Forrest M. Hoffman, Richard T. Mills, Jitendra Kumar, William W. Hargrove, and Joseph P. Spruce
Variations in vegetation phenology, the annual temporal pattern of leaf growth and senescence, can be a strong indicator of ecological change or disturbance. However, phenology is also strongly influenced by seasonal, interannual, and long-term trends in climate, making identification of changes in forest ecosystems a challenge. Forest ecosystems are vulnerable to extreme weather events, insect and disease attacks, wildfire, harvesting, and other land use change. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a proxy for phenology. NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) at 250 m resolution was used in this study to develop phenological signatures of ecological regimes called phenoregions. By applying a quantitative data mining technique to NDVI measurements every eight days over the entire MODIS record, annual maps of phenoregions were developed. This geospatiotemporal cluster analysis technique employs high performance computing resources, enabling analysis of such very large data sets, and it produces a prescribed number of prototypical phenological states to which every location is assigned in any year. Analysis of the shifts among phenological states yields information about responses to interannual climate variability and, more importantly, changes in ecosystem health due to disturbances.