INTRODUCTION
Background of the Study
Countries worldwide are grappling with challenges related to their environment, economy, and society, largely driven by land use and land cover changes due to urbanization This human-induced phenomenon significantly impacts various sectors, primarily fueled by the continuous growth of the urban population According to the United Nations (2014), urban populations surpassed rural populations in 2007, with 54% of the global population living in urban areas by 2014—a figure projected to rise to 66% by 2050 The global population is expected to increase from 3.5 billion in 2010 to 6.3 billion by 2050, leading to ongoing urban growth and development Consequently, land use change has emerged as a critical global concern amidst this rapid urbanization and population increase.
Land use and land cover change refer to both human-induced and natural alterations of the Earth's surface (Meyer, 1995; Steffen et al., 2004) Numerous studies have explored these changes, examining their relationships and impacts across various sectors.
Two studies highlight the connection between land use change, driven by increasing human populations, and various environmental issues such as climate change, urban heat islands (UHI), human health, and pollution Urbanization is identified as a primary contributor to climate change, illustrating how human activities significantly affect land use and exacerbate climate system impacts (McCarthy et al., 2010; Jin et al., 2005) Recent research in China indicates that urbanization, alongside a growing urban population, is gradually worsening air quality (Xu et al., 2016) Additionally, it has been found that urbanization significantly influences the spatiotemporal patterns of UHIs (Zhang et al., 2013).
Human-induced threats and natural phenomena are significantly impacting Earth's supporting systems, including environmental resources, water cycles, and air quality, leading to both direct and indirect consequences for humanity (Steffen et al., 2004) Land use and land cover changes are critical drivers of these global issues, yet they also present potential solutions (Turner et al., 2007) Therefore, it is essential to continuously study and prioritize land use change as a pressing global concern that cannot be overlooked.
Meanwhile, Vietnam has experienced rapid economic growth during 1980s to 2000s which had caused uncontrolled and intensive urban expansion, especially
Hanoi, the capital of Vietnam, has experienced significant urbanization, with urban areas expanding at a rate of 2.8%, particularly in the western and northern regions, as reported in 2015 (World Bank, 2015) This rapid growth can be traced back to the Doi Moi Policy, implemented in 1986, which initiated economic reforms and has since contributed to Vietnam's ongoing economic development (JICA, 2007; Vuong, 2014) From 1975 to 2015, Hanoi's urban population surged from 1.4 million to 7.6 million (GSO, 2014), and projections indicate that within the next 25 years, the urban population is expected to increase from one-third to one-half of the total population, averaging a growth rate of 6% annually (Ministry of Construction).
Due to massive disturbance of the economic growth to the environment, the Vietnam government officially implemented a proposed “Master plan of Hanoi
The "2030 Vision to 2050" initiative, launched in 2011, aims to transform Hanoi into a smart city by implementing the Hanoi Master Plan (HMP) Key objectives of the HMP include the prohibition of motorbikes, which are the primary mode of transportation in Vietnam, to reduce pollution, as well as the enhancement of the network and railway systems and the installation of multiple environmental monitoring stations To realize these goals, certain areas of Hanoi will undergo spatial development, leading to the expansion of existing urban regions, with an anticipated conversion of approximately 28% of natural land.
4 into residential area to provide accommodation for a total of 9.2 million people by the year 2030 (VIAP, 2011)
Nam Tu Liem, a district in Hanoi, is set to undergo significant changes in land use as part of a Master Plan that redefined it from a suburban area to an urban zone in 2013 To assess the impact of these changes, a study utilizing Geographic Information System (GIS) tools for land use change detection, along with a brief survey on land use awareness, was conducted.
Background of the Study Area
Nam Tu Liem, also known as South Tu Liem, is situated to the west of central Hanoi, bordered by Thanh Xuan and Cau Giay Districts to the east, Hoai Duc District to the west, Ha Dong District to the south, and Bac Tu Liem to the north Established in 2013 through Resolution No 132/NQ-CP, the district was formed by dividing the former Tu Liem District into two distinct urban areas: Nam Tu Liem and Bac Tu Liem.
Tu Liem District, originally a rural area, has been transformed into two urban districts, encompassing a total area of 7,562 hectares and housing approximately 553,000 residents Bac Tu Liem, also known as North Tu Liem, covers 4,335 hectares and is home to nearly 320,000 people This district comprises 13 wards, including Thuong Cat, Lien Mac, Thuy Phuong, and Minh Khai.
Nam Tu Liem, also known as South Tu Liem, spans approximately 3,200 hectares and is home to around 233,000 residents (HSO, 2017) The district consists of ten wards: Trung Van, Dai Mo, Tay Mo, Me Tri, Phu Do, My Dinh 1, My Dinh 2, Cau Dien, Phuong Canh, and Xuan Phuong (Nhan Dan, 2013) This study focuses on Nam Tu Liem district, encompassing all ten wards and the local self-employed workforce within the area.
Ho Chi Minh City, Vietnam's economic hub, has seen rapid spatial development, and Hanoi is also advancing in this area According to the Hanoi Master Plan for 2030 and Vision for 2050, Nam Tu Liem is poised to become one of Hanoi's central cities The Hanoi Department of Planning and Architecture has publicly announced a detailed construction plan for Nam Tu Liem, which includes 318,711 sq m (31.87 ha) designated for green spaces, lakes, public facilities, residential properties, and commercial areas in Me Tri Ward Additionally, 69,170 sq m (6.92 ha) will be allocated for low-rise residential housing and garden houses, while approximately 19,900 sq m (1.99 ha) will be developed for commercial use, including shopping malls, offices, and service spaces These developments are anticipated to significantly contribute to the overall goals of the Hanoi Master Plan.
