Urbanization and Its Impact on Physical Enviornmental Quality of Maradu Muncipality, Ernakulam District, Kerala State

URBANIZATION AND ITS IMPACT ON PHYSICAL ENVIORNMENTAL QUALITY OF MARADU MUNCIPALITY, ERNAKULAM DISTRICT, KERALA STATE.

Thesis submitted to Sree Sankaracharya University of Sanskrit, Kalady for the award of the degree of

DOCTOR OF PHILOSOPHY

By MEENU V

DEPARTMENT OF GEOGRAPHY Sree Sankaracharya University of Sanskrit Kalady-Kerala-683574 December 2016

Department of Geography Sree Sankaracharya University of Sanskrit Kalady Ernakulam-683574 DR. LANCELET. T.S Associate professor and Head Ernakulam-683574 Mob: 09495303560 Office no: 0484-2463380 Ext: 246 E-Mail:[email protected]

BONAFIDE CERTIFICATE

Certified that this thesis entitled, “URBANIZATION AND ITS

IMPACT ON PHYSICAL ENVIRONMENTAL QUALITY OF MARADU

MUNCIPALITY, ERNAKULAM DISTRICT, KERALA STATE” is a bonafide work of Ms.Meenu.V submitted for the award of Degree of Doctor of

Philosophy in Geography under my supervision after fulfilling the basic requirements specified by Sree Sankaracharya university of Sanskrit.

Certified further that to the best of my knowledge, the work reported herein does not form part of any other thesis or dissertation on the basis of which a degree or award was conferred on the earlier occasion on this or any other candidate.

KALADY DR.LANCELET.T.S 3

MEENU.V

Research Scholar Department of Geography Sree Sankaracharya University of Sanskrit Kalady Ernakulam-683574

DECLARATION

I, Meenu.V, do hereby declare that the thesis entitled” URBANIZATION AND ITS

IMPACT ON PHYSICAL ENVIRONMENTAL QUALITY OF MARADU MUNICIPALITY,

ERNKAULAM DISTRICT, KERALA STATE” is a bonafide record of work done and submitted by me for the award of the Degree of Doctor of Philosophy in Geography in

Sree Sankaracharya University of Sanskrit in the year 2016.

I further declare that this is original work, and has not been presented and published elsewhere in any university or nay body in full or part for the award of any degree, diploma or other similar recognitions.

KALADY MEENU.V

Thanks to Sree padmanabha swamy.

My Amma and Achan, words won‟t be enough to acknowledge your love and support. Whatever, I have accomplished in my life I owe it to both of them.

First of all I would like to thank Dr.T.S.Lancelet, for guiding me from the very beginning and for her patience and encouragement, till the very end of my thesis. If it was not for her I don‟t think I would be doing my PhD. Thank you for believing in me.

Besides my advisor, I am grateful to my committee Dr.R.Anilkumar, Dr.Omana Ruseel, Dr.Dilip Kumar, and Dr.Chandrashekar Rao for their insightful comments and their encouragement, but also for hard questions which helped me to widen my research. A special thanks to Dr.T.S.Saju, for patiently hearing my views and thoughts and for discussing many perspectives that I didn‟t realize while in research.

Thanks to the research scholars, faculty and students in the Department of Geography for their friendly working atmosphere. A special thanks to Dr. Monto Mani (IISC), Dr.Radhika (Icussre) and Dr.Archanan nair (IIT Guhwathi) for helping and moulding me as a researcher that I am today. Vinod chettan, Sanothosh chettan and all members of GAIA for being there for me always and encouraging me. A special thanks to all my teachers, and the non academic staff of SSUS Kalady.

This was not possible without the help of my Amma and Achan, Thank u for all your support and love. Sojin Kumar, for being the harsh critic and thanks for the words of wisdom. A special thanks to, Mr and Mrs Unnikrishna kurup and Dr. Sreedhar .U, for loving me and supporting me for all the ventures after marriage. To my whole big family in Divakara Mandiram (my uncles, sisters, brothers, cousins and all) for making me the person I am , loving me and being there for me always.

This would not have been possible without my friends helping me from the day I thought of doing PhD. My mental support pillars Jayalekshmi, Varunima and Divya, for their warmth and support. All my friends (from school level till now Nija cheechi, Vineetha), thanks for bearing my nature. Words won‟t be enough to thank you.

My kids Pachu, Paru, and Ammu for giving me the strength, their big milestones gave me an inner strength that nothing is impossible. 5

I wish my brother and grandfather was alive to see this moment, it was their dream that I become a doctorate holder just like my Ammavan. Thank you, Dr.D.Nandakumar for not interfering in my wok and for giving me space for making it my own.

I am deeply grateful to various persons in Maradu municipality office for helping in procuring data and ward level maps of Maradu. This work would not have happened without unconditional support of my friends from Maradu Geriatric clinic (KSSM staff and co coordinators) Divya Geet, Jibi, Reneesh, Ambili cheechi and Dr.Nimmy Jenson. Last but not the least; I am extremely grateful to my respondents in the study area. I encroached upon their personal spaces, time and energies unashamedly and they were amongst the most gracious hosts I have ever known. My thanks are due to them.

A very special thanks and love to my sister Dhanya, Gouri ammayi, Nija cheechi and Nandan ammavan for the proof readings and for patiently helping me with my thesis corrections. My thesis would not have turned out the way it is, without your critical comments and corrections.

Thanks to the wonderful women groups -“From the Granite top” and “Queen‟s lounge” in face book for their time and understandings, as I wouldn‟t have passed the phase of corrections and re-work without your love, support and encouragement.

There may be others whom I forgot to mention, but let me assure all such persons that it is unintentional and I seek their forgiveness.

(Meenu.V)

CONTENTS Contents Page No.

Title Acknowledgement List of Tables List of Maps and Figures List of Appendices

Section I

Chapter 1 INTRODUCTION a. Urbanization b. Physical Environment c. Urbanization and Physical Environment Status Literature Review Conceptual Frameworks a. Urban Ecology b. Urban Landscape Ecology c. Humanistic Geography Statement of the problem Objectives Research Propositions Research Design i. Area of study ii. Unit of study iii. Data sources and Analysis iv. Tools and Techniques v. Limitations of the Study Methodology Chapter Scheme

Chapter 2 STUDY AREA Introduction Location and administrative set up Physical Base Physiography Temperature and Climate Seasonal pattern of rainfall Seasonal wind patterns Geology Soil-Texture and Pattern Water Availability Land use Socio-Economic Base Demographic structure Occupational Structure Education Health Transport and communication Section II

Chapter 3 URBAN GROWTH OF MARADU Situating Maradu Demographic structure Population statistics Economic activities-Occupational structure Morphology Land types/Categories Population pressure on Land Land use/Land cover Spatio-temporal land use/land cover analysis of Maradu 8

Urban Morphology Land use change and Urban Sprawl Trend of Urban Sprawl Urban Intensity Analysis Urban Intensity Index Spatial Aeolotropy of Urban Intensity Index Conclusion-Phases of Urbanization

Chapter 4 ASSESSING PHYSICAL ENVIRONMENT STATUS USING REMOTE SENSING AND GIS Introduction Physical Environment Status Data products Data processing and Analysis Limitations of the Data Land and Vegetation Land use/Land cover Normalised Difference built up index Normalized Difference Moisture Index Normalised Difference Vegetation Index Imperviousness Land Surface Temperature Band-Ratio Assessment

Water Suspended Sediment Concentration Chlorophyll Content Salinity Normalized Difference Water Index Band-Ratio Assessment

Air Aerosol Optical Thickness 9

Sampling pattern-Physical Environment Status

Section III

Chapter 5 PEOPLE PERCEPTIONS ON PHYSICAL ENVIRONMENT STATUS Introduction Limitations of the study Field study Assessment Selection of wards and samples Ward wise socio-economic profile Socio-economic profile of the sampled households Physical environment perception and Sense of place Participatory Research Methods-Resource Mapping Conclusion-Physical environment change

Chapter 6 SUMMARY AND CONCLUSION

REFERENCES AND SELECT BIBILIOGRAPHY APPENDICES

ABBREVIATIONS Ac Archean eon(Geological Scale)

DN Digital Numbers

ERDAS Earth Resource Data Analysis Software

ETM+ Enhanced Thematic Mapper Plus

GIS Geographic Information System

LST Land Surface Temperature

MSS Multi Spectral Scanner System n Number of years

NDBI Normalized Difference Built Up Index

NDMI Normalized Difference Moisture Index

NDVI Normalized Difference Vegetation Index

NH National Highways

NRSA National Remote Sensing Agency

PET Potential Evapotranspiration

PRA Participatory Resource Mapping

RS Remote Sensing

SPSS Statistical Package for the Social Sciences t Time period

TLA Total Land Area

TM Thematic Mapper

ToA Top Of Atmosphere

UII Urban Intensity Index

ULA Urban Land Area

UN United Nations

UNESCO United Nations Educational Scientific and Cultural Organization

UPI Urban Proportional Index

USGS United States Geological Survey 11

LIST OF TABLES

Table no 2.0 showing Annual water availability-2004

Table no 2.1 showing Land use pattern-2005

Table no 2.2 showing Occupational structure-2001

Table no: 3.1 showing Total Population and Total number of Households

Table no: 3.2 Occupational Structure of Maradu

Table no 3.3Land categories and rate of change over different periods (1901, 1995 and 2003)

Table no 3.4 Population Pressure on Land over different periods (1901, 1995 and 2003)

Table no 3.5 NRSA Land use classes Table no 3.6 Land use change of Maradu over different periods in Hectares (1973, 1993 and 2003)

Table no 3.7 Urban Intensity index of Maradu 1973-2013 over 20 years

Table no 4.0 Landsat sensors and their characteristics

Table no 4.1 Spectral radiance ranges for Landsat TM AND ETM+

Table no 4.2 Indices depicting Physical Environment Status of Maradu municipality

Table no 4. 3 Physical environment status and change/no change clusters

Table no 5.1 Table showing the need for micro-level analysis

Table no 5.2 Socio-economic profile of the sampled persons

Table no 5.3 Identification of Attributes

Table no 5.4 Socio-economic elements

Table no 5.5 (a) and (b) Political –economic elements

Table no 5.6 (a) (b) and (c) (d) Bio-physical elements

Table no 5.7 (a) (b) Individual elements

LIST OF MAPS

Map no 2.1 Location map of Maradu municipality Map no 2.2 Ward map of Maradu municipality Map no 2.3 Geological map of Maradu municipality Map no 2.4 Soil map of Maradu municipality Map no 2.5 Road Network map of Maradu municipality Map no 3.1 Land use maps of Maradu over different periods from 1973- 2013). Map no3.2 Urban Sprawl Map (1973-2013) Map no 3.3 Built up Land Map (1973-2013) Map no3.4 Map shows the buffer analysis of Built up area during different intervals. Map no 3 .5 Spatial Aeolotropy of Urban Intensity index of Maradu 1973-2013 over 20 years Map no 4.0 Landsat images 1973, 1993 and 2013 Map no.4.1 Land use maps of Maradu over different periods from 1973- 2013. Map no 4.2 NDBI maps for 1993 and 2013 Map no 4.3 NDMI map for the years 1993 and 2013 Map no 4.4 NDVI map for the years 1973, 1993 and 2013 Map no.4.5 Land Impervious map for the years 1973, 1993 and 2013 Map no 4.6 LST map for the years 1993 and 2013 Map no 4.7 Salinity map for the years 1973, 1993 and 2013 Map no 4.8 Chlorophyll maps for the years 1973, 1993 and 2013 Map no 4.9 Suspended Sediment Concentration maps for the years 1973, 1993 and 2013 Map no 4.10 NDWI maps for the years 1973, 1993 and 2013 Map no 4.11 Aerosol Optical Thickness map for the years 1973, 1993 and 2013 Map no 4.12 Sampling areas in Maradu Map no 5.1 Population map of aged 60+ Map no 5.2 Population Density Map of Maradu 13

LISTS OF FIGURES

Figure no: 3.1 Figure showing Total Population and Total number of Households Figure no: 3.2 and 3.3 Occupational Structure of Maradu Fig no.3.4 Figure shows the population of Non- workers and other workers in Maradu (1971-2011). Fig no.3.5 Figure shows the Land categories/types over different periods. (1971- 2011). Fig no.3.6 Land use types of Maradu over different periods. (1973, 1993 and 2011) Figure no.3.7 Data Sources and Methodology- Urban Sprawl Figure 3.8 Urban Intensity index of Maradu 1973-2013 over 20 years Fig. 3.9 (a), (b), (c) and (d) .Changes in the UII in NE, SE, NW and SW with distance to the urban centre in different aeolotropic areas over the entire Maradu region in different time periods from 1973 to 2013 Fig No.3.10 a and b Spatial Aeolotropy of UPI based on buffer analysis during 1973-1993 and 1993- 2013 Figure 4.1 Methodology Flowchart- Physical Environment Status Fig no.4.2 Maradu land use/land cover maps 1973, 1993 and 2013. Figure no 4.3 Methodology for estimating NDBI,NDVI,NDMI and Imperviousness using Erdas modeler Figure no 4.4 Methodology for retrieving Land Surface Temperature using Erdas modeler Figure no 4.5 Methodology flowcharts for Water status assessment using Erdas modeler Figure no 4.6 Flowchart and Diagram showing Aerosol Optical Thickness (Air quality) methodology Figure no 5.1 Dimensions of Sense of Place Figure no 5.2 Sampling Scheme Figure no 5.3 (a) (b)(c) (d) showing the socio-economic profile of the sampled persons Figure no 5.4 (a) (b)(c) (d) showing the socio-cultural elements Figure no 5.5 (a) (b)(c) (d) (e) showing the political-economic elements Figure no 5.6 (a) (b)(c) (d) (e)(f)(g) (h) (i) (j) (k) showing the bio-physical elements Figure no 5.7 (a) (b)(c) (d) (e)(f) showing the individual elements 14

INTRODUCTION

The urban population of the world has grown rapidly since 1950 from 746 million to 3.9 billion in 2014 (United Nations Department of Economic and Social Affairs, 2014). Around the year 1800, 3 % of the world population lived in urban places of 5,000 people or more. This proportion has risen to more than 13% by 1900 and had skyrocketed to more than 47% by 2000 (Stanley D. Brunn et al, 2003). In developed countries, urbanization was the consequence of industrialization and economic development, but in developing countries urbanization occurred partly as a result of rising and unrealistic expectations of rural population who have flocked the cities to often escape misery (and not finding it) which led to the explosion of urban areas. Cities were thus summarized as places of opportunities. A place does not become urban until its workforce is divorced from the soil where trade, manufacturing and service provision dominates the economies of urban places (Ibid). Urban form and urban functions work together to create urban landscapes or the built environment of the city. The pressure of the real estate lobbies and increased demand for new commercial spaces have spurred on unprecedented problems which directly or indirectly affected both the physical and socio-cultural environs of the society The cities of the 21st century are spreading out and sucking food, energy water and resources from the environment without taking into due account the social, economic, and environmental consequence generated at a global level. Thus, urban landscapes are the visible manifestations of the thoughts, deeds, and actions of human beings (Ibid). Urbanism is not simply created by the forces and relations of production- it both expresses and fashions social relations and the organization of production. The 2002 UN world summit on Sustainable Development held in Johannesburg, South Africa, focused worldwide attention on the linkages between poverty and degradation of the world‟s cities i.e. protecting the environment.

The advent of industrialization made human beings strive for development and technological advancement. Thus, there came in a transition of economy from rural to urban. People were so frantically after development; it created havoc in the levels of living. A rural community was felt to be organic and rooted in the soil; in contrast to urban life with its anonymity and sense of constant change (Massey et al 1984). It created a divide in the so called urban life or sects in urban environment. The levels of 15 living affected the health and thus, people questioned the cause of such misery which ultimately answered back to development being a major issue. It is gaining momentum with the unplanned urban growth. Cities are outcomes of monumental progress and the evidence of the mighty destruction of humanity.

Asia and Africa, are urbanizing faster than the other world regions and are respectively projected to become 56% and 64% urban by 2050 (United Nations Department of Economic and Social Affairs, 2014). The fastest growing urban agglomerations are medium-sized cities and cities with less than 1 million inhabitants (Ibid). India has been considered to be a major contributor to this urban explosion, chiefly due to its large demographic weight and corresponding dynamics of urbanization. Several stylized facts – such as India‟s share of the projected world urban population increasing from the present 10 per cent to 14 per cent in 2050, and the increase in the number of 10-million-plus cities from zero in 1950 to three by the turn of the century – have been cited as evidence of “unprecedented urban growth” in India (Dasgupta.S 2005). Urban growth in India accelerated, with the advent of Europeans and other foreigners, the port cities became the flocks of urban hubs. Colonization and imperial rule in India transformed its economy and paved the way for getting urbanized especially in its interior parts with the advent of roads and railways. Urban growth in India has flourished mostly after the national independence in 1947 but the growth remained substantially confined to the major cities only. Nevertheless, we can say that urbanization is a powerful and spatio-temporal restructuring process (Ulrike, 2012). Cities are home to humanity‟s great expectations (UNCHS 2008). Local and regional impacts are visible through inefficient water services, deteriorating water quality, ground water depletion, land and air pollution.

Unlike rest of states in India, Kerala has a minimal rural-urban divide due to intense rural-urban mix and the coherence of agricultural and non-agricultural activities in conformity to its unique landscape (Eapen, 1997). Kerala urbanization shows a unique pattern with the increase in urban centers mainly as a result of urban sprawl from the main centers. The earliest urban centers were mainly port based in Kerala. Some of the placed villages begun to grow as town centre‟s with the advent of Portuguese and British. Thus, small towns began to spring up and many linkages to these villages were also made. Consequently, it paved for the way for the development of transport and communication system in the area. Ernakulam is the most urbanized district in Kerala in 16 terms of absolute number of urban population (2232564 populations as per 2011 census) (C.D.S, 2005) {See Appendix I}. Kochi Corporation acts as a nucleus of urbanization in the district from where the urban character spreads to the nearby areas (Ibid). Large scale augment in the implementation of various development projects (construction works, roads and railways, establishment of Vallarpadam container shipment, the growth of information technology) enhanced the urbanization of Cochin City.

PHYSICAL ENVIRONMENT

Environment is everything outside a given person which characterizes a community or a defined geographical area (Lester.W.Milbrath, 1978). Quality of the environment is the total of happy environmental impacts balanced off against the total of unhappy environmental impacts experienced by a given individual; this ratio should only include environmental elements important to that person (UNESCO 1978). Quality of life and quality of environment are not identical to each other, but they are connected to each other to some extent. Environmental quality of life is an important factor being amended by societal or governmental programmes, improving quality of life the highly probable that they will be targeting on environmental factors. High environmental quality of life does not lead to happiness in quality of life but it environmental improvements will be perceived as betterment in Quality of life experienced.

As per Milbrath and UNESCO 1978 , the indicators of physical environment can be distinguished into four categories; land, air, water and vegetation. „Status‟ is a Latin word meaning „state or condition of others‟. Thus, the „physical environment status‟ means the condition of physical environment of a particular area. It has a direct impact on environmental quality as well. Environmental quality is the resultant of the quality of composing parts of a given region but yet more than the sum of parts, it is the perception of a location as a whole (RMB 1996 cited by Irene van Kamp b, 2003). The composing parts (nature, open space, infrastructure, built environment, physical environment amenities and natural resources) each have their own characteristics and partial quality (Ibid). The environment includes physical elements such as land, water, air and vegetation which are collectively known as physical environment.

URBANIZATION AND PHYSICAL ENVIRONMENT STATUS

Cities are urban landscapes - spatially extended patch mosaics (Ulrike 2012). The effect of urbanization was not understood only until a century ago and after that, ecology or the physical environment became the main concern for people. (Cockshaw 1996). Degraded physical environmental status can result from and cause unsustainable development patterns. It can have substantial economic and social consequences, from health costs to reduced agricultural output, impaired ecosystem functions and a generally lower quality of life (OECD, Green Growth Indicators 2014 2014). Environmental conditions affect the quality of life of the people in various ways. It affects human health through air and water and soil pollution. Exposure to hazardous substances and noise also make things further worse. Moreover, the indirect effect of environmental conditions including climate change, transformations in the water cycle, biodiversity loss and natural disaster influences the health of ecosystem and damages the property and life of the people (OECD 2014). Environmental outcomes are important determinants of health status and well-being. People are also get benefited from environmental services, such as access to clean water and nature and their choices are influenced by the available environmental amenities in an area (Ibid). These indicators can usefully be complemented by subjective measures of people‟s perception about the quality of the environment they live in.

LITERATURE REVIEW

The purpose of this chapter is to provide a critical insight into the assessment of concept of urbanization, physical environmental status (perceptions and conditions) of the residing population. A close examination of several studies on urbanization and physical environment status has been discussed in various articles. Some of them are:

Central archives collections include:

Administration report of advancement of backward communities (1945-55), (1955-56) details on the backward community development programs and their financial incurrence‟s in Ernakulam district by Britishers. “ (Administration report of industry and commerce in Cochin State (1945-46) ) explains the different industrial training schools started in Cochin State, their location. (Ernakulam Distirct Gazetter 1965) explains the administrative boundaries and about different Taluks, their population and social- 18 economic conditions. (“Part-I VI papers relating to the preliminary items of work and the settlement proclamation by the Ruler of Cochin State n.d.) Mainly comprises of the historic documents under the feudal rule, how the land survey records have been written and about land tax revenue documents. “ (Memoir survey of Travancore and Cochin 1816-1820 1891)” explains the location settings (latitude and longitude) of different places in and around Cochin State. “ (Administration report of Village court departments 1955-56 1961)” details on the court names and their extent, number of cases in the particular jurisdiction. “ (Administration report of Panchayat department Cochin State 1944-45 1951)” gives insight into the members, construction works done by the panchayat. “

Another paper on (Problems of Urbanization in Developing countries, A case study in India 2012) by (Kadi.A.S 2012)et al presents the serious concern affecting the urban population such as degradation of the environment, increases in slum population, acute shortage of space, lack of sewage treatment facilities etc. The paper explains the interventions that can be made to overcome these problems. Paper done by (Michael 2000) discusses the historical perspective of cities, urban increase and its effect on urban environment; physio-chemical hazards are discussed in detail. The paper suggests implementing policies to cope up with the current urban context. One of the studies by (L Bull Kamanga 2003) established links between the disasters and urban development‟s in Africa and the lack of attention by urban governance. The disaster is termed rural even though two fifth of the population live in urban areas. It emphasizes the need for understanding small disasters so that large disasters can be prevented and also stresses the importance of integrating such an understanding into poverty reduction strategies. One of the studies in City Inequality (Diana Miltin 1996) addresses the inequality existing in the urban space – and defines concepts like poverty, diseases of poverty, how to map inequality and the role of government. It also discusses the patterns and perspectives of Urbanization in India. The past, present and the future projected figures, state wise urban figures and the need for sustainable urban growth is emphasized. One of chapters in the book (Cities of the Future 2011) by Stanlet.D.Burnn et al. explains the concepts like new urbanism, brown field development and Counter urbanization with examples in the world. Chapter 9 explains the evaluation and forms in South Asia and the causes it led to the expansion. One of the books (Readings in Urban Theory 2011)by Campbell details on the migrations and types in an urban setting – the world migration 19 patterns and their causes and how it has affected the urban expansion and the allied activities of an urban environs.Paper by (Kamdar 2010) is an empirical examination of the economic and non–economic dimensions of life in Mumbai. It has also discussed the human development areas of education, health, pollution levels, housing and access to publicly provided services. The paper concludes with an attempt to measure human development and has presented the human development wards of Mumbai. A study by (Shafi 1999)looks at the metropolitan cities of India and also it advocates reworking the equations and indices which should determine the living standards and quality of life in an Indian metropolis. One of the studies by (Abaleron 1995) considers how to identify the settlements and the groups in greatest need within the city and how to develop a set of social indicators for unsatisfied basic needs. Another paper by (M Maksudur Rahman 2010) explores local environmental problems at both the household and neighborhood levels in Chittagong, based on a broad spectrum household survey. (Applied Urban Ecology,A Global Framework 2012) Book is a collection of articles edited by Mathhias and Ulrike details about how environmental conditions can be assessed using satellite imageries, GIS and RS and how urban ecology is evolving. (Encyclopedia of Urban Studies (two volume set) 2010)Book edited by Ray Hutchinson explains all the terms (new and old) used in urban studies till now.

The book by (Bala 1986) by “Trends in urbanization in India” explains pattern of urbanization from the year 1901-1981.It also describes in detail urbanization regions ,some hypothesis on urban growth, spatial pattern of towns, slow urban growth and areas of rapid urban growth are identified. Degree of urbanization in 1971 and trends in urbanization during 1901-1971 are the major criteria selected for classifying urban regions after analysis, the author concluded that the coastal India was more urbanized than the interior and the south India is more urbanized than north. (Urban Geography 3rd edition 2006) by Tim Hall details on urbanization- meaning, changes in economies, urban theories, and issues of new urbanism and policy approaches and planning. Another book (Geography and Environmental Issues and Challenges 1997)explains environmental perception and preferences of any environment. Dorren Massey in her book (“Geography Matters A reader” 1994)is a collection of articles explaining the changing nature of geographies, social change and the global to local level interpretations. “Social Structure and Change” book by (edited) by (Shah 1997) and B S Bhaskar emphasis on articles on theme of Human Ecology. Out of which one paper on 20

Ecology and Development in Arunachal Pradesh particularly defines the effect of rapid development of a region on its delicate balanced ecology. The major part of the book on (GIS Spatial Analysis and modeling 2005)explores the role of GIS in socio-economic applications. In environmental applications, details about modeling of land use, environmental modeling and agent based models in different forms of satellite imageries are described.

(T.T.Sreekumar 1993), discusses an account of urbanization over the period 1900-1981. The main findings of the study are that the rapid development of services sector accounted for the unprecedented agglomeration on the third world population. The modern Kerala urbanization mainly started with a well developed transport system. Another book by (K. C. Sivaramakrishnan 2005)on (Handbook of urbanization in India ) is detailed report organized in 7 chapters. Definitional issues, for census towns and statutory towns, the process of declassification urban places and the creation of statutory towns and problem of comparability of census data. It also explores the trend of urbanization and migration and their macro level relationship with employment. It explains the urbanization and its related issues at state level in four districts (Punjab, Maharashtra, Bihar and Rajasthan) in detail. In (Cities of the Future 2011)by Stanley D Brown, new and emerging facets of urban development in Human Geography are discussed in detail. It also gives critical insights into social as well as economic emerging concepts such as Brownfield development and gated communities.

