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Chapter II : Study Area and Methodology 15

Chapter- II

Study Area and Methodology 2.1 Study area

The study area selected for the present research work lies towards the western

part of Pune district of Maharashtra state. Pune, the second largest and an important

district of Maharashtra, lies between 17 52 to 19 23 latitudes and 73 20 and 75 10

longitudes and extends over an area of 15,643 km2, which is about 5.08% of the total

area of Maharashtra state. It occupies very strategic location within western

Maharashtra. For administrative convenience, it is divided in to 14 talukas. The

population of the district according to the 2011 census is 94.25 lakh with the density

of 630 persons/ km2. Pune district has a near triangular shape with its base coinciding

with continental divide, which marks the boundary between the plateau on the east

and the Konkan to the west. It extends south west eastwards over a distance of

approximately 212 km. Along the Sahyadri ranges; it has an N-S width of about 150

km.

Entire part of study area falls within the Bhima river basin, which is one of the

important sub-basins of the Krishna River. The Bhima basin in Pune district includes

sub basin of rivers Nira, Indrayani, Mula, Vel, Ghod, Meena and Pavana. All these

rivers originates in western ghat and traverse through the Pune district. Several small

and large dams have been constructed on these rivers which provide the water for

domestic, irrigation as well as industrial purpose to the rural and urban areas of Pune

district. The water project like Warasgaon, Panshet, Khadakwasala, Mulshi, Ghod,

Kukadi and Bhima are among the few.

2.2 Geographical setup of the study area

2.2.1 Location

Study area comprises the major parts of Bhima river basin and is known as

“Khadakwasala Irrigation Project Division”. It comprises three medium size

watersheds namely Panshet, Warasgaon, and Khadakwasala itself and is the important

catchments of Mutha valley (Fig 2.1). These three catchments cover part of Velhe,

Haveli and in Mulshi tahsils. Velhe tahsil covers 185.70 km2 (41.06%), Haveli tahsil

covers 143.12 km2 (31.64%) and Mulshi tahsil covers 123.40 km

2 (27.28%)

geographical area (Fig 2.2). The extent of the study area are 18 17 45 to 18 27 20 N


latitudes and 73 25 35 E to 73 48 13 E of longitudes. All these three catchments

cover an area of about 452.22 km2 and located around 40 km. southwest from the

Pune city. Panshet reservoir is constructed on Ambi river, Warasgaon reservoir on

Mose river and Khadakwasala reservoir is constructed on Mutha river. These three

rivers are major tributaries of Bhima river.

Table 2.1 Khadakwasala Irrigation Project division - Salient features

No. Particulars Khadakwasala

Reservoir

Panshet

Reservoir

Warasgaon

Reservoir

1 a

Source : River

and Tributary Mutha river Ambi river Mose river

b Construction 1880 1973

(Reconstruction) 1993

2

a Location Maharashtra Maharashtra Maharashtra

b District Pune Pune Pune

c Taluka Haveli Haveli Haveli

d Coverage in SOI

Toposheet No. 47/F/11, 47/F/15 47/F/7, 47/F/11 47/F/7, 47/F/11

e Latitude 18 19 26 N to

18 27 14 N

18 17 44 N to

18 23 01 N

73 25 34 E to

73 37 25 E

f Longitude 73 39 54 E to

73 48 12 E

73 25 44 E to

73 38 00 E

73 25 34 E to

73 37 25 E

3 a

Yield/Utilization

catchment area 501.80 km

2 120.30 km

2 130.0 km

2

b 75 % dependable

yield 1088.45 M.cum 304.72 M.cum 397.25 M.cum

4

Dam /Reservoir

a Gross Storage 85.91 M.cum 310.60 M.cum 375.36 M.cum

b Live Storage 56.00 M.cum 298.00 M.cum 362.00 M.cum

c Dead Storage 30.00 M.cum 4.94 M.cum 12.00 M.cum

Source : Khadakwasla project, Government of Maharashtra

2.2.2 Climate

Climatic conditions of the study area are highly influenced by the „Sahyadris

mountain range. The topography determines the moisture conditions, temperature,

humidity, wind etc. The study area lies in the rain shadow zone of the Western ghat.

