Modelling Landuse Shift in Gurupavanapuriand Environs, Kerala, India

MODELLING LANDUSE SHIFT IN GURUPAVANAPURI
AND ENVIRONS, KERALA, INDIA
PROJECT REPORT
Submitted in Partial Fulfillment of the requirements for the Award of
Advanced Certificate in Geoinformatics
By
SHARIK SHAMSUDHIEN
Reg. No. 137171014
CENTER FOR ENVIRONMENT AND DEVELOPMENT
THIRUVANANTHAPURAM
NOVEMBER 2017

DECLARATION
I do hereby declare that this project entitled "Modelling Landuse Shift in Gurupavanapuri and
Environs, Kerala, India”, submitted to the Centre for Environment and Development,
Thiruvananthapuram, in partial fulfillment of the requirements for the award of the
Advanced Certificate in Geoinformatics is a bona fide record of research work done by me under
the supervision and guidance of Dr. Thrivikramji. K. P., Professor Emeritus and Dr. Chrips N. R,
Research Associate during 25th Oct. to 23rd Nov. 2017. I further declare that no part of this report
has formed the basis for the award of any other degree, diploma or certificate from any University
or organization.
Sharik Shamsudhien

CERTIFICATE
This is to certify that the report entitled "Modelling Landuse Shift in Gurupavanapuri and
Environs, Kerala, India” submitted by Sharik Shamsudhien to the Centre for Environment
and Development (CED), Thiruvananthapuram in partial fulfillment of the requirements for the
award of Advanced Certificate in Geoinformatics, is an authentic record of work done by him
under our supervision at CED during 25th Oct. to 23rd Nov. 2017, and that no part of this research
has formed the basis for the award of any degree, diploma or other similar titles of any university
or organization. We further certify that he has completed all the assigned tasks and duties to
our complete satisfaction and has impressed us with his dedication and constant strive
for excellence in work.

Dr. Chrips. N. R
Research Associate
CoGuide
Dr. Thrivikramji K. P
Professor Emeritus &
Program Director
Guide
Dr. Babu Ambat
Executive Director, CED

ACKNOWLEDGEMENTS
I express my gratitude to Dr. Babu Ambat, Executive Director, Centre for Environment and
Development (CED), Thiruvananthapuram, for providing the required facilities for carrying out
this work.
I sincerely thank Dr. Thrivikramji K. P. Professor Emeritus and Program Director for his
invaluable guidance and constant encouragement, which enabled me to enroll in this program of
study leading the award of Advanced Certificate in Geoinformatics.
I am indebted to Dr. Chrips N. R., Research Associate and Mr. Prasood S. P., Project Fellow,
Geoinformatics Division, CED for their steady and immeasurable help, valuable comments and
fruitful discussions during this project work.
I express my wholehearted gratitude to all faculty members of CED, viz., Dr. Vinod T. R., Dr.
Sabu T. and Mrs. Radha S.
I am grateful to all my course mates for their cooperation and support during the project work.
1 also wish to thank all the staff of CED who had helped me directly or indirectly for
the successful completion of my studies.
Finally, I am deeply indebted to my family for their continued and steady support and
encouragement. A special word of thanks to my parents.
Sharik Shamsudhien

ABSTRACT
Gurupavanapuri, legal name of ward 18 of Guruvayoor Municipality, Chavakkad Taluk,
Thrissur district, Kerala, is in the fast lane of population growth both due to steady  rise of
settled and floating population. Such a degree of urban population growth causes steady
increase in builtup area at the expense of vegetated and open lands. Tracking rate
conversion of vegetated lands, due to urban growth and changes in land cover in
Gurupavanapuri is essential for better management of municipal service delivery in
surrounding urban tract.
This study describes the results of land use and land cover changes in Gurupavanapuri and
its environs, from an input data set of relevant Google Earth scenes spanning two different
timelines, viz., 2002 and 2017. Specifically, in this study the building footprint type and
extent is mapped, categorized and estimated.  Also, created are a series of DEM based map
layers such as topo contour, slope, drainage network along with stream ordering. Seafloor
topography or bathymetry and depth to bedrock in the seafloor maps off Thrissur Dist.,
have been created from an input of ETOPO1.
It has been possible to identify four categories landuse types such as, building footprint,
natural vegetation cover, water body and open land. General results of land use change
study in Gurupavanapuri and its environs in Thrissur have indicated that Builtup area has
steeply grown in extent due to growth of urban population, while the vegetation and open
lands decreased continuously and steadily 2002 to 2017.

