Abstract: This paper picked up the impact factors of road distance, NDBI (Normalized Difference Buildup Index), NDVI(Normalized Difference Vegetation Index) and so on by means of analyzing the TM RS images and land use map digitally in Zhangjiagang city. In order to figure the effect of road to cropland in urbanization quantificationally, BP ANN (Back Propagation Artificial Neural Networks) was used to simulate the process of invasion and occupation of urban expansion in Zhangjiagang city. The results indicated that the probability of change from cropland to urban increased as high as 12% inside the range of 500 m around the road, and the impact of road to surrounding cropland reach the farthest to 2 000 m away. When -0.3<NDBI<0.5,the cropland neighboring the road was more easily transformed to urban. Finally, increasing training points of BP ANN could avoid the network to get into local minimum point defects.

Key words: NDBI (normalized difference buildup index), NDVI (normalized difference vegetation index), urban expansion, artificial neural network (ANN), NDBI (normalized difference buildup index), NDVI (normalized difference vegetation index), urban expansion