Modeling Land-Cover Land-Use Change a Case Study of a Dynamic

ABSTRACT

MODELING LAND-COVER/LAND-USE CHANGE: A CASE STUDY OF A DYNAMIC AGRICULTURAL LANDSCAPE IN SOUTHERN VIETNAM

by: Keelin Denver Haynes

The Mekong Delta region of Southern Vietnam is a rice agricultural region of global importance experiencing human-caused and climate change-driven landscape transformations as well as rapid economic development aligned with Vietnam's own economic growth. For example, a summer 2019 field campaign found that culturally and economically traditional rice paddy is being converted to high market value orchards, like mangoes and timber trees, aquaculture, and medicinal herbs and vegetables, like sweet potatoes. To quantify and analyze this change, we utilized land-cover/land-use maps created by the Japanese Space Agency using 10-30 m spatial resolution imagery for An Giang and Dong Thap Provinces of Vietnam. This project fuses biogeographic spatial data with unique cultural, economic, and land management variables to quantify drivers of LCLUC, with a focus on agricultural changes. To assist in-country stake- holders in risk mitigation and rural livelihood policies, we predicted LCLUC within these provinces for the year 2019 using the Land Change Modeler within the TerrSet software package developed by Clark Labs. The modeled result was validated by ground truth validation points collected during summer 2019. After model validation, we used TerrSet to predict LCLUC for 2027 given various research-derived cultural, economic, and land management scenarios.

MODELING LAND-COVER/LAND-USE CHANGE: A CASE STUDY OF A DYNAMIC AGRICULTURAL LANDSCAPE IN SOUTHERN VIETNAM

A Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Arts

Your Name: Keelin Denver Haynes

Miami University

Oxford, Ohio

2020

Advisor: Jessica L. McCarty

Reader: Stanley Toops

Reader: Jing Zhang

This thesis titled

MODELING LAND-COVER/LAND-USE CHANGE: A CASE STUDY OF A DYNAMIC AGRICULTURAL LANDSCAPE IN SOUTHERN VIETNAM

By: Keelin Denver Haynes

has been approved for publication by

College of Arts and Sciences

and

Department of Geography

______Jessica L. McCarty

______Stanley Toops

______Jing Zhang

Table of Contents

List of Tables ………………………………………………………………………….... iv List of Figures ………………………………………………………………………….. v List of Equations ………………………………………………………………………… v Acknowledgements Chapter One Introduction 1.1 Land Cover and Land Use Change and the Role of Remote Sensing ………. 1 1.2 Teleconnections and Incorporating Qualitative Knowledge ...... 2 1.3 Considerations for Model Development ...... 2 1.4 Gaps of Area, Time, and Focus ...... 3 1.5 Study Area ...... 3 1.6 Research Aims and Questions ...... 6 Chapter Two Literature Review 2.1 Introduction ...... 6 2.2 Religious & Cultural Influences on LCLUC Within An Giang & Dong Thap 6 2.3 Policy & Privatization Changes Within An Giang & Dong Thap ...... 7 2.4 Drivers of Agricultural Change: Conversion from Rice Paddy Farming ...... 9 2.5 Mixed Methods Research and Teleconnections ...... 10 2.6 Conclusion ...... 11 Chapter Three Data and Methods 3.0 Introduction ...... 11 3.1 Selected Software and Model ...... 11 3.2 R Package lulcc: Components and Requirements …………...... 12 3.2.1 Site Suitability ……………………………………...... 12 3.2.2 Permitted Transformations ...... 13 3.2.3 Demand Determination ...... 13 3.2.4 Allocation of Demand ...... 14 3.3 Land Change Modeler: Components and Functionality ...... 15 3.3.1 Use in LCLUC Modeling ...... 15 3.3.2Transitional Sub-Models ...... 15 3.3.3 Site Suitability: Log Regression and Covariate Selection ...... 15 3.3.4 Determination of Demand and Allocation of LCLUC ...... 17 3.4 Data Collection and Cleaning ...... 18 3.4.1 JAXA LCLU Map: Issues with Mangrove Classification ...... 19 3.5 Scenario Development ...... 16 3.5.1 Quantification of Scenarios and Incorporation into Models ...... 20 3.5.2 Scenario Rationale and Description ...... 21 3.6 Conclusion ...... 21 Chapter Four Results 4.0 Introduction ...... 23 4.1 Model Accuracy Assessment ...... 23

iv 4.2 Scenario LCLUC Map Output Analysis ...... 24 4.2.1 Dong Thap Control and Scenarios LCLU Map Outputs ...... 24 4.2.2 An Giang Control and Scenarios LCLU Map Outputs ...... 26 4.3 Conclusion ...... 29 Chapter Five Discussion 5.0 Introduction ...... 29 5.1 Scenario Output Implications...... 29 5.1.1 Dong Thap...... 29 5.1.2 An Giang...... 31 5.2 Questions Surrounding Cam Mountains ...... 33 5.3 Potential Future Steps ...... 33 Conclusion ...... 34 Chapter Six Conclusion ...... 34 Bibliography ...... 35

v List of Tables Table 3.1 Example Permitted Transformations Matrix …….…………………………………………… 13 Table 3.2 Example Demand per LCLU Class…………………………………………………………… 14 Table 3.3 Data Used in Model ...... 15 Table 3.4 Post-VIF Covariate List ...... ……...... ………………………………………………………... 20 Table 3.5: Comparison of Log Regressions for Dong Thap ...... 20 Table 3.6: Comparison of Log Regressions for An Giang ...... 20 Table 3.7: Adjusted Markov Matrix for Scenario 1 ...... 23 Table 3.8: Adjusted Markov Matrix for Scenario 2 ...... 23 Table 3.9: Adjusted Markov Matrix for Scenario 3 ...... 24 Table 4.1: Predicted Accuracy Per LCLU Class and Province ...... 25 Table 4.2: Percentage of LCLU of Total Area by Class by Year and Scenario ...... 28 Table 4.3: Percentage of LCLU of Total Area by Class by Year and Scenario ...... 30

vi List of Figures

Figure 1.1 Provinces of An Giang and Dong Thap ..……………………………………………… 3 Figure 1.2 An Giang and Dong Thap ...... ……… 4 Figure 3.1: Randomly Selected Points Collected During Summer 2019 Field Campaign ...... 16 Figure 3.2 Opportunistic Points Collected During Summer 2019 Field Campaign ...... 17 Figure 3.3: Comparison of An Giang 2007 LCLU Map Before and After Mango-Elimination Reclassification ...... 18 Figure 3.4 Example ROC Curve from Hasan et al. 2020 ...... 20 Figure 4.1: Comparison of Model Outputs from Control (Business as Usual) and Project Scenarios (Tables 3.7 - 3.9) ...... 27 Figure 4.2: Comparison of Model Outputs from Control (Business as Usual) and Project Scenarios (Tables 3.7 -3.9) ...... 29 Figure 5.1: Comparison of Other Crops and Orchards in Northern Dong Thap Province, 2027 ... 33 Figure 5.2: Comparison of Built Up Near Cao Lanh, Dong Thap Province, 2027 ...... 34 Figure 5.3: Comparison of Other Crops and Orchards in Northern Dong Thap Province, 2027 ... 35

vii List of Equations

Equation 3.1 VIF Calculation ………………………………………………………………………… 12

viii Acknowledgements

This thesis would not have been completed- or even possible- with the support of a number of people.

First, I want to thank the NASA LCLUC program for the financial support that it provided. Without this funding, I would have been unable to travel to my study area in Vietnam and have the amazing experience that I had while there.

I would also like to thank the members of my committee. First is Dr. Jing Zhang, whose knowledge of statistics allowed me to implement the various statistical techniques that I used. Next is Dr. Stanley Toops, whose experience in navigating and explaining foreign cultures and writing for social science audiences allowed be to successfully navigate my field campaign and the writing that followed. Finally, I have to give a massive thanks to Dr. Jessica McCarty, without whom I would not have attended graduate school in the first place. Your dedication to my success, encompassing everything from weekly thesis meetings to global networking opportunities, allowed me to make the most of my time in graduate school, depart with a wonderful position in my field, and have the skills to make a positive impact on the world.

I also must thank the wonderful guidance that Debbi provided (as well as the patience she had for my endless questions). Robbyn and John are owed massive thank yous for igniting my interest in geospatial research. Also, Jarrod and Peau- your incredible patience and willingness to answer every single one of my questions while abroad will always be remembered. The hard work that you both demonstrated while abroad made my first time out of the country a wonderful experience.

I’d also like to thank my fellow graduate students- my cohort as well as the ones preceding and succeeding mine- for the friendships we made and the support you provided. I’d also be remiss to not thank my parents for their financial and emotional support during all of my schooling from the age of 6 to 24- I wouldn’t be here today without you both. Finally, I owe a massive debt to two of my best friends: Cana and Cailin. You both helped keep me sane in the last final rush of grad school.

ix Chapter One Introduction

An Giang and Đồng Tháp (hereafter, referred to as Dong Thap) are two provinces in the southern part of the Socialist Republic of Việt Nam (Hereafter referred to as Vietnam), along the country’s border with Cambodia and separated by the Mekong River. Throughout their history, these provinces have been witness to diverse conflicts, discordant political regimes, and disparate economic paradigms. This history has influenced the peoples who live within their bounds, as well as the land on which those people live. The region has the Mekong River coursing through it, a natural feature whose large discharge transports nutrients to the region from further upstream and allows for easier irrigation of agriculture. These benefits of the river have allowed the land surrounding its banks to be an agricultural powerhouse for centuries. In particular, the annual summer flooding of the river lends itself to the growing of rice in paddies, which require massive water resources as they must be flooded as part of the growing process. Combining these abundant water resources with the relatively flat terrain of the area results in a landscape ideally suited for the cultivation of rice. With the environment so strongly attuned to agriculture, the human population in the area has been allowed to grow and remain at high density levels for the majority of the region’s habitation. Despite this high population level, the area has remained largely rural, resisting the large scale urbanization occurring throughout Southeast Asia, particularly in the nearby province of Can Tho and Ho Chi Minh City. This resistance has been explained by some as being a result of the cultural influences endemic to the provinces. This cultural influence has been described as an embrace of a “simple rural life” and is exemplified by a Buddhist sect that was founded in 1939 with the provinces, Hòa Hảo Buddhism. Hoahaoism discourages the construction of elaborate religious buildings and venerates being a smallholder rice agriculturist, codifying regional practices into an official religious doctrine. This agricultural success has also led to these provinces being attractive colonial holdings with both the French (who massively invested in infrastructure with the construction of the canals that still line the provinces) and the Japanese. Further, being surrounded by political and military powers like Ho Chi Minh City and its allies, the United States, Hanoi and its allies, China and Russia, and nearby Cambodia, under the rule of the Khmer Rouge, An Giang and Dong Thap have been the site of battles fought for control of the region. Additionally, battles of an economic nature have been waged on these provinces as Hanoi, the winning party of the above battles, attempted to enforce its desired economic policies of collectivization during the 1970s-1980s. This collectivization regime led to a lack of investment in the region unmatched by other provinces of Vietnam and that has only been counteracted in the past couple decades. To have an accurate understanding of how land cover and land use could change within these provinces, we need to understand how the landscape of the region affected its history and current situation, as well as how the landscape will affect the region going forward.

1.1 Land Cover and Land Use Change and the Role of Remote Sensing Land cover and land use (LCLU) refers, broadly, to two different concepts: land cover, or what physically covers the surface of the earth, and land use, or covering features in the context of their value to humans (Ellis, 2007). Examples of each would be the classes of forest, agricultural fields, and built up for the former; and the classes of orchard, smallholder agriculture, and high-intensity industrial for the latter. Of particular interest to this study is understanding how land cover and land use is changing, a process referred to henceforth as land cover land use change (LCLUC). While humans have been modifying their environments for millenia, the pace of modification is currently much higher than in the past, with the introduction of various technologies allowing large scale changes to take place at a faster rate (Ellis, 2007; Lambin & Meyfroidt, 2011; Sakamoto et al., 2009). One common method for measuring LCLUC is through the use of remotely sensed earth observation data which allows for wide extent, fine scale, longitudinal studies with considerably fewer personal barriers to research, including financial and labor resources (Ellis, 2007). Recent expansion of remotely sensed earth observation data, as well as

1 increased processing capability have improved LCLUC researcher’s ability to effectively quantify and analyze LCLUC across the globe (Clifford et al, 2016).

