Discussion Paper Series Vol.2007-5
Urban green space gradient analysis and building eco-network in Hanoi, Vietnam
Pham Duc UY and Nobukazu NAKAGOSHI
Graduate School for International Development and Cooperation, Hiroshima University
October 15, 2007
No part of this paper may be reproduced in any form or any means without written permission from author.
Abstract In Hanoi, the capital city of Vietnam, there has recently been a growing awareness about the roles and benefits of greening in urbanized areas. As a result, planners and decision-makers propose a combination of water bodies and green areas, using cultural as well as historic values, in a strategic concept for city planning in Hanoi. This study aims at quantifying the landscape patterns and ecological processes or clearly linking pattern to process to identify green space changes and their driving forces, based on gradient analysis combined with landscape metrics, GIS support, and FRAGSTATS 3.3, from 1996 to 2003. The results of gradient analysis taken four directions show that green spaces have been become more fragmented in this period, especially in the south and west directions. These changes could be caused by land use change, economic growth, population increase, urbanization, and weakness in planning and managing the urban development. From this context, graph theory was also applied to find any eco-networking, by mitigating the fragmentation and enhancing the green space connectivity, as a biodiversity conservation strategy for the city. Analyzing the green network based on graph theory indicates that among six different network scenarios which were produced from several models (Traveling Salesman, Paul Revere, Least Cost to User), network F with 37 links, and gamma (0.07), beta (0.62), cost ratio (0.606), circuitry (0.098) and connectivity (0.398) is the best option for ecological restoration in the Hanoi city. This will be a basis for the 2020 Green Space Planning in Hanoi. Keywords: urban green spaces, gradient analysis, graph theory, connectivity, landscape metrics.
1. Introduction
Urbanization is a vital process and one necessary for human development; and has been occurring much faster in developing countries than in developed countries. However, it also had a negative impact on city dwellers, the environment, and biodiversity. To reduce these impacts, it is found that the conservation and development of green areas are a good solution. Therefore, recently, human beings over the world are paying attention to the roles and functions of them more and more. Previous urban green space studies mention many cases where methods of landscape ecology are especially suitable for the urban process.
Gradient analysis originated from vegetation analysis, and it is found that gradient analysis based on landscape metrics is useful and effective for studying the urbanization process (Luck and Wu 2002; Ma et al. 2005; Yu and Ng 2007; Zhu et al. 2006). Kong and Nakagoshi (2006) find that this method is useful for studying urban green spaces because the results of gradient analysis show changes in the spatio-temporal pattern and give light to the driving forces behind the process as well. Luck and Wu (2002) also show that quantifying the urbanization gradient is an important first step to linking pattern with process in urban ecological studies because they found spatial pattern undoubtedly affects physical, ecological and socioeconomic processes.
How to conserve the pre-urban natural remnants and create urban green spaces will be the most important task in any effort to mitigate the potential impacts of urbanization. Linking gradient analysis with urban dynamics can help detect such spatially explicit urban green space patterns, and improve the ability of planners to integrate ecological considerations in urban planning (Yu and Ng, 2007). Also, applying graph theory, which is a useful tool in researching landscape connectivity especially ecological network research (Bunn et al. 2000; Forman and Godron 1986; Gross and Yellen 1999; Linehan et al. 1995; Rudd et al. 2002; Zhang and Wang 2006), helps to organize green space networks for ecological restoration in terms of reducing fragmentation impact and enhancing the connectivity. Because, in graph theory, like island biogeography theory, gravity model is used to express the interaction of habitat areas, which shows the greater area and number of patches, the closer they are, the higher biodiversity and colonization. Graph theory used here represents through green nodes, their interactions, and links used to connect these nodes. The root purpose of graph theory in ecological restoration is to identify the most optimal network or flow which satisfies both “least cost to builder” and “least cost to user” as the best potential network for conserving biodiversity, especially in the urban context, where number and area of green spaces are usually constrained. Moreover, in biodiversity, landscape connectivity has a special significance for seed dispersal and wildlife movement, which play a decisive role in determining the survival of a metapopulation. Rudd et al. (2002) have showed that connectivity analysis in urban green spaces, based on graph theory presented here, explores the numbers and patterns of corridors required to connect urban green spaces as part of an overall biodiversity conservation strategy.
The objectives of this study are to assess spatio-temporal changes in green spaces, as well as identify their driving forces; and examine the most effective network for biodiversity conservation based on graph theory. In addition, this study will research how to apply graph theory and landscape metrics in organizing green spaces and eco-networking, in order to optimize the benefits of urban green spaces for biodiversity.
2. Methods 2.1 Data and study area Study area: Hanoi - the capital of the Socialist Republic of Vietnam, is the political - economic - cultural - scientific and technological center of the whole country with latitude from 20o53' to 21o23' north, and longitude from 105o44' to 106o02' east. Hanoi is an ancient city with nine urban districts and five rural districts, which has been developing for almost 1,000 years, viz. since establishment in 1010. It is located in the center of the Northern Delta with a population of 3,055,300 (2004), and an area of 920.97 km2 (within downtown: 150 km2). The downtown area of Hanoi city was selected for this study (Figure 1).
Data sources: the primary data was obtained from satellite images including those from the 1996 Spot3 BW taken in September with a resolution of 10 m, band 1; and 2003 Quickbird taken in November with a resolution of 0.7 m, 3 bands. A 2005 topographic map of 1:25000 was used for geo-referencing. In addition, secondary data includes that from the 2020 Hanoi Master Plan, from the Hanoi Department of Planning and Architecture, and other sources. 2.2 Analysis methods
All satellite images were rectified, processed, and geo-referenced to the Universal Transverse Mercator (WGS_1984_UTM_Zone_48N) coordinate system, using the ERDAS image system (Version 8.5, ERDAS, Inc. Atlanta, Georgia 30329-2137, USA). The geo-referencing process was carried out with the necessary information from labeled latitude and longitude and distinct ground control points through field verification with a GPS-model Garmin-12 (Global Positioning System) and then these images were interpreted manually based on the ArcGIS 9 (Arc/Info, release version 9.1, ESRI, Redlands, California 92373-8100, USA) platform. Because the different resolution of the 1996 and 2003 satellite images caused difficulties in interpretation, we used not only the ERDAS system to perform a resolution merge but also the 1992 aerial photos, historical data and reports combined with field surveys and ground-truthing taken in August 2006 as referencing sources. This allowed for referencing, merging and validating of the necessary data to make them more reliable and accurate. Urban green spaces in Hanoi were reclassified into seven types including real green spaces or evergreen (parks, public green spaces, roadside green spaces, riverside green spaces, attached green spaces), and non-real green spaces called open green spaces (agricultural land and cultivated alluvial land) using Vietnamese standards and regulations as shown in Table 1. This allowed vector green maps for 1996 and 2003 to be created, and then converted into raster format with a pixel size of 10m x 10m with the support of Arc/Map Spatial Analysis (version 9.1, ESRI).
