Plant Phenology - Time series analysis
The purpose was to investigate changes in vegetation phenology, from 2001 to 2021, using optical satellite remote sensing data.
Remote Sensing techniques are powerful tools for studying plant phenology, since the changes in plant cycle and seasonal variations are influenced by environmental elements such as temperature, light, CO₂ concentration, and water availability (Tang et al., 2016; Cleland et al., 2007).
Alterations in those factors can lead to changes in the timing and duration of plant growth, and development (Tang et al., 2016). Thus, studying the interactions between vegetation and the environment can provide insights into the effects of climate change and other environmental disturbances on plant growth and development.
Materials
Plant Phenology Index (PPI) was made in MODIS data from 2001 to 2021.
The Time-series analysis was done in TIMESAT (MATLAB);
The image processing in IDRISI Terrset and ArcGIS Pro.
Methods
The time series were smoothed by applying the Savitzky-Golay model filtering technique.
The change over time was analyzed using four variables:
Amplitude
Start of the season (SOS);
End of the season (EOS);
Length of the season (LOS);
Trend analysis methods applied to each variable:
Median-trend (Theil-sen);
Non-linear monotonic trend (Mann-Kendall).
Results
Over 20 years, generally,
The growing season begins to offset and ends slightly earlier;
Amplitude is increasing;
Length of the season seems to become shorter in flatter regions, whilst higher elevated areas contain a longer phenological season;
Near Munich:
Shorter LOS
More pronounced delay in SOS
Alps:
Longer LOS
Slightly earlier SOS
General Trend