Figure 1.1 Location of the study area
Statement of the Problem
Hanoi is set to evolve into a smart city as outlined in the Hanoi Master Plan for 2030 and the Vision for 2050 This growth, driven by a rising population and urbanization, is already prompting significant land use changes in the Nam Tu Liem District, and these transformations are expected to continue.
Hanoi's rapid urbanization has significant positive and negative effects on its economy, environment, and society As reported by the Japan International Cooperation Agency (JICA) in 2007, this intense urban growth is straining both the environment and the health and lifestyle of its residents The continuous degradation of the environment is evident through persistent issues such as pollution.
Urban areas offer significant advantages over rural regions, including greater employment and business opportunities, which drive migration to cities (Leon, 2008) This urbanization contributes to improvements in living standards and economic growth, but it can also negatively impact environmental factors, affecting human health, biodiversity, and the overall ecosystem.
Research Objectives
This research aims to analyze land use changes in Nam Tu Liem District while examining the awareness of self-employed workers regarding these changes To achieve this goal, specific objectives will be addressed throughout the study.
1 To assess and analyze how the land use changes in Nam Tu Liem District within 2007-2017
2 To know the extent of local worker’s awareness regarding land use changes
3 To form a vision of what the individual wants their community to become in the succeeding years
4 To find solutions and mitigations for the occurring issues brought by land use change
Research Questions
1 How does the land use of Nam Tu Liem District change overtime?
2 How does the land use in Nam Tu Liem change during 2007-2013, 2013-
3 How aware the individual workers are towards land use change?
4 What are the feasible solutions or effective ways to minimize undesirable consequences of land use change?
Research Rationale
The study examines land use changes and the awareness of self-employed workers in Nam Tu Liem District, offering valuable insights for future research and observations in the area Its findings are crucial for socio-economic and environmental impact assessments of upcoming administrative projects Additionally, the research supports the development and implementation of effective land use planning policies and strategies, benefiting not only Nam Tu Liem District but also other regions in Hanoi and across Vietnam.
Limitations
This study encountered several limitations: first, it primarily addresses general land use changes while omitting specific area types like residential, commercial, and industrial zones Second, the satellite images utilized were not captured on the same date, which was affected by cloud cover Lastly, the Landsat data analyzed only included gap filling for certain images.
The study on land use change perception in Nam Tu Liem District highlights several challenges affecting image classification in 2013 A significant barrier is language, as many Vietnamese individuals lack proficiency in English, complicating research due to the prevalence of Vietnamese-language materials Additionally, time constraints necessitated the use of quota sampling, which introduces bias and may overlook crucial information by focusing solely on selected traits within a limited population The strategic analysis conducted was restricted to univariate and bivariate methods, focusing on data description and pattern identification rather than exploring variable relationships Consequently, the findings of this study cannot be generalized to represent the perceptions of all self-employed workers and individuals in the district.
Definitions and Concepts
Geographic Information System: a technology that is capable of storing, analyzing, organizing, and managing data such as satellite or geographic data which are then visualized into maps or 3D scenes
Land Cover: the bio-physical or natural earth surface structures of the land such as forests, water, vegetation, etc
Land Use: the management of the natural and built-up lands which are used by humans for agriculture, residential, recreational, government purposes, and other economic activities
Land Use Change: the conversion and modification of land surface regarding how it is being used between different years
Landsat: a satellite that is operated by the United States government organizations
Satellites play a crucial role in capturing and transmitting data about Earth's topography and various phenomena, which are essential for creating images and maps Among the eight satellites launched, two remain active: Landsat 7 ETM+ and Landsat 8 OLI/TIRS.
Remote Sensing: the process of monitoring, investigating, and collecting data of the earth’s physical traits or phenomenon by the use of satellite
Self-employed workers: individuals who work for oneself and make a living from it, which is also called a freelancer or a business owner
Urbanization: refers to the continuous growth and development of an area which is induced by and a result of population shift, specifically from rural to urban
LITERATURE REVIEW
Land Use Change
2.1.1 What is Land Use Change?
Land use and land cover changes are significant and rapid processes driven by natural and socio-economic factors, impacting both the environment and human populations In Remote Sensing, land cover refers to the bio-physical characteristics of the Earth's surface, including vegetation, water, and soil, while land use pertains to how humans utilize the land These concepts, though related, are distinct; land cover encompasses natural surface types, whereas land use involves human-induced alterations to these surfaces Understanding these differences is crucial for effective land management and planning.
Land use and land cover are interrelated, with land use exerting pressure on land cover and vice versa (Riebsame et al., 1994) Changes in land cover driven by land use can lead to negative consequences for the environment, including impacts on climate, biosphere, biodiversity, and water quality However, it is important to note that alterations in land cover due to land use do not always lead to land degradation, as human activities can also promote positive outcomes.
12 induced land use change is also considered as an improvement and enhancement to the land which brings positive influences (Riebsame et al., 1994; Meyer, 1995)
2.1.2 Importance of Land Use Change
Understanding land use change is crucial for nations and businesses to make informed decisions regarding economic growth and environmental conservation (Anderson et al., 1976; Manandhar et al., 2009) It serves as a key indicator of human-environment relationships (Li et al., 2017) and plays an essential role in environmental monitoring, helping to prevent irreversible damage to ecosystems (Manandhar et al., 2009) Addressing the challenges posed by land use change is vital for sustainable development at local, regional, and global levels (Li et al., 2017; Manandhar et al., 2009) Furthermore, timely reporting on land use changes is instrumental in effective natural resource management and tackling sustainability issues (Aspinall & Hill, 2008).