1.2 Teleconnections and Incorporating Qualitative Knowledge The trend of globalization has connected spatially distant locales and environments, allowing for communities to be affected by events at great distances and little relation to themselves. To describe this phenomenon, this paper shall use the word “teleconnections”, a term originally used in climate science to describe differing global climate events that are affected by one another. Seto et al (2012) repurposed this term to refer to the “distal flows and connections of people, economic goods and services, and land use change processes” that connect geographically far places. An example of a teleconnection is China decreasing its rice imports which then causes rice farmers in Vietnam, a major rice-exporting country, to begin growing another crop. The importance of understanding how various social, political, and economic factors influence LCLUC is seen across land use science literature, specifically by researchers conducting interviews with the local population (Gellrich et al., 2008; Lindskog & Tengberg, 1994), consulting the knowledge of subject experts (Price et al., 2012; Swetnam et al., 2011), and the use of secondary data sources (Hagenlocher et al., 2013). This study, aiming to build the most reliable possible understanding of ground conditions within the study area, makes use of all three methods by conducting interviews with local citizens and experts, as well as analyzing secondary government and international organization data.

1.3 Considerations for Model Development Common goals in LCLUC research are the creation of models to quantify previous LCLUC, understand the driving factors of that LCLUC, and forecast near-term LCLU (Amadou et al., 2018; Ellis, 2007; Lambin & Meyfroidt, 2011; McGregor et al., 2009; Willemen et al., 2002). Results of LCLUC models vary based on chosen modelling methodology. Given the impact that humans have upon their environment (Rounsevell et al., 2012), this thesis incorporates a number of social science theoretical concepts to better explain the change modellers see in human dominated landscapes. The first of these, hierarchy theory, states that local level decisions are often constrained by larger scale decisions, though at the same time, those larger scale decisions emerge from local level decisions (Allen & Starr, 2017). Verburg et al. (2002) gives as an example of hierarchy theory how the creation of a new fruit farm near a market could potentially lower the demand for the same fruit that has been traditionally grown at a farther distance. Another social science concept used in this model is connectivity, the idea that locales at varying distances are affected by one another (Goodwin, 2003). Connectivity can be seen in southwest Vietnam through how shoreline erosion and collapse has led to human settlements being rebuilt further inland in areas formerly used as agricultural land. Finally, the notion of stability, defined here as the ability of environmental and social systems to absorb internal disruptions and external pressures for a given time before changing their behavior plays a role within the study area (Adger, 2000; Holling, 1973, 1992). Stability can occur in two ways, either particular events forcing a population away from an area or activity for some time, before the population returns and continues its normal behavior. Or stability can be a population resisting immediate change exemplified by farmers experiencing lower prices for their products, but waiting for a time before switching produce based on the amount of capital invested in their current farming system.

1.4 Gaps of Area, Time, and Focus Currently LCLUC models in the Vietnamese portion of the Mekong Delta tend to focus on the urban and peri-urban region of Ho Chi Minh City, as well as the area surrounding the city of Can Tho farther south in the Delta (Berg et al., 2017; Kontgis et al., 2014; Sakamoto et al., 2009). This lack of focus on the provinces of An Giang and Dong Thap risks that threats to agriculture and human settlements within them go unstudied and unprepared for. Given that these two provinces have among the highest annual rice yields in the Mekong Delta, this lack of attention could have potentially disastrous

2 implications for rice production in the region and the Vietnamese economy (General Statistics Office Of Vietnam, 2019). Additionally, most studies of the region seek only to quantify past LCLUC and not to forecast near-term LCLUC (Binh et al., 2005; Kontgis et al., 2015, 2017; Sakamoto et al., 2009). Given that this region faces severe threats including saltwater intrusion, freshwater damming, and pollution (Geological and Topographical Features, 2009), it is imperative that policy makers and researchers plan to mitigate these harms. However, noting the speed at which land use is capable of change (Ellis, 2007; Sakamoto et al., 2009), if policy makers and researchers fail to forecast how land use could change in the near future, the mitigation plans they implement may have limited effectiveness given that they were developed for a different landscape. Further, this focus on quantification of past change (Kontgis et al., 2015, 2017; Tran et al., 2015) has likewise excluded examination of the effect that local and regional teleconnections could have on local LCLUC. Given the strong impact that humans and their societies have on the natural environment (Ellis, 2007; Rounsevell et al., 2012; Verburg et al., 2002), LCLUC researchers must analyze this impact and incorporate it into their LCLUC models.

1.5 Study Area The project study area consists of the provinces of An Giang and Dong Thap within the Mekong Delta region of Vietnam and is shown in Figure 1.1. The province of An Giang has an area of approximately 3,537 km2 and a population of 2,161,700. The other half of our study area, the province of Dong Thap, has an area of approximately 3,384 km2 and a population of 1,690,300 (Statistical Handbook of Vietnam 2017, 2018). Together, the area of the two provinces is a bit bigger than the U.S. state of Delaware (6,920 km2 to the latter’s 6,446 sq km). With a population density of 556.6 people per sq km, the two provinces have a population density significantly higher than any US state, indicating a demographic denseness in the largely rural provinces that far outstrips the population density in rural areas in western countries. Both provinces are divided into districts, which are further subdivided into communes. An Giang has eleven districts, with its capital city, Long Xuyên, located in its Figure 1.1: The provinces of An Giang eponymous district of Long Xuyên, the And Dong Thap within Vietnam. densest district in the province (Vietnam

3 Administrative Atlas, 2015). Also of note are the dense districts of Châu Đốc and Tân Châu, both home to significant cities on the border with Cambodia. Dong Thap is divided into twelve districts, with its capital city also located within an eponymous district, Cao Lãnh, which has the highest population in the province, but is second to the Sa Đéc district in terms of population density. Like An Giang, Dong Thap also has a district on the Cambodian border, Hồng Ngự, acting as a population hub for the district (Vietnam Administrative Atlas, 2015). Of further note concerning population, both provinces have

Figure 1.2: An Giang and Dong Thap Cities districts on the dividing provincial border (The Mekong River) with high populations. This produces a pattern of high population in the meeting point of the two provinces that lessens as one travels to the other ends of the provinces, with the exception of the three districts mentioned above that lie along the Vietnamese-Cambodian border.

The climate of the provinces is tropical, with a summer season (May-Sept.) experiencing high temperatures (Average of 28.1°C) and high amounts of precipitation that range from >200 mm in May, June, August, and September to >500 mm in the month of July (General Statistics Office Of Vietnam, 2019; Hickey et al., 2020). The winter season (Oct.-Feb.) experiences little fluctuation in regards to temperature (Average of 27.46°C); however the precipitation drops off considerably during this time with Dec.-Feb. having <50 mm (General Statistics Office Of Vietnam, 2019). These climate conditions, in

4 addition to the alluvial soils that are concentrated in the Mekong Delta region, allow for a thriving agricultural sector to exist within the provinces (Estelles et al., 2002; Hickey et al., 2020). An effect of this agricultural industry has marked the landscape of both provinces- with both being almost completely covered from end to end with paddy. The General Statistics Office of Vietnam states that, in 2017, 80% and 77% of the total land was used for agricultural production in An Giang and Dong Thap respectively (General Statistics Office Of Vietnam, 2019). The topography of the region, which is primarily flat, the majority of the land has a slope of less than ~8°, further helped construct the paddy (NASA JPL, 2013). An exception to this predominantly low slope are a series of small mountains in the western portion of Tịnh Biên district within An Giang province. These mountains, known in Vietnamese as the Bảy Núi (Seven Mountains) range, are heavily forested and rise from the paddy covered plains that surround them. 2019 field work in the study area showed that these mountains were the center of a tourism industry focused on them, a fact backed up by the national English language publication, Vietnam News (“Tourism Hopes for the Mekong Delta,” 2019). Dong Thap also exhibits a landscape divergence from agriculture located within its district of Tam Nông. Tràm Chim National Park, located right outside the city of Tràm Chim, is the current 7,500 ha remnant of the once 700,000 ha “Plain of Reeds”— a wetland teeming with grass, fish, and bird biodiversity (Shepherd, 2008). As described above, the population is primarily concentrated near cities and along the Mekong River within the provinces; however, population levels are still very high in all districts throughout the study area (Vietnam Administrative Atlas, 2015). Decline in infant mortality between 2005 and 2017 (An Giang and Dong Thap saw their rate of deaths per 1000 births fall from 20.9 to 13.9 and 16.1 to 11.5 respectively) has been a factor in maintaining these high population levels (General Statistics Office Of Vietnam, 2019). Interestingly however, both provinces have seen the annual population growth rate fall over the past several years. An Giang for instance saw an annual population growth of 0.51% in 2005 decrease to 0.11% in 2017. Dong Thap’s decrease was even more extreme, going from 0.79% to 0.18% (General Statistics Office Of Vietnam, 2019). This decline is also seen in the number of individuals in the labor force in An Giang falling from 1,223,900 to 1,233,600 during the same time period. However, that decline in population growth did not negatively affect the labor force in Dong Thap, as it rose from 900,900 to 1,120,500 during this time period (General Statistics Office Of Vietnam, 2019). Bảy Núi, the Seven Mountains mentioned above, are a tourist hotspot partly for their natural beauty, but they also are popular due to their importance as being home to various religious sites and traditions (Nguyen, 2003). One of these faiths, Hòa Hảo Buddhism (hereafter referred to as Hoa Hao), is an anti-colonial, activist faith constituting the majority belief system in An Giang and Dong Thap. Hoa haoism arose from the Bửu Sơn Kỳ Hương tradition, a “patriotic” Buddhist movement that emerged in the mid-19th century as Vietnamese settlers spread throughout modern-day southern Vietnam (Nguyen, 2003). This sense of patriotism increased further as French colonizers arrived in Vietnam, eventually peaking in 1939 when Huỳnh Phú Sổ—a 19 year old man who’s divinity was supported by possessing a profound knowledge of Buddhist doctrine and recently overcoming a “chronic frailty”—declared the beginning of a new religious tradition he called Hoa Hao Buddhism (Hoa Hao, 2018; Nguyen, 2003). Phú Sổ would go on to form a Hoa Hao armed force that would resist the Japanese occupation during the Second World War, the French colonization in the period following the war, and the Communist forces from Hanoi after the French exit. This resistance would cause the Hoa Hao to be treated harshly by the victorious Northern Vietnamese forces, particularly in terms of infrastructure development (Dang, 2010; Nguyen, 2003; Raymond, 2008). Hoahaoism is a faith that lacks a hierarchy, minimizes the role of monks, and extols the value of “ritual frugality”; these tenets appealed greatly to the poor peasants that were heavily present throughout the Mekong Delta region. Hoahaoism further garnered the support of the peasant masses by celebrating their predominant occupation—farming. This belief is perfectly illustrated by the well known phrase in the faith: “Practicing Buddhism by farming your land” (Cách Tu, 2018). By lauding the traditional practices of the farming community and making them a sign of religious devotion, Hoa haoism helped the practices survive the period of military and policy-based conflict that would erupt in the decades to follow

5 during the Second World War and the victory of the Northern Communist forces. One of the most obvious results of this tradition preservation is the amount of paddy that continues to dominate the landscape of An Giang and Dong Thap.

1.6 Research Aims and Questions Given these gaps in the research, and the desire of this study to address them, this thesis is guided by the following question: "How do sociocultural, governmental, and economic teleconnections affect recent, present, and near-term LCLU changes in An Giang and Dong Thap, Vietnam, 2010-2030?". To answer this question, the author aims to quantify land cover change over a ten year period, identify driving factors of LCLUC, and forecast near-term LCLUC change. To further assist in answering this question, this study makes use of remotely sensed imagery and quantitative techniques, as well as qualitative data collected from interviews by colleagues and observations during in-country fieldwork during the summer of 2019. Additionally, this project includes the expertise of multiple research partners in fields including geostatistical analysis, remote sensing, cultural geography, and philosophical environmentalism.

6 Chapter Two Literature Review

2.1 Introduction This chapter explores cultural and economic influences on LCLUC, particularly within the context of religious and government policy in the study area. It also reviews the literature on agricultural conversion and how it applies to our selected provinces. Finally, it addresses how mixed methodology research is conducted in the geospatial realm, particularly within studies utilizing remote sensing. Within this last section, it also discusses Seto et al. (2012) work on how the drivers of LCLUC differ depending on the selected study site and how these drivers are often influenced by outside factors. The literature on the specificities of the study area is used in conjunction with the literature on conversion of agricultural land to determine how the LCLU could change over time. Previous work on mixed method research and the role of outside influences on LCLUC is used in the conceptual construction of this study’s model.

