Chapter- IVshodhganga.inflibnet.ac.in/bitstream/10603/99864/12/12_chapter4.pdf · Reservoir and rivers Areas with surface water, ponds, lakes reservoir or flowing streams or rivers

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Chapter IV : Estimation of Forest Cover and Change Detection Analysis

Chapter- IV

Estimation of Forest Cover and Change Detection Analysis 4.1 Introduction

Estimation of vegetation cover is the first step towards the conservation and

sustainable management of forests resources. For the sustainability, necessity of forest

resource data which is accurate and continuously updated is the prime requirement.

Change detection analysis plays a vital role in management process. Change detection

essentially comprises the quantification of temporal phenomena from multi date

imagery that are most commonly acquired by satellite based multispectral sensors.

The present chapter deals with methods applied for the vegetation studies and change

detection in overall LU/LC pattern in the study area.

4.1.1 Methods to study vegetation

Vegetation inventory and monitoring are undertaken at a wide range of scales

depending upon the objectives. There are several methods and techniques designed

for comprehensive studies of community pattern and vegetation dynamics. These

methods of vegetation studies can be grouped in two categories.

4.1.1.1 Conventional methods

There were many traditional methods of vegetation survey and studies in use;

important among these is a quadrate method. This conventional method of vegetation

studies involves vegetation sampling by establishing various sampling units like Area

(quadrates method), Line (transact method) and Point (Point method). The method

can be applied according to the purpose of study. Among these three methods,

quadrate method is used mostly. The location and size of the quadrates depends on the

sampling strategy employed by the researcher. Approaches to sampling distribution

fall into the random, stratified or systematic sampling. Within each quadrate the

species composition can be determined by the number of individuals, presence or

absence or percent cover of each species in each quadrate. Biomass and standing crop

are measured by harvesting, drying and weighing the organic matter within each

quadrate.

4.1.1.2 Remote sensing techniques

One of the primary applications of remote sensing technology is to identify the

patterns of spatial and temporal distribution of vegetation on the ground. Remote

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sensing has several advantages over traditional methods of vegetation mapping and

has been proved to be a very powerful tool in deriving information on natural

resources and environment. Remote sensing is the science and art of obtaining

information about an object, area or phenomena through the analysis of data acquired

by a device that is not in contact with the object, area or phenomena under

investigation (Lillsand and Keifer, 1999). For this purpose, different wavelength

regions of the electromagnetic spectrum are used. Electromagnetic radiation (EMR) is

comprised of large spectrum of wavelength right from short wavelength cosmic rays

to long radio waves. In remote sensing, the most useful regions are the visible (0.4-

0.7 µ), reflective infrared (0.7-3.0 µ) thermal infrared (3.0 to 5.0 and 8.0 to 14.0 µ)

and the microwaves (1mm to 1m). Although the incoming solar radiation reaching to

the earth surface is modified by the atmospheric gaseous, aerosols and water vapor,

using atmospheric corrections to imagery good quality information can be obtained.

All type of remote sensing systems, capture radiation in different wavelengths

reflected or emitted by the earth surface features and record it either directly on the

film as in case of aerial photography or on a digital medium like tape and later, it is

used for generating image. As no two objects in nature are theoretically ditto, their

signatures are most likely to be unique.

The reflectance from vegetation is controlled by leaf pigments, cell structure

and leaf water content. The radiation absorbed in red region is primarily used for

photosynthesis. In healthy vegetation, both absorption and reflectance is more

pronounced, while diseased and senescent vegetation show lesser absorption and as

well as reflection. These spectral properties of vegetation are heavily exploited to

detect their type, density and condition through image interpretation.

The remote sensing data from aerial and satellite platform plays a significant

role in forests resource survey, monitoring forest cover, evaluating ecosystem,

studying wildlife, assessing plant disease etc. the mapping for the forest type or

vegetation is essential for forest resource survey. A complex mixture of many tree

species, in contrast to a single crop field, very often occupies a forest land. Hence the

identification of tree species is rather difficult and needs a prior knowledge of the

area. A study of the changes in appearance of trees in different seasons of the year

utilizing the multi date data helps in discriminating the species that are

indistinguishable on a single data image. It is, however difficult to interpret the

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species composition of an understory since it is beneath the forests canopy. It is

possible for example, to map a clear cut or deteriorating forest area, monitor broad

changes in forest cover, and detect a forest fire with the help of high resolution

satellite data at periodic intervals. A forest fire can be very easily detected in the

remotely sensed data since the burning of the forest canopy eliminates the infrared

reflection from leaves, allowing the radiation to reach the ground where it is highly

absorbed by the charred ground flora and thus exhibits a dark or black tone in images.

However, the mapping of forest fire damage below the smoke of fire is difficult with

the optical sensor data. In such cases the thermal data may be utilized to map the hot

pixels.

Satellite remote sensing, because of its capacity to provide a repetitive

coverage of both coarse and fine resolutions, is a very powerful tool for monitoring

forest cover and change detection. The remotely sensed data collected sequentially

provide information on any positive or negative changes in the vegetation cover,

which is reflected in the image by a corresponding change in the spectral signature. A

superimposition of two period maps delineates the areas where the changes have

taken place. It is also possible to monitor the amount of vegetation loss and

encroachment on forest lands, utilizing high resolution satellite images.

Accuracy and high resolution ability of sensors and visual and digital image

processing algorithms are now very useful to extract important vegetation biophysical

information from remotely sensed data.The use of satellite data with three dimension

analysis, particularly of the aerial photographs, can help in monitoring the ecological

status of the forests, which is essential information for rational management. The

monitoring of an actual forest at regular intervals can establish the depletion rates and

also trends in critical areas.

