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Current and future supply of ecosystem services
derived from urban green spaces in the tropical city of
Paramaribo
Joe McMeekin
Student ID: 12401234
Examiner: Dr. J. Z. Shamoun-Baranes (University of Amsterdam)
Assessor: Dr. E. van der Zanden (University of Amsterdam)
Daily Supervisor:
Dr. L.L.J.M. Willemen (University of Twente)
Research Proposal Earth Sciences
Course Code: 5264REPR6Y
Date: 18
thDecember 2019
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Contents
Summary of proposed research ... 3
1. Introduction ... 4
1.1 The urban century ... 4
1.2 Urban Green Spaces ... 4
1.3 Ecosystem services from Urban Green Spaces ... 5
1.4 Relevance and innovative aspects of proposed research ... 6
2. Study area and broader project ... 7
2.1 Study area ... 7
2.2 Towards a Green and More Liveable Paramaribo Project ... 7
3. Conceptual framework ... 10
3.1 Key urban ES in Paramaribo ... 10
3.2 Current research on the considered ES derived from UGS ... 10
3.2.1 Carbon storage ... 10
3.2.2 Coastal protection ... 11
3.2.3 Recreation ... 12
3.2.4 Local climate regulation ... 12
3.2.5 Flood protection ... 13
3.3 Methods to analyse and map ES supply ... 13
3.4 Effects of land cover change on ES supply ... 14
3.5 Methods to analyse future ES supply ... 15
4. Objectives and research questions ... 17
4.1 Objectives ... 17
4.2 Research questions ... 17
5. Methods ... 18
5.1 Modelling current ES supply – Objective 1 ... 18
5.1.1 Carbon storage ... 19
5.1.2 Coastal protection ... 19
5.1.3 Recreation ... 20
5.1.4 Local climate regulation ... 20
5.1.5 Flood protection ... 20
5.2 Developing scenarios of future land cover change – Objective 2 ... 22
5.3 Analysis of future ES supply – Objective 3 ... 23
6. Research time schedule ... 25
7. Budget & Funding ... 26
8. Insurance and safety ... 26
9. Equipment and software ... 26
10. Data management ... 27
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Current and future supply of ecosystem services
derived from urban green spaces in the tropical city of
Paramaribo
Summary of proposed research
Approximately 55% of the current global population of 7.7 billion people reside in urban areas. These urban areas are expected to absorb almost all of the projected future population growth and consequentially an extra 2.4 billion people are expected to live in urban areas by 2050. Sustainable management of cities and urban expansion has therefore never been more relevant than it is today and research focusing on urban environments is of vital importance in order to improve
understanding of urban dynamics and to facilitate sustainable management practices. Urbanisation and urban expansion result in extensive land use change on a global scale, and this land
transformation has a number of associated impacts for the local environment and local
communities. One common effect is a reduction in the amount of green spaces, such as trees, grass and shrubs within urban areas. This in turn leads to a reduction in societal benefits associated with these green spaces which has potential to inhibit sustainable development and can introduce or enhance certain challenges residents and authorities face in urban environments. The proposed research will focus on such benefits by investigating the current and future supply of ecosystem services derived from green space in the tropical South American city of Paramaribo in Suriname. The ecosystem services considered within this study will be carbon storage, coastal protection, recreation, flood protection and local climate regulation, all of which have been deemed to be relevant for the city of Paramaribo. InVEST (Integrated Valuation of Ecosystem Services and
Tradeoffs) modelling software will be used to model and map the current supply of these ecosystem services in the Greater Paramaribo region. Future scenarios for land use change resulting from urban expansion by 2035 will then be defined and modelled using the CLUE (the Conversion of Land Use and its Effects) modelling framework. These scenarios will be developed according to current development plans and initiatives, available literature and consultation with stakeholders. They will allow for quantification of potential future supply of the considered urban ecosystem services, and analysis on the differences between current and future supply will enable potential synergies and trade-offs to be identified for each of the different scenarios. Research quantifying current and future ecosystem services derived from green space is relatively common for temperate regions of the planet, particularly in the global north. However, there has been little research focusing on this in either tropical or South American cities such as Paramaribo. The research, conducted as part of the “Towards a green and more liveable Paramaribo” twinning project, therefore aims to contribute to the widening of scientific understanding on ecosystem services and the role of green space in urban environments in diverse geographic settings.
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1. Introduction
1.1 The urban century
There are an estimated 7.7 billion people on the planet in 2019 and this global population is continuing to grow, with projections indicating there could be around 8.5 billion by 2030 and 9.7 billion by 2050 (United Nations, 2019). Currently, approximately 55% of the world’s population reside in urban areas (United Nations, 2018). These areas are expected to absorb almost all of the projected future global population growth, and an estimated 2.4 billion extra people will likely be living in cities by the middle of the century (McDonald, Colbert, Hamann, Simkin, & Walsh, 2018). Cities are therefore of vital importance when considering sustainability issues arising from continued global population growth, and this importance is highlighted by recognition that we are in the midst of “the urban century” (Elmqvist et al., 2019; McDonald et al., 2018; Puppim de Oliveira, 2019; Revi, 2017).
Urban change is inevitable given the projected increase in urban population, and management of urban growth is recognised as a key challenge of the urban century. This is highlighted by The Nature Conservancy in their report exploring urban growth trends and their potential impacts entitled “Nature in the Urban Century” (McDonald et al., 2018). This report first points out that cities have numerous associated benefits in comparison to rural areas. These benefits include an increased opportunity for education, enhanced capability for economic productivity and innovation, improved access to services, and improved energy efficiency. Cities therefore provide opportunity for the development of humanity. However, besides this opportunity, the report goes on to discuss the multitude of challenges associated with the urban century. For example, urban expansion will result in extensive land use change, with a total area of 1.2 million km2 predicted to be urbanised in the
coming two decades. Such land use change can have consequences for biodiversity in surrounding rural areas, can increase exposure to hazards such as flooding, and can lead to both direct and indirect loss of agricultural land vital for food security, as well as important ecosystems such as forests (Ke et al., 2018; McDonald et al., 2018; Miller & Hutchins, 2017; van Vliet, 2019)
The central role of cities in the 21st century is also recognised in international policy and agenda,
such as within the United Nations 2030 Agenda for Sustainable Development (United Nations, 2015). At the core of this agenda are 17 Sustainable Development Goals (SDGs), each with specific targets. SDG 11, Sustainable Cities and Communities, specifically focuses on urban areas and aims to “Make
cities and human settlements inclusive, safe, resilient and sustainable”. This goal recognises that
“rapid urbanisation is exerting pressure on fresh water supplies, sewage, the living environment and
public health”. It is therefore important that this pressure is addressed in order to limit the potential
impacts of urbanisation and urban expansion. Failure to do so would likely inhibit fulfilment of SDG 11, thereby limiting sustainable development. Understanding urban environments and the impacts of urban change is therefore arguably more important than ever. Scientific research on the spatial and temporal dynamics of urban environments can contribute to a widening of this understanding and consequentially to improved sustainability efforts.