The climate of the study area is agreeable and salubrious in the western hilly zone,

whereas it becomes hot, dry and oppressive

LOCATION MAP OF THE

STUDY AREA

Fig. 2.1

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INDIAMaharashtra

KHADAKWASALA IRRIGATION PROJECT DIVISION

TAHSIL WISE DISTRIBUTION OF STUDY AREA

Warasgaon

Panshet

18

Fig. 2.2


Khadakwasala

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Fig. 2.3

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Fig. 2.3

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Fig. 2.4

CONTOUR MAP (DEM)


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Fig. 2.4Fig. 2.5


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towards the eastern part. As it forms a part of the tropical monsoon it shows a

significant seasonal variation in rainfall and temperature conditions.

The winter season is from December to about the mid February followed by

summer season which last up to the month of May. June to September is the south-

west monsoon season, whereas October and November constitute the post-monsoon

season. On an average climate of the western part is cool whereas towards eastern part

it is hot and dry.

2.2.2.1 Rainfall

Moisture conditions of the study area are governed by south west monsoon,

which brings most of the precipitation in June to September. Due to the proximity of

western ghat, Velhe and Mulshi tahsils receives high rainfall. The normal annual

rainfall over the region varies from about 500 mm to 4500 mm. Rainfall is minimum

in the eastern part to about an average of 500 mm. it increases towards western parts

of up to 4500 mm. The rainfall data for the entire catchments is collected from the

irrigation department; Pune for the year 1987 to 2012 (Table 2.2). It can be clearly

noticed that rainfall distribution decreases steadily from west to east direction. (Fig.

2.6a & 2.6b)

2.2.2.2 Temperature

April and May are the hottest months in the study area and maximum

temperature during these months often rises above 360 C. The western part of the

study area is comparatively cool whereas the eastern part is hot and dry. Cold or

winter season starts by the end of November and December being the coldest month

during this season. The mean daily minimum temperature is 9.80 C. During the period

of mid-February up to the end of May, continuous rise in temperature can be

observed. This rise in temperature in more pronounced in eastern part then in the hills

in western. May is the hottest month of the year. The mean daily maximum

temperature and the mean daily minimum temperature in the study area is 41.900 C to

21.40 C respectively. The onset of the south west monsoon commences in the first or

second week of June, which brings down temperature appreciably. The withdrawal of

the south west monsoon in October is marked by rise in temperature (Fig 2.7)


Table 2.2 Rainfall variation in the study area (mm)

Sr.No. Year Panshet Warasgaon Khadakwasala

1 1987 1438.7 1201 *

2 1988 2387.4 2282.8 *

3 1989 1592.5 1497.8 *

4 1990 2459.2 2450.9 *

5 1991 2330.4 2292.2 *

6 1992 1964.4 1843.1 *

7 1993 2083.3 2033.5 *

8 1994 3392 3556.7 *

9 1995 1676.9 1596.9 *

10 1996 2138.2 2155.2 873

11 1997 2536.3 2515.4 1079

12 1998 2079.4 2137.9 679

13 1999 1898.8 1981.9 501

14 2000 1361.1 1431.6 671

15 2001 1626.4 1703.4 691

16 2002 1331 1466.4 527

17 2003 1835.8 1817.3 505

18 2004 2304 2441 1025

19 2005 3597 3663 1587

20 2006 3902 3144 1256

21 2007 2885 3009 720

22 2008 2262 2334 823

23 2009 2306 2285 933

24 2010 2334 2368 900

25 2011 2734 2828 917

26 2012 1724 2427 639

* Data not available Source : Irrigation Depts., Pune

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

500

1000

1500

2000

2500

3000

3500

4000

0

200

400

600

800

1000

1200

1400

1600

1800

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Panshet Reservoir

Warasgaon Reservoir

Khadakwasala Reservoir

Rainfall (mm) variation in three catchments

Fig 2.6a


24 Chapter II :Study Area and Methodology

RAINFALL DISTRIBUTION MAP


25

Fig. 2.6 (b)

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TEMPERATURE DISTRIBUTION MAP


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Fig. 2.7

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GEOLOGY MAP OF THE STUDY AREA


28

Fig. 2.8

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is very resistance, while the „breccias’ part breaks down easily. Large boulders are

released and these accumulate at the base of hills. Cliffs, terraces and benches are

common (Fig 2.8).

In fact, except the thick laterites capping on the plateau tops forming the

western edge of Sahyadri and the thick alluvial soil spread along the banks of rivers,

the entire area is covered by lava flows, which are several hundred meters in

thickness, and laterites of still younger age form conspicuous rock units. Towards the

western parts of the study area few dykes, lineaments and joints are also observed.