4.2 Image classification

Image Classification is the most widely used technique in remote sensing

applications for extraction of target thematic information. For the present research

work, land use and land cover information is the prime prerequisite to understand the

spatio-temporal scenario of land use land cover pattern in general and area under

forest cover in particular. Image classification is a process of mapping to generalize

the image pixels into meaningful groups each resembling different land category

(Jensen, 1995). The process requires an optimum and specifically designed

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classification algorithm for precise application purpose because it largely varies

depending upon the type and objective of the work. Most common and typical method

for satellite image classification is based on pixel. In this method, classifier considers

different pixel values and group them into classes solely based on their spectral

properties. This practice is based on conventional statistical techniques such as

supervised and unsupervised classification where the classes are supervised by analyst

and are not supervised (i.e. fully automatic based on spectral values) respectively.

4.2.1 Selection of land use classes for image classification

The classification system amenable for use with remote sensor data varies

with the objectives of the classification and type of the satellite data that is being used.

There are many methods of land use classification like U.S. Geological Survey

scheme, NRSC etc. For the present study, “National Land use Land cover mapping

using Multi temporal Satellite data” classification scheme given by NRSC, India has

been followed. (Table 4.1)

4.2.2 Classification output

The output of the classified image was obtained as thematic maps for each

selected year. Classified satellite image, with classes defined earlier were displayed

using various traditional colors. All these classified images were later used for the

visual interpretation for understanding the changes in land use and land cover pattern

in the study area. The output images obtained through the classification were further

subjected to change detection analysis also. Before performing the further studies it

was essential to test and check the accuracy of the classification.

4.2.3 Accuracy assessment

Classification cannot be completed until its accuracy has been measured

(Lillesand T. M. 1987). Accuracy means the level of agreement between labels

assigned by the classifier and the class allocations on the ground collected by the user

as test data. After the unsupervised classification, it was necessary to assess the

accuracy of the classified maps. It is very important as it gives the idea, that at which

levels of accuracy of the thematic maps are prepared. Generally for the accuracy

assessment map based on different source of information and analyzed map obtained

through remotely sensed data is compared. Producer and user accuracy is collected for

each class. Overall accuracy for the classified IRS 1C LISS image of the year was

85% collected and for the images of IRS P6 LISS, 2008 and IRS P2 LISS, 2012 it was

No

Land Use Land Cover

Classification

Description

Level I Level II

1 Water Body

Reservoir and riversAreas with surface water, ponds, lakes reservoir or

flowing streams or rivers

Dry river bedShows maximum water level in the reservoir out of

its capacity

2Agricultural

Land

Cultivated landAreas with standing crops in the field

Agricultural fallow

Lands which are taken up for cultivation but now are

temporarily allowed to rest, un cropped for less than

year

3 Forests

Moderately dense

Forest

Areas comprising thick and dense canopy of tall

trees, predominantly remain green throughout the

year, density, usually between 40% to 70%

Open forest

Combination of evergreen and deciduous forests,

found along the margins of Evergreen forests,

covering canopy density between 10% to 40%

Scrub / Degraded

Forest

Degraded forests lands, where crown density is less

than 10% of canopy cover

4 Wastelands

Barren Land Areas un cropped or un utilized for a longer period

Rocky waste

Lands of rocky waste of varying lithology often

barren and devoid of soil erosion and vegetation

cover

5 Built up landBuilt up area- rural

(Settlements)

Lands used for human habitation for living, sized

comparatively less than urban settlements.

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Table 4.1 Land use and land cover classes under consideration and their description

NRSC classification scheme

Chapter IV: Estimation of Forest Cover and Change Detection Analysis

KHADAKWASALA IRRIGATION PROJECT DIVISION

unclassifed

Reservoir and Rivers

Dry river bed

Moderately dence forest

Open forest

Agricultural fallow

Cultivated land

Scrub/ degraded forest

Builtup area

Barren land

Rocky waste land

Land Use Land Cover - 1997

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Fig 4.1


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Dry river bed


Open forest

Agricultural fallow

Cultivated land


Builtup area

Barren land

Rocky waste land


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Fig 4.2


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Dry river bed


Open forest

Agricultural fallow

Cultivated land


Builtup area

Barren land

Rocky waste land


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Fig 4.3


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Land use class Year

Level I Level II

1997 2008 2012

Area

(km2) Area (%)

Area

(km2) Area (%)

Area

(km2) Area (%)

1

Water bodyReservoir and rivers 33.43 7.39 33.23 7.35 25.7 5.68

Dry river bed 4.66 1.03 9.1 2.01 14.53 3.21

2

Agricultural LandCultivated land 4.84 1.07 4.9 1.08 6.73 1.49

Agricultural fallow 51 11.28 50.13 11.09 42.75 9.45

3

Forests

Moderately Dense forest 168.56 37.27 108.45 23.98 53.79 11.89

Open Forests 118.24 26.15 130.92 28.95 134.61 29.77

Scrub/ Degraded forest 21.06 4.66 50.23 11.11 57.08 12.62

4

WastelandsBarren Land 11.6 2.57 16.44 3.64 27.68 6.12

Rocky waste lands 21.66 4.79 24.46 5.41 48.06 10.63

5 Built up land Built up area 17.16 3.79 24.36 5.39 41.29 9.13

TOTAL 452.22 100 452.22 100 452.22 100

Table. 4.2. LU/LC statistics for different time period of the study area


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80%. However kappa statistics was collected 0.7770, 0.7394 and 0.7695 respectively.