1.2 Urban Green Spaces
As urbanisation and its associated impacts received increased attention within the last few decades, the potential role of green space within cities was increasingly recognised (Demuzere et al., 2014; Gómez-Baggethun & Barton, 2013; Jennings, Larson, & Yun, 2016; Kabisch, 2015; McDonald et al., 2018). The term “Urban Green Space” (UGS) refers to non-built up environments within urban areas and is typically used to describe parks, forests, gardens, grass, shrubs, water bodies and street trees (Demuzere et al., 2014; Jennings et al., 2016). UGS is widely accepted to provide multiple benefits
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for society and to be important for human health and well-being (Derkzen, van Teeffelen, Nagendra, & Verburg, 2017; Rojas-Rueda et al., 2019). This acceptance is reflected in target 11.7 within SDG 11, which aims to “provide universal access to safe, inclusive and accessible, green and public spaces” by 2030. However, as pointed out by Derkzen et al. (2017), urbanisation and urban expansion lead to changes in the functionality of UGS. It is therefore important for such changes to be considered by urban planners and decision makers in order to maximise the societal benefits provided by UGS in urban environments. Examples of benefits derived from UGS are discussed in the following section.
1.3 Ecosystem services from Urban Green Spaces
Urban growth and the subsequent changes in land use result in changes to the services human society are able to derive from nature. Such services are commonly referred to as “ecosystem
services” (ES) (Demuzere et al., 2014; Gómez-Baggethun & Barton, 2013; Kabisch, 2015). Though a
number of definitions have been put forth since the introduction of this term in the 1980s, all stress a link between ecosystems and human well-being (Burkhard & Maes, 2017). The proposed research is centred around the CICES (The Common International Classification of Ecosystem Services) classification of ES (Burkhard & Maes, 2017; Haines-Young & Potschin, 2012). CICES refines and builds upon the typology of ES suggested within the Millenium Ecosystem Assessment (Haines-Young & Potschin, 2012; MA, 2005; Roy Haines-(Haines-Young & Potschin, 2018). The three categories of ES within CICES are provisioning (of raw material and energy needs), regulating (regulation and
maintenance of the environment) and cultural (non-material characteristics of ecosystems that affect physical and mental states of people) services. UGS is recognised to provide a multitude of ES within each of these categories, some examples of which are provided in table 1 (Haase et al., 2014; Kabisch, 2015; McDonald et al., 2018; Tzoulas et al., 2007; Vargas-Hernández, Pallagst, & Zdunek-Wielgołaska, 2018). UGS can be managed to promote the supply of desirable ES. Quantification and spatial visualisation of this ES supply, specifically defined as “the provision of a service by a particular
ecosystem”, is beneficial for decision makers to facilitate such management efforts (Burkhard &
Maes, 2017).
Table 1: Examples of ES derived from UGS (derived from Demuzere et al., 2014; Gómez-Baggethun &
Barton, 2013; Kabisch, 2015)
ES category Example ES Provisioning
Food provision (e.g. fruits and seeds) Raw materials
Medicinal resources
Regulating
Flood protection (via run-off mitigation) Coastal flood protection
Local climate regulation (via urban cooling) Air quality regulation
Carbon storage and sequestration Noise reduction
Maintenance of soil fertility Pollination
Biodiversity regulation (habitat and genetic diversity maintenance)
Cultural
Recreation Aesthetic
Mental and physical health Tourism
Spirituality and sense of place Education and knowledge
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1.4 Relevance and innovative aspects of proposed research
Cities around the world demonstrate clear diversity with regards to variations in factors such as culture, infrastructure, topography, development status, demographics, climate, and governance and management directions (Larondelle & Haase, 2013; Schwarz, 2010). For example, the megacity of Seoul in South Korea, with its high-rise skyscrapers, dense population, and extreme seasonal temperature variation, is notably different to the more historical and culturally diverse city of Amsterdam in the Netherlands, with its relatively low population, extensive cycling infrastructure and unique network of canals. Both of these urban contexts are different again to the bustling and sprawling tropical city of Ho Chi Minh in the rapidly developing nation of Vietnam, and to the small but historically important city of Stirling, located in a topographically diverse region of Scotland. Each of these face differing and location-specific challenges, and would therefore most likely require differing approaches with regards to UGS and its management. This highlights the need for location-specific research focusing on the role of UGS.
The proposed study will contribute to this need for location-specific research by investigating the role of UGS in the tropical city of Paramaribo in South America. The city of Paramaribo is no exception when considering the diversity of cities discussed in the previous paragraph; it faces unique challenges specific to local environmental, socio-economic and political circumstance. These challenges include common flooding events, underdeveloped and unorganised urban planning and governance, inequality, bureaucracy and corruption (Fung-Loy, Van, Ku Leuven, Hemerijckx, & Leuven, 2019; Verrest, 2010). The research will address how the local context might influence the potential role and management of UGS in order to provide location and context-specific
contributions to the wider scientific study of UGS and its associated ES supply.
An important innovative aspect of the proposed research is it will investigate UGS in a tropical climate. At present, the role of UGS and the benefits it provides are not well studied in the tropics, despite the majority of urbanisation occurring in the developing world of which a large part is concentrated in the tropical climatic zone (23.5° north and south of the equator) (Dobbs et al., 2018; Haase et al., 2014; Haase, Schwarz, Strohbach, Kroll, & Seppelt, 2012; United Nations, 2018; X. Q. Zhang, 2015). The majority of current research instead focuses on UGS in temperate regions, particularly in the global north, and suggests that biophysical context and land use policies play vital roles in determining urban ES supply (Dobbs et al., 2018). Furthermore, South America is a
particularly understudied region when it comes to both ES and UGS (Balvanera et al., 2012; Dobbs et al., 2018; Haase et al., 2014). Dobbs et al. (2018) point out that current research “rarely accounts for
the diverse and complex socio-political and ecological drivers” associated with South American urban
change. They argue that inequality and weak governance in South American cities play key roles in addition to the biophysical context and land use policy considered to be most important in the global north.
Research focusing on the dynamics of urban areas within these understudied climatic zones (the tropics) and regions (South America) can therefore contribute to an expansion of scientific understanding. The proposed research therefore aims to address this research gap by quantifying the current supply of ES derived from UGS in the tropical South American city of Paramaribo, as well as the potential future role of UGS under different urban expansion scenarios. In doing so, insights will also be identified for local management efforts regarding areas where these benefits may be particularly likely to change and regarding any synergies or trade-offs in ES supply which may occur under such scenarios.