2.2.4 Geomorphic land units

Geomorphic map of the study area has been prepared using ASTER GDEM

data. The earth surface forms are primarily due to hypogene or endogenous processes.

Geomorphology unit are dynamic in nature as they are affected by various human

activities, including the expansion of cultivated and irrigated lands, industrialization

and urbanization. In the present study area geographical area under various

geomorphic units are observed which mainly include structural hills 31.52 km2

(6.97%), denudational hills 69 km2 (15.26%), pediment 96 km

2 (21.23%), pediplain

113.2 km2 (25.03%) and valley floor 93.29 km

2 (20.36%) to the total geographical

area. (Fig 2.9a & 2.9b)

Fig 2.9b Geomorphic land units (% area)

2.2.5 Soil

Most of the soils in Maharashtra are formed from the Deccan traps, generally

from the augite or amygdaloidal basalt. These soils are black, dark brown or reddish

in color. The largest area is occupied by the black soils. These „Black cotton Soil‟,

which contain proportion of alluvium and carbonates of calcium and magnesium with

30

GEOMORPHIC LANDFORM UNITS IN THE STUDY AREA

Fig. 2.9(a)


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variable amount of potash but low nitrogen and phosphorous. The rich alluvial soil

ranging in color from pale yellow to dark brown is deposited along the river banks.

The western part of the tract has red soils. The brown coloured soil is common in

Haveli Tahsil and western part of Daund and Purandhar tahsils. These soils are

shallower and coarser than black soils.

The bottom slopes where forest growth stands for its bare existence, some soil

is grazing over the past few centuries have left vast stretches of hill slopes bereft of

tree growth, with small green pockets here and there wherever the soil conditions are

still favorable for plant growth. Along the hill slopes exposed out-crops of rocks are

common sight. On gentler slopes and plateau a poor soil layer which is coarse,

gravelly and friable is met with. The effects of denudation and depredation by cattle.

2.2.6 Flora and fauna of the study area

The type of forest occurring in different parts of the study area is governed

mainly by rainfall and altitudinal variation. The forest types are mainly three, viz,

evergreen, deciduous, and scrub. All these three types represent and correspond to the

western, central and eastern zone as the rainfall varies high, medium and low. Few

pockets are also characterized by seasonal grasslands. Detailed distribution and types

of plants has been studied in next chapters. The rich variety in vegetation supports the

habitat of various birds and reptiles. Availability of grass, wetlands near the margins

of catchments, and seasonal flowering and fruiting of large tress in the valleys attract

large number of birds. Bristled grass-bird and Painted Spur fowl are found in the

catchments. Painted Francolin, Grey Jungle fowl, White throated kingfisher, Greater

Coucal, white rumped Vulture, Indian Peafowl etc are common in the study area

(Ingalhalikar S. 2005). Along with birds diversity various reptiles including snakes,

Monitor lizards are found. Amongst the animals Blacknaped hare, Indian fox, and

Samber deer is also seen particularly in extreme western parts of the study area.

2.2.7 Rivers

Sahyadri is a source of large number of rivers and streams. These rivers flow

east and south-east direction. The chief among the rivers is the turbulent „Bhima‟. In

this tract Indrayani, Gunjawani, Mula-Mutha and Nira, are its tributaries. During the

rainy season all these rivers carry an immense volume of water, but during the hot

season they dry up completely or shrink to narrow streams in broad stretches of

gravel. At many a place, where cross spurs at some distances from the main ridge


STREAM ORDER MAP

Fig. 2.10

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occur, reducing the intervening valleys to narrow necks, advantages of this

topographical features has been taken to construct large dams and to create extensive

lakes for hydel power and irrigation. This fortunate physical formation of the

Sahyadris, not only allows storage of large volume of water behind the dams, but also

ensures, provision of water through heavy predication during the monsoons (Fig.

2.10). Three major rivers flow through the study area and those are Ambi, Mose and

Mutha river. Panshet dam is constructed river Ambi, Warasgaon on river Mose and

Khadakwasala is constructed on river Mutha.

2.2.8 Socio-economic environment

Study area covers an area of around 452.22 km2 consisting of 83 villages

(Table 2.3). The population according to the 2011 census is around 47,174. The

people in this area particularly in Panshet & Warasgaon are poor and marginal

farmers who depend heavily on traditional farming techniques and food gathering.