In the present study, major constraint was non-availability of land use and land cover

map of which can be taken as a reference point. GCP’s and Google Earth images were

used as a reference for the classification.

4.3 Spatio-temporal variation in LU/LC changes in the study area

The term, Land use and land cover are inseparable. Land use is the term that is

used to describe human uses of land, or immediate actions modifying or converting

land cover (Sherbinin A.D. 2002). On the other hand, land cover refers to the natural

vegetative cover types that characterize a particular area. Land cover is the layer of

soil and biomass, including natural vegetation, crops and manmade infrastructures

that cover the land surface. Land use is the purpose for which human make a use or

exploits the land cover (Fresco, 1994, cited in Verburg et al., 2000). Changes in Land-

use proximately cause change in land-cover pattern. Land use is obliviously

constrained by environmental factors such as soil characteristics, climate, topography

and vegetation. But it also reflects the land as key and finite resources for most human

activities comprising agriculture, industry, forestry, energy, production, settlement,

water catchments and storage (Bhat, Sulemeiman, Abdul, 2009). The knowledge of

land use and land cover is of immense important and useful to understand the natural

resources, their proper utilization, conservation and management. The driving forces

to this activity could be economic, technological, demographic, scenic and or other.

Hence, land use and land cover dynamics is a result of complex interactions between

several biophysical and socio-economic conditions which may occur at various

temporal and spatial scales (Reid R.S. et al., 2000).

Land use and land cover pattern are mostly affected by human intervention

and natural phenomena such as agricultural demand and trade, population growth,

land consumption patterns , urbanization and economic development, science and

technology, and other factors (Research on Land use change & Agriculture,

International Institute for Applied Systems Analysis, 2007). Hence, information about

land use land cover is essential for any kind of natural resource management and

action planning. Timely and precise information of land use and land cover change

detection has a great importance in understanding relationships and interactions

between human and natural phenomena for better management of decision making

(Lu D. et al., 2004) Unsupervised classification method is applied for the IRS 1C

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LISS III (1997), IRS P6 LISS III (2008) and IRS P2 LISS III (2012) images in

ERDAS IMAGINE 9.3. All these satellite images are of the month of February and

March. After systematic and accurate classification attribute data is generated

regarding the land cover classes under the consideration. This data has been

summarized in Table 4.2 & 4.3.

Fig 4.1, shows the land use classes for the February 1997 derived from the

classified image of the IRS 1C, LISS III sensor. Ten major land use classes were

obtained from the satellite data. During the classification of images major thrust is

given on vegetation dynamics. It can be clearly observed that, almost throughout the

study area, significant and noticeable patches of the moderately dense vegetation are

located along the interfluve area whereas open forest (semi evergreen) are spread in

entire study area. On the sloping lands rocky waste lands are more than barren lands.

Rocky barren lands are dominated in the central part of the study area and observed

mainly in Khadakwasala catchment. Most of the settlements are observed along the

banks of the reservoir and agricultural lands are well distributed around the

settlements and along with water bodies. Scrublands and degraded lands are almost

negligible.

Fig 4.2 demonstrate land use classes for the year February 2008 obtained from

IRS P6 LISS III image of 2008. It can be clearly seen the area under settlement within

a span of eleven years has increased significantly. This has been observed particularly

in Khadakwasala lake catchment. Western part of the catchment remained same

during the period in terms of built up land. During this period area under the

moderately dense vegetation has been decreased, especially towards the eastern parts

of the study area. Moderately dense vegetation is observed only in western part of the

study area, as this is the region away from the settlements. In Panshet catchment along

the reservoir and towards eastern part of Khadakwasala scrub and degraded forests are

considerably increased.

Fig 4.3 shows, land use classes for the year March 2012 obtained from IRS P2

LISS III image of 2012. The figures of various land cover classes from the images

shows dramatic change over in the period taken into consideration. This change is

observed more in eastern part of the study area i.e. Khadakwasala lake catchment

where most of the vegetative areas are now converted into barren and built up land,

mainly because this part of the study area is very close to the Pune city and Sinhagad

No LULC type

LULC LULC Change between Average rate

1997 2012 1997 & 2012 of change

Area

(km2) Area (%)

Area

(km2) Area (%)

Area

(km2) Area (%)

(km2)/ (%)/

Yr Yr

1 Reservoir and rivers 33.43 7.39 25.7 5.68 -7.73 -23.12 -0.52 -1.54

2 Dry river bed 4.66 1.03 14.53 3.21 9.87 211.8 0.66 14.12

3 Cultivated land 4.84 1.07 6.73 1.49 1.89 39.05 0.13 2.6

4 Agricultural fallow 51 11.28 42.75 9.45 -8.25 -16.18 -0.55 -1.08

5 Moderately Dense forest 168.56 37.27 53.79 11.89 -114.77 -68.09 -7.65 -4.54

6 Open Forests 118.24 26.15 134.61 29.77 16.37 13.84 1.09 0.92

7 Scrub/ Degraded forest 21.06 4.66 57.08 12.62 36.02 171.04 2.4 11.4

8 Barren Land 11.6 2.57 27.68 6.12 16.08 138.62 1.07 9.24

9 Rocky waste lands 21.66 4.79 48.06 10.63 26.4 121.88 1.76 8.13

10 Built up area 17.16 3.79 41.29 9.13 24.13 140.62 1.61 9.37

TOTAL 452.22 100 452.22 100

Table. 4.3. Average rate of change in LU/LC for the year 1997 and 2012 in the study area


Source : Author

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fort, a well known tourist center. Moderately dense vegetation is observed only

towards the western part along ridge line at considerable elevation. Degradation of

forests cover has increased and it can be observed in entire eastern part of the study

area. Increased cultivation patches are observed along the margins of reservoir only.

