Change

Chapter 5

Monitoring and Prediction of Land-Use and Land-Cover (LULC) Change Robert J. Corner, Ashraf M. Dewan, and Salit Chakma

Abstract This chapter looks at the use of a Markov chain–cellular automata method to model and then predict land-use change in Dhaka. Initially land-use/ land-cover maps for three separate time periods were derived from satellite images and evaluated against ground truth. The Markov chain method was then used to establish transition probability matrices between land-cover categories for the time periods represented. The use of cellular automata in this work enables neighbourhood interactions to be accounted for. After an initial calibration run, the combined method is then used to predict land use and land cover in 2022 and 2033. Keywords LULC dynamics • Modelling • Landsat TM • Markov-Cellular automata • Built-up areas • Geospatial techniques

5.1

Introduction

Increasing anthropogenic activities around the world are causing large-scale modification of the Earth’s land surface which has profound impact on the functioning of global systems (Lambin et al. 2001). One of the most visible human modifications of terrestrial ecosystem is the alteration of land use and land cover (LULC) which significantly affects the local, regional and global environment (Yu et al. 2011; Mitsuda and Ito 2011; Mahmood et al. 2010; Weng 2001). The effects of this include soil degradation (Islam and Weil 2000; Tolba et al. 1992), loss of biodiversity (Yamamura et al. 2009; de Koning et al. 2007), rampant urban sprawl (Wu and Zhang 2012; Rahman et al. 2011; Yuan 2010; Batta et al. 2010; Batta 2009;

R.J. Corner (*) • A.M. Dewan • S. Chakma Department of Spatial Sciences, Curtin University, Kent Street, Bentley, Perth 6102, WA, Australia e-mail: [email protected]; [email protected]; [email protected] A. Dewan and R. Corner (eds.), Dhaka Megacity: Geospatial Perspectives on Urbanisation, Environment and Health, Springer Geography, DOI 10.1007/978-94-007-6735-5_5, © Springer Science+Business Media Dordrecht 2014

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Taubenbo¨ck et al. 2009; Deng et al. 2009; Jat et al. 2008a, b; Martinuzzi et al. 2007; Yu and Ng 2007; Mundia and Aniya 2006; Sudhira et al. 2004), general land degradation by agricultural development and the tourism industry (Shalaby and Tateishi 2007), marked variation in biogeochemical and hydrological cycles (Foley et al. 2005; Meyer and Turner 1994; Ojima et al. 1994), nonpoint source pollution (Xian et al. 2007) and a gradual decline in ecosystem services (Quetier et al. 2007). Furthermore, human-induced changes in LULC influence the global carbon cycle and contribute to the increase of atmospheric CO2 (Alves and Skole 1996; Dixon et al. 1994). These changes result in an enormous and sometimes irreversible impact on the global environment (Abdullah and Nakagoshi 2005). Geospatial techniques such as remote sensing (RS) and Geographic Information Systems (GIS) have long been recognised as important and powerful tools in determining LULC changes at a range of spatial scales. Various image analyses and change detection techniques have been used to extract information from remotely sensed data (Lu et al. 2004, 2011). GIS, on the other hand, allows the integration of information derived from remote sensing – into the explicit understanding and modelling of LULC (Mesev 2007). In order to recognise the human dimensions of global change, the International Geosphere-Biosphere Project (IGBP) and International Human Dimensions Programme (IHDP) launched the “Landuse/Cover Change (LUCC)” plan in 1995 (Guan et al. 2011). Since then, mapping and monitoring of LULC change has become a major focus of research in different parts of the world by integrating multispectral and multi-temporal remotely sensed data with GIS (Ahmed and Ahmed 2012; Wu and Zhang 2012; Yu et al. 2011; Islam and Ahmed 2011; Bakr et al. 2010; Chen and Wang 2010; Yuan 2010; Deng et al. 2009; Kamusoko et al. 2009; Jat et al. 2008a, b; Xiao and Weng 2007; Long et al. 2007; Shalaby and Tateishi 2007; Mundia and Aniya 2006; Yin et al. 2005; Muttitanon and Tripathi 2005; Yuan et al. 2005; Seto and Kaufmann 2003; Alphan 2003; Weng 2001, 2002; Yeh and Li 1996, 1997; Giri and Shrestha 1996; Harris and Ventura 1995; Westmoreland and Stow 1992; Meaille and Wald 1990). As noted by Seto and Kaufmann (2003), there exist two broad LULC models: spatially explicit models and aspatial models. Spatially explicit models include empirical–statistical models, e.g. regression models (Jat et al. 2008a, b; Braimoh and Onishi 2007; Hu and Lo 2007; Long et al. 2007; Fang et al. 2005; Lo and Yang 2002; Lambin et al. 2000; Hazen and Berry 1997), rule-based models such as cellular automata or the Markov-cellular model (Arsanjani et al. 2013; Ahmed and Ahmed 2012; Islam and Ahmed 2011; Mitsova et al. 2011; Guan et al. 2011; Kamusoko et al. 2009; Torrens 2006; Lo and Yang 2002; Clarke et al. 1997; Xie 1996) and agent-based models (Mena et al. 2011; Evans and Kelley 2004; Barredo and Demicheli 2003; Wu 1998). These spatial models are primarily used to determine the pattern and process of LULC change and to project the locations of future changes. Among spatial models, CA in conjunction with Markov chain analysis has historically been the most favoured (Ward et al. 2000). By contrast, aspatial models are largely used to analyse driving factors along with predicting the amount of LULC in a particular geographic region (Yu et al. 2011; Huang et al. 2009; Seto and Kaufmann 2003).

