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Landscape and Ecological Engineering

, Volume 11, Issue 2, pp 259–268 | Cite as

Morphological analysis of green infrastructure in the Seoul metropolitan area, South Korea

  • Sangjun Kang
  • Jin-Oh Kim
Original Paper

Abstract

The purpose of this study is to understand changes in green infrastructure (GI) in the Seoul metropolitan area, South Korea, focusing on the critical GI components of hubs and links. We applied a morphological analysis tool, morphological spatial pattern analysis (MSPA), to explore GI in the Seoul metropolitan area. For input data to run MSPA, we used 30-m pixel-sized land cover data of 2000 and 2009 provided by the Ministry of Environment of Korea. Land cover data are used as foundational information for GI network mapping. Using MSPA, we examined morphological changes in hubs and links from 2000 to 2009 in the metropolitan area as well as in 32 municipalities in Gyeonggi-do, a major part of the metropolitan area. Our analysis showed that the area of hubs in the Seoul metropolis has decreased, while the number of links has dramatically increased, over this 10-year period. This implies that hubs have largely been fragmented into smaller ones with a rapid increase in links in a way that does not conserve and enhance GI. We also compared analysis of network area changes in the Seoul metropolitan area with the environmental conservation value assessment map (ECVAM) currently in use by the government to assess conservation value, and found that the network areas of 2009 mapped by MSPA corresponded to a 87.8 % level to the Grade I areas of the ECVAM, with variation by municipality. From these analyses, we conclude that MSPA is helpful in establishing conservation planning strategies optimized for local and regional contexts. The MSPA also provides a useful tool to complement the ECVAM for improving GI functions.

Keywords

Green network Environmental conservation MSPA 

Introduction

Green infrastructure (GI) is an interconnected network of green space that conserves natural ecosystem values and functions while providing associated benefits to human populations (Benedict and McMahon 2002). It is represented by all natural, seminatural, and artificial networks of ecological systems in urban and periurban areas at all spatial scales, with an emphasis on the quality as well as the quantity of green spaces, their multifunctional role, and the interconnection between habitats (Tzoulas et al. 2007; Turner 1996; Sandstrom 2002; Van der Ryn and Cowan 1996). The primary benefits of GI include enriched habitat and biodiversity; maintenance of natural landscape processes, e.g., for storm water and flood management; reduction of pollution; increase of recreational opportunities; and a better connection to nature and sense of place (Benedict and McMahon 2002; Wise 2008; Weber et al. 2006). Additional social benefits of GI include increased property values and a reduction in public infrastructure cost (Benedict and McMahon 2002). The concept of GI is often introduced to improve urban green space as a coherent planning entity because of its potential to guide urban development by providing a framework for economic growth and nature conservation (Walmsley 2006, Van der Ryn and Cowan 1996). According to (Benedict and McMahon 2002), in order to plan and manage GI effectively it is essential to identify and protect critical hubs and links. With a growing need for systematic land conservation on a regional scale in the face of expanding urbanization and green space fragmentation, consideration of GI in planning provides a useful basis on which to (1) recognize a variety of natural resources, (2) understand how ecological importance of a particular open space fits into a larger system, (3) assess regional landscape value for wildlife conservation, and (4) coordinate local and interjurisdictional planning (Weber et al. 2006). In the United States (US), (Lewis 1964) applied a greenways plan for Wisconsin for land acquisition, and (Hoctor et al. 2000) developed a GI network for the State of Florida (Smith 1993). In Maryland, GI was mapped to identify 2.4 million acres of ecologically significant, undeveloped land throughout the state (Conn 2009). The concept of GI was also applied to spatial planning in Cambridge in the United Kingdom to identify key issues in biodiversity, landscape, and rights of way and measures, as well as to explore options for funding and longer-term management of future assets created (European Environmental Agency 2011). In 2011, the European Commission declared the establishment of GI in urban and rural areas in the European Union (EU) to enhance ecosystems and ecological services as an action target for biodiversity strategy. However, fewer than 50 % of the mapped GI networks were protected because of land cover change (Hoctor et al. 2000; Carr 2002). Investigation of land cover change on a regional scale provides an important groundwork to understand how changes in GI structure are affected by politically fragmented land use decisions (Wickham et al. 2010).