To analyze urban green space pattern change, only landscape metrics, which is sensitive to landscape change, was chosen since it includes compositional and configurational metrics including: class area (CA), percent of landscape (PLAND), patch density (PD), largest patch index (LPI), landscape shape index (LSI), mean patch size (MPS), and a weighted mean shape index (AWSI), number of patches (NP), and mean shape index (MSI) by using the raster version of FRAFSTATS 3.3 (McGarigal et
al. 2002) (Table 2). Firstly, an analysis of green space change at class level metrics (CA, PLAND, PD, LPI, LSI, MPS, NP, AWSI) over the entire area was implemented to capture synoptic features. Then, to detect the urban green space gradient change, samples were taken along two transects: west-east and south-north, cutting across the Hanoi downtown area. The center area is identified as the ancient quarter and shown in Figure 2. The west-east and south-north transects were composed of eight and seven 2km x 2km zones respectively. Landscape level metrics were computed using an overlapping moving window across transects with the support of FRAGSTATS 3.3. The window moved over the whole landscape and calculated the selected metrics inside the window. As shown by Kong and Nakagoshi (2006), although this method can cause over-sampling in the center and under-sampling in the periphery, it does not affect the final conclusion. Moreover, it can describe the landscape pattern better; and the moving window analysis supported by FRAGSTATS combined with landscape metrics is a suitable approach for such analysis, Luck and Wu (2002), Yu and Ng (2007), Zhu et al. (2006).
Network analysis for organizing green space systems, with the purpose of ecological restoration based on graph theory, is done in terms of nodes (non-linear elements) and links (linear elements). Nodes in this study refer to green patches or habitat areas with an area of more than 10 hectares. Ten hectares was chosen as a hypothetical minimum area because it can encompass a wider range of species. Hanoi areas are home of a variety of species such as insects (595 species, 395 genera, 101 families and 13 orders), reptilia (33 species, 12 families, 3 orders), mammalian (38 species, 16 families, 6 orders) etc. Especially, there are many threatened species (9 reptiles), (3 insects), (7 small mammals) (Yen, 2005). Almost all these species have a habitat area smaller than 10 hectares, for example the musk shrew (Suncus murinus) and tree shrew (Tupaia glis) with habitat ranges 240-1200 m2 (0.024-0.12 ha), Chinese ferret-badger (Melogale moschata) with habitat ranges 4-9 hectares etc. The green patches left were considered as links acting as corridors or stepping stones. In graph theory and gravity models for analyzing networks, node weight was calculated as follows: Na = {X (ha) / S (ha)} * 10 (Linehan et al. 1995). Where: Na = the node weight for the green space, X = the area of the green space measured in hectares, S = the minimum area required for the indicator species, and multiplying by a factor of 10 normalizes the data. Connectivity analysis is based on the interaction between pairs of nodes in the gravity model as shown by Linehan et al. (1995) Gab = {Na*Nb}/ D2ab (km) and Gab = Gba; Where Gab: the level of interaction between nodes a and b; Na: the weight of node a; Nb: the weight of node b; and Dab is the distance between the centroid of node a and the centroid of node b. Then, network generation was carried out based on the concept of “least cost to user” and “least cost to builder”. There are two major groups of network models: branching and circuit, producing three graphs (Figure 3). Branching networks, for example Paul Revere model-the simplest network, are formed based on connecting all nodes but visiting once, and there are no extraneous segments (Linehan et al. 1995; Rudd et al. 2002). Thus, no circle is created. While circuit models are established based on the form of closed loops, for instances Traveling Salesman-the simplest circuit network where each node is connected only to two other nodes, and Least Cost to User-the most complex circuit network where all nodes are connected each other (Linehan et al. 1995, Rudd et al. 2002). Connectivity analysis, which is tested following the above network models, shows the level of interaction between each of the green spaces in the study area. Next, it is necessary to evaluate the circuit network and branching network
approaches. This evaluation is based on gamma, beta, and cost ratio indices (Forman and Godron 1986; Linehan et al. 1995; Rudd et al. 2002) where:
Gamma = (number of links) / (maximum number of links), Beta = (number of links) / (number of nodes), and the Cost ratio = 1 – (number of links) / (distance of links).