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2. Study area and broader project
2.1 Study area
As mentioned in section 1, the proposed research will focus on the tropical city of Paramaribo which is the capital of the smallest country in South America, Suriname (figure 1). Suriname, which
experiences an Af - tropical rainforest climate according to the Köppen classification system, has an area of 163,820 km2 and over 90% of this is forested (Fung-Loy et al., 2019). The population of the
country is currently around 575,000 people, and over half reside in Paramaribo and the surrounding area (Tropenbos Suriname, 2019; World Bank, 2018). Paramaribo itself has an area of around 182km2 whilst the surrounding area falls within the region of Greater Paramaribo, which is
approximately 650km2 . This region of Greater Paramaribo will be used as the study area for the
proposed research (figure 1). It consists of the districts of Paramaribo, Wanica (West and South of Paramaribo), and a section of Commewijne (East of Paramaribo), each of which are divided into resorts (Fung-Loy et al., 2019) (see figure 1).
Paramaribo provides a relevant case study for assessing ES derived from UGS because as mentioned it is located in an understudied climatic zone (the tropics) which is in an understudied region of the planet (South America) (Balvanera et al., 2012; Dobbs et al., 2018; Haase et al., 2014). Furthermore, the city is also growing. Between 2000 and 2015 the population of Greater Paramaribo increased by 14%. This has largely been attributed to the urban pull effect of the city on rural populations and has resulted in urban sprawl in the districts surrounding Paramaribo since this is where free space is still available (Fung-Loy et al., 2019). This urban sprawl is largely unplanned and uncontrolled by the government. Suriname lacks a coherent system of land registration and consequentially there is unreliable information on the ownership, availability and status of land (Verrest, 2010). On top of this inadequate spatial planning, there are a lack of finances, technology, data, and expertise within the government (Fung-Loy et al., 2019; Verrest, 2010). Continued urban expansion of Paramaribo will influence the ES supplied via UGS, an aspect which will be addressed by consideration of future urban expansion scenarios within the proposed research.
2.2 Towards a Green and More Liveable Paramaribo Project
The proposed research will be part of a larger twinning project between the knowledge based Non-Governmental Organisation (NGO) Tropenbos Suriname (TBIS) and the Faculty of Geo-information Science and Earth Observation of the University of Twente (UT-ITC) in the Netherlands. This twinning project, entitled “Towards a Green and more Liveable Paramaribo”, recognises the following central problem; “The benefits of urban greenery are insufficiently known in Suriname, are insufficiently
valued and are not included in urban planning and management” (Tropenbos Suriname, 2019). The
project therefore aims to “promote a green Paramaribo in which ecosystem services contribute to a
healthy and more liveable environment for its inhabitants” (Tropenbos Suriname, 2019). Within the
project there will be coordinated research with students from Anton de Kom University of Suriname, interactions with stakeholders, continued monitoring, and educational material developed. The proposed MSc thesis can contribute to these elements by providing a spatial overview of current ES supply derived from UGS in Paramaribo and estimating how this could change with alternative and plausible future urban development scenarios. It could therefore also serve as a basis for future monitoring of urban expansion and change in the functioning of UGS. The knowledge attained will be utilised to highlight the associated benefits of UGS in Paramaribo, thereby increasing awareness amongst urban planners and decision makers on the role of UGS within the city.
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Figure 1: Above: The location of
Suriname in South America.
Right: Suriname and Paramaribo. Below: Aerial image with resort
boundaries indicated within the study area of Greater Paramaribo (yellow) and the city of Paramaribo (red)
All maps produced using ESRI ArcMap v. 10.6.1. Basemap layers are available within this software. For the above two maps the National Geographic basemap is used. For the lower map ESRI aerial imagery is used.
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As part of the project, a pixel-based classification of Sentinel satellite imagery has been produced (Figure 2) (Taus, IN PREP). This allowed for land cover of the area to be identified. Green spaces distinguished within this classification are trees, mangroves, grass and mixed low vegetation.
Figure 2: Classified land cover of Greater Paramaribo based upon Sentinel satellite imagery (Taus, IN
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3. Conceptual framework
3.1 Key urban ES in Paramaribo
The proposed research will focus on quantifying the current and future supply of five ES derived from UGS, namely; carbon storage, coastal protection, recreation, local climate regulation and flood protection. These services are generally considered to be relevant for health and human well-being in cities (Derkzen, van Teeffelen, & Verburg, 2015; Salmond et al., 2016; Y. Zhang et al., 2018). Furthermore, initial consultation with TBIS has confirmed their importance and relevance in
Paramaribo as they carried out a small survey in March 2019 with participants representing national government, NGOs and the private sector. They are also commonly considered urban ES in research outside of the tropics which will allow for some reflections to be made on how ES supply compares with such research (Derkzen, van Teeffelen, & Verburg, 2015; Farrugia, Hudson, & McCulloch, 2013; Zhang et al., 2018). This also means there are established approaches for quantification which can be adapted for use within this research.
A literature review was conducted in order to; (i) explore current understanding on the supply of the considered ES from UGS (section 3.2); (ii) gain insights into methods used to quantify and map ES supply (section 3.3); (iii) gain insights into how land cover change can affect future ES supply (section 3.4); and (iv) gain insights into methods used to develop future scenarios of land cover change (section 3.5).
3.2 Current research on the considered ES derived from UGS
3.2.1 Carbon storage
Understanding carbon dynamics in urban areas is recognised to be a key research challenge and relative policy concern due to increased effort to address anthropogenic global climate change (Chen, 2015). Although the carbon storage offered by UGS is recognised to be relatively small on a national scale, there is still recognition that it is an important and relevant urban ES (Chen, 2015; Derkzen et al., 2015; Haase et al., 2012; Holt, Mears, Maltby, & Warren, 2015; Jiang, Deng, Tang, Lei, & Chen, 2017). When considering carbon in cities, some researchers attempt to quantify carbon dynamics through time via sequestration estimates. For example, Zhang et al. (2018) noted there to be an uneven spatial distribution of carbon storage in the city of Wuhan, China, indicating carbon sequestration across the city varies from 0 to 1573.85 g cm-2 year-1. This highlights how much carbon
dynamics can vary across relatively small areas in an urban setting.