Agricultural activities are confined close to the margins of reservoir. The

transportation and communication facilities are very low. After the development of

„Lavasa lake city‟ that transportation and infrastructural facilities are developed only

in Warasgaon catchment. The forest areas act as an economic strength to the people

for daily basis although it is prohibited. The poverty forces most of the young adult

male members to migrate to urban areas in search of job opportunities. In

Khadakwasala dam due to nearness to the Pune city, development of tourist centers

like „Chaupati‟, and particularly Sinhagad fort, tourism related activities like, hotels,

lodging etc are well developed. (Fig 2.11 & 2.12)

2.3 Database and methodology

A detailed and systematic methodology for the present research work has been

adopted to meet the various objectives. It mainly includes development of spatial and

attributes database, base map, various thematic maps generation using toposheets and

high resolution satellite data. Present chapter also includes information about satellite

data, ancillary data and instruments used to meet different objectives of the study. The

methodology of the present research work has been summarized in (Fig.2.13).

Table 2.3.Total villages in the study area (Census-2011)

Village Census Area Village Census Area

No Name Code (km2) Population No Name Code (km2) Population

1 Dhamanohol 3104300 16.0051 321 43 Pole 3141600 8.0542 197

2 Koloshi 3104400 1.8502 55 44 Mangaon 3141700 10.6631 254

3 Mugaon 3104500 11.8542 321 45 Tekpole 3141800 10.043 229

4 Bhoini 3104600 6.7829 279 46 Aglambe 3109800 15.978 1610

5 Dasave 3104700 9.816 505 47 Kudje 3109900 5.8589 1492

6 Padalghar 3104800 2.2448 7 48 Gorhe B.K 3110000 4.0946 3178

7 Ugavali 3104900 0.7318 * 49 Donje 3110100 9.5422 3565

8 Gadale 3105000 10.4945 196 50 Gorhe Kh. 3110200 2.4335 2126

9 Sakhari 3105100 3.2106 14 51 Khadakwadi 3110300 8.99 733

10 Wadavali 3105200 5.0549 123 52 Mandavi Kh. 3110500 1.1767 638

11 Admal 3105300 5.1491 89 53 Mandavi Bk. 3110400 4.482 718

12 Palase 3105400 3.9772 69 54 Sangrun 3110600 6.0971 1245

13 Bembatmal 3015500 1.6345 28 55 Malkhed 3110700 1.8495 770

14 Patharshet 3105600 2.6775 77 56 Khanapur 3110800 1.756 3191

15 Tav 3105700 17.307 176 57 Mankewadi 3110900 3.21 1080

16 Dhadawali 3105800 3.3166 34 58 Sambarewadi 3111000 6.55 480

17 Mose Kh. 3105900 3.1454 74 59 Ghera Sinhgad 3111100 26.114 655

18 Saiv Kh. 3106000 3.7383 160 60 Mordhari 3111200 8.413 313

19 Mose Bk. 3139300 9.0551 330 61 Thoptewadi 3111300 5.38 500

20 Saiv Bk. 3139400 8.8077 468 62 Bhagatwadi 3111400 7.35 694

21 Varasgaon 3139500 6.8833 298 63 Wardade 3111500 4.5722 1183

22 Panshet 3139600 0.8854 1570 64 Sonapur 3111600 4.0644 818

23 Dapsare 3138100 8.3319 121 65 Khamgaon Maval 3111900 3.7893 743

24 Kurtavadi 3138200 5.4862 35 66 Mogarwadi 3112000 6.102 708

25 Gondekhal 3138300 4.8999 73 67 Osade 3140000 2.8824 847

26 Ghodkal 3138400 3.7741 16 68 Nigade Mose 3140100 3.2061 911

27 Kasedi 3138500 2.2769 139 69 Kondgaon 3140200 3.6012 463

28 Chikhali Kh. 3138600 1.2236 * 70 Ranjane 3140300 7.7319 1049

29 Balavdi 3138700 2.6061 111 71 Khamgaon 3140400 3.3913 450

30 Koshimghar 3138800 1.6508 90 72 Ambed 3140500 3.17 496

31 Kambegi 3138900 2.0851 55 73 Bahuli 3109700 15.43 716

32 Gholapghar 3139000 1.7549 * 74 Katavadi 3103300 2.1041 542

33 Ambegaon Kh. 3139100 6.0179 129 75 Jambhli 3111700 4.0618 688

34 Kuravati 3139200 1.9294 38 76 Ambee 3111800 8.5707 1704

35 Kadhve 3140700 8.8367 991 77 Rule 3140600 10.2835 1550

