# GEOG 566

April 30, 2018

### Advanced Spatial Statistics: Blog Post 1 (.5) : Temporal autocorrelation of phenological metrics

Filed under: 2018,Exercise/Tutorial 1 2018 @ 11:11 am

## 1. Key Question

How has the phenology of production changed over time at two grassland sites, and does this change differ between and C3 and a C4 grassland?

## 2. Approach used

My approach uses an autocorrelational analysis to assess whether differences in phenological indices are indicative of change over time, or cyclical patterns.

## 3. Methods / steps followed

To answer this question, I used the R package greenbrown to extract phenological indices from time series of MODIS NDVI data from 2001-2015 at two locations in C3 and a C4 grassland. The sites correpond to Eddy Covariance Flux tower locations at the University of Kansas Biological Station and Konza Prairie Biological station, in eastern Kansas.

Phenological metrics include the start of the growing season, the end of the growing season, the length of the growing season, the peak growing season productivity. The Phenology() function calculates the phenological metrics by 1) identifying and filling permanent (i.e., winter) gaps in the time sereis, 2) smoothing and interpolating the time series, 3) detecting the phenology metrics from the smoothed and interpolated time sereis, and 4) correcting the annual DOY time series such that the metrics associated with days of the year (e.g., start of season, end of season) don’t jump between years. The Phenology() function provides several different approaches to calculate phenology metrics and to conduct temporal smoothing and gap filling. For this analysis, I used the “White” approach to calculation phenology metrics by scaling annual cycles between 0 and 1 (White et al. 1997) and used Linear interpolation / a running median for temporal smoothing and gap filling. The code for the phenology calculation function is “kon_phen <- Phenology(kon_ndvi, tsgf=”TSGFlinear”, approach=”White”)”. The end result is a dataframe with annual phenology metrics.

After calculating the annual phenology metrics, I used the acf() function to assess whether annual differences in phenology were a product of change over time, or cyclical trends.

## 4.1 Results: Phenological metrics

The phenological metrics appear to differ distinctly between the sites, which reflects established differences between the phenology of C3 vs. C4 grasses. The C3 site has a consistently longer growing season than the C4 site, with an earlier start of season and an later end of season. Based on the NDVI data, the sites have similar mean growing season (MGS) and peak growing season values of NDVI.

## 4.2 Results: autocorrelation analysis

In the autocorrelograms above, the dashed lines represent the upper and lower thresholds for statistically significant autocorrelation. Vertical lines represent 1-year lags, and a line at 0 is provided for reference. In each plot, the C4 site is orange, and the C3 site is blue.

The autocorrelational analysis reveals only a few instances of temporal autocorrelation that appear to be marginally significant. Overall, there does not appear to be strong temporal autocorrelation in the phenological metrics, suggesting that there are not annual or interannual cycles influencing the phenological metrics.

There appear to be different, but not significantly distinct, patterns of autocorrelation between the C3 and the C4 site, suggesting that the production patterns are being controlled by the same environmental drivers.

The few instances of statistically significant autocorrelation are:
– 3- and 4-year lags for EOS for the C3 site, indicating that the first positive peak in a cyclical pattern of EOS would occur at 3 years, and the first trough for EOS would occur at 4 years. This pattern is not evident at the C4 site.
– 2-, 3-, and 5-year lags for MGS at the C3 site, indicating that the first negative trough in a cyclical pattern of the mean growing season value would occur at 2 and 3 years intervals, and that the first positive peak in the cycle would occur at 5 years. Again, the C4 site does not exhibit a similar pattern.

Anecdotally, the result that the C3 site shows more statistically significant autocorrelation might indicate that the C3 site follows more cyclical patterns of phenology than the C4 site– perhaps suggesting that production at the C3 site is less sensitive to interannual variation in climate.

## 5. Critique

The distinction between the autocorrelational patterns at the C3 and C4 site may be due to a change in the land management at the C3 site over the course of the time series analyzed. In 2007 management shifted from an irrigation / field management type to lleaving the area in a more natural prairie state, when the Eddy Covariance Flux tower was installed. In contrast, the C4 site was maintained as a natural, unirrigated prairie for the duration of the time series.

Further, though NDVI is easy and accessible, comparison of the NDVI record with the Eddy Covariance Flux record at these sites suggests that it does not accurately capture intra-annual variation in production dynamics between the C3 and C4 sites. Eddy covariance flux tower records show that the C4 site has consistently higher max annual production than the C3 site, and a more distinct phenology. Because NDVI is a proxy of vegetation health by measuring greenness, rather than the physiology of plant production, it is less useful when plants look the same, but have distinct resource-use efficiences.

This method appears to work well on NDVI data; the greenbrown package appears to have been optimized for a ~2-week temporal resolution. When I attempted to use the package on Eddy Covariance flux data at daily resolution, the Phenology() function returned errors or missing data, and was unable to produce a smooth time series. Next steps include further processing the flux data for use with the greenbrown package, and performing a bivariate analysis to link annual phenological metrics with annual climate variables (e.g., mean annual temperature, mean annual precipitation, monthly precipitation variables, growing degree days).