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Forecasting the impacts of climate change on plant communities over large spatial extents requires understanding population dynamics at similar extents. However, virtually all studies of plant population dynamics rely on demographic observations recorded at the meter to sub-meter scale. Local-scale demographic data make building population projection models an easy task, but it is very difficult to extrapolate small-scale studies to large spatial extents. Alternatively, large-scale trends in populations are easily detected using widely available monitoring data, but such data are rarely used to project future population states. We propose combining the best features of local-scale population modeling and large-scale monitoring data. Specifically, we describe a new approach for modeling population dynamics at large spatial extents based on the theory and mechanics of individual-based models (IBMs) as applied to a 27 year remotely-sensed time series of sagebrush (Artemisia spp.) cover at two sites in southwestern Wyoming (5 x 5 kilometer study areas). We fit regressions for pixel-level processes (30 meter resolution) analogous to growth, survival, and colonization by linking climate covariates to interannual changes in remotely-sensed cover. These regressions are the basis for a pixel-based model (PBM) which we used to forecast sagebrush cover at the landscape scale in response to altered climate. The PBM successfully reproduces the observed sagebrush cover distribution, with the simulated mean and standard deviation similar to the observed time-series distribution at both study areas. Based on outputs from climate models for the 2050-2100 period relative to the 1950-2000 period, our study area is projected to experience increased temperature and precipitation. Under projected future climate, we forecast a modest increase in sagebrush cover at one site and a modest decrease at the other. Positive growth responses may be most likely at high elevation sites near the thermal limit for sagebrush species.