Environmental and socio economic impact assessment of urbanization in sta rosa city philippines

Hồ Ngọc Sơn Thai Nguyen University of Agriculture and Forestry Supervisor’s signatures Abstract: This study focuses on determining the impacts of urbanization on land use/land cover m

THAI NGUYEN UNIVERSITY

UNIVERSITY OF AGRICULTURE AND FORESTRY

MARY JOY CAMARGO ONGKIATCO

ENVIRONMENTAL AND SOCIO-ECONOMIC IMPACT ASSESSMENT OF URBANIZATION

IN STA ROSA CITY, PHILIPPINES

BACHELOR THESIS

Study Mode: Full-time

Major: Environmental Science and Management Faculty: Advanced Education Program Office

Batch: 2015-2018

Thai Nguyen, 15/11/2018

DOCUMENTATION PAGE WITH ABSTRACT

Thai Nguyen University of Agriculture and Forestry

Degree Program Bachelor of Environmental Science and Management

Student Name Mary Joy C Ongkiatco

Student ID DTN1454290105

Thesis Title Environmental and Socio-Economic Impact Assessment of

Urbanization in Sta Rosa City, Philippines Supervisor(s) Prof Damasa Magcale-Macandog (Institute of Biological

Sciences, University of the Philippines, Los Baños)

Dr Hồ Ngọc Sơn (Thai Nguyen University of Agriculture and Forestry)

Supervisor’s

signature(s)

Abstract:

This study focuses on determining the impacts of urbanization on land use/land cover modifications (1993-2017), total waste generation (2015), air quality (2017), population and population growth (1990-2015), and economic status (1980-2013)

of Sta Rosa City, Philippines Landsat images of 1993, 2005, and 2017 were used

in order to estimate the rate and extent of urbanization within the 24-year period and

to produce land use/land cover change map It was observed that the built-up land use of Sta Rosa City in 2017 increased to 38.52% or 1,834.69 Ha This was paralleled by a corresponding decline of agricultural land use Moreover, it was observed that the increase of population has resulted to increased total waste generation of the city in 2015 It was also found out that PM 2.5 annual mean concentration amounting to 34.12 µ𝑔/𝑁𝑚3 in 2017 exceeded the US EPA guideline value by 19.13µ𝑔/𝑁𝑚3 Ambient air concentration of finer air pollutants (PM 2.5)

is strongly negatively correlated with wind velocity Majority of the working population is employed in commercial sector and least in agricultural sector Results

of this study will be useful for future urban development

Keywords: Urbanization, Land Cover/Land Use Change,

Environmental, Socio-economic, Impact Assessment Number of pages: 62

ACKNOWLEDGEMENT

I am extending an overwhelming gratitude first to my thesis supervisor, Prof

Dr Damasa Macandog, from Institute of Biological Science, University of the Philippines at Los Banos (UPLB) for the guidance, encouragement, understanding, patience, and knowledge which greatly contributed to this research Her suggestions and constructive criticisms from the research topic to the research manuscript have manifested, and her influence will never be forgotten I am thankful to Mr Donald Luna for always answering my inquiries and for always imparting knowledge and assistance especially in mapping activities I am also thankful to everyone at Ecoinformatics Lab for the warm welcome and the kindness they have shown Also,

to my second thesis supervisor, Dr Hồ Ngọc Sơn, deputy dean from Thai Nguyen University of Agriculture and Forestry, for the supervision, patience, and knowledge that he contributed from beginning to the end of this research

To Ms Vanessa Bernadette B Atienza from the Environmental Management Bureau - Department of Environment and Natural Resources, Region IV-A CALABARZON office, Ma’am Linda Creencio and Sir Pots F Ramos from City Environment and Natural Resources Office for the accommodation and the effort of providing all of the available data that I needed for this research

To Dr Mariano L Macaleng Jr (Sir Macaleng), the administrator of my dearest alma mater – The Refiner’s Christian School, for being one of the pioneers of the acquired opportunity to study full-time and with full scholarship at Thai Nguyen University of Agriculture and Forestry under Advanced Education Program Truly, TRCS have a great influence in the lives of their students

To my family, thank you for the support right from the start of this journey, for letting yourselves as an outlet of inevitable frustrations, and for not giving up on me

To my spiritual family – Word International Ministries Calauan/Bay outreach, for making an atmosphere of a true family, for the unceasing prayers, and for the unending guidance towards growth and maturity To Alessandra, Joshua, Tonio, Ate Joice, Niecer, King, JD, Lian, Ghia, Luis, Jess, Pau, Lester, Aj, Enzo, Vea, Tinay, Fritz, Ate Kat, Ate Rosette, Ate Tina, Kuya JM, family friends from Victoria, and to all the acquaintances and friends I’ve met who helped throughout this journey, thank you for showing different sorts of support