CONTENTS
SI. No. Title Page No.
1.1 Background 1
1.2 Landuse and Landcover 1
1.3 Landuse classification , India 2
1.4 Urbanization and its effects 3
1.4.1 Effects 3
1.5 Application of GIS and Remote sensing 4
1.6 Literature review 4
1.7 Objectives 5
2.1 STUDY AREA 6
2.2 Methodology 8
2.3 Materials 8
2.4 Tools used 8
3.1 Land use map of 2002 10
3.2 Land Use map of 2017 10
3.3 Change detection 10
3.4 Topographic Contour map 14
3.5 Digital Elevation Model (DEM) 15
3.6 Triangular Irregular Networks (TIN) 16
3.7 Slope map 17
3.8 Drainage network 18
3.9 Bathymetry and Oceanic Bedrock Surface 19
3.10 Toposheets 20
3.10.1 Survey of India (SOI) Toposheets 20
3.10.2 Toposheet from University of Texas
library
21
3.11 Discussion 22
4 CONCLUSIONS 24
References 25
AppendixI 26
Biographic sketch 29

LIST OF FIGURES
SI. No. Title Page No.
2.1 Location of study area 7
2.2 Work flow diagram 9
3.1 LULC   2002 11
3.2 LULC  2017 11
3.3 Pie diagram, LU types, 2002,
Gurupavanapuri and environs
12
3.4 Pie diagram, LU types, 2017,
Gurupavanapuri and environs
13
3.5 Contour Map of Thrissur District (includes
AOI).
14
3.6 DEM model of Thrissur District (includes
AOI).
15
3.7 TIN  model of Thrissur District (includes
AOI).
16
3.8 Slope map of Thrissur District (includes
AOI).
17
3.9 Drainage network model of Thrissur
District (includes AOI).
18
3.10 Bathymetry  of Coastal ocean (ETOPO1) –
Off Thrissur
19
3.11 Oceanic Bedrock Surface (ETOPO1) – Off
Thrissur
20
3.12 Mosaic of SOI Toposheets, Thrissur
District
21
3.13 Toposheet of  University of  Texas
indicating Thrissur District
22
3.14 Comparison of the LCLU change,  2002
and 2017
23
List of Tables
S I. No. Title Page No.
3.1 Areal extent of LU types, 2002 12
3.2 Areal extent of LU types, 2017 13

3.3 Comparison of Landuse change, 2003 to
2017
23

Chapter 1
INTRODUCTION
1.1 Background
Land is one of the most important natural resources, as life and various developmental
activities are based on it. Landcover change has been identified as one of the most
important drivers of changes in ecosystems and their services. These changes result from
population growth and migration of poor rural people to urban areas for economic
opportunities. However, information on the consequences of land cover change for
ecosystem services and human wellbeing at local scales is largely absent. The sprawling
process of expansion is disordered, unplanned, leading often to inefficient and
unsustainable. Land is considered to be the most important component of earth system as it
is the immediate victim of all sorts of the human activities.
1.2 Landuse and landcover
Landuse, LU, is influenced by economic, cultural, political, and historical and land – tenure
factors at multiple scales. Land use referred to as man’s activities and the various uses, are carried
on land. Landcover, LC, is referred to as natural vegetation, water bodies, rock/soil, artificial
cover and others resulting due to land transformation. Since India has an agrarian economy, the
prosperity of the country, to a great extent depends on the judicious and rational use of land. Land
use can be considered as an interface between the biophysical and socioeconomic systems.
In other words, LU is defined in terms of human activities such as agriculture, forestry,
building construction and others. LULC is dynamic in nature and it provides a
comprehensive understanding of the interaction and relationship of anthropogenic activities
with the environment. Changes in LULC are the direct and indirect consequences of
human actions to ensure essential resources. Increasing human population has been a threat
to future production of food and other essentials by the transformation of productive land
to other uses, such as conversion of agricultural land to residential use, degradation of land
by over grazing and forest destruction for recreations and faith. Current rates and extents of
LULC changes are far greater than ever in history and are driving unprecedented changes
in ecosystems and environmental processes at local, regional and global scales (Tsegaye
2014). These changes may cause climate change, biodiversity loss and the pollution of
water, soil and air and finally lead to threat for life survival. Monitoring and mediating the