2.2 Religious and Cultural Influences on LCLUC Within An Giang and Dong Thap The study area is in the southern part of Vietnam, on the border with Cambodia, and far from the capital in Hanoi. Being so far from the capital of the state, the provinces of An Giang and Dong Thap, as well the rest of the Mekong Delta region, have had a different path of development and culture than the provinces in the north. One example of this is the religious tradition known as Đạo Bửu Sơn Kỳ Hương, a faith that emerged as Vietnamese settlers took up residence throughout the region in the 1800’s. The faith combined Buddhist ideology with a strong sense of patriotism, a necessity to the settlers living in a difficult and unstable colonizing region (L. T. Nguyen, 2003). Đạo Bửu Sơn Kỳ Hương was a millenaristic faith that sought to eliminate superstitious beliefs in favor of abiding by Buddhist doctrine, a necessity given that it also preached an end of end of days scenario with a final day of judgement and deliverance for the true followers of the faith (L. T. Nguyen, 2003). The faith rejected a formal hierarchy, but did have a leader in the form of “spontaneously enlightened” prophets, or living Buddhas, whose authority was not limited to religious matters, but included the social movement that Đạo Bửu Sơn Kỳ Hương instigated throughout the region. One interesting item of note is how many of the prophets possessed healing capabilities and had a connection to the Seven Mountain range—this is a pattern continued through the advent of Hoahaoism (L. T. Nguyen, 2003). Of final note of this faith is its message of the importance of a connection to the earth, particularly through the peasant farming that was so prevalent through the area (Cách Tu, 2018). As mentioned above, the patriotic sentiment that Đạo Bửu Sơn Kỳ Hương coaxed increased as the French began their colonization of Vietnam, before culminating in the advent of the Hoa Hao faith in 1939. The founder of the faith, Huỳnh Phú Sổ, was a sick child whose malady forced him to drop out of school at 15 after failing to be healed by local healers (L. T. Nguyen, 2003). This frailty disappeared suddenly when Phú Sổ was 19. Taken as a sign of spiritual power and enlightenment, Phú Sổ declared the beginning of a new faith, one that was an extension of Đạo Bửu Sơn Kỳ Hương. Along with his announcement, he made a series of pilgrimages to the Seven Mountains and began performing healings in his home village of Hoa Hao, a place that would soon give its name to the fledgling faith (L. T. Nguyen, 2003). Following in the footsteps of previous Prophets with these actions provided legitimacy to Phú Sổ’s claim and he soon began gathering followers throughout the region. While initially not viewed as a threat by the French imperialists, the spread of the faith eventually caused some to worry. To squash the faith, the French began a series of forced moves of Phú Sổ to different locations throughout the Mekong region. This plan backfired however, as Phú Sổ continued his proselytization and gained followers with each move, simply creating new centers of the faith wherever the French sent him (L. T. Nguyen, 2003). Eventually escaping the French, Phú Sổ shifted from solely a religious leader to the developer of an armed force that would protect the Hoa Hao from the French colonists and the Japanese occupiers. The tenets of Hoahaoism are similar to Đạo Bửu Sơn Kỳ Hương. They include a rejection of superstition, encourages simple worship practices, and the encouragement of the peasant agricultural way

7 of life. Due to this similarity, Hoahaoism is better understand as a continuation of Đạo Bửu Sơn Kỳ Hương, than a wholly new religion, helped the local citizens of the area become practitioners as it really was simply an extension of the beliefs they had grown up with. Phú Sổ also discouraged the practice of expensive funerals, weddings, or temples, instead encouraging the faithful to give their money to the poor as a testament of their faith (L. T. Nguyen, 2003). Pushing the importance of peasant agriculture, it helped solidify agriculture throughout the provinces of An Giang and Dong Thap and limited urban expansion. Crucially, the emphasis on smallholder farming helped encourage the notion of private property and a strong connection to the land. This belief would later come into conflict with collectivization policies in the mid 1970’s-1980’s. Hoahaoism also shared with Đạo Bửu Sơn Kỳ Hương a strong sense of patriotism, a fact that would help motivate its followers to take up arms in the perceived defense of their homeland. Villages throughout Hoa Hao dominant areas trained citizens to act as local “Self-Defense Forces”, units that would eventually be combined in a fighting force that would strike at the Japanese through the end of the Second World War, the French forces until their departure, and the Communist forces up through and past the fall of Ho Chi Minh City in 1975 (Hays, 2018; L. T. Nguyen, 2003). The reunification of Vietnam would prove to cause further tension between the Hoa Hao and the unified, communist government in Hanoi, specifically when it came to matters of land ownership and redistribution (Dang, 2010; Raymond, 2008)

2.3 Policy and Privatization Changes Within An Giang and Dong Thap As mentioned above, the strong sense of land ownership that Hoahaoism incubated would conflict with the goals of the newly dominant Communist government. Specifically, this clashed with the collectivization policies of the new regime. In northern Vietnam, where farmland was largely owned by landlords and farmed by peasant tenants, the transition to collectivized land was politically easier and less controversial. In the south however, given the emphasis on privately held land, the collectivization process amounted to confiscating the farms of individual households and was met with much more hostility (Dang, 2010; Raymond, 2008). In An Giang province, many citizens farmed land both nearby their homes and farther away in neighboring communes. This farming outside of one’s home commune was outlawed in 1976, causing many local farmers to lose their land as it was redistributed to those without land in the province (Dang, 2010). By February 1976, the Việtnamese government had confiscated 2,214 hectares of land (Dang, 2010). In a region that placed such emphasis on land ownership, these actions led to high levels of anger in the area. Wealthier land owners started carrying weapons to defend their larger tracts of land, while poorer peasants refused to accept redistributed land as it was so antithetical to the local culture (Dang, 2010). When land was collectivized, local Hòa Hảo citizens abandoned the collectivized land and established new cropland in non-collectivized areas, leading to an expansion of agricultural lands in the provinces (Dang, 2010). This resistance had mixed results as only 6% of all land in the south was ever collectivized (i.e. made publicly owned) compared to 96% in the north; however some farmers did have parts of their land confiscated and redistributed (Dang, 2010; Pingali & Xuan, 1992). Pingali and Xuan (1992) note another government policy that outlawed the private ownership of draft machinery, such as tractors and combines, in the late 1970’s. Instead, this machinery had to be purchased by each province and made available to the different cooperatives within it. Given that the private land ownership remained so prevalent, there were few cooperatives in the area and this meant that there was limited machinery for labor intensive tasks like plowing and clearing fields. This shortage caused an increase in local manual “labor sharing” between neighboring farms, but it was unable to fully overcome the limitations caused by banning private ownership of draft machinery and rice production fell. All of these policies- land confiscation, redistribution, and not allowing private ownership of farming machinery caused not just productivity to fall, but also for land use to shift as more cropland either went fully untended or had fewer crops grown per year (Dang, 2010; Raymond, 2008). The fall in rice production and resulting food shortages caused the Communist government to abandon its land collectivization plans and adopt a policy of country-wide economic liberalization, known

8 as Doi Moi in 1986 (Kontgis et al., 2014). One piece of Doi Moi was to change how land ownership was handled legally. Prior to Doi Moi, farmers had no ownership of collectivized land in the eyes of the government, meaning that the farmers could have their land reassigned on a whim. This uncertainty made the farmers hesitant to invest in the infrastructure necessary for productivity increase (Pingali & Xuan, 1992). In 1988, the government, in concert with other Doi Moi reforms, allowed land to be leased for 10- , 15-, and 20-year periods by families. Additionally, individuals were given land tenure, meaning that families could pass on land if the owner passed away within the lease frame (Pingali & Xuan, 1992). Another impact of Doi Moi was the abolishing of forced rice production contracts between farmers and the government. Instead, after paying taxes, assessed by an impartial land-quality scale and paid in rice, farmers can sell their rice to whoever they wish. This has has lead to an influx of private foreign traders (Pingali & Xuan, 1992). With these land reforms, the lifting of earlier prohibitions on owning draft machinery, and the entrance of foreign capital, rice productivity in the region rose rapidly. Young et al. (2002) note that Vietnam in 1987 was a net importer of rice, but had become the second largest exporter of rice in 1995, only seven years later. Kontgis et al. (2015) shows that this trend has continued as Vietnam has seen its rice production grow by 25% between 2001 and 2011.

2.4 Drivers of Agricultural Change: Conversion from Rice Paddy Farming Agricultural land conversion refers to the process in which land changes from agricultural land to another LCLU class. Azadi et al. (2011) found that the levels of conversion varies among, as they refer to them, less developed, developing, and developed states. Specifically, they found that urbanization pressures led to the push in agricultural conversion and that developed states were more successful in managing this agricultural to urban conversion. They also found that less developed countries who are benefiting from “rapid economic growth” and are seeing their economic structure transform quickly are the states most prone to agricultural land loss to conversion (Azadi et al., 2011). Nelson (1990) found that one important determinant for agricultural land conversion to an urban class is the presence of relatively flat land, which based on the abundance of flat land observed during summer 2019 field work and described in Geological and Topographical Features (2009) would indicate a strong desirability of ag- urban conversion. However, this desirability is overshadowed by other features of the landscape in the study area. Firman (1997) points out the necessity of dry and well draining ground to urbanization. Given the regular flooding and heavy irrigation network built throughout An Giang and Dong Thap, the land is often too inundated and would require significant infrastructure projects to sufficiently drain for urban development. Additionally, the limited major highways, abundance of single lane roads, and the lack of four wheel vehicle paths between rice paddies prevent urbanization according to Levia and Page’s (2000) research on how development is spurred by short distance to major roads and cities. Finally, Levia and Page also found that farm size was a strong factor in ag-urban conversion, which given the small farm size described above in section 2.1 is yet another limiting element to urbanization in the study area. Economic liberalization policies enacted in Vietnam have led to fast and strong economic growth for the country, especially in the agricultural sector (Le Coq & Trebuil, 2005). This growth is exemplified in how Vietnam reached food self-sufficiency, became a massive rice exporter, and average rice yields increasing 39% between 1985 and 2000 (Le Coq & Trebuil, 2005). However this success has not been universal as the “pace of capital accumulation [is] unequal among farmers” and, thus, the country has seen a diversification in agricultural production that is notably shown by the percentage of paddy as a percent of total farmland falling from 81% to 70% during the same 15 year period. (Le Coq & Trebuil, 2005). This is further intensified by the decreased demand and low prices that farmers can get for their rice harvests (“Rice Exports,” 2019). For example, the first five months of 2019 saw Vietnam export 239,000 tonnes of rice- a significant drop from the 1.44 million tonnes during the same period in 2018 (“Rice Exports,” 2019). For example, in the first five months of 2019, Vietnam saw its rice exports drop 8% in terms of volume and 24% in value, indicating a lower demand, but also a significant drop in the price of Việtnamese rice compared to the same time period in 2018 (Chanh, 2019). This difficulty in rice farming has led to an increase in other forms of agriculture occuring in the study area. As Shibuya (2015) points out, farmers in southern Vietnam do not acquire new land when

9 shifting their agricultural produce, but convert their existing, often rice producing, land. This can be massively beneficial as foregoing growing solely rice and adding vegetables can earn farmers income 3.7 - 5.8 times greater than what they would earn from rice (“Southern region,” 2019). One particularly notable shift in LCLU class has been from rice paddy to orchard, including mango, papaya, guava, and jackfruit. This fails to surprise given that the average expected income for a hectare of paddy runs around VND22 million, while a hectare of pomelo or oranges earns VND500 million, and a hectare of durian fetches VND900 million (Chanh, 2019; Dan, 2019). Another notable shift has been towards aquaculture- both alongside rice paddy and orchards, and, to a lesser extent, complete conversion to full time ponds (“Kien Giang,” 2019). This shift has been occurring for a significant amount of time- the 25,000 ha of freshwater ponds in the Mekong Delta region of the country in 1994 grew by 200,000 ha in a single year—2001— through the conversion of “unproductive rice land” to aquaculture (Wilder & Phuong, 2002). This method of agriculture is favored by farmers given its accompanying increased income and the ability to save some of the harvest to diversify home diets. However, Nhan et al. (2007) found that aquaculture farming was highly dependent on income levels, with poor, intermediate, and rich households adopting the practice at 6%, 42%, and 60% respectively. Despite this unequal distribution, aquaculture in the Mekong Delta region of Vietnam is undeniably significant, as the region alone produces 85% of the national aquaculture production (Wilder & Phuong, 2002).