Aspinall and Hill (2008) identified various aspects of land use change reporting, including areal change, transformation, dynamics, and prediction Areal change refers to the fluctuations in the extent of land, highlighting both the loss and gain of specific land areas over time.
Land transformation refers to the process of converting one type of land use to another, while dynamics involves analyzing the ratios and trends of these transformations Additionally, predictions utilize models to forecast future changes in land use patterns Understanding the primary drivers of land use change is essential for implementing effective land use planning strategies moving forward.
Understanding land use and land cover change is crucial for developing effective land use systems that address societal needs and welfare This knowledge also aids in assessing the impact of historical and current policies, as well as identifying existing drivers of land use change, which are vital for future research.
2.1.3 Land Use Change Detection using RS and GIS
Change detection involves identifying alterations in land and phenomena through remote sensing data and techniques (Anderson et al., 1976) Numerous studies have demonstrated the efficacy of remote sensing and sensor databases in mapping land use and land cover changes, even over extensive areas Additionally, the capability for real-time data acquisition, broad observational coverage, and consistent reporting enhances the monitoring of urbanization and other change detection initiatives (Belal & Moghanm, 2011; Kontgis et al., 2014).
In the 1940s, Francis J Marschner created a comprehensive land use map of the United States by utilizing aerial photographs from the 1930s and 1940s, compiling data from aerial photograph mosaics (Anderson et al., 1976).
In the 1970s, the introduction of more remote sensing (RS) data, satellite data, and related applications marked a significant advancement in the field (Kontgis et al., 2014) Today, the integration of RS and Geographic Information Systems (GIS) enables efficient mapping of land use and cover changes (LUCC) with reduced costs, shorter timeframes, and enhanced accuracy (Kachhwala, 1985).
Land Use Change Factors, Impacts, and Mitigations
2.2.1 Major Factors of Land Use Change
Land use change is influenced by a variety of factors, including biophysical, social, economic, demographic, technological, and institutional aspects, as highlighted by Partoyo and Shrestha (2017) The following sections will explore these factors in detail, starting with biophysical influences.
Land use is influenced by biophysical factors, which can indirectly drive changes in land cover that subsequently affect land use allocation (Partoyo & Shrestha, 2017) Key biophysical factors include soil type, topography, climate, and water quality and availability The suitability of land for various purposes, such as horticulture, pastoral activities, farming, or residential construction, often depends on these factors Additionally, climate and water availability play crucial roles in determining appropriate land use.
Water quality is a critical factor influencing agricultural land use, particularly in crop growth and irrigation Certain soil types with low potential and unsuitable locations require additional inputs, such as land preparation and fertilizers, to enhance their usability (Journeaux et al., 2017; Verburg et al., 2004) Furthermore, changes in land use patterns can lead to the degradation of irrecoverable lands, like desertified areas and forests, which are challenging to restore In such cases, converting these lands into structures may be considered to maximize their benefits (Verburg et al., 2004).
Moreover, Journeaux et al (2017) stated phenomenon such as climate change to be a future driver of land use change However, Zondag and Borsboom
Climate change indirectly influences land use by affecting biophysical factors, as noted by Rutledge et al (2009, 2011) One significant impact is on the hydrological cycle, which alters water resource distribution and availability, thereby affecting infrastructure and planning Additionally, climate change influences mineral production, which can disrupt agricultural output through changes in nutrient cycling Variations in temperature, carbon dioxide levels, rainfall patterns, and droughts also contribute to this disruption Consequently, these factors may negatively impact farm productivity, leading to alterations in land use and potential land abandonment.
The United Nations (2014) defines a city as an area of persistent urban development characterized by structures within 200 meters of each other, housing over 10,000 residents Cities play a crucial role in global development, attracting individuals, businesses, and cultural activities (Singh, 2014), and currently serve as homes for half of the world's population (Cohen, 2006; Kontgis et al., 2014) People are increasingly migrating to urban areas, drawn by the greater opportunities they offer compared to rural regions (Leon, 2008) For instance, Nepal has experienced a rapid increase in its urban population due to migration from rural areas (Gurung et al., 2012) This trend is primarily driven by the superior education, healthcare, services, and employment opportunities available in urban settings, which are often lacking in rural areas (Moore et al., 2003; Gurung et al., 2012).
In the late 1950s, rapid urbanization emerged as a significant global challenge, particularly affecting developing countries (Elhadary et al., 2013; Roshanbakhsh et al., 2017) By that time, approximately 30% of the world’s population lived in urban areas, a figure that rose to 54% by 2014 and is projected to reach 66% by 2050 If birth rates remain constant, the global urban population could surge to 7.4 billion by 2050, up from 6.3 billion (UN, 2014) Notably, cities occupy only 2% of the world’s land yet consume 75% of its resources and produce waste at a similar rate (ADB, 2008).
Urbanization is a significant human-induced factor leading to land loss, habitat destruction, and environmental degradation, particularly affecting natural vegetation (Kontgis et al., 2014) The expansion of urban areas into rural regions creates urban-rural and peri-urban boundaries, altering the rural landscape and impacting community life (Elhadary et al., 2013) This phenomenon results in changes to rural land use in various parts of the world (Williams & Schirmer, 2012).
