Monitoring and modeling of urban sprawl through remote sensing and GIS in Kuala Lumpur, Malaysia
© Boori et al. 2015
Published: 4 November 2015
Kuala Lumpur is currently experiencing a large-scale urban transformation. This study explores a multi-buffer ring method to investigate the spatial and temporal dynamics of urban growth in the Kuala Lumpur metropolitan area by using Geographical Information Systems (GIS) and remote sensing imageries from1989 to 2014.
The first Landsat images from 1989, 2001, and 2014 were classified with maximum likelihood classification to produce land cover maps and identify four types of land cover: urban/built-up, agriculture, forest, and water. Then, the area coverage for all the land use types at different points in time were measured and combined with distance from the city center. Later on, urbanization densities from the city center to the outside for every 1-km distance from 1 to 50 km are calculated.
City density on the distance of 5, 8, 15, 20, and 30 km was very high in the years 1989 and 2014. Temporally, urban expansion shows fast and slow growth stages with high-speed growth shifting to the east side of the city.
In general, the population, traffic conditions, industrialization, and policy are the major factors thatinfluenced the urban expansion
KeywordsUrbanization Landsat Change detection Remote Sensing GIS
Kuala Lumpur is one of the most important historical, cultural, industrial, and commercial cities in Malaysia as well as one of the most important cities in Asia. It has a booming economy with dynamic growth. During the last three decades, growth in population, industry, and economic activities has been strongly concentrated in the Kuala Lumpur metropolitan area. Formed in the 1850s, the Federal Territory of Kuala Lumpur is the national capital of Malaysia. The city has an estimated population of 1.627 million (2010 census). Malaysia’s rich and diverse urban history developed primarily from the second half of the twentieth century when it shifted from a largely rural to a largely urban population. Following independence in 1957 and the formation of Malaysia in 1963, Kuala Lumpur became the administrative center and took center stage in national development. From the mid-1980s, Kuala Lumpur underwent policy shifts, notably privatization and economic liberalization (Malaysia 1991). Major development and supporting policy shifts were undertaken, and within two decades, there were major transformations and dramatic landscape changes in Kuala Lumpur (Saboori et al. 2012).
In the mid-1980s, there was a gradual but obvious change in the nature of the Malaysian economy, as was examined earlier. In 1987, for instance, the agricultural sector share in GDP was exceeded by the share of the manufacturing sector. In 1990, the agricultural share was 19.4 % compared with 26.6 % for the manufacturing sector (Saboori et al. 2012). Thus, there has been a change in the structure of Malaysia from an agricultural base to a manufacturing base since 1987. This, indeed, marks a milestone in the country’s transition towards an industrializing economy (Malaysia 1991). It has often been argued that the process of urbanization and urban growth in the third world has taken place with the expansion of the industrial sector. That is true to a large extent in that, even without a substantial industrial base prior to 1957, Malaysia had experienced a large increase in its urban population. However, with the growth of manufacturing since 1957, the impact on the process of urbanization and urban growth has been most tangible as the employment opportunities generated were located in the urban areas (Salari et al. 2013; Boori and Vozenilek 2014). These issues make the necessity to the study of urbanization in Kuala Lumpur.
The Kuala Lumpur Structure Plan 2020 indicates that the population base is set to increase from 1.4 million to 2.2 million over the next 20 years, and that optimizing limited land resources will be a priority. The new Kuala Lumpur 2020 Strategy Plan was undertaken in the early 2000s to revise the 1984 Kuala Lumpur Structure Plan and to cope with the unprecedented growth and changes in the urban landscape. It is made compulsory to study Kuala Lumpur urban growth. Urbanization has been traditionally understood using the frameworks of demography, urban planning, urban infrastructure, transportation, affordable housing, and urban development (Yin et al. 2011; Boori et al 2014a). Urban growth is characterized by significant gains in urban/built-up areas at the expense of green or open spaces. In this study, the urban/built-up land cover class is defined as the areas dominated by buildings, asphalt, and concrete, including residential, commercial, industrial, and transportation space (Yin et al. 2011; Boori and Vozenilek 2014; Salari et al. 2013). Other types of urban land use, such as golf courses and urban green parks are not included in the urban/built-up class. Urbanization studies are useful to planners who seek to avoid the irreversible and cumulative effects of urban growth and to optimize the allocation of urban services (Dewan and Yamaguchi 2009a; Barnsley and Barr 1996; Boori et al. 2014b, Thinh et al. 2013). Furthermore, such information is indispensable for the assessment and evaluation of sustainable urban and environmental planning strategies (Wu and Hao 2012; Alphan 2003; Jensen and Im 2007). Land use planning is an integral part of the viability and liveability of a metropolitan area because it affects decisions about where and how we live. Over the last few decades, government agencies and local councils in different countries have been continually developing and adopting land use planning strategies to contain and manage urban growth (Yin et al. 2011). For example, “Smart Growth” in the USA and the “compact city” and “intensification” in Europe were devised and adopted to regulate and contain urban sprawl. There are few other studies such as Yagoub (2004) who focused his attention on Al-Ain in the United Arab Emirates. In another study, Al-Awadhi (2007) used available maps, aerial photographs, and satellite images covering the period between 1950 and 2003 to study the city of Muscat in Oman.
