Mar Sesamum/Til S:Aug-Oct; H:Dec-Apr S:Dec-Feb; H:Mar-May S: Apr-Aug;

H: Aug-Oct Soyabean S: Jun-Aug;H:Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct Rape&Mustard S: Sep-Nov; H: Feb-Mar Oilseeds S: Jun-Jul; H: Nov-Dec S: Sep-Nov; H: Feb-Mar Cotton S:Jun-Jul; H: Nov-Dec Sugarcane S:Oct-Feb; H:Oct-Dec Table 38: Season wise crops of Kerala Source: (Agricultural Statistics at a Glance, 2008)

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G O A

Kharif Rabi Paddy S:May-Jul; H:Sep-Dec S:Nov-Jan; H:Mar-Apr Wheat S: Oct-Dec; H: Feb-Jun Bajra S: Jun-Jul; H: Sep-Nov Arhar/Tur S: Jun-Jul; H: Dec-Feb Blackgram/Urd S: Jun-Jul; H: Sep-Nov Green Gram S: Jun-Aug; H: Sep-Dec Gram S: Oct-Dec; H: Mar-Apr Pulses/Lentil S: Jun-Oct; H: Nov-Dec S: Oct-Nov; H:Mar-May Groundnut S: Jun-Jul; H: Oct-Nov S: Nov-Jan; H: Feb-May Linseed S: Sep-Oct; H: Jan-Mar Castor Sesamum/Til S: Oct-Oct; H: Jan-Feb Soyabean S: Jun-Aug; H: Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct S: Sep-Jun; H: Jan-Mar Rape&Mustard S:Aug-Sep; H: Nov-Dec S:Dec-Jan: H:Mar-May Oilseeds S: Jun-Jul; H: Nov-Dec Potato S: Jul-Dec; H: Dec-Feb Maize S: Sep-Jan; H: Jan-May

Table 39: Season wise crops of Goa Source: (Agricultural Statistics at a Glance, 2008)


G U J A R A T

Early Kharif Kharif Rabi Summer Paddy S:Jun-Aug; H:Oct-Dec S: Nov-Feb; H:Mar-May Wheat S:Oct-Nov; H:Feb-Mar Bajra S: Jun-Jul; H:Sep-Nov S:Feb-Feb;

H:May-May Arhar/Tur S: Jun-Jul; H: Dec-Feb Blackgram/Urd S: Jun-Jul; H: Sep-Nov Green Gram S:Jun-Aug;

H: Aug-Sep S:Jun-Aug; H: Sep-Dec

Gram S:Oct-Nov; H:Feb-Mar Masur S:Sep-Nov; H: Feb-Apr Pulses/Lentil S:Jun-Oct; H: Nov-Dec S:Oct-Nov;H:Mar-May Groundnut S: Jun-Jul; H: Sep-Nov S:Jan-Jan;

H:May-Jun Linseed S: Sep-Oct; H: Jan-Mar Castor S: Apr-Jun; H: Oct-Feb Sesamum/Til S:May-Jul; H: Aug-Oct S: Oct-Oct; H: Jan-Feb Soyabean S:Jun-Aug;H: Sep-Nov Sunflower S: Jun-Aug; H: Sep-Oct S: Sep-Jun; H: Jan-Mar Rape&Mustard S:Sep-Nov;H: Feb-Mar

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Oilseeds S: Jun-Jul; H: Nov-Dec S:Sep-Nov;H: Feb-Mar Potato S: Jul-Dec; H: Dec-Feb S: Jul-Dec; H: Dec-Feb Cotton S:May-May;H:Oct-Apr Maize S: Jun-Jul; H: Sep-Nov Table 40: Season wise crops of Gujarat Source: (Agricultural Statistics at a Glance, 2008) 3.6 Problems of vegetation:

In recent years, the natural vegetation of Western Ghats has declined due to many reasons (Tewari, 1995):

1. Deforestation 2. Urbanization 3. Logging 4. Lopping of leaves 5. Cattle grazing 6. Cultivation 7. Conversion of forests into plantations and dry season fires.

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4. Methodology & Results

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4.1 Remote Sensing Sensors: A sensor in a satellite is a device embedded to it in space that can capture the data of material, an object or scene with or without physical contact. They collect the radiated spectrum and project it to the processing machine, through which the data is compared to the relevant spectrum and gives a clear picture to the operator. They use some form of light energy to identify the target location such as sunlight. In this case, the emitted spectral wavelength of different plants are collected and processed to give us a better understanding of the vegetation of an area. This remote sensing sensor gives a synoptic view of the larger areas and temporal data from time to time.