Cities are typically located near valuable natural resources like rivers, lakes, and fertile agricultural land However, as urban areas expand, they consume these resources, leading to land use changes that destroy habitats and threaten biodiversity This expansion also causes erosion and desertification due to deforestation, jeopardizing food production as agricultural land is encroached upon Despite these challenges, urban landscapes can still offer ecosystem services and goods Additionally, urban sprawl contributes to health issues by increasing carbon emissions linked to higher energy consumption, greater reliance on automobiles, and further land use changes.
Social value plays a crucial role in influencing land use change, encompassing various factors such as knowledge, age, educational background, experiences, spending habits, attitudes, and preferences.
2017) Moreover, Zondag and Borsboom (2009) explains how a certain scenario
Changes in society's lifestyle are directly influencing housing types and locations, while agricultural production and other economic activities are being indirectly impacted by societal values and perceptions, particularly due to shifts in government budget priorities.
Technology significantly impacts land use change across various sectors, including agriculture and land use planning Advances in technology enhance agricultural productivity and facilitate the desalination of underground water, proving essential for effective land use planning In urban contexts, these technological developments support the formulation of policies aimed at promoting sustainable land use practices (Zondag & Borsboom, 2009).
Land use change plays a crucial role in economic development, driven by factors such as profit, capital, markets, and infrastructure These economic indicators are vital for maintaining a functional economy and influence significant land use conversions, including the transformation of agricultural and vegetative lands into urban areas Such changes, especially the conversion of croplands into settlements, can reduce primary production and the essential output required for agricultural economies.
The continuous reduction of arable land is driven by increasing population demands for manufactured products and urban space Additionally, external economic factors such as commodity prices, market demands, and land value, influenced by population growth, economic development, and government policies, contribute to land use changes (Rutledge et al., 2011; Journeaux et al., 2017; Wu, 2008; Zondag & Borsboom, 2009).
Institutional factors serve as external drivers of land use change, categorized by various policy dimensions such as scale level (international, national, and territorial), sectoral level (including spatial planning), and type (financial, communications, and instruments) Future land use will be significantly influenced not only by current land use policies but also by forthcoming policies (Zondag & Borsboom, 2009).
Land use plays a crucial role in benefiting the economy and society, but it can negatively impact the environment, leading to inefficient land allocation This inefficiency arises from the interplay between private and public land use influenced by planning and decision-making processes Often, these decisions prioritize economic costs over environmental considerations, highlighting the need for improved land use regulations to balance these factors effectively.
20 and policies between private and public plans must be balanced (Wu, 2008; Journeaux et al., 2017)
Related studies on Land Use Change Worldwide
Numerous studies have successfully utilized Remote Sensing (RS) and Geographic Information Systems (GIS) technologies for Land Use and Land Cover (LUCC) detection One notable study by Yesmin et al (2014) focused on LUCC detection in the Mirzapur union of Gazipur district, Bangladesh, analyzing Landsat imagery from 1989 and 2009 This research aimed to assess the spatio-temporal patterns of land use and cover changes over two decades, revealing significant reductions in forest and water areas, alongside increases in settlement and bare land, primarily driven by unplanned urbanization and population growth Similarly, Rawat and Kumar (2015) conducted a study in Almora District, Uttarakhand, India, comparing land use from 1990 to 2010, which indicated that vegetation became the predominant land use due to afforestation efforts.
Between 1990 and 2010, agricultural and bare land have diminished significantly due to urban expansion A study by Belal and Moghanm (2011) highlights that urban sprawl has led to a reduction in productive and fertile agricultural lands in Egypt's Nile Delta.
Moreover, a study in Ssese Islands in Uganda resulted a land use change trend encouraged by agricultural investments and economy changes through
Government policies have led to significant agricultural expansion, notably impacting forest lands in the Ssese Islands, especially on Bugala Island The development of an oil palm plantation project has resulted in the degradation of both forests and grasslands in the area.
In 2007, Vientiane, Laos, experienced a transformation as forest and farmland were converted into urban areas, leading to the displacement of residents due to paddy field reclamation, which also contributed to deforestation and a decline in forested land However, local villagers seized this change to enhance their livelihoods by transporting agricultural products sourced from the forests (Okamoto et al., 2014) Similarly, in the Amboseli region of Kenya, there has been a notable reduction in pastoral resources, driven by the conversion of grazing land into croplands and the establishment of human settlements (Kimiti et al., 2016).
Related Studies on Land Use Change in Vietnam
A land use/land cover (LULC) study was conducted by Tran, H et al
In 2015, a study was conducted in Tran Van Thoi District, Ca Mau Province, Vietnam, focusing on the spatio-temporal dynamics of land use and cover change (LUCC) over a 40-year period The research analyzed six distinct time frames, spanning from 1973 to 2015, to assess how land use patterns have evolved in the district.
Satellite images from 2011 were collected, georeferenced, classified, and validated for comparison with previous years The findings reveal significant changes in land use and land cover (LULC) patterns in coastal and rural districts since the end of the Vietnam War Over the past 40 years, all land types have undergone transformations, with cultivated lands, aquaculture ponds, and built-up areas experiencing the most significant alterations Although cultivated land remains the dominant land use type, its extent has likely decreased.
The study identifies 29 as the predominant type in the analysis Tran, H et al (2015) emphasize that this research in Cau Mau Province marks the initial phase in identifying the key drivers of Land Use and Cover Change (LUCC) in the delta region, alongside economic policies and shifts in demographic, socio-economic, and environmental factors.