In the past few years, significant research has been carried out on the use of satellite data and Geographical Information Systems (GIS) for measuring city growth and sprawl development patterns. Herold et al. (2002, 2003) conducted a comprehensive study on urban land cover dynamics using remote sensing data for the development of spatial (landscape) metrics that were analyzed and interpreted in conjunction with results from the spatial modeling of urban growth. Remote sensing provides spatially consistent data sets that cover large areas with both high spatial detail and high temporal frequency. Dating back to 1960, remote sensing can also provide consistent historical time series data. The importance of remote sensing was emphasized as a “unique view” of the spatial and temporal dynamics of the processes in urban growth and land use change (Dewan and Yamaguchi 2009b; Herold et al. 2003; Boori and Amaro 2010). Satellite remote sensing techniques have therefore been widely used in detecting and monitoring land cover changes at various scales with useful results (e.g., Wu et al. 2013; Stefanov et al. 2001; Wilson et al. 2003; Boori et al. 2010; Muller and Zeller 2002; Weng 2002). Recently, remote sensing has been used in combination with GIS and Global Positioning Systems (GPS) to assess land cover change more effectively than by remote sensing data only (Dewan et al. 2012; Boori et al. 2015a). It has already proved useful in mapping urban areas and as data source for the analysis and modeling of urban growth and land use/land cover change (e.g., Rodriguez-Galiano and Chica-Olmo 2012; Grey et al. 2003; Herold et al. 2003; Wilson et al. 2003).
The objective of this study is to determine the spatial patterns of urban growth over time in Kuala Lumpur. Thus, the growth has been systematically mapped, monitored, and accurately assessed using satellite images in concert with conventional ground data. Mapping provides a “picture” of where growth is occurring, helps to identify the environmental and natural resources threatened by such development, and suggests the likely future directions and patterns of growth. The current study has three specific objectives: (1) investigate the growth of Kuala Lumpur City during 1989–2014 by using remote sensing and GIS; (2) analyze the existence of urban sprawl in Kuala Lumpur; and (3) evaluate current local environmental and natural resource protection and development policies.
The geography of Kuala Lumpur is characterized by the huge Klang Valley. The valley is bordered by the Titiwangsa Mountains in the east, several minor ranges in the north and the south, and the Strait of Malacca in the west. Kuala Lumpur has a tropical rainforest climate, which is warm and sunny, along with abundant rainfall, especially during the northeast monsoon season from October to March.
A selection of multi-sensor and multi-temporal images was used for this study (Ding and Shi 2013; Gamba et al. 2005). The specific satellite images used were Landsat TM for 1989, Landsat ETM+ for 2001, and finally Landsat 8 for 2014, an image captured by different types of sensor. So, three different date satellite images were used with less than 10 % cloud cover. The spatial resolution is 30 m for band numbers 1 to 5 for all sensors, which is used in this research work. All images were from the USGS website, which is free of cost. All images are radiometric and geomantic corrected. As all images have the same spatial resolution for specific bands, one simply goes through maximum likelihood classification. Here, at least 10 years’ time difference was used in two dates’ data (Jensen and Im 2007) so that sufficient land use/cover change information can be investigated (Dewan and Yamaguchi 2008; Anderson et al. 1976). Another reason for selecting these images was their availability.
Description of land use/cover classes
Urban or built-up
Residential, commercial and services, industrial, transportation and roads, mixed urban and other urban
Agricultural and vegetation
Agriculture field, small vegetation, sand soils, landfill sites, areas of active excavation, dry salt flats
Forest or tree/parks
Pine, coniferous trees and citrus orchards, parks, grass belts
River, permanent open water, lakes, ponds and reservoirs, permanent and seasonal wetlands, swamps
Post-classification refinement was used to improve the accuracy of the classification. In addition, because the urban surface is heterogeneous with a complex combination of features (buildings, roads, grass, trees, soil, and water), mixed pixels and the salt-and-pepper effect are common problems when using medium spatial resolution data, such as Landsat (Byomkesh et al. 2012; Lu and Weng 2005; Boori et al. 2014c, Thinh 2003). In this step, a 3 × 3 majority filter was applied to all the classified land covers (Were et al. 2013; Lillesand and Kiefer 1999).