One of the chapters in (Cities and Development 2009) on “Urbanization and development in historical perspective”, book edited by Jo Bell and Sean Fox details how urbanization became a tool for development and its stages as referred from different parts of the globe. In book (Urban Problems and Policy perspectives 1981)explains the impact on urbanization on natural and manmade resources. Jeg brumann (2009) in his book (Welcome to the urban revolution)” explains in Chapter 10th the transformation of eco- systems and adaptation in plant and human behaviour. Recent works on micro climatic urban environments and it also explores the two main paradigms such as environmentalism and possiblistic view on Urbanization. In World Watch Paper by (Brown and Jacobson 1776 ) explains the ecological and economic constraints of urbanization on different economies of the world. It also brings out the works done by various individuals in establishing the above linkage. In book (Splintering Urbanism 21

2011) gives a critical insight into the technological connected urban spaces – how an urban space is evolving globally and how the local characteristics are lost. Another interesting book ( Decision centered view of Environment Planning 1987) by Andreas Faludi mainly deals with an understanding the planning approaches, on re constructing planning theories, pragmatic view of planning and modern trends of planning. ( A Companion to Environmental Geography 2009) systematically explaining human- environment relations, new research frontiers and international works by many renowned scholars. An article on “ (The New Geography of Contemporary Urbanization and the Enviornment 2010) by “Karen et al (2010)” explains how contemporary urbanization, history of urban growth in different scales, relationship with environment, and public policies. An article on “Application of Remote Sensing and GIS technique for Urban Environmental Management and Sustainable Development of Delhi, India” by “ (Rahman 2008)” details on the applications on Remote Sensing and GIS for urban environment and the need of sustainable urban planning of Delhi. An article “Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing” by “ (Elizabeth A Wentz 2014) et al ” explains the use of GIS and Rs for understanding Global Environment change. Spectral properties of satellite images are used to manipulate and create resultant images for understanding the socio- economic changes of an area. An article by (Pacione 2003) on “Urban environmental quality and human well being- a social geographical perspective” explains the social geographical approach to research and on urban environmental quality of life. A paper on “Quality of life or Urban poor in Shillong” by (Das 1998) explains with the case study on Shillong how urban social planning is essential to measure quality of life of any area addressed. An article by (Phadke 2014) on “Mumbai Metropolis region: Impact of Recent Urban Change on the Peri-Urban Areas of Mumbai” details on how urbanization not only leads to development of a core nucleus city but how it affects it surroundings and its social and economic consequences. A paper by (Mathew 2015) on “Port Building and Urban Modernity” discusses the emergence of Cochin harbor and how it recreated urban space. An article on “Traffic Potentials and prospects of an elevated road corridor between Aroor and Edapally in Kochi”by (T.Elangovan 2010) et al (1996) explains the need for transport planning and the traffic congestion causes of Cochin outer areas.

A selection of leading articles on urbanization and its related consequences has been compiled by (Chaplin 1999) on “Cities, Sewers and poverty; India’s politics of 22 sanitation” which addresses the political circumstances that explain why the insanitary living conditions of such a large section of India‟s population have been ignored. In India, there is little middle class pressure for sanitary reforms. The paper ends by reflecting on what factors might change this. The book (Green Households: Domestic Consumers, the environnment and Sustainability 2014)by “Klaas Jaan Norman and Ton School Uiterkamp” explains how sustainable development the concept came into being, with physical environment playing as a constraint and how is it linked with environmental quality. An interesting article by (Jamtsho 2010) on “Urbanization and water, sanitation and hygiene in Bhutan” the urbanization and its effects on water and sanitation facilities of Bhutan, and the policies adopted by the government to overcome these problems.

Additional studies on Physical environmental quality were also refereed and they are:

A paper by Giuilettea Fadda and Paolo Jiron on (Quality of life and gender, a methodology for urban research 1999) discusses how to undertake research on quality of life from a gender perspective. It also explains the difference in the meaning of concepts such as quality of life and environmental quality. The paper also details the methodology adopted for the study of low income group in Santiago. A research paper by (Marans 2003)on “Understanding environmental quality through quality of life studies; the 2001 DAS and its use of subjective and objective indicators”, aimed at measuring the quality of community life using subjective and objective indicators in Detroit metropolitan area. The study is useful in understanding the methodology adopted for the quality of life or such related studies. A research paper by Ivan (The role and Status of Geography in the Quality of Life Research 2010) Andrasko, The Role and status of Geography in the quality of life research” focuses on the concept of quality of life and its interdisciplinary nature. It also discusses the role played by geography in past and present in undertaking the quality of life studies and why such a view point has gained importance. It also examines the problems related with such studies such as aspect of geographical scale and usefulness of information obtained via the geographical quality of life research. Another paper by (Rogerson 1999)on “Quality of Life and City Competitiveness” explains how quality of life is considered as a part of competitiveness of any city- how factors such as migration, economic production and capital ahs an important role in determining quality of life of any city. 23

A paper on “ (Landscape and Landschaft 2004)by Cosgrove explains how landscape as a research topic evolved through Geo-politics, European romanticism and of new spatial turns. Another interesting paper on “Morphology of Landscape” by (Saucer 1925)discusses how landscapes are culturally produced and modified. Landscape is interdependent on many factors. An article by “ (Antrop 2004)” on “Landscape change and the urbanization process in Europe” mainly studies the history and spatial process of urbanization in Europe, and how changing landscapes should be understood as an multi disciplinary framework.

Another book by Peter E Lloyd and Peter Dicken on ( Location in Space, a theoretical approach to economic geography 1973) to economic geography explains the spatial order and analysis of location patterns in different environments such as urban and rural. Area development policies of different countries are discussed in detail. An interesting book by Richard Peet and Nigel Thrift in (New Models in Geography-Vol-I 2001) VOL.1 and 2 explores the concept of political economy and the different paradigms in geography. It mainly emphasizes the role of geography as an interdisciplinary subject, which has a wider applicability while conjoining with other social science subjects.

Another paper by (Rene P Schwarzenbach 2010) on “Global Water Pollution and Human Health” reviews the water quality issues faced by humanity and addresses current scientific advances to cope up with the great diversity of pollutant. Water quality issues such as aquatic contaminants, chemical pollution of water resources, aspects of water borne diseases and need for improved sanitation is also explained in detail. (Craig A Milroy 2001) In his paper describes the application of statistical technique such as Principal Components and Cluster Analysis to quantify sanitary conditions and to identify groups of area with similar environmental quality. This type of analysis helps to identify the need based assessment of each community and also some baseline conditions that later allow the impacts of interventions needs to be assessed.

One section of the book by (Sudhakar M. Rao 2008) et al in the “Advances in Water Quality and Management” gives a detailed account on water and sanitation in human settlements and associated pollutants on water and land resources. It also explores associated health problems. A report by (CWRDM 2001) on “Interim report-Impact of Urbanization on Stream Flow and Ground water Recharge in the city” discusses on 24 impact of urbanization on hydrological models and mathematical models suited for same hydrological landscapes. A M. Phil Thesis of Jose 2003 on (Urbanization processes and changes in landuse pattern: A cast study in Maradu Panchayat of kochi Urban Agglomeration 2003) explains urbanization and its implications of land use pattern with the help of secondary data from census report and Land survey department. A Report published by (Beltran 2004) on Global International Water Assessment (GIWA) on Caribbean islands describes the influence of human actions such as dumping of wastewater and improper sanitation treatment on coastal waters and environment. Another interesting study published by (FAO 2001) Report and Studies (elucidate, with ample case studies, on Land-based Activities affecting the quality and use of marine, coastal and associated freshwater environment. One of chapter in the book on “Human Geography- Society, Space and Social Science” by (Derek Gregory 1994) explains about Landscape school in Geography came into being and how it is related to cultural geography.

The following articles and books were referred and apt methodologies for GIS and Remote Sensing studies on Physical Environment were adopted from the following. Articles on assessing Air quality

Research paper on “Building density and its impact on aerosol diffusing” by (Yang 2004) employs remote sensing technique to correlate and to identify the spatial patterns in aerosol diffusion. A strong correlation between building density and aerosol status are identified in Guangzhou, China. The two main terms aerosol diffusion and building density are explained with the help of appropriate equations. For defining aerosol status, spatial distributions of grades of aerosols are interpreted. The ETM data is divided into two groups. One group is atmospherically corrected using Dark Substraction method and its ratio is calculated between band 1 and band 4. Another group ratio is between band 1 and band 4, is taken without atmospheric correction. The diverge between two groups are found as ∆L. For calculating aerosol index, the resultant

∆L is divided by ∆Lat (which is clean air). Urban area is classified in land cover classes using principal component analysis. The ETM data is atmospherically corrected and Principal component analysis is applied. First principle component represents light and the other represents vegetation and water. By subtracting the first and second principal components, the region covered by buildings is analyzed. Building volumetric ratio (BVR) is total built up area per spatial unit. In remote sensing data, BVR is calculated 25 using equations combining height of the building, length of the top side of the building, number of the building unit and area occupied by buildings divided by the pixel area. After applying remote sensing methods its is inferred that the building resists aerosol diffusing , so by limiting build density the effects of air pollution can be minimized to some extent.

Another paper by (Kyriacos Themistocleous December 2013) on “Development of an image based integrated method for determining and mapping aerosol optical thickness over urban areas using the darkest pixel atmospheric correction method, RT equation and GIS; A case study of the Limassol area in Cyprus” presents the development of an image based integrated method for determining and mapping aerosol optical thickness. Eleven Landsat images with low cloud cover (four Landsat-5 TM and Landsat-7 ETM+) from 2010-11 were selected and Bands 1-4 were retrieved using RT equations. Band 1 was used to derive AOT levels through algorithm. AOT values were taken using Microtops and Climel Photometer and also from AERONET station which was used to compare the satellite AOT values. For attaining precision in atmospheric correction, darkest pixel method was used. Spectroradiometric measurements were used to measure the reflectance from large asphalt surface (non variant target surface with same reflectance value throughout the year). The in situ reflectance values were assessed with atmospheric corrected reflectance value, so as to improve the accuracy of the AOT derived algorithm. The image based integrated method uses an image based technique using RT equations to retrieve AOT values. For calculating AOT of every pixel it was necessary to re calculate or simply the exponents or the power functions within the equation. The equations for path radiance, Mie scattering and Rayleigh scattering were combined into one equation using Mc Laurian Taylor series (consists of rewriting a function as a sum of infinite sum of other functions that are calculated from the values of the functions derivatives at a single point, with a centre within the possible AOT values).The equation was solved for positive and negative signs to obtain the Ta (Aerosol single scattering Albedo) between 0 and 4. Negative values were not used, as negative AOT values do not exists. The equation was inserted and processed through Matlab program and Erdas imagine. This model was applied to band 1 of Landsat image and the AOT value for each pixel was calculated. All the AOT values were exported to ASCII files to create an AOT datasets which were then imported into ARC GIS software. Thematic maps illustrating GIS software were mapped using Kriging interpolation tool. 26

The resultant map showed variations from high to low AOT values using different colours. This map was overlaid with GIS vector data from the land and survey department, which makes it easier to identify sources of AOT values within the city. The in situ and the satellite derived AOT values were found statistically significant. Therefore it can be considered Image based integrated method is an effective alternative to calculate AOT values from satellite images, especially were meteorological or atmospheric input data are not available.

Research paper by (Hadjimitsis, Aerosol optical thickness (AOT) retrieval over land using (2009) ) on “Aerosol optical thickness retrieval over land using satellite image based algorithm” monitors the aerosol concentration over urban areas (near Heathrow airport and Paphos airpor (Kyriacos Themistocleous December 2013) in Cyprus). For analysis, a defined subset of study area is taken into consideration. The darkest pixel atmospheric correction method is used. The dark pixels in the image is taken as pseudo invariant targets, represents the atmospheric path radiance in the region. The atmospheric path radiance is a function of total optical thickness which is the term used to define the prevailing atmospheric conditions. In Landsat TM; band 1 is used to minimize the effect of water vapour absorption. Rayleigh scattering is considered to be constant and ozone has a variable but minor effect. If these conditions are met, then any significant variations of path radiance between the images will be due to changes in the distribution and changes in the concentration of aerosols. Input parameters are ozone transmittance

(t03=1), water vapour transmittance (th2o=1) and aerosol single scattering Albedo =1 (perfectly scattering aerosol) and surface reflectance of the dark target as standard value if derived from spectral library or consider it as zero. Iterative tool is calculated and a maximum number of iterations refine the atmospheric corrections until the image reaches maximum contrast value. The atmospheric path radiance is determined and contour map is drawn on its basis. Radiative transfer calculations are run to determine the aerosol optical thickness. Insitu aerosol value is calculated using sun photometer and satellite calculated aerosols are found having close agreement.

Another interesting paper by (M. N. Sai Suman 2014) on “Role of coarse and fine mode of aerosols in MODIS AOD Retrieval; a case study over southern India” compare the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth with data obtained from sky radiometer at a remote location in southern India. The total AOD from MODIS shows a correlation of about 0.71 for Terra and 0.77 for Aqua. 27

The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R value of 0.74 for Terra and 0.66 for Aqua. It is shown that both the total AOD and fine mode AOD are significantly underestimated with slope of regression line 0.75 and 0.35 respectively, whereas the coarse mode AOD is overestimated with a slope value of 1.28 for Terra. Similar results are found for Aqua where the slope of the regression line for total AOD and fine mode AOD are 0.72 and 0.27 whereas 0.95 for coarse mode. The fine mode fraction derived from MODIS data is less the none-half of that derived from the sky-radiometer data. Based on these observations and comparison of single scattering Albedo observed using sky-radiometer with that of MODIS aerosol models, we argue that the selection of aerosol types used in the MODIS retrieval algorithm may not be appropriate particularly in the case of southern India.

Paper by (Anup.K.Prasad 2004) on “Variability of Aerosol Optical Depth over Indian sub continent using MODIS data” investigates AOD distribution in spatio- temporal domain over Indian sub continent since 2000. Aerosol optical depth have been obtained using Level-3 MODIS gridded atmosphere monthly global product „MODIS 08_M3‟ (Modis/Terra Aerosol Cloud Watervapour Ozone monthly L3 Global 10). Monthly average MOD08_M3 product files are available in Hierarchical data format at spatial resolution of 10 by 10. The paper presents gives an insight into the growing spatial distribution of aerosols over Indian sub continent with Ganga plain showing a high AOD content summer 2000-Summer 2004.

Paper by (Jahwee Lee 2010) “Algorithm for retrieval of aerosol optical properties over the ocean from the geostationary ocean colour imagery” explains an aerosol retrieval algorithm for Geostationary Ocean Colour Image (GOCI). The algorithm retrieves aerosol optical depth (AOD), Fine –mode fraction (FMF) and aerosol type in 500X500m resolution. All other products except AOD are derived from clear water where as aerosol properties are obtained from turbid waters due to its high surface reflectance. Optical properties of aerosol are analyzed from AERONET stations to generate a look up table. For assessing AOD from turbid waters, SSR threshold method by using a 30 day composite data is used for getting surface reflectance (second minimum reflectance value is used) from the atmosphere corrected images. In the case of clear waters, using MODIS TOA surface reflectance is computed using Fresnel reflection and average wind speed. AOD and FMF are thus defined and types are retrieved from LUT. The aerosol retrieval algorithm of GOCI is assessed and it 28 application capability compared using MODIS. Thus the retrieved AOD from GOCI is compared with MODIS data and AERONET sun photometer shows reliable results.

Research article on “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions” by (Zhihua Mao 2013) explains a new approach to accurately estimate epsilon spectrum (aerosol scattering ratios) for coastal turbid waters. The epsilon value is determined using darkest pixel approach in two NIR bands and then epsilon spectrum is defined from aerosol models. Using insitu measurements of water reflectance and AOT distribution was used as look up tables, epsilon values can be assessed. For finding epsilon spectrum, AOT data from similar coastal areas are collected and four types of aerosol properties are derived. Standard deviation and mean values are calculated. AOT values are then fitted using Angstrom law and then exponential equation. The derived AOT values provide excellent data source for spectral variations. The non-linear fit is applied to each spectrum of AOT and the mean relative error is calculated. For finding the epsilon value from satellite data Rayleigh scattering, sun glint and whitecaps are computed using different equations. Aerosol water reflectance is measured using satellite measured reflectance. Look up table of water leaving radiance is established from in situ measurements. Candidate aerosol scattering difference can be obtained from subtracting aerosol water reflectance from all water leaving reflectance in Look up tables. The two models Obtained AOT and satellite derived AOT are assessed for finding epsilon spectrum. Matching of epsilon spectrum will give more stable estimate for epsilon values.

Paper by (D.Diouf 2013) on “ Retrieving aerosol characteristics and sea- surface chlorophyll from satellite ocean colour multispectral sensors using a neural variational method” developed a two step algorithm and to monitor the concentration of Saharan dust and sea surface chlorophyll from satellite ocean colour multi spectral observations. In order to retrieve the atmospheric parameters from sea wifs, two statistical models were applied- Self Organizing Map (SOM) and NeuroVaria method. In Self Organizing Map, that data from SeaWifs DATAobs was classified according to their similarity and parameter associated with each vector of a class to that of the parameter associated with that class. From DATAobs a homogenous sample was extracted Learnobs. SOM model was calibrated on Learnobs, aimed at summarizing and producing a 600 referent vectors (rv). These were determined through an iterative learning process by minimizing a 29 quadratic cost function representing the distance between the referent vector and analyzed vector. The data vectors were then projected on SOM-A-S (A-S Angle spectrum), using an algorithm-best fitting neutron with respect to geometry constraint and spectral constraint. The first coinciding values were the value chosen for SOM-A-S neurons. Thus different groups were labeled and the represented in SOM model, showing aerosol properties and chlorophyll concentration. Second statistical model NeuroVaria was applied to provide accurate atmospheric correction for the ocean colour measurements. The algorithm minimizes the weighted quadratic cost function. The difference between the satellite measurements and stimulated measurements were modeled by supervised neutral networks (Multi Layer Preceptors). The MLP were synthesized with DATA expert base (look up table for modeling the atmospheric reflectance from SeaWifs image) for extracting the aerosol type of the given area. Thus this method is useful in assessing the seasonal variability of aerosol and chlorophyll characteristics over an ocean surface.

ARTICLES ON ASSESSING WATER QUALITY

An article by Felipe et al on (Time-series analysis of Landsat-MSS/TM/OLI images over Amazonianwaters impacted by gold mining activitie 2014) explains how Landsat images are used to identify the impact of gold mining on Amazon River. Research article by (M.Rodny March 2012)on “Can suspended sediment concentrations be estimated from multispectral Imagery using only Image-derived Information?” explains a simplistic approach by which the suspended sediments can be retrieved from multispectral satellite image using only image derived information. In remote sensing, Digital numbers (DN) have to be converted into radiance values (L), using calibration constants specific for every satellite sensor. The radiance thus received is the total of Mie and Rayleigh scattering and the target leaving radiance (radiance produced by water and water leaving radiance). The ratio between upwelling and down leaving radiance for retrieving the suspended sediment concentrations under varying illumination conditions (Sun azimuth, sensor viewing angle etc.)This ratio is called remote sensing reflectance. With increasing concentrations of suspended sediments the reflected signal reaching a remote sensor increases up to a point at which it exhibits stagnation, or asymptotic saturation. The relationship between SSC and RS in the visible domain of wavelength are defined: - 1) Reflectance increases with suspended sediments 2) The second derivative or slope becomes shallower with increasing SSC 3) Reflectance has to be 30 always positive. Simplified model by Schiebe has been adopted to establish relationship between SSC and reflectance. The saturation effect is therefore wave-length dependent. Reflectance recorded in the infra red domain remains almost linear for a broad range of SSCs. Therefore, the near infrared region of wavelengths is most suitable for examining suspended solid. Thus SSC for near infra red can be approximated with a linear function. But it cannot be solved due unknown parameters. SSC of visible domain and infra red were plotted in scatter diagram to obtain co ordinate one denoted as DNvisual. In order to rescale SSC of visible domain and infra red are combined to produce at sensor radiance recorded in the visual domain. But the unknown parameters are assigned with the help of Non Linear optimization methods such as Quasi Newton technique to obtain the optimal model parameters by minimizing the pre defined “objective function”. Objective function was calculated as a weighted error to be eliminated the possible influence of large scatter. Optimizing can be done in iterative runs, minimizing weighted error in each run. The initial values can be selected arbitrarily, but to ensure that the iterations will propagate into a desired direction and physically plausible values, some “educated guess” is needed. Spatial patterns of suspended sediments can be found, but assumption is essential in order to properly optimize the exponential relationship between SSC and remote signal. An article by (Sayantan Das 2013) et al on “Assessment of Impervious surface growth in the Mula-mutha watershed in Maharashtra” is assessment of water shed area nearby Pune city. Remote Sensing and GIS are used to identify the amount of increasing impervious layer which is seen on part with the built up of the area.

Another paper by (Raaj R.M Ramalingam March 2008) et al on “Mapping of suspended sediments using site specific seasonal algorithms” explains the preliminary work on two site specific seasonal algorithm for estimating concentration of SSC. In situ measurements were taken and analysed gravimetrically. The OCM abroad IRS-P4 makes available the opportunity to estimate the concentration of SSC at sea surface and atmospheric aerosol in the marine environment. The three processes in identifying suspended sediments in near shore water are 1) Atmospheric correction of visible channels to obtain water leaving radiance 2) Geometric correction of satellite image to obtain positional accuracy 3) Retrieval of suspended sediments. Atmospheric correction is done through darkest pixel substraction approach. While choosing dark pixel preference was given to offshore ocean pixel unaffected by shade, clouds and land. Thus the dark pixel was subtracted from the water leaving radiance to get total measured 31 radiance. The value thus gained eliminate nearly all haze, cloud and land pixels while preserving turbid and clear water pixels from suspended sediment concentration analysis. Geo correction of each image was performed using the pre defined ground control points and resample using cubic convolution interpolation technique to keep spatial distortions to minimum. The in situ measurements and water leaving radiance were plotted in regression analysis. Thus satisfactory comparison is seen between sea and lagoon environment. The proposed algorithms are still in preliminary stages of development and further extension is currently in progress.

Journal by (Brian B barnes 2014) on “Use of Landsat data to track historical water quality changes in Florida Keys Marine environments” demonstrate an approach to identify historical water quality events through improved atmospheric correction approach using Landsat and cross validation with concurrent MODIS data. After aggregation, Landsat 5 TM 30-m to 240-m pixels (to increase signal to noise ratio using binning and neighborhood averaging) was changed to match MODIS data. Atmospheric correction was done in Landsat SWIR bands with the help of MODTRAN, was developed and applied to entire Landsat 5 images. The TM Rrs were then reprojected to geographic latitude/longitude cylindrical projection were each pixel has 1 km resolution using bilinear interpolation to match MODIS ocean colour bands. MODIS data was atmospherically corrected using SeaDaS to obtain Rrs for each pixel. Pixels determined to be cloudy were removed using CLDICE algorithm for further analysis. The resultant data was also re-projected to geographic latitude/longitude cylindrical projection to match Landsat Rrs data. Cross sensor agreement between the 2 sensor in linear regression and over shows accuracy over 90% for all three bands for the same period of 2002-2010. Remote sensing anomalies were applied to in 1980 and 1990 which are corroborated by known eco-system changes.

Research paper on “Turbidity retrieval and monitoring of Danube delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western north-western Black sea coastal zone” by (Fabio.N.Guttler 2013) et al studied the turbidity patterns in delta plain and a coastal zone. Two distinct but complementary, methodologies for retrieving turbidity was employed. For assessing the turbidity along the Danude coastal zone, the following equation was used:

Turbidity= 0.54 (non_algal SPM+ 0.234 Chl0.57) 32 were non_algal SPM is data available (insitu/satellite reflectance value in the red or green part of the spectrum is considered) For assessing the turbidity in Danube delta plain, pseudo invariant target is applied, to minimize the atmospheric effects in the study area. Two areas, Beluciug Lake (minimum radiance) and Caraorman dunes (maximum radiance) were selected and computed with radiance values in obtaining turbidity index for each pixel. Two additional empirical constants were added to the former equation in order to create a turbidity index presenting adjusted percentage value. To avoid single pixel potential errors, the radiance of the invariant targets were computed as an arithmetic mean from a 3 X3 pixel window. After testing the pertinence of each band, red band was chosen for representing turbidity index. A second image product on phyotoplankton index is also calculated with the same equation as the one given above. This procedure uses green, red and infra red bands to separate clear water, turbid and biological turbid water. Only for phyotoplankton dominated areas the former turbidity index is recalculated and the former turbidity index is recalculated using phyotoplankton co efficient. Third product can be created with the combination of the phytoplankton and turbidity index known as fused product. This work presents new results for the characterization of Danube waters and also explores multi sensor turbidity algorithms.

Paper by (Matthew.W.Becker 2006 Vol 44 No.2)Becker on “Potential for satellite remote sensing of Ground water” explains the use of satellite remote sensing for predicting hydrological behavior. It‟s mainly based on T´oth‟s conceptual model of topographically driven ground water flow systems. The remote sensing potential of groundwater is organized into hydraulic potentials and hydrological fluxes. Hydraulic potential is indication of groundwater storage and hydraulic gradient.

Topics like humanistic geography, sense of place, data analysis and participatory resource mapping the following literature was used;

A book “ Aging in Place” by (Callahan 2005) describes how aged people describe the phenomena of growing older, dynamics of reassuring the role of personal and environmental fit etc. An article (Topophillia- A study of enviornmental perception, attitudesand values 1974)by Yi Fu Tuan explains the perception, attitudes and bond between people and place. The humanistic approach forms the central core of the book. An interesting article by (Haydea Izazola 2006) on “Environmental perceptions, Social Class and Demographic changes in Mexico City: A Comparative Approach: “discusses 33 the impact of physical environment deterioration and population responses. The data was analyzed in two group‟s low income and middle income households. Income as well as region highland or lowland had an influence on environment perception. The crucial findings are that individual factor play a decisive role in responses to environmental change.

Online video lecture by (Prof:A.K.Sharma 2012)“” on “Participatory Rural Appraisal” details on participatory rural appraisal (sources, history, methods and analysis of qualitative methods and their importance in research and public policy formation). One of the chapters in “Key Texts in Human Geography” by (Relph, Place and Pacelessness 2008) describes the phenomology of space and place, critique and in depth explanation of places etc. One of the chapters in the book “Modern Geographic Thought” by “Richard Peet” on (Existentailism,Phenomenology and Humanisitc Geography 1998) discusses the history of terms existentialism and Phenomology and how it Place and placelessness and then to Humanistic geography. An interesting chapter “Humanism and People centered Methods” in the book “Approaches to Human Geography “by (Roadway 2006) discusses how humanism and humanistic geography methods are researcher, their critical, participatory methodology, reflections on human society and the critiques on human geographical concept.