2.5 Mixed Methods Research and Teleconnections Mixed methods research in geography refers to research that incorporates both quantitative and qualitative methods. Mixed methods has been discussed within the field of geography going back a significant time (Gould, 1970) and continues to be a critical methodology taught to students in the field (Clifford et al., 2016). Sui and DeLyser (2012) lay out an argument that the divide between quantitative and qualitative methods needs to be abolished and that research benefits greatly from incorporating both kinds of methods: “We write together in an effort to bury the qualitative-quantitative divide in our discipline (and in the social sciences and humanities more broadly) and we contend that this divide has hindered cooperation, collaboration, and constructive engagement of diversity (pg. 111).” This sentiment is echoed by Cheong et al. (2012), Rounsevell et al. (2012), and Verburg et al. (2016). Rounsevell (2012), for instance, said that, “Interactions between decision-making, governance structures, production and consumption, technology, ecosystem services and global environmental change influence human activities at the local and regional scale… Land system research therefore has to cope with the substantial challenge of multi- and inter-disciplinarity to bridge the nature-society divide (pg. 900).” A number of LCLUC studies noted this demand and began to use a mixed methods approach to investigate the object of their focus. de Beurs and Henebry (2004) compared remotely sensed vegetation indices from different epochs defined by institutional stability in Kazakhstan to replicate the LCLUC history of sub-national units at spatiotemporally explicit scale. Doing this they were able to discover that the institutional instability that resulted from the collapse of the Soviet Union contributed to the unstable agricultural outputs of Kazakhstan during the latter part of the 20th century. Another study conducted by Gellrich, Baur, Robinson, and Bebi (Gellrich et al., 2008) combined remote sensing analysis with interviews of local citizens and government officials to determine why grasslands sometimes reverted back to forest in sub-alpine zones in the Swiss Alps. Classification trees were effective in determining where reforestation would occur, but “additional information from interviews was necessary to understand the patterns and determinants of reforestation.” Finally, Muller and Zeller (2002) estimated LCLUC in the central highlands of Vietnam from “primary-recall” data gathered during interviews with local citizens, focusing on how socio-economic policies caused LCLUC. Likewise, Seto et al. (2012) outlines current issues in land use change conceptualization and an alternate way to develop LCLUC models. They explain that currently, land change science views land as being geographically bounded and limited, such that land change transitions are solely driven by local demands. These concepts go against ideas outlined in urbanization literature stating that places are capable of having multiple identities and having multiple social connections to other places (Massey,

10 1991). Seto et al. (2012) also see that the drivers of LCLUC in North America or Europe are not always the same drivers of change in Asia, South America, and Africa and that these drivers can be greatly influenced by the mixture of local cultural drivers and the demands of globalization. To overcome these shortcomings of the field, Seto et al. (2012) introduced the concept of “teleconnections”, which they describe as the “distal flows and connections of people, economic goods and services, and land use change processes that drive and respond to urbanization” (pg. 7678). Seto gives the example of the global demand for rare earth metals creating and driving a local mineral extraction job market in areas where the minerals are found. Teleconnections can consist of a number of sociocultural factors including but not limited to local cultural practices, political history and structure, and economic development and demands and can have a large impact on how land changes over time. Seto et al. (2012) explains that by considering teleconnections in a land change study, a researcher is given “more opportunity to identify the leverage points to intervene in complex global land and urban systems” (pg. 7678).

2.6 Conclusion The literature within this section permits for a better understanding of how the landscape could change over time within the study area, specifically how agricultural patterns can shift from one crop to another. The literature also helps in the building of a model that effectively combines qualitative and quantitative data to predict future LCLUC.

11 Chapter Three Data and Methods

3.0 Introduction This chapter details the data and methodology used within the study. Due to unforeseen complications with the originally selected model (CLUE-S), a new modeling software (TerrSet) had to be chosen far within the second year of the thesis. On the wishes of my committee members, and because of the author’s desire to document the history of this study, the sections on the originally selected model, CLUE-S, have been maintained in this chapter. These are section 3.1 - 3.2.4. Section 3.1 introduces the model (CLUE-S), while 3.2 describes the components and requirements of CLUE-S. Of particular importance is section 3.2.1 which discusses the statistical assumptions of logistic regression. This is relevant and should be noted as logistic regression forms a core part of the secondary model selected, TerrSet. The next section of this chapter discusses the components and functionality of TerrSet. The chapter then moves on to discuss the data used in the model, how it was acquired and pre-processed. Finally, this chapter describes how the LCLUC scenarios were constructed and implemented in the model.

3.1 Selected Software and Model To maximize transparency and reproducibility, this study uses the lulcc modeling package in the open source R programming language (R Core Team, 2019). The lulcc package was developed by Moulds et al. (2015) for a PhD dissertation at Imperial College London in an effort to “resolve limitations associated with the current land use change modelling paradigm”; specifically the lack of access to the source code of the modelling software and the necessity of having to use multiple software packages to calculate regressions and determine change allocation. Using lulcc, the underlying code used for the model can now be reviewed to test model effectiveness and there is no risk of incorrectly incorporating the regression algorithms within the LCLUC allocation (Moulds et al., 2015). Further lulcc is based off the CLUE-S LCLUC allocation framework developed by Verburg et al. (2002), a modelling methodology used successfully by a number of LCLUC researchers (Ty et al., 2012; Verburg, 2010; Verburg et al., 2002, 2016; Verburg & Overmars, 2009; Willemen et al., 2002)

3.2 R Package lulcc: Components and Requirements 3.2.1 Site Suitability The package lulcc has four main components. The first, site suitability, or how likely each unit in a landscape, defined in this project as a window of 10 m2 in our LCLU raster layer, is to exist as a certain land cover type. Site suitability is determined by a list of biophysical and sociodemographic covariates of the study site. A logistic regression is calculated from these variables to estimate the suitability of each LCLU class within individual windows (Moulds et al., 2015) . Statistical assumptions must be met before the logistic regression model can be implemented. Logistic regression assumes no multicollinearity among independent variables, i.e., that the explanatory covariates we use to predict land cover are not correlated with one another (Kamwu et al., 2018). To meet this assumption, a stepwise variance inflation factor (VIF) selection was implemented. Succinctly summarized, VIF calculations find the reciprocal of the difference of 1 and the r2 value for a given explanatory variable (EV). The higher this calculated reciprocal, the larger the degree of collinearity of the EV with other EVs in the regression (Chun & Griffith, 2013). The stepwise approach simply iterates the IVF calculation, dropping the EV with the highest IVF value, until all EVs have IVF values less than the determined threshold, which in this case was chosen to be 10 as supported by Kamwu et al. (2018).

Equation 3.1: VIF Calculation

Second, logistic regression requires a large sample size for accurate implementation. Generally, it is recommended to have a minimum of 10 observations of the lowest occurring outcome (Starkweather & Moske, 2011). The LCLU products in this analysis provide more than 35 million observations for An Giang (with the smallest LCLU class having 49,454 observations) and more than 33 million observations for Dong Thap (smallest LCLU class having 24,856 observations). Finally, logistic regression assumes independence of observations, meaning that the land cover classes cannot be dependent upon one another (Chun & Griffith, 2013). The concept of spatial autocorrelation, or that the distribution of observations across a landscape are not randomly determined is an extension of the Tobler’s first law of geography: “Everything is related to everything else, but near things are more related than distant things” (Chun & Griffith, 2013). In other words, phenomena like population, elevation, and soil type of one space are often highly correlated with the population, elevation, and soil type of a contiguous space. To eliminate spatial autocorrelation, a subset is created using randomly selected samples from the original data. Even with subsetting the original LCLU data, sufficient independent observations are available for the logistic regression.

3.2.2 Permitted Transformations Five optional parameters concerning LCLU class transformation can be configured within the lulcc package. The first of these is the ability to “mask” out areas in the study area that are forbidden to transition to a different LCLU class than what they currently are. A raster file of the study area is used with the observations having values of either “0”, indicating that change is not allowed, or “1”, where change is permitted. Secondly, the user is allowed to create “Neighborhoods”, or weighted matrices indicating restrictiveness of LCLU transition within each window. This is useful for LCLU transitions highly correlated with existing LCLU; for instance the expansion of built up land is more likely to be more contiguous to already built up land and forested land is more likely to appear next to already forested land. Relatedly, the third parameter is the setting of transition elasticity, or how likely each LCLU class is to become another class. Each LCLU can be assigned a value from 0-1, indicating the elasticity restrictiveness. As an example, built up is very unlikely to transition to forest or agricultural land, causing the user to give it an elasticity value close to “1”. The fourth parameter allowed to be set by the user are the transition permissions, whether or not a LCLU class is allowed to transition to another LCLU class. The input for this parameter is a matrix with equal number of rows and columns with the former representing current LCLU classes and the latter representing potential LCLU classes. At the intersection of each current and potential LCLU, a value of “0”, not allowed to change to this class, or “1”, permitted to change to this class, is input. An example of a permission matrix is shown in Table 3.1.

Table 3.1: Example Permitted Transformations Matrix

13 The last customizable parameter is the land use history, a vector stating the timesteps each window in the study site has been its current LCLU class. This is used for transitions where time plays a major factor, for example, given the capital investment, an orchard would likely be maintained as an orchard for at least a few years before the owner would transition it to another LCLU class. Given the complication associated with determining the length of time each window in the study area has been a particular LCLU class, along with the time restraints of the study, this last transition parameter was not used in this model.

3.2.3 Demand Determination Within LCLUC models, demand is a nonspatial data input whose selection is critical to the model’s accurate running (Price et al., 2012; Swetnam et al., 2011; Verburg et al., 2002). In lulcc, demand can be determined using two different methods. Both methods, however, require that the total amount of LCLU for every timestep in the model be specified before the model is run. In a model predicting LCLUC for the years 2020 to 2025, for instance, the user must specify how much of the land should be rice at 2020, 2021, 2022, 2023, 2024, 2025. The first method to determine this demand is an interpolation of the LCLUC between at least two known LCLU maps, representing different time periods.. This method has an initial and final demand for each LCLU class provided by the first and last known LCLU map, respectively. The determination method then linearly interpolates how that demand may have changed per year over the intervening period. The output is the total demand each LCLU class has per year inside of a matrix, an example of which can be seen in Table 3.2.

Table 3.2: Example Demand Per LCLU Class

While this technique can fail to be a realistic projection given how capricious different time periods can be given the socioeconomic and political phenomena that occur within them, linear interpolation can provide a great starting point for demand determination as well as allow for the inclusion of “business as usual” LCLUC scenario (Moulds et al., 2015). The second method of demand determination requires the user to create change scenarios from a variety of gathered interviews with local citizens and policymakers, the analysis of government projections and reports, and or the construction of complex econometric models (Swetnam et al., 2011; Verburg et al., 2002). If this method is chosen, demand is fed into the model the same as with the first method, a matrix containing the amount LCLU class per year.

3.2.4 Allocation of Demand Allocation is the process by which the model determines which observations in the study site transition or remain as the same LCLU class. A large portion of the allocation procedure in lulcc is the implementation of the decision rules on transition discussed in section 4.1.2 and so will not be covered here. However, it should be known that at the beginning of the allocation procedure, each window’s ability/ willingness/ difficulty to transition is determined based upon these rules (Moulds et al., 2015). Once the rules are accounted for, the model iteratively meets the demand for each LCLU class at each

14 timestep with the use of the CLUE-S LCLUC modeling framework developed by Verburg et al. (2002). In the first iteration, the CLUE-S framework assigns each window in the study site to the LCLU class determined to be most suitable based on the results of the logistic regression. It then checks the number of observations each LCLC class occupies against the given demand for that timestep. If the two values are not equal, then the model will either decrease or increase the suitability for the LCLU class for each window in the study site based on whether the LCLU was assigned too many or too few observations in the first iteration. It continues this until the demand for the LCLU classes is met. It then saves the resulting map and repeats the procedure for the next timestep (Verburg et al., 2002; Verburg, 2010). An important feature to be mentioned are the allocation parameters the user can adjust to ensure the effectiveness of the model. Moulds et al. (2015) remarks that these parameters will likely need to be fine-tuned after the model is run to ensure the model “achieves congruence”. While not heavily exhaustively explained, the parameters are: “jitter.f”, which slightly modifies the suitability raster in each iteration by a certain amount, allowing the user to determine the deterministic force within the model; “scale.f”, which determines the amount by which suitability if increased or decreased in the iteration if demand is not met; “max.iter”, which determines the maximum amount of iterations permitted for each timestep; “max.diff”, the maximum permitted difference between desired demand and actual allocation; and “ave.diff”, the average permitted difference between desired demand and actual allocated area.

3.3 Data Collection and Cleaning To determine which variables were needed for the site suitability regression, a number of LCLUC models were consulted to determine the best covariates for explaining LCLU class occurrence (Gharbia et al., 2016; Kamwu et al., 2018; Kusratmoko et al., 2017; Swetnam et al., 2011; Ty et al., 2012). A full list of data used in the logistic regression, including LCLU class, elevation, soil type, and distance from roads, can be found in Table 3.5, along with the original creator, date of creation, and the original spatial resolution.