Urban growth rate
Results and discussion
Kuala Lumpur land use/cover classes for 1989, 2001, and 2014
In 2001, density of urban/built-up and agriculture is the same like 1989 till distance of 8 km. Later on, urbanization reduces continuously. In 2001, agriculture has 10 % density from 8- to 50-km distance. Forest has 0 % density on 1- and 50-km distance. Forest has the highest 15 % density on 9 km and 10 % density on 7-, 8-, 10-, 11-, 16-, and 17-km distance. Water class was very less on maximum distance and has 0 % density (Fig. 5).
In 2014, in the city center, urban density was more than 95 % and other classes (agriculture, forest, and water) were less than 5 %. From 1- to 3-km distance, urban/built-up areas reduce till 75 %, but agriculture increases and reaches up to 22 %, and later on reduces or varies around 10 to 15 % in all distances. Urban/built-up density again increases after 3 to 6 km and later on reduces continuously and reaches 0 % density on 50-km distance. Forest is variant on 10 % and vise versa with agriculture class. In all distances, water is close to 0 % density (Fig. 5).
The census data indicate that the main phase of population growth in Kuala Lumpur occurred between 1989 and 2001. The main causes of this growth were a high birth rate in the early years of the Islamic revolution, rural to city migration, and the merging of nearby villages with the city as it developed (Wikipedia 2014). Urban expansion and subsequent landscape changes are governed by geographical and socioeconomic factors such as population growth, policy, and economic development. In most cases, urban expansion and associated land use/cover changes resulted from a combination of these factors (Wikipedia 2014). For example, socioeconomic policy can strongly affect urban expansion and under the changes driven by urban expansion, the land use patterns of the urban outskirts are altered or adjusted in pursuit of high economic returns (orchards or croplands changing to vegetable land). Even though it is difficult to clarify the influence of these factors in these changes, their influence is examined by analyzing the relationships between developed area (including urban and residential area) and socioeconomic factors such as population, industrialization, traffic conditions, and infrastructure (Wikipedia 2014).
Asia is the most populous and arguably the most vulnerable continent in the world because of the high rate of urbanization and lack of facilities. This variability in Asia has and will continue to impact all sectors, from national and economic security to human health, food production, infrastructure, water availability, and ecosystems. Thus, many large Asian cities have become vulnerable, as more countries are unable to feed themselves and fewer countries produce exportable surpluses (Yin et al. 2011). In Asia, where most of the urban growth has concentrated in metropolitan areas, the problem of basic infrastructure and access is becoming more acute. In these urban centers, uneven distribution of incomes, the prevalence of poverty, diminishing farmlands, inefficient distribution systems, and rising expectations have all contributed to increasingly critical problems, particularly as they affect the urban poor (Dewan and Yamaguchi 2009a). A recent World Bank study maintained that as many as 360 million inhabitants of cities in developing nations suffer from chronic calorie deficits (Wu et al. 2013). Five of every six urban families in India typically spend 70 % of their income on basic needs. Even in relatively prosperous Kuala Lumpur, between 45 and 50 % of total household expenditure goes to basic needs or twice the proportion spent in the UK. Consequently, the lower-income groups in cities of Asia are often worse off nutritionally than their rural counterparts (Dewan et al. 2012).