4.4 Change detection analysis of the LU/LC in the study area

Mapping of land use land cover (LULC) and change detection analysis using

remotes sensing and GIS technology is an area of interest that has been attracting

increasing attention of researchers and planners. Land use land cover change detection

is very essential for better understanding of landscape dynamics during a known

period of time for sustainable management. The process of change detection is most

frequently associated with environmental monitoring, natural resource management

and measuring urban development. Understanding landscape patterns, changes and

interactions between human activities and natural phenomena are essential for proper

resource management and decision making (Prakasam C. 2010).

Land use land cover change has been recognized as an important driver of

environmental change on spatial as well as on temporal scales (Tansey et al., 2006).

Remote sensing technology for capturing the spatial data on various resolutions, GIS

for undertaking integrated analysis and presentation of attribute data are found to be

much more effective to know the change detection of land use and land cover

(Lillesand T.M. et al., 1987).

It can be clearly observed that, the land use land cover pattern of the study

area has been changed dramatically during the last fifteen years. Therefore, the data

interpretation and data analysis is based on the comparison of LU/LC for three

different time periods viz 1997, 2008, and 2012. Area under major land use land

cover category has been categorized into five major classes (Level I) and ten

subclasses (Level II). Following observations for this time period of fifteen years has

been noted as following. (Table 4.3 & 4.4)

4.4.1 Change in water bodies

The rivers and three reservoirs namely Panshet, Khadakwasala and Warasgaon

are considered in this category. The changing rate of water bodies is showing

deceasing trend as it totally depends on and the annual rainfall in the study area and

requirements of beneficiary regions. All these three catchments provide water for

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drinking and agriculture purpose to adjacent cities like a Pune as well as Baramati and

Indapur tehsils. Since the last decade the demand of water for the domestic, industrial

and agricultural purpose has been increased. Hence up to the month of April the water

level significantly goes down. During the year 2011, due to the drought conditions all

these three catchments are showing only 5.68 % water. It can be observed from the

area under the dry river bed which is 3.21% in 2012.

4.4.2 Change in agricultural lands

The observations gained through the image interpretation reveals that area

under the cultivated land is showing very negligible change as the entire study area is

characterized by hilly terrain and undulating topography. Very few places are having

flat surfaces for cultivation and these areas are mostly confined to the banks of

reservoirs. During the monsoon, paddy cultivation is performed on hill slopes. It

shows that in the year 1997 area under cultivation was 1.07 % which is increased up

to 1.49% in 2012. During the field visit, it was also observed that Nachani is grown

in the western hilly tract of the study area, towards eastern part, lentils like pawata

(Dolichos lablab), masoor (Lens esclenta), gram (Cicer arietinum) are grown. Along

the banks of reservoir particularly Khadakwasala, creepers of cucumber, red pumpkin

along with vegetables like math, rajgira, chilli, and sweet potato are grown. The area

under the cultivation has shown an increase of 39.05% within this span.

Agricultural fallow lands are those lands which are available for cultivation

but not used for one or the other reason. The agriculture activities in such land are

totally dependent on monsoon. Most of the surface area under agricultural fallow is

utilized in rainy season only. This area under fallow was 11.28 % in the year 1997 and

it has been decreased slightly to 9.45% in 2012. Average annual decrease of fallow

land is observed to be 1.08% per year.

4.4.3 Change in forests

In order to compute the growth and decrease in the forest cover, following

formula is common use by forest survey of India. This formula has been used in the

computation of forest growth from the base year 1997.

Percentage Growth/Loss = B – C

B × 100

Where, B is Base year value, and C is current year value

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Land cover class under the forest is continuously decreasing year by year and

shows negative change. It was found that area under the moderately dense forests,

which was 37.27% in the year 1997, comes down to 11.89% in the year 2012. It has

been decreased by 68.09% within the fifteen years of span. Although open forests

show slight positive growth by 13.84% from 1997 to 2012, most of the previously

moderately dense forests are now thinning and few plantations around the settlements

are observed. Forests patches are mostly found at high altitudinal zone and in villages

towards western part of the study area. Scrub or degraded forests however shows

continuous and tremendous increase by 171.04% from 1997 to 2012. The negative

change in the forest is only because of increasing human population, as well as need

of forest product to satisfy daily livelihoods. The rate of degradation or increase in

scrub forest was found to be 11.40% per year.

4.4.4 Change in wastelands

One of the significant changes in land cover is observed in degradation of

lands i.e. increase in wastelands, which is showing continuous increasing trend. In the

year 1997 area under barren land was 11.6 km2 (2.57%), which increased up to 27.68

km2 (6.12%) in the year 2012. The total 138.62% growth during this period has been

observed. Area under rocky waste has also been increased by 121.88 % from 4.79%

in 1997 to 10.63% in 2012. Both barren land and rocky waste shows significant

increase after the year 2008 from 3.64% to 6.12% and 5.41% in 2008 to 10.63% in

2012 respectively. During the extensive field survey it was observed that the intensive

encroachment of anthropogenic activities are increasing ecological stress and leading

to soil erosion on hill slopes and land degradation. In period of heavy monsoon due

the lack of vegetative cover along the slopes, most of the valuable soils washed out

leaving behind the open bare land surfaces.

4.4.5 Change in built up land

The observation also indicated that built up area has increased from 3.79% in

1997 to 9.13% in 2012 and this growth is about 140.62%. One noticeable change

behind this is increase in human population in villages under the study area and

expansion of fringe area around the Pune city. Average rate of increase in built up

area was observed to be 9.37% per year. Since 2008 this rate of increase more

pronounced as the area under built up land has been doubled in four years.