An article by (Nicole.M.Ardoin 2006) on “Towards an Interdisciplinary Understanding of a Place: Lessons for Environmental Education” explains place as a holistic concept which comprises of many of the components such as individual, social, political-economic and bio-physical settings. The author also defined Sense of place as measure to understand people‟s environment conscious. An article “From space to place: place- based explorations of text” by (Purves and Curdin 2015) explains how digital repositories and spatial data can contribute in Place based studies, The study is based on place based experience of “mountain in the alps” which generates macro maps , to understand the place and the subjective experience called sense of place. An article by (Stephanie A Clemons 2016) et al on “The importance of Sense of Place and Sense of Self in Residence Hall Room Designs” tries to understand hoe resident halls of a house has been connected with sense of place and sense of self. Sense of place has a direct influence on Sense of self. An interesting article by (Chick 2007) on “The Social Construction of Place” explains meanings of recreation tents of an agricultural fair. Symbolic framework consisting of Old photos guided interviews were used to 34 understand the place meanings. The place meanings were found to be connected with the physical settings of the place. An article by (Wyly 2007), “Sense of Place” explains different context and their explanations by different scholars. An interesting article by Kianicka et al on (Locals' and tourists' sense of place: a case study of a Swiss alpine village 2005) examines the difference between locals and residents on their sense of place of Swiss alpine village. Qualitative interview methodology was used. Locals needed economic development and landscape development to be on par be with each other whereas tourist more keen on landscape development. An article on “Landscapes, absence and the geographies of Love” by (Wylie 2009) explains how people perceive memorial bench at Mullion cove. The study describes phenomenological perspective of love, landscapes and fear. An interesting article “What is Sense of Place” by (Cross 2001) explains the concept sense of place in various field of study and discusses elements that makes up sense of place. The conclusion of this paper is as follows; place and people are inter-connected and their bond determines whether a place is conserved or not. A paper by (Hashem Hashemnezhad 2013) on “Sense of Place and Place Attachment” discusses the concepts such as sense of place and place attachment. And factors that affect them. After explaining various literatures on both concepts the author concludes place attachment is a subset of sense of place. One of chapters, in the book “Spaces of Geographical thought” by (Johnston 2005) et al explains how space and place are inter-related.

Since urban humanistic geographical studies on physical environment status and urbanization in Kerala is scarcely found, this paper would substantiate the need for further studies in this area.

CONCEPTUAL FRAMEWORK “Theory has to be grasped in the place and time out of which it emerges” (EDWARD SAID)The facts don‟t and never speak for themselves, and no one in humanities or social science can escape working with a medium that seeks to make social life intelligible and to challenge the matter of factness of the facts”. Geography acts as an eye for history (Cosgrove 2004). When it comes to the understanding of the cultural meanings and the geopolitical processes that shaped our world, regardless of how modern historians view and use space in the 21st century, history and geography are interrelated fields that support the understanding of societies. (Arias 2009). 35

Urbanization

Urban Landscape Physical Environment Status Ecological Approach

Land, Air People’s Perceptions

Humanistic Geography Remote Sensing Sense of place Participatory Resource Mapping and GIS Water and Vegetation

Source: Prepared by the researcher

Geographers have long investigated the way in which humans have shaped and transformed the landscape and how they situate themselves in nature. More recent decades have witnessed emergence of historical approaches which posit ecological or environmental change as the consequence of cultural choice and human action rather than environmental circumstances.

Urbanization is one of the important demographic trends of all time due to the environment changes that it brings about. Urbanization can be understood through a variety of approaches like Locational Approach, Regional Approach and Spatio- Structural Approach. Among these, locational approach has been widely studied, where site and situation of the place has immense importance. Regional wise approach would be helpful in effective planning where each region is considered as a special entity. Spatio-Strucutral approach is defined as a system of internal relations which is being structured through the operation of its own transformation rules” (Harvey 1996). For this particular work, ecological approach is considered. „Ecology‟ as a term, connotes complex interrelatedness and as such, has proven a popular label for a suite of scholarly approaches, primarily in anthropology and geography, for analyzing society-environment relations (cultural ecology, human ecology, political ecology). An ecological perspective, land development is one of the most disturbing processes since it dramatically alters the natural energy and material cycles of eco-systems. Some of the ecological approaches are - Political Ecology interlinks relationship between social and ecological change 36 without reducing social to ecological (Watts 1983) or ignoring ecological relations influencing resource properties. The main drawback of political ecology is that it ecological relations are not fully incorporated in the broad schema emphasis is given to unequal distribution of resources. Landscape ecology, explains land as a sense of the country or pays, to a natural object, land is understood, as a physical phenomena like soil. It, thus makes landscape an object of scientific interest, as in landscape ecology. This approach also implies that human cultural phenomena can be treated in the same way, thus reducing cultural phenomena to a subset of the natural.

URBAN ECOLOGY

The traditional research core of urban ecology was extended as a part of the historical development of urban land use patterns and its changes and various environment and health consequences. Urban ecology is expected to play an instrumental role in improving existing cities and developing new ones that are more sustainable ecologically, economically, and socially. Urban ecology plays an important role in understanding the urban systems. This approach has been called as”the line of tradition rooted in natural history”. It has an inter-disciplinary research field which investigates the inter relations between environment compartments and human activities. The land transformation modifies the near surface energy budgets by reducing evapotranspiration, crowding, solar energy absorption surfaces, natural vegetation is replaced by impervious surface (Asphalt, roof tops, buildings, concrete and walls) and create heat trapping canyon- like urban morphology. Urban morphology is affects, and affected by socio- economic process and ecological processes.

URBAN LANDSCAPE ECOLOGICAL APPROACH Urban landscape ecological approach emphasizes on the relationship between land cover pattern and ecological processes on multiple scales, as well as holistic and humanistic dimensions of the city, as a spatially extended system. The urban landscape ecological approach conceptualizes cities as urban landscapes - spatially extended mosaic patches. It emphasizes the relationship between landscape pattern and ecological processes and it includes several interactive components such as quantifying urban landscape pattern and its change is essential for the monitoring and assessment of ecological consequences of urbanization through simulation modeling and scenario analysis. Urbanization exhibits a cyclic pattern in time and space by two alternative 37 processes; diffusion that spreads from urban areas to peripheries and gap filling of existing urban areas and in process of diffusion urban areas increase monotonically, increase to a peak then decrease into a uni modal shape.

Environmental changes are also expressed in terms of land use changes. Land cover and the land use change influences the urban climate and atmosphere deposition of pollutants. Urbanization/physical environment quality can be classified and quantified using a number of other pre-classification change detection methods using multi temporal remote sensing data. Pre-classification change deduction techniques are based on manipulation with image spectral bands, creation of composite of multi-data images that can be classified into change or no-change clusters using Simple Band Rationing. This classification does not get to the processes directly although they can be used to help to identify the underlying drivers and link patterns and processes in urban landscapes.

HUMANISITC GEOGRAPHY

Human beings are the re-presenters of the world. Universal human traits are sought. . Man is ecologically dominant and explains the way in which how humans perceive and structure their world. Environment perception and attitude act as a determinant interaction between culture and environment, attempts to extract environment attitude with the help of surveys. Changes in the environment evaluation act as a part of study of the history of ideas of cultural history- to understand the meaning and history of environment. According to Conzen, the historical layers of urban landscapes represent accumulated experience are thus a precious assets and valuable tool for conservation planning.

Humanistic geography has developed a distinct research strategy and a series of people-centered methods (or adaptations of existing methodologies) to explicate a detailed and reflective understanding of the relationship between people and place, geography of the world as home (Tuan, 1977 : Relph, 1976). Humanistic approaches in geography sought to explicate the meaning of individual (and social) experience, and the sense of place (and a world) as it were from within or with the flow of the living wholeness of being, that is our being in the world or dwelling (Heidegger, 1983). It is supplemented by reading texts about the history and recognizing the meaning of a place. Place has been distinguished by various scholars as a global sense of place (Massey, 38

1991), as meaningful locations (Agnew, 1987), how “lived experience” create places (Tuan, 1977 : Relph, 1976). Place is a meaningful location consisting of three key aspects; Location, Locale and Sense of place (Agnew, 1987). Sense of place is related to the way in which we might (or indeed might not) identify with a place and is necessarily based on our associations and experiences, or lack thereof, of a place (Ibid). Nicole has defined the Dimensions of Sense of place through an extensive inter-disciplinary literature review and preliminary field based research four consistent dimensions (of “sense of place”) - the bio-physical environment, the personal/psychological element, the social/cultural element and the political/economic element (Nicole.M.Ardoin, 2006)

STATEMENT OF THE PROBLEM

The overall goal of the study is to develop a better understanding of the relationship of urbanization on changing physical environmental status. The myriad strands of urbanization provide a strong stimulus to change in physical environment status of any environment. The factors‟ affecting such change has to be understood through a humanistic approach. Thus, the research problem is to analyze the changing physical environment status of Maradu municipality. The problem has been addressed to understand the underlying influencing factors on physical environment status and of urbanization. A systematic and scientific approach has been attempted by way of concretizing the research problem. These have been tested empirically to reach some generalization so as to answer research problem. A systematic scheme of research activities like data collection, interpretation, and analysis has been evolved to understand

OBJECTIVES

The specific objectives of the study are:

1. To understand the urban growth of Maradu Municipality in Ernakulam district. 2. To assess the Physical Environment status using Geographical Information System (GIS) and Remote Sensing (RS). 3. To examine the people‟s perception on physical environment status of the residing population of Maradu.

Research Propositions

Following research questions were posed in order to achieve the above objectives:

 What are the main driving forces behind the urban growth of Maradu Municipality?  Is there any difference in the physical environmental status during different years addressed in the study?  What are the elements influencing the changes in Physical environment status of the area?  Is Urbanization the sole element which led to the change in physical environment status

RESEARCH DESIGN

i. Area of Study

Maradu is a region located in the Ernakulam District, Kerala State which is found in south eastern part of India. Maradu Municipality is one of 11 municipalities in Ernakulam District (Census, 2011). Maradu is located between the latitude 9⁰ 54' 40.24" to 9⁰ 57' 17.121" N and 76⁰ 18' 13.147" to 76⁰ 20' 29.166" E longitude. Maradu acts as a corridor between two districts - Ernakulam and Alleppy, which is the main reason for its well connectivity by roads. It is bordered by Kochi Corporation on the North, Thripunithura Municipality and Udayamperoor Grama Panchayat on the East, Kumbalam Grama Panchayat on the West and Kumbalam Grama Panchayat and Kochi Corporation on the South. It has been detailed in chapter 2.

ii. Unit of Study

The study was attempted at three levels- by taking Maradu municipality as a whole (macro level), calculating the physical-environment status of the area (meso level) and considering the perception of the population aged above 60 (micro level). At macro level, the study was aimed to evaluate the urban intensity and at meso level, the physical environment status of the study area was analyzed. This analysis helped to conduct a micro level case study to carve out the areal characteristics of the area by taking into account of the perception of the population aged above 60 in the municipality (micro level). 40

iii. Data Source and Analysis

The research design utilizes both primary and secondary data sources.

Primary data were collected using field study, questionnaire survey. A schedule was prepared (Structured Questionnaire) to conduct the survey so as to understand the perception of the population on physical environmental changes over years. The collected data was analyzed using SPSS. Non parametric tests- Mann Whitney U tests was used for analyzing primary data with the help of SPSS and cross tabulation was done. The resultant data was graphically represented using bar diagram and Comparative diagram using Microsoft Excel and SPSS.

Secondary data was collected from Archival records from Central Archives and are used to understand the urban growth of Maradu from 1970‟s to present. The data provides to have the spatial and temporal background of study area. Panchayat level census data from 1981 to 2011 was collected from the census abstracts of Census of India. Landsat images (MSS, ETM+ AND TM) were downloaded from USGS Glovis. The Urban intensity analysis and Pre-classification methods (Band Rationing) were analyzed using ERADS and GIS software to find out the physical environment status of the study area.

iv. Tools and Techniques

Tools: The interview schedule (Appendix B) collecting information on physical environment changes and socio-economic attributes of ward wise level. Information on resource usage, status and job, and neighborhood knowledge was also sought.

Cartographic technique: Arc GIS 10.1 and Erdas 9.1 has been used to process the satellite images was analyzed using Band rationing method and urban intensity analysis mapping, land use land cover maps. Each components of physical environment was assessed. The resultant maps were classified into low, very low, medium and high as per ach image data. Flow charts have been used to explain the methodology. The secondary data were processed and analyzed using appropriate diagrams in Microsoft Excel and chloropleth maps. For primary data, graphs and diagrams have been used. Flowcharts have been used to explain the methodology of all maps done. 41

Statistical Technique- The primary data were coded on the basis of suitably designed coding structure for individual level information separately and then put for further processing. The data was edited using Non parametric tests and cross tables were used using Statistical Package for Social Science (SPSS 20)

v. Limitations of the study 1. Time Constraint 2. Monetary constraint 3. Concepts like physical environment quality of life have been narrowed into four or more components 4. Cloud free satellite images with needed resolutions

METHODOLOGY

First step involved in identifying the statement of the problem, which was analyzed with the help of pilot survey and local interaction. The data sources, needed for the study can be divided into two i.e. Primary and Secondary Sources. Details from were primary and secondary sources are explained in above sections. For obtaining each objective both primary and secondary data sources have been used. The scenario analysis was made for three different time periods in the first two analysis chapters (Chapter 3 and Chapter 4). For fulfilling the third objective, primary data analysis with SPSS and Participatory Resource Mapping was done to spatially represent the people‟s perception on physical environment change in Maradu. Thus, the study concluded in understanding the different elements that cause physical environment change of a place. The methodology followed for fulfilling each objective has been detailed in the flowchart (Fig no CHAPTER SCHEME The study consists of five chapters. The first chapter deals with literature review and theoretical framework including research questions, data methods, and sample design. The second chapter defines the study area with location aspects with its physical and cultural characteristics. The third chapter is the core of the study which evaluates the urban intensity of the area using multi-temporal Landsat images. It also provides the details of urbanization trend and the growth of built up within a spatial framework. The emphasis is given mainly to the urban phases from past to present. The forth chapter explores the physical environment status using image-oriented techniques in GIS and RS 42 and to identify the areas with change or no change clusters over various urban phases. The fifth chapter investigates the perception of the people about the place and the elements which affects the changing physical environmental status. The sixth which is the final chapter is all about the major findings and conclusions drawn in the study. The study not only throws light on the spatial analysis of urbanization and physical environment status of Maradu, it provides crucial background

Identifying SOP and STUDY AREA

Pilot survey Local interaction

Primary data sources Secondary source 1. Structured questionnaire survey 1. Census data 2. Ground Truth Verification 2. Data from various offices- 3. Satellite Images (LANDSAT SERIES MSS, Panchayat, corporation, Archival TM &ETM) data.

GROWTH OF MARADU PHYSICAL ENVIORNMENT PEOPLE PERCEPTION STATUS DATA ANALYSIS Purposive Sampling: Persons LANDUSE CHANGE Post classification URBAN SPRAWL above 60 years old (202 Simple Band sample) /4157 URBAN INTENSITY Rationing INDEX ERDAS / ARC GIS URBAN SPSS analysis- Mann Whitney U test PROPOTIONAL Participatory Resource Mapping INDEX

Identifying factors and Scenario elements which causes Analysis changes in urbanization and Physical environment status

Conclusions

Source: Prepared by the researcher 43

STUDY AREA

2.1 Introduction 2.2 Location and Administrative set up 2.3 Physical Base 2.3.1 Physiography 2.3.2 Temperature and Climate 2.3.2.1 Seasonal Pattern of Rainfall 2.3.2.2 Seasonal Wind Patterns 2.3.3 Geology 2.3.3.1 Soil-Texture and Pattern 2.3.4 Water Availability 2.3.5 Land Use 2.4 Socio-Economic Base 2.4.1 Demographic Structure 2.4.2 Occupational Structure 2.4.3 Education 2.4.4 Health Care Facilities 2.5 Transportation and Communication 44

STUDY AREA

2.1 Introduction Ernakulam, that comprises 11 municipalities and a municipal corporation, is the most urbanized district in Kerala with 68.07 % of its residents living in urban areas (Census 2011). Of the total population of 32, 79,860 in this coastal district, 22, 32,654 live in urban areas. While the total population of the district has gone up by a mere 5.60 percent since 2001, the urban population has witnessed a spurt of 51.15 percent (Census 2011).

Maradu, one of the 11 municipalities, is located 15kms south of Ernakulam Town, in Kanniyannur Taluk, Vytilla Block. The total area is 12.35sq.kms including parts of coastal area. A panchayat was formed in Maradu on May 18, 1953 and was upgraded to the municipal status in November 2010. Maradu Municipality is divided into 33 wards (PCA 2011). This area has a good network of roads (NH 47, NH 47A and NH 49) and water connectivity. Ernakulam Town and Ernakulam City are the main railway stations. The International Convention Centre and three luxury five star hotels are located in Maradu. Kochi International Airport is the nearest airport and is located at about 40kms from the study area. The projected population estimate of Maradu in 2031 is about 1, 10,635 (CDP 2010)

2.2 Location and Administrative set up Maradu is located between the latitude 9⁰ 54' 40.24" to 9⁰ 57' 17.121" N and 76⁰ 18' 13.147" to 76⁰ 20' 29.166" E longitude. Maradu acts as a corridor between two districts - Ernakulam and Alleppy, which is the main reason for its well connectivity by roads. It is bordered by Kochi Corporation on the North, Thripunithura Municipality and Udayamperoor Grama Panchayat on the East, Kumbalam Grama Panchayat on the West and Kumbalam Grama Panchayat and Kochi Corporation on the South.

The Municipality is divided into 33 wards. These wards can be divided into two types – coastal wards and central wards. Coastal wards are water frontage wards and central wards are land locked wards. There are 20 coastal wards with water frontage. They are Nettoor North, Kudanoor North, Kannadikkad East, Kunnalakad, Thuruthi, J.B School, Kudanoor Junction, Ambedkar Nagar, Ayurveda Hospital, Valantakad, Santhivanam, Nettoor South, Thandaseri, Peringattuparamb, S.U.V.P School, Thekepattupurakal, Ambalakadav, North Colony, Vadakepattupurakkal, and Thattekkad. The 13 central wards are Maradu North, Kattithara, Sankara Nagar, Martinpuram, Sasthrinagar, Kairali 45

Nagar, Society, Pandavath, Neravath, Sub Registrar Office, Mangayil, Mannaparamb and Purakkely (Panchayat data 2016). During 2010, about 37,881 foreign tourists and 73, 568 domestic tourists visited Maradu. (PCA 2011)

Map no 2.1 Location map of Maradu municipality

SOURCE: Prepared by the researcher

HISTORY OF MARADU

In the earlier days, land in this area was occupied by the erstwhile landlords, the temple organizations and the „Manna‟ - landlord‟s residence. There are indications that centuries ago this area was occupied by a few Namboothiri / royal families. In due course of time, these families were ruined, and the ownership of the land passed on to a few Nair families of which “Mangayil” was the prominent one.

2.3 Physical Base 2.3.1 Physiography Maradu portrays a coastal lowland area, where the relief is less than 20 meters above mean sea level and the relative relief is less than 10 meters. Coastal plain and Mud/Tidal flat can be noticed along this area. The area is gently sloping. (Resource atlas 1991)

Geomorphology

Maradu municipality depicts more or less uniform geomorphology. The coastal landforms are conspicuous in the coastal plain region, consisting of mangrove swamps and tidal/mud flats.

The coastal wards have a frontage to backwaters. Vytilla block comes under the stable zone in the landslide hazard zonation (Resource atlas 1991)

Map no 2.2 Ward map of Maradu municipality

SOURCE: Prepared by the researcher

2.3.2 Temperature and Climate

Ernakulam district has a wet monsoon type of climate. March, April and May months are the hottest. December to February months is the coldest. The mean monthly maximum temperature ranges from 28.1⁰C to 31.4⁰C and the minimum ranges from 23.2⁰C to 26⁰C. The maximum temperature occurs during March and April months and the minimum temperature occurs during December and January months. The humidity ranges from 68% to 89% during morning hours and 64% to 87% during evening hours. The maximum humidity is observed during May to October months. (IMD Kochi 2010)

2.3.2.1 Seasonal Pattern of Rainfall

The annual rainfall ranges from 3233 mm to 3456 mm at different places of the district. The district receives on an average 3359.2 mm (based on 1901-99 data) of rainfall annually and during the said period the maximum rainfall in the district was around Neriamangalam area. The rainfall is less in the Western part of Ernakulam District and increases towards the Eastern part of the district. The normal annual rainfall in Neriamangalam is around 5883 mm. Cochin in the Western part, receives around 3233 mm annually. Rainfall during the South-West monsoon season, contributes nearly 67.4% of the total rainfall of the year, followed by the North-East monsoon which contributes nearly 16.6% and the balance of 16% is received during the months of January to March from May to November indicating a water surplus for recharge into the ground water regime. (IMD Kochi 2010)

2.3.3 Geology

Geology of Maradu comprises of Quartz feldspar hypersthenes granulite (charnokcite), charnokcite gneiss and hypersthenes diopside gneiss. Thus it can be inferred that the area belongs to Charnokcite (older) (Ac) 2600 Ma period in the Archean era.

Map no 2.3 Geological map of Maradu municipality

SOURCE: Geology Survey Of India 48

2.3.3.1 Soil-texture and Pattern

The main soil texture and erosion pattern found along this region are of two types, which are clay/ Loamy clay /Sandy clay /Silty clay that have a moderate erosion pattern, and Loamy sand/Sand that have none to slight/slight erosion. Clay is the main content found in this soil, which reduces its strength due to its high water content.

Map no 2.4 Soil map of Maradu municipality

SOURCE: Soil Survey

2.3.4 Water availability

There are 14 public wells and 2 public tanks in Maradu. The groundwater found along shallow wells range from 100-200m3 per day and for 50-100 m3 per day ((Resource Atlas 1991). Rainfall is the major source of recharge to groundwater in the district. The annual water availability (MCM) as on March 2004 for Vytilla block is given below:

The withdrawal of ground water is mainly for irrigation purpose followed by domestic and industrial purposes. Based on the stage of development and long term groundwater trend, the blocks are categorized as safe, semi critical and critical. Thus, since Vytilla is showing a declining water trend and is classified as critical, there is a need for harnessing and recharging the groundwater. 49

Table no 2.0 showing Annual water availability 2004

Block Total Annual Net Annual GW Existing gross Existing gross Existing gross Stage of GW Categorization Recharge, Availability, GW draft for ground water for groundwater draft development % for future GW MCM MCIM irrigation, domestic and for all users development MCM industrial purposes

Vytilla 10.46 9.25 2.72 2.57 5.29 57.14 Critical

Source: CWRDM 2004

2.3.5 Land use

Till the1960s, more than half of the rice needed for sustenance of the residing population was cultivated. Agriculture was the main occupation of this area. As per the Panchayat Level Statistics (2011), 1025 plots and 1, 19, 853 cents are in wet lands and1193 plots and 1, 85,116 cents are categorized as dry lands. From Table no. 2.1. , we can infer that Mixed crops occupies more area followed by Built up Land.. The paddy Puncha type of cultivation is seen only 0.002 square kilometers. Paddy land converted into different land forms comes about 0.85 square kilometers. It can be inferred from the table there has been a extensive conversion of Agricultural Land. 50

Table no 2.1 showing Land use pattern 2005

Area (in Sq. SL No. Land use Km)

1 Aqua Culture 0.24

2 Built up Land 2.44

3 Coconut 0.07

4 Cultivable Waste Land 0.68

5 Manchiyam 0.14

6 Mixed Crops 4.41

7 Mixed Trees 0.02

8 National Highway 0.18

9 Paddy Puncha 0.002

10 Paddy reclaimed to Built-up Land 0.53

Paddy reclaimed to Cultivable Waste 11 Land 0.22

12 Paddy reclaimed to Mixed Crops 0.08

13 Paddy reclaimed to Water Logged Area 0.02

14 Prawns Field 0.92

15 Railway land 0.16

16 Water body 2.25

17 Grand Total 12.35

Source: Land Use Board 2005

2.4 Socio-Economic Base

2.4.1 Demographic structure

Maradu has a total population of about 44704 with total number of households as 11065. The male population is 22176 and the female population is 22528. The total number of those that fall in the age group of under six years is about 3983, of which 1998 are males and 1985 are females. The Scheduled Caste population in Maradu is 3939, with 1954 males and 1985 females. Scheduled tribe population is about 261, with 129 females and 132 males. The total number of literates is 39565 with male literacy of about 19835 and female literacy of 19730. (PLS 2011) There are 7 Scheduled Caste colonies and 1 Scheduled Tribe colony with a total of 410 houses (Directorate of Economics and Statistics 2001). As per rural development report, 6762 BPL families are residing in this area.

2.4.2 Occupational Structure, Table no 2.2 showing Occupational structure 2001

MAIN WORKERS

2001 OCCUPATION Male Female

Total workers 12678 4225

Main Workers 11836 3499

Main Cultivators 17 4

Main Agriculture Labourers 129 35

Main Household industry 147 59

Main other workers 11543 3401 Marginal workers 842 726

Marginal Cultivators 3 9

Marginal Agriculture Labourers 21 14

Marginal Household industry 14 16

Marginal Other workers 804 687

Non workers 9498 18303

Source: District Census handbook 2001 52

From the above table, we can infer that the number of Main workers, Main other workers and Marginal other workers is very high in both genders. The minimum number of workers is found in Marginal and Main Cultivators. The Non workers population is also seen as high. Being a coastal area, Maradu also has some boat building industry.

2.4.3 Education

There are 2 Government and 2 private aided Lower Primary Schools, 2 private aided Upper Primary schools and 1 Government Higher Secondary School. 2 Vocational Higher Secondary Schools (one government aided and one private aided) are also seen in the study area. (PLS 2011).

2.4.4 Health Care Facilities

There are two Primary Health Centers and private dispensaries in Maradu. There are eleven private medical institutions in Allopathy, five in Ayurveda, and seventeen in Homeopathy dispensaries. (PLS 2011). There are about forty Anganwadis in this area (ibid)

2.5 Transport and Communication

There are two post offices and one telephone exchange within the limits of the study area. There are 3 National Highways- (45.6 kilometers in length), Public Works Department roads- (6.53 kilometers in length) and village roads (52.13 kilometers in length).

Map no 2.5 Road Network map of Maradu municipality

Source: Prepared by the researcher

4. URBAN GROWTH OF MARADU 3.0 INTRODUCTION

Understanding the past helps geographers and others to understand their environs and societies. All individuals are products of the past, not just of the present. The present cannot be understood in ignorance of the past. Studying the past provides answers not merely to questions about how things were but also about how things are now (and, more tentatively, about how things will be in the future) (Massey 1991). So, Geography acts as an eye of History (Cosgrove 2004). “Without a thorough understanding of place as it has human significance, one would find it difficult to describe why a particular place is special and impossible to know how to repair existing places in need of mending” (Relph 1976). Urban geography emphasis the studies onto past and present of places.