negative consequences of LULC change while sustaining the production of essential
resources have therefore become a major priority of researchers around the world. LLULC
change has become a central component in current strategies for managing natural resource
and monitoring environmental changes.
1.3. Landuse classification, India
Statistics on land use are collected at present, in the form of a ninefold classification on a
yearly basis. Out of a geographical area of 329 million ha (reporting area) statistics are
available only from 305 million ha (nonreporting area), which leaves some areas (7%)still
not covered or classifiable under the ninefold classification. The reporting area is
classified into the following nine categories:
 Forests: This includes all lands classed as forest under any legal enactment dealing with
forests or administered as forests, whether stateowned or private, and whether wooded or
maintained as potential forest land. The area of crops raised in the forest and grazing lands
or areas open for grazing within the forests should remain included under the forest area.
 Area under Nonagricultural Uses: This includes all lands occupied by buildings, roads and
railways or under water, e.g. rivers and canals and other lands put to uses other than
agriculture.
 Barren and Unculturable Land: includes all barren and unculturable land like mountains,
deserts, etc. Land which cannot be brought under cultivation except at an exorbitant cost,
should be classed as unculturable whether such land is in isolated blocks or within
cultivated holdings.
 Permanent Pastures and other Grazing Lands: includes all grazing lands whether they are
permanent pastures and meadows or not. Village common grazing land is included under
this head.
 Land under Miscellaneous Tree Crops, etc.: This includes all cultivable land which is not
included in ‘Net area sown’ but is put to some agricultural uses. Lands under Casuarina
trees, thatching grasses, bamboo bushes and other groves for fuel, etc. which are not
included under ‘Orchards’ should be classed under this category.
 Culturable Waste Land: This includes lands available for cultivation, whether not taken up

for cultivation or taken up for cultivation once but not cultivated during the current year
and the last five years or more in succession for one reason or other. Such lands may be
either fallow or covered with shrubs and jungles, which are not put to any use. They may
be assessed or unassessed and may lie in isolated blocks or within cultivated holdings.
Land once cultivated but not cultivated for five years in succession should also be included
in this category at the end of the five years.
 Fallow Lands other than Current Fallows: This includes all lands, which were taken up for
cultivation but are temporarily out of cultivation for a period of not less than one year and
not more than five years.
 Current Fallows: This represents cropped area, which are kept fallow during the current
year. For example, if any seeding area is not cropped against the same year it may be
treated as current fallow.
Net area Sown: This represents the total area sown with crops and orchards. Area sown
more than once in the same year is counted only once.
1.4 Urbanization and its effects
Urbanization is the increasing number of people that migrate from rural to urban areas. It
predominantly results in the physical growth of urban areas. It is the rapid rise in urban
population. In India, it is leading to many problems like increasing slums, decrease in
standard of living in urban areas, also causing environmental damage. The urbanization of
India is taking place at a faster rate than in the rest of the world.
The urban populace of Kerala has registered a huge growth over the last decade as the
number of towns in the State increased three times during the period. The first everserious
attempt to review and analyze the urbanization process and formulate policies for
integrated urban development in the country began with the appointment of the National
Commission on Urbanization in the 1980s.
1.4.1 Effects
With a high rate of urbanisation significant changes have taken place. The effect of
urbanizationcan be summed up as:

 Positive effect
Migration of rural people Io urban areas, employment opportunities in urban centers,
transportand communication facilities, educational facilities and increase in the standard of
living.
 Negative effects
Problem of over population,disintegration of Joint family, Cost of living, Increase in Crime
Rates, Impersonal relations, Stress, Problem of Pollution and much more. Thus
urbanization has its own merits and demerits. Urbanization can't be avoided. But
thenegative effects of urbanization can be minimized.
1.5 Applicationof GIS and remote sensing
GIS is a method of digital (i.e., computerized) mapping that can represent where particular
people, events, things, or conditions are and provide other information about them as well.
It links data to its geographic location. GIS is a technology that enhances the users’
understanding of regional opportunities by allowing them to visualize the spatially
distributed nature of the data.GIS allows us to view, understand, question and interpret
data in many ways that reveal relationships, pattern and trends in the form of maps, charts
and reports. GIS helps answer questions and solve problems by looking at data in a way
that is quickly understood and easily sheared.
To understand how LULC changes affects and interacts with global earth systems,
information is needed on what changes occur, where and when they occur and the rates at
which they occur. This global or local change in LULC can be monitored using GIS and
remote sensing (RS) data in combination with ground survey using GPS or Geographic
Positioning System. The technique has been used extensively in the tropics for generating
valuable information on the forest cover, vegetation type and land use changes.
RS is the process of acquiring information about earth's surface from a distant vantage

