In the last few years, landscape researchers have sought to understand temporal and spatial patterns of landscape changes in order to develop comprehensive models of land cover dynamics. To do so, most studies have used similar methods to quantify structural patterns, usually by comparing various landscape structural indices through time. Whereas the necessity for complementary approaches which might provide insights into landscape dynamics at some finer scale relevant to local managers has been expressed, few studies have proposed alternative methodologies. Moreover, the important relationship between the physical constraints of the landscape and land use dynamics has been seldom emphasized. Here we propose a methodological outline which was applied to the study of a rural landscape of Southern Quebec, Canada, to detect spatial and temporal ( 1958 to 1993) patterns of land cover changes at field, patch and landscape level. We then relate these patterns to the underlying physical structure of landscape elements using GIS and canonical correspondence analyses. We use the different geomorphological deposit types as stable discriminant factors which may constrain land use. Canonical correspondence analyses showed relations of land use and land use changes to the physical attributes of the landscape elements, whereas spatial analyses revealed very dynamic patterns at finer spatial and temporal scales. They highlighted the fact that not only the physical attributes of the landscape elements but also their spatial configuration were important determinants of land use dynamics in this area. Thus more land use changes occurred at the boundary between geomorphological deposit types than in ally other locations. This trend is apparent for specific small-size changes (e.g. forest to crop), but not for the large-size ones (e.g. abandoned land to forest). Although land use changes are triggered by socioeconomic forces in this area, these changes are nevertheless constrained by the underlying physical landscape structure. A thorough comprehension of historical changes will enhance our capability to predict future landscape dynamics and devise more effective landscape management strategies.