Experiments that use one simulated and three actual MODIS-Landsat datasets featured by different types of land surface changes were conducted to demonstrate the performance of RASTFM.
This approach also provides valuable insights into land surface changes captured by multi-temporal elevation models.
Deep insight into types of vegetation variability provided by AVHRR space scanner images of vegetation index spatial distribution helps reveal impact of land surface changes on environment.
In RASTFM, land surface changes are reorganized into non-shape changes (including phenological changes and land cover changes without shape changes) and shape changes (i.e., land cover changes with shape changes), which are handled differently.
However, a critical challenge confronting the further development of STIF is to systematically and robustly address complex land surface changes, which include land cover changes without shape changes (e.g., crop rotation) and land cover changes with shape changes (e.g., urban expansion), in addition to conventional land surface changes (e.g., phenological changes of vegetation).
In offline simulations, these alterations are manifested as changes in latent and sensible heat fluxes accompanied by reduced land surface or skin temperature.
However, discrete land cover classifications of such data cannot sufficiently quantify land surface heterogeneity or change.
The ground surface temperature is mostly affected by land use changes (due to changes in albedo, evapotranspiration and thermal insulation) and surface air temperature changes (due to non-local effects such as global climate change) (Ferguson and Beltrami 2006; Bense and Beltrami 2007; Gunawardhana et al. 2011).