without direct physicalcontact with it. RS is done with the help of orbiting and
geostationary satellites and it is important for studies which aim at understanding the
LULC dynamics. RS data provides information about earth system functions including
changes at local, regional and global scales over time. Such data provide a vital link
between intensive, localized ecological knowledge and the regional, national, and
international conservation and management of land resources and biodiversity.
1.6. Literature review
Monitoring the changes for the LULC change in India has been done by several
government agencies, scholars and researchers in different parts of the country, and there
has been growth in rapid sequence, especially in the last fifteen years. Here are some of the
previous studies that have taken place usingRS and urbanization monitoring.
Baby andArickal (2010) did a study on Land Use /Land Cover Mapping with Change
Detection Analysis of AluvaTalukcity in a span of 10 years using RS and GIS. The results
show that there is noticeable increase in Builtup land, Plantations at 6% and 3%
respectively.
Jainisara (2016) did a study on rapid urbanization that took over in the Pamba region and
its environs during the year 1978, 2002 and 2014, which indicated a clear reduction of
natural forest due to the human constructions. The percentage of manmade features from
1978 to 2002 had increased from 3% to 10% respectively
Tsegaye (2014) did an analysis of LULC change of West Guna Mountain; South Gondar
Zone, Amhara National Regional State, Ethiopia (between 1973 and 2014) using RS data
and GIS analysis with field verifications. In the study, land use/land cover maps of 1973,
1986, 2003 and 2014 were derived and studied for the land use changes.
Sundarakumar et al. (2012) studied the urban expansion and land cover changes that took
place in Vijayawada City in a span of 36 years from 1973 to 2009. Landsat images of TM
and ETM+ of Vijayawada city area are collected from the USGS Earth Explorer web site
and image classification has been performed to classify the images in to different land use
categories.
Pradeep and Thirumalaivasan (2015) did a study to identify the changes in LULC,

particularly with respect to builtup area spread in the Adyarwatershed, Tamil Nadu,India.
Images from the Corona and Landsat 8 satellites were used for this study. The results
revealed that built up area has increased around 400% in a span of 35 years.
Jayapradeep (2007) conducted a study on land use changes in Kanjirapalli village between
1968 and 2004 using GIS approach. Increase in rubber cultivated area and its reasons were
studied with the help of land use maps and soil fertility maps.
1.7 Objectives
The principal aim of this studies is to apply RS data and geospatial analysis tools to detect,
quantify, and analyze the changes that have taken place inGurupavanapuri and its
environs,Thrissur District, Kerala. The specific objectives of the research are as follows:
• Monitoring of the urbanization expansion and land cover changes since 2002 till
2017 inGurupavanapuri and its environs.
• Making topography contour map.
• Prepare TIN for 3D visualization and
• Familiarize with GIS various tools.
Chapter 2
MATERIALS AND METHODS
2.1 Study Area
Gurupavanapuriin the Guruvayoor Municipality is the ward and abode of the famous
Guruvayoor Sri Krishna Temple an important Hindu pilgrimage center in ThrissurDist,
Kerala.This has the fourth rank among temples in India in terms of the number of devotees
visiting per day. It is believed to have benn built around 5000 years.
Gurupavanapuri and environs had been steadily growing in terms of builtup area and
pilgrim’s facilities since independence. Broad gauge rail link and the Cochin International
Air Port have had amplified the no of nonstate visitors to this temple.Consequently the

infrastructure facilities in Gurupavanapuri and its surrounding have increased in
comparison with the past years or decades, especially to meet the comfortable stay of
devotees. This caused tremendous land use changes in the Gurupavanapuri area. Some of
these are:
 Resting stations (hotels/lodges) and Sanitation facilities
 Dispensaries (Allopathy, Ayurveda and Homeopathy)
 The improvement of transportation facilities (Roads and railways)
 Hotels and Restaurants
 Parking facilities
So here Gurupavanapuri and its environs is taken for enumerating and analyzing land use
and land cover changes through the year’s 2002 to 2017.
Guruvayoormunicipality inChavakkadTaluk,Thrissur district, Kerala, Indiahas 43 wards
and the area of interest, AOI (Fig.2.1), covers the Gurupavanapuri –the temple ward No.
18 and also parts of wards 13 and 28.The total area is 50.8259 ha(N. Lat 10° 35' 51.61"and
N 10° 35' 37.11"andE.Long. 76° 2'12.68" and 76° 2' 42.17" ). And further, the messages
of this study are national in scope and synthesizes the findings of other related studies
regarding LCLU change through time in response to evolving economic, social, and
biophysical conditions.Many of these changes can be quantified from measurements using
specific orbiting satellite imageries,for. E.g., Google Earth scenes or aerial photographs.
This study however chiefly uses Google Earth scenes of two time lines.
Study area