Above and utmost of all, I am extending my deepest gratitude to the Most High, Jehovah Shalom, Prince of Peace, for being the most stable and everlasting support system throughout this phase of my life He worked everything together for good throughout this research and I can’t thank Him enough for it His grace is always sufficient for my weaknesses

- MJCO

TABLE OF CONTENTS LIST OF FIGURES vi

LIST OF TABLES vii

LIST OF ABBREVIATIONS viii

PART I INTRODUCTION 1

1.1 Research Rationale 1

1.2 Research Objectives 5

1.3 Statement of the Problem 5

1.4 Significance and Limitations of the Study 7

1.5 Definition of Terms 9

PART II: LITERATURE REVIEW 10

2.1 Urbanization and Land Use/ Land Cover Change 10

2.2 Impacts of Urbanization to Environment and People 11

2.2.1 Urbanization and Waste Generation 13

2.2.2 Urbanization and Air Quality 14

2.3 Remote Sensing and Geographic Information System 16

PART III: MATERIALS AND METHODS 18

3.1 Materials 18

3.1.1 The objects of the research 18

3.2 Conceptual Framework 18

3.3 Land Use/ Land Cover Change Mapping 20

3.3.1 Data Collection 20

3.3.2 Image Pre-Processing 21

3.3.2.1 Radiometric Calibration 21

3.3.2.3 Image Subset 23

3.3.4 Accuracy Assessment 26

3.3.4.1 User’s Accuracy 26

3.3.4.2 Producer’s Accuracy 26

3.3.4.3 Overall Accuracy 27

3.3.4.4 Cohen Kappa’s Coefficient 27

3.3.5 Change Detection Analysis 28

PART IV RESULTS AND DISCUSSION 30

4.1 Results of Land Use/Land Cover Change Detection Analysis of Sta Rosa City 30

4.2 Total Waste Generation and Waste Composition of Household and Non-Household Sources 35

4.2.2 Particulate Matter (PM 2.5 and PM 10) Concentration of Sta Rosa City 41

4.2.3 Population and Population Change, and Economic Activity of Sta Rosa City 45

PART V CONCLUSION 49

REFERENCES 51

APPENDICES 59

LIST OF FIGURES

Figure 1: Map of Sta Rosa City Divided Into Eighteen Barangays 4 Figure 2: Conceptual Framework of Land Use/ Land Cover Change Analysis 19 Figure 3: Conceptual Framework for Spatial Maps 20 Figure 4: Pre-processed Images of (a) 1993, (b) 2005, (c) 2017 in Natural Color

Combination (Bands 321 for Images a and b; Bands 432 for Image c 22 Figure 5: Clipped Images of the Study Area - Santa Rosa City for years 1993, 2005 and

2017 23 Figure 6(a) and (b): False color composite (FCC) and Normalized Difference Vegetation Index (NDVI) for 1993, 2005, and 2017 Images 25 Figure 7: Land Use/Land Cover Classification of Sta Rosa City in 1993, 2005, and 2017 31 Figure 8: Land Use/ Land Cover Change Map of Sta Rosa City from 1993-2017 34 Figure 9: Composition of Total Waste Generation from Household Sources of Sta Rosa City in 2015 Presented in Percent 37 Figure 10(a) and (b): Choropleth maps of Total Waste Generation and Population of Sta Rosa City in 2015 37 Figure 11: Associated Choropleth Maps of Total Waste Generation and Population of Sta Rosa City in 2015 38 Figure 12: Scatter Plot Showing the Correlation between Population and Total Waste Generation 39 Figure 13: Monthly Average of PM 2.5 and PM 10 Concentrations in Sta Rosa City in

2017 expressed in micrograms per cubic meter 42 Figure 14: Scatterplot Showing the Monthly Average of PM 2.5 and Wind Speed of Sta Rosa City in 2017 43 Figure 15: Comparison of Annual Means of Guideline Values (from NAAQGV and US EPA and Sta Rosa City’s PM 2.5 and PM 10 Concentration Values in 2017 44 Figure 16: Population of Sta Rosa City from 1990-2015 45

LIST OF TABLES

Table 1: Collected Satellite Images and their Attributes 21

Table 2: Description of the Land Use/Land Cover Classification Used in the Study 30

Table 3: Land Use/Land Cover Change Statistics of Sta Rosa City from 1993-2017 32

Table 4: Changes of Sta Rosa City from 1993-2017 Presented in Percent 33

Table 5:Rate and Extent of LULC from 1993-2017 35

Table 6: Total Waste Generation, Waste Composition, and Population of Each Barangay in 2015 36

