  Leaf Area index is widely used metric for the assessment of vegetation
dynamics and can be used to assess the impact of regional/local climate
conditions. The underlying continuity of high resolution spatio-temporal
phenological processes in the presence of extensive missing values poses a
number of challenges in the detection of changes at a local and regional level.
The feasibility of functional data analysis methods were evaluated to improve
the exploration of such data. In this paper, an investigation of multidecadal
variation of leaf area index (LAI) is conducted in the Columbia Watershed, as
detected by NOAA AVHRR satellite imaging, and its inter- and intra-annual
correlation with maximum temperature and precipitation using the ERA-Interim
Reanalysis from 1996 to 2017. A functional cluster analysis model was
implemented to identify regions in the Columbia Watershed that exhibit similar
long-term greening trends. Across these several regions, the primary source of
annual LAI variation is a trend toward seasonally earlier and higher recordings
of regional average maximum LAI. Further exploratory analysis reveals that
although strongly correlated to LAI, maximum temperature and precipitation do
not exhibit clear longitudinal trends.