2.2Methodology
In order to detect the LULC change through two different timelines, the procedures
followed are shown below in the workflow flow diagram (Fig.2.2). The first thing is to
Fig: 2.1Location Map, study area

consider the remotely sensed data   to extract information about the area of interest (AOI)
for two different time lines.
The proposed methodology includes two main points:
• Remote Sensing data collection, such as SRTM DEM, ETOPO1 data, Toposheets,
Google Earth images of the AOI 2002 and 2017 and such other relevant materials.
• Data verification, preparation/processing,  reprojection/setting of coordinate system,
clipping, digitizing, classification, etc. and importing data to personal/file Geodatabases
2.3Materials
 Toposheet of Survey of India (1978) on a scale 1:50000 and Toposheet(1:250000)
downloaded from university of Texas library
 Google Earth images for the years 2002 and 2017
 SRTM DEM from United States geological Survey (USGS)
 Shapefile of the study area downloaded from the  DIVAGIS website.
 ETOPO1  global relief model of the Earth and oceanic bed rock data covering land
topography and ocean bathymetry.The tile is downloadable as a
geotiffreferenced TIFF or KMZ file from National oceanographic atmospheric
administration(NOAA).
  2.4Tools Used
ArcGIS 10.2platform is used in the geodatabase creation, geoprocessing and presentation
of results in well designed cartograms of thematic maps. GIS is an integrated system of
Hardware, Software, Data & Skilled persons to capture, update, manipulate and display
geographically referenced spatial information.It provides an alternative for efficient
management of large and complex data base, whereas for detecting and analyzing the
changes for two time periods in the study area, Google Earth was used.

Modelling Landuse Shift in Gurupavanapuriand Environs, Kerala, India

Chapter 3
RESULTS AND DISCUSSION
3.1 Land use map of 2002
Land Use map (Fig.3.1) of Gurupavanapuri and its environs of year 2002 was prepared by
digitizing from Google Earth. In 2002, Gurupavanapuri and its environs had more
vegetation and less human involved transformation of land cover. There was more Open
space in the year 2002 as compared to the year 2017
3.2 Landuse map of 2017
Land Use map of Gurupavanapuri and its environs in year 2017 was also prepared by
digitizing the corresponding Google Earth scene (Fig.3.2). It is clear that a lot of land use
changes happened compared to the year 2002. The expansion of the temple area is
mainly attributed to rise in the number of pilgrims descending to Guruvayor temple. The
intraregional variations in growth are mainly associated with acceleration inadding new
hotel rooms, economic activity, transportation network, administrative
and government interventions.
3.3 Change detection
An important aspect of the change detection is to determine what is actually changing to
the other land use class. This information will also serve as a vital tool in management
decisions. The figures below show the state of the LCLU between 2002 and 2017. The
observation here is that there is a concrete contrast between the two reference years in
terms of builtup area and the open space.
The builtup area has considerably increased in 2017 compared to 2002 (see Fig. 3.1and
3.2) respectively and there is also very much visible changes in the extent of open space
between the two reference years with a gap of more than a decade in between. The list of
the landusetypes for the two years are listed in Table. 3.1 and 3.2, whereas pie diagrams
for the reference years are represented in Fig. 3.3 and 3.4.

Fig: 3.1 LULC 2002
Fig: 3.2 LULC 2017

Table: 3.1 Areal extent of LU types, 2002
LANDUSE (2002) AREA (ha) PERCENTAGE (%)
Building footprint 11.7279 24%
Open area 4.542 9%
Pond 0.666 1%
Vegetation 33.2824 66%
Fig: 3.3 Pie diagram, LU types, 2002, Gurupavanapuri and environs

Table 3.2. Areal extent of LU types, 2017
LANDUSE (2017) AREA (ha) PERCENTAGE (%)
Buildings 21.2292 42%
Open area 2.3278 5%
Pond 0.6956 1%
Vegetation 26.5733 52%

Fig: 3.4. Pie diagram, LU types, 2017, Gurupavanapuri and environs
3.4. Topographic Contour map
These are generaluse maps at medium resolution that present elevation (contour lines),
surface hydrologic network, geographic place names, and a variety of cultural features.
Currentgeneration topographic maps are created from digital GIS databases. A contour
line is a line that connected two points or similar points with same elevations Contour lines
are curved, straight or a mixture of both on a map describing the intersection of a real or
hypothetical surface with reference to one or more datum. Contour Map of the study area is
created in ArcGIS from SRTM DEM with the help of Arc Toolbox, ‘Raster Surface’ in
‘3D Analyst Tools’ is used to create the contour with an interval of 20m. Contour interval
of a contour map is same in elevation between successive contour lines.
Fig:3.5 Contour Map,Thrissur District (includes AOI).