36 Dhindali 3140900 1.6456 14 78 Vanjalwadi 3140800 0.62 83

37 Vadghar 3141000 4.5843 236 79 Kuran bk 3139700 4.6077 742

38 Ambegaon Bk 3141100 5.2246 175 80 Kuran kh 3139800 2.1158 213

39 Givashi 3141200 3.8485 107 81 Kondhur 3103500 3.0458 1054

40 Shirkoli 3141300 13.1582 276 82 Davaje 3103400 3.6159 319

41 Ghodshet 3141400 1.4144 9 83 Ranvadi 3139900 0.7399 380

42 Thangaon 3141500 2.9992 38 TOTAL 438.17 47174

* Data not available


Chapter II :Study Area and Methodology

Source : Census handbook (dist. Pune 2011)

34

Fig. 2.11

35

VILLAGE BOUNDARY OVERLAY


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VILLAGE MAP OF THE STUDY AREA WITH CENSUS CODE 2011

36

Fig. 2.12


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To be more precise, the overall methodology adopted for the present research

work is broadly divided into two major phases, namely

i Data collection and data analysis

ii Extensive field work and ground data collection

2.3.1 Data collection and data analysis

2.3.1.1 Data used

The holistic understanding of the complex dynamism that envisages spatial

and temporal dynamics requires synergetic approach. The data requirement therefore

is of both spatial and non spatial in nature and also of various time scales. For the

present research work primary and secondary data is collected from numerous

relevant and valid sources. Satellite data is used to study temporal changes in

vegetation types, land use land cover and developing different empirical models,

whereas, ancillary data i.e. topographic maps and socioeconomic survey reports were

used for geo coding, gathering socioeconomic information and validation of results.

Field data has been collected on seasonal and yearly basis from the permanent

sampling plots.

2.3.1.1.1 Satellite data

Since the study area includes entire catchment having geographical area of

around 452.22 km2,

high resolution with large coverage multi temporal satellite data

has been considered useful and thus, cloud free satellite data are acquired from

National Remote sensing Center (NRSC) Hyderabad (India). The Indian Remote

Sensing Satellites (IRS) - 1C, Resourcesat-1 (P6) and Resourcessat-2 LISS III (Linear

Imaging Self Scanning Sensor) MSS (Multi Spectral Scanner) data were procured

from NRSC, Hyderabad. The orbital characteristics of the IRS satellites are given in

table no 2.4. This multi seasonal satellite data is used to address seasonal behavior.

LISS III sensor has aerial coverage of 141 km2,

with of 23.5 m resolution and

repitivity of 24 days. It provides information in four bands; i.e. Band 2 (Green) 0.52-

0.59, band 3 (Red) 0.62-0.68, band 4 (NIR) 0.77-0.86 and band 4 (SWIR) 1.55-1.70.

Due to its high resolution and high swath LISS III covers an appropriate area in a

single scene, which reduces the cost, saves processing time and avoids issues

associated with voluminous data handling.


Table 2.4 Major characteristics of satellites

No Characteristics IRS IC IRS P6

(Resource Sat-1)

IRS P2

(Resource Sat-2)

1 Path 95 95 95

2 Row 60 60 60

3 Number of Bands 4 4 4

4 Principal sensor MSS MSS MSS

5 Spatial

Resolution(m) 23.5 23.5 23.5

6 Swath (km) 141 141 141

7 Spectral Bands

(micron)

0.52-0.59 (G)

0.62-0.68 (R)

0.77-0.86 (NIR)

1.55-1.70 (SWIR)

0.52-0.59 (G)

0.62-0.68 (R)

0.77-0.86 (NIR)

1.55-1.70 (SWIR)

0.52-0.59 (G)

0.62-0.68 (R)

0.77-0.86 (NIR)

1.55-1.70 (SWIR)

8 Date of Pass 06.02.1997 21.02.2008 31.03.2012

2.3.1.1.2 Ancillary data

For the present research work, following ancillary data has been used.