To analyze networks here, the formulae of circuitry and connectivity (Forman and Godron 1986) were also used, where L and V are links and nodes respectively. Circuitry: = L-V +1/2V-5 where zero means no circuitry, and positive values mean more circuitry. Connectivity: γ = L/3(V-2) in that greater values mean more connectivity 3. Results 3.1 Synoptic characteristics of urban green spaces in Hanoi A study of the synoptic characteristics using landscape metrics over the entire study area will provide general information on urban green space patterns in Hanoi. In the year 1996, there were 357 green patches totalling 8449.6 ha; and in the year 2003, there were 669 green patches totalling 7139.4 ha. Comparing these two years, there was a reduction in green space area of 1310.2 ha and an increase in the number of patches by 312. The reduction in the whole area: parks, attached green spaces, and agricultural land was 2.2, 3.4, 2.7 and 3.1% per year. The patches increased at about 12.5% per year. Likewise, the increase rate of patches for P, PGS, AGS, AA, CAL, RiSP, RoSP were 14.3, 23.8, 11.6, 11.1, 5.3, 14.3, 20.95% (Table 3 a&i) respectively. The increase in the fragmentation index, such as in the number of patches (NP) and patch density (PD), indicates that the landscape was highly fragmented providing less connectivity, greater isolation and a higher percentage of edge area in patches. McGarigal et al. (2002), Luck and Wu (2002) have shown that NP and PD are two important metrics which are usually used for assessing the landscape fragmentation. As expressed in Table 3 a&b, agricultural land (AA), attached green spaces (AGS) and parks (P) had a reduction of area of 1170 ha, 247 ha, and 20.5 ha, respectively. This suggests that the urban sprawl process is occurring strongly in the peri-urban areas, and the city became more compact. However, public green spaces (PGS) and roadside green spaces (RoGS) showed a remarkable increase. PLAND (percent of land) of real green spaces (parks, public green spaces, riverside green spaces, roadside green spaces) showed a slight increase from 18% in 1996 to 19% in 2003. However, non-real green spaces or open-green spaces (agricultural land) reduced from 63% to 58% in the period 1996-2003. This reflects the dominance of this green space type. AA exists at the periphery of urban areas. Thus, a decrease of its PLAND suggested an increase in the urban sprawl process. The ranking of PLAND for urban green spaces is AA>CAL>AGS>RoSP>PGS>P>RiSP for both of the years mentioned. The density of all types of green spaces increased from 1996 to 2000 (Table 3c). This index indicated a higher fragmentation of all green space types and could be confirmed by the decrease in mean patch size index (MPS) of all green space types (Table 3f).
The largest patch index (LPI) of AA reduced from 12.8 to 10.34 indicating that AA patches became smaller (Table 3d). An increase in LSI (landscape shape index) showed that the total length of edges within the landscape increased, and shape become more irregular as these green spaces suffered more impact from surrounds. The AWMSI (area weighted mean shape index) of almost all green space types increased also, indicating that the patch shape became more irregular. However, the decrease of AWMSI for RiGS (river green spaces) combined with an increase of CA and PLAND indicated an improvement of this green space type over that of other green spaces. In general, fragmentation of green patches increased from 1996 to 2003. Green patches became smaller and more isolated. 3.2 Gradient analysis of landscape level metrics Gradient analysis of landscape level metrics is shown in Figs. 4a-g & 5a-g. By comparing NP and PD (Figures 4a&b) in the west-east transect, there was a shift in peak position, as well as an increase of NP and PD in going from the center in 1996 to 4km west in 2003. Fluctuation in NP and PD in the east was smaller than that in the west. Both indicators suggest that the dynamic for this variation might be the urbanization process. The above judgment was confirmed by considering Mean Patch Size (MPS), where the lowest values were distributed from 4km west to the center, the closer to the center the higher the MPS. The MPS peaked at a distance 4km to the east. A decline of NP from 1996 to 2003 indicated that green patches became smaller. This is obvious since they are under pressure from human impact more and more. The LPI in 1996 at 4km to 2km west was higher than that of the year 2003 showing that green spaces at this distance became more fragmented and smaller except other distances. This may indicate that some green spaces were preserved as core areas while other green spaces were reducing in area. Combining this result with configurational metrics, we can quantify and understand better the variation in urban green space patterns. As shown in the Figure 4e, LSI peaked at a distance around 4km west and in the transect center, suggesting that at these distances the shape of urban green spaces is the most complex. This seems to reflect different stages in urban development. The center area is the old quarter and is very compact; the neighboring areas belong to the government and French colonial towns; and outside these are new urbanized areas and urban fringes. However, the Mean Shape Index (MSI) was stable along the transect, and over time. While there was a big fluctuation of AWMSI in the year 2003, especially in the center area to 3km west, it then decreased slightly on going eastward. Like the west-east transect, the peak position of NP in the south-north transect varied from near center (1996) to 4km south (2003) and then reduced in both directions. The NP of 2003 was much larger than that of 1996 and its fluctuation in the south was stronger as well (Figure 5a). Together with NP, PD is one of the most important fragmentation indices, the PD of 1996 and 2003 peaked at 4 km south and its change in the north was lower than that during 2003. The LPI for urban green spaces varied irregularly with multiple peaks. At 4km south, the variation of NP and PD was the strongest, but the fluctuation of LPI and MPS was the lowest. For MPS closer to the center, there was a remarkable decrease comparing 2003 to 1996, especially from a distance of 6km southward. This is evidence that these green patches here suffered more pressure from surrounds. A decrease of LSI at 4km south suggested that the shape of
green patches at this distance became more complex, while in the center area there was an improvement. The MSI showed no big changes along the transect and a slight increase toward the center. Compared to the west-east transect, variation in the MSI of the south-east transect was bigger. The AWMSI seems to be similar to the LSI, with the highest values being found at a distance of 2km south where the AWMSI then showing a reduction at 4km south. This was consistent with an increase in NP and PD. The AWMSI then slightly increased again at a distance of 6 km south. However, it decreased toward the center when comparing 2003 and 1996. In general, the variation in landscape metrics of urban green spaces in the south was stronger than that of the north. The peak change was at around 4 km south indicating that land use change at this distance was greatest. Moreover one of the more interesting results, in terms of configurational metrics, was found at the center where the LSI, MSI and AWMSI for urban green spaces declined on comparing 2003 and 1996. This revealed an improvement in green patch shape. 3.3 Network analysis
The result of the node interaction (gravity model) of the 33 existing green patches with an area larger than 10 hectares (Table 4) and the common network types (Figure 3) have produced six different network scenarios from A to F (Figure 6). Specifically, the theory maximum expresses all nodes connected each other including unfeasible links and feasible links. Feasible links to connect these nodes are identified based on the existing land use including corridors (road green ways, etc), open spaces, or other small green spaces, and unfeasible links are virtual links or do not exist in the reality (business areas, busy highways, etc) (Linehan et al., 1995). The network A based on the network model “Least Cost to User”, namely project max, expresses the highest connectivity or connects all green spaces with all feasible links. The network B, based on circle networking, represents the connection of all largest nodes only. The network C was built based on the network model “Paul Revere” or branching network. The Network D was developed following the network type “Traveling Salesman” or circle networking. The network E represents the connection of the closest green patches as its name “Minimum Spanning Tree”. Finally, the network F, based on the “Least Cost to User”, expressed the connection of selected groups of green patches. The gamma, beta and cost ratio were used to evaluate each graph model or network scenario (Table 5). In addition to using gamma, beta and cost ratio scenarios to evaluate networks, the circuitry () and connectivity (γ) indices were also used to analyze network structure. These formulae were adopted by Forman and Godron (1986); Hagget et al. (1977). In analyzing networks, these indices are not as sensitive as the other mentioned indices but they support connectivity analysis more efficiently and clearly (Table 5).