In addition, same scenario like the study of Tran, H et al (2015) happened in Y Yen district, Nam Dinh, Vietnam within 2006 to 2014 that is conducted by
Le et al (2016); in Da Nang City within 1979 to 2009 conducted by Linh et al (2012); and in Ho Chi Minh City within 1990 to 2012 conducted by Kontgis et al
A 2014 study revealed a significant increase in non-agricultural land, primarily consisting of built-up areas The research indicates that these built-up lands are predominantly converted from agricultural fields, water bodies, forests, shrubs, and bare land.
Numerous studies have explored the impact of land use change, including a significant investigation in Hanoi by Trihamdani et al (2017), which focuses on the urban heat island (UHI) effect This research aims to analyze the implications of land use and cover change (LUCC) resulting from the Hanoi Master Plan.
A study examining the impact of the Housing Master Plan (HMP) on urban heat islands (UHI) revealed that land use changes associated with HMP did not significantly alter the average air temperature in urban areas, which remains steady at 37°C However, the findings indicate an increase in the number of hotspots and a rise in night-time average temperatures in newly developed urban regions.
The Land use and the Workers Interactions in Vietnam
The following paragraphs in this section follow the study of JICA (2007), unless stated otherwise
The adoption of the Doi Moi Policy in 1986 significantly enhanced migration patterns in Vietnam, contributing to job creation across the nation Research indicates that many poor migrants moving to urban areas are often temporary or low-wage workers in their home provinces Additionally, the findings reveal that rural residents tend to migrate to smaller towns, while those from smaller towns are drawn to larger cities.
(2008), people move to and stay in the cities because there are more opportunities awaits in the cities
According to a JICA report from 2007, urban areas such as Hanoi offer enhanced safety, security, and services related to leisure, education, health, and overall living standards compared to suburban and rural regions However, the rapid pace of urbanization has significantly affected residents' lifestyles, leading to increased traffic congestion, environmental degradation, and other challenges associated with ongoing urban development and land use changes.
To develop effective sustainability plans and policies, it is crucial to understand people's perceptions, as highlighted by Tran, K C et al (2002) and Elhadary et al (2013) This understanding serves as a valuable technique to identify, prevent, and address undesirable development issues and their impacts.
RESEARCH OBJECT AND METHODS
Research Object, Scope, and Content
3.1.1 Research Object a Land use of Nam Tu Liem District
• Land use map in 2007, 2013, and 2017
• Land use change detection map from 2007 to 2017 b Self-employed workers in Nam Tu Liem District
• General information of the individual workers
• Perception towards land use change
3.1.2 The scope of the research a Time scope: The entire research was conducted for 4 months, starting from
April 2018 until August 2018 b The spatial extent: The designated area for the research is the whole Nam
Tu Liem District in Hanoi, Vietnam which includes the ten (10) wards/communes of the district
3.1.3 The content of the research
The study focuses on the analysis of land use change and the worker’s perception towards changes that have occurred and have been occurring in Nam
The study focuses on Tu Liem District in Hanoi, Vietnam, examining land use changes within the area It includes a comprehensive mapping of these changes and an awareness survey targeting self-employed workers across various commercial sectors.
32 wholesale and retail services, accommodation and catering services, and other types of services.
Research Methods
The research background reading and method completion occurred from April to May 2018, alongside the collection of materials for land use change mapping in April 2018 Data on worker perceptions regarding land use change was gathered in June 2018 in Nam Tu Liem District, Hanoi, Vietnam Finally, the GIS and awareness survey data were analyzed between June and July 2018 at Thai Nguyen University, Vietnam.
The study utilized both primary and secondary data for its analysis Primary data was collected through an awareness survey using questionnaires, while secondary data for land use change detection was sourced from the United States Geological Survey (USGS) website.
The land use change study employed an observational research design that incorporated both quantitative and qualitative data Qualitative data were obtained through the classification and alteration of land use, while quantitative data were derived from measuring changes in specific land use areas.
Data for land use change analysis and mapping were sourced from the Earth Explorer website of the United States Geological Survey (USGS) The study utilized satellite images from Landsat TM 5, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, all with a spatial resolution of 30 by 30 meters, covering the years 2007, 2013, and 2017 The selected Landsat images were chosen for their accessibility and minimal cloud cover, particularly those captured in May 2007.
2013, and June 2017 A detailed description of the data used in the study is shown in Table 3.1
Table 3 1 Detailed description of collected satellite images
3 Landsat 8 OLI/TIRS 127/45 1-11 9/10 5.54 2017-06-04 b Data Collection for Awareness Study
The study employed a descriptive research design to assess land use awareness, utilizing questionnaires as the primary tool for quantitative data collection Two versions of the questionnaire were developed: one in English and another translated into Vietnamese The survey questions were categorized into basic respondent information and perceptions of land use change, drawing on assessments from Neupane (2016) and The Geauga County Planning Commission (2008) Prior to implementation, the questionnaire underwent face validity checks and pretesting to ensure its effectiveness.
The survey questionnaire consists of 18 questions divided into two categories: seven questions gather general information about respondents, while eleven focus on their perceptions of land use changes in Nam Tu Liem District It includes multiple-choice, multi-response, and filter questions, as well as open-ended questions that allow participants to express their thoughts and ideas freely The complete survey questionnaire is available in Appendix 1.
3.2.2 Detection of Land Use Change
The land use changes in Nam Tu Liem District, Hanoi, Vietnam within
From 2007 to 2017, a study utilized GIS tools, specifically ArcGIS and ENVI software, to assess land use change (LUC) through various indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-Up Index (NDBI), and Bare Soil Index (BSI) The topography datasets were sourced from USGS and the Vietnam Government, and the study involved the analysis of remotely sensed imagery and its pre-processing.