3.5 Digital Elevation Model (DEM)
A Digital Elevation Model (DEM) is a specialized database that represents the relief of a
surface between points of known elevation. By interpolating known elevation data from
sources such as ground surveys and photogrammetric data capture, a square grid of DEM
can be created. GIS software can use DEM for 3D surface visualization, generating
contours, and performing viewshed visibility analysis. A GIS represents both features and
surfaces as a layer symbolized by a color ramp (Fig. 3.6). A DEM is the base for creating
new surfaces such as slope and aspect. DEM of the study area is created SRTM DEM with
the help of ArcGIS by extractingSRTM DEM with our AOI.
Fig: 3.6DEM model,Thrissur District (includes AOI).

3.6. Triangular Irregular Networks (TIN)
TINs are a form of vectorbased digital geographic data and are constructed by
triangulating a set of vertices (points). The vertices are connected with a series of edges to
form a network of nonoverlapping triangles. There are different methods of interpolation
to form these triangles, such as Delaunay triangulation or distance ordering. The map of
this study is created by the Delaunay triangulation method which is supported in ArcGIS.
The main purpose of creating TIN map is for the visual representation of topographical
features and appearance of the physical land surface (Fig. 3.7).TIN map of the study area is
created in ArcGIS from an input ofSRTM DEM with the help of Arc Toolbox. ‘TIN
Management’ in ‘3D Analyst Tools’ is used to create the TIN.
Fig :3.7TIN model,Thrissur District (includes AOI).

3.7 Slope map
A slope is a surface that is at an angle, so that one endis higher than the other.Slope is a
physical feature, landform or constructed line refers to the tangent of the angle of that
surface to the horizontal. It is a special case of the slope, where zero indicates horizontality.
A larger number indicates higher or steeper degree of "tilt". Often slope is calculated as a
ratio of "rise" to "run", or as a fraction ("rise over run") in which run is the horizontal
distance and rise is the vertical distance.It is themeasure of change in elevation. The grades
or slopes of existing physical features such as canyons and hillsides, stream and river
banks and beds are often described. Slope Map of the study area is created in ArcGIS from
SRTM DEM with the help of Arc Toolbox, ‘Surface’ in ‘Spatial Analyst Tools’.
Fig: 3.8Slope map, Thrissur District (includes AOI).

3.8.Drainage network
It is the study of the way in which the pattern of streams in a drainage basin is organized.
Drainage systems, also known as river systems, are the patterns formed by
the streams, rivers, and lakes in a particular drainage basin. They are governed by the
topography of the land whether a particular region is dominated by hard or soft rocks, and
the lay of the land. Geomorphologists and hydrologists often view streams as being part of
drainage basins. A drainage basin is the topographic region from which a stream receives
runoff, throughflow, and groundwater flow. The number, size, and shape of the drainage
basins found in an area vary andthe larger the topographic map, the more information on
the drainage basin is available.*. Stream net of the study area is created in ArcGIS from
SRTM DEM with the help of Arc Toolbox, ‘Hydrology’ in ‘Spatial Analyst Tools’.
Fig: 3.9Drainage network model,Thrissur District (includes AOI).

3.9.Bathymetry and Oceanic Bedrock Surface
Bathymetry is the study of underwater depth of lake or ocean floors. In other words,
bathymetry is the underwater equivalent to hypsometry or topography. Bathymetric
(or hydrographic) charts are typically produced to support safety of surface or subsurface
navigation, and usually show seafloor relief or terrain as contour lines (called depth
contours or isobaths) and selected depths (soundings), and typically also provide
surface navigational information. Bathymetric maps (a more general term
where navigational safety is not a concern) may also use a Digital Terrain Model and
artificial illumination techniques to illustrate the depths being portrayed. Oceanic bedrock
is the oceanic crust which is the uppermost layer of the oceanic portion of a tectonic
plate. The data is retrieved as ETOPO1 global relief model of the Earth, covering land
topography and ocean bathymetry. The tile is downloadable as a geotiff
referenced TIFF or KMZ file from NOAA.
Fig. 3.10.Coastal oceanic bathymetry(ETOPO1) – Off Thrissur