Other researchers focus instead on the amount of carbon stored for quantification purposes rather than carbon dynamics over time (Derkzen et al., 2015). Quantification of carbon storage is typically based upon two important factors; biomass volume and vegetation type (Derkzen et al., 2015). The carbon pool in aboveground vegetation is most relevant when considering the storage offered by UGS. Vegetation is the second largest pool of organic carbon in urban ecosystems, after soil, and most carbon within this pool is found in trees (Haase et al., 2012). Exemplifying the importance of trees, Davies et al. (2011) found there to 3.16 kg C m-2 of above-ground carbon stored in vegetation
in the British city of Leicester, of which 97.3% was associated with trees. Trees within the proposed study area are therefore expected to be most relevant to this ES supply. However, Donato et al. (2011) point out that mangroves are among the most carbon rich forests in the tropics, so a high carbon storage can be expected from this class of UGS found along the coast of Paramaribo (figure 2). Quantification of soil carbon storage can offer increased understanding of carbon dynamics within urban systems, but such quantification is recognised to be complex in urban areas due to high levels of mixing and disturbance (Derkzen et al., 2015).
11 3.2.2 Coastal protection
The effects of climate change are particularly experienced in coastal areas (Mcgranahan, Balk, & Anderson, 2007; Spalding et al., 2014). Of these effects, rising sea level is a particular concern for coastal cities. However, predictions also suggest there will be an increase in storm frequency or intensity, as well as an intensification in storm surge and wave impacts in the coming decades. The combination of such change will most likely result in increased coastal storms, coastal erosion, flooding, inundation of low-lying land, and salinization of groundwater (Gedan et al., 2011; Spalding et al., 2014). Climate change therefore adds to the hazards coastal communities already face, yet people continue to be drawn towards cities on the coast. The Low Elevation Coastal Zone (coastal areas with an altitude less than 10m) covers only 2% of the world’s surface, but is home to approximately 10% of the global population (Mcgranahan et al., 2007; Spalding et al., 2014).
Coastal vegetation, such as seagrass, marshes, coastal forest and mangroves (figure 3), is recognised to protect shorelines and therefore human development due to the reduction of coastal hazard impacts (Gedan et al., 2011; Guannel et al., 2015; McIvor, Möller, Spencer, & Spalding, 2012). In an ES context, this is often referred to as coastal protection (Cilliers, Cilliers, Lubbe, & Siebert, 2013) and the proposed research will consider coastal protection offered by green space along the coast of Greater Paramaribo. Vegetation on coasts can reduce wave height, moderate current strength and decrease wave runup extent on beaches (Guannel et al., 2015). This provides benefits including the prevention of coastal flooding and the reduction of coastline erosion, thereby preventing economic damage or even loss of life (Das & Vincent, 2009; Guannel et al., 2015). Quantification of the coastal protection offered by vegetation is necessary given the exposure and vulnerability of coastal
communities, but also due to the threat of a removal or reduction of such vegetation in response to urban expansion. Urban areas also prevent a natural landward migration of coastal habitats in response to rising sea level meaning such habitats can be lost due to “coastal squeeze” (Spalding et al., 2014). The mangrove presence along the coast of Paramaribo (figure 2) is expected to play a major role in coastal protection within the context of the proposed research.
Figure 3: Schematic demonstrating wave attenuation offered by mangroves and factors affecting
12 3.2.3 Recreation
UGS is also recognised to provide the cultural ES of recreation (Cilliers et al., 2013; Haase et al., 2012). This ES, which is closely linked with aesthetics, generally considers the potential of UGS for outdoor recreational activities such as exercise, dog walking and relaxation (Derkzen et al., 2015). UGS therefore plays an important role in determining the quality of life for urban dwellers. Research indicates that green areas can improve physical and mental health, and even reduce mortality (Rojas-Rueda et al., 2019). Furthermore, Derkzen et al. (2015) claim that cities become more
attractive when recreational opportunity increases. This could potentially lead to other benefits such as increased visitation and tourism rates, which in turn could improve economic status in a city. As opposed to the other four ES considered within the proposed research, the supply of recreation as an ES largely depends upon human perception. It therefore potentially varies in different regions and cultures. For example, Zhang et al. (2018) found the recreation potential to be higher for water bodies and forested areas than in other urban areas in Wuhan, China. However, research by Derkzen et al. (2015) in Rotterdam, The Netherlands, suggests water bodies and herbaceous vegetation provide the highest recreation potential. Based upon these examples, it is expected that the UGS classes of trees and grass identified in the underpinning land cover classification (figure 2), particularly those near water bodies, will be most influential on the ES supply of recreation in Paramaribo.
3.2.4 Local climate regulation
Built-up urban areas generally have less green vegetation present than surrounding rural areas, and an increased area of impermeable artificial surfaces such as concrete, pavements, roads and buildings. This creates a difference in the urban energy balance, altering albedo and reducing
evapotranspiration and latent heat flux, which results in urban areas being warmer than surrounding rural areas (Chapman, Watson, Salazar, Thatcher, & McAlpine, 2017; Luber & McGeehin, 2008). Anthropogenic heat sources, such as traffic and temperature control in buildings, contribute further to this temperature difference. This phenomenon is commonly known as the urban heat island effect, and this term can describe either a quantified surface urban heat island (variation in surface temperature) or canopy layer heat island (variation in urban air temperature) (Anniballe, Bonafoni, & Pichierri, 2014). Global climate change is recognised to enhance this effect, leading to decreased energy efficiency, increased exposure of urban residents to heat stress, health risks from increased air pollution, and lower work productivity (Brown, Vanos, Kenny, & Lenzholzer, 2015; Chapman et al., 2017; Luber & McGeehin, 2008). Urbanisation, or more specifically the transformation of rural, vegetated land to built-up, urban land, further reduces green space which can exacerbate the urban heat island effect.
The potential for UGS to mitigate this urban heat island effect has received increased focus in recent decades (Alexandri & Jones, 2008; Cameron et al., 2012; Chapman et al., 2017; Takebayashi & Moriyama, 2007). The majority of research primarily focuses on quantifying the reduction in air temperature offered by UGS and therefore specifically addresses the canopy layer heat island (Brown et al., 2015). The research demonstrates that air temperature in UGS is typically lower than in the surrounding urban area, though there is variation in the magnitude of cooling which can be provided. For example, Wong and Yu (2005) highlighted that temperatures in Singapore, another tropical city, were up to 4°C cooler in green areas than in non-green areas such as the central business district (CBD). In Toronto, Canada, temperatures were similarly 4.9°C cooler in UGS, though in Phoenix, USA, temperature variation was only 1.9°C (Brown et al., 2015). Despite these
differences, the mitigation offered by UGS in an ES context translates to recognition that UGS provides the important regulatory service of local climate regulation, often referred to as urban
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cooling (Farrugia et al., 2013; Haase et al., 2012). The major mechanisms contributing to this cooling are the provision of shade and increased evapotranspiration. Since trees offer more shade and evapotranspiration than other vegetation, they can be expected to play an important role in the cooling of the urban heat island in Paramaribo.