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4.5 Catchment wise change in the study area

Table 4.4 shows change detection analysis between 1997 to 2012. The upper

Mula sub basin has undergone a sharp change during recent years. Entire study area

has witnessed drastic changes in its land use and land cover pattern since last few

years. The major reasons of these changes are the expansion and urbanization of small

sub urban areas in the eastern part of the watershed. Urbanization is one of the most

widespread anthropogenic causes of the loss of arable land (Lopez, et al., 2001),

habitat destruction (Alphan, 2003), and the decline in natural vegetation cover. One of

the major reasons of urbanization is rapid population growth in the urban area. Due

the over expansion of urban areas in Haveli, Daund and Baramati tahsils the

requirement of water for domestic as well as for agricultural and industrial purpose is

increased, hence to satisfy this need two new small sized watershed namely

Warasgaon and Temghar are constructed on Ambi and Mutha river respectively. Prior

to this, Panshet on Mose River and Khadakwasala on Mutha River were the only

sources of water. The primary aim of these four reservoirs is to provide the water for

agriculture, drinking and industrial purpose.

As urban population increases, the demand of land for various urban activities

also increases. In India, the process of urbanization gained momentum with the start

of industrial revolution and globalization way back in 1970s. Forests, grasslands,

wetlands and croplands were encroached upon under the influence of expanding

cities, yet never as fast as in the last decade. Various studies have revealed that, main

basis of urbanization is the socio-economic transformations and in particular the

growth of secondary and tertiary occupation in urban areas (Fazal, 2001).

Villages in study area belong to three rapidly changing tehsils of Pune district

and those are Mulshi, Velhe and most importantly Haveli. Overall growth has been

putting pressure on the existing land use pattern of study area. As a result of it

vegetation cover, agricultural land and fallow land has been now transforming into the

built up land. Haveli tehsil, particularly in Pune city Information Technology (IT)

sector has expanded the margins of the area. Many small IT hubs are newly being

opened up in the vicinity of Pune city. On the other hand, among all the tehsils of

Pune, Mulshi is rapidly developing in tourism activity. Population trend in last few

years has been responsible in changing the pattern of land use and land cover in the

entire catchment.

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4.5.1 Change in LU/LC pattern of Khadakwasala reservoir catchment

Khadakwasala irrigation project include three main catchments namely

Panshet, Warasgaon and Khadakwasala itself. For all these three catchments land use

land cover analysis have been exercised using IRS 1C LISS III and IRS P2 LISS III

satellite data for the year 1997 and 2012 respectively. Entire study area has

undergone sharp changes since last decade. Table shows the data of land use land

cover of three watersheds. The analysis between the year 1997 and 2012 has provided

important insight in to the land cover dynamic in all three catchments.

It can be observed that, in Khadakwasala catchment, barren land has been

increased tremendously by 316.48% per year within last fifteen years. These bare

surfaces may now be used in future for other land cover classes. Change in built up

by 4.93% per year can be observed. A famous Sinhagad Fort, backwater of

Khadakwasala reservoir, Nilkantheshwar temple etc are the major attraction points

for the weekend hence the tourism activities has been increased considerably.

Out of these three catchments, Khadakwasala lake catchment has large land

surface area under the reserved forests. This area has still extremely rich flora and

fauna including some rare endangered species (Ingalhalikar S., 2005). It is known as a

hot spot habitat for biodiversity. Positive aspect in this catchment is only that the

barren hill slopes now have good plantation and trees are being planted by the forests

department mainly around the Sinhagad Fort. The forest at lower level is dominated

by plantations made by the forests department, which includes prominent exotic

species like Teak, Australian Acacia and Eucalyptus. Native flora of plant species has

been disappeared and exotic species are introduced. At the foot hills where the soils

are deep, some survival of planted species can be noticed. Large scale deforestation in

the foothills around the Sinhagad fort has been observed. There is about 96.05% of

reduction in moderately dense forest where as 270.62 % increase in scrub or degraded

forest. Most of the areas showing increase in open forests were previously densely

forested and now due to the encroachment the area under dense forests are decreasing.

The massive change in the forests cover is due to the human induced activities and

unsound forests management practices. Major consequences in the forests of

Khadakwasala catchment, apart from the destruction is thinning process of forests, i.e.

the forests are altered which makes change in forests type form dense to open or

scrub/ degraded. Native tracts of forests are separated into patches due to the

increasing anthropogenic activities.

Khadakwasala

LULC 1997 LULC 2012

Change between Average rate

Catchment 1997 & 2012 of change

No Land cover class Area (km2) Area (%) Area (km2) Area (%) Area (km2) Area (%) km2/yr %/yr