4. Discussion What is the driving force of green space change in Hanoi?
Analyzing green space patterns over the entire landscape, and analyzing gradients based on landscape metrics along two transects, showed that green spaces have changed at different distances and in different directions, from 1996 to 2003. However, analyzing synoptic characteristics of landscapes as traditional ways that the averaging of landscape metrics over an entire study area may lead to incorrect interpretation of the causal dynamics in the region. As shown by Kong and Nakagoshi (2006, p12), “It is difficult to link changes in green space patterns in local areas accurately with the processes that produced these changes”. This difficulty can be solved by using gradient analysis or the “moving window” method combined with spatially explicit landscape metrics. This method can provide adequate quantitative information about the structure and pattern of urban green spaces. Therefore, a better link between pattern and process, and a more effective capture of the dynamic changes can result.
Generally, there are two main driving forces causing the urbanization process: population and economy (Ma et al. 2005). In addition, Luck and Wu (2002) recognize urbanization as one of the most important driving forces for land use and land cover change. When studying the spatio-temporal green space change in Jinan City (China), Kong and Nakagoshi (2006) found that the driving forces are policy affecting the development and management of urban green spaces, and urbanization. Moreover, the urban sprawl direction was influenced by green space changes and vice versa urbanization caused changes in the spatial pattern of green areas. It is obvious that in different conditions, the driving forces will be different. In Hanoi, through an analysis of the spatio-temporal change of green space pattern combined with economic, social data, and development policy, we found that there were several reasons for this change. Firstly, the population increase in the downtown area in the period 1995-2005 was from 1.275 million to 2 million with a rate of increase of 4.6%. The rural population decreased from 52% (1996) to 42.4% (1999), and the agricultural labor force and non-agricultural labor force in this period were 32, 68% and 30.2, 69.8 % respectively. This is mainly rural-urban migration because the birth rate is around 1.3%. Especially, the establishment of new urban districts including Thanhxuan, Tayho and Caugiay in this period from rural districts at the south and west of Hanoi was a main factor which contributed to an increase of the urban population (www.hanoi.gov.vn). Moreover, analyzing land-use showed that the rapid reduction in area of agricultural land (1200 ha) and attached green spaces (200 ha), especially in the south and west as indicated by gradient analysis reflects that the development of the city not only occurred at the fringes because of the urban-sprawl process, but also in the city itself making it more compact in terms of population density. Another important driving force is the growth of economy. The year 1995 marked a turning point for Vietnamese economy in general and Hanoi in particular when Vietnam and the United States of America normalized the relationship. This led to the first wave of foreign investment in this period. As a result, the economic growth of Hanoi city was over 10% per year. The economic mechanism for industry, services, and agriculture changed from 38, 58.2 and 3.8% (1996) to 41.5, 55.5 and 3% (2005). New urbanized areas, roads, business areas and other infrastructure areas were built to meet the needs of urbanization, whereby mainly agricultural land (AA) was converted into built-up areas. One of the most important driving forces was
the lack of suitable planning, and weakness in controlling and managing the development of Hanoi. This was evident through the orientation of the Hanoi Master Plan being mainly westward and northward (Decision 108/1998/QĐ-TTg), yet our analysis showed that development mainly occurred in the westward and southward directions. In other words, the planning policy for non-west regions in Hanoi up to 2020 prioritizes development to the north, but not the south (Decision 108/1998/QĐ-TTg). However variation in urban green spaces occurred mainly in the south, not the north, suggesting that changes in land use in the south have been stronger since 1996. This might result from the urbanization process. Finally, we could say that policy on the orientation of the Hanoi development was not strongly effective and well controlled in that direction. Besides, a plan for developing urban green spaces in the period 2001-2005 increased from 4 m2 to 5-6 m2 but this analysis has showed that some types of urban green spaces (public green space, riverside green space and roadside green space) had a slight increase around 100 ha while other green spaces (attached green space, park) decreased around 250 ha. Thus, it is necessary to inspect and evaluate the effectiveness and efficiency of this plan.
In brief, the five main reasons leading to changes in green spaces from 1996 to 2003 were land use change, economic growth, population increase, urbanization, and weakness in planning, controlling and managing the urban development. These changes are reflected in a reduction not only in area and quality of green spaces, but also that green patches were more irregularly shaped and unevenly distributed. Changes in urban green space patterns will affect ecological processes, leading to a decline in eco-service quality and in making the city less sustainable. To improve this circumstance, it is imperative to conserve and build a green network, which optimizes the benefits of green spaces as much as possible. Which network is the best?
Graph theory and the gravity model give us useful methods in analyzing networks and are especially suitable for planning eco-networking because they are unbiased or non-discriminatory methods in determining different levels of interaction between nodes. Connectivity indices are found to be useful measures for describing the degree to which green spaces are connected (Linehan et al. 1995). A good network is one that satisfies all criteria (gamma, beta and cost ratio), is appropriate for the site conditions, and takes into account the feasibility of the network. Habitat connectivity is analyzed by linking high-quality habitat patches along least-cost paths though this parameterized cost surface (O’Brien et al. 2006).