In remote sensing (RS), two essential pre-processing methods are employed: geometric and radiometric processing Geometric processing involves distorting images to align with a specific map projection, and it is most effective when applied after the final classified map is created Conversely, radiometric processing focuses on correcting the image's brightness and color values to enhance the accuracy of the data.
Radiometric processing plays a crucial role in minimizing atmospheric effects and noise that can interfere with surface reflectance measurements obtained from remotely sensed imagery This technique enhances classification accuracy, as highlighted by Horning (2004) In this study, radiometric processing was employed, and the subsequent sections detail the pre-processing methods utilized.
Table 3.2, 3.3, and 3.4 present various bands utilized in the analysis, where the bands are layer stacked to merge distinct image bands into a comprehensive multispectral image For land use change detection, the selected bands include 1, 2, 3, 4, 5, and 7 from the Landsat 5 TM and Landsat data.
7 ETM On the other hand, bands 2, 3, 4, 5, 6, and 7 are used from the Landsat OLI/TIRS
Band 4 - Near Infrared (NIR) 0.76-0.90 Band 5 - Shortwave Infrared (SWIR) 1 1.55-1.75
Band 4 - Near Infrared (NIR) 0.77-0.90 Band 5 - Shortwave Infrared (SWIR) 1 1.55-1.75
Table 3 4 Landsat 8 OLI/TIRS bands
Band 1 - Ultra Blue (coastal/aerosol) 0.435 - 0.451
Band 5 - Near Infrared (NIR) 0.851 - 0.879 Band 6 - Shortwave Infrared (SWIR) 1 1.566 - 1.651 Band 7 - Shortwave Infrared (SWIR) 2 2.107 - 2.294
Band 10 - Thermal Infrared (TIRS) 1 10.60 - 11.19 Band 11 - Thermal Infrared (TIRS) 2 11.50 - 12.51
Source: https://landsat.usgs.gov
Before classifying multispectral images, Dark Object Subtraction (DOS) is applied to correct them by removing haze caused by additive scattering in remote sensing data (Chavez Jr, 1988) While atmospheric correction may not always be crucial for classification and change detection, it significantly enhances accuracy (Song, 2001) This widely used method is particularly effective for detecting land use changes.
The first step in atmospheric correction is radiometric calibration, which involves converting digital number (DN) values into Top of Atmosphere (TOA) Reflectance, followed by the transformation into Surface Reflectance using the Dark Object Subtraction (DOS) method Surface reflectance is crucial for analyzing land use changes and conducting studies, as it effectively reduces or eliminates atmospheric additives, enhancing the precision and comparability of data over time.
Since May 2003, all Landsat 7 data collected have exhibited data gaps, as reported by USGS This study focuses on the Landsat 7 TM data acquired on May 16, 2013, which is the sole dataset that underwent gap filling using ENVI Classic software.
After completing layer stacking, gap filling, and image correction for each image, the next step involved clipping to emphasize the true shape of the study area The shapefile utilized in this process was sourced from the Hanoi Natural Resources and Environment Department's website The actual outlines of the study area for the years 2007, 2013, and 2017 are illustrated in Figure 3.1.
Figure 3 1 Clipped image of the study area in 2007, 2013, and 2017
There are two main classification methods in data analysis: supervised and unsupervised classification Supervised classification relies on user-provided training data to assign land classes to pixels, while unsupervised classification automatically categorizes land classes without user intervention.
RESULTS AND DISCUSSION
Land Use Change Detection
Nam Tu Liem District's land use classification includes five categories: water, bare land, vegetation, agriculture, and built-up areas Analysis of land use maps from 2007, 2013, and 2017 reveals a significant increase in built-up land, which has become the dominant type since 2007, rising by 18.71% or 601 hectares Conversely, agricultural land has declined by 11.70%, equating to 375.99 hectares, indicating a substantial transformation over the years Meanwhile, water, bare land, and vegetation types have remained relatively stable, with minimal changes observed; for instance, bare land decreased by only 1.68% from 2007 to 2013, while water and vegetation showed almost no variation during that period Overall, from 2007 to 2017, all land types experienced a decrease, except for the built-up areas.
Table 4 1 The land use classes’ area and percentage in hectares
Figure 4 1 The land use classes’ area in hectares
Figure 4.2 illustrates a significant increase in built-up land, contrasting with a decline in agricultural land Additionally, Figure 4.1 presents a percentage breakdown of land use classes over the years, highlighting the decrease in various land use categories alongside the expansion of built-up areas.
4.1.2 Accuracy Assessment of Land Use Classification
The study conducted an accuracy assessment to determine whether to accept the classified images prior to performing change detection analysis According to Anderson (1971) and Thomlinson et al (1999), the overall accuracy for land use and land cover categories should be at least 85% Additionally, Thomlinson et al (1999) state that an accuracy rate of 85% is deemed acceptable as long as each class maintains a minimum accuracy of 70% The accuracy assessment results include User’s Accuracy, Producer’s Accuracy, and Overall Accuracy.
WaterBare SoilVegetationAgricultureBuilt-Up
Cohen’s Kappa Coefficient for the year 2007, 2013, and 2017 are shown in Table 4.2, 4.3, and 4.4, respectively
The confusion matrix for the classified image of 2007, presented in Table 4.2, reveals that the reference data is represented in the rows while the classified classes appear in the columns With an impressive overall accuracy of 99.20%, the classified image is deemed acceptable Additionally, the Kappa coefficient of 0.99 indicates an almost perfect agreement between the classified image and the actual data.