1 Reservoir and rivers 7.7 5.68 9.55 7.05 1.86 24.12 0.12 1.61

2 Dry river bed 0.17 0.12 0.93 0.69 0.77 459.47 0.05 30.63

3 Cultivated land 2.43 1.8 4.3 3.18 1.87 76.92 0.12 5.13

4 Agricultural fallow 26.13 19.29 25.86 19.09 -0.28 -1.06 -0.02 -0.07


6 Open Forests 21.04 15.53 30.11 22.23 9.07 43.11 0.6 2.87


8 Barren Land 0.21 0.15 10.11 7.47 9.9 4747.21 0.66 316.48


10 Built up area 9.04 6.68 15.73 11.62 6.69 74 0.45 4.93

TOTAL 135.46 100 135.46 100

Panshet

LULC 1997 LULC 2012




1 Reservoir and rivers 10.75 9 9.32 7.81 -1.43 -13.28 -0.1 -0.89

2 Dry river bed 2.06 1.73 2.85 2.39 0.78 38 0.05 2.53

3 Cultivated land 0.06 0.05 0.4 0.33 0.34 620.91 0.02 41.39

4 Agricultural fallow 8.41 7.04 1.38 1.16 -7.03 -83.59 -0.47 -5.57


6 Open Forests 35.92 30.08 37.01 31 1.09 3.03 0.07 0.2


8 Barren Land 1.17 0.98 7.55 6.33 6.38 543.11 0.43 36.21


10 Built up area 1.63 1.37 8.76 7.34 7.13 436.44 0.48 29.1

TOTAL 119.39 100 119.39 100

Warasgaon

LULC 1997 LULC 2012




1 Reservoir and rivers 15.14 11.35 6.93 5.19 8.21 -54.25 0.55 -3.62

2 Dry river bed 2.58 1.94 10.1 7.57 -7.51 290.73 -0.5 19.38

3 Cultivated land 0.23 0.17 0.42 0.32 -0.19 83.6 -0.01 5.57

4 Agricultural fallow 4.86 3.64 5.15 3.86 -0.29 6.01 -0.02 0.4

5 Moderately Dense forest 45.79 34.31 31.33 23.48 14.46 -31.57 0.96 -2.1

6 Open Forests 46.97 35.19 42.27 31.67 4.7 -10.01 0.31 -0.67

7 Scrub/ Degraded forest 8.53 6.39 11.99 8.99 -3.46 40.6 -0.23 2.71

8 Barren Land 3.94 2.95 3.59 2.69 0.35 -8.92 0.02 -0.59

9 Rocky waste lands 2.48 1.86 11.42 8.55 -8.93 359.98 -0.6 24

10 Built up area 2.93 2.19 10.25 7.68 -7.32 250.11 -0.49 16.67

TOTAL 133.46 100 133.46 100

Table 4.4 Catchment wise LU/LC and change detection


Chapter IV: Estimation of Forest Cover and Change Detection Analysis79

81


4.5.2 Change in LU/LC pattern of Panshet and Warasgaon reservoir catchment

Panshet and Warasgaon reservoirs are located towards about the 40 km south

west of Pune city in Velhe and Mulshi tehsils of Pune district in the flanks of Western

Ghats. Panshet reservoir is built in 1971 (reconstructed after breaking in 1961) on the

river Ambi, which originates near village Dapsare, while Warasgaon reservoir is built

in (1976) on the river Mose, which originate near village Dhamanohol. The terrain

consists of low lying valley to high dissected ridges forming ridge valley topography.

Panshet and Warasgaon have formed extensive lakes in the valleys, flanked on either

side by hills which rises more than 600 m. the hill slopes are dotted by small villages

with populations ranging from 100 to 400. The catchment area of these reservoirs is

119.39 & 133.46 km2 which include around 45 villages with combined population of

8,974 according to 2011 census.

These catchments lie just next to east of the crest line of the Western Ghats at

the altitude of about 600 m. In some parts the terrain is very much broken with narrow

valleys of less than half a km in extent separated by steep hills rising to altitudes of

around 1200 m. The people are poor and mostly marginal farmers depending heavily

on traditional farming techniques which is called as shifting cultivation by slash and

burn technique of forest according to the seasons. Previously the villagers cultivate

paddy on the flat land in the valley, most of which are now submerged under the

reservoir. Hill slopes particularly in the Panshet are fairly remote from the villages

which are still covered by forests. The land in Warasgaon is purchased by the Lake

City Development Corporation, Pune and they are developing Model Hill Station

known as “Lake Town”. The work of one small city known as “Lavasa” is recently

developed.

Hill slopes had a good tree cover of Mangifera indica (Mango) and Terminilia

chebula (Hirda), for these cash-yielding trees used to be spared by the peasants while

clearing for millets cultivation. The nuts of hirda used extensively for tanning

supported a flourishing industry at Bhor. The upper hill slopes were clothed by rich

natural forests of semi evergreen type, constituted into state owned forests reserves.

These forests were hardly exploited due to the lack of transport facilities (Gadgil &

Vartak, 1976). During the decade of 1950-60 all these forest were cut down. This has

led to condition that most of the land is now entirely barren and heavily eroded; also

82


the encroachment by the villagers in remained forests area has increased as the

productivity of their fields is declining. In 1963, government acquired the land of the

farmers for construction of Panshet dam. When the construction of the dams

completed, most of the lands never comes under water or catchment and this land

surface area thus remained unutilized. Most of the land is tilling by the local farmers

with the agreement of state government.

Table 4.4 shows that area under cultivated land in Panshet and Warasgaon is

very low, i.e. 0.4 and 0.42 km2

respectively. Physical constraint is the only factor

which has retarded the agricultural growth in these catchments. However during

monsoon this area under agriculture increases as, paddy cultivation is practiced

mostly in valleys. Change in moderately dense forest land is also noticed in Panshet

and Warasgaon catchment and it shows decrease by 3.77% and 2.1% per year

respectively. Scrub land is also increasing by 12.66% and 2.71% per year in Panshet

and Warasgaon respectively. Growth in barren and rocky waste land in Panshet is

more as compared to Warasgaon and it is 36.21% and 40.95% per year as in

Warasgaon it is 0.59% and 24% per year. Rate of growth in built up area in Panshet is

observed to be 29.1% and in Warasgaon it is to be 16.67% per year. The

encroachment now can be seen in remote and in accessible parts of the study area as

the transportation facility has been substantially improved.

4.6 LU/LC change assessment using Markov chain method

Land Use Land Cover Change (LULCC) is a major driver of a global change.

