Most models of land cover change predict change using physical and socio-economic factors in raster grids where temporal and spatial scales must be selected to optimize prediction and calculation time. This study tests the impacts of spatial extent and spatial resolution (cell size) on land cover change modelling. Spatial extent here is equivalent to increasing the area of a dormant category. Two extents (33.6 km² and 79.1 km²) and three resolutions (25 m, 50 m and 100 m) were tested on study zones located in SE France in the Var department. The 50 m and 100 m resolutions were downscaled back to 25 m and compared to the initial 25 m maps. Land cover maps dated from 1950, 1982, 2003 and 2011, and IDRISI’s Land Change Modeler (LCM) was used to predict 2011. Dormant category improved Cramer’s V values (1.3 to 1.5 time greater) and quantity and allocation disagreement values. Actual change predictions were similar for the two zones, but the high persistent forest in the large window artificially improved prediction statistics, so increasing dormant category area (spatial extent) artificially inflates prediction statistics. Spatial resolution appeared to have little impact at first, but upscaling/downscaling revealed that coarser cell sizes lose predictive power (1.5-2 times greater allocation errors). The dormant category area should be minimized and upscaling/downscaling should be done if data are modelled at coarser resolutions than original cell size.

The Euro-Mediterranean area has experienced widespread land cover change since 1950, but few studies of land cover change explicitly explore spatial constraints on land cover change patterns. The main objective of this study was to analyze the spatial dynamics of land cover change from 1950 to 2008 in a French Mediterranean catchment. Aerial photographs (1950, 1982, and 2008) were screen digitized, and surfaces were classified into five categories: forest, vineyard, grassland, urban, and suburban. Land cover changes were concentrated mainly in the alluvial plain. Although forest remained the dominant land cover in the catchment (>85.0%), it underwent significant swapping with vineyard and grassland. Vineyard decreased (34% of initial loss) while grassland increased (43% of initial). Urban and suburban areas remained minor in the catchment (0.2% in 1950 and 3.0% in 2008), but showed a dramatic relative increase (about 20x). Changes occurred mainly at low altitudes and slopes. Vineyard located near streams was converted mainly to grassland. Built areas were dependent on roads and former built areas for expansion but expanded little near streams due to flooding risks. The rate of change was greater during the latter part of the study (1982-2008) than in the earlier phase (1950-1982).

Land cover has been changing rapidly throughout the world, and this issue is important to researchers, urban planners, and ecologists for sustainable land cover planning for the future. Many modeling tools have been developed to explore and evaluate possible land cover scenarios in future and time scales vary greatly from one study to another. The main objective of this study is to test land cover change prediction at different time scales in a Mediterranean catchment in SE France. Land cover maps were created from aerial photographs (1950, 1982, 2003, 2008, and 2011) of the Giscle catchment (235 Km2) and surfaces were classified into four land cover categories: forest, vineyard, grassland, and built area. Explanatory variables were selected through Cramer's coefficient. Different time scales were tested in the study: short (2003-2008), intermediate (1982-2003), and long (1950-1982). To test the model's accuracy, Land Change Modeler (LCM) of IDRISI was used to predict land cover in 2011 and predicted images were compared to a real 2011 map. Kappa index and confusion matrix were used to evaluate the model's accuracy. Altitude, slope, and distance from roads had the greatest impact on land cover changes among all variables tested. Good to perfect level of spatial and perfect level of quantitative agreement were observed in long to short time scale simulations. Kappa indices (Kquantity = 0.99and Klocation = 0.90) and confusion matrices were good for intermediate and best for short time scale. The results indicate that shorter time scales produce better predictions. Time scale effects have strong interactions with specific land cover dynamics, in which stable land covers are easier to predict than cases of rapid change and quantity is easier to predict than location for longer time periods.