Table 4 2 Confusion matrix for the year 2007
The confusion matrix for 2013, presented in Table 4.3, indicates that the classified image achieved an impressive overall accuracy of 95.60%, mirroring the accuracy levels of 2007 Additionally, the Kappa Coefficient for 2013 stands at 0.945, reflecting a perfect agreement in the classification results.
Table 4 3 Confusion matrix for the year 2013
In 2017, the confusion matrix indicates that the classified image achieved an overall accuracy rate of 92%, the lowest among the three years analyzed, yet still deemed acceptable Additionally, the Kappa Coefficient for 2017 is 0.9, reflecting an almost perfect level of agreement in the classification results.
Table 4 4 Confusion matrix for the year 2017
The overall accuracy and kappa coefficient for the years 2007, 2013, and
2017 showed a high value which makes classified image in each year acceptable to be used in the study and the change detection analysis
Figure 4 2 The land use maps of Nam Tu Liem district in 2007, 2013, and 2017
LAND USE MAP OF NAM TU LIEM DISTRICT IN 2007, 2013, AND 2017
The Land Use Change Detection study aimed to analyze the conversions between different land use classes over specific periods: 2007 to 2013, 2013 to 2017, and 2007 to 2017, as illustrated in Table 4.5 and Figure 4.3 Additionally, a detailed land use change map covering the years 2007 to 2017 is provided in Figure 4.4 The findings indicate that the built-up area has consistently been the dominant land use category across all examined periods.
Between 2007 and 2017, land use changes revealed significant conversions, with agriculture to built-up areas increasing by 9.46%, vegetation to built-up by 8.28%, agriculture to vegetation by 4.65%, and bare land to built-up by 3.26% The transformations of bare land and water areas from other land use classes were minimal, primarily involving the development of reservoirs and ponds in water areas, while bare land consisted mainly of uncultivated land and exposed soil.
Between 2007 and 2017, the conversion of agricultural land to built-up areas in Nam Tu Liem increased significantly, rising from 5.93% (190.30 ha) to 9.46% (303.53 ha) Similarly, the transition from vegetation to built-up land grew by 6.36%, from 203.91 ha to 265.40 ha Conversely, the area shifting from agriculture to vegetation decreased from 6.55% (210.14 ha) to 4.65% (148.98 ha) Currently, built-up land occupies 59.09% of Nam Tu Liem, which encompasses a total area of 1,899.08 ha.
2017 It is then followed by agriculture which have decreased by 11.70% or 375.99 ha mainly due to its conversion to built-up land
Table 4 5 Land Use Class Conversions from 2007, 2013, and 2017
Built-Up to Bare land
51 Figure 4 3 Land use change maps of Nam Tu Liem district from different years
Figure 4 4 The land use change map of Nam Tu Liem district from 2007 to 2017
Figures 4.5, 4.6, and 4.7 illustrate the annual gains and losses across different land classes, derived from the data in Table 4.5 The analysis indicates a rise in built-up areas, contrasted with a decline in agricultural and vegetative classes from 2007 to 2013, 2013 to 2017, and overall from 2007 to 2017 Additionally, changes in water and vegetation areas are noted during this period.
From 2007 to 2013, the data indicates minimal numerical changes, as illustrated in Figure 4.5, while spatial variations are evident in Figures 4.2(a) and 4.2(b) A similar trend is observed in water area and agriculture from 2013 to 2017, as depicted in Figure 4.6 Overall, these findings highlight a consistent pattern of slight fluctuations in the examined periods.
2007 to 2017 shows that built-up gained the most and agriculture lost the most land (Figure 4.7)
Figure 4 5 Gains and losses by each class between 2007 and 2013 (by hectares)
Table 4.6 illustrates the changes in land use from 2007 to 2017, detailing the gains and losses in hectares across different classes, including water, bare land, and vegetation The data highlights the extent of area lost versus the area gained in each category, providing a comprehensive overview of land transformation over the decade.
Water Bare land Vegetation Agriculture Built-Up
Gains and Losses in each class (2007-2013)
54 built-up land shows tripling hectares throughout the years Moreover, the number of unchanged hectares within 2007 to 2017 is also shown in the Table 4.6
Figure 4 6 Gains and losses by each class between 2013 and 2017 (by hectares)
Figure 4 7 Gains and losses by each class between 2007 and 2017 (by hectares)
Table 4 6 Gains and losses by each class between 2007 and 2017
Water Bare land Vegetation Agriculture Built-Up
Gains and Losses in each class (2013-2017)
Water Bare land Vegetation Agriculture Built-Up
Gains and Losses in each class (2007-2017)
Worker’s awareness towards land use change
A survey conducted among 100 workers in the commercial sector of Nam Tu Liem District assessed their awareness of land use changes (LUC) The participants were categorized into three sectors: wholesale and retail services, accommodation and catering services, and other urban services, with respective representation of 48, 18, and 34 workers.
The study involved 34 respondents representing various businesses, including pharmacies, convenience stores, cosmetic shops, coffee shops, and small canteens, as detailed in Appendix 4.1 A total of 67 respondents work on-site, either in their own or rented homes, with many commuting via motorbikes or private vehicles Notably, most tenants work at their locations, with 73 respondents renting and 27 being homeowners (refer to Appendices 4.3 and 4.4 for more information).
A recent study on workers' perceptions of LUC revealed that 69% of respondents in the district are aware of LUC, while 31% are not aware of it (Table 4.7).