Table 7: Population, Total Waste Generation, and Per Capita Generation of Sta Rosa City in 2015 38

Table 8: Total Waste Generation of Non-Household Sources of Sta Rosa City in 2015 40

Table 9: Monthly Average of Wind Speed and PM 2.5 Concentration of Sta Rosa City in 2017 43

Table 10: Number of Commercial and Industrial Establishments in Sta Rosa City from 1980-2013 46

Table 11: Intercensal Estimates of Sta Rosa City’s Employment Status in Various Sectors of Economic Activity 47

Table 12: Total Number and Rates of Registered Job Applicants and Qualified Job 48

LIST OF ABBREVIATIONS

CALABARZON Cavite, Laguna, Batangas, Rizal, Quezon

CENRO City Environment and Natural Resources

DENR Department of Environment and Natural Resources

DN Digital Numbers

DOS Dark Object Subtraction

EMB Environmental Management Bureau

ENVI Environment for Visualizing Images (image processing

software; Research Systems, Inc.) FLAASH Fast Line-of-sight Atmospheric Analysis of Hypercubes

GIS Geographic Information System

LULC Land Use/Land Cover

LULCC Land Use/Land Cover Change

NAAQGV National Ambient Air Quality Guideline Value

NASA National Aeronautics and Space Administration

OLI/TIRS Operational Land Imager and Thermal Infrared Sensor

PM Particulate Matter

PSA Philippine Statistics Authority

RS Remote Sensing

Sta Rosa City Santa Rosa City

SLEX Southern Luzon Expressway

TM Thematic Mapper

TOA Top-of-atmosphere

TWG Total Waste Generation

US EPA United States Environmental Protection Agency

USGS United States Geological Survey

USGS EE United States Geological Survey Earth Explorer

WACS Waste Analysis and Characterization Study

PART I INTRODUCTION

1.1 Research Rationale

The concept of urbanization started about ten thousand years ago when gatherers eventually learned early farming techniques which led to the expansion of semi-permanent settlements instead of moving to different places to search for food

hunter-As territories enlarged and trade occurred, more people were drawn in and out the center of trades because of more labor or job opportunities Urbanization is not considered as a mere modern phenomenon, but through time, urbanization became dynamic by the advancement of technology to what is evident today, the modern cities (Kite, 2013)

Urbanization is mainly attributed to demographic and structural changes It has implications other than conversion of lands from rural to urban or from non-built-up

to built-up, but it is a complex process that changes economic, social, technological, demographic, political and environmental aspects of a community (Stelter & Artibise, 2006) Mostly, urbanization results to urban areas with high density of human population and infrastructures such as railways, skyscrapers and establishments which are central to residential, commercial and industrial activities and almost no agricultural activities exist (National Geographic Society, 2011) By 2030, it is predicted that about 60% of global population will live in urban areas (Yadav, 2017)

It was reported by the World Bank Group in 2017 that Philippines is one of the fastest urbanizing countries in the East Asia and Pacific region About 50 million Filipinos

live in urban areas and in 2050 it is predicted to double at 102 million The increasing demand for more land to be converted for residential, commercial, and industrial uses and services cannot be overlooked and it becomes inevitable The continuous alteration of land use/land cover amplifies environmental degradation such as too much generation of wastes and air pollution

Sta Rosa City is selected as the study area for it is the “fastest growth center”

of the country located at the region of South Luzon, one of the most sub-dynamic regions in the Philippines today (https://www.santarosacity.gov.ph/about-sta-rosa/history/) It is a first class component city in the province of Laguna that has a total land area of 5,543 ha, and lies at 40 kilometers south of Manila, the country’s capital Sta Rosa City is one of the municipalities surrounding Laguna Lake – the largest lake in the Philippines (LLDA, n.d.) It is bounded by Biñan on the northwest, Cabuyao on the southwest, Cavite on the west and Laguna de Bay on the northeast

It became a first-class municipality in 1993 and officially became a city in 2004 which was considered as an economic success (http://www.santarosacity.gov.ph/investment-profile/) The city has now evolved into

a major residential, industrial, and commercial center Figure 1 shows the location of the study area

Monitoring of land use/land cover change, efficient and effective detection, and analytical techniques are essential to urban planners especially at the local and regional scale in order to assess the patterns and trends of urbanization (Mundhe &

