Closed G-Lomax closed 1 year ago
Completed R code that separates each NDVI time series by hydrological year (Oct-Sep), automatically defines the segment representing the dry season decline and then fits either a linear or exponential decay model to it. It does this for both the original data (including cloud gaps) and for a smoothed version of the data using the Savitzky-Golay smoother, although the latter introduces some problems with artefacts (e.g., false dips in the curve immediately before the following season increase).
This initial analysis suggested that the average R-squared for linear models was greater than for exponential decays, and that the smoothed data provided a slightly better fit than the original. I think that this is probably a consequence of the simplistic method used to define the curve length, which resulted in many curves defined as starting early in the season when the first peak is reached rather than when the exponential period of the decay starts.
For a future iteration, I could try one of the following:
Next steps:
Phew! I've actually taken another route and fitted logistic curves to the data, automatically isolating the biggest growth and decay segments and then optimising for parameters to fit those curves. It's worked fairly well for growth, less so for decay.
Next steps:
Following supervision meeting, I need to focus my work on fitting simple models and extracting a few key metrics:
I've completed these three tasks and have concluded:
Now I just need to plot a few nice graphs to show the results ready to share with Peadar/Peter.
Completed this analysis for TPW data points and sent a cleaned-up HTML Markdown document/notebook to Peter and Peadar.
Fit exponential decay curves to the dry season declines in NDVI back to baseline levels for each year, starting with the TPW points.