Study results showed that networks based on theory max and project max models (network A) were ideal for conservation because they had the greatest consecutiveness (1 for theory max and 0.115 for project max). They also had the most complicated networks with a beta index of 16 and 1.85 respectively, but their existence is not real and feasible. As demand for land to develop grows with the population, cities can usually only afford to preserve a few large green spaces (Rudd et al. 2002). Network B was built based on major nodes (10 nodes). These major nodes were connected to make a single circle. Network B had the lowest raw and adjusted gamma indices with 0.019 and 0.164 which expressed the lowest connectiveness within the network so that it had the lowest value in maintaining biodiversity among the six scenarios although the cost
ratio was the greatest at 0.73. If we only considered cost ratio index, network B would be the best. However, a good eco-network needs to satisfy all gamma, beta and cost ratio indices. Therefore, using only one or a few indices could lead to a misleading network interpretation (Linehan et al. 1995; Rudd et al. 2002). Networks C and E were also not suitable for building an eco-network because their beta indices were under 1, indicating that the networks were not complete circuits and all nodes were not linked together. These factors act to reduce the accessibility or ease of movement and dispersal of species between nodes. The beta index of networks C and E was 0.525, which was lower than that of networks D (0.54) and F (0.62). For network E, the cost ratio index was the lowest (0.48). This means that “cost to user” (wildlife) was highest. The results of analysis of networks B, C and D were consistent, with node structure analysis, circuitry, and connectivity indices of: 0.05; 0; 0 and 0.107; 0.343; and 0343 respectively.
Networks D and F seem to be more attainable in terms of this urban context because their network structure (beta indices of 1 and 1.15 respectively) had enough complexity to maintain biodiversity in urban areas. Based on assessment using gamma, beta, and cost-to-user criteria, and on using circuitry and connectivity sub-indices, network F was the best because it had the greatest raw gamma and adjusted gamma values. Networks A and B which had similar or higher such values were excluded because of lack of feasibility. The beta index of network F was 1.12 with four loops so that its network structure was complicated and ecologically better than that of network D (1). The “cost to user” value of network F (0.62) was also greater than that of network D (0.58), or 4 %. However, the link-use efficiency for network D was 0.42, i.e. higher than that of F’s at 0.37. Linehan et al. (1995) showed that the effects of the various links can be systematically tested in terms of link efficiency, as measured by the amount of connectivity achieved per unit distance. From this perspective, Network F was the best option or best model of the six network scenarios. Thus, network F needs to be maintained as the primary greenbelt or inner greenbelt in Hanoi’s planning in the near future. Network F not only resists the sprawl process of urbanization but also meets the requirements of eco-network building and biodiversity. The next best alternative network was network D. Some researchers argue that building eco-networks by using graph theory applied to a similar habitat such as paddy fields, should be connected with that, i.e. paddy field, ecology but in fact in this study it is not necessary because of two reasons. Firstly, there are many species that live in multiple habitat areas even some species only exist in a specific habitat, the different habitats are considered as open spaces acting as corridors or stepping stones for the survival of species. Moreover, this network is to cover a variety of species. Secondly, they have potential to develop into equivalent habitats such as parks, public green spaces or protected areas.
The results of this analysis give some implications for the 2020 Urban Green Space Planning in Hanoi (Hanoi Government, 2005). In this plan, Hanoi city will allocate the per capita 18 m2 for green spaces and sports-fields. In that, improvements are planned for green spaces, parks, and flower gardens together with developing green spaces near big lakes as heart green spaces of city. The creation of newly planted rows of trees and shrubbery is for ecological protection of landscapes between the banks of the rivers (Figure 7). In addition, at the regional scale, a greenbelt will be created with a width of 1-4 km for natural and ecological preservation. From the results of this analysis, almost all green nodes of network F are consistent with the 2020 planned green spaces. However, many of them are not connected or still isolated. Thus, network F will provide a basis for enhancing the connectivity of the planned green spaces by maintaining and
creating suitable corridors. This is appropriate with the Vietnamese standard (TCXDVN 362: 2005) which requires that the city has to allocate the per capita roadside green space about 1.7-2 m2. Moreover, in the 2020 Green Space Planning, it mainly focuses on the roles and functions of parks and public green spaces but ignores those of small green spaces such as attached green spaces etc which also play an important role in urban green structure. This structure will create a green network ecologically for the city more effective than the sum of the individual green spaces.
In conclusion, gradient analysis with the support of FRAGTATS 3.3 is useful and effective for quantifying spatial pattern and ecological processes. The results of this study showed spatial-temporal changes of green spaces in Hanoi from 1996 to 2003. Green spaces became smaller and smaller, and more fragmented. This causes not only the loss of biodiversity but also a reduction in the quality of ecological services and the quality of life of urban dwellers in this period. Thus, it is necessary to build and preserve green spaces. Graph theory has been proved to be a useful tool in studying the landscape connectivity, especially in studying ecological networks. Linehan et al. (1995) stated that graph theory was well suited to landscape ecology and landscape planning on a theoretical and scientific basis, and that graph theory helped systematize greenway planning which in turn, helped give it additional credence as an important land-use strategy. Based on the analysis of graph theory, we have selected one network as the best option for building an urban ecological network and preserving green spaces in the greater Hanoi city region. This is a potential network for conserving biodiversity and is fundamental in planning comprehensive urban green spaces in Hanoi. Acknowledgments
We would like to thank the Hanoi government offices for offering data on the Hanoi Master Plan; Prof Dr. Xiuzhen Li, Institute of Applied Ecology, Chinese Academy of Sciences, Shengyan for giving her valuable suggestions; and many thanks to all members of the Nakagoshi laboratory for giving their comments and encouragement and Mr Nick Walker for English checking.