Jaybhaye, 2014) Sta Rosa City is a well-researched place and there are many existing studies about impacts of urbanization such as flooding This study would be able to contribute an updated status of the land use/land cover change of the study area as well as the link to rapid urbanization and its environmental and socio-economic impacts in terms of waste generation, air pollution, population growth, and economic status/activities of the city Hence, in order to assess the impact of urbanization based

on land use/land cover changes (LULCC) that occurred in the last 24 years 2017), Landsat data were used aided by modern technologies such as Remote Sensing and Geographic Information System Secondary data from various sectors were also used in analyzing waste generation, air quality and understanding other socio-economic factors in various years (based on the available data)

(1993-Figure 1: Map of Sta Rosa City Divided Into Eighteen Barangays

1.3 Statement of the Problem

Rapid urbanization is seen as one of the most critical issues nowadays because

it may result to unplanned growth or urban sprawl and one major concern related to this is dramatic land use (LU)/ land cover (LC) changes (Iizuka et al., 2017) It was stated by Reis in 2008 that land use/ land cover (LULC) alterations especially the anthropogenic activities negatively affect climate patterns, natural hazard, and socio-

economic factors on a global and local scale Nevertheless, the City of Santa Rosa now relishes the status of being residential, commercial, and industrial center in the region of South Luzon, the most dynamic sub-region in the Philippines today Although increasing urbanization rates have beneficial effects in economic development such as providing job opportunities, and generating higher income; rapid urbanization when left unchecked results to adverse effects such as overcrowding, excessive conversion of land use/land cover from non-built-up to built-up, increased generation of wastes, and pollution to name a few

Hence, the study will be conducted to answer these questions:

1 What is the rate of urbanization in Sta Rosa City from 1993 to 2017 in terms of land area conversion?

2 Which land use (forest/trees, agricultural land, and bare land), changed the most from initial state to a final state of built-up from 1993 to 2017?

3 What is the relationship of total waste generation and population of Sta Rosa City in 2015?

4 What are the significant trends of the PM concentration of Sta Rosa City

in 2017?

5 How did urbanization affect socio-economic factors such as population growth, population shift, and economic status of Sta Rosa City?

1.4 Significance and Limitations of the Study

Urbanization and the continuous expansion of urban areas are the results of plans often focused on development to economically sustain a growing population which often fail to consider various factors such as climate patterns, natural hazards, sanitation, etc (Iizuka et al., 2017) The use of approaches and techniques that produce certain results aided by remotely sensed data has become notable as it is difficult to conduct field surveys and other ground data collection approaches in estimating the changes brought about by urbanization on land use/land cover Land use/land cover changes and its adverse impacts will be a major concern in the future

if not monitored, analyzed, and assessed

Hence, finding out and estimating the current extent and rate of urbanization, the patterns of major land use/land cover alterations, the significant issues related to

it such as waste generation and air pollution as well as the overview to some economic factors are important especially to urban planners and policy makers in formulating future policies and ordinances for the residents of Sta Rosa City This study could also provide baseline information to other researchers who are interested

socio-in this topic

In mapping land use/land cover change, only free downloadable archive Landsat satellite data with a resolution of 30 meter by 30 meter and shapefile (administrative boundary) were used The study was not able to gather Landsat images within the same months due to the limiting availability of cloudless data for Sta Rosa

City Mapping of the rate and extent of urbanization only focused on land use/land cover and not on the physical characteristics of the features such as residential, industrial, and commercial buildings, elevation, and characteristics of soil

In identifying the quantity, main composition, and major sources of solid wastes generated in Sta Rosa City, as well as the significant relationship to urbanization, the study was not able to conduct field survey and only secondary data

of 2015 acquired from the City Environment and Natural Resources were used due to the distance of the study area, and the amount of time it will take to finish the study

In assessing the impact of urbanization to Sta Rosa’s air quality, only two

parameters (PM 2.5 and PM 10) were gathered from only one observation point or roadside ambient air quality monitoring system in Sta Rosa City The data were gathered from the Department of Natural Resources – Environmental Management Bureau Regional Office of Region IV-A CALABARZON Thus, the study only focused on analyzing the trends of PM 2.5 and PM 10 occurrence in 2017 instead of the proposed preparation of air quality index map of Sta Rosa City In understanding the trends of socio-economic factors: population, migration, and employment status

of Sta Rosa City, only those available secondary data/censuses were used

1.5 Definition of Terms

Definition of Terms can be found in Appendix 1

PART II: LITERATURE REVIEW 2.1 Urbanization and Land Use/ Land Cover Change

Currently, 55% of the world’s population lives in urban areas By 2030, the

population of global urban communities will grow to 4.9 billion On the other hand, between 2005 and 2030, the world’s rural population will decrease by 28 million

(Bhatta et al., 2010) Asia, despite its relatively lower level of urbanization, is home

to 54% of the world’s urban population (UN, 2018) In fact, Philippines is one of the fastest urbanizing countries in the East and Asia Pacific region, with 2-5% of its total area occupied by cities (World Bank Group, 2017)

