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

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: Number of pages: Date of submission: Urbanization, Land Cover/Land Use Change, Environmental, Socio-economic, Impact Assessment 62 15/11/2018 ii 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 iii TABLE OF CONTENTS LIST OF FIGURES vi LIST OF TABLES vii LIST OF ABBREVIATIONS viii PART I INTRODUCTION 1.1 Research Rationale 1.2 Research Objectives 1.3 Statement of the Problem 1.4 Significance and Limitations of the Study 1.5 Definition of Terms 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 3.3.3 Image Classification 24 iv 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 NonHousehold 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 v LIST OF FIGURES Figure 1: Map of Sta Rosa City Divided Into Eighteen Barangays 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 vi 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 vii LIST OF ABBREVIATIONS CALABARZON Cavite, Laguna, Batangas, Rizal, Quezon CENRO DENR DN DOS EMB ENVI City Environment and Natural Resources Department of Environment and Natural Resources Digital Numbers Dark Object Subtraction Environmental Management Bureau Environment for Visualizing Images (image processing software; Research Systems, Inc.) Fast Line-of-sight Atmospheric Analysis of Hypercubes Geographic Information System Land Use/Land Cover Land Use/Land Cover Change National Ambient Air Quality Guideline Value National Aeronautics and Space Administration Operational Land Imager and Thermal Infrared Sensor Particulate Matter Philippine Statistics Authority Remote Sensing Santa Rosa City Southern Luzon Expressway Thematic Mapper Top-of-atmosphere Total Waste Generation United States Environmental Protection Agency United States Geological Survey United States Geological Survey Earth Explorer Waste Analysis and Characterization Study FLAASH GIS LULC LULCC NAAQGV NASA OLI/TIRS PM PSA RS Sta Rosa City SLEX TM TOA TWG US EPA USGS USGS EE WACS viii PART I INTRODUCTION 1.1 Research Rationale The concept of urbanization started about ten thousand years ago when hunter- 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 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-starosa/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 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 & Table 12: Total Number and Rates of Registered Job Applicants and Qualified Job Registered Job Applicants Qualified Job Applicants Rates (%) of Registered Job Applicants Rates (%) of Qualified Job Applicants 1998 707 304 70 30 2005 13,706 5,230 72 28 2013 31,542 30,581 51 49 48 PART V CONCLUSION The general objective of the study was to assess the environment and socioeconomic impacts of urbanization by analyzing land conversion, waste generation, air quality, population, population growth and economic activities in Sta Rosa City It was found out that the land of Sta Rosa City has been experiencing alterations over the 24-yr period from 1993-2017, with built-up having the highest rate of final state and agricultural land continuously decreasing Drastic changes in land use/land covers affect the city’s livelihood especially those who work in agriculture sector after selling their land at inexpensive price The economic activity of Sta Rosa City has indeed benefited its population, however, it was also inevitable and undeniable that the increase of population has also contributed to environmental degradation particularly the increase in total waste generation of Sta Rosa City in 2015 which has exceeded the average per capita generation Intensified occurrence of air pollutants, specifically PM 2.5 in 2017 has notably exceeded the average annual guideline value of US EPA which was found out to be negatively correlated with wind speed Thus, setting more ambient air quality monitoring stations could help to produce air quality index map(s) of Sta Rosa City to better observe and visualize the intensity, frequency, and causes of occurring air pollutants It is also suggested to conduct actual waste sampling, quantifying, classifying, and locating sources and dumps/landfills for the study that will produce actual and updated status of total waste generation of Sta Rosa City Raising 49 awareness and encouraging every household and commercial establishment to resort to urban gardening and composting could reduce biodegradable wastes (which has the highest generation in 2015), address food