3.1 SITUATING MARADU

Kochi, formerly known as Cochin (Queen of Arabian Sea) is one of the main port cities in the south west coast of India, and is a part of Ernakulam District in Kerala state. Kochi was formerly a princely state known as Cochin State under the rule of Cochin royals known as “Perummpadapu Swaroopam”; who were descendants of Kulashekaras of Mahodayapuram. Their capital was Vanchi and they moved to Cochin in 1405 A.D (A. S. Menon 1975).

The Cochin area was divided into "Janapadhas" under the rule of the king and “Nadus” under “Naduvazhis”- the landlords as in the Zamindari system. Tripunnithura, which is hardly 5 kilometers from Maradu, was the capital of the Cochin royals. Maradu was one of the many Nadus which were under the land lordship of Brahmins and Nairs, people belonging to the upper caste communities. All the land was divided into Taluks, and then further divided into properties and villages as sub categories. In the land revenue papers of 996 M.E1, 1012 M.E AND 1032 M.E, there is mention of „Maradu muri‟ (a village extending to 3304 acres) under Nettoor “proverthy” (Cochin under the Kingship was divided into „proverthies‟ for revenue administration purposes) under Kaniyannoor taluk. Another reference is made on Maradu in the Settlement Registry (1907). In Survey no.344, there is a reference of a palace in Maradu in the eastern “parambu” (land) and in Survey no: 1352/2, it is mentioned that the land got its name as it is near the Maradu goddess temple. An

1 M.E- Malyalam Era is a solar, sidereal Hindu calendar used in Kerala. 55 interesting reference to the name of Maradu is made by (V.V.K.Vallath 1990), who states that the name “Maradu” must have originated from two sources. According to him, in Kokasandesham2 (Shloka3: 26), it is said that the king‟s palace ruled the area and the royal designation given to the king was “Maradi” which might have converted in due course of time for the land to get its present name “MARADU”. Secondly, he says that the uniqueness of the land and soil must have given the area its name. In another reference, the 1931 census, Maradu is shown as one of the villages in Cochin-Kanniyanoor taluk. In the Kerala Sthalanamakosham (1984), a directory of names of land in Kerala (Villakudi 1984), land named Maradu is mentioned as existing under the Kanniyanoor Taluk and mention is made of the presence of a Post Office and Public Telephone in the Maradu Village and Maradu Panchayat. So, it is clear from the above references that Maradu was part of a „Nadu‟ and was a village. For categorizing or assessing the development of any area, it is important to understand all the criteria ranging from the settlement structure to functional structure.

A single criterion is not satisfactory while defining any area. The issue has to be decided on the basis of a set of suitable criteria, which are:

 Demographic Structure- Population Statistics  Occupational Structure  Morphology- Land and Urban Morphology 3.2 DEMOGRAPHIC STRUCTURE

Demographic structure depicts the population size of any area. In the Census of India 2011, the definition of an urban area is as follows:

1. All places with a municipality, corporation, cantonment board or notified town area committee, etc. (Census 2011) 2. All other places which satisfies the following criteria: (Ibid) i) A minimum population of 5,000; ii) At least 75 per cent of the male working population mainly engaged in non-agricultural pursuits; (Ibid) iii) A density of population of at least 400 persons per sq. km. The first category of urban units is known as Statutory Towns. These towns are notified under law by the concerned State/UT Government and have local bodies like Municipal Corporations, Municipalities,

2 It’s an old Malyalam literary work 3 Shloka means “verse” 56

Municipal Committees, etc. The second category of urban units (as in item 2 above) is known as Census Towns. (Ibid) As per the Indian Census 2011, Maradu comes under the second category, viz. Census Towns. An administrative hierarchy exists at the State government level to delineate a place as it attains certain norms. As per the Kerala government norms, it was decided in 2010 that Maradu would come under a municipality but it was implemented officially only after 2011. The affair of the ward distinctions and the redefining of the boundaries caused the delay in the implementation. Hence, on the basis of having a municipality Maradu has attained the status of a Census Town (Ibid). However, the urban nature of Maradu is still very unclear based on the census definition. Population is an important criteria and mainly the population growth rate is the important factor in determining an area as urban or rural.

3.2.1. Population Statistics

Population statistics such as Total population, Male and Female population, Institutional population etc are used in determining the nature of the population statistics in Maradu. Population criteria have been defined by various organizations such as United Nations, Indian Census etc. But for the basic understanding of an area, it is necessary to bring out the unique characteristics of the population of that area.

Marxist and Neo-Marxists as well as Malthusian theories have acknowledged population characteristics like birth and death rates, fertility and mortality rates and also immigration and emigration rates, as indicators of an area being urban or rural. So, an understanding of the increasing population is needed for assessing the character of any area.

Table no: 3.1 Table showing Total Population and Total number of Households 1971,81,91,01 and 201.

Year Total Male Female House Holds Wards

1971 22817 11418 11399 3487 3 1981 28749 14306 14443 4823 4 1991 34995 17487 17508 6769 11 2001 41012 20301 20711 9123 22 2011 44704 22176 22528 11065 33 Sources: Census Handbook 1971-2011 57

The Total population is seen increasing two fold from 22817 in 1971 to 44704 in 2011. The Female and Male population has been following the same pattern as that of the total population. The Total number of Households has increased from 3487 in 1971 to 11065 in 2011 (i.e. more than twice from 1971). The number of inhabitants of Maradu was very less because of the particular ecology of land, being island type. Being water bound, the main connection between Maradu and the surrounding areas was by valloms4 (Boats) (Field Survey 2016).

The increase in population in 1991 is mainly due to the migration of families from Venduruthy in the North Western part of Maradu (Field Survey 2016). Caplow considers migration as a change of residence. It also concerns with occupational shifts from one job to other (Caplow 1954). The location of Maradu (water bound area) acted as an easily accessible area for people working in Port Trust and in Naval Base. The number of wards has increased from 3 to 33 without change in area. None of the adjacent panchayat has been joined to form the present Maradu Municipality. This is a clear indication that there has been no addition to the area and the whole population pressure is on the same area of land.

Figure no: 3.1 Figure showing Total Population and Total number of Households (1971 - 2011)

Sources: Census Handbook 1971-2011

4 Vallom is a small canoe made of Aini’s wood and was extensively used in water transportation. 58

3.3 ECONOMIC ACTIVITIES- Occupational Structure

Demographic and Non –demographic factors affect the development of any area. Under demographic factors, high population growth has a direct effect on work participation rates. Economic activities are the census defined classification showing different sections of people engaged in different work forces. Occupational structure in turn defines the economic activity of an area or the division of population based on different occupations. Occupation has been divided into three types. Primary activities which include Agriculture, Fisheries and Animal husbandry, Secondary activities that consist of Manufacturing Industries both small scale and large scale and Tertiary activities which comprise Banking and Finance sector, Transport and Communications etc. Total population is classified as workers and non- workers. Work is defined as participation in any economically productive activity (Census 2011). Workers are further classified into Main and Marginal workers. Those who have worked for the major part of the year preceding the enumeration are termed as Main workers. Marginal workers are those who have worked at any time during the year preceding the enumeration, but have not worked for the major part of the year.

Table no 3.2 shows that the Total Main Workers have increased two fold from 4707 males and 755 females in 1971 to 11836 males and 3499 females in 2011. Main Cultivators have decreased to 12 in 2011 from 77 in 1971. Main Agricultural Labourers show a declining trend till 2001 but have increased to 129 males and 35 females in 2011. Compared to 1971, Household Industries & Manufacturing processing show a decrease in male and female population till 1991, but in 2001 and 2011 the participation rate of male and female is seen as rising and has remained more or less the same in both years.

Table no: 3.2 Occupational Structure of Maradu

WORKERS

1971 1981 1991 2001 2011

Femal OCCUPATION Male Female Male Female Male Female Male Male Female e Total Main Workers 4707 755 5457 923 8609 1409 9731 2074 11836 3499 Main Cultivators 65 12 58 2 43 0 5 2 3 9

Main Agriculture 439 127 103 51 87 16 6 1 129 35 Labourers

Household Industries& Manufacturing 157 106 49 42 27 7 139 27 147 59 processing, servicing

and repairs Other Workers 801 89 5247 828 1478 196 9581 2044 11543 3401

Marginal Workers 0 0 777 313 163 51 1369 395 842 726

Non Workers 6711 10644 8072 13207 8715 16048 9201 18242 9498 18303 Sources: Census Hand Book 1971-2011 60

Figure no: 3.2 Occupational Structure of Maradu

Sources: Census Hand Book 1971-2011 61

The above figure no 3.2 shows the sub divided bar diagram representing the Total Main Workers, Main Cultivators and Main Agricultural Labourers of Maradu from 1971 to 2011. As per the 1971 census, Total Main Workers constitute about 23.9 % of the total workers. The total number of Male Main Workers is seen as having more than doubled from 4707 in 1971 to 11836 in 2011and the total number of Female Main Workers is seen as increasing double fold in a gradual increase from 755 in 1971 to 3499 in 2011. Among the Main Workers, the Female Workers are only 13 %, and the majority i.e. 86 % are Male Workers.

A person is classified as a cultivator if he or she is engaged in cultivation of his or her own land or land held from Government, private persons or institutions for payment in money, kind or share. Cultivation includes effective supervision or direction in cultivation ( Census 2001). The number of Main Male Cultivators is showing a declining trend from 65 in 1971 to 3 in 2011. On the other hand, though the number of Female Main Cultivators is displaying a negative trend till 1991, after a gradual increase to 2 in 2001, a steep increase to 9 is seen in 2011. A person who works on another person's land for wages in money or kind or share is regarded as an agricultural labourer. An agricultural labourer has no right of lease or contract on land on which she/he works and also has no risk in the cultivation (Census 2001). The total population of both male and female Main Agricultural Labourers in Maradu is seen as 2.48 % in 1971. Although it shows a declining trend and falls to 0.29 % in 1991, there appears to be a slight improving trend in 2011 with a rise to 0.36 %.

Those workers who had not worked for the major part of the reference period (i.e. less than 6 months) are termed as Marginal Workers. Marginal workers are absent in 1971. Subsequently, a sudden emergence of a considerable number Marginal Workers is seen in 1981 wherein 777 men and 313 women engaged in this manner of work. Then, after a gradual decline in 1991, a threefold increase is seen for male and female populations in 2001, and finally, the latest census of 2011 shows a decrease in Male Marginal Workers and a twofold (726) increase in Women Marginal Workers

Household Industry is defined as an industry conducted by one or more members of the household at home or within the village in rural areas and only within the precincts of the house where the household lives in urban areas (Census 2001). The larger proportion of workers in the household industry consists of members of the household. 62

Figure no: 3.3 Occupational Structure of Maradu

Sources: Census Hand Book 1971-2011

The industry is not run on the scale of a registered factory which would qualify or has to be registered under the Indian Factories Act. The Occupational Structure of Maradu (Fig. No. 3.3) shows that the Household Industry and Manufacturing processing, servicing and repairs are at a peak in 1971, then a gradual decrease is seen till 1991 and by 2001 and 2011 a steep increase is displayed. It was noted that Household Industry like coir making, rope making and weaving flourished in the 1970s. After the 1970s, most of the traditional Household Industry workers deviated from their work to being daily wage labourers. (Field Survey 2016) A person who did not work at all during the reference period is treated as a Non- Worker (Census 2001). Non Workers constitute about 62 % of the total population in 2011, whereas it was 66 % in 2001, 70% in 1991, 74% in 1981 and 76 % in 1971. There is a gradual decrease in the non working population from 1971 to 1991.The reason may be the migration pull and push factors, development of apartment buildings and flats in the study area.

People engaged in some economic activity during the last one year, but are not cultivators, agricultural labourers or in Household Industry, are known as –„Other Workers 63

(OW) (Census 2011). Other Workers constitute about 30 % of the total population in 2011, whereas it was 28 % in 2001, 4.7% in 1991, 21 % in 1981 and 3.9 % in 1971. The percentage of population in Other Workers is seen as highly fluctuating, especially a sudden increase is observed in 1981 and then in 1991, there is a sudden drop. From then onwards, it is steadily increasing. The construction of roads along maradu region and the main bridge named Thycoodam Bridge connecting Vytilla with Maradu might be the reason behind the growth of other workers in this area (Cochin,71-81). In 1991, the National Highway and a bridge named “Aroorkutti palam” (connecting Ernakulam district with the adjoining Alappuzha district) came into existence, converting the whole island area into land with a connected system of roads. This network of roads paved way for development along roadside in the form of five star luxury hotels, luxury car showrooms, shopping malls etc., and the whole area underwent a major shift to an urban manner of life with people engaging in activities as Other Workers. This shift occurred predominantly between 2001 and 2011.

Fig no.3.4 Figure shows the population of Non- Workers and Other Workers in Maradu (1971-2011).

Source: Census Handbook 1971-2011

3.4 MORPHOLOGY

Morphology means the study of form or structure. In order, to assess the growth of Maradu - Land and Urban Morphology is explained. Land Morphology denotes the structure or the layout of land in simple terms. Human interventions (occupational structure) on land use and land types play an important role in influencing and superimposing the natural habitat of a place (Ulrike 2012). Environmental changes are also expressed in land use changes. Social, political and economic trends are conveyed spatially. Such an insight to land change/land use/ cover will help to understand the development pathways or growth of a city. Land morphology is assessed using two ways:

1. Land types / categories- For classification purposes, land types or categories are adopted by Basic Tax Registry. Basic Tax Registry and Settlement Registry are taken for reference for yester years (1901, 1995) and the same division is followed to assess the difference or the change in land types or categories up to the present. Land revenue used to be collected by the land lords or “Jenmies” until the Malayalam Era 1012 (i.e. after Tippu Sultan‟s attack on Cochin). After that point in time, land revenue system was modernized. The land revenue began to be collected by officials from the tenants (A. S. Menon 1975). Division of land into types or categories for the purpose of Land Tax collection was devised as follows 1) Nilams or cultivated wetlands that was fit for paddy cultivation 2) Parambu or garden land used for planting coconut, areca nut etc 3) Poramboke land which is government owned such as roads, railways etc. (Papers relating to preliminary works and settlement proclamation Central Archives)

.3.4.1 Land-Types/Categories

Table no 3.3 Land categories and rate of change over different periods (1901, 1995 and 2003)

YEAR NILAM RATE OF PARAMBU RATE OF PORAMB RATE OF TOTAL ACRE CHANGE CHANGE OKE CHANGE ACRE ACRE ACRE

1901 1238 NIL 1263 NIL 802 NIL 3305

1995 760 -38 1560 26 806 -4 3126

2003 300 -169 2120 36 706 -12 3126

Source: Settlement Registry 1901, Basic Tax Registry 1995 and 2003 Secondary data 65

It should be noted that there is a drastic change in Nilam, which has reduced from 1238 acres in 1901 to 300 acres in 2003. The rate of change from 1901 to 2003 is about -169 acres, which itself determines the land use change from agriculture land to others. The second category, Parambas has increased from 1263 acres in 1901 to 2120 in 2003. The rate of change has been drastically increased from 26 in 1995 to 36 acres in 2003.

It can be inferred that the change in land category is striking. Nilam (which was primarily used for agricultural purposes-paddy cultivation and aqua culture) and Puramboke land (land under government) is noticed as declining whereas Parambas (garden land) is seen increasing at a high rate. This change also highlights the changing phases in Maradu from rural to Urban. Facilities of urban nature such as Five star hotels like Le Meridian, BTH Sarovaram; many of the leading car company showrooms (Mercedes, BMW, Audi, Hyundai etc) are located along the Nilam area (Field Survey conducted by the researcher 2016).

Fig no.3.5 Figure shows the Land categories/types and changes in their area over different periods 1901, 1995 and 2003.

Land types/Categories

PORAMBOKE PARAMBAS NILAM

706 2003 2120 300

806 1995 1560 760

802 1901 1263 1238

Source: Settlement Registry 1901, Basic Tax Registry 1995 and 2003 Secondary data

3.4.2 POPULATION PRESSURE ON LAND

The concept of population pressure on land is employed in a remarkably wide range of disciplines and debates, and it has been forcefully critiqued within numerous fields. The historical usage is till obscure. In 18the century it was Thomas Albert Malthus, who stated that unchecked population growth will have an effect on economic growth. Clarke stated that population pressure (Clarke 1967) detremines agricultural growth and not economic growth. Population pressure is defined as the force exerted by a growing population upon its environment, resulting in dispersal or reduction of the population (N.Ajithkumar 2011). Concern with population pressure is dominant in many countries. Rapid population growth has often been held responsible for population pressure. According to Edward O. Wilson, the raging monster upon the land is population growth and in its presence, sustainability (i.e. sustainable development) is but a fragile theoretical construct (Wilson 1992). A policy document from the World Bank affirms that the causes of environmental degradation are as varied as its manifestations. But rapid rate of population growth is at the heart of the problem in many developing countries (Margalin 1993).

Mukerjee (1938) has suggested a coefficient for determining the pressure of population on land using the absolute minimum requirement per person, viz. one acre of land (0.4 hectare). It is a simple calculation where the land required per person is divided by the per capita availability of arable land. If the coefficient is unity, the land has optimum population (R.K.Mukherjee 1938). A coefficient of more than one indicates over-population and higher the deviation from unity, greater is the pressure of population on land. If the coefficient is below unity, it indicates that there is scope for further increase in population from the point of view of land availability (Ibid). Population pressure on Land was found for three different years 1901, 1995, and 2003 using the Nilam area or the area under paddy cultivation. The population pressure on Land is 0.0008 which is less than the optimum unity and is seen increasing to 0.0013 in 1995 and 0.0033 in 2003. The co efficient is seen increasing which means that there is further scope of an increase in population from the point of land availability. The population pressure on the land can be easily identified and the population pressure on the productive lands are seen high which is a clear indicator of urban transformation where population pressure on Land is increasing, and area under paddy cultivation decreasing. 67

Table no 3.4 Population pressure on Land over different periods (1901, 1995 and 2003)

Year Nilam in Rate of Population Total Rate of change Acres change pressure on population Land 1901 - 0.0008 22817 - 1238 1995 -2.3 0.0013 34995 12178 760 2003 -1.86 0.0033 41012 6017 300 SOURCE: Basic Tax Registry and Panchayat level statistics 1901, 1995, 2003.

The rate of change in land is seen increasing but it doesn‟t point out to the fact that land is used for agriculture purposes. Most of the wetlands are abandoned; shrimp culture sites, but is classified under the category of wetlands. The officials need to delineate agricultural lands from wetlands. The rate of change in population is seen decreasing; most of the residents of the Maradu are economically backwards, so most of them sell their land and migrate to the adjoining municipality.

2. Land use/ Land cover- The National Remote Sensing Agency (NRSA) in the year 1988-89 at the behest of the Planning Commission have classified the land by visual interpretation technique and digital techniques into twenty two-fold land use classes. The definitions of the 22 categories adopted by them are as follows: Land use /land cover classification is not adopted entirely because most of the features mentioned in the NRSA classification are not seen in the study area. So, modified form of NRSA classification is adopted suiting the study area needs.

Table no 3.5 NRSA Land use classes Sl. No. Land use types -CLASS II Class I 1 Urban 2 Rural 3 Mining Built up land 4 Cropland 5 Plantation 6 Current shifting cultivation 7 Fallow land Agricultural land 8 Evergreen/Semi-evergreen 9 Deciduous forest 10 Degraded forest/Scrub 11 Forest plantation 12 Mangrove/Swamps Forest 13 Salt-affected area 14 Guilled/Ravenous land 15 Land with or without scrub 16 Sandy area(Coastal and Desertic) Barren rock/Stony waste/Sheet rock 17 area 18 Rann Barren/uncultivable/wasteland 19 Inland wetland 20 River/Stream/canals 21 Water bodies Wetlands/water bodies 22 Grass land/Grazing land Grass/Grazing land Snow covered /Glacial areas Snow covered /Glacial areas Source: NRSA Land use/Land cover classification 2011

SPATIO-TEMPORAL LAND USE/LAND COVER ANALYSIS OF MARADU

Map no.3.1 Land use maps of Maradu over different periods from 1973- 2013).

SOURCE: Prepared by the Researcher

Table no 3.6 Land use change of Maradu over different periods in Hectares (1973, 1993 and 2013) AREA IN HECTARES Land use Types 1973 1993 2013 Water Bodies 390.854 303.457 260.69 Islets(Manmade and Natural) 120.54 247.25 122.06 Agricultural Land 408.48 262.28 97.63 Mixed trees with settlement 224.34 218.33 418.43 Built up 147.09 260.001 458.39

SOURCE: Source: Calculated and compiled by the researcher

Land use has been classified into 5 categories as observed in the study area. They are Water bodies, Islets5, Agricultural Land, Mixed trees with settlement and Built up. The study area is situated in the lower course of Periyar river (hence sediment deposition is common) and it is also near the Vemabanad estuary adjacent to Cochin harbour (tidal zone). These two factors are the reason for the classification of land use type, Islets. Man made islets can be noted along the coastal wards of the panchayat, owing to the dredging in water bodies. This waterway is mostly used by the barges for the transshipment of Ammonium to the FACT, Ambalamugal (See Appendix Photos VII). For this purpose, the canal was widened and dredged and the soil thus dredged is dumped along the sides of the coastal wards in Maradu. A recent incident of Ammonium leak happened in Champakara, a place in Maradu. It can be observed from the above table that the water bodies in the study area have declined from 1973 to 2013 (approximate change of 130 hectares). Islets are seen increasing at a double rate from 120 hectares in 1973 to 247 hectares in 1993, and then it shows a decreasing trend and drops down to 122 hectares in 2013. Islets (Natural and Manmade) can be noticed along this area. Man made islets, resulting from the dredging in the water channel surrounding Maradu, are used for inlets and also for Shrimp cultivation which was at its peak in and around the 1990s. The same trend can be noticed in mixed trees with settlement. An inverse relation can be observed in Agricultural Land and Built up i.e. Agricultural land is steadily decreasing whereas Built up area is increasing. In fact, Built up, from 1993 to 2013,

5 Islets- mudflats on the banks of rivers/canals cause Maradu comes under Inter tidal zone as per Coastal Regulation Zone report by National Centre for Earth Science (2014). 71 is observed as doubling from 260 to 458 hectares. The adjacency to Cochin Corporation and Thripunnithura Municipality would be the reason for this kind of land use change.

Fig no.3.6 Land use types of Maradu over different periods. (1973, 1993 and 2013)

Land Use Types

Area in Hectares 1973 Area in Hectares 1993 Area in Hectares 2013

458.39 418.43 390.854 408.48 303.457 260.69 247.25 262.28 260.001 224.34218.33 147.09 120.54 122.06 97.63

Water Bodies Islets Agricultural Land Mixed trees with Built up settlement

SOURCE: Calculated by the Researcher

3.4.3 URBAN MORPHOLOGY

Urban Morphology is defined as the physical pattern of the urban environment. It focuses on the development and phases of growth of urban areas including physical, social and cultural aspects of social form. In order to understand the change in growth of Maradu, its physical change has been assessed with the help of Modified NRSA Land use/ Land cover classification. Urban Sprawl is found with the assistance of Built up area from Land use/ Land cover classification and is usually defined as the “haphazard growth”, of relatively low density, over an extended region, of residential units dominated by single family homes. Urban Sprawl is implicated in the following social problems- social isolation ,obesity, asthma, global warming, flooding and erosion, demise of small farms and extinction of wildlife and unbalancing the nature (Mark Gottdiener 2005). So, analyzing Urban Sprawl and Land Use Change will help to chart out the phases in the growth on Maradu.

LAND USE CHANGE AND URBAN SPRAWL Methodology

Three sets of Landsat MSS, TM (1973/1993, resolution 30 m, seven bands) and ETM+ (2013, resolution 15m, seven band) images were used in the study. These images were processed with ERDAS IMAGINE 9.1 software, which involves geometric correction, unsupervised classification and GIS reclassification methods. False Colour Composite (FCC) was produced by stacking different bands in the Landsat Imageries. The Panchromatic data were merged with multispectral FCC. The Maradu mucipality map (base map of the study area) was imported in computer environment and geo referenced in GIS environment. By using this, shape files were generated. The sub-setting of satellite images were performed for extracting the study area by taking a geo-referenced outline of the Maradu boundary as seen in the Municipality map of 2011. The subset images were subsequently re-projected. All three images were then classified using ISODATA unsupervised classification algorithm. In iso-data unsupervised algorithm, thirty spectral clusters with 95% convergence value were selected. Through visual examination of satellite imageries, and by using top sheets and Google earth images, digitally classified images were interpreted and reclassified. Through the use of spectral classification, the built up land was extracted, which include the high density residential areas as urban areas.

Secondary data sources were mainly used for fulfilling the objectives of this paper. Land sat series from the year 1973, 1993 and 2013 were downloaded from USGS GLOVIS. Images with less than 10% cloud cover - January and February month‟s data were downloaded. Images were submitted from AOI file in ERDAS IMAGINE 9.1. These images were classified using Unsupervised Classification using ISO-DATA Classification Algorithm with 95 % convergence value with 20 iterations. Unsupervised classification was used because software itself clusters the Digital Number values as per the spectral reflectance value.

The resultant images were rechecked using base map as well as ground control points and also with ground truth data. Thus, Built up Area Maps and Urban Sprawl Maps were created. To check the consistency of the data from 1973, the so called urban Built Ups were cross checked with the Archival Data from Ernakulam State Archives and field visits. Thus, the conclusions were derived.

Fig no.3.7 DATA SOURCES AND METHODOLOGY-URBAN SPRAWL

Landsat MSS 1973 Landsat TM 1993 Landsat ETM+ 2013

BASE MAP

ERDAS/GIS SOFTWARES Geo reference Field survey Subset Unsupervised-ISO DATA 1. LAND USE MAPS Classification Algorithm with 95% 2. URBAN SPRAWL Central Archives convergence value MAP

SOURCE: Compiled by the Researcher 74

3.4.4 TREND OF URBAN SPRAWL

Urban Sprawl is the spread of built up area, or to find the direction/trend of the spread of urban area. In Map no: 2 we can infer that the built up area is mainly scattered along the North eastern and North western directions, along some patches in eastern and western direction. The Urban Sprawl in 1973 is mainly based around temple locations. The two prominent temples in the study area are located in the North eastern and North western parts of Maradu. In 1993, the Built Up is seen spread around the 1973 Built Up and in the north eastern side because of the location of the railway line which connects Ernakulam to the Southern districts of Kerala. Around 2013, the Built Up is mainly spread from the northern side to the southern side of Maradu in a long strip because of the presence of the two lane National Highway acting as a corridor between South and North districts of Kerala. 3.4.6 URBAN INTENSITY ANALYSIS

Liu has formulated formulae to measure and quantify the magnitude and pace of urban growth (S.Liu 2000). Urbanization Proportional Index (UPI) and Urbanization Intensity Index were calculated using the following expression

UPIi, t~t+n = ( ULAi, t+n - ULAi,t) *100/ TLAi

UIIi,t~t+n = [( ULAi,t+n - ULAi,t) /n] *100/ TLAi

The variables UPIi,t~t+n, UIIi,t~t+n, ULAi,t+n and ULAi,t are indices of the proportion, of urbanization and the intensity of urbanization, within a spatial unit „i‟ during a time period t~t+n, and the areas of urban land-use for years t+n and t, respectively. TLAi, is the total area of the spatial unit i. The UPI, expresses the percentage of the total area occupied by urban expansion for a given spatial unit over the entire course of the study from 1973 to 2013, and it reveals the total magnitude and spatial distribution patterns of urban expansion throughout this period. The UII is used to compare, the intensity and pace of urban expansion over various periods (Ibid).