3.2.5 Flood protection
An common challenge in urban areas which is being exacerbated by anthropogenic climate change is an increase in flood risk, as runoff in urban areas has greater velocity due to the prevalence of smooth impervious surfaces such as concrete, pavements and road surfaces (Demuzere et al., 2014). Up to 60% of rainwater becomes runoff in non-vegetated cities whilst in vegetated areas only 5% to 15% does so (Demuzere et al., 2014). Vegetation is recognised to reduce surface runoff during precipitation events due to interception of water via leaves and stems (Farrugia et al., 2013).
Furthermore, soil underlying vegetation can reduce infiltration rates and, as described by Farrugia et al. (2013), soil can therefore essentially act as a sponge as water is stored in pore spaces until percolation and through-flow occur. Vegetation can also absorb water once it is in the soil via its roots. Water therefore takes longer to pass through soils in habitats with a thick layer of soil and dense vegetation than those without such characteristics. Vegetation essentially reduces peak discharge and induces groundwater recharge (Demuzere et al., 2014). Exemplifying the role of vegetation, green areas in the highly urbanised and flood prone catchment of Como Lake in Italy were found to reduce stormwater runoff by up to 100% in years with normal levels of precipitation and up to 88% during years with high levels of precipitation (Demuzere et al., 2014). In an ES context, UGS can be considered to provide the regulatory service of flood protection via runoff mitigation (Kabisch, 2015). Street trees are particularly recognised for their ability to intercept and store rainfall in their canopy (Salmond et al., 2016). However, Cameron et al. (2012) point out how urban gardens are also recognised to “provide storm attenuation services to the urban matrix”, mainly due to the interception by vegetation which reduces peak flow and can ease demand on urban drainage systems.
3.3 Methods to analyse and map ES supply
Analysis of ES is essential for understanding how ecosystems contribute to human well-being (Burkhard & Maes, 2017). A large and rapidly growing field of research therefore seeks to analyse ES (Bagstad, Semmens, Waage, & Winthrop, 2013). Consequentially, a multitude of methods have been developed to conduct such analyses at varying scales and for varying needs (Bagstad et al., 2013; Ochoa & Urbina-Cardona, 2017). Within the last decade or so, there has been development of decision support tools which integrate ecological, economic and geographical information to quantify ES and to display results in spatially explicit ES maps (Bagstad et al., 2013; Burkhard & Maes, 2017; Ochoa & Urbina-Cardona, 2017). Maps are recognised to be a vital tool for practical application and awareness raising of ES as they can effectively communicate complex spatial information. Furthermore, maps are central to decisions relating to landscape planning, environmental resource management and land use optimisation (Burkhard & Maes, 2017).
Production of maps quantifying and spatially representing ES supply are therefore a primary focus, and considered to be state of the art, for research aiming to understand and communicate the status of ES in a particular area. Table 2 provides examples of some of the more commonly utilised modelling tools which are available for ES modelling and mapping. A major benefit of such tools is that they can usually be applied to different locations and scales. However, they also therefore have several associated assumptions and limitations (Burkhard & Maes, 2017).
Some researchers instead choose to develop their own models or approaches to quantify ES supply and produce ES supply maps (Derkzen et al., 2015; Yang et al., 2015). This can allow for a more
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locally and context-specific quantification and visualisation of ES, since a lot of the aforementioned decision support tools are limited with regards to which ES they can quantify and how much they can be adjusted for specific research needs. These approaches are commonly based upon proxies relating geographic information and land cover data in a method often referred to as the “lookup
table approach” (Burkhard & Maes, 2017). Alternatively, approaches can rely upon expert
knowledge for values in lookup tables or causal relationships between ES supply and spatial
information which is well established within literature. Other methods for mapping ES supply include approaches which estimate values based upon extrapolation from primary data such as surveys linked with spatial information, or approaches which utilise quantitative regression and socio-ecological models to combine spatial data, field data and information from literature (Burkhard & Maes, 2017).
Table 2: Common tools used for quantification, spatial modelling and mapping of ES (based upon
Burkhard and Maes (2017) and Ochoa and Urbina-Cardona (2017))
Tool Details Recent examples of use
InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs
Open-source software models used to map and value the goods and services from nature that sustain and fulfil human life
Modelling of ES supply for water yield, nutrient retention and carbon storage (UK river catchment) (Sharps et al., 2017)
Modelling carbon storage in the Chinese Changsha-Zhuzhou-Xiangtan urban agglomeration (Jiang et al., 2017)
Modelling carbon storage, sediment erosion and pollination in an urban setting (UK) (Grafius et al., 2016) ARIES - ARtificial intelligence models of ecosystem services ARIES provides an intelligent modelling platform capable of composing complex ecosystem services models
Modelling of ES supply for water yield, nutrient retention and carbon storage (UK river catchment) (Sharps et al., 2017)
Modelling the effects of urban expansion on ES (carbon storage, flood regulation, sediment retention, open space proximity, and scenic viewsheds) in Washington, USA
LUCI – Land
Utilisation Capability Indicator
Open source GIS toolbox to map areas providing services and potential gain or loss of services under management scenarios
Modelling of ES supply for water yield, nutrient retention and carbon storage (UK river catchment) (Sharps et al., 2017)
Modelling agricultural potential (Wales) (Emmett et al., 2016).
SWAT – Soil and water assessment tool
Developed to assist water resource managers in the assessment of water supply
Modelling water flow regulation and erosion regulation in Western Siberia (Schmalz, Kruse, Kiesel, Müller, & Fohrer, 2016)
3.4 Effects of land cover change on ES supply
As discussed in section 1.1, the world is in a midst of an urban century. This term has been adopted in response to continued increase in global population and in urban population which are in turn leading to the expansion of many urban areas. Urban expansion inevitably leads to land use change, since non-urban land is transformed to urban land. This transformation generally results in a loss of UGS. ES are accepted to be vulnerable to such land use change (Metzger, Rounsevell, Acosta-Michlik, Leemans, & Schröter, 2006). More specifically, the supply of ES can change over both space and time due to changes in land use (Peña, Onaindia, Fernández de Manuel, Ametzaga-Arregi, & Casado-Arzuaga, 2018). Transformation of vegetated land into built up land can result in a degradation of multiple ES (Y. Zhang et al., 2018). For example, urbanisation can result in losses of carbon storage if forested areas are replaced by built up areas as this eventually leads to a release of carbon to the atmosphere (Haase et al., 2012; Jiang et al., 2017). However, removal of forest is also detrimental for
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flood protection and local climate regulation services, and depending on local context, potentially recreation too.
Most ES trade-off analyses have focused on natural or protected areas, but in recent years there has been increased attention on urban areas (Peña et al., 2018). Trade-offs in ES can arise when planning or management decisions lead to the optimisation of some ES as this can lead to reduction or
deterioration of others (Peña et al., 2018). Spatial visualisation of trade-offs via mapping can contribute to environmental and landscape decision making (Peña et al., 2018).