4.2 Change detection:

As the name itself implies that it detects the change, and in this case it detects the change in the vegetation phenomenon of an area from time to time making it easy for comparison and looking into the future. In order to understand interactions and relationships between humans and natural phenomena for better management and use of resources an accurate, reliable and timely change detection of earth’s surface features should be the foundation. Good change detection research should provide the following information

• Area change and change rate • Spatial distribution of changed types • Change trajectories of land-cover types • Accuracy assessment of change detection results (Lu et al, 2004)

When implementing a change detection project, three major steps are involved: (Lu et al, 2004)

• Image pre-processing including geometrical rectification and image registration, radiometric and atmospheric correction, and topographic correction if the study area is in mountainous regions

• Selection of suitable techniques to implement change detection analyses • Accuracy assessment

The accuracies of change detection results depend on many factors: (Coppin et al., 2004)

• Precise geometric registration • Availability and quality of ground truth data • Complexity of landscape • Algorithms used, as well as • Calibration and normalization of the satellite images

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And also depends on other factors like: (Lu et al, 2004)

• Classification and change detection schemes • Analysts skill and experience • Knowledge and familiarity of the study area • Time and cost restrictions

4.3 Methodology:

4.3.1 Vegetation Classification using NDVI:

Vegetation indices (VI) have been extensively used for monitoring and detecting vegetation and land cover changes (De fries et al., 1995). The development of vegetation indices is based on differential absorption, transmittance and reflectance of energy by the vegetation in the red and near infrared regions of the electro-magnetic spectrum (Jensen, J. R., 1996). Plants absorb light energy with the help of chlorophyll, a green pigment present in leaves in order to perform photosynthesis. Light is absorbed more in the red and reflected in near-infrared regions. As a result the leaves appear brighter indicating the high vegetation in the red band and dark indicating low or sparse vegetation in the near infrared regions. With the help of NDVI it is possible to monitor and identify the amount of vegetation greenness. Frequently, red and near infrared (NIR) image bands have been used to compute a Normalized Difference Vegetation Index (NDVI) which can be defined as follows:

NDVI = ρNIR - ρred / ρNIR + ρred

where ρNIR (846-885 nm) and ρred (600-680 nm) are the surface bidirectional reflectance factors for their respective MODIS bands (Kawamura et al., 2005). The NDVI is functionally equivalent to the simple ratio SR = NIR/ Red such that NDVI = SR-1/ SR+ 1 NDVI has been classified using variable DN value limits.

EVI = G ((ρnir – ρred) / (ρnir + C1 x ρred – C2 x ρblue + L))

where the ρ values are partially corrected (Rayleigh and ozone absorption) surface reflections, L is the canopy background adjustment L= 1, C1 and C2 are coefficients of aerosol resistance term that uses 500 m blue band (458-479 nm) of MODIS to correct for aerosol influences in the red band (C1 = 6 and C2 = 7.5) and G is the Gain factor (G= 2.5) (Huete et al., 1994, 1997, 1999). “The equation produces values ranging from –1 to 1. Negative values are indicative of clouds, snow, water and other non-vegetated, non-reflective surfaces, while positive values denote vegetated or reflective surfaces” (Burgan 1993). The most commonly used vegetation index is the NDVI and widely employed for

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global monitoring of vegetation which has proved to be very useful in vegetation change detection and is employed in this study work. 4.3.2 Generation of NDVI:

In this study MODIS 32 day composite images of spectral bands 1 and 2 of the Red and Near Infrared bands ratio imagery with a spatial resolution of 500 m having wavelengths of 620-670 nm and 841-876 nm have been used for the generation of NDVI. 4.3.3 Image extraction and conversion in IDRISI:

MODIS 32-day composite images for the five year period from 2000 to 2005 were downloaded from the web based satellite data archives of the Global Land Cover Facility (GLCF) offered by USGS and University of Maryland for natural resource research. All the downloaded images were in tiff format compressed in zip. They were unzipped and converted in IDRISI32 environment which is a raster based geographical information systems. 4.3.4 Goode’s Projection: “The Goodes projection or Interrupted Goode Homolosine projection, developed by J.P. Goode in 1923, is an equal-area pseudocylindrical composite map projection which is interrupted to reduce distortion in the major land masses formed by more than one map projection” (Goode, 1925). The whole world is represented on a single map which has twelve discrete regions and is divided into two six lobes. Six lobes represent Mollewide projection mapping Polar Regions and remaining six represent Sinusoidal projection mapping equatorial regions. 4.3.5 Image preprocessing: This is an important process before classification and change detection methods. The raw satellite image obtained from the sensor is not clear and is very difficult to interpret. After preprocessing the image looks like as if it is acquired from the same sensor. 4.3.6 Geometric Correction: Satellite images obtained from the sensors are either distorted or degraded and can be repositioned using Ground Control Points (GCP) in order to represent an original image. In this the GCP are taken from the map of that area and also from the distorted image. The ground control point file is prepared by using old and new latitude and longitude information. For classification of the image it is necessary to have the accurate geometric registration.