The purpose of this study is to understand the changes in GI structure in the Seoul metropolitan area in South Korea. We applied a morphological analysis tool focusing on the critical GI components of hubs and links. The resulting outputs indicate the changes in GI structures in the entire Seoul metropolitan area as well as municipalities. We also examined how these changes in hubs and links are correlated with conservation areas represented by the environmental conservation value assessment map (ECVAM). These analyses will help the Seoul metropolis to manage systematically its GI in a way that enhances hubs and links.

Approaches to green infrastructure in the Seoul metropolis

The Seoul metropolitan area is a region consisting of Seoul, Incheon and Gyeonggi-do (Fig. 1). The area has experienced rapid urban growth over recent decades and significant changes in green open spaces (Kim and Han 2012; Kang et al. 2010). Since the 1980s, as a way to meet the rapidly growing demand for housing, the Korean government has developed suburban areas for satellite towns through aggressive land acquisition, resulting in the reduction of green space from 6,050.7 km2 (59.4 %) in 1985 to 5,513.5 km2 (54.1 %) in 2003 (Kim and Han 2012). Since 1971, greenbelt regulation has been enforced to restrict urban sprawl in the Seoul metropolitan area, but a significant proportion of the area has been rezoned for development in the last decade. (Sung 1996) pointed out the problem of green space fragmentation and offered strategies to establish green networks through the creation of eco-bridges, biotopes, ecological parks, green corridors, street trees, roof-top gardens, ecological restoration of rivers and ponds, and new urban parks. This approach helped provide practical guidelines for each municipality to enhance the green network, but it did not offer specific ideas to assess which parts of the area are most important to maintain or restore in terms of green network function. The lack of assessment tools spurred governmental efforts to evaluate the quality of green spaces and led to the creation of a new map. In 2005, the Ministry of Environment of Korea introduced the ECVAM, a topographic map developed to be used for decision making for effective conservation and development. The ECVAM classifies the entire national land into five grades, with Grade I representing the most environmentally valuable areas and Grade V meaning the most suitable for development. Grade II areas are environmentally valuable, but allow limited development, whereas Grade IV areas are intended for environmentally friendly development. Grade III represents buffer areas between conservation and development and allows flexible use of the land depending on the social and environmental contexts. The ECVAM was produced from map overlay using equivalent weighting and least index methods. The map incorporated 67 assessment values based on criteria of naturalness, diversity, rarity, vulnerability, and stability (Ministry of Environment of Korea 2005). The ECVAM has undergone regular updates since its introduction in 2005 and has been widely used primarily by governmental sectors at multiple stages in land development decision making, including land use planning and environmental impact assessment. Despite its usefulness in land use decision making, the quality of the ECVAM has been criticized for its tendency to assign relatively higher values than other assessment tools because of even-valued overlay and minimal indicator methods, ignorance of local contexts, and its questionable function in GI systems (Jeon et al. 2010).
Fig. 1

Administrative boundaries of the Seoul metropolitan area (smaller fonts indicate municipalities of Gyeonggi-do)

Methods

For systematic assessment, GI relies on understanding hubs and links (Weber et al. 2006). Hubs (cores) are anchors of green infrastructure networks that provide origins and destinations for the wildlife and ecological processes moving through them, whereas links (bridges) are the connections tying the system together to make the networks work (Benedict and McMahon 2002).1 The overlay of different thematic layers that was popularized by McHarg (1969) has commonly been used for mapping GI, together with the rapid advance of geographic information systems (GIS) (Wickham et al. 2010). Using GIS, hubs are identified through overlay of several environmental features of interest, and links are defined primarily by river networks. However, the overlay method requires a wide range of data in exactly the same format from multiple resources, which is rarely feasible in locally fragmented jurisdictional contexts such as the Seoul metropolis.