3.5 Methods to analyse future ES supply
In attempts to try and predict and highlight the potential impacts of future land use change on ES supply, researchers often consider future scenarios of land use change (Jiang et al., 2017; Verburg & Overmars, 2009). Such scenarios can be useful for identifying areas to focus management efforts in order to limit potential reductions in ES supply, but can also be useful for predicting the impacts of certain management or policy decisions (Burkhard & Maes, 2017). Scenario analysis can therefore support the development of adaptive management strategies and allow for exploration of
alternative socio-ecological development pathways (Burkhard & Maes, 2017).
The development of a scenario generally involves three major phases (Burkhard & Maes, 2017). Firstly, major tendencies for the specific region or subject are defined and drivers of change likely to be related to these tendencies analysed. This initial phase results in a few plausible scenarios. Secondly, these scenarios are translated (quantitatively or qualitatively) into variables which
describe the key drivers of change. These drivers of change are used as inputs in models which relate changes to environmental changes, such as land use change (Burkhard & Maes, 2017). Finally, the outcomes of these models are analysed and desirable and undesirable options highlighted. Over the past two decades a multitude of land change models have been developed to simulate future changes in land use (Mas, Kolb, Paegelow, Camacho Olmedo, & Houet, 2014; van Vliet et al., 2016). Land change models can be categorised in different ways. They can for example be spatial or non-spatial, static or dynamic and contain parameters based on statistical correlation or on
descriptions of the process of interest (Mas et al., 2014). Some examples of frequently used models to simulate future land use change are the CLUE (Conversion of Land Use and Its Effects) framework, DINAMICA EGO and Land Change Modeller (Mas et al., 2014). These are summarised in table 3.
Table 3: Modelling frameworks frequently used to simulate future land cover change (Mas et al.,
2014).
Model Description
CLUE (Conversion of Land Use and Its Effects) Based upon location suitability utilising logistic regression. Simulates competition and interactions between different land cover types. Has been applied to a large variety of topics such as deforestation, farmland abandonment and urban expansion.
DINAMICA EGO Based upon transition probability maps that simulate landscape dynamics using Markov chain matrices and cellular automata. Has been applied for a variety of studies on urban growth, fire regimes and forest degradation.
Land Change Modeller A suite of tools in which land use change analysis can be modelled in combination with biodiversity and emission assessments. Has been applied for example in research on urban growth, erosion and habitat modelling.
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As an alternative to using such modelling frameworks, some researchers develop their own model to simulate land cover change. A recent example of such research is available for Paramaribo. Fung-Loy et al. (2019) simulated urban expansion using a relatively simple linear regression approach under a business as usual scenario based upon historical trends of land cover change. Results of this model suggested built up area could grow by 49% between 2015 and 2030 in Greater Paramaribo (Fung-Loy et al., 2019). However, aside from this research no other literature was found during the literature review which quantified potential urban expansion in Paramaribo.
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4. Objectives and research questions
This section outlines the specific objectives and related research questions of the proposed research.
4.1 Objectives
The primary objective of the proposed research is;
To identify the current and future supply of key ecosystem services derived from green space in the Greater Paramaribo area.
Three secondary objectives have been identified. Combined fulfilment of these will allow for achievement of the primary research objective.
1. To quantify and map the current supply of key ecosystem services derived from green space in Greater Paramaribo.
2. To develop three plausible future scenarios in which urban expansion and land use change, and therefore changes in green space, are estimated and visualised for the year 2035. 3. To analyse how the identified scenarios would affect future ecosystem service supply
derived from green spaces in Greater Paramaribo.
4.2 Research questions
The following primary research question will be asked in order to address the primary objective; What is the current and future supply of key ecosystem services derived from green space in the Greater Paramaribo area?
Sub-questions have also been identified to address the secondary objectives. These will be
addressed in the order presented below throughout the following proposed methods section and in the subsequent thesis.
1. What is the current supply of key ecosystem services derived from green space in Greater Paramaribo?
2. What are three plausible scenarios for future urban expansion and land use change, and how would these alter green space in Paramaribo by 2035?
3. What impact would the identified future scenarios have on future ecosystem service supply derived from green space in Paramaribo?
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5. Methods
This section outlines the proposed methods to address the research objectives and questions presented in section 4. Method choices for the proposed research are based upon the literature review carried out in section 3 which identified state of the art approaches for quantifying ES and for developing land cover change scenarios. As discussed in section 3, quantification and mapping of current and future supply of ES is most commonly approached via spatial modelling and mapping (Ochoa & Urbina-Cardona, 2017). The proposed research will therefore largely be centred around geospatial modelling and mapping. Throughout the research, best practices for mapping ES outlined by Willemen et al. (2015) will be followed to ensure robustness, stakeholder relevancy and
transparency. The specific method for each of the proposed secondary objectives (1, 2 and 3) is outlined in sections 5.1, 5.2 and 5.3 respectively. A flowchart summarising the proposed methodological process can be seen in figure 4.
Figure 4: Workflow summarising objectives, associated modelling frameworks and final outputs.
At the beginning of the research, a two week fieldwork period will be carried out in Paramaribo. This fieldwork will allow for an on-the-ground experience of the local situation in the city and in-depth discussion with project partners and stakeholders. The main purpose of this fieldwork is; (i) to justify assumptions associated with modelling and mapping ES supply; (ii) to acquire as much relevant local data as possible in order to enhance the accuracy of the modelling and mapping of ES supply and to ensure local context is included; and (iii) to gain an understanding of planned and expected urban expansion and changes in green space to inform future scenario developments.
5.1 Modelling current ES supply – Objective 1
Supply of the five considered ES will be modelled and mapped via application of the commonly applied suite of models provided by the Natural Capital Project under the name InVEST (Integrated
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Valuation of Ecosystem Services and Tradeoffs) (Natural Capital, 2019). This is a free, open-source software designed to “map and value the goods and services from nature that sustain and fulfil
human life” and it is often therefore used to inform decisions about natural resource management
(Natural Capital, 2019). InVEST was chosen as; (i) it is the most commonly used tool for spatial assessment of ES (Ochoa & Urbina-Cardona, 2017); (ii) all considered ES within this study are covered by models available within the software; (iii) it is best suited to analysis of multiple ES and has low input data requirements relative to more complex approaches (Natural Capital, 2019), and; (iv) the models are largely land use based. The low data requirement is particularly relevant as there is expected to be limited data available in Paramaribo which is relevant to the considered ES. Furthermore, the models being land use based is ideal since a land cover map has been produced as part of the UTC and TBIS twinning project (Figure 2, Section 2). This 2019 raster data, which covers Greater Paramaribo and has a spatial resolution of 10 metres, will therefore be a primary input and will allow for identification of UGS within Paramaribo. This in turn will allow for quantification and spatial visualisation of the supply of the considered ES.