Table 3.3: Data Used in Model

All data used in the regression were reprojected from its original projection to UTM zone 48N based on the Vietnam 2000 datum (EPSG 3405). Additionally, all data were resampled using the nearest neighbor technique to a common spatial resolution of 10 meters, as well as being “clipped” to the extent of our study areas, Dong Thap and An Giang. The extent, or boundaries, of the study areas were determined using shapefiles obtained from Natural Earth, a repository of publicly available geospatial data (Natural Earth, 2019). During summer of 2019, I traveled to the An Giang and Dong Thap to assist in conducting interviews as well as for collecting ground truth LCLU points. These points were collected with the intention of being used for model validation. To make them statistically useful, 500 points were randomly selected using ENVI ArcMap software before traveling to the study area. These points were traveled to while in the field and the dominant LCLU class present at each point was recorded. Additionally, 695 opportunistic points were collected while travelling through the study area. These points also record the dominant LCLU class present. The 500 randomly selected points are shown in Figure 3.2 and the opportunistic points are shown in Figure 3.3.

Figure 3.1: Randomly Selected Points Collected During Summer 2019 Field Campaign

Figure 3.2 Opportunistic Points Collected During Summer 2019 Field Campaign

3.3.1 JAXA LCLU Map: Issues with Mangrove Classification and Other Preprocessing Needs The 10 m 2007 and 2017 LCLU maps produced by JAXA overestimated the class ‘mangrove’, which was not observed within the study area during field work or verified with very high resolution (VHR) satellite imagery. To rectify this classification error, a comparison of the 2017 LCLU map with VHR satellite imagery (< 5 m in spatial resolution) from Google Earth Pro was conducted. For 2017 LCLU map, the ~18.7 km2 (0.27% of total LCLU) of mangroves class was compared to coincidental imagery in Google Earth Pro, and based on visual interpretation, this class appeared to be forests rather than the coastal and near-shore environments required for mangroves. The mangrove class was manually re-mapped to the “forest” class. The 2007 LCLU map proved a more significant problem, as the pixels marked as mangrove (~537.5 km2 or 7.78% of total LCLU) had to be converted to a mixture of “forest”, “paddy”, and “water”. Using Google Earth Pro, a scheme for reclassification was created in which mangrove-classified pixels were reclassified to the most probable class based on proximity to verified forests, rice paddy, and rivers, irrigation channels, and waterbodies. The original and post-mangrove reclassified 2007 LCLU maps are shown below in Figure 3.2. Further, the initial plan was to mask out the land in Dong Thap province within the bounds of Tram Chim National Park. It was determined however that it would be preferable to simply remove all pixels within the park bounds and make the step of masking it out unnecessary. Corresponding pixels from each covariate raster layer were likewise deleted.

Figure 3.3: Comparison of An Giang 2007 LCLU Map Before and After Mango-Elimination Reclassification

Finally, after further analysis of the LCLU maps, it was noted that the classes grassland and bare land were incredibly small (The sum of both classes for An Giang and Dong Thap makes up 2.98% and 2.06% of the total study area respectively). To simplify analysis, these classes were combined into a new wildlands class and the LCLU maps were reclassified accordingly.

3.4 Land Change Modeler: Components and Functionality 3.4.1 Use in LCLUC Modeling Land Change Modeler (LCM) is a module within the TerrSet software package that is useful for using past LCLUC patterns to predict future potential LCLUC and has been used in a number of studies ranging from deforestation in Vietnam (Khoi & Murayama, 2010), urbanization in China (Hasan et al., 2020), and the spread of malaria in Sudan (Fuller et al., 2012). Of importance to this thesis is the functionality of LCM and how it operates. It requires two LCLU maps of different time steps from which it computes a tabulation of which LCLU classes transitioned into another LCLU class between the initial and final time steps. It then requires the inputting of covariate spatial data layers so it can determine the site suitability of each LCLU class in the study area. It then determines demand with a markov chain it constructs from the initial and final state LCLU maps before allocating the demand using an interactive process. These steps are explained in greater detail below.

18 3.4.2 Transitional Sub-Models TerrSet performs a cross tabulation of the initial and final state observed LCLU maps. This allows the user to see the amount of land that transitioned from each class to every other potential class. This permits interesting analyses to be performed. TerrSet requires the user to create “transition sub-models” for every LCLU transition the user is interested in, e.g. “rice paddy to built up”, “forest to orchards”, etc. Using simple math (seven LCLU classes, each able to transition to another LCLU class), we see that this would require us to run 49 logistic regression to cover all possible transitions. However, as pointed out in the TerrSet manual, most LCLUC modeling is not interested in every potential change but primary transitions. Using the above described cross tabulation results, we narrowed down the transitions to modeled by limiting it to transitions that occurred in at least 1,000 hectares between 2007 and 2017. Given that An Giang is 35,218.488 hectares and Dong Thap is 33,858.584 hectares, this sets a threshold of 2.84% and 2.95% respectively. Following research objectives set in this thesis’ funding grant, we further narrowed our selected transitions to those resulting in Built Up, Rice Paddy, Other Crops, and Orchards.

3.4.3 Site Suitability: Logistic Regression and Covariate Selection As mentioned earlier in the chapter, both CLUE-S and TerrSet use logistic regression to determine site suitability for a given LCLU class. Remembering that logistic regression requires covariates to not have a collinear relationship, we performed a stepwise VIF as described earlier. Input points for the stepwise VIF were selected using a stratified random sampling methodology in which 1,000 points were selected for each province, with the caveat that each point be a minimum of 500 m away from it’s nearest neighbor. Covariates selected for inclusion in the logistic regression had to meet two requirements:

1. Not be eliminated for collinearity in either 2007 or 2017 2. Have a p-value of 0.05 or smaller

Covariates that meet these requirements for each province are shown in Table 3.3.

After covariate selection, logistic regressions for each transition were performed. To test the effectiveness of the logistic regressions, we are using ROC analysis, which is a commonly used method to determine the effectiveness of logistic regression models (Mas et al., 2014). ROC (receiver operating characteristic) curves are graphs that show the relationship between the percentage of true positives and the percentage of false negatives. An example of a ROC curve (red line marked “model”) from Hasan et al. (2020) can be found in Figure 3.1. ROC curves are described using what is called the AUC (area under the curve) which is the percentage of the graph that is under the ROC curve. In Figure 3.1, the AUC is the percentage of the graph under the red line. AUC ranges from 0 to 1, where 0.5 is a random fit and 1 a perfect fit. The farther the ROC curve is from the diagonal line, the higher the AUC. The example in Figure 3.1 has an AUC of 0.914.

Figure 3.4: Example ROC Curve from Hasan et al. 2020

Table 3.4: Post-VIF Covariate List

Unfortunately, the effectiveness of each model, as represented by the AUC, was deemed to be particularly low. In an effort to raise AUC and create a more effective model, we re-ran the logistic regressions, this time including all covariates regardless of significance value, only excluding those deemed to be collinear by the VIF. This dramatically improved the effectiveness of the logistic regression. A comparison of the logistic regressions for each selected LCLU transition can be seen in Tables 3.4 and 3.5.

Table 3.5: Comparison of Logistic Regressions for Dong Thap

Table 3.6: Comparison of Logistic Regressions for An Giang

20 3.4.4 Determination of Demand and Allocation of Land-Cover/Land-Use Across Study Area CLUE-S, as mentioned above, determines the amount of LCLUC for each class using a user- supplied matrix listing the total area of each LCLU class for each year of the model. TerrSet differs from this methodology as it relies upon a markov matrix it computes from two previously observed LCLU maps. Markov processes, as defined by the TerrSet manual, are “ where the state of a system can be determined by knowing its previous state and the probability of transitioning from each state to each other state” (Eastman, 2016). What this means for this model is that the markov matrix informs the model of the total area for each LCLU class at each time step as well as how often each LCLU transition will occur (Mas et al., 2014). Once amount is determined, LCM determines spatial allocation using an iterative process that ranks the suitability maps for each transition sub-model, assigns LCLU classes to every pixel and then checks for assignment conflicts. When conflicts are found, it resolves them using a “minimum distance to the ideal point rule” that is based on the ranked suitability maps (Mas et al., 2014).

3.5 Scenario Development 3.5.1 Quantification of Scenarios and Incorporation into LCLUC Model Scenarios for the model were developed using a combination of remote sensing-derived LCLU products, scientific literature, and field research. Once the narratives (described below) were determined, it became necessary to quantify them and incorporate them into the model. We did this using the markov chain that TerrSet computed using the initial and final time step LCLU maps. TerrSet provides this in the form of a matrix that lists the probability that a given LCLU class will remain the same or transition to another LCLU class. An example of such a markov matrix is seen in Table 3.7. Note that the rows indicate the initial LCLU class and the columns represent potential final LCLU classe. Given that the probability of a LCLU class to remain the same or transition to another class has to be 100%, each row sums to 1. To incorporate our narratives into the model, we then modified these transition probabilities according to the given scenarios. For example, if our fictional scenario predicted a massive capital influx into the region focused on infrastructure development, we would increase the likelihood of the other LCLU classes transitioning into built up land and decrease their probability of remaining as their initial state. Determining the proper changes to make each probability was determined using various literature sources, as well as by investigating the initial cross tabulated change between each LCLU class. One such work was the previously mentioned Shibuya (2015) which found that farmers in Vietnam, when switching production from rice, are more likely to convert their paddy than to acquire new land. This caused us to primarily decrease the area rice paddy when the scenario called for increased other crop and orchard area. Another important concept that influenced our decisions was the notion of stability first mentioned in the introduction of the thesis. As described there, stability refers to the ability of a location to maintain its state despite internal and external pressures for a set amount of time (Adger, 2000; Holling, 1973, 1992). Given this, and the observed amount of change between 2007 and 2017 in the study area, we decided to err on the side of caution and chose to increase and decrease our probabilities in relatively small increments. Also of importance was the need to do this change in a systematic manner that could be reproduced and allowed for comparison between the scenarios. With both of these objectives in mind, we decided to increase and decrease our probabilities by 100% or 50% respectively. For example, let’s assume our initial markov matrix had the probability of rice transitioning to built up as 1.5% and the probability of rice remaining as rice to be 87%. If the scenario called for an increase in built up from largely rice paddy, we multiplied the probability of rice

21 transitioning to built up by 100% (1.5* 2 = 3%) and subtracted that new increase from the probability of rice remaining rice (87-1.5%= 85.5%). This is also necessary to keep the sum of each row at 1. Another example (using the same initial markov matrix) in which rice production was deemed to be more important than the creation of new built up land, we would decrease the probability of rice converting to built up by 50% (1.5%/2 = .75%) and add that decrease to the probability of rice remaining rice (87% + .75% = 87.75%). After the scenario output LCLU map is created and analyzed, changes to the probabilities can be altered further as necessary. The only situation in which we strayed from this pattern was in the third scenario, where we had multiple LCLU transition probabilities being modified. This scenario focused on increased capital injection and land tenure and so looks at conversion of rice and forest to other crops and orchards. Our previously described methodology was used for rice to other crops and rice to orchards. However, given the small percentage of forest that converted to other crops or orchards in the observed period, we instead simply increased the probability of conversion from forest to other crops by 2% and of forest to orchards by 3%. This decision was informed by insight gained from Fox et al. (2012) who determined that LCLUC forecast models focused on SE Asia showed a higher loss of forest classes than bare land classes and (To a lesser degree) grassland. Note that the green cells in the following markov matrices for each scenario (Figures 3.7 - 3.9) indicate an increase in the percentage of the given transition. Red cells indicate a decrease in the percentage of the given transition.

3.5.2 Scenario Rationale and Description The first scenario is cultural and focuses on the beliefs of the Hòa Hảo, the religious majority within the study area. The group’s beliefs toward small-holder, rice agriculture were initially the main driver of this scenario. Interviews conducted by colleagues Drs. Jarrod Brown and Stanley Toops with local religious leaders and Hòa Hảo farmers, however, determined that while many might idealize the notion of rice paddy farming, economic reality did not allow them to also live up to the ideal. However, the desire to own, and pass on, a smallholder farm did persist in the visited communities and, thus, forms the basis of this scenario. Given the desire to maintain their smallholder farms, predicted demand for the built up class was maintained at the same rate as 2007-2017. The economic drivers were slightly reduced in this scenario to allow for strong religious motivation to stick to paddy farming. As a result, classes orchard and other crops did increase (to the loss of rice pixels), but at a smaller rate than in the economic scenario. Likewise, the transition of rice to built up was also decreased.

Table 3.7: Adjusted Markov Matrix for Scenario 1

The second scenario is a market-conforming scenario without the cultural affinity to maintain rice farms. This scenario allows for a drop in demand and price of Việtnamese rice, with higher prices for vegetables and fruits. In this scenario, built up increases at the same rate as from 2007-2017. The transition from rice to orchards and rice to other crops increased more than they did during the 2007-2017 time frame. The corresponding decline is taken from rice remaining as rice based on Shibuya’s (2015) findings on paddy conversion and ignoring any potential local affinity for rice production.