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4.3.7 Atmospheric Correction: The atmosphere acts as a barrier between the earth’s surface and the sensor preventing the sun light from reaching the earth and then to the sensor. As a result some light is scattered or absorbed or reflected due to the present of haze. Atmospheric correction is employed to remove or minimize the haze on the images. It eliminates atmospheric haze by rescaling each frequency band so that its minimum value (usually realized in water bodies) corresponds to a pixel value of 0. The MODIS satellite data was atmospherically corrected by the GLCF team and is used for this study purpose. 4.3.8 Radiometric Correction: When satellites take multi temporal image of same location over and over during different times, no two images would give in the same data as there would be changes of senor calibration, difference in the angle of the sun’s rays, the climate and atmospheric changes. These effects gives raise to some errors and these are rectified using radiometric correction. The MODIS satellite data was radiometrically corrected by the GLCF team and is used for this study purpose. 4.3.9 Windowing:

This is an important tool used to select the specific interested area by removing the unnecessary parts using the same row and column from the given images. 4.3.10 Overlay:

MODIS NDVI bands in the Red and Near-Infrared were overlaid one above the other in order to detect the vegetation change between two images of the same year using the IDRISI 32 software.

4.3.11 Image Classification: In this the image is classified into different classes or categories such as land use, vegetation etc. In this four classes of vegetation have been developed for our convenience from very low to very dense using IDRISI Andes software to classify and compute NDVI values. Four vegetation classes have been generated based on NDVI scores in relation with four different seasons in India which can best determine the phenological vegetation changes. 4.4 Monitoring vegetation phenology using remote sensing:

According to (Zhang et al., 2003), the annual cycle of vegetation phenology is characterized by four key transition dates, which define the key phonological phases of vegetation dynamics at annual time scales. These transition dates are:

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1) Greenup, the date of onset of photosynthetic activity 2) Maturity, the date at which photosynthetic activity and leaf area is maximum 3) Senescence, the date at which photosynthetic activity and green leaf area begin to

rapidly decrease. 4) Dormancy, the date at which physiological activity becomes near zero.

4.5 Results:

Characterizing the vegetation phenology using NDVI time series:

Fig 6: NDVI time series graph The above figure shows the NDVI time-series curve of vegetation in India for the years 2000-2005 indicating growth and ungrowth cycles. India has four seasons according to meteorological survey of India. The winter in India starts from December to February and the temperature is pretty low. In this season, vegetations stops growing in Himalayas at higher altitudes where NDVI is zero and in some parts of Western Ghats and Eastern Ghats where NDVI is also all-time low. The hot weather starts from March to May and the temperature increases all over India especially in Rajasthan, Gujarat and all coastal areas due to humidity and moisture and vegetations become to grow; but, generally, NDVI is not very high. The Monsoon or Rainy season is from June to August, when the temperature is above normal; both sunshine and radiation in summer are very strong and rainfall is very plentiful and is the best season for vegetation growth owing to good sunshine, moderate temperature and sufficient water. Western Ghats, Himalayas and some parts of Eastern Ghats will have very high NDVI values and the remaining parts with high NDVI approaches to the peak value gradually. The autumn is from September to November when the temperature becomes to decrease; NDVI decreases along with

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vegetations turning withered and yellow. It is mostly observed in Himalayan regions and in some parts of North India. 4.6 Vegetation change detection and comparison of Images:

Fig 7: Vegetation change in November, 2000

Fig 8: Vegetation change in February, 2001

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Fig 9: Vegetation change in November, 2001

Fig 10: Vegetation change in March, 2002

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Fig 11: Vegetation change in December, 2002

Fig 12: Vegetation change in January, 2003

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81



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Fig 17: Vegetation change in September, 2005 4.7 Markov Model prediction algorithm: “The Russian mathematician Andrei Andreyevich Markov (1856–1922) developed the theory of Markov chains in his paper ‘Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain” (Markov, 1907). This phenomenon would indict that the elements would change into different elemental states over a period of time. This helps us to predict the status of the element in the future with the results from the past, enabling us to define the time for which the element would be active in that period. The results obtained from the Markov Model would help us choose how the land could be used effectively in the future. A discrete-time stochastic process specifies how a random variable changes at discrete points in time. Let Xt denote a random variable representing the state of a system at time t, where t= 0, 1, 2…… A. Stationary Markov chain is a special type of discrete time scohastic process with the following assumptions: (Winston, W. L., 1994)

• The probability distribution of the state at time t+1 depends on the state at time t, and does not depend on the previous states leading to the state at time t,