An alternative way to map systematically GI networks is with morphological spatial pattern analysis (MSPA) (Soille and Vogt 2009). Based on concepts from mathematical morphology (Soille 2003), MSPA allows generic segmentation of binary patterns into categories representing specific geometric features such as size, shape, and connectivity (Soille and Vogt 2009; Vogt et al. 2007). The principle of MSPA can be applied to identify hubs and links from a single land cover map by creating structure from the spatial relationships among land cover features that are critical for GI (Wickham et al. 2010).

The elements of GI defined by MSPA are core, islet, bridge, loop, branch, edge, and perforation (Soille and Vogt 2009) (see definitions in Table 1 and an example in Fig. 2). Core and bridge elements (equivalent to hubs and links, respectively) are mapped based on the interplay among the values of their own and other elements. MSPA processes data by identifying the core, based on the connectivity rule used to define neighbors and the value of edge width (Soille and Vogt 2009). The size of the core can be reduced by an increase in edge width, resulting in gains for other elements. For example, if edge width is increased, core can change to islet when the area of the core is small and to bridge when the area of the core is narrow. MSPA is supported for its strengths in describing the fragmentation of spatial patterns, for examining edge effects affected by a range of width, in a more user-driven and flexible way, and for distinguishing possible differences between interior (perforation) and exterior edge effects (Soille and Vogt 2009; Wickham et al. 2010).
Table 1

Definition of MSPA classes (Wickham et al. 2010)

MSPA class

Definition

Core (hub)

Foreground pixels surrounded on all sides by foreground pixels and greater than the specified edge width distance from background

Bridge (link)

Foreground pixels that connect two or more disjunction areas of the core

Loop

Foreground pixels that connect an area of the core to itself

Branch

Foreground pixels that extend from an area of the core, but do not connect to another area of the core

Edge

Pixels that form the transition zone between foreground and background

Perforation

Pixels that form the transition zone between foreground and background for interior regions of foreground. Consider a group of group of foreground pixels in the shape of a doughnut. The pixels forming the inner edge would be classified as perforations, whereas those forming the outer edge would be classified as edge

Islet

Foreground pixels that do not contain the core. Islet is the only unconnected class. Edges and perforations surround the core, and loops, bridges, and branches are connected to the core

Fig. 2

Example of MSPA for Namyangju-si with 60-m edge width (color figure online)

We used MSPA to explore GI in the Seoul metropolis. For input data to run MSPA, we used 30-m pixel-sized land cover data of 2000 and 2009 provided by the Ministry of Environment of Korea. Land cover data are widely used as foundational information for GI network mapping (Hoctor et al. 2000; Carr 2002; Weber 2004). The land cover maps are based on 14 classifications, of which we chose forest, wetland, grassland, and recreational areas such as urban parks for MSPA. Forests and wetlands are important land cover classes that are widely used for GI network mapping (Hoctor et al. 2000; Carr 2002), and grassland and recreational areas are regarded as important land cover classes in Korea for their potential ecological functions in rural and urban areas. To perform MSPA, we used the Graphical User Interface for the Description of image Objects and their Shapes (GUIDOS) program distributed online for no charge by the European Commission Joint Research Centre (http://forest.jrc.ec.europa.eu/download/soft-ware/guidos). MSPA and GUIDOS have already been adopted by researchers from different fields and government entities, including the U.S. Forest Service and the U.S. Environmental Protection Agency (Vogt 2010). In the data input process of the program, we applied the four classes selected as focal classes for GI network mapping while assigning all other classes to background. Using raster image data, MSPA conducts a segmentation of the image foreground data into mutually exclusive feature classes.

Using MSPA in conjunction with GIS, we first examined morphological changes of hubs and links from 2000 to 2009 in the metropolitan area as well as 32 municipalities (Incheon-si was omitted because of lack of data). An analysis of network changes over 10 years was also conducted to understand how changes in hubs and links enhanced or weakened green infrastructure. Finally, we compared our analyses of network changes with the ECVAM to determine how effective ECVAM is in conserving networks, a key component of green infrastructure.