Urbanization is defined by the United Nations (UN) as the increase in the proportion of a population living in urban areas and the process by which cities are formed as large number of people becomes permanently concentrated on small areas

It is a global phenomenon that has different expressions within countries and in their level of development Richer countries already have a large number of people living

in urban areas while third world countries are still mostly rural but will urbanize faster than other regions (United Nations, 2018) In addition, urbanization is a physical transformation of landscapes from natural land covers to developed ones, mostly impervious surfaces for residential, industrial, and commercial purposes as well as schools and hospitals (Sharma, 2014)

Land, an irreversible resource is central to primary production systems (Prasad

& Sreenivasulu, 2014) Land cover is the physical characteristics of the surface of the

earth whether be it a manmade feature such as housing/settlements or a natural feature such as vegetation, water bodies and soil Land use is the human activity that is being done on the feature of the earth’s surface which is the land cover (Rawat & Kumar,

2015) Land use and land cover are terms that are always correlated and often used interchangeably because land cover is the direct result of land use Generally, LULCC

is a term that refers to the human modification of Earth's terrestrial surface Gutierrez, 2015) Land use and land cover are considered as essential elements in understanding the earth as a system which is important for planning and management activities (Lillesand & Kiefer, 2000) They are regarded as important factors in assessing environmental issues, population shift, and economic conditions (DeFries, 2012)

(Engay-2.2 Impacts of Urbanization to Environment and People

Land use/ land cover changes are the major issues and challenges to the three dimensions of sustainable development: economic, social and environmental (Rawat

et al., 2013; United Nations, 2018) According to Wu (2008), land use/ land cover change is arguably the most extensive force that causes degradation of the environment Rawat et al in 2013 stated that land use/ land cover change could be beneficial if every available land is used correctly for economic improvement Rapid urbanization and city expansion are driven by the pressure of an ever growing population and the increase of economic activities for sustaining the needs of the population which has benefits for economic development However, factors such as climate patterns, natural hazards, sanitation, etc were often overlooked when

expanding cities which eventually leads to land, air, and water pollution, traffic congestion, overcrowding, increased temperature, diseases, etc

The transition of land uses/land covers of Sta Rosa City has been dramatic since the year 1946 but most notably in 1990 when built-up areas started to boom and are continuously increasing, while agricultural lands are continuously decreasing The change in land uses/land covers of the city has also influenced the change in the main sources of livelihood Historically, a large proportion of the city were devoted to agricultural lands in 1946 when large number of farmers were still able to grow and produce crops such as rice, corn, coffee, sugarcane, and fruit-bearing trees Residents were also living off livestock like poultry and piggery and fishing was an option too for those who live in/near the lakeshore barangays/areas The opening of South Luzon Expressway (SLEX) in 1980s has slowly paved way for local and foreign investors who influence rapid development of the city Since then, the agricultural lands and the number of people living off agriculture sector has been decreasing Sta Rosa City has also been consequentially contributing to the unfavorable Class C status of Laguna Lake (https://www.santarosacity.gov.ph/waste-management/)

Flooding, as intensely experienced in Sta Rosa City when Typhoon Ondoy hit

in 2009 is one of the impacts of urbanization to environment which is due to large generation of unmanaged solid wastes by the residential areas along with informal settlers that clogs canals, waterways, and river banks Groundwater depletion is also being experienced which is due to the rising demand of water as economic activities

and population are also increasing Furthermore, the severe use of fertilizers and pesticides is one of the factors contributing to the declining productivity of Laguna Lake along with pollution from industries, siltation and sedimentation, encroachment

of shoreline areas, and rapid conversion of prime agricultural lands into industrial and residential lands Thus, fish growth and fish harvest were also diminishing due to pollution (Eugenio, 2018)

2.2.1 Urbanization and Waste Generation

Globally, especially in developing countries, an increasing population, uncontrolled urbanization and industrialization have greatly increased the rate of waste generation and its poor disposal and management (Ugwuanyi & Isife, 2012) Increasing rates of waste generation along with poor disposal and management have affected the environment and its sustainability which includes: pollution, availability

of space, waste leaching, contamination of ground and surface water, clogged drainage that intensifies flood during rainy seasons, and diseases such as cholera and dengue fever also arise due to the attraction of rodent and insect vectors to wastes According to U.S Public Health Service, 22 diseases are found out to be linked to improper solid waste management (Pervez & Ahmade, 2013) It has been recognized

by the global community that solid waste management requires serious awareness along with action

