Dormant categories and spatial resolution affect the perception of land cover change model performance

Abstract

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.

Keywords

• land cover
• spatial resolution
• Land Change Modeler
• area
• landscape modeling/modelling