Map no 3.2 Urban Sprawl Map (1973-2013)

Source: Compiled by the researcher

GIS-based buffer analysis was adopted in this study. ArcGIS software was used for the buffer analysis. Each buffer zone, was employed as a basic spatial unit to characterize distance-dependent urban growth behaviour with their UPI and UII values for a given time period (Jayalekshmy S.S 2016). For the purpose of the study, four different buffer systems were established. The first was a circular buffer zone system with a buffer width of 3500 meters covering the entire region. This was designed to explore the overall urbanization process over the study area comprising the Kudanoor the main junction (joining two National Highways) as the nucleus. The second system consisted of an Aeolotropic buffer and a Multiple Point Buffer Analysis for the entire area. For this, the entire region was divided into four parts based on direction. Calculations of the UPI and UII were made separately for each part to explore the directional trends in urbanization process. The division was (in clockwise order): North-East (0º to 90º), South-East (90º to180º), South-West (180o to 270o) and North- West (270o to 360o). In this analysis, the buffer zones covering the Kudanoor junctions were defined as having 500 meters width which was enough to include the principal urban centre of Maradu and its adjoining 33 wards.

In all the buffer analysis, urban areas of Maradu region indicated by Landsat data from 1973, 1993 and 2013 were extracted and used to represent the urban centre as a baseline for creating the buffer zones. Because the urban centre of study area was generally not obvious, the place which acted as centre during the first time phase (1973) was used as the origin in creating the buffer zones.

Map no 3.3 Built Up Land Map (1973-2013)

SOURCE: Compiled by the Researcher

SPATIOTEMPORAL TRENDS OF UII BASED ON BUFFER ANALYSIS DURING 1973-2013

Map no 3.4 Map shows the Buffer Analysis of Built up area during different intervals.

SOURCE: Compiled by the Researcher

3.4.5 URBAN INTENSITY INDEX

Urban Intensity Index measures the Urban Built Up from the core to adjacent areas. The study area Maradu comes under the 3500m Buffer and the whole area is represented in an interval of 500m Buffer each, i.e. 500m, 1000m, 1500m, 2000m, 2500m 3000m and 3500 m. Urban Built-Up from the CBD –Kudanoor junction is measured.

Table no 3.8 Urban Intensity Index of Maradu 1973-2013 over a 20 year time interval

Distance in Meters Years 500 1000 1500 2000 2500 3000 3500 1973-1993 0.02 0.08 0.16 0.13 0.03 0.03 0.03 1993-2013 0.03 0.085 0.02 0.09 0.02 -0.03 -0.03

Figure 3.7 Urban Intensity Index of Maradu 1973-2013 (over 20 years)

Source: Calculated and compiled by the researcher

Map no. 4 displays the expansion of urban area for entire Maradu over 20 years time interval. Figure 3.7 displays the UII resulting from buffer zone analysis for the entire study area over each time interval. During the 1973-1993 time interval, buffer zones 500-1000 m show a gradual upward increase in UII. Kudanoor junction, the nucleus acts as the core of the city and it is well developed from 1973 onwards and is considered as CBD. Well- connected transportation network has been of great advantage to the urbanization of this 80 region. A prominent Nair „Nalukettu‟ (an ancestral homestead of matrilineal family system) once existed in this area. Between the buffer zones 1000-1500 m, the UII (0.08 to 0.16) shows a high increase. This is because these zones, which include Nettoor, have been well developed from historical times. The earlier settlements of Maradu were developed in Nettor due to the presence of two prominent temples namely Nettoor Mahadeva Temple and Maradu Kottaram Bhagavathy temple. Around the Nettoor Temple, there were Namboodthiri families and their ancestral homes known as „Mana‟ were situated there. One hundred and one „Manas‟ are said to have existed in historical times. On the other hand, Nair settlements are prominently seen around the Maradu Kottaram Bhagavathy temple. This area, the educational hub of Maradu , was the location of Mangayil School established by the landlords belonging to the Mangayil family of that time. The zone had developed as a typical orient market town, with commercial activities (remnants of wood industries can be noticed) distributed along the waterfronts. These areas also served as pit stops for water front trade activities. Next, the UII shows a sharp decrease from the buffer zones 2000 to 3000 m. This might be because of the adjacency to the backwater and it served as inlets to various water inlets. Finally, between 3000 and 3500m, the UII shows as being more or less constant gradual decline as the land area is limited, and the area consists mainly of an island called Valanthakadu.

In the 1993-2013 periods, the UII is at its peak in and around 500-2000 meters of the CBD. It shows a high intensification of urbanization within 1000m of Maradu. The area has a high number of international luxury car showrooms and has two five star luxury hotels. Then, between 2000- 3000 m, a steady declining trend is visible from the city centre because the zone is seen having a high number of abandoned man made islets where the remnants of the aquaculture industry which once existed can be noticed. Lastly, in this time interval also the UII shows a steady decline (-0.03) from 3000-3500 m because the zone is vacant lands which are reserved for other housing projects and which are far from the stipulated minimum distance maintained by the Coastal Zone Regulation. Moreover, the land area is limited too.

SPATIAL AEOLOTROPY OF UII BASED ON BUFFER ANALYSIS DURING THE PERIODS 1973-2013

Fig 3.10 displays the results of the Spatial Aeolotropy Analysis of the UII for Maradu. In 1973-1993, up to 500 meters NE, NW and SE slices show a similar trend of urban growth as the city core comes under these slices. SW shows a high growth in UII. In Zone 1, 81

(between 500-1000 m buffer zone), NE and NW follow the same trend of UII, whereas SW and SE show an increasing trend in UII. This part covers the aristocratic housing area of the Namboothiri and Nair families, and also one of the most prominent temples comes under this area. In Zone 2, (between 1000-2000 m buffer zone), NE, NW, and SE show a similar trend in UII, while SW shows a decreasing trend. The North West slices cover their peak value in this buffer zone as the residential population, the railway station, the rail line and two most prominent temples are concentrated in this zone. All the slices show a declining trend in Zone 3 (after 2000 -3500 m) and this represents a medium trend of urbanization. This area is waiting to be urbanized, i.e. most parts of this area is vacant land because of its adjacency to water bodies, the presence of islets and the abandoned aquaculture sites. Some villas are seen along this area.

Regarding Zone 1, in 1993, Spatial Aeolotropy Analysis of the UII for Maradu up to 500 meters NE, SW and SE slices show a similar trend of urban growth as the city core comes under these slices. NW shows a high growth in UII. Between the 500-1000 m buffer zone, except NE, all the other directions follow the increasing trend of UII. This may be due to the presence of the National Highway Bypass and the NH Bridge connecting Maradu with Nettor, which could have also prompted the growth of the road side developments. Moreover, in Zone 2 (between 1000-2000 m buffer zone), it is seen that the NE, NW, and the SE, SW directions show an increasing trend which is due to the highly populated residential areas and the slices cover their peak value in this buffer zone as the residential population, railway station, rail line and the two most prominent temples are concentrated in this zone. Additionally, in Zone 3, all the slices show a steady trend after 2000 -3500 m, pointing to a medium trend of urbanization. This area , as mentioned earlier, is waiting to be urbanized which means that most parts of this area is vacant land because of its adjacency to water bodies, the presence of islets and the abandoned aquaculture sites. Some villas are seen along this area.

Map no 3.5 Spatial Aeolotropy of Urban Intensity Index of Maradu 1973-2013 over 20 years

SOURCE: Calculated and compiled by the researcher

Fig No 3.9 (a)

Fig No 3.9 (b)

Source: Calculated and compiled by the researcher

Fig No 3.9 (c)

Fig. 3.9 (c)

Source: Calculated and compiled by the researcher

Fig No 3.9 (d )

Fig. 3.9 (a), (b), (c) and (d) .Changes in the UII in NE, SE, NW and SW with distance to the urban centre in different aeolotropic areas over the entire Maradu region in different time periods from 1973 to 2013

Fig No.3.10 a and 3.10 b Spatial Aeolotropy of UPI based on buffer analysis during the period 1973-1993 and 1993- 2013 Fig No.3.10 a

Fig No 3.10 b

Source: Calculated and compiled by the researcher

Based on the results of buffer zone analysis, the spatial aeolotrophic characteristics of Maradu displayed in Fig: 3.10 (a) and (b) is summarized as follows. In the1973-1993 time 86 interval, urban expansion is concentrated mainly in three directions- NE, SW and SE directions. The urban expansion is limited in the other directions due to the physiographic constraints (water bodies). The Champakra Market is located on the North Eastern direction which has accelerated the urban growth to that direction. In the South Eastern direction, the development of a bridge known by the name - Irumbu palam, has served as a connection between Thripunnithura and Maradu, which has boosted the urban expansion in that region. The well-developed road, the rail network, as well as the temple oriented activities in this region have hastened the spread of urban area in the South Western direction. .

For the period of 1993-2013, the urban area has spread over North East, South East and SW directions. The growth of hospitals and commercial activities like shopping malls, institutions (relating to education, hospitality, and automobile) and the development of transport in the South East and North East directions have led to this progress. The apartment villa projects in Maradu and further expansion activities (like improvement in road network) have fast-tracked the urbanization process in the South East direction. In South West direction, urban spread, is due to the development of four lane roads (in commuting/accessibility), i.e. Aroor bridge acting as a transport conduit between North and South Kerala districts. Furthermore, the location of the renowned “Lakeshore hospital” has changed the whole geography of the area.

CONCLUSION

To be urban means to render urban, to remove the rural character of a district or a population. Whilst urbanity generally means „of the city‟, it can also mean refined or polished in manner of style (Chadwick 2016). With occupational change and population growth, the process of Urbanization of Maradu has accelerated and the area of urban land has increased quickly. Three distinct phases of urbanization are discernible in Maradu: a) Phase of Measured Urbanization (Till 1973)

Measured urbanization had prevailed in the Maradu till 1973 because the form and layout of land was completely controlled by the Upper caste gentry and the Kings, and the land was observed as three main territories - Nettoor, Maradu and Valanthakadu. The Nettoor area was dominated by the Namboothiri Mana (One hundred and One manas) and the land mainly comprised of Paddy fields and Parambs under the Nairs of Maradu. The south eastern part was a colony for the Scheduled Caste population (Ulladan Community) (Administration 87 report of advancement of backward communitites 1954-55,55-56 1956). The Maradu region had five prominent Nair families who were in charge of agriculture, and law and order. The rest of the population was mainly agricultural labourers, and women engaged in household activities (like coconut husking, rope making etc). In the North Western part, the Champakara market was seen, which served as the main market for Maradu and the surrounding areas. Since water bodies surrounded the entire land, boats were the major mode of transportation. Only in Maradu there were rickshaws collies available. 5 out of 1000 people had access to cycle (Field survey 2016). b) Phase of Diffusive Urbanization (1973-1993)

In this period, urban expansion had already caused considerable growth of urban area, and the outward expansion of the urban-suburban transition zone. The main reason, being the coming of bridges and roads connecting the three islands. The extent of urban expansion zone had also continued to increase and the top values of UII were at points further from the original urban centers, but with lower values. The urbanization was characterized by a transformation from localized environment to high intensity, and uniform to diffusive, regionalized and complementary. Therefore, the UII peak values were getting increased and steady values were observed till the peripheries of the Municipality. c) Phase of Rapid Urbanization (1993-2013)

This phase is characterized by a large scale urban expansion and the urbanization intensity is seen as increasing as a whole. There is a rapid growth in the urban expansion zone. This is because the centre and the Kudanoor regions immediately surrounding the centre have become already developed and urbanized and the wards surrounding the water front areas and the road front areas have partly transformed into new urban centers. The urban expansion zone has rapidly moved outward with a drastic increase in area. On the outer side of the urban expansion zone, the urban expansion intensity has decreased due to the obstruction from water bodies.The major directional growth is observed to be in the NW, NE and SE slices as new developmental activities and projects are being undertaken in this zone. In the NE and NW slices, this growth was seen even earlier because of the Market oriented activities and also the adjacency of (the city/ township of) Thripunnithura on the Eastern side of the Municipality. The developmental activities such as The Cochin Metro Rail, the coming of National Waterway , Heritage and Tourism activities, Fishing, Residential development 88 activities and Cosmopolitan activities like Shopping Malls (Nucleus Mall and Abad Mall, which are yet to come) predict the acceleration in the trend of urban expansion for the future.

Among the satellite towns of Ernakulam District, the urban expansion was observed as active for Maradu, Kalamassery and Thripunithura. Residential apartments in Maradu, IT parks at Kalamassery, Heritage Tourism and Residential buildings in Thripunithura have accelerated the urban growth (Jayalekshmy S.S 2016). The Satellite Town Theory has originated from the Garden City Theory developed by Ebenezer Howard, who expounded that around the central city there would be smaller towns which resemble the satellites around a planet. The presence of a satellite town is an inevitable product of modern city development. The Satellite Towns have been categorized into four generations (Z. Shao 2015). The first generation satellite towns are dormant cities. People live there but travel to the main city. The second generation satellite towns have some factories and public facilities where people can work close to home. The third generation satellite towns are almost independent of the main city. They supply employment opportunities and the centre is modern too. The fourth generation satellite towns are connected by high speed traffic lines so that the function of the main city can be extended to the satellite cities.

This chapter demonstrates the use of RS and GIS, to study the urban sprawl mapping and identify changes of urban land use/ land cover through different years. Satellite data, are found to be helpful in mapping and measuring the degree of urban area in different time periods. New Built Up area in the Maradu is growing largely towards North, North-West and North East directions along the main transport route of the Cochin city. New Urban Development is taking place mainly on the vegetation and agricultural land. The above study provides a methodology for better estimation of urban growth and population, using various land uses over different time periods. The secondary data sources such as Archival Data and Records can be used as a base for understanding the historical changes of an area. This is useful for the urban planning authorities in developing countries, where land use data is not available regularly. GIS and Remote Sensing, with Archival Data can help a lot in monitoring urban sprawl compared to conventional techniques. And finally the conclusions from the above data and their analysis necessitates that an assessment be made of the unsustainable conversion of land for urbanization, and its effect on the Physical Environment Status. And this paves way for another enquiry….whether the Physical Environment Status is being altered with the dynamic changes in the population and the changing land use trends. 89

4. ASSESSING PHYSICAL ENVIRONMENT STATUS USING REMOTE SENSING AND GIS

4.0 INTRODUCTION

Urbanization is the change from rural to urban and its impact on environment has been widely assessed and studied for an effective resource management. Urban sprawl associates large scale alteration of the natural landscape which will continue to escalate and have a profound effect on environmental conditions and processes. Geography helps to analyze the spatial aspect of variation of particular, relevant components and processes. Remote sensing is the science and the art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation (Lilliesand, 2004). Remote sensing has an important role in environment quality evaluation and management strategies. The advantages of remote sensing like spatio-temporal analysis and availability of multi spectral data can be used effectively for monitoring physical environment quality studies. However, use of remote sensing is limited to surface measurements on Land and Vegetation, Water and Air using indices like turbidity, suspended sediment, chlorophyll and eutrophication and temperature. Works on various other physical environment parameters are still going on.

4.1 PHYSICAL ENVIRONMENT STATUS

“Environmental quality” the term was first included in meeting of experts in the division of socio-economic analysis held by UNESCO headquarters in December 1976. These experts and academicians suggested a methodological approach or approached for the formulation of quality of life (quality of the working life and quality of the working environment). Milbrath defined environment and quality and proposed methodology for measuring them. He proposed subjectively as well as objectively measuring the environment conditions and environment quality. Environmental quality was assessed using many variables out of which physical environment was analyzed through Topography, soil, climate ,air, water and variety of birds and animals.

Milbrath is a pioneer to develop a methodology for assessing environment quality in order to highlight the very different indicators surrounding physical environment. Accordingly, objective measures will not ipso facto lead to a high quality environment and 90 vice versa. In this sense, the usage of both objective and subjective measure are imperative for understanding physical environment quality.

P.L.Knox, promoted the mapping of social and spatial variations in assessing environmental quality in quality of life studies was suggested as a methodological approach in later years (Andrasko, 1993). Spatial approach to the study on urbanization and physical environment status/ is widely used in geographical studies (Brian B Barnes, 2014) (Rodny, 2012). Studies mainly on physical environment are mainly analyzed using primary data. These scholars mainly used integrated approaches on Geographic Information Systems and Remote Sensing and also in situ primary data collected from the source.

As can be seen, most of the physical environment studies are situated in European and American settings, although some methodological approaches could have been adopted by Indian context as well. The larger the bigger the study area and the population are, the higher is the degree of our knowledge about physical environment status is generalized. The present study is an attempt to assess the physical environment status in the context of Maradu, by identifying the change clusters/no change clusters which are a repercussion of urbanization and to understand an underlying spatial pattern in the area.

4.1.1 Data products

Cloud free Landsat images were downloaded from USGS for the year 1973, 1993 and 2013. Landsat MSS (bands 1-4) for the year 1973, Landsat TM 7 (bands 1-7) 1993 and Landsat 7 ETM+ data (bands 1-8) 2013 and pan was selected. The sensors have an overpass frequency of 16 days.

Table no 4.0 Landsat sensors and their characteristics Sensor type Date of Path/Row Cloudiness (%) Sun Azimuth acquisition Elevation Landsat MSS 1973-02-10 155/53 Less than 10 % 46.5 123.72

Landsat TM 1993-01-24 144/53 Less than 10 % 46.84 136.86

Landsat ETM+ 2013-01-14 144/53 Less than 50 % 46.8 136.86

SOURCE: USGS HANDBOOK

Figure 4.1 Methodology Flowchart- Physical Environment Status

LANDSAT MSS LANDSAT TM LANDSAT ETM+ 1973 1993 2013

ERDAS/GIS SOFTWARES BASE MAP GEO REFERENCE OUTPUT IMAGES LAYER STACK/SUBSET

FIELD SURVEY POST CLASSIFICATION METHODS- BAND RATIO IMAGE RECLASSIFICATION

PHYSICAL ENVIRONMENT STATUS

Source: Compiled by the author

4.1.2 Data processing and analysis

Cloud free Landsat images were downloaded from USGS for the year 1973, 1993 and 2013. Maradu, study area falls under Landsat World reference system Path 144 row 53(See Map no 4.0). The downloaded images were selected on the basis of the percentage of cloud cover. As per the Meta data, the first two images for the year 1973 and 1993 cloud cover was less than 10 % but for the year 2013 the downloaded image has a cloud cover less than 50 %.

ERDAS 9.1 and Arc GIS 10.1 were used for data processing and analysis. These images were processed with ERDAS IMAGINE software, which involves geometric correction, unsupervised classification, Band rationing and GIS reclassification. The different bands of imageries were stacked to produce a False Colour Composite (FCC). The Panchromatic data were merged with multispectral FCC. The base map (Maradu Municipality map, 2014) of the study area was imported in computer environment and geo referenced in GIS environment. By using this shape files were generated. The sub-setting of 92 satellite images were performed for extracting study area by taking geo-referenced outline Maradu Municipality boundary of 2011 map. The images were in Geo tiff format which was converted to imagine files. Post- classification techniques band ratio was performed in each of these images for finding change clusters/ no change clusters (Ulrike, 2012). A band ratio image is created by dividing brightness values, pixel by pixel, of one band by another (Lilliesand, 2004). A digital image-processing technique, is which enhances contrast between features by dividing a measure of reflectance for the pixels in one image band by the measure of reflectance for the pixels in the other image band (Ibid). The primary purpose of such ratios is to enhance the contrast between objects by dividing brightness values at peaks and troughs in a spectral reflectance curve. This tends to enhance spectral differences and suppress illumination (topographical) differences. Ratios can be used to differentiate objects if those objects have characteristic spectra. (Ibid). Thus the band files for all the images were layer stacked to form a union of images and (DN) Digital Numbers were used for analyzing the images.

4.2 LIMITATION OF THA DATA

• Availability of cloud free satellite images/ Images of same resolution/band combinations • The narrowing of physical environment using Band ratio Images

Map no 4.0 Landsat images 1973, 1993 and 2013

Source: Usgs Glovis Landsat Mss, Tm and Etm+

PHYSICAL ENVIRONMENT STATUS

The rapid urbanization and its impact on physical environment status have been widely acknowledged in remote sensing. Features of the urban environment- the social and the physical environment, as well as the infrastructural resources- all in turn are influenced by municipal, national and global forces and trends. The spatial agglomeration of built surface in most cities has different physical characteristics from most naturally occurring environments. These physical characteristics influence environmental conditions by changing the flux of mass and energy through the environment. Physical environment has been defined using the following factors

1. LAND AND VEGETATION 2. WATER 3. AIR

4.4. LAND AND VEGETATION

Land and vegetation are important components of soil and provide many benefits to surface soil including protection of surface land. This type of assessment provides an understanding of the current status of land and vegetation conditions of the area assessed. The expansion of urban areas into other land covers/vegetation drives environmental change, including climate, hydrological systems, and biogeochemistry and habitat loss. This methodology process incorporating remote sensing and GIS helps to understand or predict the nature of the landscape, the types of changes occurring, the casual structure connecting the underlying factors of change, and the hypothesis tested. The urban physical environment refers to the built environment pollution, noise, traffic congestion and the geological and climate conditions of the city occupy (Guy Carrin, 2010). The built environment refers to the “housing form, roads and footpaths, transport networks shops and markets, parks and other public amenities and the disposition of the public space (Scott Weich, 2002).Recent studies have suggested that poor-quality of built environment is associated with depression, drug overdose, and physical activity. The cost of development of new areas and longer transportation routes may contribute to higher green house gas emissions and larger energy use.

Land quality was assesses using the following indices

1. Land use/Land cover 2. Normalized Difference Built Up index (NDBI) 3. Normalized Difference Moisture Index (NDMI) 4. Normalized Difference Vegetation Index(NDVI) 5. Imperviousness 6. Land Surface Temperature(LST)

1. Land use/Land cover

The National Remote Sensing Agency (NRSA) conducted a land use survey using Remote Sensing Technique in the year 1988-89 at the behest of the Planning Commission in which they had classified the land by visual interpretation technique and digital techniques into twenty two-fold. The definitions of the 22 categories adopted by them are defined and interpreted in Chapter 3.Land use /land cover classification is not adopted entirely because most of the features mentioned in the NRSA classification are not seen in the study area. So modified form of NRSA classification is adopted suiting the study area needs.

SPATIO-TEMPORAL LAND USE/LAND COVER ANALYSIS OF MARADU

A modified NRSA land use/land cover classification is adopted in my study area for three different time periods.

Map no.4.1 Land use maps of Maradu over different periods from 1973- 2013.

SOURCE: Calculated and compiled by the author 97

The land use has been categorized into 5 categories as observed in the study area. They are Water bodies, Islets, Agricultural Land, and Mixed trees with settlement. The study area is situated in the lower course of Periyar river (hence sediment deposition is common) and also near the Vemabanad estuary adjacent to Cochin harbour (tidal zone). These two factors are the reason for classification of land use type Islets. Man made islets can be noted along the coastal wards of the panchayat, owing to the dredging in water bodies. This waterway is mostly used by the barge to transshipment of Ammonium to the FACT Ambalamugal. For this purpose, the canal was widened and dredged; the soil thus dredged is dumped along sides of the coastal wards in Maradu. A recent incident of Ammonium leak happened in this Champakara, a place in Maradu.

Fig no.4.2 Land use maps of Maradu over different periods. (1973, 1993 and 2013)

SOURCE: Calculated by the researcher

It can be observed from the above table that the water bodies in the study area are declining from 1973 to 2013 (approximately change of 130 hectares). Islets are seen increasing in double rate from 120 hectares in 1973 to 247 hectares in 1993 and then decreasing trend to 122 hectares. Islets (Natural and Manmade) can be noticed along this area. Man made islets are a result of dredging in the water channel surrounding the Maradu. They are also used for inlets and also for Shrimp cultivation which was at its peak in and around 1990‟s. The same trend can be noticed in mixed trees with settlement. An inverse relation can be observed in Agricultural Land and Built up i.e. agricultural land is steadily decreasing were as built up area is increasing- Built up from 1993 to 2013 is observed 98 doubling from 260 to 458 hectares. The adjacency to Cochin Corporation and Thripunnithura municipality is another reason for this kind of land use change.

2. Normalized Difference Built Up index (NDBI)

National Remote Sensing Agency has defined “Built up land- as an area of human habitation developed due to non-agricultural use and that which has a cover of buildings, transport, communication utilities in association with water vegetation and vacant lands” (NRSA 2010). Normalized Difference Built up Index is automated process of mapping built up areas. Built up area thus mapped has an accuracy of more than 90%, and it‟s a reliable tool (J.Zha, 2003).

NDBI = MIR-NIR (Xu, 2007)

MIR+NIR

Fig 4.3 Methodology flowchart for estimating NDBI, NDVI, NDMI and Imperviousness‟ using Erdas modeler

Source: Compiled by the researcher 99

Landsat images were layer stacked in Erdas 9.1. Layer stacked image was used for calculated using the Radiative Transfer Equation given above. Land sat images the appropriate bands are used and the equation is defined as: Band 4-Band 5/Band 4+Band 5, and its run in Erdas 9.1 imagine modeler using conditional statement. The output image is further reclassified using Natural Jenks in Arc GIS 10.1. The resultant images shows high, medium and low built up areas based on Digital Numbers.

Map no 4.2 NDBI maps for 1993 and 2013

SOURCE: Calculated and compiled by the author

The following maps no 4.2 shows built up index for the years 1993 and 2013. In 1993, the medium built up area constitute more than 25 % in the land area, but for 2013 no medium built up land is seen where as high built up structures and only patches of medium built up area are noted.

3. Normalized Difference Moisture Index (NDMI)

Normalized Difference Moisture Index (NDMI) estimates the level of moisture in vegetation. High value indicates existence of high soil moisture and low value denotes low moisture content of soil. Moisture content of vegetation has a direct effect on soil, and also 100 has a cooling effect on the surrounding environment from Land Surface Temperature (Guan, 2012). The following formula is applied:

NDMI = Band 5-Band 6 Band 5+ Bands 6

From Landsat images, the appropriate bands are used and the equation is defined as: Band 5- Band 6/Band 5+ Band 6 and its run in Erdas 9.1 imagine modeler using conditional statement. The output image is further reclassified using Natural Jenks in Arc GIS. The resultant images shows high, medium and low built up areas based on Digital Numbers.