References Bunn AG, Urban DL, and Keitt TH (2000) Landscape connectivity: A conservation application of graph theory. J Environ Manage 59: 265-278. Circular No 20/2005/TT-BXD (2005) Instruction on managing plants in urban areas (in Vietnamese). Ministry of Construction, Vietnam. Decision 108/1998/QĐ-TTg (1998) Decision of Prime Minister about the approval on adjustments of Hanoi Master Plan up to 2020 (in Vietnamese). Hanoi, Vietnam. Forman RTT, Godron M (1986) Landscape ecology. Wiley, New York. USA. Gross J, and Yellen J (1999) Graph theory and its application. CRC Press, Florida. USA. Hagget P, Cliff AD, Fry A (1977) Locational Analysis in Human Geography, 2nd edn. Wiley, New York, p454. Hanoi Government (2005) The 2020 Hanoi Master Plan. This is available at http://www.hanoi.gov.vn/hnportal/render.userLayoutRootNode.uP. Kong F and Nakagoshi N (2006) Spatial and temporal gradient analysis of urban green spaces in Jinan, China. Landsc Urban Plan 78: 147-164. Ma K, Zhou L, Niu S, Nakagoshi N (2005) Beijing urbanization in the past 18 years. Journal of International Development and Cooperation, Hiroshima University 11: 87-96. McGarigal K, Ene E, Holmes C (2002) FRAGSTATS (version 3): FRAGSTATS Metrics. University of Massachusetts-produced program. Available at the following website: http://www.umass.edu/landeco/research/fragstats/documents/Metrics/Metrics%20TOC.htm Linehan J, Meir G, and John F (1995) Greenway planning: developing a landscape ecological network approach. Landsc Urban Plan 33: 179-193. Luck M and Wu J (2002) A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc Ecol 17: 327-339. O’Brien D, Micheline M, Andrew F, Fortin M-J (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: An application of graph theory. Biol Conserv 130: 70-83. Rudd H, Jamie V, Valentin S (2002) Importance of backyard habitat in a comprehensive biodiversity conservation strategy: A connectivity analysis of urban green spaces. Restor Ecol 10: 368-375. TCXDVN 362: 2005 (2005) Vietnamese construction standard: Greenery planning for public utilities in urban areas-Design standards (in Vietnamese). Yu XJ and Ng CN (2007) Spatial and temporal dynamics of urban sprawl along two urban-rural transects: A case study of Guangzhou, China. Landsc Urban Plan 79: 69-109. Yen MD (2005) Inventory and evaluation of biodiversity in Hanoi – Enhancing awareness of community and proposing the solutions for conservation(In Vietnamese). Summary report, Hanoi Biological Association. Zhang L and Wang H (2006) Planning an ecological network of Xiamen Island (China) using landscape metrics and network analysis. Landsc Urban Plan 78: 449-456. Zhu M, Xu J, Jiang N, Li J, Fan Y (2006) Impacts of road corridors on urban landscape pattern: a gradient analysis with changing grain size in Shanghai, China. Landsc Ecol 21: 723-734.
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0 2.5 51.25 KmLegendWater body
Green spaces and open green spaces
Built-up area-
Figure 1. Hanoi (left down) and the studied urban area of Hanoi, Vietnam.
Table 1. Reclassification of urban green spaces
Vietnamese standards/regulations
Circular
No 20,
2005
TCXDVN 362: 2005 Reclassification Abbreviation Description
Park (urban forest > 50 ha, central park
15ha < and ≤ 50 ha, multiple functional
park >10 ha and ≤ 15 ha, small park
Parks P
Big area, open to public with natural or planted
vegetation and higher bio-diversity
Public green space
(1-6 ha) Public green spaces PGS
Small area, open to public and providing recreational
areas such as flower gardens, squares, historical sites and
others
Roadside green spaces RoSP
Public
use
plants
Roadside green space
(linear element) Riverside green spaces RiSP
Trees planted beside transportation routes, creeks, canals
to prevent dust, noise, add beauty and create corridors
Limited
use
plants
Not applicable Attached green spaces AGS
Privately owned trees, planted in schools, hospitals,
factories, temples and other organizations
Cultivated alluvial
land CAL
Outside of river banks, inundation areas, places
sometimes cultivated in the year, grassland, and aquatic
plants.
Special
use
plants
Not applicable
Agricultural land AA Paddy fields, orchards and other cultivated activities
Table 2. Definitions of landscape metrics (adopted from McGarigal et al., 2002)
Landscape metrics
Abbreviation Description Units Range
Compositional measures Class area CA CA equals to the sum of the areas (m2) of all patches of the corresponding patch
type divided by 10.000 (to convert to hectares). Hectares CA>0, no limits
Number of patches
NP Total number of patches in the landscape or the corresponding patch type (class).
None NP≥ 1 without limit
Percent of landscape
PLAND The proportion of total area occupied by a particular patch type; a measure of landscape composition and dominance of patch type.
Percent 0<PLAND≤ 100
Patch density
PD The number of patches per 100 hectares. Number per 100 hectares
> 0 without limit
Mean patch size
MPS The area occupied by a particular patch type divided by the number of patches of that type.
Hectares MSP>0 without limit
Largest patch index
LPI LPI equals the area (m2) of the largest patch of the corresponding patch type divided by total landscape area (m2), multiplied by 100 (to convert to a percentage).
Percent 0<LPI≤ 100
Configurational measures Landscape shape index
LSI The total length of edge involving the corresponding class divided by the maximum length of class edge for a maximally aggregated class, a measure of class aggregation or clumpiness.
None LSI ≥ 1 without limit
Mean shape index
MSI MSI equals to the sum of the patch perimeter (m) divided by the square root of patch area (m2) for each patch of the corresponding patch type, divided by the number of patches of the same type or MSI equals to the average shape index of patches of the corresponding patch type.
None MSI ≥ 1 without limit
Area weighted mean shape index
AWMSI AWMSI equals the sum, across all patches of the corresponding patch type, of each patch perimeter (m) divided the square root of patch area (m2), multiplied by the patch area (m2), divided by total class area or AWMSI equals to the average shape index of patch of the corresponding patch type, weighted by each area.
