The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008 
where Y n = the urban area in year n 
Xi n = the real GDP in year n 
X2n = the population in year n 
It is estimated (Table 4Table 4) that the urban land for 2010, 
2015 and 2020 would be 2895.06Km\ 3393.91 Km 2 and 
4198.89 Km 2 , respectively. This implies that by year 2020, the 
urban area in the study area would rise to a high ratio, which 
may be nearly 179.37% of the 2005 in 15 years. Thus, the 
pressure on land would further grow and the farm land areas, 
open grounds and region around the highways are likely to 
become prime targets for urban expansion. 
Year 
Item 
1984 
Real GDP 
(100 Million Yuan) 
196.14 
Population 
( 10000 Persons) 
965.66 
Urban Area 
(Km 2 ) 
1241.63 
1988 
282.77 
1061.00 
1348.11 
1991 
341.41 
1094.00 
1404.14 
1994 
485.14 
1125.00 
1499.07 
1997 
652.04 
1240.00 
1642.24 
2001 
988.77 
1385.10 
1898.73 
2005 
1561.17 
1538.00 
2340.94 
2010 
2318.97 
1577.92 
2895.06 
2015 
3751.38 
1694.86 
3393.91 
2020 
6068.57 
1811.80 
4198.89 
Table 4. Population, GDP and Urban area from 1984 to 2020 
where data for 1984-2005 are from BMSB (2007) and data for 
2010-2025 are from the regression analysis result. 
5. CONCLUSION 
(1) Land use/land cover change detection using multi-temporal 
images by means of remote sensing and ration research of 
model of urban expansion by GIS are good means of research of 
urban expansion. Through this process, we obtain the seven 
temporal distribution maps of land use in the study area from 
1984 to 2005 (Figure 4). And the spatio-temporal distribution 
maps of ratio of urban land represent a strong expansion in 
urbanization. 
(2) Research of time sequence land use/ land cover change 
through analysis of urban expansion trajectories and index 
reveals of urban distribution rules in terms of spatial-temporal. 
According to analysis of 1000 randomly selected sample, the 
trajectory of urban expansion in the land use change tracked by 
29 series (Table 3); and track series from cultivated land to 
urban land, taken for the largest proportion of 77.7 percent. The 
maps of urban expansion intensity (Figure 6Figure 6) show that 
the expansion of urban land in study area experiences a 
transition from center city to suburb. 
(3) Combined the analysis of social economic data, the 
simulation of expansion urban land is given in amount. With the 
analysis of socio-economic data, population and real GDP in 
this study, we obtain the result of prediction urban land area 
through a linear regression. It is estimated (Table 4) that the 
urban land for 2020 would be 4198.89 Km 2 . Thus, the pressure 
on land would further grow and the farm land areas and other 
type of land nearby the highways are likely to become prime 
targets for urban expansion. 
Future studies will investigate the impacts and interactions of 
spatial variables on urban expansion patterns and suitable 
method to quantify other socio-economic factors that may also 
play important roles in urban expansion. Appropriate and 
practical methodologies for expansion simulation in terms of 
spatial should also be further studied. 
REFERENCES 
Beijing Municipal Statistical Bureau, 2007. Beijing Statistical 
Yearbook. Beijing: China Statistical Press. 
Epstein, J., Payne, K., Kramer, E., 2002. Techniques for 
mapping suburban sprawl. Photographer Engineering Remote 
Sensing. 63 (9), pp. 913-918. 
He, C., Okada, N., Zhang, Q., Shi, P., Zhang, J., 2006. 
Modeling urban expansion scenarios by coupling cellular 
automata model and system dynamic model in Beijing, China. 
Appl. Geogr. 26(2006), pp. 323-345 
He, C., Okada, N., Zhang, Q., Shi, P., Li, J., 2008. Modelling 
dynamic urban expansion processes incorporating a potential 
model with cellular automata, Landscape Urban Plan.(200S), 
ARTICLE IN PRESS. 
Jiang, Q., 2004. Monitoring and change analyzing of the 
temporal and spatial urban expansion pattern based on remote 
sensing. Beijing: Beijing Normal University. 
Xu, X., Zhou, Y., Ling, Y., 1997. Urban Geography. Beijing: 
Higher Education Press. 
Li, X., Yeh A., 2000. Modelling sustainable urban development 
by the integration of constrained cellular automata and GIS. Int. 
J. Geographical Information Science. 14(2), pp. 131-152. 
Li, X., Yeh, A., 2001. Calibration of cellular automata by using 
neural networks for the simulation of complex urban systems. 
Environ. Plan. A. 33, pp. 1445-1462. 
Li, X., Yeh A., 2002. Neural-Network-Based Cellular Automata 
for Simulating Multiple Land Use Change Using GIS. 
International Journal of Geographical Information Science. 
16(4), pp. 323-343. 
Liu, H., Zhu, Q., 1999. Studies of methodologies and their 
development on land use/ cover change detection by using high 
spatial resolution remote sensing data. Resources Science. 21 
(3), pp. 23-27. 
Liu, H., Zhou, Q., 2004. Accuracy analysis of remote sensing 
change detection by rule-based rationality evaluation with post- 
classification comparison. Int. J. Remote Sens. 25(5), pp. 1037- 
1050. 
Liu H., Zhou Q., 2005. Developing urban growth predictions 
from spatial indicators based on multi-temporal images. 
Comput., Environ, and Urban Systems 29 (2005), pp. 580-594. 
Mundia, C.N., Aniya, M., 2005. Analysis of land use/cover 
changes and urban expansion of Nairobi city using remote 
sensing and GIS. Int. J. Remote Sens. 26(13), pp. 2831-2849. 
Mills, H., Cutler, M. E. J. and Fairbaim, D., 2006. Artificial 
neural networks for mapping regional-scale upland vegetation 
from high spatial resolution imagery. Int. J. Remote Sens. 
27(11), pp. 2177-2195.