In the Philippines, with the rapid growth of population, waste management has become a major environmental challenge (Castillo & Otoma, 2013) About 35,580

tons of garbage is generated every day in the Philippines Waste mismanagement in the Philippines has become prevalent that resulted to the passage of Republic Act (RA) 9003 or Ecological Solid Waste Management Act of 2000 which is an act providing for an ecological solid waste management program, creating the necessary institutional mechanisms and incentives, declaring certain prohibited acts and providing penalties, and appropriating funds Solid wastes is defined by Sta Rosa City’s Environmental Code (City Ordinance No.1720-2011) as all discarded

household, commercial wastes, non-hazardous institutional and industrial wastes, street sweepings, construction debris, agricultural wastes, and other non-hazardous/non-toxic solid wastes According to the City Environment and Natural Resources Officer, the average individual waste generation or per capita generation

in 2015 is 0.7 per kg/day The increased generation of solid wastes by high-density residential areas or informal settlers along with the illegal dumping of wastes to water ways have contributed to intense flooding in the city (Eugenio, 2018)

2.2.2 Urbanization and Air Quality

Urban development has paved the way for the increase of human-engineered structures such as buildings, factories, and vehicles that produce smoke which amplifies air pollution Air pollution is one of the key environmental problems associated with urbanization and land use/ land cover change It was considered that industrial emissions and vehicle exhausts are the main sources of air pollution In an instance, built-up is central for human activities, therefore it directly produces air

pollutants to the surrounding environment There are also land uses that indirectly emit air pollutants but still attract vehicular sources ((Xu et al., 2016) Air pollutants include gaseous substances and particulate matter such as PM 2.5 and PM 10

Particulate matter (PM) also called as particle pollution which includes (PM 2.5 and PM 10) is a major air pollutant in which its concentrations are directly affected

by human activities and the surrounding areas (EPA, 2016; Chan & Yao, 2008) Particulate Matter is the term for a mixture of solid particles and liquid droplets found

in the air These particles vary in size, as there are particles that are large or dark enough to be seen by the naked eye such as dust, dirt, soot, and smoke; while others are small that are only visible through the use of microscope (EPA, 2016) PM 2.5 which stands for particulate matter 2.5 (with diameters that are 2.5 micrometers and smaller) are fine particles that generally causes smog and aerosol in high concentrations (Xing et al., 2016) They can penetrate the lungs and cardiovascular system (DENR, 2017) On the other hand, PM 10 which stands for particulate matter

10 (with diameters that are 10 micrometers and smaller) are coarse particles that are produced by all types of combustion, including motor vehicles, power plants, residential wood burning, forest fires, agricultural burning, and some industrial processes (AirNow, 2017)

Air pollution is now seen as the leading problem for environmental health risk with 92% of the world’s population being exposed to areas that exceed World Health Organization’s ambient air quality guidelines One out of 4 deaths in the Philippines

is attributed to air pollution Air pollution is one of the key concerns in the physical environment of Sta Rosa City As the number of industries increase and traffic congestion worsens, a great risk is being faced especially by those who reside near the industries The city of Santa Rosa started monitoring ambient air quality in 2016 through the installation of the ambient air monitoring station by the Environmental Management Bureau (CLUDP, 2000-2015)

Republic Act 8749 requires the monitoring of ambient air quality, the average purity of the atmosphere in major cities in the country In order to monitor the ambient air quality, the Department of Environment and Natural Resources – Environmental Management Bureau has set-up regional monitoring systems in several major cities in the country which have different monitoring types and criteria of the pollutant being monitored Monitoring types can be general air pollution monitoring systems that is used for fixed areas or roadside air quality monitoring systems used for assessing air pollution caused by motor vehicles within large traffic systems

2.3 Remote Sensing and Geographic Information System

Remote sensing is the science of obtaining information about an object or an area indirectly or from a distance, by the use of various instruments such as sensors (NOAA, 2017) Generally when scientists use this term, it means that the use of the gathered information is primarily to study the earth system and its dynamics (European Space Agency, 2010) Remote sensing serves as the ‘eye’ providing repeated and completed remotely sensed images of the earth from an aerial or above-

view point (Lillesand & Kiefer, 2000) Remote Sensing advances in characterizing spatial (space) and temporal (time) analysis of urban growth’s patterns and processes rather than causes and consequences by using multi-stage images (Sun, et al., 2007) Geographic Information System (GIS) is a system or framework used for collecting, storing, processing, and modeling all types of geographical data It helps in indicating landscape in terms of structure, function, and change (Sudhira, et al., 2004)

It is through the integration of Remote Sensing (RS) and Geographical Information System (GIS) that researches on various fields become widely comprehensible (Vallesteros, 2002) This integration has provided an almost daily basis improvement for the mapping of land use/cover change which became more vast, detailed and useful Most importantly, people become more aware of the interrelation and fragility of the elements on earth as a system and the role of RS and GIS integration in acquiring necessary information, processing, managing and modeling the earth as a system (Lillesand & Kiefer, 2000)