Fig. 3.11.Ocean Bedrock surface(ETOPO1) – Off Thrissur Dist.
Thematic Maps of coastal ocean bathymetry and oceanic bedrock off Thrissurwer,e created from
ETOPO1 Ice Surface and ETOPO1 bed rock created in ArcGIS using Arc Toolbox, ‘Raster
Surface’ in ‘3D Analyst Tools’ is used to create the contour with an interval of 250 m. And for the
creation of the oceanic bed rock was done by extracting ETOPO1 bed rock with the area of
Interest with ArcGIS.
3.10 Toposheets
3.10.1 Survey of India (SOI) Toposheets
Toposheets published under the Survey of India (1978) on a scale of 1:50000 is used for creating a
mosaic of Thrissur Dist. A set of eleven Toposheets were combined using ArcGIS, where they
were imported to ArcGIS and was masked into a single tile by‘Mosaic to new Rastor’ Tool in the
‘Datamanagement tools’. It was then extracted to the Thrissur district shapefile by using ‘Extract
by mask’ tool in ‘Extraction’ under ‘Spatial analyst tools’.

Fig. 3.12.Mosaic of SOI Toposheets,Thrissur District
3.10.2 Toposheet from University of Texas library
Toposheet (NC 437) published under the University of Texas library(1954) on a scale of
1:250,000 is used for determining the Location and boundary ofThrissur district. Out of a
single Toposheetand using ArcGIS, and after Georeferencing, extracted to the Thrissur
district shapefileby using ‘Extract by mask’ tool in ‘Extraction’ under ‘Spatial analyst
tools’.

Fig :3.13Toposheet of University of Texas; note Thrissur Dist. boundary
3. 11 Discussion
The change detection process is to determine the changes and compare the same as to what
was before in terms of the land use classes or types versus say now. The information will
also serve as a vital tool in the resource management decisions. The landusedata and
figures for 2002 and 2017 show the land use change (based on Google earth scenes).
The identified classes were built up area, road, vegetation and open space the latter
occupied largest area. It was observed that, compared to year 2002 the builtup area of
2017 has clearly increased with a much larger area under it.
The emerging urbanization and increase in industrialization are among the many drivers of
this shift.Table. 3.3andbar diagram (Fig. 3.14) illustrate the changes.

LANDUSE Extent, ha, 2002 Extent, ha, 2017 CHANGE
Building footprint 11.7279 21.2292 9.5013 +
Open area 4.542 2.3278 2.214
Vegetation 33.2824 26.5733 6.7091
Water body 0.666 0.6956 0.0296 +
Table: 3.3, Comparison of landuse change, 2002 to 2017
Fig. 3.14. Comparison of the LCLU change, 2002 and 2017

Chapter 4
CONCLUSIONS
The results of this study are based on Google Earth scenes and interpretation. The data
gathering and analysis for the two different time lines, 2002 and 2017, helped in the
preparation of a series of maps and infographics corresponding to land use shifts.
The work was mainly aimed to identify the rapid urbanization that took over in the
Gurupavanapuri and its environs during the year 2002 and 2017 and on analysis there was
a clear reduction of natural vegetated area due to the human influenced constructions.
In conclusion, the urban land use of the AOI has vastly increased over the last decade.
Compared to the year 2002, the builtup area in the fringes has also increased in the year
2017, in terms of roads and new buildings.
All these observations point towards the change in the land use pattern and the study
clearly defines this change, which can be used in the future planning of Gurupavanapuri
and similar areas.

REFERENCES
Jainisara, .2016. Estimation of landuse changes around Pamba and environsusing Remote
Sensing and GIS, Centre for Environment and Development, Thiruvananthapuram.
Ohri, A. andPoonam 2012. Urban sprawl mapping and land use change detection using
Remote Sensing and GIS”,International Journal of Remote Sensing and GIS, 1(1):
1225.
Pradeep, C. and Thirumalaivasan, D. 2015. Use of Corona, Landsat 8 Images to Assess 30 Years
of BuiltUp Landuse Changes in Adyar Watershed, India,International Journal of Remote
Sensing and GIS, 4(1): 1722.
Sundarakumar, K., Harika, M., Aspiya S. B., Yamini, S., Balakrishna,K. 2012.Land use
and land cover change detection and urban sprawl analysis of Vijayawada city
using multitemporallandsatdata,International Journal of Engineering Science and
Technology 4(1): 170178.
Tsegaye, L. 2014. Analysis of Land Use and Land Cover Change and Its Drivers Using GIS and
Remote Sensing: The Case of West Guna Mountain, Ethiopia,International Journal of
Remote Sensing and GIS, 3(3): 5363.
V.M Jayapradeep. 2007. Assessment of Land use changes in Kanjirapalli Village using
GIS and Remote Sensing,Centre for Environment and Development,
Thiruvananthapuram.
Vattoly JD, Baby L and Arickal AP (2010) Land Use /Land Cover Mapping With Change
Detection Analysis of AluvaTalukcityUsing Remote Sensing and GIS International
Journal of Science,Engineering and Technology
 USEFUL WEBSITES
 http://www.divagis.org/gdata/
 https://earthexplorer.usgs.gov/
 https://data.noaa.gov/dataset/
 http://www.lib.utexas.edu/