• A state transition from time t to time t+1 is independent of time. Let pij denote the probability that the system is in a state j at time t+1 given the system is in the state t at time t. If the system has a finite number of states, 1, 2, 3….s, the

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stationary Markov chain can be defined by a transition probability matrix: (Winston, W. L., 1994)

(1) And an initial probability distribution:

(2) where qi is the probability that the system is in state i at time 0, and j=s

∑ pij = 1 (3) j=1 The probability that a sequence of states X1,…. XT at time 1…., T occurs in the context of the stationary Markov chain is computed as follows: T

P(X1,….., XT) = qx1π PXt – 1Xt (4) t=2 The transition probability matrix and the initial probability distribution of a stationary Markov chain can be learned from the observations of the system state in the past. Provided with the observations of the system state X1, X2, X3… XN-1 at time t=0,…, N-1, we learn the transition probability matrix and the initial probability distribution as follows: (Mitchell, T.M., 1997)

Pij = Nij (5) Ni

Qi = Ni (6) N where Nij is the number of observation pairs Xt and Xt+1 with Xt in state i and Xt+1 in state j; Ni is the number of observation pairs Xt and Xt+1 with Xt in state i and Xt+1 in any one of the states 1…..s; Ni is the number of Xt‘s in state i and N is the total number of observations.

P =

Q1 Q2 Q3 ………………... QS Q =

P11 P12 P13 ……………….. P1S

P21 P22 P23 ………………. P2S

P31 P32 P33 ………………. P3S

…. …. …. …. PS1 PS2 PS3 ………………... PSS

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4.8 Future Change Prediction: It has been observed that the dominant vegetation classes have changed significantly during the year 2000-2005. A five year period of Markov prediction has been applied in this given study report and a further 5 year period that is till 2010 for future prediction and the following results were found. From the Markov model transition probability matrix it has been observed that Class 1 has the probability of changing to Class 2 at 18%, Class 2 has the probability of changing to Class 3 at 33%, Class 3 has the probability of changing to Class 2 at 14% and Class 4 has the probability of changing to Class 3 at 33%.

Fig 18: Markov prediction of Class 1

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Fig 19: Markov Prediction of Class 2


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0

0,2

0,4

0,6

0,8

1

C1 C2 C3 C4

Markov Transition Probaility Matrix

Class 1

Class 2

Class 3

Class 4

Fig 22: Markov Transition Probability Matrix Graph

C1 C2 C3 C4 Class 1 0,8136 0,1833 0,0031 0,0001 Class 2 0,0167 0,6485 0,3336 0,0012 Class 3 0,0114 0,1471 0,7534 0,0881 Class 4 0,0145 0,0223 0,3356 0,6276

Table 41: Markov Transition Probability Table

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0

1000000

2000000

3000000

4000000

C1 C2 C3 C4

Markov Transition Areas

Class 1

Class 2

Class 3

Class 4

Fig 23: Markov Transition Area Graph

Table 42: Markov Transition Area Table

Fig: 24: SeaWiFs NDVI Time Series Graph for the year 2005-2007

C1 C2 C3 C4 Class 1 1131728 254913 4346 79 Class 2 90733 3524838 1813484 6286 Class 3 55907 721871 3697248 432168 Class 4 4890 7522 113146 211633

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5. Conclusions

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Conclusions: The objective of this study report was to assess the MODIS 500m 32 day composite time series images in detecting the vegetation change. Remote Sensing is the only effective tool for monitoring, mapping and analyzing the earth’s surface on large scales at low cost. Remote Sensing techniques are superior than conventional ground based methods of vegetation mapping and has proved to be successful in lot of research studies. A developing country like India needs necessary measures to attain sustainable land use and land cover planning. Predicting the future change in vegetation is important for future land use planning and overall management. The potential of MODIS Satellite images has proved successful in determining the vegetation change detection for the given period 2000-2005 in this study area. Each image of MODIS in this study area was taken at a time interval of 32 days. This research used the Normalized Difference Vegetation Index (NDVI) method for accurate classification of images which is used widely for estimation of changes in vegetation condition or state. It has been observed that a significant change has occurred all over in India during the study period. The changes are due to afforestation, human interventions, forest regeneration, cattle grazing, deforestation and agricultural expansion. This study also used Markov probability model algorithm for change detection and forecasting of vegetation. For this, study work has employed MODIS satellite images during the period 2000 to 2005. The number of time between the images was four years and the number of time to project forward from the later image was five years that is for 2010. The results obtained in this research will help to prevent loss of forest cover and agriculture vegetation from being disappeared and also help take necessary decisions for a better sustainable land use and land cover management in the future.

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Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, H. C. F., Gao, F., Reed, B. C and Huete, A., (2003), Monitoring Vegetation Phenology using MODIS, Journal of Remote Sensing and Environment, Vol.84, pp.471-475.

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