Table 3.8: Adjusted Markov Matrix for Scenario 2

The third scenario—policy—focused on how government policy and investment would affect landscape change in the study area. This scenario drew from the changes in land tenure outlined in the Revised Land Law of 2014, specifically the increase in land lease terms (Dezan Shira and Associates, 2019). It is assumed that increased terms of land use rights encourages development on the land, both on

23 agricultural land (conversion to orchards which require longer waiting period before harvest) and in built up areas. Further, the Revised Land Use law of 2014 creates new regulations on land acquisitions, specifically requiring that the land being acquired was originally planned to have been leased in an annual plan by the district People’s Committee, as well as requiring developers to have equity capital holdings of 15-20%, and the payment of deposits to ensure timely land development (Nguyen, 2014). Despite these new regulations however, the law also equalizes the investing process for foreign and citizen investors, allowing foreign investors greater access to land acquisition opportunities (V. Q. Nguyen, 2014). This increased access was determined to likely exceed the limitations posed by the new regulations and lead to an increase in the change rate of built up class pixels. As such, this scenario has the number of built up pixels increasing according to the scheme described in the preceding section with paddy decreasing the same percentage to absorb this conversion. Agricultural land use due to increased tenure lengths by farmers was predicted to increase other crop classes and orchard transition probability. By using Shibuya (2015) and Fox (2012), we accounted for this increase in those classes by decreasing paddy and forest classes.

Table 3.9: Adjusted Markov Matrix for Scenario 3

3.6 Conclusion This chapter introduced a modeling software that uses statistics to determine the most suitable land for given LCLU classes and a methodology to implement cultural, economic, and policy-driven scenarios into the model. It details the validation of the logistic regressions for each LCLU transition and how the models were adjusted to increase effectiveness. It also describes which data was included in the model, vital information for those choosing to test the findings of this study.

24 Chapter Four Results

4.0 Introduction This chapter covers a wide range of topics. It details the accuracy assessment of the model and the results the assessment produced. After validation of the model is discussed, the chapter lays out the simulated outputs from each scenario (cultural, economic, and policy) and describes spatial patterns within each simulation, specifically where high amounts of LCLUC change occurs within the study area; what forest and wildlands convert to; how clusters of simulated built up corresponds to observed built up; and finally how percentages of built up, rice paddy, other crops, and orchards have shifted from 2007 to 2027.

4.1 Model Accuracy Assessment Likely the most common method of assessing the accuracy of a predictive LCLUC model output is by comparing a simulated map that the model produced with a real-life observed LCLU map (Chang- Martínez et al., 2015). Unfortunately, the maps used in this study, produced by JAXA were only available for the years 2007 and 2017. Additionally, given the high spatial resolution of the maps, it proved impossible to find a LCLU for the area that could effectively be used for validation in the manner commonly used. To conduct an accuracy assessment, we instead used a combination of ground truth collected field points and sample points that were labeled using very high resolution imagery in Google Earth Pro. Both types of points were randomly created to ensure statistical effectiveness. Ground truth points are thought to be the most accurate type of validation information (Bai et al., 2015). Unfortunately due to the limitations of field work conducted during the summer of 2019, we were unable to collect a sufficient number of points for each LCLU class. To overcome this, we created a random sample of points throughout both provinces and used very high resolution collected during 2019 to label the LCLU class at each point as done by Bai et al. (2015) and Jain et al. (2017). We then predicted LCLU for both provinces for the year 2019 to allow for coincident spatial comparison of the model and real life known LCLU data.

Table 4.1: Predicted Accuracy Per LCLU Class and Province

As can be seen in Table 4.1, there is a fairly wide range of accuracy in the model’s ability to predict different LCLU classes. Overall accuracy for both provinces is 74%, however that average conceals some deep shifts in reliability. Classes water, built up, rice paddy, and orchards all had high accuracy, while classes other crops, forest, and wild lands had lower accuracy. This lower accuracy for the latter classes could be due to the fact that these classes occur less often within the two provinces under review.

25 Given the earlier mentioned funding grant’s objectives, we are most interested in the ability of the model to predict future decreases in rice paddy and increases in other crops and orchards. The model had >80% in predicting the location of rice paddy and > 75% in predicting the location of orchards, so we feel confident in its ability to predict those classes. Its ability to predict other crops is considerably lower at 54% and 60% and does draw into question the model’s effectiveness in predicting this class. However, it is important to note that both classes are greater than random chance (50%) and thus do have some predictive ability. We draw the audience’s attention to work from Rykiel (1996) wherein they define validation as “a demonstration that a model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model”. Based on this definition, the accuracy of a model is not a simple binary accurate / non-accurate and thus we conclude that our model has “satisfactory” accuracy when predicting future rice paddy and orchard (along with water and built up). We do, however, urge consumers of our model to consider the accuracy for classes: other crops, forest, and wild lands when interpreting the outputs obtained from the scenarios.

26 4.2 Scenario LCLUC Map Output Analysis 4.2.1 Dong Thap Control (Business As Usual) and Scenarios LCLU Map Outputs

Figure 4.1: Comparison of Model Outputs from Control (Business as Usual) and Project Scenarios (Tables 3.7 - 3.9). Note that the maps show a line of missing data due to an allocation issue with Terrset. Clark Labs has been notified of the issue.

27 All 2027 simulations show extensive conversion of rice paddy to other crops in the northeast corner of the province. As expected, the Cultural scenario does have less conversion as it prioritizes rice paddy, however there is still significant conversion in the region. Another area showing extensive change is the rice paddy in the north/central western region of the province on the island and land on the east side of the river that has been converted to orchard. Like with other crops class, the Cultural scenario has less, but still significant amounts of rice to orchard conversion. Also of interest is the development of built up land throughout the province. During summer 2019 field work, we noticed that both provinces have a unique spatial pattern. The canals that were largely built by the French during the colonial period cross the province in numerous straight lines that split the land into strips. Outside of the urban areas, the same pattern repeats almost without fail. The pattern goes: canal -> street -> row of houses -> small plots of other crops and orchards -> very large plots of rice -> small plots of other crops and orchards - row of houses -> street -> canal. Given this pattern, rural increases in built up will result visually in thicker parallel lines of (according to our chosen visualization parameters) red lines. Looking at the control output, we can see increased thickness of the built up class lines from the 2017 class. However this varies in each of the three scenarios. Unsurprisingly, the cultural scenario has the thinnest built up lines throughout rural areas. The economic scenario is thicker and the policy-driven scenario is thicker still.

Table 4.2: Percentage of LCLU of Total Area by Class by Year and Scenario

Additionally, given the observed spatial patterns during field work, one would expect to see an increase in other crops alongside the built up area. Investigating the four 2027 simulated maps, we can see that this pattern does occur- parallel to the thickened built up (red lines), we see increased other crop area. We also wished to examine the pattern of built up in more urban areas- particularly if the expansion of built up corresponds to previous urban built up land. Looking through the map, there is only one urban area that shows extensive expansion- the capital city of Cao Lanh. This is interesting as it reveals limited urban expansion in the province- our model predicts that urban growth is primarily limited to the rural landscape.

28 4.2.2 An Giang Control (Business As Usual) and Scenarios LCLU Map Outputs

Figure 4.2: Comparison of Model Outputs from Control (Business as Usual) and Three Scenarios (Tables 3.7 - 3.9). Note that the maps show a line of missing data due to an allocation issue with Terrset. Clark Labs has been notified of the issue.

29 In the control and all three scenarios’ LCLU map outputs, we see conversion of rice paddy to orchard occurring alongside the banks of the river running south east through the province, as well as on the Cam mountains in the west of the province. As in Dong Thap, the cultural scenario shows the smallest amount of conversion away from rice paddy. However as can be seen, there is still a significant amount of conversion to orchards from rice paddy. Areas north of Chau Doc and on the island immediately east of Long Xuyen show large amounts of rice paddy conversion. Unlike Dong Thap, we also see orchards appear alongside the canal network throughout the province.

Table 4.3 Percentage of LCLU of Total Area by Class by Year and Scenario

There is also noticeable expansion of built up land in the area around the Cam mountains, as well as alongside rural roads like in Dong Thap. The area around the Cam mountains lacks major cities, but still shows large amounts of built up land. Due to field research, we know that this is due to high levels of tourism occurring in the area. The Cam mountains contain numerous religious sites and also become home to numerous shops, movie theaters, parking lots, and assorted other entertainment-type structures. Unlike Dong Thap, we also see the emergence of new built up hubs in the south and western part of the province. While we would expect this built up increase alongside the canals and near established towns, the increase is less expected in these new hubs of built up land. If accurate, these locations of new built up would be of importance to government officials as they would require the construction of settlement necessities like sewage, electrical delivery, etc. In an attempt to determine the likelihood of these hubs emerging, we looked at the coincident areas in the year 2017 using very high resolution imagery in Google Earth Pro to see if, and to what degree, built up land already is found in the area. Our investigation found that the southern hub did have a small amount of built up land in 2017. The hub in the west however, had just a strip of buildings alongside the canal. That hub is somewhat close to the Cam mountains (and thus the tourism industry there) and so could potentially be built up as that industry expands. However, that explanation is not certain and we regard this hub with a higher level of suspicion. Finally, we see significant increase in the area of other crops on the west side of the river in the central part of the province. This does not occur in the cultural scenario, but is easily noticeable from basic visual examination in the economic and policy scenarios. Somewhat surprisingly, we don’t see significant expansion of other crops alongside the canal network like we did in Dong Thap. Given the spatial pattern that we observed in both provinces and described in the preceding section, we would

30 expect to see more other crops alongside the canals. However, while not conforming exactly to our expectations, this occurrence is still very plausible. We see a similar prediction of a large clump of other crops in the north east corner of Dong Thap. Additionally, our field research revealed a large area outside of the city of Sa Dec that was almost entirely non-rice crops, specifically sweet potato.

4.3 Conclusion This chapter explored the accuracy assessment process we used to validate our model. We predicted LCLU for both provinces for the year 2019 and compared randomly selected points within our study area to coincident high-resolution imagery. Our results showed accuracies that varied depending on the LCLU class, with classes water, built up, rice paddy and orchard having generally high accuracies and classes forest, other crops, and wild lands having lower accuracies. We then explored the model outputs for each scenario, as well as the control (business as usual) output.

31 Chapter Five Discussion

5.0 Introduction This chapter delves into the ramifications of the outputs for each scenario. It notes patterns and items to pay attention to going forward. We also discuss potential issues with our results and urge appropriate caution for those pieces. We then end the chapter by discussing potential future steps and lessons learned.

5.1 Scenario Output Implications 5.1.1 Dong Thap The primary goal of this thesis is to determine the degree and spatial pattern of agricultural conversion, particularly rice paddy, other crops and orchards. In the cultural scenario we see limited conversion from rice paddy compared to the other scenarios (as well as the control output). As mentioned in the previous chapter, there is significant conversion from rice to other crops in the northeast corner of the province. Additionally, there is a noticeable increase in other crops alongside roads and canals throughout the province. This is interesting as it shows that even with consideration for the Hoa Hao infinity for rice agriculture, there will still be noticeable conversion away from rice cultivation. The same phenomenon occurred with orchards in Dong Thap. The island on the north western side of the province shows significant conversion to orchard primarily from rice paddy. Interestingly though, this did not occur with the built up class. In this scenario, most of the new built up occurs around Cao Lanh, the capital city, and converted from orchard and to a lesser degree rice paddy.

32 The economic scenario, with its focus on the economic difficulties associated with rice paddy shows much more extensive growth in other crops in the eastern half of the province. Additionally, we can see expansion of orchards in the same spot area as in the cultural scenario, but the area being converted to orchard is much denser in this scenario. Also noticeable is how the perimeter of the new orchard in this scenario is much larger as it extends along the river. This increased area and increased density results in a much larger amount of rice being converted in this scenario.

Figure 5.1: Comparison of Other Crops and Orchards in Northern Dong Thap Province, 2027. Note that the maps show a line of missing data due to an allocation issue with Terrset. Clark Labs has been notified of the issue.

33 The policy scenario with a focus on orchard and other crops, but also built up, shows the most change in the province. We see very similar patterns when it comes to the conversion of other crops and orchards as in the economic scenario. However, we also see much expanded built up in this scenario. As mentioned in the previous chapter, the increase in built up land appears primarily in rural areas along the canal network. The only significant built up growth in an urban area is near the capital city of Cao Lanh.

Figure 5.2: Comparison of Built Up Near Cao Lanh, Dong Thap Province, 2027

When determining allocation of public funds in areas like infrastructure or social surfaces, this information could prove critical in helping plan the distribution of said funds. One interesting item to note are the two patches of forest in the eastern portion of the province. In all scenarios, they are being converted to (in differing amounts) rice paddy. This is interesting as it demonstrates that although rice paddy may be being converted to other LCLU classes throughout the province, it still remains a desirable enough commodity to justify new paddy fields being constructed.