APPENDIXI
Google Earth
 Monitoring the changes for the land use and land change of Gurupavanapuri and Environs
was done by using Google Earth,
 Features digitized manually by ‘Add path’ option.
 Digitisation of 2002 and 2017 are done separately and was saved in a ‘kml’ format.
ArcGIS
 The digitized features converted from ‘kml’ to Geodatabasewith theArcGIS from Arc
toolbox, Conversion tools, from kmltool
 The converted geodatabase is then converted to feature to polygon,byData management
tools, features, feature to polygon tool.
 The converted polygons are then separated according to the feature type, such as building
patterns, vegetation, water body, etc. to each of the study periods done seperately.
 After separation of features, the area of each feature is identified by selecting the
attribute table of the feature.
 Finally the layout of the Landuselandcover map is prepared for each of the study periods
done seperately.
 Open the digitized map – georeferenced and with features added.
 Select ‘layout view’ in left bottom frame.
 Layout tool bar will become active. Otherwise, right click on top menu bar and select
‘layout’ tool bar. Select ‘Change layout’ tool > select A4 landscape
 Select preferredpreferred layout
 Use zoom and pan tools in the layout tool bar to move map as a whole with the layout
frame.
 To move the map alone without the frame, use the pan tool in the ‘Tools’ toolbar.
use ‘select elements’ in Tools toolbar, right click on frame > select ‘properties’
> ‘Grid’ tab > new grid , then click next > select labels only grid interval as
need
 To change labelling, click on ‘properties’ > go to ‘labels’ tab and select vertical label
orientation
1. To give Title, click ‘Insert’ from main menu > select ‘Title’ > give title >change
symbol and edit font and font size if needed.
2. To add North arrow, go to Insert menu, select north arrow
3. To add scale bar, go to Insert menu, select ‘scale bar’> properties > change scale and
unit as needed.

4. To insert legend, go to Insert, select legend –apply. Then to manually edit the legend, right
click on the legend > ‘Convert to graphics’ > right click on legend again > ‘Ungroup’.
. Then select all and right click > ‘Group’ to group again.
5. Save map using save option in menu bar.
Save as Arc map (.mxd file) to be able to edit the map again in ArcGIS
Location map
Location map of the study area  was downloaded as a shape file from  the  DIVAGIS website.
(URL given below)
 http://www.divagis.org/gdata/
Then the country wise, state wise and district wise map layouts is created as similar to
above steps of creation of map layouts
Prepare thematic Maps using GIS tools, ETOPO1 data and SRTM DEM
Bathymetry and oceanic bedrock
The data is retrieved as ETOPO1   global relief model of the Earth, covering land
topography and ocean bathymetry. The tile is downloadable as a geotiff
referenced TIFF or KMZ file from NOAA (URL given below)
 https://data.noaa.gov/dataset/
Thematic Maps of  coastal ocean bathymetry and oceanic bedrock off Thrissur was created from
ETOPO1 Ice Surface and ETOPO1 bed rock created in ArcGIS from with the help of Arc
Toolbox, ‘Raster Surface’ in ‘3D Analyst Tools’ is used to create the contour with an interval of
250m.
And for the creation of the oceanic bed rock was done by extracting ETOPO1 bed rock with the
area of Interest with ArcGIS.Mask  study area  with Data Source–ETOPO1
SRTM DEM
SRTM Digital Elevation Model  (DEM) data is downloaded  from United States geological
Survey (USGS) .  (URL given below)
 https://earthexplorer.usgs.gov/

The downloaded DEM data helps in the creation of :
 Topographic contours
 TIN
 Slope map
 Drainage pattern
Map layout should be done after the creation of the above the themes from the DEM data

Modelling Landuse Shift in Gurupavanapuriand Environs, Kerala, India

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