security, and lessen the impacts of urbanization to air quality and health In fact, converting biodegradable wastes to organic fertilizers through composting was already made possible in one of the eighteen barangays of the city (Brgy Market Area) with its “Eco-waste Center” Other barangays can adapt this kind of program/project Overall, urbanization affected the modifications of land attributes as well as its uses which can be rapid and uncontrolled This study showed that air pollution and increasing waste generation are exacerbated by the increasing population and high demands of land conversion which also influenced the population’s main sources of living according to their environment Knowing these conditions can be helpful in taking necessary and win-win solutions without neglecting both the environment and the people 50 REFERENCES AirNow (2017) Particle Pollution Retrieved https://www.airnow.gov/index.cfm?action=aqibasics.particle from (accessed on 08/24/2018) Bhatta, B., Saraswati, S and Bandyopadhyay, D (2010) Urban sprawl measurement from remote sensing data Applied Geography, vol 30, pp 732-740 Retrieved from https://www.scribd.com/document/207702125/Urban-sprawl-measurement-fromremote-sensing-data (accessed on 07/26/2018) Castillo, A.L and Otoma, S (2013) Status of Solid Waste Management in the Philippines Retrieved from https://www.jstage.jst.go.jp/article/jsmcwm/24/0/24_677/_pdf/- char/en (accessed on 08/09/2018) Chan, C and Yao, X (2008) Air pollution in mega cities in China Atmospheric Environment, 42(1), pp 1-42 Retrieved from https://www.sciencedirect.com/science/article/pii/S1352231007007911 (accessed on 8/21/2018) City of Santa Rosa (n.d.) 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has at least one establishment with a minimum of 100 employees; has five or more establishments with a minimum of 10 employees and five or more facilities (PSA, 2017) Urbanization – a historical process relating to the growth of modern society which implies change in demographic and socio-economic structure; an urban way of life and new settlement (PSA, 2017) 60 Appendix 2: Landsat TM band numbers with corresponding band names Band Number Band Band Band Band Band Band Band Band Name Blue Green Red Near Infrared (NIR) Shortwave Infrared (SWIR 1) Thermal Shortwave Infrared (SWIR 2) Source: https://landsat.usgs.gov Appendix 3: Landsat OLI/TIRS band numbers with corresponding band names Band Number Band Band Band Band Band Band Band Band Band Band 10 Band 11 Band Name Ultra Blue (coastal/aerosol) Blue Green Red Near Infrared (NIR) Shortwave Infrared (SWIR 1) Shortwave Infrared (SWIR 2) Panchromatic Cirrus Thermal Infrared (TIRS) Thermal Infrared (TIRS) Source: https://landsat.usgs.gov 61 Appendix 4: Accuracy Assessment of Classified Image (1993) Classes from Classified Maps Built-Up Vegetation Agricultural Land Idle Land Column Total Ommission Error (%) Producer’s Accuracy (%) BuiltUp 50 Ground Truth/ Reference Classes Agricultural Idle Vegetation Land Land 47 0 50 50 44 50 38 50 12 24 100 94 88 76 Row Total 56 54 48 42 200 % Commission User’s Error Accuracy 10.71 89.29 12.96 87.04 8.33 91.67 9.52 90.48 Overall Accuracy: Kappa Coefficient: 0.90 0.86 Appendix 5: Accuracy Assessment of Classified Image (2005) Classes from Classified Maps Built-Up Vegetation Agricultural Land Idle Land Column Total Ommission Error (%) Producer’s Accuracy (%) BuiltUp 42 Ground Truth/ Reference Classes Agricultural Idle Vegetation Land Land 36 50 50 37 50 47 50 16 28 26 84 72 74 94 Row Total 45 38 54 63 200 % Commission User’s Error Accuracy 6.67 93.33 5.26 94.74 31.48 68.52 25.40 74.60 Overall Accuracy: Kappa Coefficient: 0.81 0.75 Appendix 6: Accuracy Assessment of Classified Image (2017) Classes from Classified Maps Built-Up Vegetation Agricultural Land Idle Land Column Total Ommission Error (%) Producer’s Accuracy (%) BuiltUp 45 Ground Truth/ Reference Classes Agricultural Idle Vegetation Land Land 43 50 50 43 50 50 50 10 14 14 90 86 86 100 Row Total 48 47 48 57 200 % Commission User’s Error Accuracy 6.25 93.75 8.51 91.49 10.42 89.58 12.28 87.72 Overall Accuracy: Kappa Coefficient: 0.91 0.87 62 ... 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:... 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. .. concentration of PM 2.5 and PM 10 in Sta Rosa s air quality from 2017; To understand the trends of population, migration, and economic activity/status of Sta Rosa City 1.3 Statement of the Problem Rapid urbanization