Since last few decades, the magnitude and spatial reach of human impacts on the

earth’s land surface is unprecedented (Lambin et al, 2001). Change in land cover and

land use has been accelerating as a result of socio-economic and biophysical drivers

(Turner et al. 1995, Lambin et al, 1991). These are closely linked with the issues of

the sustainability of a socioeconomic development since they affect essential parts of

natural capital such as vegetation, biodiversity and water resources (Mather and

Sdasyuk, 1991).

To understand and to infer what and where changes have occurred, and at

what pace such changes will happen a reliable model is require in this context. It will

also provide the information about future trends if the driving forces continue to

function in the same or alternative way.

1997-

2008 1 2 3 4 5 6 7 8 9 10 1997

1 27.94 2.51 0.63 1.02 0.62 0.17 0.20 0.14 0.19 0.03 33.43

2 0.37 1.56 0.12 0.89 0.27 0.05 0.68 0.29 0.29 0.15 4.66

3 4.28 2.69 56.76 51.73 14.56 2.80 18.92 7.04 4.87 4.90 168.56

4 0.05 0.32 31.56 45.64 10.01 0.17 15.25 5.63 5.16 4.46 118.24

5 0.23 0.64 9.06 12.83 10.56 1.38 4.65 4.38 2.56 4.71 51.00

6 0.03 0.04 0.94 1.07 1.44 0.21 0.35 0.58 0.01 0.18 4.84

7 0.06 0.86 4.15 6.67 2.64 0.07 4.00 1.57 0.51 0.54 21.06

8 0.02 0.19 1.97 3.66 4.51 0.03 2.47 2.19 0.49 1.63 17.16

9 0.26 0.30 0.67 2.72 2.07 0.00 1.62 1.33 1.01 1.63 11.60

10 0 0.00 2.59 4.70 3.46 0.00 2.09 1.22 1.35 6.23 21.66

2008 33.23 9.10 108.45 130.92 50.13 4.90 50.23 24.36 16.44 24.46 452.22

2008-

2012 1 2 3 4 5 6 7 8 9 10 2008

1 18.22 5.78 0.49 1.92 0.71 0.34 1.40 0.41 2.87 1.08 33.23

2 1.81 2.65 0.57 1.15 0.27 0.07 0.66 0.61 0.78 0.53 9.10

3 1.29 0.92 20.39 37.35 6.70 1.03 16.20 8.13 5.70 10.75 108.45

4 1.29 2.12 24.73 43.68 10.04 1.62 16.41 11.47 6.50 13.06 130.92

5 1.22 0.62 0.80 12.84 12.00 1.86 6.19 7.92 3.60 3.07 50.13

6 0.45 0.18 0.12 3.05 0.42 0.25 0.08 0.21 0.04 0.11 4.90

7 0.60 1.24 4.99 18.30 3.37 0.39 9.06 4.95 4.06 3.26 50.23

8 0.47 0.53 0.85 5.98 5.15 0.70 2.68 3.64 1.38 2.99 24.36

9 0.26 0.33 0.56 3.18 0.44 0.04 1.16 1.37 1.17 7.93 16.44

10 0.10 0.15 0.29 7.16 3.65 0.42 3.25 2.58 1.59 5.28 24.46

2012 25.70 14.53 53.79 134.61 42.75 6.73 57.08 41.29 27.68 48.06 452.22

Table 4.5a LU/LC Transition of the successive periods (Area km2)


1 Reservoir and rivers

2 Dry river bed

3 Moderately dense forest

4 Open forests

5 Agricultural fallow

6 Cultivated land

7 Scrub/ degraded forest

8 Built up area

9 Barren land

10 Rocky waste lands

INDEX

Source : Author

Ch

ap

ter IV: E

stima

tion

of F

orest C

ov

er an

d C

ha

ng

e Detectio

n A

na

lysis

83

1997

2012 1 2 3 4 5 6 7 8 9 10 1997

1 20.83 6.82 0.48 1.15 0.3 0.15 0.68 0.18 2.81 0.03 33.43

2 1.57 1.72 0.09 0.3 0.01 0 0.06 0.1 0.69 0.13 4.66

3 1.6 2 27.27 55.59 14.43 2.43 25.43 13.81 11.16 14.85 168.56

4 0.18 0.59 22.08 42.28 8.43 0.66 13.97 11.81 4.34 13.88 118.24

5 0.39 1.19 1.26 13.15 9.46 1.63 6.73 6.52 3.64 7.03 51

6 0.59 0.13 0.15 1.09 1.07 0.58 0.58 0.44 0.22 0.01 4.84

7 0.42 1.29 1.63 5.75 2.17 0.43 3.33 2.46 1.33 2.25 21.06

8 0.12 0.49 0.3 4.45 2.98 0.44 1.87 2.98 0.81 2.7 17.16

9 0 0.18 0.32 4.01 1.29 0.17 1.16 1.19 1.2 2.08 11.6

10 0.01 0.13 0.2 6.85 2.61 0.23 3.28 1.79 1.48 5.09 21.66

2012 25.7 14.53 53.79 134.61 42.75 6.73 57.08 41.29 27.68 48.06 452.22

Table 4.5b LULC Transition of the successive periods (Area km2)



