Abstract
This paper presents the entropy method to analyze urban sprawl based on the integration of remote sensing and GIS. The advantages of the entropy method are it simplicity and easy integration with GIS. The measurement of entropy is devised based on the tow location factors distance from town centers and distance from roads, to reveal and capture spatial patterns of urban sprawl. The application of the method in the Pearl River Delta, one of the fastest growing regions in China has demonstrated that it is very useful and effective for the monitoring of urban sprawl. It provides a tool of the quantitative measurement that is needed for rapidly growing regions in identifying internal variations and temporal change of urban sprawl patterns.

1. Introduction
Remote sensing data are especially important in the areas of rapid land use changes where the updating of information ti tedious and time-consuming. The monitoring of urban development is mainly to find out the type, amount, and location of land conversion. There are numerous studies in using remote sensing to monitor land user change and urban development ( Howarth, 1986 ; Fung and LeDerw, 1987; Eastman and Fulk , 1993; Jensen et al., 1993, 1995; Li and Yeh, 1998). Various techniques haven been developed to improve change detection accuracy , including image differencing ( Toll et al., 1980) , image rationing ( Nelson , 1983), post -classification comparisons ( Howarth and Wickware, 1981), masking method ( Pilon et al., 1988) ,and principal component analysis ( Fung and LeDrew, 1987; Li and Yeh , 1988).

In this study, the main focus is one the urban sprawl that has appeared in the Pearl River Delta, China. The region has witnessed widespread ' leap-frog ' development due to lack of proper planning. The fragmented conversion of agricultural land into urban use discords with rationalized land development patterns ( Yeh and LI, 1998) . many advantages have been associated with a larger patch of land use , such as reduction of environmental cost and development cost ( Buiton, 1994). There is also a long history in estimation of landscape change and its impacts on wildlife ( Mc Arthur and Wilson, 1967). It is found that fragmentation of land user in harmful to biological conservation as well as to urban growth. A larger area usually contains a grater diversity of habitats because it provides great spatial and temporal variation in resources ( Mc Arthur and Wilson, 1967 ; O'Connor et al, 1990) .

2. Methodology
Although various studies have been dedicated to the measurement or urban form, they have limitations in capturing the characteristics or urban sprawl. There methods are just developed in the context of image analysis or fractal theory ( Webster, 1995; Batty and Longley, 19940; in this paper, an alternative technique, entropy, specifically to measure the extent or urban sprawl is developed with the integration of remote sensing and GIS. The measurement is directly carried out with in GIS to facilitate the convenient access to GIS spatial database. The measurement is based on entropy theory, as Shannon 's entropy (E) can be used to measure the degree of spatial concentration and dispersion exhibited by geographical variable ( Xi) ( Theil, 1967; Thomas, 1981 ) . Entropy is calculated by :

Where p_i = and x_i is the observed value in the it zone in a total of n zones. It ranges from 0 to 1. if the distribution is maximally concentrated in one region, the lowest value, zero. Will be obtained. Conversely, an evenly disperse distribution across space will give a maximum value of 1.

The major difference between entropy and entropy and traditional indices of spatial dispersion is that its value is invariant with the value of zones, the number of observation (n) ( Thomas, 1981). In contrast, Gini coefficient or Lorenz curve, which has been widely used in geography to describe location pattern, has deficiency because the coefficient is sensitive to the size and shape of the area units of observation. The modifiable area unit problem may exert influences on the results of spatial analysis and lead to the loss of detailed information ( Openshaw, 1991). However, there is no such problem when entropy is used.

In this study, the buffer function of GIS will be used to define buffers or zones for calculating entropy. This can allow some independent variables form GIS database to be easily embedded in entropy. Other morphological approaches have limitations to explore spatial relationships between urban sprawl and spatial factors because those methods are not directly developed within GIS. The information from GIS database is important because urban sprawl is always dependent on other geographical variables.

Since entropy can be used to measure the distribution of a geographical phenomenon, thus the measurement of the difference on entropy between time t+1 can t can be used to indicate the change in the degree of dispersal of land development or urban sprawl.
DE=(t+1)-E(t)           (2)
The dispersal or urban areas from a town center will lead to an increase in the entropy value . the change of entropy can be used to identify whether land development is toward a more dispersed ( sprawl ) or compact pattern. The following section will discuss how to use the entropy method to measure the rapid urban sprawl in a fast growing region with the integration of remote sensing and GIS.