The proposed InVEST modelling approaches, data requirements, potential data sources and model outcomes are summarised in table 4 and briefly discussed individually in the following sections. Whenever possible, locally sourced data will be used as opposed to the global defaults suggested or included within InVEST in order to make the models as representative of the local context as possible. Efforts will be made to obtain such data during and following the fieldwork once
connections with relevant stakeholders are established. The Natural Capital Project is in the process of developing a new suite of models under the name Urban InVEST. At present, two are available, but if more become available at a convenient time during the research, they will be used. These models are being developed to specifically focus on ES in an urban area and are therefore more suited to the proposed research. The default InVEST models, while still applicable to urban
environments, are not specifically designed for such purposes and are often used for more natural environments.
5.1.1 Carbon storage
The InVEST Carbon Storage and Sequestration model will be used to quantify carbon derived from green space in Greater Paramaribo (Sharp et al., 2018). This model maps carbon storage densities to land cover raster data. It requires an estimate of the amount of carbon in at least one of four carbon pools; aboveground biomass, belowground biomass, soil and dead organic matter. Since the focus in this research is on carbon storage offered by green space, estimates for carbon in aboveground biomass will be used for each of the green space classes in the land cover map (trees, mangrove, mixed low vegetation, and grass). The sequestration aspect of the model is optional and therefore will not be utilised within this research. In order for the model to be suitable for the local context, data will be sought during the fieldwork for estimates of carbon stock in vegetation present in each of the mapped classes of UGS. In the absence of data, allometric equations suggested by local stakeholders will be used to calculate estimates.
5.1.2 Coastal protection
The InVEST Coastal Vulnerability model will be used to gain insights into the coastal protection offered by green space along the coastline of Greater Paramaribo. However, important to note is that unlike the other four models within this research, this model does not directly quantify an ES. It instead produces a qualitative estimate of exposure in a vulnerability index and differentiates areas with relatively high, moderate or low exposure to coastal erosion and inundation (Sharp et al., 2018). It is therefore proposed that the output of the model will allow for protective services derived from coastal land cover such as mangroves to be highlighted. The model also does not directly
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require a land cover raster. Instead, coastal habitat polygons are used as inputs. These will be derived from the land cover raster used for all other models. There may also be further specification of coastal habitats such as the location of wetlands or marshes, though this will depend if there is geospatial data available on these in the region. This will allow for the effect of such habitats to be distinguished. Efforts will be made to seek this data during the fieldwork.
5.1.3 Recreation
The InVEST model entitled Visitation: Recreation and Tourism will be used to quantify recreational services derived from green space in Greater Paramaribo (Sharp et al., 2018). The model is based upon relative visitation rates across a landscape and can be used to identify which features of the environment influence the spatial pattern of visitation and what the relative influence of these features is. Polygon shapefile data derived from the land cover map (figure 2) will be used to include green space and its type in Greater Paramaribo. The model will allow for visitation rate within these polygons to be spatially visualised. Furthermore, a linear regression calculated within the model will allow for estimation of contributions of green space, as well as other features incorporated such as roads, to these visitation rates. Geotagged photographs from the website flickr are used to
parameterise visitation within the model. This introduces a large assumption; that residents of Paramaribo upload photographs to flickr. Efforts will be made to justify this during the fieldwork and this will be further reflected upon in the final thesis. Email consultation with a Natural Capital Project member involved in the development of InVEST has also revealed that a new Urban InVEST
Recreation model is almost complete. Should this become available during the research then this may be used instead, though no further information is available at this time.
5.1.4 Local climate regulation
The Urban Cooling Model, from the new Urban InVEST suite of models currently under development by the Natural Capital Project, will be used to quantify the cooling effect provided by green space within the study area (Sharp et al., 2018). The ecosystem supply this biophysical model will
specifically quantify will be a reduction of air temperature provided by green space. The estimation of this cooling effect is based upon data relating to climate and land cover for the study area, and once again, the land cover raster data (figure 2) will be used to specify areas and types of green space. A cooling capacity index (CCi) is calculated according to the following equation within the model:
CCi = (0.6 x shade) + (0.2 x albedo) + (0.2 x Evapotranspiration index) A city-scale estimated urban heat island magnitude is then used to estimate air temperature reduction provided by green space throughout the city. This output will be in raster form and will match the resolution of the land cover raster used as input (10m).
5.1.5 Flood protection
The Urban Flood Risk Mitigation Model, also from the new aforementioned Urban InVEST models, will be used to quantify water retention provided by green space in Paramaribo during storms (Sharp et al., 2018). This retention can provide the service of a reduction in the volume of floodwater in flood-prone areas. Again, a main input to this model is the land cover raster data (figure 2). Soil characteristics per land cover type will also be included if available. The model calculates the amount of runoff retained per pixel compared to the considered storm volume. This is based upon the SCS curve number method, a common approach for determining runoff from an associated rainfall event in a given area (Sharp et al., 2018).
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Table 4: Proposed models for quantifying and mapping ES supply in Greater Paramaribo along with data requirements, potential data sources and primary model outputs.
Based upon information available in the InVEST User Guide (Sharp et al., 2018).
Note: The column entitled “potential local source of data” lists local stakeholders and is based upon initial consultation with TBIS. These stakeholders will be contacted during the fieldwork to determine if such data is available and can be shared. If it is not, then the globally available data will be used instead.
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5.2 Developing scenarios of future land cover change – Objective 2
Future scenarios focusing on urban expansion and change in UGS area in Greater Paramaribo will be considered to address secondary objective 2. The freely available CLUE (The Conversion of Land Use and its Effects) modelling framework will be used to develop these scenarios (CLUE, 2019; Verburg & Overmars, 2009). This framework is typically used for simulation of large scale land use change processes, but is also applicable to smaller scale change and for simulation of urban expansion. For example, it was utilised alongside InVEST for modelling impacts of urban change on carbon storage in an urban setting in China (Jiang et al., 2017). It has also been used alongside InVEST in other studies considering the impacts of urban expansion on ES (Liang, Liu, & Huang, 2017; D. Zhang, Huang, He, & Wu, 2017). It was therefore deemed to be suitable for the proposed research. No literature was found during the underpinning literature review in which the other land cover change modelling frameworks mentioned in section 3.5 were used alongside InVEST in an urban context. The specific model which will be used is entitled Dyna-CLUE, an adapted version of the CLUE-s (the Conversion of Land Use and its Effects at Small regional extent) model (figure 5). This spatially explicit land use land cover change model is used to simulate land use change based upon empirical relationships between land use and its determining and driving factors in combination with dynamic modelling of competition between different land use types (CLUE, 2019). Information within the model is subdivided into four categories which combine to create a set of conditions for which the model calculates the optimal solution. These categories, highlighted in figure 5, are; land use requirements (demand); spatial policies and restrictions; land use type specific conversion settings, and; location characteristics. User-decisions relating to each of these categories can be combined for the development of a land use change scenario.