5 Monitoring and Prediction of Land-Use and Land-Cover (LULC) Change

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Although Earth observation satellites provide an excellent opportunity to characterise LULC (Loveland et al. 1999) with their high temporal frequency and wealth of historical detail (Donnay et al. 2001), human-induced changes in LULC has typically been poorly enumerated in developing counties. As a result, serious environmental degradation resulted from dramatic change in LULC is believed to be threatening the sustainability of development (Li et al. 2009; Liu et al. 2007; Xiao et al. 2006; Li and Yeh 2004). It has been recognised that the analysis of LULC patterns, including historical patterns, assists in determining the preferred areas for future development (Mitsuda and Ito 2011), helps in assessing the directions and degree of human-related environmental changes (Xiao and Weng 2007), supports informed decision making (Costanza and Ruth 1998) and promotes land-use planning and the formulation of policy (Muttitanon and Tripathi 2005; Verburg et al. 2004), all of which lead to effective management of resources. Geospatial techniques have proved their efficacy for updating and managing spatial data in developing countries (Dong et al. 1997) and are particularly useful for providing accurate and timely geospatial information, such as that which is essential to the efficient management of a large metropolis (Yang 2002). Bangladesh is one of the most densely populated countries in the world, with a very low per capita income. Until 1970, the major contribution to the gross domestic product (GDP) was from the agricultural sector; currently the service sector has taken that position and now contributes 62 % to the GDP. In contrast, industrial growth shows a very flat trend and contributes only 18 % to the GDP (BBS 2011; The World Bank 2007). These economic changes have led to considerable loss of arable lands and exert tremendous pressure on limited natural resources, particularly on land and vegetation. It is estimated that every year, more than 800 km2 of agricultural land is converted to cities, roads and infrastructure in Bangladesh (BBS 1996). To elaborate on this, a 0.3 % per annum decline in cultivated areas has been observed by the agricultural census of 2008 (BBS 2010). In addition, the proportion of the country covered by forest has progressively been decreasing in the areas with higher population density (Giri and Shrestha 1996). Simultaneously there has been conspicuous urban growth, with the urban population of the country rising from 14.1 million in 1981 to 33.6 million in 2011 (BBS 2012, 2003, 2001). One of the most important reasons for this population explosion in the cities of Bangladesh is large-scale rural-urban migration, primarily due to collapse of the rural economy (Islam 1999). This loss of agricultural land, once the powerhouse of the national economy, has a number of social and economic effects. An increase in landlessness causes social upheaval in a traditionally agrarian society and leads to unrest. In addition, the decline in agricultural production poses food security risks with the possibility that in future, Bangladesh will be even less able to meet the food demands of its ever-growing population. Due to the economic and sociopolitical significance of Dhaka, marginalised rural people are often attracted to the area in search of better employment opportunities and improved lifestyles. Dhaka became one of the world’s top ten megacities in 2011, and if the current rate continues, Dhaka will be larger than Beijing by 2025, with a projected population of 22.9 million (UN 2012). LULC change from 1960 to 2005 for Dhaka was first mapped by Dewan and Yamaguchi (2009a, b; 2008) using geospatial techniques and drawing on historical

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topographic maps, Landsat and IRS-LISS III data. These studies reported that the rate of LULC conversion is extremely fast, which results in rapid depletion of precious natural resources such as cultivated land and wetlands. In contrast, urban growth is prominent mostly in an unplanned and piecemeal manner causing severe environmental degradation (Dewan et al. 2012). For example, built-up areas grew by 344 % between 1960 and 2005 (Dewan and Yamaguchi 2009b). A more recent study reported a total increase of urban built-up areas between 1990 and 2006 of 232 % (Griffiths et al. 2010). Econometric modelling to identify the underlying causes of LULC changes suggests that population growth is the major factor contributing to the rapid changes in LULC in that area (Dewan and Yamaguchi 2009a). This chapter describes the results of LULC classification in Dhaka megacity that are derived from multi-temporal remotely sensed data but differ from previous studies in terms of their areal extent and method of analyses. Specifically, the aims of this study are to map and monitor LULC changes from 1990 to 2011 and to simulate future land-use change using a combined Markov chain analysis and cellular automata model.

5.2 5.2.1

Materials and Methods Data Acquisition and Preparation

Six cloud-free Landsat TM 5 scenes were acquired for the years 1990, 2000, and 2011 all from the period late winter to early summer. The study area is on the boundary between two Landsat Rows, so two scenes were required for each year. Each Landsat TM image was enhanced using histogram equalisation to help identify ground control points for rectification. A minimum of 75 ground control points (GCPs), taken from topographic maps of 1990, were used to register each pair of images to the Bangladesh Transverse Mercator (BTM) system, an areaspecific standard UTM projection system for Bangladesh. GCPs were well dispersed throughout the scenes, yielding an RMS error of