Remote sensing and GIS techniques are used to monitor the dynamic phenomenon of urbanization with the help of satellite images and census data for Kuala Lumpur. In comparing 1989 and 2014 on distances of 5, 8, 15, 20, and 30 km, the city density was increased very high. In 1989 to 2001, on the distances of 8, 15, and 19 km, the city density has been highly increased. From 2001 to 2014, on the distances of 4 and 30 km, density has increased and on 50-km distance, the city density was 0 % in the last three decades. In Kuala Lumpur, urban land expansion has largely been shaped by the terrain such as mountains and lakes and social factors such as population growth, migration, and economic development. All open spaces such as gardens and vegetation cover were destroyed and converted to built-up areas; this study demonstrated that development occurred mainly in available open spaces in the city and remaining lands between the buildings. Conversion of vegetation and orchards to built-up area, however, has been a more recent phenomenon. The empirical investigation of urban residential density patterns presented in this paper offers new insights into the spatial implications of policy changes. Analysis has the potential to aid urban planners and policy practitioners in evaluations of urban consolidation or compact city policies.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Al-Awadhi T (2007) Monitoring and modeling urban expansion using GIS and RS: case study from Muscat, Oman. In: Proceedings of urban remote sensing joint event, 2007. Paris, FranceGoogle Scholar
- Alphan H (2003) Land use change and urbanization in Adana, Turkey. Land Degrad Dev 14(6):575–586View ArticleGoogle Scholar
- Anderson JR, Hardey E, Roach J, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. US geological survey professional paper, Washington, DC. 964., p 28Google Scholar
- Barnsley MJ, Barr SJ (1996) Inferring urban land use from satellite sensor images using Kernel-based spatial reclassification. Photogramm Eng Remote Sens 62:949–958Google Scholar
- Boori MS, Amaro VE (2010) Land use change detection for environmental management: using multi-temporal, satellite data in Apodi Valley of northeastern Brazil. Applied GIS 6(2):1–15Google Scholar
- Boori MS, Ferraro RR (2015) Global Land Cover classification based on microwave polarization and gradient ratio (MPGR). Geo-informatics for ntelligent Transportation 71:17–37. doi:https://doi.org/10.1007/978-3-319-11463-7-2 Google Scholar
- Boori MS, Vozenilek V (2014) Assessing land cover change trajectories in Olomouc, Czech Republic. Int J Environ, Ecol, Geological and Mining Engineering 8(8):540–546Google Scholar
- Boori MS, Amaro VE, Vital H (2010) Coastal ecological sensitivity and risk assessment: a case study of sea level change in Apodi River (Atlantic Ocean), Northeast Brazil. Int J Environ, Earth Science and Engineering 4(11):44–53Google Scholar
- Boori MS, Vozenilek V, Choudhary K (2014a) Land use/cover change and vulnerability evaluation in Olomuc, Czech Republic. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci II 8:77–82. doi:https://doi.org/10.5194/isprsannals-II-8-77-2014
- Boori MS, Vozenilek V, Burian J (2014b) Land-cover disturbances due to tourism in Czech Republic. Advances in Intelligent Systems and Computing 303: 63–72. doi:https://doi.org/10.1007/978-3-319-08156-4-7
- Boori MS, Vozenilek V, Choudhary K (2014c) Land use/cover disturbances due to tourism in Jeseniky Mountain, Czech Republic: a remote sensing and GIS based approach. The Egyptian Journal of Remote Sensing and Space Sciences 17(3):01–10. doi:https://doi.org/10.1016/j.ejrs.2014.12.002
- Boori MS, Vozenilek V, Choudhary K (2015a) Exposer intensity, vulnerability index and landscape change assessment in Olomuc, Czech Republic. Int Arch Photogramm Remote SensSpatial Inf Sci XL-7/W3:771–776. doi:https://doi.org/10.5194/isprsarchives-XL-7-W3-771-2015
- Boori MS, Netzband M, Vozenilek V, Choudhary K (2015b) Urban growth in last three decades in Kuala Lumpur, Malaysia. IEEE: Urban Remote Sensing Event (JURSE), Joint, pp. 01-04. Doi:https://doi.org/10.1109/JURSE.2015.7120536
- Butt MJ, Waqas A, Iqbal MF, Muhammad G, Lodhi MAK. (2012) Assessment of urban sprawl of Islamabad metropolitan area using multi-sensor and multi-temporal satellite data. Arab J Sci Eng 37.1(2012):101–114Google Scholar
- Byomkesh T, Nakagoshi N, Dewan AM (2012) Urbanization and green space dynamics in Greater Dhaka, Bangladesh. Landsc Ecol Eng 8(1):45–58View ArticleGoogle Scholar
- Dewan AM, Yamaguchi Y (2008) Effect of land cover change on flooding: example from Greater Dhaka, Bangladesh. Int J Geoinformatics 4(1):11–20Google Scholar
