Intensity and spatial pattern of urban land changes in the megacities of Southeast Asia
Introduction
At the current stage of our geological epoch, the Anthropocene (Crutzen, 2002), we are entering an urban era (Seto and Reenberg, 2014). From a mere 13% (220 million) in 1900, the world urban population has increased to 29% (745 million) in 1950 and 52% (3.56 billion) in 2010 (UN, 2006, UN, 2012). If the current trends continue, the population of the world’s urban dwellers is projected to be at approximately 67% (6.25 billion) by 2050 (UN, 2012). It has also been projected that rapid urbanization will happen in the developing countries, including those in the Southeast Asian region, whose urban population has increased from about 15% (27 million) in 1950 to 44% (262 million) in 2010 and has been projected to increase to approximately 66% (500 million) by 2050 (UN, 2012). This shows that urban landscapes are continuously becoming more and more important for the everyday living of the majority of the Southeast Asian population in particular and the global population in general.
Urbanization has brought improvements to social welfare and economic development, while cities have been playing important roles in our society, e.g., as symbols of creativity, imagination, the power of humanity, the heart of socio-cultural transformations, and engines of economic growth (Wu, 2010, Estoque and Murayama, 2014a). Through political, economic and technical systems, but more importantly through the Earth’s biophysical life-support systems, cities are connected globally (Jansson, 2013, Gómez-Baggethun et al., 2013). Cities are also the cradles of innovation and knowledge creation (Wu, 2010). They also provide critical leadership in the global sustainability agenda (Folke et al., 2011, Gómez-Baggethun et al., 2013). However, urbanization also has a serious impact on the natural environment, both locally and globally (Grimm et al., 2008, Wu, 2010, Seto et al., 2011, Estoque and Murayama, 2014a). Poorly planned urban development can lead to many negative socio-economic and environmental impacts, e.g., poor quality of life and poor urban environments (Bloom et al., 2008, Dahiya, 2012), while urbanization itself is arguably the most drastic form of land transformation that results in irreversible landscape changes (Estoque and Murayama, 2014a).
Knowledge about the intensity and spatial pattern of urban land changes (ULCs), defined in this paper as the changes in land-use/cover from non-built-up to built-up lands, might help in landscape and urban development planning and environmental resources management. The emergence of land change science (Gutman et al., 2004, Turner et al., 2007), also known as land-use science (Aspinall, 2006, Müller and Munroe, 2014) and land-system science (Reenberg, 2009, Verburg et al., 2013), amidst the advances in remote sensing and GIS technologies provides a platform for monitoring landscape changes, including the intensities and spatial patterns of ULCs. In fact, remote sensing and GIS have been widely used as tools for studying spatial structures of urban landscapes and characterizing ULC patterns (e.g., Li and Yeh, 1998, Herold et al., 2003, Azocar et al., 2007, Liu et al., 2010, Estoque and Murayama, 2014b, Gong et al., 2014, Puertas et al., 2014).
In the broad context of land change studies, a technique called land change intensity analysis has been proposed (Aldwaik and Pontius, 2012). This technique is designed to better understand land change patterns. Intensity analysis at the interval level, for instance, examines how the size and speed of change vary across time intervals (Aldwaik and Pontius, 2012). More particularly in the context of ULC studies, a formal theory of urban growth dynamics has also been proposed (Dietzel et al., 2005a). Based on previous work (e.g., Blumenfeld, 1954, Boyce, 1966), the theory suggests that the process of urban growth can be characterized into two phases: diffusion and coalescence (Dietzel et al., 2005a, Dietzel et al., 2005b). Diffusion is a process in which new urban areas are dispersed from the origin point or ‘seed’ location, while coalescence is the union of individual urban patches, or the growing together of the individual urban patches into one form or group (Dietzel et al., 2005b). In connection with this theory, three general spatial patterns of ULC – namely outlying, edge-expansion, and infilling – have also been identified (Liu et al., 2010).
Several ULC studies in Asian cities, including the megacities of Southeast Asia, have been conducted (e.g., Yamashita, 2011, Angel et al., 2012, Bagan and Yamagata, 2014, Estoque et al., 2014), but the concepts of intensity analysis and the above-mentioned urban growth theory have not been applied. Furthermore, the past two decades, i.e., the 1990s and the 2000s, were important periods in the growth of the megacities of Southeast Asia, including Metro Manila, Philippines and Bangkok Metropolitan Region, Thailand. Also, although these two megacities are both prime urban centers in the region, displaying dominance in their respective countries, a comparison of their landscape-change characteristics based on intensity analysis and the urban growth theory has not been done. Thus, by using the concepts of intensity analysis and the diffusion-coalescence urban growth theory, with the aid of GIS and remote sensing tools and techniques, in this study we examine and compare the intensities and spatial patterns of ULCs in Metro Manila and Bangkok Metropolitan Region, during the 1990–2000 and 2000–2010 periods. We also explore some of the possible factors influencing ULC intensity trends and spatial patterns, and discuss some major land use policy-related development plans of the two megacities. In this paper, we define ‘megacity’ as a metropolitan area with a total population of 10 million people or more.
