Measurement and Monitoring of Urban Sprawl in a Rapidly Growing Region Using Entropy

Rapid urban development and dramatic change of landscape have been recently witnessed in some developing countries as a result of rapid economic development. The measurement and monitoring of land-use changes in these areas are crucial to government officials and planners who urgently need updated information for planning and management purposes. This paper examines the use of entropy in the measurement and monitoring of urban sprawl by the integration of remote sensing and G I ~ . The advantages of the entropy method are its simplicity and easy integration with GIs. The measurement of entropy is devised based on two locational factors-distances from town centers and roads-to capture and reveal spatial patterns of urban sprawl. The entropy space can be conveniently used to differentiate various kinds of urban growth patterns. The application of the method in the Pearl River Delta, one of the fastest growing regions in China, has demonstmted that it is very useful and effective for the monitoring of urban sprawl. It provides a useful tool for the quantitative measurement that is much needed for rapidly growing regions in identifying the spatial variations and temporal changes of urban sprawl patterns. lntroductlon Urban sprawl is the scattering of new development on isolated tracts, separated from other areas by vacant land (Ottensmann, 1977). It is also often referred to as leapfrog development (Gordon and Richardson, 1997). Urban sprawl has been criticized for inefficient use of land resources and energy and large-scale encroachment on agricultural land. There are many problems related to fragmented conversion of agricultural land into urban use. Many advantages have been associated with a larger patch of land use, such as the reduction of environmental cost and development cost (Buiton, 1994). It is found that fragmentation of land use is also harmful to biological conservation. A larger area usually contains a greater diversity of habitats because it provides greater spatial and temporal variation in resources (McArthur and Wilson, 1967; O ' C o ~ o r et al., 1990). There have been a lot of debates on how to confine urban sprawl and conserve agricultural land resources (Bryant et a]., 1982; Ewing, 1997; Daniels, 1997). However, there is a general lack of good indicators to measure urban sprawl and assess their impacts. This paper examines the use of Shannon's entropy to measure urban sprawl with the integration of remote sensing and GIs. Remote sensing and GIs have been successfully applied in land-use inventory, land-use change monitoring, and assessment of environmental impacts of land development (Jensen et al., 1993; Jensen et al., 1995; Yeh and Li, 1997; Li, 1998). Remote sensing has great potentials in studying urban environments and urban/suburban landscape when high spatial resolution imagery is available (Jensen and Cowen, 1999). An important part of urban monitoring is to obtain information about the geometric elements of urban landscape. Studies have shown that entropy is a good statistic for measuring the spatial distribution of various geographic phenomena (Batty, 1972; Thomas, 1981). In this study, Dongguan, a city in the Pearl River Delta which is one of the fastest growing regions in China, is used to show how entropy can be conveniently calculated using remote sensing and GIs for the measurement and monitoring of urban sprawl.  Remote sensing data are capable of detecting and measuring a variety of elements relating to the morphology of cities, such as the amount, shape, density, textural form, and spread of urban areas (Webster, 1995; Mesev et al., 1995). Remote sensing data are especially important in areas of rapid land-use changes where the updating of information is tedious and time-consuming. The monitoring of urban development is mainly to determine the type, amount, and location of land conversion. There are many studies on the use of remote sensing to monitor land-use changes and urban development (Howarth, 1986; Fung andLeDrew, 1987; Martin, 1989; Eastman and Fulk, 1993; Jensen et al., 1993; Jensen et al., 1995; Li and Yeh, 1998). Various techniques have been developed to improve change-detection accuracy, including image differencing (Toll et al., 1980), image ratioing (Nelson, 1983), post-classification comparison (Howarth and Wickware, 1981), the masking method (Pilon et al., 1988). and principal component analysis (Fung and LeDrew, 1987; Li and Yeh, 1998). A conversion matrix which can be generated from change detection techniques is hequently used to indicate the details of land-use conversion. The encroachment of urban areas on other land-use types can be clearly shown in the conversion matrix. Morphological measurement of urban development is important for land-use planning and the study of urban development (Webster, 1995). Urban planners and researchers are concerned with the change in shape, size, and configuration of the built environment. The measurement of urban form can provide a more systematic analysis of the relationships between urban form and process. Urban economics, transport, and social structure are predicted in spatial terms, and, thus, the effects of such a theory are often articulated through geometric notions involving the shape of urban land use and the manner in which it Centre of Urban Planning and Environmental Management, The University of Hong Kong, Pokfulam Road, Hong Kong (hdxugoy@hkucc.hku.hk). X. Li is also with the Guangzhou Institute of Geography, Guangzhou, P.R. China (lixia@graduate.hku.hk). Photogrammetric Engineering & Remote Sensing VO~. 67, NO. 1, January 2001, pp. 83-90. 0099-1112/01/670143$3.00/0 6 2001 American Society for Photogrammetry