- Dewan AM, Yamaguchi Y (2009a) Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl Geogr 29(3):390–401View ArticleGoogle Scholar
- Dewan AM, Yamaguchi Y (2009b) Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environ Monit Assess 150(1–4):237–249Google Scholar
- Dewan AM, Yamaguchi Y, Rahman MZ (2012) Dynamics of land use/cover changes and the analysis of landscape fragmentation in Dhaka Metropolitan, Bangladesh. Geo J 77(3):315–330Google Scholar
- Ding H, Shi W (2013) Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City. Int J Remote Sens 34(15):5503–5517View ArticleGoogle Scholar
- Gamba P, Dell'Acqua F, Dasarathy BV (2005) Urban remote sensing using multiple data sets: past, present, and future. Information Fusion 6:319–326View ArticleGoogle Scholar
- Grey WMF, Luckman AJ, Holland D (2003) Mapping urban change in the UK using satellite radar interferometry. Remote Sens Environ 87:16–22View ArticleGoogle Scholar
- Herold M, Clarke KC, Scepan J (2002) Remote sensing and landscape metrics to describe structures and changes in urban landuse. Environment and Planning A 34(8):1443–1458View ArticleGoogle Scholar
- Herold M, Goldstein N, Clarke KC (2003) The spatio-temporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86(3):286–302View ArticleGoogle Scholar
- Jensen JR, Im J (2007) Remote sensing change detection in urban environments. In: Jensen RR, Gatrell JD, McLean D (eds) Geo-spatial technologies in urban environments: policy, practice and pixels, 2nd edn. Springer-Verlag, Heidelberg, pp 7–30View ArticleGoogle Scholar
- Lillesand TM, Kiefer RW (1999) Remote sensing and image interpretation. John Wiley and Sons, New YorkGoogle Scholar
- Lu Q, Weng D (2005) Urban classification using full spectral information of Landsat ETM imagery in Marion County, Indiana. Photogramm Eng Remote Sens 71(11):1275–1284View ArticleGoogle Scholar
- Malaysia (1991) Economic structural change and urbanization.Google Scholar
- Muller D, Zeller M (2002) Land use dynamics in the central highlands of Vietnam: a spatial model combining village survey data with satellite imagery interpretation. Agric Econ 27:333–354View ArticleGoogle Scholar
- Rodriguez-Galiano V, Chica-Olmo M (2012) Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. Appl Geogr 35(1):208–218View ArticleGoogle Scholar
- Saboori B, Sulaiman J, Mohd S (2012) Economic growth and CO2emission in Malaysia: a cointegration analysis of the environmental Kuznets curve. Energy Policy 51:184–191View ArticleGoogle Scholar
- Salari HE, Hengsbach T, Henke C, Kopec J, Kozinski M, Sumpelmann P (2013) Detection and analysis of land use changes and growth of Hanoi using remote sensing methods and Landsat satellite images. Technische University Dortmund 2013:01–12Google Scholar
- Stefanov WL, Ramsey MS, Christensen PR (2001) Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote Sens Environ 77:173–185View ArticleGoogle Scholar
- Thinh NX (2003) Contemporary spatial analysis and simulation of the settlement development of the Dresden city region. 17th International Conference Informatics for Environmental Protection, Cottbus, pp 253–261Google Scholar
- Thinh NX, Arlt G, Heber B, Hennersdorf J, Lehmann I (2013) Pin-pointing sustainable urban land-use structures with the aid of GIS and cluster analysis. Sustainability in the information society 15th symposium informatics for environmental protection, metropolis Verlag Marburg., pp 559–567Google Scholar
- Weng QH (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manage 64:273–284View ArticleGoogle Scholar
- Were KO, Dick OB, Singh BR (2013) Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya. Appl Geogr 41:75–86View ArticleGoogle Scholar
- Wikipedia (2014) http://en.wikipedia.org/wiki/Kuala_Lumpur. Accessed date: 09/2014
- Wilson EH, Hurd JD, Civco DL, Prisloe MP, Arnold C (2003) Development of a geospatial model to quantify, describe and map urban growth. Remote Sens Environ 86:275–285View ArticleGoogle Scholar
- Wu KY, Ye ZY, Qi, ZF, Zhang H. (2013) Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: the case of fast-growing Hangzhou metropolitan area, China. Cities 31:276–284Google Scholar
- Xiao J, Shen Y, Ge J, Tateishi R, Tand C, Liang Y, Huang Z (2006) Evaluation urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc Urban Plan 75:69–80View ArticleGoogle Scholar
- Yagoub M (2004) Monitoring of urban growth of a desert city through remote sensing: Al-Ain, UAE, between 1976 and 2000. Int J Remote Sens 25(6):1063–1076View ArticleGoogle Scholar
- Yin J, Yin Z, Zhong H, Xu S, Hu Z, Wang J, et al. (2011) Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environ Monit Assess 177(1–4):609–621Google Scholar