Table 4 7 Frequency and percentage of respondents’ awareness about land use changes in Nam Tu Liem district
B1 Are you aware about land use changes in Nam Tu Liem District?
The land use awareness among various business sectors is detailed in Appendix 4.2 In the wholesale and retail services sector, 66.67% of respondents (32 individuals) are aware of land use change (LUC), while 32.33% (16 individuals) are unaware In the accommodation and catering services sector, awareness is higher, with 83.33% of respondents (15 individuals) aware and 16.67% (3 individuals) unaware Lastly, findings from the further services sector reveal similar trends in awareness levels.
Out of the total respondents, 22 (64.71%) were aware of LUC, while 12 (35.29%) were unaware Among the aware respondents, which make up 69% of the total, a question was posed regarding the source of their awareness The results, detailed in Table 4.8, indicate that the primary source of awareness for most respondents was local television channels, accounting for 27.8% of the responses.
45 responses It is then followed by internet with 25.9% or 42 responses and by own observation with 21% or 34 responses
Table 4 8 Source of awareness of the respondents towards land use change
B2 What made you aware about land use changes in your district?
According to Appendix 4.5, 76.8% of aware respondents expressed satisfaction with the changes in the district, while 23.2% reported being dissatisfied.
A survey of 69 informed respondents revealed that 92.8% (64 respondents) view land use change (LUC) positively, while 7.2% (5 respondents) perceive it negatively Additionally, most respondents believe that LUC benefits both the country and the district's future When asked about ongoing changes in the district, 82.6% (57 respondents) support the continuation of land use change, whereas 17.4% (12 respondents) oppose it The reasons for these differing opinions are detailed in Tables 4.9 and 4.10.
A significant 82.6% of respondents expressed a desire for continuous changes in their lifestyle, with 36.8% (39 responses) specifically wanting to make adjustments Additionally, 26.4% (28 responses) believe that land use changes could help alleviate poverty Conversely, the 17.4% of respondents who disagreed feel that ongoing land use changes would lead to a persistent rise in living costs in the district, accounting for 28% of the total responses.
7 responses It is then followed by the statement of keeping their culture, having 24% or 6 responses
Table 4 9 Respondents who want land use change to continue
Percent (%) Increase the employment opportunities 14 13.2 24.6
Table 4 10 Respondents who do not want land use change to continue
Negative impacts on the biodiversity 4 16.0 33.3
Do not like to change the lifestyle 2 8.0 16.7
Respondents identified significant issues related to land use change (LUC) and urbanization affecting their businesses and living conditions, as detailed in Table 4.11 The primary concerns highlighted were overpopulation (20%), congestion (17.9%), and poor waste management (17.4%) Additionally, pollution emerged as another critical issue, with 15.9% of respondents acknowledging its impact Other problems associated with LUC are also presented in the table below.
Table 4 11 Major problems in the district due to land use change
Major problems brought by Land Use
The survey included questions aimed at all respondents, both aware and unaware, focusing on their perceptions of security and satisfaction regarding changes in their district Notably, 89% of respondents reported feeling safe in their wards, while 11% expressed feelings of insecurity Additionally, 86% of participants indicated satisfaction with the developments in their area, whereas 14% were dissatisfied.
In a survey regarding desired changes in their wards and the district, respondents expressed a strong preference for increased open spaces, with 42% highlighting the need for parks, outdoor recreation, and scenic areas This was followed by a demand for more residential development at 35% and closer employment opportunities at 33% Notably, among those who were aware of the issues, 46.38% (32 respondents) specifically advocated for more open spaces.
A survey of 60 respondents revealed that 32.26% favored more residential development, more open space, and closer employment opportunities, each receiving 10 votes Approximately 14% of participants chose "none," indicating satisfaction with current changes in the district Among those who are aware of the land use changes, it is suggested they have experienced the impacts over the years Additionally, regarding individual income status, 71% reported improvements, 28% noted no change, and only 1% indicated a decline over the past decade.
Table 4 12 Changes that the respondents would like to see in the district
B10 Changes respondents would like to see
More residential development 25 36.23 10 32.26 35 Provision of central water and sewer services 17 24.64 7 22.58 24
Closer shopping opportunities 8 11.59 8 25.81 16 Closer employment opportunities 23 33.33 10 32.26 33
More open space, parks, outdoor recreation, scenic areas, etc
The awareness of respondents is influenced by various factors such as education level and years of residency, though this study does not explore these aspects in depth Among the 69 respondents who are aware, only 4 completed junior high school, while 24 have finished secondary high school In contrast, among the 31 unaware respondents, 12 also completed secondary high school, highlighting a potential correlation between educational attainment and awareness levels.
Among the 31 respondents surveyed, 19 individuals who were unaware of LUC had completed a college degree, indicating that 61.29% of the unaware group were at the College/University level, compared to 38.71% at the Senior High School level In contrast, 59.42% of aware respondents also held a College/University degree Overall, 60% of respondents completed higher education, while 36% finished Senior High School, and only 4% completed Junior High School, with the latter group being aware of LUC in the district.
Table 4 13 The respondent’s level of education
According to the residency data, only 37% of respondents are originally from Nam Tu Liem District, while a significant 63% are migrants from various provinces, including Thai Binh, Nam Dinh, and Nghe An, as well as other districts within Hanoi such as Hoai Duc, Ba Vi, and Ha Dong.
Table 4 14 Number of unaware and aware respondents which are residents and migrants in Nam Tu Liem district
A4 Are you originally from Nam