5.1.2 An Giang As with Dong Thap, we are primarily interested in agricultural conversion within the province. However, An Giang also shows some interesting built up patterns worth examining. The cultural scenario shows limited expansion of other crops. It does show extensive expansion of orchard however in two areas along the river. Despite such a large amount of conversion, we regard this prediction as being very likely as we have seen this level of conversion in the island on the far side of the province. That island, between the years 2007 and 2017, went from primarily rice paddy to almost entirely orchard. This

34 scenario, unsurprisingly given the parameters we set, didn’t have a significant increase in the amount of built up throughout the province. The economic scenario shows much greater amounts of orchard expansion in the province. We see large tracts along the river as well as new orchards along the canal network throughout the province. This scenario also introduces new areas for other crops. These non-rice crops occur alongside newly developed orchards on the western side of the river. This would seem to indicate that the 5-10 km of land west of the river in An Giang are highly susceptible to conversion away from rice paddy. An interesting pattern that can be seen is that this new non-rice crop and orchard occurs alongside a major road that extends from the city of Chau Doc to the capital city of Long Xuyen. Likewise, most of the other new orchards in the province occur close to the built up areas alongside the road network in the province. The large tracts of rice paddy that were described in the previous chapter appear to remain largely undisturbed in An Giang, as well as in Dong Thap. This, combined with the observation in 5.1.1 of forest being converted to paddy, indicates that even if economic pressures continue to disfavor rice production, we should still expect to see significant portions of land devoted to paddy.

Figure 5.3: Comparison of Other Crops and Orchards in Northern Dong Thap Province, 2027. Note that the maps show a line of missing data due to an allocation issue with Terrset. Clark Labs has been notified of the issue.

The policy scenario has similar agricultural results, but differs in its new built up area. In this scenario, we see significant growth in built up areas alongside the canal/ road network, just like in the policy scenario for Dong Thap. This observation is interesting as it points to an increase in rural built up rather than an expansion of urban land like developed towns and cities. Of course, it is vital to remember that western understanding of “urban” and “rural” is very different from the reality within our study area.

35 In the west rural areas are associated with low population density, however, as observed during our time in the field, “rural” areas in our study area are much denser than a western mindset would imagine. They are less dense than large towns and cities of course, but it is rather rare to encounter a human dwelling that is more than a km from a neighbor.

5.2 Questions Surrounding Cam Mountains The Cam mountains are the range in the western part of An Giang province. In all of our scenarios, even the limited urban development cultural scenario, it shows extensive built up land around the mountains. It also shows significant conversion of forest on the mountains themselves to orchard and rice paddy. Our experience in the field showed previously mentioned extensive tourism and luxury accommodation/ entertainment in the area surrounding the mountains. This lends some credence to the built expansion around the mountains. However, using high resolution imagery, the amount of built up land shown in the 2017 LCLU map is slightly overexaggerated. This observation does increase the uncertainty in the results of the model around the base of the Cam Mountains as the model output can only be as good as the data put into the model. We remind users to be aware of this potential over- building in the region when analyzing the outputs. We also use this as an opportunity to remind potential modelers of the importance of input data. Additionally, the forest on the mountain appears to be largely converted to orchard and rice paddy. Neither of these are implausible as we can see in both the 2017 LCLU map and in high resolution imagery, the presence of orchards on the mountains already. These indicate that orchards at least can, and are, grown even on surfaces that have natural slopes. The potentially limiting factor though is the history and popularity of the Cam Mountains as a religious pilgrimage and luxury, rural vacationing spot. This land use raises questions on the possibility of mass agriculture occurring on the mountain. When this model was being developed, we searched for publicly available data on protected lands and the Cam Mountains were not found to be protected, so they were included in the model. Despite this lack of official (or easily found information on protected status), the mountains could be protected through methods like being publicly owned or local ordinances limiting development. We again use this as an opportunity to remind the user of the complexity of modeling foreign locales and that model results can only be as good as the knowledge that goes into their construction.

5.3 Potential Future Steps In the experience of creating and running this model, we noticed a few items that would improve future modeling experience if implemented. The first of these is ensuring the availability of LCLU maps to validate the model. As explained above, our model was interested in modeling at a fine spatial resolution as our study area has lots of minute LCLU classes that could be hard to model at courser resolutions. However, this did provide difficulty during the validation process, as using such fine resolution maps severely limited our ability to find a suitable map at the same resolution. Our solution was effective and supported by the literature, however, we do urge modelers to think through the whole process, including validation, before advancing too far into their study. Additionally, as explained in the previous section, as well as in the third chapter, we have had a variety of problems with the JAXA-produced LCLU maps that had to be resolved. We still have high confidence in their accuracy, however they do produce some uncertainty (around the base of the Cam Mountains for instance) in specific areas. This uncertainty is, to a degree, expected given the fine spatial resolution of the map which is more likely to introduce LCLU label classification errors. We note for future modelers that finer resolution modeling efforts can result in a higher possibility of misclassification which can have negative ramifications further in the modeling process. As seen in Figures 4.1, 4.2, 5.1, and 5.3, Terrset produced output maps with a data issue due to an allocation error. More information can be found in the Clark Labs support center post titled “Stripes on the result of my prediction in LCM”. This data error was removed, resulting in the line of empty space in the shown maps. Clark Labs has been notified of this issue and we are working together to resolve this issue.

5.4 Conclusion One major conclusion we can draw from our modeling scenarios is that, regardless of cultural affinity for rice agriculture, there will be significant conversion of rice paddy to built up, other crops, and orchards. However, it is also important to remember that we will not see a complete elimination of paddy within An Giang and Dong Thap- in fact, a majority of the land remains rice paddy in both provinces for all scenarios. So while it is important to acknowledge and account for significant decreases in paddy area (and likely production), we can’t disregard the still massive impact that rice agriculture will have on the local environment and society.

Chapter Six Conclusion

The outputs of this model show that regardless of scenario we can expect to see significant conversion of rice paddy to other crops, orchards, and built up. In both An Giang and Dong Thap, we can expect to see large plots of land used for other crops. As mentioned above, this is similar to what we’ve already observed with the fields of sweet potatoes in southern Dong Thap. We also can expect to see the hectarage of orchards to increase both alongside the canal network and in large tracts on the east and western side of the Mekong River. As for built up, we can expect to see it occur primarily in “rural” areas along the road/ canal network. Excluding the two capital cities, we don’t expect to see significant expansion of the towns and cities in the two provinces. This understanding will be incredibly beneficial for policymakers planning for distribution of infrastructure development and social services. The simulated conversion of rice paddy to other crops and orchards reminds consumers of the model to account for potential decreases in rice production (due to lessened paddy area) and the resulting consequences regarding food. Finally, by acknowledging increased other crops and orchards, those involved in selling and buying those commodities can plan for increased amounts. Additionally, this model shows that incorporating sociocultural, economic, and government policy data into predictive models is vital in creating useful results. Our three scenario outputs all differ from our business as usual output that simply extrapolated trends and didn’t incorporate local factors. Additionally, the gathering of this non-geophysical data is an arduous task for any researcher as trying to account for all (or more realistically, most) of the factors influencing LCLUC is incredibly difficult. When this difficulty is compounded with a study in a land that is foreign to the modeler with a different history, culture, and language, this task becomes almost impossible if they attempt it on their own. This thesis was only successfully completed because of the team that surrounded myself. Their expertise in the study area’s history, culture, and governance; geospatial techniques; and social science research ensured this model would function as well as it does and produce results that are able to be studied and learned from. Anyone undertaking such a project in the future would be well served to remember the necessity of having a good multi-disciplinary team.

Bibliography

Adger, N. (2000). Social and Ecological Resilience: Are They Related? Progress in Human Geography, 24(3), 347–364. Allen, T. F. H., & Starr, T. (2017). Hierarchy: Perspectives for Ecological Complexity (2nd ed.). University of Chicago Press. Amadou, M. L., Villamor, G. B., & Kyei-Baffour, N. (2018). Simulating agricultural land-use adaptation decisions to climate change: An empirical agent-based modelling in northern Ghana. Agricultural Systems, 166, 196–209. https://doi.org/10.1016/j.agsy.2017.10.015 Azadi, H., Ho, P., & Hasfiati, L. (2011). Agricultural land conversion drivers: A comparison between less developed, developing and developed countries. Land Degradation & Development, 22(6), 596– 604. https://doi.org/10.1002/ldr.1037 Bai, Y., Feng, M., Jiang, H., Wang, J., & Liu, Y. (2015). Validation of Land Cover Maps in China Using a Sampling-Based Labeling Approach. Remote Sensing, 7, 10589–10606. https://doi.org/10.3390/rs70810589 Berg, H., Söderholm, A. E., Söderström, A.-S., & Tam, N. T. (2017). Recognizing wetland ecosystem services for sustainable rice farming in the Mekong Delta, Vietnam. Sustainability Science, 12(1), 137–154. https://doi.org/10.1007/s11625-016-0409-x Binh, T. N. K. D., Vromant, N., Hung, N. T., Hens, L., & Boon, E. K. (2005). Land Cover Changes Between 1968 and 2003 In Cai Nuoc, Ca Mau Peninsula, Vietnam. Environment, Development and Sustainability, 7(4), 519–536. https://doi.org/10.1007/s10668-004-6001-z Cách Tu Hiền và Sự Ăn Ở Của Một Người Bổn Đạo. (2018, December 18). A Brief Description of Hoa Hao Buddhism. Phật Giáo Hòa Hảo. https://hoahao.org/a3489/a-brief-description-of-hoa-hao- buddhism Chang-Martínez, L. A., Mas, J.-F., Valle, N. T., Torres, P. S. U., & Folan, W. J. (2015). Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling. ISPRS International Journal of Geo-Information, 4(4), 1791–1812. https://doi.org/10.3390/ijgi4041791 Chanh, T. (2019, June 29). A Flight from Rice Farming. Saigon Times, 18–19. Cheong, S.-M., Brown, D. G., Kok, K., & Lopez-Carr, D. (2012). Mixed methods in land change research: towards integration. Transactions of the Institute of British Geographers, 37(1), 8–12. https://doi.org/10.1111/j.1475-5661.2011.00482.x

39 Chun, Y., & Griffith, D. (2013). Spatial Statistics and Geostatistics. SAGE Publications Inc. Clifford, N., Cope, M., Gillespie, T., & French, S. (Eds.). (2016). Key Methods in Geography (Third Edition). Sage. Dan, N. (2019, November 12). 12,593 hectare of low-yield rice fields converted into orchards. SGGP English Edition. https://sggpnews.org.vn/content/ODEzMTQ=.html Dang, T. D. (2010). Post-1975 Land Reform in Southern Vietnam: How Local Actions and Responses Affected National Land Policy. Journal of Vietnamese Studies, 5(3), 72–105. https://doi.org/10.1525/vs.2010.5.3.72 de Beurs, K. M., & Henebry, G. M. (2004). Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment, 89(4), 497–509. https://doi.org/10.1016/j.rse.2003.11.006 Dezan Shira and Associates. (2019, June 25). Land Rights in Vietnam - What Are They and How You Can Acquire Land. Vietnam Briefing. https://www.vietnam-briefing.com/news/land-rights-vietnam- what-they-are-and-how-you-can-acquire-land.html/ Eastman, R. (2016). Terrset Geospatial Monitoring and Modeling System Manual. Clark Labs. Ellis, E. (2007, January 31). Land Use and Land Cover Change. http://ecotope.org/people/ellis/papers/ellis_eoe_lulcc_2007.pdf Estelles, P., Jensen, H., Sanchez, L., & Vechiu, G. (2002). Sustainable Development in the Mekong Delta. Center for Environmental Studies, University of Aarhus. Firman, T. (1997). Land Conversion and Urban Development in the Northern Region of West Java, Indonesia. Urban Studies (Routledge), 34(7), 1027–1046. https://doi.org/10.1080/0042098975718 Fox, J., Vogler, J., Sen, O., Ziegler, A., & Giambelluca, T. (2012). Simulating land-cover change in Montane Mainland Southeast Asia. Environmental Management, 32(2), 420–432. Fuller, D. O., Parenti, M. S., Hassan, A. N., & Beier, J. C. (2012). Linking land cover and species distribution models to project potential ranges of malaria vectors: an example using Anopheles arabiensis in Sudan and Upper Egypt. Malaria Journal, 11(1), 264. https://doi.org/10.1186/1475- 2875-11-264 Gellrich, M., Baur, P., Robinson, B. H., & Bebi, P. (2008). Combining classification tree analyses with interviews to study why sub-alpine grasslands sometimes revert to forest: A case study from the Swiss Alps. Agricultural Systems, 96(1), 124–138. https://doi.org/10.1016/j.agsy.2007.07.002 General Statistics Office Of Vietnam. (2019). https://www.gso.gov.vn/Default_en.aspx?tabid=766 Geological and Topographical Features. (2009, August 8). https://web.archive.org/web/20090808231341/http://www.cbd.int/programmes/areas/water/toolkit /html/1.2.0a_physical_geological.html