Section snippets
Study areas and data used
Fig. 1 shows the locations of the two study areas, namely Metro Manila, Philippines and Bangkok Metropolitan Region, Thailand, hereafter referred to as Metro Manila and Bangkok MR. Both megacities are situated in the coastal regions of Southeast Asia. More specifically, Metro Manila is located on Luzon Island, east of Manila Bay and west of Laguna de Bay, while Bangkok MR is located in the north of the Gulf of Thailand, in the Chao Phraya River delta (Fig. 1). This study examines the
LUC mapping results
Fig. 5, Fig. 6 present the classified LUC maps of Metro Manila and Bangkok MR, respectively. The accuracy assessment revealed an overall accuracy of 90.62% and 89.69% for the 2001 and 2009 classified LUC maps of Metro Manila. For Bangkok MR, its 1999 classified LUC map had an overall accuracy of 91.62%, while the 2009 classified LUC map had 89.46% overall accuracy. Due to the unavailability of reference imageries for the 1993 and 1988 classified LUC maps for Metro Manila and Bangkok MR,
Intensity and spatial pattern of ULCs
The results show that for Metro Manila, its ULC was more intense during the 1990s (fast) than in the 2000s (slow). For Bangkok MR, it was more intense during the 2000s (medium fast) than during the 1990s (medium slow) (Fig. 7). In both time periods, the results also show that ULCs of Metro Manila have been dominated by both the edge-expansion and infilling patterns, while the ULCs of Bangkok MR have been mainly characterized by an edge-expansion pattern (Fig. 8, Fig. 9; Table 1).
Analogous to
Conclusions
In this study, we have examined and compared the intensities and spatial patterns of ULCs in two megacities of Southeast Asia (Metro Manila and Bangkok MR), employing the concepts of intensity analysis and the diffusion-coalescence urban growth theory, with the aid of GIS and remote sensing tools and techniques, and by using their respective administrative boundaries as units of analysis. The results show that as of 2009, approximately 80% of the total land area of Metro Manila had already been
Acknowledgements
This study was supported by the Japan Society for the Promotion of Science (Postdoctoral Fellowship Grant: ID No. P 13001; and Grant-in-Aid for Scientific Research B: No. 26284129, 2014-16, Representative: Yuji Murayama). The comments and suggestions of the three anonymous reviewers are gratefully acknowledged.
References (68)
- et al.
Intensity analysis to unify measurements of size and stationarity of land changes by interval category, and transition
Landscape Urban Plann.
(2012) - et al.
Urbanization patterns and their impacts on social restructuring of urban space in Chilean mid-cities: the case of Los Angeles, Central Chile
Land Use Policy
(2007) - et al.
Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data
Appl. Geogr.
(2010) - et al.
Geographic object-based image analysis – towards a new paradigm
ISPRS J. Photogramm. Remote Sens.
(2014) Object based image analysis for remote sensing
ISPRS J. Photogramm. Remote Sens.
(2010)- et al.
Ecosystem services in urban areas
Ecol. Econ.
(1999) - et al.
An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution
Remote Sens. Environ.
(2008) Accuracy of classifications of remotely sensed data
Remote Sens. Environ.
(1991)Cities in Asia 2012: demographics, economics, poverty, environment and governance
Cities
(2012)- et al.
Deforestation in Central Africa: estimates at regional: national and landscape levels by advanced processing of systematically-distributed Landsat extracts
Remote Sens. Environ.
(2008)
Examining the potential impact of land use/cover changes on the ecosystem services of Baguio city: the Philippines: a scenario-based analysis
Appl. Geogr.
Landscape pattern and ecosystem service value changes: implications for environmental sustainability planning for the rapidly urbanizing summer capital of the Philippines
Landscape Urban Plann.
Object-oriented change detection for the city of Harare: Zimbabwe
Exp. Syst. Appl.
The intensity change of urban development land: implications for the city master plan of Guangzhou, China
Land Use Policy
The spatiotemporal form of urban growth: measurement: analysis and modeling
Remote Sens. Environ.
Reaching for a sustainable: resilient urban future using the lens of ecosystem services
Ecol. Econ.
Per-pixel vs. object based classification of urban land cover extraction using high spatial resolution imagery
Remote Sens. Environ.
Effect of category aggregation on map comparison
Ecosystem services in practice: challenges to real world implementation of ecosystem services across multiple landscapes: a critical review
Appl. Geogr.
Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago metropolitan area, 2010–2045
Land Use Policy
Land system science: between global challenges and local realities
Curr. Opin. Environ. Sustain.
Atlas of Urban Expansion
Editorial
J. Land Use Sci.
Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells
Environ. Res. Lett.
Changing relationships between land use and environmental characteristics and their consequences for spatially explicit land-use change prediction
J. Land Use Sci.
Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications
Urbanization and the wealth of nations
Science
Tidal wave of metropolitan expansion
J. Am. Inst. Planners Winter
The edge of the metropolis: the wave theory analog approach
Br. Columbia Geographic Ser.
Geology of mankind: the anthropocene
Nature
Spatio-temporal dynamics in California’s Central Valley: empirical links to urban theory
Int. J. Geographic Inf. Sci.
Diffusion and coalescence of the Houston metropolitan area: evidence supporting a new urban theory
Environ. Plann. B
IDRISI Selva Manual (Manual Version 17)
Cited by (122)
Landscape modeling for urban growth characterization and its impact on ecological infrastructure in Delhi-NCR: An approach to achieve SDGs
2023, Physics and Chemistry of the EarthUrbanisation of a growing tropical mega-city during the 21st century — Landscape transformation and vegetation dynamics
2023, Landscape and Urban PlanningA distance-driven urban simulation model (DISUSIM): Accounting for urban morphology at multiple landscape levels
2023, CitiesCitation Excerpt :Findings from previous studies of urban expansion also support the feasibility and generalizability of the philosophy used to design the distance-driven components in the proposed model. Previous studies have shown that the distribution of new urban land from city centers to the periphery displays a wave-like pattern (Blumenfeld, 1954; Dietzel et al., 2016; Estoque & Murayama, 2015; Newling, 1969). Suburban areas are empirically proven to be popular zones of new urban development.