Topographical maps on 1:50,000 scale series of 47/F/7, 47/F/11, and 47/F/ 15

have been collected from Survey of India, Pune. Soil maps are procured from „Soil

Survey Department, Pune‟. Rainfall data is obtained from Indian Meteorological

Department (IMD) Pune, Warasgaon and Khadakwasala irrigation office. Socio-

economic and population data is obtained from „Census Handbook‟ government of

India, for the year 2001and 2011. Forest working plan of Pune district division

prepared by forest department is procured and used to infer forest types in the study

area. Other documents related to the status of forest of Pune district division; i.e.

Forests Survey of India (FSI), Status of the forest report, State forest survey report

„Four Decades of Forestry‟, District Economic Survey, Pune 2011 and District

Environmental Atlas, Pune district (2006) has been used.

2.3.2 Field work and ground data collection

One of the most important components of field visits was to collect

information on plants or species richness. During the vegetation survey random

sampling approach was followed and numbers of sample points were distributed to its

probability proportional to its size. Field data has been collected from 22 sample sites.


Along with vegetation surveys, ground truth and verification of the LULC classes

derived through satellite data has also been done. This reconnaissance survey has

been carried out in the post monsoon seasons in order to develop acquaintance with

the vegetation type, patterns, their dynamics and socioeconomic environment of the

study area.

During reconnaissance survey, using GPS (Global Positioning System) GCPs

were taken. GCPs are identifiable features located on the surface of the earth, whose

ground coordinates in X, Y and Z is known. These GCPs corresponding attributes

were marked down for representative vegetation types. GPS is an instrument that

provides information about two dimensional locations on the ground. The process by

which GPS determines the location of the ground is known as „Satellite ranging‟. In

this process, the time acquired for transmission of a signal to reach the ground is

computed. Speed of the light is known, hence, distance between the satellite and the

ground object can be determined to know the precise location of the object on the

surface.

GPS coordinates were later used to test the accuracy of the final classified

map. Based on the reconnaissance survey, land cover classes in general and forest

classes in particular were delineated.

2.4 Methodology

2.4.1 Pre-processing of the remote sensing data

Before performing the classification of the Remote Sensing data, it is

important to pre-process the data to correct the errors which occurs during scanning,

transmission and recording of the data. Image preprocessing refers to all those

functions which are frequently performed to improve geometric and radiometric

qualities of the satellite images. Typically, the pre-processing operations involves in

(a) radiometric correction to compensate the effects of atmosphere (b) geometric

correction i.e. registration of the image to make it usable with other maps or images of

the applied reference system , and (c) noise removal to remove any type of unwanted

noise due to the limitation of transmission and recording processes. For the present

research work following pre processing steps has been followed.

2.4.1.1 Geometric correction

Geometric errors in remote sensor data are of an external origin. Normally

these errors are systematic and nonsystematic and occur due to space borne sensor

TOPOGRAPHY GEOLOGY REMOTE SENSING FIELD WORK LABORATORY

WORK

SOI Toposheet

47/F/7, 47/F11

& 47/F/15 on

1:50000 scale

SOIL

Geology Map •IRS IC LISS III

•IRS (P6) LISS III

•IRS (P2) LISS III

•Soil Texture

•Soil Depth

•Soil Erosion

•Soil Drainage

Vegetation

Survey

Digitization of

•Drainage

•Sub-watersheds

•Villages

Georeferencing

LAND USE & LAND

COVER

Map

Preparation of thematic

maps using-

•ERDAS IMAGINE 9.3

•ArcGIS-9.3

•Global Mapper 11.02

Priotization of sub

watersheds

Ground Check &

Truthing (GCP)

Using GPS

NDVI Analysis

•Absolute Relief

•Relative Relief

•Dissection Index

•Slope

FOREST MANAGEMENT PLAN

DATABASE & METHODOLOGY

Soil

Conservation

Fig. 2.13

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platform velocity and simultaneous earth rotation. Also the fluctuations in the altitude

and attitude of the sensor platform cannot be corrected by analytical means. All these

errors cause deformations in an image, hence before identifying the geographic reality

on the ground these deformations need to be removed. Spatial distortions in satellite

images may corrected by matching up image coordinates of physical features as they

are not dynamic, or by Ground control points (GCP‟s). For the present research works

geometric corrections are done by image to image registration techniques in ERDAS

IMAGINE 9.3 software. The IRS-1C LISS III and IRS-P6 LISS III images were

already geo corrected; only an IRS P2 LISS III image is corrected geometrically.