Map no 4.3 NDMI map for the years 1993 and 2013

SOURCE: Calculated and compiled by the author

The following maps no 4.3 shows moisture index for the years 1993 and 2013. In 1993, the medium and low moisture index area constitute more than 50 % in the land area, but for 2013 medium and low moisture index is seen where as high built up structures are noted.

101

4. Normalized Difference Vegetation Index (NDVI)

Fragmentation of natural patches is one of the best-known impacts of human activities on the diversity, structure and distribution of vegetation. Connectivity is the critical property of landscapes facilitating or limiting the movement of resources and organisms among natural patches. Urban growth affects connectivity directly by modifying the landscape and indirectly by changing biophysical patterns and processes (Tischendorf et al. 2005). A bio diversity gradient decreases from urban fringe to urban core (Pickett et al., 2001). .Normalized Difference Vegetation Index is a representative condition and its photosynthetic apparatus capacity and biomass concentration (Groten, 1993). It is time varying due to change in meteorological and environmental parameters. Healthy vegetation will have a high Digital Number value. Bare rock soil, water and clouds will have low Digital Number value. NDVI is calculated using the following equations

NDVI = (Near Infra Red - Red) (Near Infra Red + Red)

The appropriate bands are used and the equation is defined as: Band 2-Band 4/Band 2+Band 4, and its run in Erdas imagine modeler using conditional statement. The output image is further reclassified using natural jenks in Arc GIS. The resultant images, shows high, medium and low vegetation based on Digital Numbers.

The following maps no 4.4 shows Vegetation index for the years 1973, 1993 and 2013. In 1973, more than 50 % of the land area comes under high vegetation (higher than 144 digital numbers). In 1993, the medium and high vegetation area constitute more than 50 % in the land area, but for 2013 medium and low vegetation index is seen where as high vegetation index are noted as patches.

102

Map no 4.4 NDVI map for the years 1973, 1993 and 2013

SOURCE: Calculated and compiled by the author

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5. Imperviousness- Land Imperviousness

Imperviousness cover is often the primary disturbance contributing to altering hydrology in urbanizing water bodies, affecting various components of hydrological balance. It helps to understand how future developments will influence urban environment especially hydrology and all related environmental factors. Gillie (Gillies 2003) found that empirical evidence, in terms of aquatic species richness, was available to assess the relative impact of changes in impervious surface area using multi-temporal Landsat imageries.

NDVI provides a standardized method for comparing vegetation greenness between satellite images. NDVI low values are considered as low greenness value which corresponds to the cells of potential impervious characteristics. The image thus derived is further divided into high, low and medium impervious layer based on their Digital Number Values.

The following maps no 4.5 shows impervious map for the years 1973, 1993 and 2013. In 1973, more than 50 % of the land area comes under high vegetation (higher than 144 digital numbers). In 1993, the medium and high vegetation area constitute more than 50 % in the land area, but for 2013 medium and low vegetation index is seen where as high vegetation index are noted as patches.

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Map no.4.5 Land Impervious map for the years 1973, 1993 and 2013

SOURCE: Calculated and compiled by the author

105

6. Land Surface Temperature (LST)

The urban surface regulates much of the urban climate through land use and regional climatic change. The urban heat island is well established and documented as affected by the shape and size and geometry of the buildings. The land use/land cover influences the urban atmospheric and surface temperatures. The relationships between surface temperature and landscape features were meditated by socio economic factors, so that richer neighborhood tends to be greener and collar- another example of the “luxury effect”. A scientific report by the Earth System Science Partnership also supports the fact that rising temperatures along with stronger rainfall events and surface sealing (land use change) will lead to improved conditions for vector breeding. (Confalioneri and McMichael 2007) Land Surface temperature maps were generated through two steps. First, the digital numbers were transformed into absolute radiance using

Lλ = (Lmax − Lmin)/255 × DN + Lmin

(1) where Lλ is the spectral radiance, Lmin and Lmax (mW/(cm2 ·sr·µm) are spectral radiances for each band at digital numbers 0 and 255, respectively.

High gain and Low gain mode in ETM+ are characteristics of image. Low gain means surface is brighter in image statistics were as high gain mode surface brightness is low. Low gain LMAX and LMIN are used for spectral radiance.(Refer table no 2.1)

The next step was to convert the spectral radiance into a satellite brightness temperature (i.e., blackbody temperature) under the assumption of uniform emissivity using the following conversion formula:

TB = K2 ln(K1/Lλ + 1)

(2) where TB is effective at-satellite temperature in Kelvin, K2 and K1 are calibration constants in Kelvin, and Lλ is spectral radiance at the sensor‟s aperture. For TM, K1=607.76 and K2=1260.56 mW/(cm2 ·sr·µm) and for ETM+ ,K1=666.09 AND 1282.71 mW/(cm2 ·sr·µm).

106

Table no 4.1 Spectral radiance ranges for Landsat TM AND ETM+

Spectral Radiance Range watts/(meter squared * ster * μm) ETM+ TM Low Gain High Gain Low Gain High Gain Bands LMIN LMAX LMIN LMAX 1 -6.2 293.7 -6.2 191.6 -1.52 169 2 -6.4 300.9 -6.4 196.5 -2.84 333 3 -5.0 234.4 -5.0 152.9 -1.17 264 4 -5.1 241.1 -5.1 157.4 -0.51 221 5 -1.0 47.57 -1.0 31.06 -0.37 30.2 6 0.0 17.04 3.2 12.65 1.237 15.303 7 -0.35 16.54 -0.35 10.80 -0.15 16.5 8 -4.7 243.1 -4.7 158.3 NA NA Source: (USGS Handbook).

Figure no 4.4 Methodology for retrieving Land Surface Temperature

Source: Calculated and compiled by the auother using Erdas modeler 107

Map no 4.6 LST map for the years 1993 and 2013

SOURCE: Calculated and compiled by the author

The following maps no 4.6 shows Land Surface Temperature for the years 1993 and 2013. In 1993, the LST area with more than 29 degree Celsius six patches in entire study area constitute more than 25 % in the land area, but for 2013 high LST area is seen more than 50 % and also a clear indication of Asphalt road surface National Highway can be clearly identified, passing through the middle of Maradu.

LAND AND VEGETATION- BAND RATIO ASSESSMENT

All of the above, six indices attempts to assess the Land and the Vegetation patterns of Maradu using Multi-temporal Landsat images 1973,1993 and 2013. Band rationing was successfully conducted for calculation each indices. In all the maps a certain trend in Land quality can be noticed where the Western portion of the study area Nettoor, showing an increase land activities towards the eastern side, shift from water locked side towards the eastern side (National highways is present). The second change is in eastern part of the study area known as Maradu, where the land is being developed over the years from 1973-2013

The entire area shows a diffusive changing pattern from all the sides. The third unique pattern is shown by the island part Valanthakadu, area showing an increasing land area but the status is not changing drastically compared to the other two sides. Land and Vegetation, 108 only first part of assessing Physical Environment Status, which led us to analyze the second factor -Water status.

4.5 WATER STATUS ASSESSMENT

The rapid urbanization and its impact on water resources have been widely acknowledged in remote sensing. Water being the elixir of life is accounted and measured and studied in order to sustainably manage and to preserve for our future generations. Remote sensing has an important role in water quality evaluation and management strategies. The advantages of remote sensing like spatio-temporal analysis and availability of multi spectral data can be used effectively for monitoring water quality studies. However, use of remote sensing is limited to surface measurements on turbidity, suspended sediment, chlorophyll and eutrophication and temperature. Works on various other water quality parameters are going on.

METHODOLOGY

For analyzing the images ERDAS 9.1 imagine was to develop a model for assessing the water quality for the study area. The Digital Numbers (DN) values were taken as (ToA) Top of Atmospheric Reflectance and were used for analyzing images for different time periods (M.Orderka, 2012). Band ratio using different band combinations were calculated and the resultant images were assessed for finding water quality and the point were the change is seen. So it can be further assessed and studied for a fruitful outcome for my PhD study. Three water quality parameters were assessed. Suspended sediment concentration was calculated using Bands 1,2 and 3 cause the reflectance in these bands for water is seen highest (Rodny, 2012). Salinity calculated using Band 1 and band 3, chlorophyll content in water assessed using band 3 and band 4 (Brian B Barnes, 2014). Model Maker in Erdas was used for creating appropriate model for finding water quality changes for the study area and the methodology is given below with the help of a flow chart.

• Suspended Sediment Concentration =Band 1/Band 1+Band 2+Band 3 (M.Rodny, M.Orderka and, March 2012)

• Chlorophyll Content =Band 3/Band 4 (Brian B Barnes, 2014)

• Salinity =Band 1/Band 3(Fabio et al 2013) • Normalized difference water index 109

Figure no 4.5 Methodology flowcharts for water status assessment using Erdas modeler

Source: Compiled by the author

The following maps no 4.7 shows Salinity concentration for the years 1973, 1993 and 2013. In 1973, High saline content (Digital Number 111-166) occupies more than 25 % of the water bodies, medium saline content (Digital Number 56-110) preceding areas with high saline content, low saline content (Digital number less than 55) is mainly noticed as patches in the water body channel. For 1993 image, most of the area is covered by medium saline content consisting of more than 50 % in the image. In 2013, the entire area is filled with \ high saline content and some patches showing low saline content.

110

Map no 4.7 Salinity map for the years 1973, 1993 and 2013

SOURCE: Calculated and compiled by the author

111

Map no 4.8 Chlorophyll maps for the years 1973, 1993 and 2013

Source: Calculated and compiled by the auothr 112

The following maps no 4.8 shows Chlorophyll content for the years 1973, 1993 and 2013. In 1973, the high chlorophyll (Digital Number more than 187) content is only found in small patches, medium chlorophyll content (Digital Number 109-187) which constitutes more than 50 % of the study area, no low chlorophyll content is seen. In 1993 image, only two patches identified having high chlorophyll content, consisting of more than 50 % of the water channel and less than 25 % of the medium chlorophyll content are seen along the stretch of water channel between Maradu and Nettor. In 2013, the entire area is filled with medium and low chlorophyll content and high chlorophyll content in seen in some patches mainly south eastern, north western and Northern part of the study area.

The map no 4.9 shows Suspended Sediment concentration (SSC) for the years 1973, 1993 and 2013. In 1973, the high SSC (80-99) content is only found in one patch ,i.e. middle side of the study area , Medium SSC (Digital Number60-79) occupies area surrounding were very low SSC is seen and also small patches along North Western and South Western extent of the water channel, less than 20 % of the water bodies constitutes medium SSC (Digital Number preceding areas with low SSC content is mainly noticed along the middle part of the study area and to the South Western- North Western water body channel, (Digital number less than40-59) and Very low SSC (Digital Number <40) constitutes more than 50 % of the entire study area. In 1993 image, Very high SSC content is seen in two patches along the north eastern side and towards the middle of the water channel. High SSC is noticed along three patches (surrounding the Very high SSC content) , rest of the area is covered by Medium SSC content consisting of more than 25 % of the water channel and less than 25 % of the low SSSC content are seen along the stretch of water channel towards South Eastern and South Western side of Maradu. Very Low SSC content is not seen in this year. In 2013, the area with very high SSC content is seen increasing mainly along the middle part of the water channel. High SSC content is noticed alongside Very High SSC content and the entire area is covered with medium and Low SSC contents.

113

Map no 4.9 Suspended Sediment Concentration maps for the years 1973, 1993 and 2013

Source: Calculated and compiled by the author

114

NORMALIZED DIFFERENCE WATER INDEX (NDWI)

Information extraction is the major feature of Remote Sensing. Normalized Difference Water Index is adopted to enhance water information in the method. These methods take the spectral reflection of water and separate other features, or suppress other features. Thus water bodies can be assessed spatially and their distribution can be mapped properly. The formula is as follows:

NDWI = GREEN-NIR (Samuel, 2016) GREEN+NIR

Methodology

The following maps no 4.10 shows Normalized Difference Water Index for the years 1973, 1993 and 2013. In 1973, the medium water index (Digital Number 100-149) occupies more than 20 % of the water bodies, low water content (Digital number less than 100) constitutes more than half on the land area. For 1993 image, High water index area is partly converted medium water content area consisting of more than 50 % of the water channel and less than 25 % of the low water content. In 2013, the entire area is filled with medium water content and some patches showing low water content.

115

Map no 4.10 NDWI maps for the years 1973, 1993 and 2013

Source: Calculated and compiled by the author

116

WATER STATUS: BAND RATIO ASSESSSMENT

Water status is assessed using the four indices given above. All of these indices show a decreasing water status in the study area. Since the water is being surrounded the whole study area, it cannot be physically bound to any of the portions in the study area. Thus overall water status is assessed and found to be having decreasing status. The suspended sediment concentration is increasing in patches, should be further pursued in future studies. The last factor in assessing Physical Environment status is –Air Quality

4.6 AIR STATUS ASSESSMENT

Common pollutants in the surface (ground level air) are Aerosols, Nitrogen dioxide, carbon monoxide, and sulphur-di-oxide (M. N. Sai Suman 2014). Air quality is mainly assessed through ground stations. But observation captured near surface or at the surface cannot measure/ capture vertical variation or spatial variation of pollutants. Remote sensing (Image derived information) is widely used in understanding spatial variation in ground level air mainly concentrating aerosol particles. A complex mixture of organic and inorganic particles (aerosols) can be found suspended in the atmosphere in solid, liquid, or both physical states (Gupta P, 2006). Aerosols are considered as one of the major air pollutants responsible for human health problems related to the respiratory system. Studies on the particles have demonstrated the existence of a link between the presence of fine and ultrafine particulate of non-natural origin and some effects on the health of human and other living beings especially due to industrial and urban emissions which are a major source of aerosols (Pelon et al 2002). The suspension and transport potential of aerosols as well as its relationships with SO2 AND NO2 makes aerosols a good indicator of air pollution at urban and regional scales thus emphasizing the importance of developments in satellite aerosol retrieval.

THE PRINCIPLE IN INTERPRETING THE AEROSOL

Aerosol is solid or liquid particles floating in the air. It reflects and scatters electromagnetic wave. For analyzing the images ERDAS 9.1 imagine was to develop a model for assessing the air quality (aerosol optical thickness) for the study area. The Digital Numbers (DN) values were taken as (TOA) Top of Atmospheric Reflectance and were used for analyzing images for different time periods (M.Orderka 2012). Band ratio using different band combinations were calculated and the resultant images were assessed for finding air 117 quality and the point where the change is seen (Hadjimitis 2009). Aerosol Optical Thickness Air quality parameters were assessed (Yang 2004). It was calculated using Bands 1 and 4 causes the reflectance in these bands for aerosol optical thickness is seen highest. The TM data is divided into two groups. One group is atmospherically corrected using Dark Subtraction method and its ratio is calculated between band 1 and band 4. Another group ratio is between band 1 and band 4, is taken without atmospheric correction. The diverge between two groups are found as ∆L. ∆L is taken as ratio for Aerosol Optical Thickness. (Adopted by Yang et al. 2004).

Figure no 4.6 Flowchart and Diagram showing Aerosol optical thickness (air quality) methodology

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Map no 4.11 Aerosol Optical Thickness map for the years 1973, 1993 and 2013

Source: Source: Calculated and compiled by the author 119

The following maps no 4.11 shows Aerosol Optical Thickness for the years 1973, 1993 and 2013. In 1973, the medium aerosol index (Digital Number 85-170) occupies more than 50 % of the study area, high index (Digital number less than170) constitutes more than southern portion of the study area. For 1993 image, High aerosol index area is mostly consisting of more than 50 % and less than 25 % of the low aerosol content. In 2013, the entire area is filled with high aerosol content.

4.7 SAMPLING PATTERN – PHYSICAL ENVIRONMENT STATUS

In the following table overall distribution of Physical environment changes and setting in which they occur is shown in Table no 4.3 .The table iterates Table no 4. 3 and 4.4 represents indices measuring physical environment status and change/no change clusters of Maradu based on Physical environment status.

From the above table ,after examining various indices, on the physical environment status in study area [such as Land (NDVI, NDBI, NDMI, Land use/ land cover and Imperviousness and Land Surface Temperature), the spatial distribution is non- comparable among wards. But a clear pattern is seen among Maradu and Nettoor area, the following where all the land and vegetation variables the change clusters are mainly changing from Low to High for Nettoor and in Maradu the development trend is seen in an increasing pattern. Valanthakadu area shows a unique pattern with aquaculture sites surrounding the whole island area and containing only certain of population.

Water status is more or less the same in all sample areas. The whole study area is surrounded by water. The analysis of status change need further, in detail study to understand point source pollution caused due to urbanization. But with the above analysis, the comparison among sample areas are clear with Maradu and Nettoor showing change compared to Valanthakadu and up to an extent has satisfies the objective of assessing the physical environment status.

Air status is high in aerosol content in all three areas of Maradu municipality.

For better understanding the perceptual difference in physical environment status the study area is divided into three sampling units. 120

Table no 4. 3 Indices depicting Physical environment status of Maradu municipality

Nettoor Maradu Valanthakadu

Physical Environment Status Indices 1973 1993 2013 1973 1993 2013 1973 1993 2013 Land use/Land cover Low To High Low To High Low To Medium

Normalized Difference Built Up Index Low To High Low To High Low To Medium Land and Vegetation Normalized Difference Moisture Index Low To High Low To High Low To Medium

Normalized Difference Vegetation Index Low To High Low To High Low To Medium

Imperviousness Low To High Low To High Low To Medium

Land Surface Temperature Low To High Low To High Low To Medium

Suspended Sediment Concentration Very Low to High Very Low to High Low to High

Chlorophyll Content Medium to Low Medium to Low Medium to Low Water Status Salinity Content Very Low to High Very Low to High Very Low to Medium

Normalized Difference water index Low to High Low to High Medium to High

Air Status Aerosol Optical Thickness Medium to High Medium to High Medium to High

SOURCE: Source: Calculated and compiled by the author 121

Table no 4. 4 Physical environment status and change/no change clusters

Physical Environment Status Indices Nettoor Maradu Valanthakadu Land use/Land cover ** ** * Normalized Difference Built ** ** * Up Index Land and Vegetation Normalized Difference ** ** * Moisture Index Normalized Difference ** ** * Vegetation Index Imperviousness ** ** * Land Surface Temperature ** ** * Suspended Sediment *** *** ** Water Concentration Chlorophyll Content * * * Salinity Content *** *** ** Normalized Difference water ** ** * index Air Aerosol Optical Thickness *** *** *** *Low to Medium Clusters**Low to High Clusters*** Very Low to High Clusters

SOURCE: Source: Calculated and compiled by the author

Map no 4.12 Sampling areas in Maradu

Source: Calculated and compiled by the author

Though for identifying Physical environment status change and understanding urbanization spatial analysis has been proved successful, but to explore casual relationships which caused 122 changes in physical environment status and urbanization, recently scholars have extensively studied and postulated and the concept of people‟s perception. The concept has also been loosely linked with Sense of Place people‟s perception towards factors influencing physical environment status and urbanization, which also has been used in this study. The next chapter follows this conceptual framework through primary data obtained with the help of Schedule and analysis with the help of SPSS.

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PEOPLE PERCEPTIONS ON PHYSICAL ENVIRONMENT STATUS Introduction

Urbanization, its spread and type, have implication on Physical Environment status, which has been already stated in the previous chapters. Physical Environment cannot be just understood or analyzed with only quantitative data; it has to be examined with the help of qualitative data also. In this case, physical environment is analyzed using four factors: 1) Land 2) Air 3) Water and 4) Vegetation. The previous analysis bought forth three distinct spatial pattern of Physical Environment Status of Maradu whereby Maradu – showing a diffusive pattern, Nettoor showing a reducing trend from peripheral wards ( having water access) to increase in change in physical environment status in Central wards (near roads) and Valanthakadu showing a unique character with an island type of nature. However, ward wise population data of three distinct areas shows the same as above three distinct spatial representations.

Perception is a term which defines precise definition. Perception: is an activity, a reaching out to the world” (Tuan, Topophillia- A study of enviornmental perception, attitudesand values 1974). The potential role of people perception of physical environment status has been an important determinant in determining the urbanization. Physical Environment perception is the multiple ways in which people perceive their environment and allowing them to know their environment. People perception and attitudes, controls the way in which an individual uses a particular physical environment and in turn it affects physical environment status. Thus, it has been argued that most important influence on physical environment status is perception and attitude of the residing population. That is the level, or attitude of people towards particular resources- physical environment-water, land, air and vegetation. The physical environment also has an effect on perception, with culture mediating: thus the development of visual acuity is related also to ecological qualities of environments (Ibid). Another issue is that, different sections of people differ in their opinion for a particular resource. Such reasoning is drawn from observation that their own individual perception, alternatively called physical environment perception, mainly the status change is directly related to its use by the people. Further, people‟s attitude and perception of physical environment has profound impact on its status and on its perception.

Given the focus of this research, the underlying argument of this study is that there is a link between physical environment status and environment perception/usage. In the process 124 of understanding this complex link between the influences of perception, attitudes, the query has been: how difference in environment perception can affect change in physical environment status of an area, i.e. to explore the status of the physical environment, the factors which causes such changes and how people perceive them. People‟s responses on attitudes/ resource usage were understood with the help of qualitative and Participatory Resource Mapping (PRA techniques).

The frame work of analysis can be loosely placed in Kurt Lewin‟s (1997 [1943]) concept of the lifespace described how elements of the environment make up a sort of force field within which people live their lives (Mangold 2014). Lewin, felt that the social and physical environment or field (borrowing from the Gestalt psychological framework) is dynamic and changes over time, across spaces, and with experience; as such, people change over time as well (Ibid). In effect, people and space are connected and co-produce one another, rather than exist as distinct and autonomous entities. These theories, have further probed the relationship between people and environment, through the questions of perception and experience- mainly through the medium of Humanistic geography.

Humanistic geography developed a distinct research strategy, through a series of people centered methods (or adaptations of existing methodologies) to explicate, a detailed and reflective understanding of the relationship between people and place, geography of the world as home (Tuan 1977, Relp 1976, (Murgerauer 1985)). Humanistic approaches in geography sought to explicate the meaning of individual (and social) experience, and the sense of place (and a world) as it were from within or with the flow of the living wholeness of being, that is our being in the world or dwelling (Heidegger 1983).It is supplemented by reading of the texts about the history and meanings of the place. Place has been distinguished by various scholars as a global sense of place (Massey 1991), as meaningful locations (Agnew 1987), how “lived experience” create places (Relph, Place and Placelessness 1976) (Tuan, 1977). Out of which Agnew definition of place as meaningful locations consist of three key aspects (1) Location (2) Locale and (3) Sense of place. Sense of place, is related to the way in which we might (or indeed might not) identify with a place and is necessary based on our associations and experiences, or lack thereof, of a place. Nicole et al 2006 has defined Dimensions of Sense of place through an extensive inter disciplinary literature review and preliminary field based research four consistent dimension of “sense of place” have emerged the bio-physical environment, the personal/psychological element, the social and the cultural context and the political economic milieu (Nicole.M.Ardoin 2006). 125

Figure no 5.1 Dimensions of Sense of place

Source: (Nicole.M.Ardoin 2006).

1) Biophysical dimension-Sense of place relates to Human –Environment relation, an important element of which is a connection to place. Without physical environment as a context, there could be no sense of place (Nicole et al 2006). The bio physical elements attract humans, should be considered in relation to place identity, place attachment or sense of place. Thus physical environment settings can be considered as a “stage” (Steele 1981) (Basso 1996) for human/environment interactions. 2) Individual Dimension- Individual Dimension is a type of; psychological dimension mainly explores the individual‟s experience of places. It strives to understand, people‟s interactions with biophysical places (H.M.Proshanky 1976). Place identity and Place dependence are two individual dimensions in which the environment is an important factor as a self developing concept. Place identity develops through relationships not only with people, but also with places that represent setting for everyday life (A.R.Graefe 1994). Place dependence reflects the importance of a place in providing features and conditions that support specific goals or desired activities (J.J.Vaske 2003). They both contribute to place attachment. Place attachment refers to an emotional and functional bond between human and place. (Hashem et al2013) . 3) Socio-cultural dimensions- Socio cultural dimensions are central to developing countries and maintain a sense of place to creating a cultural background for understanding and interpreting places (M.Hufford 1992) (S.M.Low 1992) . William 2002 attributes to the intimacy of people- place interactions to the social construction 126

of places. The individual functions as a part of the society, which develops, portrays, and often promotes an aggregate understanding of place and cultural symbolic elements sustain society‟s view of beliefs related to place. (Nicole.M.Ardoin 2006).Basso states that “place making is... a form of cultural activity, and so.... it can be grasped only in relation to the ideas and practices with which it is accomplished: (Basso 1996). 4) Political –economic dimensions- Places are not isolated entities but, innumerable visible and invisible connections with other places (Gustafson 2002).Industrialization and global-scale societies that exist further than ever before from ecological systems, bio regionalists fear that society ability to change is diminishing alongside declining biodiversity (MV 1999). The political economic dimension provides fertile ground for deeper understanding of the large-scale implications of people-place connections.

LIMITATIONS OF THE STUDY

For lack of location- information specific, the socio-economic profiles are drawn from the sample area data and also from the surveyed persons. There are obvious limitations here- ward level data do not represent precisely neither the needed information nor the sample area data, which are small for generalizing, yet together they still provide some over all background information in a comparative framework.

FIELD STUDY ASESSMENT

In any such study, people perception and attitudes on Physical environment can be assessed only through qualitative techniques. However several studies have pointed out the use of qualitative methods. The challenge of this data collection is to represent it in a collective level.