None AWMSI ≥ 1 without limit
_̂_̂
1
2
3
4
5
6
7
8
9
1011
12
13 14
1516
17 18
19
20
21
22
23
24
25
26
2728
29
30
31
32
33
Figure 2. The 1996 and 2003 green transects for gradient analysis
From 1 to 33: Green nodes for graph theory
Legend_̂ Center
<all other values>
Agricultural land
Attached green space
Cultivated alluvial land
Riverside green space
Public green space
Roadside green space
Park .1996
0 2.5 51.25 Kilometers
Table 3. Class level metrics of green spaces
Year Type P PGS AGS AA CAL RiSP RoSP (a) Class area (CA)
1996 86.66 50 1285.95 5326.8 1612.2 27.7 60.31 2003 66.2 89 1039.4 4156.8 1638.2 30 119.8
(b) Percent of landscape (PLAND) 1996 1.03 0.59 15.22 63.04 19.08 0.33 0.71 2003 0.93 1.25 14.57 58.27 22.97 0.34 1.68
(c) Patch density (PD) 1996 0.07 0.07 3.24 0.32 0.1 0.07 0.35 2003 0.51 0.22 6.95 0.67 0.15 0.17 1.04
(d) Largest patch index (LPI) 1996 0.4 0.4 2.57 12.8 8.71 0.13 0.18 2003 0.51 0.46 2.7 10.54 11.15 0.11 0.63
(e) Landscape shape index (LSI) 1996 6.65 6.28 28.4 11.8 6.04 8.36 14 2003 9.12 9.38 38.66 13.7 7.45 9.65 24.65
(f) Mean patch size (MPS) 1996 14.44 8.33 4.69 197.29 201.52 4.6 2.01 2003 5.51 5.56 2.1 86.6 148.92 2 1.62
(g) Area weighted mean shape index (AWMSI)
1996 3.53 3.56 2.85 2.9 2.4 4.5 3.9 2003 4.38 3.77 3.71 2.91 2.95 3.79 7.34
(i) Number of Patches 1996 6 6 274 27 8 6 30 2003 12 16 496 48 11 12 74
Paul reserve Traveling salesman Least cost to user
Where Node:
and Link:
Figure 3. Examples of branching and circuit networks
Number of Patches (NP)
0
20
40
60
80
-8 -6 -4 -2 0 2 4 6
Patch density (PD)
0
30
60
90
120
-8 -6 -4 -2 0 2 4 6
Mean Patch Size (MPS) (ha)
0
30
60
90
-8 -6 -4 -2 0 2 4 6
Largest Patch Index (LPI)
0
20
40
60
80
100
-8 -6 -4 -2 0 2 4 6
Landscape Shape Index (LSI)
0
4
8
12
16
-8 -6 -4 -2 0 2 4 6
Mean Shape Index (MSI)
0
1
2
3
4
5
-8 -6 -4 -2 0 2 4 6
Area w eighted mean shape index (AWSI)
0
1
2
3
4
5
6
7
-8 -6 -4 -2 0 2 4 6
Distance to center (km)
19962003
Figure 4. Gradient changes in landscape level metrics of Hanoi urban green spaces,
from west to east in the period 1996-2003.
f
a
e
b
dc
g
West East
Number of patches (NP)
0
10
20
30
40
50
60
-8 -6 -4 -2 0 2 4
Patch Density (PD)
0
50
100
150
200
-8 -6 -4 -2 0 2 4
Largest Patch Index (LPI)
0
25
50
75
100
-8 -6 -4 -2 0 2 4
Mean Patch Size (MPS) (ha)
0
10
20
30
40
50
-8 -6 -4 -2 0 2 4
Landscape Shape Index (LSI)
0
4
8
12
16
-8 -6 -4 -2 0 2 4
Mean Shape Index (MSI)
0
0.5
1
1.5
2
2.5
-8 -6 -4 -2 0 2 4
Area w eighted mean shape index (AWSI)
0
2
4
6
-8 -6 -4 -2 0 2 4Distance to center (km)
1996
2003
Figure 5. Gradient changes in landscape level metrics of Hanoi urban green spaces,
from south to north in the period 1996-2003.
a b
c d
e f
g
South North
Table 4. Node interaction based on gravity model Node/ node
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
1. 23320.5 3280.6 2892.2 203.1 146.5 122 1368.1 101.2 49 55 110.5 252 15 29.8 23.8 112.3 116.9 344.2 184.2 1407.1 171.5 1111.4 1959.9 430 57.3 1066.2 806.9 1485.9 9039.6 1040.4 1072.6 1320
2. 0000 14080.9 8013.5 1469.2 520.8 222.6 2408.4 161.6 79.5 84.9 162.9 448.5 23 45.3 35.4 164.7 151.5 400.9 142.6 1192 92.7 826.3 1562.1 300.2 39.3 713.6 473.8 783.9 3063.6 543.7 332.9 256.3
3. 0000 7191.3 126.9 104.6 211.5 1601.9 108.2 52.7 53.6 101.3 287.9 14.5 29 22.4 101.7 94.2 273.3 90.1 609.4 39.4 333.2 622.2 119.2 22.1 250.7 164.6 267 845.8 177.6 86.3 57.7
4. 0000 1053 994.2 1897 6560 372 168.2 167.4 279.2 933.9 42.3 84.7 62.5 275.5 240.3 606.6 225.5 1194.8 55.7 455.2 839.8 156.3 20.9 332 205.7 330.4 930.5 225.3 97.9 62.2
5. 0000 78.5 21.2 141.4 8.7 4.2 4.4 7.9 23.4 1.2 2.3 1.8 8 7.2 18 6.9 41.6 2.5 21.2 38.7 8.6 1 16 12.3 19.2 38.8 13 6.6 4.6
6. 0000 20.1 141.8 9 4.3 4.5 8.1 24 1.2 2.3 1.8 6.2 7.3 18.2 7 38.8 2.5 18.7 38.3 6 1 15.7 9.8 27.5 47.9 10.9 5 3.2
7. 0000 2071 80.5 36.2 13.8 19.3 189.6 7.2 14.5 10 42.1 34 77.2 31 153.6 6.9 45.5 64.3 11 1.4 22.1 22.3 19.7 53.3 13.4 5.2 3.3
8. 0000 11061 3246 1954.2 1735.3 16130 366.6 733.2 428 1651.6 1131 1374 552.1 2733 122.1 6.5 1145 195 25.8 393.8 192.5 241.2 601 161.8 59 35.2
9. 0000 1819.5 469.8 92.9 2119.9 602.2 40.9 51.4 181.8 120.6 249 104.3 457 18.6 124.7 166.6 17.7 2.5 34.3 15.6 25 46.7 12.9 4.6 2.7
10. 0000 847.5 247 543.8 214.7 29 17.2 70.2 63.3 107.5 44.2 203 8.5 57 77.7 8.5 1.2 16.2 7.4 9.9 22.8 6.5 2.3 1.3
11. 0000 546.7 612.8 12.1 24.3 15.5 65.3 61.2 111.1 44.7 251.4 9.3 61.1 86.7 9.6 1.4 18.3 7.6 11.3 25.7 7.4 197 1
12. 0000 529.8 19.4 33 23.2 102.4 101.3 205.4 80 472.2 26.3 177 183.1 21.3 3 19.6 15.3 24.9 55 16.1 5.5 3