2.4.1.2 Re-projection of satellite images

It is always desirable to bring all the images to one common single projection

platform, so that comparison among all the study aspects under consideration

becomes easy. For the present research work data brought from NRSC was of a same

resolution i.e. LISS III, 23.5 meter but it was in different coordinates system. Hence

data were re-projected in Universal Transverse Mercator‟s projection (UTM 43) using

ERDAS IMAGINE 9.3 software. This has helped in further calculations of LU/LC

classes.

2.4.1.3 Image enhancement

Image enhancement is the modification of an image to alter its impact on

viewer and to carry out this; the original digital values are changed at some extent.

However this technique can make the features more prominent only if there are

significant spectral differences in the digital data.

2.4.2 Obtaining the subset for the area of interest (AOI)

Sub-setting refers to breaking out a portion of a larger file into one or more

smaller files. Often images contain areas much larger than a selected study area;

hence it is helpful to reduce the size of the image to include only the area of interest.

This not only eliminates the extraneous data in the file but it speed up processing due

to the smaller amount of data to process.

In order to extract the area of the interest (AOI) from the entire image, a vector

layer was prepared in the same projection as that of the image. This AOI was the

boundary of the study area. This AOI, digitized as polygon feature overlaid on the

raster images and subsets were obtained. Subsets were done for all three satellite

images.


2.4.3 Image classification and accuracy assessment

2.4.3.1 Image classification method

Classifying the satellite images to extract the land use / land cover theme is

one of the important and major steps. Classification is the process of assigning classes

to the pixels in images. Moreover, successful utilization of remotely sensed data for

LULC studies demands careful selection of an appropriate data set and image

processing techniques (Lunetta R. S. et al, 1998). The most common image analysis

for extracting LULC is digital image classification. Image classification techniques

are most generally applied to the spectral data of a single-date image or to the varying

spectral data of a series of multi-date images (Sabins F. F. 1997). The purpose of

image classification is to label (class) the pixels in the image with the real information

(Jensen L. L. F et al, 2001). Through classification of image, thematic maps such as

the LULC can be obtained (Tso and Mather, 2001). Classification involves labeling

(class) the pixels as belonging to particular classes using the spectral data available.

There are two major types of classification procedure and each finds

application in processing of remote sensing image.

Unsupervised classification is based on the fact that similar classes cluster in a

feature space. Clustering is done by applying suitable algorithms on the basis of

spectral signature, generating‟ spectral class‟. By ground verification or verifying with

other maps each spectral class is assigned to class on the ground.

In supervised classification method, the analyst, based on the prior

information on the spectral characteristics of these classes, „trains‟ the computer to

generate boundaries in the feature space within which each class should lie. Then each

pixel lying within a class boundary is assigned to that class. Analyst determines the

land cover class associated with each „spectral cluster‟ from the prior knowledge

already available from ground reference data. Typical supervised classification

involves in training, classification and output stage.

For the present research work, an unsupervised image classification technique

is used. The thematic maps prepared and classes obtained from the classification, later

on verified in the field work. All this verification process was almost correct to an

extent


2.4.3.2 Accuracy assessment

Accuracy assessment is an essential and most crucial part of studying image

classification and thus LULC change detection in order to understand and estimate the

changes accurately. It is correctness of standards assumed to be correct and a

classified image of unknown quality. The post-classification method for LULC

change detection has dependency on the accuracy of individual classification results

(Foody G. M., 2002).

2.4.4 NDVI analysis

The NDVI analysis has been performed in ERDAS IMAGINE 9.2 by running

the indices option. This vegetation index uses the combination of band 3 (0.63-0.69

Red), band 4 (0.76-0.90 Near IR) bands. NDVI is a representative of plant

assimilation condition and its photosynthetic apparatus capacity and biomass

concentration (Groten S.M.E., 1993). NDVI for Landsat TM, ETM+ and IRS LISS III

images can be calculated by using following formula.

NDVI = NIR – R

NIR + R

2.4.5 Change detection of LU/LC and NDVI

Change detection is the process of identifying and analyzing the differences of

an object or a phenomenon through monitoring at different times (Singh A., 1989;

Mouat D.A. et al., 1993). Now the detection and analysis of changes in multi-

temporal remote-sensing data has got ever-increasing strategic importance in several

application domains. As present research work mainly deals with vegetation studies, it

was necessary to study other LU/LC classes. Hence along with LU/LC and NDVI

assessment change detection studies has also been carried out.

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