PART III: MATERIALS AND METHODS 3.1 Materials

3.1.1 The objects of the research

Land use/ land cover change analysis

 Classified images of Sta Rosa City in 1993, 2005 and 2017

 Land use/ land cover change map of Sta Rosa City in 1993-2017 Secondary data analysis

 Population Map

 Total Waste Generation Map

 Graphs and Tables of air quality and socio-economic factors

3.2 Conceptual Framework

The conceptual framework for land use/ land cover change map is presented in Figure 2 This framework includes methods in order to analyze the changes that happened in Sta Rosa City’s land use/ land cover Figure 3 presents the methods in

creating maps for population and solid waste generation of Sta Rosa City These methods will be further discussed from the collection of data to the analysis of the results

Data Acquisition

LANDSAT 5

TM 1993

LANDSAT 8 OLI/TIRS 2017

3.3 Land Use/ Land Cover Change Mapping

3.3.1 Data Collection

The data used for land use/land cover change map of Sta Rosa City were collected from United States Geological Survey EarthExplorer (USGS EE) website where satellite images are archived Free and downloadable Landsat images – Landsat

5 TM and Landsat 8 OLI/TIRS images were used for years 1993, 2005, and 2017 Due to the limited availability of cloudless data in Sta Rosa City, selection of images with the same months was not possible Landsat 5 TM were both in the month of May

in years 1993 and 2005; while Landsat 8 OLI/TIRS was in the month June in 2017

 Population Density Map

 Solid Waste Generation Map

Figure 3: Conceptual Framework for Spatial Maps

In the Philippines, the dry season starts in November and ends in May, while rainy season begins in June and ends on October (see table 1)

3.3.2 Image Pre-Processing

Image pre-processing is important before proceeding to the main processing of the images because raw satellite data often contain defects such as distortions, low brightness of images, haziness, etc which needs corrections and enhancements Many studies have mentioned the importance of pre-processing in accurate detection and analysis of land use/ land cover change (Rawat & Kumar, 2015) The type of correction to be applied depends on the type of error that the raw data contain

3.3.2.1 Radiometric Calibration

Radiometric errors are due to the influence of the atmosphere and the differences of the functions of sensor types that are recording the data Raw data/ images have digital numbers (DN) or electromagnetic radiation per pixel that are recorded by the collecting sensors The DN numbers can be converted to meaningful units in real world like radiance, reflectance or brightness temperature (Humboldt State University, 2015) Radiometric calibration is important when images from

Table 1: Collected Satellite Images and their Attributes

Spacecraft ID Sensor ID Date Acquired Spatial

Resolution

Image Quality Landsat 5 TM May 5, 1993 30 meters 7/10

Landsat 5 TM May 21, 2005 30 meters 7/10

Landsat 8 OLI/TIRS June 6, 2017 30 meters 9/10

different sensors are used It is also important before applying atmospheric correction

so that DN values are converted to TOA or top-of-atmosphere reflectance

3.3.2.2 Atmospheric Correction

Atmospheric correction is the process of reducing the effects of atmospheric scattering which is caused by clouds, aerosol, and gases ENVI, an image processing software that has tools that can readily calibrate the data was used Dark Object Subtraction or DOS was applied to Landsat 8 OLI/TIRS 2017 DOS is a simple kind

of technique for atmospheric correction and it is also the most used method which is available in ENVI FLAASH or Fast Line-of-sight Atmospheric Analysis of Hypercubes which was available in the Atmospheric Correction Module of ENVI was applied to Landsat 5 TM 1993 and 2005 Figure 4 shows the results of the pre- processing methods

Figure 4: Pre-processed Images of (a) 1993, (b) 2005, (c) 2017 in Natural Color Combination (Bands 321 for Images a and b; Bands 432 for Image c

3.3.2.3 Image Subset

The images were clipped to the boundary of Sta Rosa City using ArcMap The administrative boundary shapefile that was used was acquired from PhilGIS.org, a website that provides free GIS data for the Philippines The administrative boundary

or the shapefile was dated 2011 and has a total land area of 4,764.78 Hectares Figure

5 shows the subset or clipped images of the study area in their respective years These images were clipped with natural color band combination, specifically bands 321 for Landsat 5 TM and bands 432 for Landsat 8 OLI/TIRS The list of band designations

of Landsat 5 TM and Landsat 8 OLI/TIRS can be found in Appendices 2 and 3

Figure 5: Clipped Images of the Study Area - Santa Rosa City for years 1993, 2005 and