Figure 5: An overview of the information flow associated with the four categories within CLUE-S
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Within the model, the sub-category “spatial policies and restrictions” primarily indicate areas where land use change is restricted or implausible. “Land use type specific conversion settings” relate to the temporal dynamics of the land cover change simulations. Values ranging from 0 (easy
conversion) to 1 (irreversible change) will be assigned representing the elasticity to change for each of the 8 land cover types mapped in Greater Paramaribo (figure 2). A conversion matrix will also be used to specify to which other land cover types the present land cover type can or cannot be
converted to and over which temporal scale such conversion is possible. For example, it is impossible for water to be converted into trees so this will be specified within this conversion matrix. The sub-category “land use requirements (demand)” defines the total required change in land use.
Extrapolation of historical trends in land use change into the future is a common approach for determining this, though for the proposed research land use requirements will be based upon current plans for Greater Paramaribo and expected requirements indicated by key stakeholders. Finally, “location characteristics” involves a list of biophysical or socio-economic factors which are related to the likelihood of a specific land cover occurring at a particular location. These factors can be essentially considered as determinants of land use change. Of particular interest in the proposed research are factors relating to urban expansion and change in UGS. A preliminary list of factors expected to be relevant is distance from road, elevation, distance to coastline, land price, slope and distance from the urban centre. However, the factors included within the research largely depend on data which is obtainable for the study area and may change after consultation with local stakeholders.
In order to inform decisions to be made within the Dyna-CLUE model, interviews will be carried out with identified experts and stakeholders during the fieldwork in Paramaribo. These interviews will be designed to address the four aforementioned categories in which user-based decisions are made within the model. Between 5 and 10 stakeholders and experts from different organisations and institutions associated with spatial and urban planning in Paramaribo will be interviewed. The interviews will focus on how these stakeholders see the future of Paramaribo with regards to urban expansion and changes in green space in the next 15 years. Such a time-period has been chosen as any less would likely be too small a time for significant land cover change to occur, but longer term change would likely be more difficult to predict and therefore be less reliable.
Responses from the interviews alongside any available projects or plans for Paramaribo will determine the defining and development of three different scenarios which will be considered. Currently, it is expected that one of these will focus on minimal and controlled urban expansion in certain areas, one on extensive, uncontrolled urban expansion, and one on the introduction of an initiative or idea aimed at promoting, protecting and increasing green space during moderate urban expansion. This will allow for comparisons to be made between each of these, and insights to be provided for local stakeholders and policy makers for how these differing scenarios could affect the city. However, the specifics of the scenarios to be considered have potential to change after consultation with experts and stakeholders. The final outcome of this secondary objective will be three different land cover maps for the year 2035, at the same spatial resolution as the current map (10m) (Figure 2).
5.3 Analysis of future ES supply – Objective 3
A further advantage of utilising the InVEST modelling software is it allows for exploration of different future scenarios due to it being land cover based. The outputs of the Dyna-CLUE model (secondary objective 2) will be used as inputs for the same InVEST models used to map the current supply of ES derived from green space during secondary objective 1. All other data inputs will remain the same as used for modelling current ES supply. This will first enable future ES supply for the five considered ES
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to be estimated and spatially visualised. It will also enable comparison between the results from secondary objective 1 (current ES supply) and secondary objective 3 (future ES supply). Differences in ES supply will be statistically analysed and potential trade-offs or synergies between specific ES identified for each of the developed scenarios. This statistical analysis will primarily be carried out using R statistical software and will first involve transformation of the quantified ES supply data via min-max normalization so that a range of each ES is set to a fixed scale. Correlation analysis will then be performed on each pair of ES using the Pearson correlation test. ES bundles will then be identified via cluster analysis and visualised in starplots according to the steps utilised by Yang et al. (2015) in their research aimed to detect trade-offs and synergies in urban-rural complexes.
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6. Research time schedule
A planned schedule for different aspects of the proposed research is presented in figure 6. The entire thesis will take place over 7 months from December 2019 until June 2020. Overlaps within the objectives in figure 5 are to allow for possible delays.
Figure 6: Proposed time schedule for the Master’s thesis
Proposed landmarks during the research are as follows: Proposal presentation on 5th February 2020
Objective 1 complete by the end of February Objective 2 complete by the end of March Objective 3 complete by the end of April
First draft of thesis submitted by the end of May Final presentation on 3rd June 2020
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7. Budget & Funding
The majority of the proposed research involves a desktop-based study in which freely available software or software covered by licenses from the University of Amsterdam will be used (see section 9). Therefore, the only major costs are associated with the fieldtrip to Paramaribo scheduled from 21st November 2019 until 5th December 2019. The costs for this trip are covered by the Tropenbos
Suriname twinning project (explained in section 2.2) which is funded by the UTSN twinning facility. These costs are summarised in table 5.
Table 5: Overview of associated costs which will be covered by the UTSN twinning facility Component of research Approximate cost
Flight ticket to Paramaribo €800
Daily spending allowance for fieldwork period €1050
Suriname visa €50
Total €1900
8. Insurance and safety
Insurance and safety during the fieldwork will be carried out according to guidance and the fieldwork insurance provided by the University of Amsterdam. Prior to the trip a fieldwork declaration form and medical form will be submitted via Datanose. Personal travel insurance will also be taken out to cover any additional emergencies. Normal travelling precautions will be followed and supervision and assistance throughout the fieldwork will be available from the project partners Tropenbos Suriname.
9. Equipment and software
No special equipment will be required for the research since there will be no direct raw data
collection. Only currently available data and insights from interviews will be used. Software required for the proposed research is listed below, all of which is already installed and licensed for the duration of the research on a personal laptop. Interviews will be recorded on a personal mobile phone.
ESRI ArcGIS Desktop
Microsoft Office (Excel, Word and Powerpoint) RStudio
InVEST
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10. Data management
All data, information, knowledge and products generated or collected within the framework of the proposed research will be handled according to rules and regulations of project UTSN 31-123-M-G ‘Naar een groen en leefbaarder Paramaribo’. These are outlined in a signed agreement between the faculty of Geo-Information Science and Earth Observation ITC University of Twente, Tropenbos Suriname and myself.
As discussed in section 5, during the fieldwork a number of stakeholders will be contacted for data acquisition purposes or for interviews. Personal contact details will be stored separate to the data collected and deleted once the project is complete. Recordings of interviews will be stored inaccessible to third parties and will also be deleted upon completion of the research. A consent form will be produced outlining these terms to ensure clear understanding and transparency during interviews.
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