Analysis

Morphological changes in MSPA classes

We first observed how the areas of hubs and links are affected by edge widths. MSPA is based on the connectivity rule to define neighbors and the value defining edge width, and connectivity can be defined by either queen’s (eight neighbors) or rook’s (cardinal direction only) contiguity (Soille and Vogt 2009). In this study, we used eight-neighbor connectivity and three different edge width values to determine the size and number of the GI components hubs and links. The number of cores decreases as edge width increases, and the edge width has a nonlinear effect on the number of cores (Wickham et al. 2010). Although edge width functioning as corridor width has a significant impact on the number and size of cores, corridor width may not be a significant factor for explaining the abundance and richness of forest interior species such as birds (Kohut et al. 2009). For some species, corridor presence can be a determining factor (Levey et al. 2005). In this study, as we used 30-m pixel-sized land cover data, the edge widths become 30, 60, and 90 m, respectively. After MSPA analysis, we found that core areas decreased 16 % with 30-m edge, 20 % with 60-m edge, and 21 % with 90-m edge. The change in link areas was more dramatic; link areas increased 342 % for 30-m edge, 179 % for 60-m edge, and 137 % for 90-m edge (Table 2). These results indicate that the link areas are more sensitive to edge effects than core areas (Figs. 3, 4). We applied 60-m edge width to examine the morphological changes in GI, and observed that the overall area of the green spaces in the Seoul metropolis has changed from 2000 to 2009. The GI area was obtained from the summation of each MSPA class area—core, bridge, loop, branch, edge, perforation, and islet—for each year. The total size of GI for 2000 and 2009 was 605,357 and 548,279 ha, respectively. However, unlike the overall decrease in size, a few MSPA classes such as islet, edge, bridge, and branch increased during this period; in particular, the branch size increased by approximately 106 % from 2000 to 2009.
Table 2

Area changes (ha) in MSPA classes with the edge widths of 30, 60, 90-m

Edge (m)

Year

Core

Bridge

Islet

Perforation

Edge

Loop

Branch

30

2000

509,287

2,179

5,206

10,896

66,771

2,906

8,112

2009

427,767

7,457

3,728

4,825

81,255

4,331

18,916

60

2000

436,160

16,224

12,531

8,657

96,433

15,437

19,916

2009

350,624

29,004

13,762

4,057

98,690

14,913

37,228

90

2000

391,242

34,384

18,463

6,114

103,819

25,122

26,212

2009

307,256

47,042

21,712

3,893

99,458

22,918

46,001

Fig. 3

Core (hub) changes with 30-, 60-, and 90-m edges

Fig. 4

Bridge (link) changes with 30-, 60-, and 90-m edges

Changes in hubs and links

Based on GIS analysis, we showed that core areas in the entire Seoul metropolis decreased between 2000 and 2009 from 436,014 to 348,571 ha, representing a 7.48 % reduction (Fig. 5). We also examined 32 municipalities in the Seoul metropolis to understand the changes in core areas for each administrative area over 10 years, and found that Siheung-si had the highest rate of loss of core areas, corresponding to a 52.97 % loss, followed by Bucheon-si, Hwasung-si, and Gimpo-si. The lowest loss of core areas in the metropolitan area was observed in Gwacheon, which had a 5.86 % reduction. From the analysis of bridges, we found that rapidly growing land developments in the Seoul metropolis increased link areas from 5,765 ha in 2000 to 12,857 ha in 2009, representing a 122.9 % increase. Dongducheon-si created the most new links with a 1,198 % increase, while Ansan-si lost the most links with a 41 % decrease (Fig. 6). We also observed that only 10.74 % of the link areas present in 2000 remained in 2009. This implies that, although not all links deserve the same level of conservation, almost 90 % of the links present in the metropolitan area in 2000 that were potentially important green infrastructure elements were lost without consideration of their value. The proportion of links present in 2000 that remained in 2009 varied by municipality. In Gwacheon-si, all of the links present in 2000 were completely removed and replaced by new links over 10 years. Anyang-si and Gunpo-si retained as little as 0.67 and 1.73 % of the original link areas of 2000, respectively, and created more than 98 % new links. Dongducheon-si retained the most link areas at 23.71 %. This analysis indicates that the area of hubs in the Seoul metropolis has been constantly decreasing over 10 years, perhaps as a result of greenbelt reform and growing development for housing and industry. In fact, the greenbelt area in the Seoul metropolis was decreased nearly 10 % from 1,566.8 km2 in 1980s to 1,424 km2 in 2010, allowing new urban development (Bae 2013).
Fig. 5

