Evaluating and Exploring Patterns of Satellite-predicted Forest Phenology in the Southern Appalachians

Current research indicates that warming global temperatures are capable of altering phenological cycles in deciduous forests.  Potential climate inducted changes to deciduous forest phenology are particularly important as they form the base for higher level trophic interactions and affect carbon and water cycles.  Remote sensing technology provides an efficient means to monitor changes in vegetation phenology continuously through space and time, though application is limited by the difficulties in relating satellite signals to local phenological events.  In this research we employed MODIS satellite data to predict the timing of spring leaf emergence for the years 2001 – 2012 and validated this approach by comparing to intensive ground observations we collected at the Coweeta Hydrologic Laboratory.  Additionally, we used a network of 50 temperature loggers and 7 long term climate stations to create a spatially explicit daily temperature model for our study region.  Finally, we utilized a multimodel inference approach to test hypotheses regarding the relative influence of spring temperatures, winter temperatures, and photoperiod on the timing of satellite-predicted spring forest phenology.  Our evaluation of MODIS satellite data indicates that on average predicted dates of leaf onset are accurate to within 3 days, and are more sensitive to canopy rather than understory greenup.  We also find that warmer spring temperatures advance spring leaf emergence, however the magnitude of the response to warmer temperatures varies widely at a local scale both within and among species.  Finally, we conclude that these satellite-based techniques will be useful for monitoring potential future shifts in forest phenology as well as informing related studies on trophic dynamics and global climate change.