Phenological data collected in a spatially explicit manner offer considerable opportunities for gauging landscape-level spatial variations crucial for accurate scaling-up of flux measurements to larger areas or downscaling regional-scale atmospheric circulation models. In this project, spring tree leaf phenological data from a spatially intensive sampling area (216 trees in a 300m x 600m area) located in a mixed forest of northern Wisconsin were recorded during annual field campaigns in the springs of 2006 and 2007. These data, together with concurrently collected microclimatic, soil, terrain, and biotic information, were analyzed and characterized with spatial association methods and spatial regression models across the landscape, and compared to MODIS-scale (250m) remote sensing data.

Results/Conclusions

The results show that phenological development at this scale, though related to overall changes in air temperature, does not display patterns of spatial autocorrelation, even among trees of the same species. This is apparently due to the initiation of spring growth (bud burst) being driven by non-environmental factors (genetics) among individuals. The implications of these results are that representative phenological sequences for large areas can be constructed using a sufficiently large sample and generalized measurements of environmental conditions. Preliminary comparisons to satellite measurements suggest that high temporal resolution surface phenological sequences can improve techniques used to simulate plant development from satellite-derived indices.