13. 0000 609.7 1219.3 441.6 1461.9 726.6 1490.3 438.8 1479 69.2 541.6 671.9 76 9.9 134.6 53.9 68.4 129.4 37 13.1 7.4
14. 0000 116.7 187.3 151 111.3 147.5 48.2 166.7 6.5 41.7 50.4 5.6 0.74 11.6 4.4 5.6 8.4 2.8 0.72 0.4
15. 0000 319.1 940.3 260.5 284.7 152.4 463 15.6 105.4 115.7 13 1.7 27.7 10.6 13 22.7 6.6 1.4 0.8
16. 0000 679.7 369 422 149.5 430.1 65.4 97.2 106.2 11.6 1.6 25.4 7.8 11.8 15.9 6.2 1.2 0.7
17. 0000 4038.7 2516.7 1615.7 3575.5 109.8 727.5 717.2 73.5 10.2 169.6 62 75 107 35.5 9.0 4.4
18. 0000 9229.4 6223 6824.2 175.3 1196.4 1049 104.4 13.5 250.8 90.3 94.5 141.7 48.6 9.5 6
19. 0000 8227 21103 542 3700 3244 323 41.7 775.5 278 193.6 438.3 150.4 29.5 18.5
20. 0000 20478.8 358 2182.7 1696.7 149.9 19.5 371.6 127.2 142.6 194.4 63.4 12 7.8
21. 0000 12521 75934 28125 1950.3 296.3 5942.5 1708.7 3241 2270 734.2 125 88.4
22. 0000 4513.8 5015.5 230.4 31.7 353.2 101.6 106 134.9 43.6 7.4 5.3
23. 0000 47870.8 1773.3 255.9 6559.7 1475.7 1277 1439.2 452.2 72.1 52.8
24. 0000 13409 2103 11065.4 2490.8 2211.1 2530.3 816.9 131.7 95.9
25. 0000 1055 2744.5 776.2 602.4 598.3 188.4 27.3 21.5
26. 0000 1105.2 134.5 90 88 27.3 3.8 3
27. 0000 6928.7 2624.3 2022.9 601 70.4 61.6
28. 0000 5069.5 2211.5 595.6 53.3 54.8
29. 0000 5035.7 2544 132 104.9
30. 0000 1216 2548.2 2025
31. 0000 262.5 57
32. 0000 110.4
33. 00.00
Note: Because Gab=Gba so that this table is symmetrical and it is unnecessary to calculate both values (Linehan et al., 1995)
Table 5. Evaluating networks
Name Network model Nodes Links Total distance (km)
Gamma Raw Adjusted
Beta Cost ratio
Circuitry index
Connectivity index
Theory max 33 528 Not applicable
1 16 Not appli-cable
Not appli-cable
A Project max 33 61 149 0.115 1.85 0.60 1 0.457 0.656 B Major nodes 10 10 44 0.019 1 0.73 0.164 0.05 0.107 C Paul Revere 33 32 76.8 0.06 0.97 0.58 0.525 0 0.343 D Traveling salesman 33 33 78.9 0.0625 1 0.58 0.54 0.015 0.352 E Minimum spanning
tree (MST) 33 32 61.5 0.06 0.97 0.48 0.525 0 0.343
F Small circuit group 33 37 99.3 0.07 1.12 0.62 0.606 0.098 0.398
Legend
<all other values>
Agricultural land
Attached green space
Cultivated alluvial land
Riverside green space
Public green space
Roadside green space
Park
Link 0 2.5 51.25 Kilometers
.
Figure 6. The different scenarios from A to F based on graph theory
A B
C D
E F
Figure 7. The 2020 Hanoi Master Plan (sources: Hanoi Government, 2005)
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Development and Cooperation, Hiroshima University) “Social Capacity for Environmental Management for Developing Sustainable City” 2007/2/8 (Japanese, English abstract provided).
Vol.2006-9 Satoru KOMATSU, Shunji MATSUOKA, Katsuya TANAKA (Graduate School for
International Development and Cooperation, Hiroshima University) “Estimating Willingness to Pay (WTP) for Rural Water Supply Improvements for Pastureland Conservation in Mongolia” 2007/2/20.
Vol.2006-10 Atsushi OHNO (Graduate School for International Development and Cooperation,
Hiroshima University) “The National Development Policy and International Development Approaches: the case of Indonesia” 2007/2/21 (Japanese, English abstract provided).
Vol.2006-11 Teppei YAMASHITA (Graduate School for International Development and Cooperation,
Hiroshima University) “Correlation between Efficiency of Energy Consumption and Governance Index in East Asian Countries: Market and Government Failure related to Environmental Policy” 2007/2/22 (Japanese, English abstract provided).
Vol.2007-1 Daisaku GOTO (Graduate School for International Development and Cooperation,
Hiroshima University) “Rewards versus Intellectual Property Rights in Green Innovation: Incentive Design for Capacity Development” 2007/5/23.
Vol.2007-2 Metin SENBIL, Akimasa FUJIWARA and Junyi ZHANG (Graduate School for
International Development and Cooperation, Hiroshima University) “What can we do to decrease private car ownership and its usage in developing countries: A Capacity Development Approach for Jabotabek MA (Indonesia)” 2007/6/6.
Vol.2007-3 Teppei YAMASHITA (Graduate School for International Development and Cooperation,
Hiroshima University) “Social Capacity and Air Quality Management in Indonesia: The Case of AQMS (Air Quality Monitoring System)” 2007/7/31 (Japanese, English abstract provided).
Vol.2007-4 Junyi ZHANG and Akimasa FUJIWARA (Graduate School for International
Development and Cooperation, Hiroshima University) “Development of the DPSIR+C Framework for Measuring the Social Capacity of Environmental Management” 2007/10/4.