SELECTION OF WARDS AND SAMPLES

The introductory chapter gives an insight into discussion of methodology and selection of wards etc. As can be recalled the identification of survey and samples consist of an two stage process, one is data collected from people aged 60+ of understanding the changes in time frames and second, is to categorize places in the study area based on physical environment status and within these areas to find variations i.e. the perception and attitudes on usage of Physical Environment. This recognition or division was needed to find out the cause or factor which is driving these environment changes of any area. The schema would 127 be clearer in the following diagram (Figure 5.1). With confidence level of 90 per cent and confidence interval of 5, for a population of about 4157 (Refer Map no: 5.1) the required sample is 267, which was calculated through an online sample size calculator (Raosoft systems Inc.). For convenience, the researcher has selected 202 samples for the present study. Figure no 5.2 Sampling Scheme

Target Population- aged Target study area-Maradu 60+ populations –Total (Urbanization and Physical 4157 Environment Status

Sample population- Sample Areas- Areas of 202 persons change cluster-Maradu and Nettoor

Maradu- 101 persons Nettoor-101 persons

SOURCE: Compiled by the author

Although geographical distribution was considered, the aim of this thesis and the finalization of sample areas were derived from the above two chapters. (Micro level detail study)

Table no.5.1 Table showing the need for micro-level analysis

OVERALL SAMPLE AREAS WARD NUMBER PHYSICAL URBANIZATION ENVIRONMENT STATUS STATUS Nettoor 1,33,32,31,30,29,28,27 Area of change Phases 1 to 3 26,25,24,23 clusters Maradu 22,21,20,19,18,17,16 Area of change Phases 1 to 3 15,14,13,12,11,10,9,8, clusters 7,6,5,4,3,2,1, Valanthakadu 22 Area of no change Phases 1 clusters Source: Calculated and Compiled by the author

Since, Maradu and Nettoor shows changes in both urbanization and Physical environment status, further more investigation into these areas will reveal the factors responsible for their consecutive change. 128

Map no 5.1 Population map showing aged 60+

Source: Compiled by the author

WARDWISE SOCIO ECONOMIC PROFILE Maradu, the study area was of island type with three islands –Nettoor, Maradu and Valanthakadu (Field Study 2015). Maradu and Nettoor, has been famous for has been 129 identified as Temple based places, dominated by higher caste people (Hindu, Brahmins and Nair). Valanthakadu had and is still known as Scheduled Tribe colony (Field and Municipal Records). As per, Maradu municipality records 2016, Maradu has been divided into 33 wards. Their population characteristics, density are given below in Map no: 5.2 (Appendix : ).

Map no 5.2 Population density map of Maradu

Source: Compiled by the author It can be seen that no particular feature characterize the sampling areas. In general, however the ward wise population density shows that the high density wards (greater than 7001/sq.km) are located along Maradu and Nettoor. A distinct pattern can be seen in Valanthakadu in terms of population density, and Physical environment status where as Maradu and Nettoor is more developed compared to Valanthakadu. Thus, the sampling areas selections are justified. 130

SOCIO ECONOMIC PROFILE OF THE SAMPLED PERSONS

Table no 5.2 Socio-economic profiles of the sampled persons

SOCIO-ECONOMIC PROFILE SAMPLE AREAS Particulars Maradu Nettoor Less than 65 16 35 65-70 48 36 AGE OF THE 70-75 20 13 RESPODENTS Greater than 75 17 17 Male 33 36 SEX Female 68 65 Till 10th 61 39 Pre-Degree 7 7 Degree and More 3 7 Not Educated 4 1 EDUCATION LEVEL Less than 5 th 26 47

Less than 2 6 3 2 TO 4 11 18 FAMILY MEMBERS Greater than 4 84 79 Daily wage labourer 39 59 Retired 12 12 LIC 1 1 OCCUPATION None 39 59 Source: Compiled by the author

Table 5.4 provides some basic statistics on sample respondents. It may be seen from the above table that no particular feature highlights in socio-economic data. The age composition does differ in sample areas. Maradu shows slight high percentage of respondents of age group pertaining to 65-70 where as Nettoor has more diverse group of age group. Sequential developments in sense of place were most evident from those who were raised in the same place (Hay 1998). Females are more in both the sampled areas. Maradu has highest percentage of Population who had education till 10th. The main reason being the location of Mangayil School, which was established at 1970‟s (Fieldwork 2015). There are no variations of family members/types and family size in both areas. Both Maradu and Nettoor have family size of having more than 4 and 2 to 4 (nuclear families). Above 60 populations still a high 131 percentages are Daily wage labourers (Female mostly engaged in housework and Men for Daily wage works such as Painting, Carpentry etc.

Figure no 5.3 (a) and (b) Bar diagrams showing the Age and Sex of respondents

132

Figure no 5.3 (c) and (d) Bar diagrams showing the Present occupation and Education level of the respondents

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PHYSICAL ENVIORNMENT PERCEPTION AND SENSE OF PLACE

Keeping in view of the physical environment factors earlier defined in the methodology part, Land, Water, and Vegetation, these variables have been chosen. To understand physical environment perception, the whole schedule was divided into 1) socio- economic profile 2) Occupational structure 3) Land 4) Transport and Road 5) Water 6) Population 7) Flora and Fauna.

Table no 5.3 Identification of Attributes

CONCEPTS (REFERENCES) OPERATIONAL QUESTIONS

SOCIO-CULTURAL ELEMENTS Gender, Education, Age, Occupation, (Entrikin 1991,Harvey 1996) Traditional Job, Family Members, Old cooking technique, Migrated and inhabitants percentage, reason for change

POLITICAL –ECONOMIC Sample Areas: Occupation, other job location ELEMENTS (Gustafson 2002), (MV Transport old, first bus, bridges and their connectivity 1999)

BIO-PHYSICAL SETTINGS Sample areas: Land use, change, harvesting frequency (Basso 1996), (Steele 1981), (Kellert (Agriculture), acres, at present ,Number of ponds, At SR 2005), (Pyle RM 1993) present, Water bodies, Usage/Why not, sides of water bodies, Water logging/flood. Status of Snakes, Turtles, Duck, Trees. I INDIVIDUAL ELEMENTS Individual: Occupational change, Reason, (S.M.Low 1992), (H.M.Proshanky Changes in Job, Son/daughter job, 1976) Traditional/Non/traditional job Individual: Drinking water source old/new, number of ponds, families using ponds, at present Individual: Years of stay , Migrated, Reason, Old ward, Price per cent, New ward, Price per cent,

SOURCE: Compiled by the Author

The analysis has so far tried to visualize the urbanization pattern and Physical environment status of Maradu, and the role of perception/attitude of people on theses physical environment factors. However, there exists a pattern in the above analyzed factors in the study area. In order to assess the factors influencing the sample areas, Non parametric tests- 134

Mann-Whitney U test is applied. The hypothesis for the tests H0: elements differ significantly for sample areas and H1: the elements don‟t differ (same) significantly for sample areas.

Independent sample- Sample areas (Maradu and Nettoor) are two independent samples. These areas were concluded from analysis done in first and second chapters. The main aim of this analysis is to find whether there is any change of elements or factors in each ward.

Exploratory set of variables: We examine all the variables which are used to identify the cause of Urbanization and Physical Environment Status. Thus the factors which differ are assessed and an investigation to those factors will reveal the main cause of change in Physical Environment Status.

The tables are about the various variables and their influence on sample areas, mainly to find which variables differ in these two areas. It also predicts which the variables are and how likely they influence Physical environment status on Urbanization. Tables 4.3-4.13 summarize the results from Mann-Whitney U tests and estimate the effects of each factor on Maradu and Nettoor. The results have been presented in various subs heading as to analyze the effect of individual factors in each variable.

135

SOCIO-CULTURAL ELEMENTS

Table no 5.4 Socio-cultural elements

Age of the Sex Education Religion Family Grand Son/daughter Cooking Percentage respondent members daughter education source people /son olden residing days 4949.00 4797.50 Mann-Whitney U 4254.500 3864.500 4483.500 4938.000 4394.500 5100.500 4641.000 0 0 10100.0 9948.50 Wilcoxon W 9405.500 9015.500 9634.500 10089.000 9545.500 10251.500 9792.000 0 0 Z -2.143 -.444 -3.264 -1.633 -.564 -1.809 .000 -2.480 -1.130 Asymp. Sig. (2- tailed) .032* .657 .001* .102 .573 .070 1.000 .013* .258 a. Grouping Variable: SAMPLE AREAS *statistically significant at 0.05 SOURCE: Calculated and Compiled by the Author

From the table no 5.4 we can infer that all the Age of the respondent, Educational Level, and cooking source used in olden days are statistically significant at that these factor are different in the sample areas (having p value >0.5) 2 tailed tests. The educational level differ significantly with 60 % in Maradu with education level of 10th standard and 46 % of the respondents of education less than 5th standard. The presence of school in Maradu (Mangayil School) has a direct influence in the educational level of this place. The socio-cultural change the occupational transformation, the job change of children (next generation), the cooking gas methods all indicate the change of different households from resource based life to consumer based services, which had an impact on physical environment. The occupational change from cultivators and agricultural labourers became construction workers in sample areas, due to the growth of construction sector (ibid). 136

Figure no 5.4 (a) (b) and (c) Bar diagrams showing the socio-cultural elements 137

138

POLITICAL - ECONOMIC ELEMETS - In political-economic elements mode of transportation, the mode of transport and construction of bridges has a great influence over development of these areas. But in the present context, the occupation of the respondents differs significantly in both areas.

Table no 5.5 (a) and (b) Political-economic elements

Present Job1 Job2 Job3 Why change Traditional job Son job Son/daughter occupation in job change job Mann-Whitney U 3910.500 4538.000 4605.000 4650.500 5098.500 4848.000 5100.500 4232.500 Wilcoxon W 9061.500 9689.000 9756.000 9801.500 10249.500 9999.000 10251.500 9383.500 Z -3.150 -1.478 -1.320 -1.199 -.005 -1.105 .000 -2.387 Asymp. Sig. (2- .002* .139 .187 .231 .996 .269 1.000 .017* tailed) Transportation

\ If other occupation where is situated How people travelled First bus travel/started First bridge connected

Mann-Whitney U 5078.000 4293.000 3190.000 3592.500 Wilcoxon W 10229.000 9444.000 8341.000 8743.500 Z -.110 -2.111 -6.090 -3.831 Asymp. Sig. (2-tailed) .913 .035* .000* .000* a. Grouping Variable: SAMPLE AREAS *statistically significant at 0.05 SOURCE: Calculated and Compiled by the Author

Son/Daughter change of Job and present occupation of the respondent is statistically significant having a value less than 0.5, which means there is difference in both the wards. About 59% of the respondents from Maradu are retired from their work and are jobless due to their old age 139 ailments and 48% of the respondents in Maradu are Daily wage laborers. From the above table we can infer that all factors are statistically approach roads and side roads to National highway played crucial role for all land uses changes in Maradu (DR.V.T.Jose 2003).

Figure no 5.5 (a) (b) (c) and (d) (e) Bar diagrams showing the political-economic elements

140

141

Source: Calculated and compiled by the auother

142

BIO-PHYSICAL ELEMENTS Bio physical elements are represented in 3 categories 1) Land Status 2) Water Status 3) Vegetation LAND STATUS Table no 5.6 (a) (b) (c) (d) Bio-physical elements

Location of Years of Reason for Migrated How Old Land New house Land firewood stay migration reason many house price- value source cents Past Present 5050.00 4442.00 Mann-Whitney U 3901.500 4301.500 4836.500 481.000 12.000 18.500 4799.000 0 0 10201.0 9195.00 Wilcoxon W 9052.500 9452.500 9987.500 1111.000 22.000 63.500 9849.000 00 0 Z -3.049 -2.028 -.698 -1.598 -.382 -1.000 -1.014 -1.662 -1.292 Asymp. Sig. (2-tailed) .002* .043* .485 .110 .702 .317 .310 .097 .196 *statistically significant at 0.05 SOURCE: Calculated and Compiled by the Author

From the above table we can infer that all factors except Location and Years of stay are statistically significant. The variables interpreting Land status has to be understood in detail in both the sample areas (having p value >0.5).

143

Figure no 5.6 (a) (b) (c) (d) (e)(f)(g) (h) (i) (j) (k) Bar diagrams showing the bio-physical elements

144

LAND USE CHANGE

Table no 5.6(b)

Past Harvesting How many Present Change in Percentage Pattern of Water occupation frequency acres situation years migrated water logging logging Mann-Whitney U 4754.500 4053.000 4846.500 4949.000 4904.000 4527.000 3930.000 4999.500 Wilcoxon W 9905.500 9204.000 9997.500 10100.000 10055.000 9678.000 9081.000 10150.500 Z -.957 -3.049 -1.438 -1.741 -.958 -1.405 -2.942 -.290 Asymp. Sig. (2- .002* .150 .082 .338 .160 .003* .772 tailed) a. Grouping Variable: SAMPLE AREAS *statistically significant at 0.05 SOURCE: Calculated and Compiled by the Author

From the above table we can infer that all factors are statistically significant and that these factor are more or less same in both the sample areas (having p value >0.5) for (1 tailed) tests respectively. From the above table we can infer that all factors are statistically significant and that these factor are more or less same in both the sample areas (having p value >0.5) for both (1 tailed) tests respectively. Water and Land status differ significantly for both areas and are entirely different, it has also led to a change in pattern of water logging.

145

Figure no 5.6 (c)

146

Figure no 5.6 (d)

147

WATER STATUS AND ITS USEAGE Table no 5.6(c)

Drinking Old drinking How How Sides of Sides of Present water now water source many many Water water pond ponds families bodies- bodies- Past Present 4866.50 4259.00 Mann-Whitney U 4949.000 5007.000 4797.500 4898.500 5100.500 0 0 10017.5 9410.00 10049.50 10251.50 Wilcoxon W 10100.000 10158.000 9948.500 00 0 0 0 Z -1.741 -.288 -1.087 -2.901 -.788 -2.015 .000 Asymp. Sig. (2-tailed) .082 .773 .277 .004* .431 .044* 1.000 *statistically significant at 0.05 SOURCE: Calculated and Compiled by the Author

From the above table we can infer that all factors are statistically significant and that these factor are more or less same in both the sample areas (having p value >0.5) for (1 tailed) tests respectively, except present condition of ponds. From the above table we can infer that all factors are statistically significant and that these factor are more or less same in both the sample areas (having p value >0.5) for (1 tailed) tests respectively. The usage of land and water resources differed significantly and it can be associated with the caste hierarchy dominating both the areas. Reason for the change in study areas is striking different with % people stating money as a factor for changing source of water from ponds to wells and then to pipes. 148

FLORA AND FAUNA STATUS

Table no 5.6(d)

Fish Reason for poisonous Snakes types Fruit trees Turtle Duck Mann-Whitney U 4350.500 4117.500 5050.000 4950.500 4749.500 4315.500 Wilcoxon W 9501.500 9268.500 10201.000 10101.500 9900.500 9466.500 Z -2.772 -2.519 -.140 -1.342 -1.534 -2.523 Asymp. Sig. (2-tailed) .006* .012* .888 .180* .125* .012* a. Grouping Variable: SAMPLE AREAS *statistically significant at 0.05 **statistically significant at 0.01 SOURCE: Calculated and Compiled by the Author

From the above table we can infer that all factors are statistically significant and that these factor are more or less same in both the sample areas (having p value >0.5) for (1 tailed) tests respectively. The bio-physical change of land and water are striking because it has been directly or indirectly influenced by the occupational change. The change in land use pattern in chapter 1 clearly indicates the change from agricultural land to urban purposes which led to decrease change in agricultural purposes. The change in land use pattern can affect storm-water can wash litter, grease, heavy metals, pathogens, petroleum products and more, directly waterways which will affect stream biota (Bottom dwelling flora and fauna, alteration of fish species and erosion of channel banks)( Wolman and Schick in (CWRDM 2001))

149

Figure no 5.6 (e) (f) Bar diagrams showing the bio-physical elements 150

Figure no 5.6 (g) (h) Bar diagrams showing the bio- physical elements

151

Figure no 5.6 (i) (j) Bar diagrams showing the bio-physical elements

152

Figure no 5.6(k) Sources: Calculated and compiled by the auother 153

INDIVIDUAL ELEMENTS Table no 5.7 (a) (b) Individual elements

Water bodies If yes then None Now also Cause of Water Water If yes then Change Old use what why use ponds change in usage bodies use what in ward neighbor hood water change knowledg e 4870.00 Mann-Whitney U 4898.500 4896.500 4649.000 4949.000 3833.500 4898.500 4896.500 3002.000 4423.500 0 10021.0 Wilcoxon W 10049.500 10047.500 9800.000 10100.000 8984.500 10049.500 10047.500 5852.000 9574.500 00 Z -1.188 -1.199 -2.148 -1.151 -3.202 -.824 -1.188 -1.199 -.789 -2.156 Asymp. Sig. (2- .235 .230 .032* .250 .001* .410 .235 .230 .430 .031* tailed) Table no 5.7 (b) WASTE DISPOSAL Solid waste disposal Plastic Old toilets

Mann-Whitney U 4888.500 4898.500 4444.000 Wilcoxon W 10039.500 10049.500 9595.000 Z -.909 -.901 -1.924 Asymp. Sig. (2-tailed) .364* .367* .054* a. Grouping Variable: SAMPLE AREAS *statistically significant at 0.01 154

Source: Compiled and calculated by the auother From the above table we can infer that all factors are statistically significant and that these factor are more or less same in both the sample areas (having p value >0.5) for (1 tailed) tests respectively. The Individual elements such as reason for not using water bodies or ponds, their neighborhood knowledge and waste disposal methods have had a profound impact o their understanding/preserving nature on physical environment. The residential status has been development towards the sense of place (Hay.R 1998). Perceptions of the local environment have a direct and independent effect on neighborhood attachment (Mesch G S 1998 ). History of water use, perceptions which affect water use and management of community resources affect water resources. (Menash et al in (CWRDM 2001) ).

Figure no 5.7 (a) (b) (c) (d) (e) and (f) Bar diagrams showing the Individual elements

155

156

157

Sources: Calculated and compiled by the auother 158

Figure no 5.7 Comparitive diagram showing the signficant variables influencing the Sense of place

Age Pattern of .050 Education waterlogging Present Snakes types .040 occupation Reason for .030 Children Job poisonous .020 Old Fish population .010 neighbourhood

Sides of water .000 Fuel source bodies

Families using Mode of transport ponds Harvesting Bus service frequency Years of stay bridge connected change in water None why source Asymp. Sig. (2-tailed)

Sources: Calculated and compiled by the auother

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Table no 5.10 Elements- Level of significance for sample areas of Maradu

Level of QUESTIONS Past Present significance changing 1.Political- 1. Mode of transportation, ** *** economic 2. First mode of transportation elements ** 3. Bridge connecting places ** 2.Socio-cultural 4. Age* ** **** elements 5. Educational qualification* 6. Present Occupation * 7. Harvesting frequency ** 8. Cooking gas methods** 9. Son/Daughter Job * 3.Bio-physical 10. Location of firewood source * ******* elements ** 11. Sides of water bodies * 12. Fish population * 13. Reason for poisonous snakes * 14. Snake types * 15. Pattern of water logging * 16. Duck population * 17. Turtle population * 4.Individual 18. Number of families using *** ***** elements ponds in olden days** 19. Years of stay * 20. Reason for not using ponds * 21. Reason for not using water bodies * 22. Old neighborhood ** 23. Plastic disposal * 24. Old toilet usage ** 25. Solid waste disposal * * Variables indicating Present situation ** Variables indicating Past situation Source: Compiled by the author

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Fig 5.7 and Table 5.10 suggests the changing elements from past to present as per people perception. Sense of place that is composed of different elements and their changes of sample areas have been defined from the above analysis,- were bio-physical, individual and socio-cultural elements also play an important role, as per people‟s perception on Maradu. Participatory resource Mapping has been used to represent these changes in Maradu.

PARTICIAPTORY RESERCH METHODS-RESOURCE MAPPING

Participatory rural appraisal is an approach to incorporate the knowledge and opinions of rural people in the planning and management of development projects and programs. Robert Chambers, a Fellow at the Institute of Development Studies (UK), first used the term Rapid Rural Appraisal in 1983. In 1985, the first international conference to share experiences relating to RRA was held in Thailand. This led to a rapid growth in the development of methods that involved rural people in examining their own problems, setting their own goals, and monitoring their own achievements. By the mid 1990„s, the term RRA had been replaced by a number of other terms including Participatory Rural Appraisal (PRA) „and Participatory Learning and Action„(PLA). PRA involves local people and outsiders from different sectors and disciplines. Outsiders facilitate local people in collection and analyzing the information, practicing critical self-awareness, taking responsibility and sharing their knowledge of life and conditions to plan and to act. The tools used in PRA are secondary data reviews, participatory observations, semi-structured interviews, mappings, time-line, trend analysis, seasonal review, workshops etc. “PRA, methods are often called, are visual and tangible and usually performed by small of people” (Chambers 2007).The methods are divided into many categories out of which , resource maps are used. Resource map is space related method which is generated at a given point of time in space. Resource Mapping can be used as ice breaking exercise as well as tool to investigate the knowledge of the people about their own locality, their resources and their spatial distribution.

Resource Mapping can help communities

 Rethinking and assessing there existed resources  To make them understand the impact of land use  To inter connect resource usage and their existing problems Facilitator being the researcher itself was one on one discussion with respondent.

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CHOICE OF LOCATION AND ACTIVITY

The activity started in two shifts one in the morning around 9.30 am; it was organized with the Vayogana clinic6 Kerala Social Security Mission (KSSM), when most of people came to the clinic (where Consultation and medicines are also given free of cost). The frequent visits to the clinic helped in creating a good rapport between author and the respondents, which helped a lot in schedule as well as Resource mapping session. The Facilitator/author had boundary map of each ward and when one person was explaining the area, others also joined and told their views and joined the team. After initial reluctance all were enthusiastic and the entire mapping was done by people.

The Mapping process

Due to constraints, mapping was done with reference to present major road and features like Anganwadi, post office, police station etc. People were able to identify these features and helped in to marking out land use of the area, which once existed. The marking areas of ponds, streams and other line features were done. Finally the point features location of wells, ponds, markets, and trees were marked. Some of the landmarks such as temples and settlements were introduced by people, randomly for their better understanding of direction and position. But this did not affect the flow of the process. Most of the drawings were done with colour pencils/crayons, because it could represent different colour for each class.

Other benefits from the activity

Minor details on land use/ water bodies / their experiences that are not covered in schedule were got this exercise.

From, the participatory resource mapping one can infer the drastic change from agriculture/garden land to built up and the conversion of these areas into aquaculture sites and built up areas. The road networks have doubled through the years and have completely changed the entire landscape. The roads and railways have converted the entire land and development or change in land use along both sides of roads can be well documented with the help of participatory resource map and recent image from Google earth.

6 Vayogana clinic is a state government initative under Kerala Social Security Mission (KSSM) for elderly above 60 – free medical checkups and medicines once in a week in Anganwadi’s. In Maradu there are about 20 clinics for 33 wards. 162

Map no 53. (a) and (b) Participatory Resource Mapping and Google earth image 2016

SOURCE: Compiled and calculated by the author 163

CONCLUSION

The situation of Sense of place in the two change clusters of Maradu reveals multi- layered dimensions and yet several generalizations are possible. Whether its Maradu or Nettoor, Participatory Resource Mapping brought a lot of insights into the land use, water resources of the area. The old true picture of Maradu will help the researcher to identify the place respondents are speaking of.

After the Non Whitney U tests, the author was able to accomplish that the infrastructure mainly modes of transport was concentrated along Maradu than Nettoor cause of its prominence of temple and the adjacency of Thripunnithura in the eastern side of the study area in olden days. For Socio cultural elements the education level of the respondents, caste, their age all seems to have an impact on Physical environment status. The Individual elements especially the reason of change was interesting with people often stating money as a main factor in deciding the use of resources. The bio- physical settings – land, water and vegetation status has changed entirely, i.e. in present context the change in vegetation is due to the dynamic change in land use. The pattern of water logging sheds light into the improper development of the area, which to some extent satisfy that water logging commonly occurs in area of high soil moisture content, This can be established by the fact that more than 70 % of the land was agricultural fields (Participatory resource mapping-Field Study 2016).The neighborhood knowledge of respondents in older days which also shed light that the area had half the population that its now sustaining. Participatory resource mapping helped in giving a spatial assessment of resources once available, and its comparison to the current context with the help of Google Image 2016.

In sum, withholding the limitations and complexities involved in assessing physical environmental status that were also to be located in a spatially confining setting as required by the conceptual and theoretical constructs, it may be said that the study within its scope has been able to establish the association between the factors -urbanization and changing the physical environmental status.

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CONCLUSION AND SUMMARY

Urbanization and Physical Environment Status had no open and significant influence in geographical research related to Humanistic geography in Kerala until date. Earlier research on Physical environment status and Urbanization focused primarily on the hotspots and intervening areas of pollution, or to assess the trend of urbanization etc. Conceptually, such research provided a satisfactory explanation of Physical environment status and Urbanization, but its failure to address the role of people‟s perception within the society reduces its acceptability in the present context. In the discussion of these processes, Humanistic geographical concept of Sense of place- how lived experiences create a sense of place (Tuan 1977) and the different elements that makes it, need to be understood. According to him, no models are considered complete unless they include a description of the human experience that change and form urban areas. This is, because an urban change of an area affects people perception and attitudes about the physical environment status. The concept of Humanistic geography (Sense of place) –theory tries to understand if urbanization influences the change in Physical Environment Status. The multi dimension of sense of place defines the different elements that are present in area, and the changing elements and their inter connections due to urban changes. Thus, humanistic geography thus provides a framework within which Physical Environment Status and urbanization become mutually conclusive through complex elements.

As detailed out in the introductory chapters, such an understanding of Urbanization and Physical Environment Status promotes a theoretical development away from the ecological perspective towards a greater people oriented discourses, which on one hand revives the perspective from the society rather than views being suggested. The complexity play of Urbanization on Physical Environment Status with an integration of Humanistic geography is a complex phenomenon. Increasingly, however, it has been accepted that sense of place have been widely addressed to understand Physical Environment Status in several discourses earlier.

Many studies have argued that Urban Ecological Landscape Approach has quantified Urbanization and Physical Environment Status. With this background, the present study deals with the issue of urbanization and its implications on physical environment status with the help of primary and secondary data, Geographical Information System (GIS) and Remote Sensing (RS) for Maradu. The analysis is carried out under three levels - Urban growth, 165 assessing Physical environment status, both based on multi-temporal Landsat images using GIS and RS, and tries to understand the elements or the drivers influencing the changing physical environment status using primary data - Field survey and Participatory Resource Mapping.

Briefly, objectives of the study are:

3. To understand the urban growth of Maradu 4. To assess the Physical Environment status using GIS and RS. 5. To examine the people‟s perceptions on physical environment status of the residing population.

Following research questions were posed in order to achieve these objectives:

 What are main driving factors behind the urban growth of Maradu?  Are there any differences in the physical environmental status in the areas addressed by the research?  Is there any particular elements influencing the change in Physical environment status of this area?

The study consists of five chapters. First chapter deals with literature review, discusses the theoretical framework. It also contains research framework including research questions, data methods, and sample design. The second chapter defines the study area with locational aspects. The third chapter draws from urban intensity analysis of multi-temporal Landsat images. This chapter provides details of urbanization trend and growth of built up within a spatial framework. The emphasis is mainly on the urban phases from past to present. The fourth chapter explores the physical environment status using image oriented techniques using GIS and RS tools and to identify areas with change or no change clusters during different urban phases. These four chapters together not only throws light on the spatial analysis on Urbanization and physical environment status of Maradu, which provide crucial background information against which the subsequent analysis has been placed. The fifth chapter investigates people‟s perception on sense of place and the elements which are affects the changing physical environment status. The sixth which is also the final chapter is about the major findings and conclusions drawn from the study.