40 Gharbia, S., Abd, S., Gill, L., Johnston, P., & Pilla, F. (2016). Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms. Modeling Earth Systems and Environment, 2, 151. https://doi.org/10.1007/s40808-016-0210-y Goodwin, B. (2003). Is Landscape Connectivity a Dependent or Indpendent Variable? Landscape Ecology, 18, 687–699. Gould, P. (1970). Is Statistix Inferens the Geographical Name for a Wild Goose? Economic Geography, 46, 439. https://doi.org/10.2307/143157 Hagenlocher, M., Delmelle, E., Casa, I., & Kienberger, S. (2013). Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based modeling. International Journal of Health Geographics. http://www.ij-healthgeographics.com/content/12/1/36 Hasan, S., Shi, W., Zhu, X., Abbas, S., & Khan, H. U. A. (2020). Future Simulation of Land Use Changes in Rapidly Urbanizing South China Based on Land Change Modeler and Remote Sensing Data. Sustainability, 12(11), 1–24. Hays, J. (2018, December 18). Cao Dai and Hoa Hao Religions: The Beliefs, History, and Military Wings. http://factsanddetails.com/southeast-asia/Vietnam/sub5_9d/entry-3379.html Hickey, G., Buttinger, J., Duiker, W., Edgeworth, M., Jamieson, N., & Turley, W. (2020, February 19). Vietnam. Encyclopedia Britannica. https://www.britannica.com/place/Vietnam Holling, C. S. (1973). Resilience and Stability of Ecological Systems. Annual Review of Ecology and Systematics, 4, 1–23. Holling, C. S. (1992). Cross‐Scale Morphology, Geometry, and Dynamics of Ecosystems. Ecological Monographs, 62(4), 447–502. Jain, M., Mondal, P., Galford, G. L., Fiske, G., & DeFries, R. S. (2017). An Automated Approach to Map Winter Cropped Area of Smallholder Farms across Large Scales Using MODIS Imagery. Remote Sensing, 9(6), 566. https://doi.org/10.3390/rs9060566 Kamwu, J., Cho, M., Kaetsch, C., Manda, S., Graz, F., & Chirwa, P. (2018). Assessing the Spatial Drivers of Land Use and LandCover Change in the Protected and Communal Areasof the Zambezi Region, Namibia. Land. Khoi, D. D., & Murayama, Y. (2010). Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing, 2(5), 1249–1272. https://doi.org/10.3390/rs2051249 Kien Giang spreads out efficient rice farming and aquaculture models. (2019, June 27). Viet Nam News, 4. Kontgis, C., Schneider, A., Fox, J., Saksena, S., Spencer, J., & Castrence, M. (2014). Monitoring peri- urbanization in the greater Ho Chi Minh City metropolitan area. Applied Geography, 53, 377–

41 388. Kontgis, C., Schneider, A., & Ozdogan, M. (2015). Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data. Remote Sensing of Environment, 169, 255–269. https://doi.org/10.1016/j.rse.2015.08.004 Kontgis, C., Warren, M. S., Skillman, S. W., Chartrand, R., & Moody, D. I. (2017). Leveraging Sentinel- 1 time-series data for mapping agricultural land cover and land use in the tropics. 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), 1–4. https://doi.org/10.1109/Multi-Temp.2017.8035199 Kusratmoko, E., Albertus, S. D. Y., & Supriatna. (2017). Modelling land use/cover changes with markov- cellular automata in Komering Watershed, South Sumatera. IOP Conference Series: Earth and Environmental Science, 54, 012103. https://doi.org/10.1088/1755-1315/54/1/012103 Lambin, E. F., & Meyfroidt, P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465–3472. https://doi.org/10.1073/pnas.1100480108 Le Coq, J.-F., & Trebuil, G. (2005). Impact of Economic Liberalization on Rice Intensification, Agricultural Diversification, and Rural Livelihoods in the Mekong Delta, Vietnam. Southeast Asian Studies, 42(4), 519–547. Levia, D. F. J., & Page, D. R. (2000, February 12). The use of cluster analysis in distinguishing farmland prone to residential development: A case study of Sterling, Massachusetts. Environmental Management. https://eurekamag.com/research/011/557/011557549.php Lindskog, P., & Tengberg, A. (1994). Land Degradation, Natural Resources and Local Knowledge in the Sahel Zone of Burkina Faso. GeoJournal, 33(4), 365–375. JSTOR. Mas, J.-F., Kolb, M., Paegelow, M., Camacho Olmedo, M. T., & Houet, T. (2014). Inductive pattern- based land use/cover change models: A comparison of four software packages. Environmental Modelling and Software, 51, 94–111. https://doi.org/10.1016/j.envsoft.2013.09.010 Massey, D. (1991). A Global Sense Of Place. Marxism Today, 38, 24–29. McGregor, H. V., Dupont, L., Stuut, J.-B. W., & Kuhlmann, H. (2009). Vegetation change, goats, and religion: a 2000-year history of land use in southern Morocco. Quaternary Science Reviews, 28(15), 1434–1448. https://doi.org/10.1016/j.quascirev.2009.02.012 Moulds, S., Buytaert, W., & Mijic, A. (2015). An open and extensible framework for spatially explicit land use change modelling: the lulcc R package. Geoscientific Model Development, 8, 3215– 3229. https://doi.org/10.5194 Muller, D., & Zeller, M. (2002). Land use dynamics in the central highlands of Vietnam: a spatial model combining village survey data with satellite imagery interpretation. Agricultural Economics,

42 27(3), 333–354. https://doi.org/10.1111/j.1574-0862.2002.tb00124.x NASA JPL. (2013). NASA Shuttle Radar Topography Mission Global 1 arc second. ASA EOSDIS Land Processes DAAC. Natural Earth. (2019). Natural Earth. naturalearthdata.com Nelson, A. C. (1990). Economic critique of U.S. prime farmland preservation policies. Towards state policies that influence productive, consumptive, and speculative value components of the farmland market to prevent urban sprawl and foster agricultural production in the United States. Journal of Rural Studies, 6(2), 119–142. https://doi.org/10.1016/0743-0167(90)90001-O Nguyen, L. T. (2003). Hoa Hao Buddhism in the course of Vietnam’s history. Nova Science Publishers. Nguyen, V. Q. (2014). Vietnam’s New Land Law: The Impact on Foreign Developers. tilleke.com Nhan, D. K., Phong, L. T., Verdegem, M. J. C., Duong, L. T., Bosma, R. H., & Little, D. C. (2007). Integrated freshwater aquaculture, crop and livestock production in the Mekong delta, Vietnam: Determinants and the role of the pond. Agricultural Systems, 94(2), 445–458. https://doi.org/10.1016/j.agsy.2006.11.017 Pingali, P. L., & Xuan, V.-T. (1992). Vietnam: Decollectivization and Rice Productivity Growth. Economic Development and Cultural Change, 40(4), 697–718. https://doi.org/10.1086/451973 Price, J., Silbernagel, J., Miller, N., Swaty, R., White, M., & Nixon, K. (2012). Eliciting expert knowledge to inform landscape modeling of conservation scenarios. Ecological Modelling, 229, 76–87. https://doi.org/10.1016/j.ecolmodel.2011.09.010 Raymond, C. (2008). “No Responsibility and No Rice”: The Rise and Fall of Agricultural Collectivization in Vietnam. Agricultural History, 82(1), 43–61. https://doi.org/10.3098/ah.2008.82.1.43 Rice Exports Face Tough time Amid Huge Global Supply. (2019, June 25). Vietnam News, 13–14. Rounsevell, M. D. A., Pedroli, B., Erb, K.-H., Gramberger, M., Busck, A. G., Haberl, H., Kristensen, S., Kuemmerle, T., Lavorel, S., Lindner, M., Lotze-Campen, H., Metzger, M. J., Murray-Rust, D., Popp, A., Pérez-Soba, M., Reenberg, A., Vadineanu, A., Verburg, P. H., & Wolfslehner, B. (2012). Challenges for land system science. Land Use Policy, 29(4), 899–910. https://doi.org/10.1016/j.landusepol.2012.01.007 Rykiel, E. (1996). Testing ecological models: The meaning of validation. Ecological Modelling, 90, 229– 244. https://doi.org/10.1016/0304-3800(95)00152-2 Sakamoto, T., Van Phung, C., Kotera, A., Nguyen, K. D., & Yokozawa, M. (2009). Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery. Landscape and Urban Planning, 92(1), 34–46. https://doi.org/10.1016/j.landurbplan.2009.02.002

43 Seto, K. C., Reenberg, A., Boone, C. G., Fragkias, M., Haase, D., Langanke, T., Marcotullio, P., Munroe, D. K., Olah, B., & Simon, D. (2012). Urban land teleconnections and sustainability. Proceedings of the National Academy of Sciences, 109(20), 7687–7692. https://doi.org/10.1073/pnas.1117622109 Shepherd, G. (Ed.). (2008). The Ecosystem Approach: Learning rom Experience. International Union for Conservation of Nature and Natural Resources. Shibuya, S. (2015). Living with Uncertainty: Social Change and the Vietnamese Family in the Rural Mekong Delta. Insitute of Southeast Asian Studies. Southern region plans to grow other crops on low-yield rice fields. (2019, April). Vietnamnews.Vn. http://vietnamnews.vn/economy/518294/southern-region-plans-to-grow-other-crops-on-low- yield-rice-fields.html Starkweather, J., & Moske, A. K. (2011). Multinomial Logistic Regression. Statistical Handbook of Vietnam 2017. (2018). Center for Data and Services Statistics- General Statistics Office. Sui, D., & DeLyser, D. (2012). Crossing the qualitative-quantitative chasm I: Hybrid geographies, the spatial turn, and volunteered geographic information (VGI). Progress in Human Geography, 36, 111–124. Swetnam, R. D., Fisher, B., Mbilinyi, B. P., Munishi, P. K. T., Willcock, S., Ricketts, T., Mwakalila, S., Balmford, A., Burgess, N. D., Marshall, A. R., & Lewis, S. L. (2011). Mapping socio-economic scenarios of land cover change: a GIS method to enable ecosystem service modelling. Journal of Environmental Management, 92(3), 563–574. https://doi.org/10.1016/j.jenvman.2010.09.007 Tourism hopes for the Mekong Delta. (2019, October 13). Vietnam News. https://vietnamnews.vn/english-through-the-news/536767/tourism-hopes-for-the-mekong- delta.html Tran, H., Tran, T., & Kervyn, M. (2015). Dynamics of Land Cover/Land Use Changes in the Mekong Delta, 1973–2011: A Remote Sensing Analysis of the Tran Van Thoi District, Ca Mau Province, Vietnam. Remote Sensing, 7(3), 2899–2925. https://doi.org/10.3390/rs70302899 Ty, T. V., Sunada, K., Ichikawa, Y., & Oishi, S. (2012). Scenario-based Impact Assessment of Land Use/Cover and Climate Changes on Water Resources and Demand: A Case Study in the Srepok River Basin, Vietnam—Cambodia. Water Resources Management, 26(5), 1387–1407. https://doi.org/10.1007/s11269-011-9964-1 Verburg, P. (2010). The CLUE Model. http://www.ivm.vu.nl/en/Images/Exercises_tcm234-284019.pdf Verburg, P., Dearing, J. A., Dyke, J. G., Leeuw, S. van der, Seitzinger, S., Steffen, W., & Syvitski, J. (2016). Methods and approaches to modelling the Anthropocene. Global Environmental Change,

44 39, 328–340. https://doi.org/10.1016/j.gloenvcha.2015.08.007 Verburg, P., & Overmars, K. P. (2009). Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 24(9), 1167–1181. https://doi.org/10.1007/s10980-009-9355-7 Verburg, P., Veldkamp, W., Espaldon, R., & Mastura, S. (2002). Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management, 30(3), 391–405. https://doi.org/10.1007/s00267-002-2630-x Vietnam Administrative Atlas (3rd ed.). (2015). Vietnam Publishing House of Natural Resources, Environment, and Cartography. Wilder, M., & Phuong, N. T. (2002). The status of aquaculture in the Mekong Delta region of Vietnam: Sustainable production and combined farming systems. Fisheries Science, 68(sup1), 847–850. https://doi.org/10.2331/fishsci.68.sup1_847 Willemen, L., Verburg, P., Castella, J.-C., & Nguyen, V. (2002). Modelling of land cover changes with CLUE-S in Bac Kan province, Vietnam. Mountain Agrarian Systems, 10. Young, K., Wailes, E., Cramer, G., & Nguyen, T. K. (2002). Vietnam’s Rice Economy: Developments and Prospects. http://arkansas-ag-news.uark.edu/pdf/968.pdf