Hubs and links present in 2000 (left) and 2009 (right)

Fig. 6

Changes in hubs and links in Ansan-si (red indicates hubs and links lost, and blue represents hubs and links gained between 2000 and 2009) (color figure online)

The area of links, however, increased considerably through fragmentation of the hubs and the creation of new links. Notably, the links observed in 2000 were mostly lost in 2009.

Network changes

Network analysis was conducted by GUIDOS using the MSPA results. In these analyses, the network is composed of nodes and links, and the remaining MSPA classes are ignored. Network analysis by GUIDOS defines nodes and links as cores and bridges, respectively, where bridges are connectors between different cores. A connected set of nodes and links is called a ‘component’ (Vogt 2010). Thus, a set of cores with no links—nodes only—is not considered a component. In Fig. 7 showing the results of network analysis, individual components of the network are displayed in different colors.
Fig. 7

GI networks in 2000 (left) and in 2009 (right)

From the network analysis, we found that the number of components in the Seoul metropolis increased from 745 in 2000 to 829 in 2009, while the total area of the components decreased from 446,834 to 375,608 ha, representing a 15.9 % area reduction. This indicates that GI networks are fragmented, resulting in an increased number of smaller networks with a decrease in total network area (Table 3). We also found that the municipalities lost and gained network areas between 2000 and 2009 in different proportions. Among the 32 municipalities, Euiwang-si showed the greatest decrease in network areas with 763.2 ha lost, indicating a 14.16 % reduction, followed by Yangpyeong-gun and Siheung-si with 10.72 and 10.32 % reduction, respectively. Euiwang-si, the 27th in territory size out of 32 municipalities with a significant portion of greenbelt, lost the most network areas as a result of growth in development in the greenbelt zone over the past 10 years with relatively less creation of new networks (Fig. 8). Yangpyeong-gun, the second largest municipality, ranked second in the percent reduction of network areas, but actually lost the most network area in the Seoul metropolis. We observed that most of the municipalities with high network area loss, including Siheung-si and Namyangju-si, are located in greenbelt zones but have promoted urban development with relaxation or abolition of the restrictive greenbelt regulation for the past 10 years. This implies that these municipalities that spurred land development have not effectively preserved GI or been developed in a way that enhances the network functions of GI.
Table 3

Change in network (component) features

 

Number of networks

Total area of the networks (ha)

Link area (ha)

Node area (ha)

2000

745

446,834

16,216

430,617

2009

829

375,608

28,986

346,621

Fig. 8

Network changes in Euiwang-si between 2000 and 2009 (color figure online)

Evaluation of environmental conservation value assessment map

By comparing network area changes in the Seoul metropolis with the ECVAM, we observed how the network areas in 2009 mapped by MSPA correspond to the Grade I areas of the ECVAM, i.e., those with the highest conservation value. We found 87.8 % concordance between network areas in 2009 mapped by MSPA and Grade I areas of the ECVAM, with some variation by municipality (Fig. 9). Bucheon-si and Ansan-si share the least network areas with the ECVAM, both 69.71 %, followed by Gimpo-si and Pyeongtaek-si. Hanam-si, on the other hand, shows the greatest concordance with 98.63 % of the network area in Grade I. We also observed how changes in network areas over 10 years are associated with Grade I areas of the ECVAM. From the analysis of individual municipalities, we found that Bucheon-si and Ansan-si lost a relatively small amount of network area between 2000 and 2009, with 5.03 and 7.79 % reduction respectively. However, 43.21 % and 44.34 % of the network areas identified in 2000 were no longer included in Grade I areas of the ECVAM in 2009 (Fig. 10). This implies that, although useful for land use planning and environmental impact assessment, the ECVAM does not effectively account for the conservation value in terms of network function.
Fig. 9