2017

3.3.3 Image Classification

Image classification is the process of extracting and simplifying spectral information into interpretable classes There are two primary methods of classification: Unsupervised and Supervised Unsupervised classification is the automatic classification executed by the software, which computes the pixels’ similarity according to spectral characteristics It does not require inputs from the user

to train the selection On the other hand, supervised classification requires the input

of preferred and created training samples by the user Supervised classification was used to assign classes as built-up, forest/tree, agricultural land, and idle land; a minimum of 20 samples per class were trained in order to execute the image classification Different band combinations (bands 432 for Landsat 5 TM and bands and 543 for Landsat 8 OLI/TIRS) and Normalized Different Bands Combination (NDVI) were also used as an aide to classify the images with satisfactory which are presented in Figure 6a & 6b Maximum Likelihood Classification (MLC) was the classification algorithm used to carry out supervised classification This algorithm is being widely used for LULC change assessment According to Kamrul et al in 2018, MLC is mostly used and convenient to apply with satisfactory accuracy

Figure 6(a) and (b): False color composite (FCC) and Normalized Difference Vegetation

Index (NDVI) for 1993, 2005, and 2017 Images

3.3.4 Accuracy Assessment

Accuracy assessment is an important process in evaluating the accuracy of the classification as well as the agreement between the classified images and the actual world It requires the comparison of the classified image to another data that is considered to be accurate or ground truth data Fifty random points were created per class in each of the images, so there were a total of 200 points per year (1993, 2005,

& 2017) Ground truth data or reference data were obtained from mid-high resolution imagery (accessed through Google Earth Pro), previously classified imagery and GIS data layers Accuracy assessment also provides assessments that can identify the classification errors User’s accuracy, producer’s accuracy, overall accuracy, and Cohen Kappa’s coefficient were computed and produced into error matrices

3.3.4.1 User’s Accuracy

User’s accuracy is according to the perspective of the map user It tells about

the reliability of how often the class of the map will likely appear in the actual world

It is computed by dividing the total number of correct classification of a class and by the row total or the ground truth total In addition, user’s accuracy corresponds to commission error which is computed by subtracting user’s accuracy of a class from

100% Thus, the higher the commission error, the lower the user’s accuracy

3.3.4.2 Producer’s Accuracy

Producer’s accuracy is according to the perspective of the map producer or

maker It tells about how often the features on the actual world will likely appear on

of a class to the column total or the total number of classification points In addition, producer’s accuracy corresponds to omission error which is computed by subtracting the producer’s accuracy of a class from 100% Thus, the higher the omission error, the lower the producer’s accuracy

3.3.4.3 Overall Accuracy

Overall accuracy implies the proportion that was classified correctly out of all the ground truth or reference sites It is computed by dividing the total number of correctly classified sites to the total number of reference sites It is the easiest to calculate and to understand but only provides basic accuracy information The highest overall accuracy rate is at 100% where all the reference sites are classified correctly

3.3.4.4 Cohen Kappa’s Coefficient

Cohen Kappa Coefficient is generated from statistical test often used to test the interrater reliability, in this case, to measure the accuracy of classification It is used

in measuring the agreement between the two variables Its value ranges from -1 to 1 where in -1 to 0 = no agreement, -0.01-0.20 = none to slight agreement, 0.21-0.40 = fair, 0.41-0.60 = moderate, 0.61-0.80 = substantial and 0.81-1.00 = almost perfect agreement Below is the formula in computing Kappa Coefficient:

𝐾 = 𝑁 ∑𝑘𝑖=1𝑥𝑖𝑖 − ∑𝑘𝑖=1(𝑥𝑖+ × 𝑥+𝑖 )

𝑁2− ∑𝑘𝑖=1(𝑥𝑖+ × 𝑥+𝑖 )

Where:

N = total number of samples

𝑥𝑖𝑖 = correctly identified samples

𝑥𝑖+ = Classified data total

𝑥+𝑖 = Ground reference total

3.3.5 Change Detection Analysis

Post-classification (thematic) change detection is the method used in analysis

in which land use/land cover change map along with change matrix is produced from overlaying two independent thematic maps Post-classification change detection evaluates the initial (before) and final (after) state/ class of each pixel

3.4 Secondary Data Collection and Analysis

Secondary data including demographic profiles, waste analysis and characterization study (WACS), socio-economic profiles were acquired from City Environment and Natural Resources Office and Planning Office of Sta Rosa City, and Philippine Statistics Authority (PSA) website The data for roadside ambient air quality of Sta Rosa City were acquired from the Department of Environment and Natural Resources Regional Office of CALABARZON – Environmental Management Bureau