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The third chapter amply demonstrates the use of Remote Sensing and GIS to analyze the urban sprawl mapping and detect changes of urban land use/ land cover through different year. Secondary data sources such as Archival data can be used as a base for understanding the historical changes of an area. Satellite data are found to be useful in mapping and quantifying the extent of urban area in different time periods. Urban Intensity Analysis (Liu 2000) and Urban Proportional Index quantified and measured, pace of urban growth. New urban development (New built up area) occurs mainly on vegetation and agricultural land is growing largely towards North, North-West and North East direction along the main transport route where major developments will take place Cochin metro, Capitol Mall etc. The above study provides a methodology for better estimation of urban growth and population using various land uses over different time periods. GIS and Remote Sensing with archival data can help a lot in monitoring urban sprawl compared to conventional techniques. Thus the unsustainable conversion of land for urbanization has been established. The fourth chapter examines various variables, on the physical environment status such as Land (NDVI, NDBI, NDMI, Land use/ land cover and Imperviousness and Land Surface Temperature), Water [Salinity, Suspended Sediment Concentration, Chlorophyll Content and NDWI] and Air (Aerosol Optical Thickness) in Maradu. These indices were produced with the help of multi-temporal Landsat images using {Post-classification method} Band rationing in Remote Sensing and Geographical Information System. A clear pattern was seen among Maradu and Nettoor area, all the indices - change clusters are mainly changing from Low to High for Nettoor and in Maradu the status is seen in an increasing pattern. Though for identifying Physical environment status change and understanding urbanization spatial analysis has been proved successful, but to explore casual relationships which caused changes in physical environment status and urbanization, humanistic geographical approaches has been used. The concept has also been loosely linked with Sense of Place people‟s perception towards physical environment factors, which also has been used in this study.

The fifth and the final chapter explain humanistic approach - situation of Sense of place in the two change clusters of Maradu. Primary data was collected through means of a Schedule. Out of 4157 (60+ aged population) a purposive sample of 202 was selected and surveyed for getting the needed results. Non parametric tests- Mann Whitney U tests was analysed with the help of SPSS. The main change was noticed in Individual elements, socio- cultural and Bio-physical elements of the sample areas. The striking feature was the 167 difference in both sample areas that of political-economic elements, on transportation, bridges connecting these places and the mode in which people travelled roads in olden days where as at present Urbanization played a major role in equally distributing political- economic elements. The socio-cultural change the occupational transformation, the next generation job change, the cooking gas methods all indicate the change of different households and their transformation from resource based life to consumer based services has an impact on physical environment status. The occupational change, of cultivators and agricultural labourers to construction workers in sample areas, due to the growth of construction sector. The bio-physical change of land and water are striking because it has been directly or indirectly influenced by the occupational change. The Individual elements such as reason for not using water bodies or ponds, their neighbourhood knowledge and waste disposal methods have had a profound impact on their understanding/preserving nature on physical environment. Whether its Maradu or Nettoor, Participatory Resource Mapping brought a lot of insights into the land use, water resources of the area. The old true picture of Maradu will help the researcher to identify the place respondents are speaking of. Participatory resource mapping helped in giving a spatial assessment of resources once available, and its comparison to the current context with the help of Google image 2016.

In brief, with holding the limitations of various sources of data and complexities involved in considering and assessing Urbanization and Physical Environment Status that were also to be located in spatially confining place as requires by the conceptual and theoretical constructs of Humanistic geography, it may be said that the study within its scope has been able to establish an association between Urbanization and Physical environment Status.

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List of Appendices

Appendix I: Kerala District wise urban population – 2011

Sl.no Districts Urban population 1 Kasargod 5,05,176 2 Kannur 16,42,892 3 Wayanad 31,577 4 Kozhikode 20,74,778 5 Malappuram 18,16,483 6 Palakkad 6,77,193 7 Thrissur 20,89,790 8 Ernakulam 22,32,564 9 Idukki 52,025 10 Kottayam 5,65,611 11 Alappuzha 11,47,027 12 Panthanamthitta 1,31,461 13 Kollam 11,86,340 14 Thiruvananthapuram 17,79,254 Source: Census 2011

Appendix II: Municipalities of Ernakulam district-2016

Sl.no Muncipalities 1 Aluva 2 Angamaly 3 Eloor 4 Kalamassery 5 Kothamangalam 6 Kothattukulam 7 Maradu 8 Muvattupuzha 9 North Paravur 10 Perumbavoor 11 Piravom 12 Thrippunithara 13 Trikkakkara Source: www.ernakulam.nic.in

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Appendix III: Details of different wards of Maradu Municpality-2011

Total Ward number Name of ward population-2011 Population density 1 Nettoor north 1322 3672 2 Kundanoor north 1203 2406 3 Kannadikkadu east 1185 9875 4 Kunnalakadu 1288 21467 5 Thuruthy 1203 20050 6 Maradu north 1218 17400 7 Kattithara 1218 4872 8 Sankar nagar 1225 5104 9 Martinpuram 1303 9307 10 Sasthrynagar 1185 7406 11 Kairali nagar 1292 8075 12 Society 1333 9521 13 Pandavathu 1162 4469 14 Neravathu 1222 3491 15 J b school 1177 2737 16 Kundanoor junction 1214 3195 17 Sub registrar office 1226 1977 18 Mangayil 1166 3761 19 Mannaparambu 1252 6260 20 Ambedkar nagr 1148 2014 21 Ayurveda hospital 1178 1870 22 Valanthakadu 1167 1122 23 Santhivanom 1300 1711 24 Nettoor south 1270 4379 25 Thandassery 1307 13070 26 Peringattu parambu 1251 4314 27 Purakeli 1192 5183 28 Svups 1333 3508 29 Thekkepattupurackal 1292 4969 30 Ambalakadavu 1300 4194 31 North colony 1303 4654 32 North pattupurackal 1222 5313 33 Thattekadu 1315 5260 Source: Maradu Municipality 2011

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Appendix IV: CENSUS DEFINITIONS-WORKERS

1. CULTIVATOR - A person who has given out his/her land to another person or persons or institution(s) for cultivation for money, kind or share of crop and who does not even supervise or direct cultivation of land, is not treated as a cultivator. Similarly, a person working on another person's land for wages in cash or kind or a combination of both (agricultural laborer) is not treated as a cultivator. Cultivation involves ploughing, sowing, harvesting and production of cereals and millet as wheat, paddy, jowar, bajra, ragi, etc., and other crops such as sugarcane, tobacco, ground-nuts, tapioca, etc., pulses, raw jute and kindred fibre crop, cotton, cinchona and other medicinal plants, fruit growing, vegetable growing or keeping orchards or groves, etc. Cultivation does not include the following plantation crops - tea, coffee, rubber, coconut and betel-nuts (areca nut). 2. The main criterion of a Household Industry even in urban areas is the participation of one or more members of a household. Even if the industry is not actually located at home in rural areas there is a greater possibility of the members of the household participating even if it is located anywhere within the village limits. In the urban areas, where organized industry takes greater prominence, the Household Industry is confined to the precincts of the house where the participants live. In urban areas, even if the members of the household run an industry by themselves but at a place away from the precincts of their home, it is not considered as a Household Industry. It should be located within the precincts of the house where the members live in the case of urban areas. Household Industry relates to production, processing, servicing, repairing or making and selling (but not merely selling) of goods. It does not include professions such as a Pleader, Doctor, Musician, Dancer, Waterman, Astrologer, Dhobi, Barber, etc., or merely trade or business, even if such professions, trade or services are run at home by members of the household. Some of the typical industries that can be conducted on a household industry basis are: Foodstuffs : such as production of flour, milking or dehusking of paddy, grinding of herbs, production of pickles, preservation of meat etc. Beverages: such as manufacture of country liquor, ice cream, soda water etc., Tobacco Products : such as bidi, cigars, Textile cotton, Jute, Wool or Silk, Manufacture of Wood and Wood Products, Paper and Paper Products, Leather and Leather Products, Petroleum and Coal Products : such as making foot wear from torn tyres and other rubber footwear, Chemical and Chemical Products: such as manufacture of toys, paints, colours, matches, fireworks, 171

perfumes, ink etc., Service and Repairing of Transport Equipments : such as cycle, rickshaw, boat or animal driven carts etc. 3. Non worker- . The Non-Workers broadly constitute students, who did not participate in any economic activity paid or unpaid; homemakers, who were attending to daily household chores like cooking, cleaning , looking after children, fetching water etc. and did not help in any work beyond the precincts of the house viz. any kind of productive work in the family farm like cultivation or mulching ; dependants such as infants or very elderly people not included in the category of a worker; pensioners who are drawing pension after retirement and are not engaged in any economic activity; beggars, vagrants, prostitutes and persons having unidentified source of income and with unspecified sources of subsistence and not engaged in any economically productive work during the reference period. 4. Other worker- The type of workers that come under this category of 'OW' include all government servants, municipal employees, teachers, factory workers, plantation workers, those engaged in trade, commerce, business, transport banking, mining, construction, political or social work, priests, entertainment artists, etc. In effect, all those workers other than cultivators, or agricultural laborers or household industry workers are 'Other Workers‟

Appendix V: LAND USE CLASSIFICATION BY National remote Sensing Agency

a. Built up land- It is defined as an area of human habitation developed due to non- agricultural use and that which has a cover of buildings, transport, communication utilities in association with water, vegetation and vacant lands.

b. Agricultural land- It is defined as the land primarily used for farming and for production of food, fibre, and other commercial and horticultural crops. It includes land under crops (irrigated and un-irrigated), fallow, plantation, etc.

c. Crop Land- It includes those lands with standing crop (per se) as on the date of the satellite imagery. The crops may be of either Kharif (June-September) or Rabi (October – March) or Kharif Rabi seasons.

d. Fallow land- It is described as agricultural land which is taken up for cultivation but is temporarily allowed to rest un-cropped for one or more seasons, but not less than one 172

year. These lands are particularly those which are seen devoid of crops at the time when the imagery is taken of both seasons. e. Plantations- It is described as an area under agricultural tree crops, planted adopting certain agricultural management techniques. It includes tea, coffee, rubber, coconut, areca nut, citrus, orchards and other horticultural nurseries. f. Forest- It is an area (within the notified forest boundary) bearing an association predominantly of trees and other vegetation types capable of producing timber and other forest produce. g. Evergreen/Semi-evergreen forest- It is described as a forest, which comprises of thick and dense canopy of tall trees, which predominantly remain green throughout the year. It includes both coniferous and tropical broad-leaved evergreen trees. Semi- evergreen forest is a mixture of both deciduous and evergreen trees but the latter predominate h. Deciduous forest- It is described as a forest which predominantly comprises of deciduous species and where the trees shed their leaves once in a year. i. Degraded forest or Scrub- It is described as a forest where the vegetative (crown) density is less than 20% of the canopy cover. It is the result of both biotic and abiotic influences. Scrub is a stunted tree or bush/shrub. j. Forest Blank- It is described as openings amidst forests without any tree cover. It includes openings of assorted size and shapes as seen on the imagery. k. Forest Plantations- It is described as an area of trees of species of forestry importance and raised on notified forest lands. It includes eucalyptus, casuarina, bamboo, etc. l. Mangrove- It is described as a dense thicker or woody aquatic vegetation or forest cover occurring in tidal waters near estuaries and along the confluence of delta in coastal areas. It includes species of the general Rhizophora and Aviccunia.

Wastelands- It is described as degraded land, which can be brought under vegetative cover with reasonable water and soil management, or on account of natural causes. Wastelands can result from internal/imposed constraints such as, by location, 173

environment, chemical and physical prosperities of the soil or financial or management constraints (NWDB, 1987). m. Salt-affected land- The salt-affected land is generally characterized as the land that has adverse effects on the growth of most plants due to the action or presence of excess soluble or high exchangeable sodium. Alkaline land has an exchangeable sodium percentage (ESP) of about 15, which is generally considered as the limit between normal and alkali soils. The predominant salts are carbonates and bicarbonates of sodium. Coastal saline soils may be with or without ingress or inundation by seawater. n. Waterlogged land- Waterlogged land is that land where the water is at/or near the surface and water stands for most of the year. Such lands usually occupy topographically low-lying areas. It excludes lakes, ponds and tanks. o. Marshy/Swampy land- Marshy land is that which is permanently or periodically inundated by water and is characterized by vegetation, which includes grasses and weeds. Marshes are classified into salt/brackish or fresh water depending on the salinity of water. These exclude Mangroves. p. Gullied/Ravenous land- The gullies are formed as a result of localized surface runoff affecting the friable unconsolidated material in the formation of perceptible channels resulting in undulating terrain. The gullies are the first stage of excessive land dissection followed by their networking which leads to the development of ravenous land. The word „ravine‟ is usually associated not with an isolated gully but a network of deep gullies formed generally in thick alluvium and entering a nearby river, flowing much lower than the surrounding high grounds. The ravines are extensive systems of gullies developed along river courses. q. Land with or without scrub- They occupy (relatively) higher topography like uplands or high grounds with or without scrub. These lands are generally prone to degradation or erosion. These exclude hilly and mountainous terrain. r. Sandy area (coastal and desertic) - These are the areas, which have stabilized accumulations of sand in-site or transported in coastal riverine or inland (desert) areas. These occur either in the form of sand dunes, beaches, channel (river/stream) islands, etc. 174 s. Barren Rocky/Stony waste/Sheet rock area- It is defined as the rock exposures of varying lithology often barren and devoid of soil cover and vegetation and not suitable for cultivation. They occur amidst hill forests as openings or scattered as isolated exposures or loose fragments of boulders or as sheet rocks on plateau and plains. It includes quarry or gravel pit or brick kilns.

Water bodies- It is an area of impounded water, areal in extent and often with a regulated flow of water. It includes man-made reservoirs/lakes/tank/canals, besides natural lakes, rivers/streams and creeks. t. River/Stream- It is a course of flowing water on the land along definite channels. It includes from a small stream to a big river and its branches. It may be perennial or non-perennial. u. Reservoir/Lakes/Tanks/Canals- It is a natural or man-made enclosed water body with a regulated flow of water. Reservoirs are larger than tanks/lakes and are used for generating electricity, irrigation and for flood control. Tanks are smaller in areal extent with limited use than the former. Canals are inland waterways used for irrigation and sometimes for navigation.

Others- It includes all those, which can be treated as miscellaneous because of their nature of occurrence, physical appearance and other characteristics. v. Shifting Cultivation- It is the result of cyclic land use practice of felling of trees and burning of forest areas for growing crops. Such lands are also known as Jhum lands. w. Grassland/Grazing land- It is an area of land covered with natural grass along with other vegetation, often grown for fodder to feed cattle and other animals. Such lands are found in river beds, on uplands, hill slopes, etc. Such lands can also be called as permanent pastures or meadows. Grazing lands are those where certain pockets of land are fenced for allowing cattle to graze. x. Snow-covered /Glacial area- It is snow-covered areas defined as a solid form of water consisting of minute particles of ice. It includes permanently as on the Himalayas. Glacier is a mass of accumulated ice occurring amidst permanently snow-covered areas. 175

Appendix VI: Ward wise Voters list of Maradu Municipality

Total Total Female Male Ward voters - voters (aged 60 (aged 60 number Name of ward 2011 (aged 60 +) +) +) 1 Nettoor north 1330 183 83 100 2 Kundanoor north 938 98 52 46 3 Kannadikkadu east 994 120 45 75 4 Kunnalakadu 997 101 60 41 5 Thuruthy 877 95 50 45 6 Maradu north 784 98 49 49 7 Kattithara 734 98 52 46 8 Sankar nagar 851 92 46 46 9 Martinpuram 1065 107 58 49 10 Sasthrynagar 902 111 50 61 11 Kairali nagar 889 146 74 72 12 Society 1018 162 79 83 13 Pandavathu 773 141 83 58 14 Neravathu 702 117 55 62 15 J b school 751 134 77 57 16 Kundanoor junction 899 124 74 50 17 Sub registrar office 1092 168 101 67 18 Mangayil 1078 148 82 66 19 Mannaparambu 905 95 45 50 20 Ambedkar nagr 839 106 52 54 21 Ayurveda hospital 1281 175 95 80 22 Valanthakadu 871 124 70 54 23 Santhivanom 960 90 52 38 24 Nettoor south 724 81 44 37 25 Thandassery 1010 111 58 53 26 Peringattu parambu 915 73 41 32 27 Purakeli 982 112 57 55 28 Svups 1057 94 44 50 29 Thekkepattupurackal 1033 130 65 65 30 Ambalakadavu 1214 180 85 95 31 North colony 1144 159 86 73 32 North pattupurackal 1064 149 72 77 33 Thattekadu 917 118 74 44 SOURCE: Kerala State Election Commision-2011

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Appendix VII:

a. DATA COLLECTION-PHOTOS

177

178 b. PARTICIPATORY RURAL/URBAN APPRAISAL-Resource Mapping

179

180

Appendix X

QUESSTIONAIRE IN REGIONAL LANGUAGE MALAYALAM –used for data collection

നഗരവ配കരണവ Ԃ ഭൗതിക അവസ്ഥയ Ԃ

മര絍 നഗരസഭ – എറണാക ളԂ

1. ൅േര്: വാര്‍ഡ്: വര്‍ഡഗം: M/F

2. വയസ്സ്:>65 | 65 – 70 | 70 – 75 | >75

3. മതം : H / C / M / Ohers

4. ൄതാഴില് : NA / വിട്ടു ൅ ാലി/ ൄതാഴിലുറപ്പ് (Rs/day)

5. മുന്‍കാല ൄതാഴില് :

6. ൄതാഴില് മാറനുള്ള കാരണം?

7. കുലൄതാഴില് ആയിരു൅നാ? അ൅ത / അലല

8. വീട്ടിൄലo അ ഗങ്ങള് :

൅േര് വയസ്സ് ൅ ാലി/േഠിക്കുകയാ൅ണാ? സ്ഥലം

ോചക ൅്ശാത: വിറ啍 മൄെെ ൅വൄറ

വിറ啍 ആൄണങ്ങില് എവിൄെ നിനും ലഭിച്ചു?

കാട്ടില് ൅മെിച്ചിരുനു

181

കാ絍 വാര്‍ഡഡിൄെ എവിൄെ സ്ഥിതി ൄചയുനുനു?

കിഴക്ക് േെിഞ്ഞാറ് വെക്ക് ൄതക്ക്

ഭൂമി

1. എ്ത വര്‍ഡഷമായി മരെില് താമസം തുെങ്ങിയിട്ട്?

30 – 35 | 35 – 40 | 40 - 45 | >45

2. നിച്ചു വളര്‍ഡന സ്ഥലം

കലയാണം കഴിച്ചു വനവര്‍ഡ

കുെി൅യറിയവര്‍ഡ

മൄെൄെങ്കിലും

3. കുെി൅യറിയവരാൄണങ്കില് എൊണ് കാരണം?

4. ഭാഗം വച്ച൅പ്പാള് ൅വൄറ

5. േഴയകാല വീ絍: സവെം ൄസെ്, രൂേ

Ward no: വെകയ്ക്ക്ക് ൄസെ്, രൂേ

൅മെിച്ചു ൄസെ്, രൂേ

6. വാര്‍ഡ് മറിയതാൄണങ്കില് എെുൄകാണ്ട്?

േട്ടയം സൗകരയം ൅റാ് െസ്

മൄെൄെങ്കിലും

182

7. ഓര്‍ഡമവച്ച കാലം മുതല് മരെിൄെ ്േനാന വരുമാന മാര്‍ഡഗം?

എ്ത ൅േര്‍ഡ ?

ൄനല് കൃഷി മല്സയ ധംം ൅വൄറ

8. ൄകായ്ക്തു എ്ത ്േവശയം?

2 തവണ 3 തവണ

9. ആരുൄെ ോെമായിരുനു

10. ഏക൅േശം എ്ത ഏക്കര്‍ഡ ഉണ്ടായിരുനു?

< 2 2-3 3-4 >4

11. ഇ൅പ്പാള് ഉ൅ണ്ടാ?

ഉണ്ട് ഇലല

12. ഇൄലലങ്കില് അവിൄെ എൊണ്?

നിര്‍ഡമിതി ്േ൅േശം ൄവള്ളൄക്കട്ട് ൅വൄറ

13. ോെം നികതിയിട്ട് എ്ത വര്‍ഡഷമായി?

< 10 10 – 15 >15

14. ൅വൄറ / മെു ൅മഖല?

15. േണി സ്ഥലം എവിൄെ ആണ് സ്ഥിതി ൄചയുനുനത്?

16. യാ്ത ൄചയുന്തിരുനത്?

കെത് കാല്നെ െസക്കിള്

17. ധ വനത് ഏക൅േശം എ൅പ്പാള് ?

18. തൃപ്പൂണിതുറ

െവെില

183

19. ോലങ്ങള് :

അരൂര്‍ഡക്കുെി

െതക്കുെം ോലം

NH ോലം

20. സ്ഥലൄത കുറിച്ചുള്ള ൅കട്ടു ൅കള്വി

21. േണ്ട് ചുെുവട്ടം? വീട്ടുകാര്‍ഡ

<5 5 – 10 >15

22. ഇ൅പ്പാഴൄത ചുെുവട്ടം?

<5 5 – 10 >15

23. കുെി൅യറിയവര്‍ഡ ഇവിെുതുകാര്‍ഡ

24. മഴ കഴിഞ്ഞാല് ഇവിൄെ ൄവള്ളൄകട്ട് അനുഭവൄപ്പൊറു൅ണ്ടാ?

ഉണ്ട് ഇലല

25. ഉൄണ്ടങ്കില് കരണൄമെ്? എവിൄെ?

26. ൄവള്ളൄപ്പാക്കം ഉണ്ടായിട്ടു൅ണ്ടാ?

ഉണ്ട് ഇലല

27. േുരിതാശവാസ ൅ക്രം എവിൄെയായിരുനു?

വവള്ളԂ

28. ്േനാന കുെിൄവള്ള ൅്ശാത?

െേപ്പ് കിണര്‍ഡ ൅വൄറ

29. േണ്ടൄത ്േനാന കുെിൄവള്ള ൅്ശാത?

കുളം കിണര്‍ഡ േുഴ ൅വൄറ

184

30. എ്ത കുളങ്ങള് ഉണ്ടായിരുനു?

5 5-10 10-15 >15

31. എ്ത കുെുംധങ്ങള് ഒരു കുളതില് നിനും ൄവള്ളം എെുതിരുനു?

>2 2-4 4-6 >6

32. അവയുൄെ ഇ൅പ്പാഴൄത അവസ്ഥ?

നികതി നിര്‍ഡമിതി ൅്േ൅േശം

33. േുഴയിൄല ൄവള്ളം ഉേ൅യാഗിക്കാറു൅ണ്ടാ?

ഉണ്ട് ഇലല

34. ഉൄണ്ടങ്കില് എെിൄനലലാം?

അെുക്കള കുളിക്കാന്‍ ൅വൄറ

35. ഇൄലലങ്കില് എെുൄകാണ്ട്?

ഉപ്പ് രസം േച്ച നിറം മലിനം

36. േുഴയുൄെ ചുെും എൊയിരുനു?

കണ്ടം ചള്ള കണ്ടല്ക്കാ絍

37. ഇ൅പ്പാള് േുഴ൅യാരതില് എൊണ്?

നിര്‍ഡമീതി ്േ൅േശം

185

38.

േണ്ട് ഇ൅പ്പാള്

മത്സ്യം

ോമ്പ്

മാ핍, പ്ലാ핍

കാള, ൅ോത്

േശു

൅കാഴി

താറാ핍

ആമ

39. േുഴ ക്ഷയിക്കാനുള്ള? കാരണം

40. ഖരമാലിനയങ്ങള് ? : കതിക്കുനു

41. പ്ലാസ്റ്റി啍 : കതിക്കുനു

42. േണ്ടൄത കക്കൂ

േറമ്പു ഓവ്വറ

186

Appendix X

QUESTIONNAIRE (translated in English)

URBNIZATION AND ITS IMPACT ON PHYSICAL ENVIORNMENTAL QUALTIY OF MARADU MUNCIPALITY - ERNAKULAM DISTRICT

9. Name: Ward: Sex: M/F

10. Age: >65 | 65 – 70 | 70 – 75 | >75

11. Religion : Hindu / Christian / Muslim / Others

12. Present Job: NA / Domestic Help / NREGS (Rs/day)

13. Past job:

14. Reason for Job change?

15. Traditional job? Yes/ No

16. Family Members:

Name Age Working/Studying? Location

Cooking Source: Wood Kerosene Others

If wood, from where they procured it?

Forest bought

187

If forest, which location in ward?

North West East South

LAND

43. How long you have been staying in Maradu?

30 – 35 | 35 – 40 | 40 - 45 | >45

44. Born and brought up

Married and came

Migrated

Others

45. If Migrated, state reason?

46. Land partition Others

47. Old house: Own cent, Rs

Ward no: Rented cent, Rs

Bought cent, Rs

48. If changed ward why?

Land Lease convience Roadside

Others

49. What was the primary source of income in Maradu?

50. Rice fields Aquaculture Others

188

51. Harvesting frequency?

2 times 3 times

52. Whose rice fields

53. How many acres under rice cultivation (acres)?

< 2 2-3 3-4 >4

54. Presently there?

Yes No

55. If no, what’s presently in its place?

Built up land Water logged area others

56. How many years since the paddy fields have been filled up?

< 10 10 – 15 >15

57. (q no:6 )others/what job?

58. location of other job?

59. how people travelled?

Vallom/boat Walk Cycle

60. Which year did transport bus came?

61. Thripunnithura

Vytilla

62. Bridges:

Aroor bridge

THaikudam Bridge

NH Bridge 189

63. Old saying about Maradu

64. Neighbourhood: in past, how many houses?

<5 5 – 10 >15

65. Presently, how many houses?

<5 5 – 10 >15

66. Migrated(%) Born and brought up

67. Is there water logging after rain?

Yes No

68. If yes , reason? Where?

69. Flood occurance?

Yes No

70. Flood relief centre location?

WATER

71. Water source: Present?

Pipe Well Others

72. Drinking water source: Past?

Pond Well Canal Others

73. How many ponds where there in the past?

5 5-10 10-15 >15

74. How many families took water from a pond?

>2 2-4 4-6 >6

75. Present situation of Ponds?

Filled Built up land 190

76. Do you use water from water canal?

Yes No

77. If yes, for what purpose?

Kitchen Bathing Others

78. If No, Why?

Salinity Green Waste dumping

79. Features surrounding water canal: Past?

Rice fields Swampy land Mangroves

191

80. Features surrounding water canal: Present?

Built up land

81.

Past Present

Fish

Snakes

Fruit bearing trees (Mango,jackfruti)

Ox/Bull

Cow

Chicken

Duck

Turtle

82. Reason for water canal degradation?

83. Solid waste : Being burnt

84. Plastc waste : Being burnt

85. Old toilet

Garden land Manual scavenging toilet

192

APPENDIX XI

Participatory Rural appraisal 193

194

195

196

197

198

199

200

201

202

203

204

205

206

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