Urbanization is a significant cause of change in the urban landscape. Landscape analysis and remote sensing provide effective means for researching the process of urbanization, but the problems of scale and accuracy in landscape studies are yet to be resolved. In particular, the impacts of sensor spatial resolutions and classification themes on the urban landscape with changing scale size have rarely been reported. Roads are an important land-use type in urban areas. Unfortunately, this is also one of the land-use types that are relatively easy to neglect because of sensor resolutions and classification themes. To better understand the characteristics of the urban landscape, the landscape pattern of Shanghai was analyzed using land-use maps produced from Landsat Thematic Mapper (TM) and Indian Remote Sensing satellite PAN (IRS-PAN) images with changing grain size (ie, resolution) and extent size (ie, size of study area). Landscape metrics were computed along 51 km2transect cutting across Shanghai with a moving window. The results showed that both sensor spatial resolutions and classification themes provide incomplete information, such as missing linearity corridors, from urban landscape analysis. This could greatly affect urban landscape analysis and planning. The missing information may be detected by changing both the grain and extent sizes in the analysis parameters. However, varying the sensor resolutions did not produce the same effects as varying the classification themes. Varying the classification themes led to within-class variance, and thus was more significant in fine-resolution imagery than in coarse-resolution imagery. Linearity corridors such as urban roads should be identified and classified in research focusing on the details of urban fragmentation, urban ecologies related to road corridors,

Landscape analysis and remote sensing are effective tools for studying the urbanization process, but the problems of scale and precision in landscape studies remain to be solved. In particular, the impacts of the spatial resolution of sensors and classification themes on the urban landscape as a function of changes in scale have been little addressed. Roads are an important type of land use in urban areas. Unfortunately, among the different types of land use, it can be easily overlooked due to the resolution of the sensors and classification themes. To better understand the characteristics of the urban landscape, the pattern of the Shanghai landscape was analyzed using land use maps produced from TM and PAN images of IRS according to variable grain dimensions and extent. Landscape measurements were compiled along a 51 × 9 km transect2crossing Shanghai using a movable window. The results showed that the spatial resolution of the sensors and the classification themes provide incomplete information, for example missing linear corridors, in the conduct of landscape analyzes. This could significantly affect landscape analysis and planning. Missing information can be detected by varying the grain and extent in the analysis parameters. However, varying the sensor resolution did not produce the same effects as varying the classification themes. The variation in classification themes resulted in variance within classes and was therefore more significant in fine resolution images than in coarse resolution images.