2 Dry river bed

3 Moderately Dense forest

4 Open Forests


6 Cultivated land

7 Scrub/ Degraded forest

8 Built up area

9 Barren Land


INDEX

Source : Author

Ch

ap

ter IV: E

stima

tion

of F

orest C

ov

er an

d C

ha

ng

e Detectio

n A

na

lysis

84

1 2 3 4 5 6 7 8 9 10 1997 Total

1997-

2012Land cover class

Reser &

rivers

Dry river

bed

Mod.dense

forest

Open

forest

Agri

fallow

Cultivated

land

Scrub/degr

forestBuilt up

Barren

land

Rocky

waste

1 Reservoir and rivers 20.83 6.82 0.48 1.15 0.30 0.15 0.68 0.18 2.81 0.03 33.43

2 Dry river bed 1.57 1.72 0.09 0.30 0.01 0.00 0.06 0.10 0.69 0.13 4.66

3 Moderately Dense forest 1.60 2.00 27.27 55.59 14.43 2.43 25.43 13.81 11.16 14.85 168.56

4 Open Forests 0.18 0.59 22.08 42.28 8.43 0.66 13.97 11.81 4.34 13.88 118.24

5 Agricultural fallow 0.39 1.19 1.26 13.15 9.46 1.63 6.73 6.52 3.64 7.03 51.00

6 Cultivated land 0.59 0.13 0.15 1.09 1.07 0.58 0.58 0.44 0.22 0.01 4.84

7 Scrub/ Degraded forest 0.42 1.29 1.63 5.75 2.17 0.43 3.33 2.46 1.33 2.25 21.06

8 Built up area 0.12 0.49 0.30 4.45 2.98 0.44 1.87 2.98 0.81 2.70 17.16

9 Barren Land 0.00 0.18 0.32 4.01 1.29 0.17 1.16 1.19 1.20 2.08 11.60

10 Rocky waste lands 0.01 0.13 0.20 6.85 2.61 0.23 3.28 1.79 1.48 5.09 21.66

2012 Total 25.70 14.53 53.79 134.61 42.75 6.73 57.08 41.29 27.68 48.06 452.22

Change Sq km -7.73 9.87 -114.77 16.37 -8.25 1.89 36.02 24.13 16.08 26.40

Change % -23.12 211.80 -68.08 13.84 -16.17 39.04 171.03 140.61 138.62 121.88

Table 4.6 Overall change in LULC pattern of the study area

85


Ch

ap

ter IV: E

stima

tion

of F

orest C

ov

er an

d C

ha

ng

e Detectio

n A

na

lysis

2012 1 2 3 4 5 6 7 8 9 10 1997

1 0.623 0.204 0.014 0.034 0.009 0.005 0.020 0.005 0.084 0.001 1

2 0.336 0.369 0.020 0.064 0.001 0.000 0.013 0.022 0.147 0.028 1

3 0.010 0.012 0.162 0.330 0.086 0.014 0.151 0.082 0.066 0.088 1

4 0.002 0.005 0.187 0.358 0.071 0.006 0.118 0.100 0.037 0.117 1

5 0.008 0.023 0.025 0.258 0.186 0.032 0.132 0.128 0.071 0.138 1

6 0.121 0.026 0.032 0.224 0.220 0.121 0.119 0.090 0.046 0.001 1

7 0.020 0.061 0.078 0.273 0.103 0.020 0.158 0.117 0.063 0.107 1

8 0.007 0.029 0.018 0.259 0.174 0.026 0.109 0.174 0.047 0.158 1

9 0.000 0.016 0.027 0.345 0.111 0.015 0.100 0.103 0.103 0.179 1

10 0.000 0.006 0.009 0.316 0.120 0.011 0.151 0.082 0.068 0.235 1

Table 4.7 Land cover change : Transition Probability Matrix- 1997-2012

86



2 Dry river bed

3 Moderately Dense forest

4 Open Forests


6 Cultivated land

7 Scrub/ Degraded forest

8 Built up area

9 Barren Land


INDEX

Source : Author

Ch

ap

ter IV: E

stima

tion

of F

orest C

ov

er an

d C

ha

ng

e Detectio

n A

na

lysis

87


Markov chain model aim at predicting the spatial distribution of the specific land use

land cover classes in later year by utilizing the knowledge gained from the previous

years. Markov chain is spatial transition based models and one of the most accepted

methods for modeling land use land cover changes (LULCC) using current trends.

Markov chain models are particularly useful in the geographical studies which are

mainly concerned with problems of movements or change. Markov chain models are

neat and elegant conceptual devices for describing and analyzing the nature of

changes generated by the movement or change by particular variables. It also useful

for forecast the future changes. Markov chain method analyses a multiple images and

outputs a transition probability matrix. The transition probability matrix shows the

probability that one land use class will change to others. The transition area matrix

tells the number of pixels that are expected to change from one class to the other class

over the specified period (Eastman J.R. 2006).

Land use change transition probability in Markov analysis indicates the

probability of making a transition from one land use class to another within a two

discrete times. Transition probability matrix is produced by multiplication of each

column in the transition probability matrix to the number of cells of corresponding

land use in the in the later image.

4.6.1 Transition of land use and land cover change process in the study area

The Transition Probabilities governing the period 1997-2012 are calculated.

Table no 4.7 shows Transitional Probabilities (TP’s) between 1997 and 2012. For

instance transition probability from moderately dense open to open forests is 0.220

and to scrub or degraded forests is 0.103 and settlement is 0.120 and so forth upto the

year 2027. This computation is based on the assumption that the land use and land

cover change process is Markovian.

Analysis of the LU/LC pattern during 1997, 2008 and 2012 using satellite

derived maps it is observed that, socio-economic drivers mainly residential

development have influenced the spatial pattern of forests.

Documents

Chapter- IVshodhganga.inflibnet.ac.in/bitstream/10603/99864/12/12_chapter4.pdf · Reservoir and rivers Areas with surface water, ponds, lakes reservoir or flowing streams or rivers