Network changes and correspondence with the ECVAM (color figure online)

Fig. 10

Disproportion of networks and Grade I of the ECVAM in Bucheon-si (color figure online)

Findings and discussion

From the MSPA results, we derived five important findings. First, the total area of hubs decreased while the number of links increased dramatically over 10 years from 2000 to 2009. In particular, the area of hubs in the Seoul metropolis decreased by 7.48 %. Among 32 municipalities, Siheung-si had the highest rate of hub loss, corresponding to a 52.97 % reduction, followed by Bucheon-si, Hwasung-si, and Gimpo-si. The lowest rate of hub loss in the metropolitan area was in Gwacheon, with a 5.86 % reduction. The number of links, however, increased greatly with an overall 122.9 %. Dongducheon-si created the most new links, with a 1 198 % increase, while Ansan-si lost the most links with a 41 % percent decrease.

Second, from our analysis of network changes from 2000 to 2009, we learned that hubs in the Seoul metropolis have largely been fragmented into smaller ones with a rapid increase in links, in a way that did not conserve and enhance networks. In particular, Euiwang-si, Yangpyeong-gun, and Siheung-si lost the most network areas during this 10-year period, with 14.16, 10.72, and 10.32 % reductions, respectively.

Third, we found that MSPA is useful for understanding spatial changes in green space in terms of hubs and links, two important components of GI. This tool will be helpful to establish conservation planning strategies that are optimized for local and regional contexts. For example, Siheung-si, Bucheon-si, and Hwasung-si, which lost the highest proportion of hub areas based on MSPA, need to pay more attention to enhancing or creating large patches of green space. In cases such as Euiwang-si and Yangpyeong-gun, which lost the most network areas, it is critical to focus on improving the network function of green spaces. This is an important implication because most municipalities, not only in the Seoul metropolis, but also in the rest of the country, tend to rely on stereotypes of green space planning and management such as sidewalk planting, roof-top gardening, and the creation of urban parks, without paying careful attention to the spatial relationship of green spaces in regional contexts.

Fourth, MSPA provides a useful tool to complement the ECVAM for improving GI functions. The method used for the ECVAM relies heavily on overlay analysis to identify areas of high conservation value, paying less attention to horizontal structural relationship among land uses and green spaces. Theoretically, the overlay methods popularized by (McHarg 1969) focus on vertical spatial relationship among the layers of important environmental elements, and have often been criticized for their lack of attention to horizontal spatial relationships among different land uses and open spaces (Forman 1995). Thus, the combined use of MSPA and the ECVAM is theoretically complementary and may provide better insight for identifying or prioritizing areas for conservation in terms of ecological value of the site as well as hub, link, and network functions to enhance GI.

Finally, we note some limitations and constraints that emerged in the use of MSPA. Although MSPA provides a good analytical tool to identify hubs, links, and networks that are lost or gained, it does not provide other important values such as those related to biodiversity, aesthetic, or sociocultural issues. Such values have to be understood through effective planning process, techniques, and stakeholder engagement by planners and local governments. Nonetheless, MSPA will provide important insight to enhance the spatial structure of GI, thus preventing fragmentation of green spaces.

Footnotes

  1. 1.

    In this study, the terms hub and core are used interchangeably with link and bridge, because the MSPA software uses the latter terminology.

Notes

Acknowledgments

This work was supported by a research grant from the Kyung Hee University in 2013 (KHU-20130682).

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Copyright information

© International Consortium of Landscape and Ecological Engineering and Springer Japan 2015

Authors and Affiliations

  1. 1.Department of Landscape ArchitectureKyung Hee UniversityYongin-SiKorea
  2. 2.Department of EnvironmentGyeonggi Research InstituteSuwon-SiKorea

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