Methods of Phenological Observation

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Lecture 2 Modeling of Land Surface Phenology
with satellite imagery
Kirsten M. de Beurs, Ph.D. Assistant Professor of
Geography Center for Environmental and Applied
Remote Sensing (CEARS) Virginia Polytechnic
Institute and State University Kdebeurs_at_vt.edu ht
tp//www.mapseasons.net
Madison LSP Workshop 08 APR 2008
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Southwest Virginia
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Northeastern Maine
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Death Valley
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Corn/Soy belt Central Illinois
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Tundra Northern Alaska
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Phenological Metrics

• Phenological metrics describe the phenology of
  vegetation growth as observed by satellite
  imagery.
• Standard metrics derived are
• Onset of greening
• Onset of senescence
• Timing of Maximum of the Growing Season
• Growing season length
• However, there are many more metrics available.

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Maximum NDVI
Rate of Senescence
Rate of Greenup
Time Integrated NDVI
End of Season
Start of Season
Duration of Season
SOS
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Phenological Metric ? Phenological Interpretation

• Time of SOS (EOS)
• beginning (end) of measurable photosynthesis.
• Length of the growing season
• duration of photosynthetic activity.
• Time of Maximum NDVI
• time of maximum photosynthesis.
• NDVI at SOS (EOS)
• level of photosynthetic activity at SOS (EOS).
• Seasonal integrated NDVI
• photosynthetic activity during the growing
  season.
• Rate of greenup (senescence)
• speed of increase (decrease) of photosynthesis.

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Ground Validation

• It is desirable to compare the satellite derived
  phenological estimates with data observed at
  ground level.
• However this is not a trivial task due to
• Large pixel sizes of satellite imagery.
• Composited data.
• Thus, it is often unclear what LSP metrics
  actually track.

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Four Categories

• A diversity of satellite measures and methods has
  been developed.
• The methods can be divided into four main
  categories
• Threshold
• Derivatives
• Smoothing Algorithms
• Model fit

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Thresholds

• When do you estimate that the growing season
  starts?

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Thresholds
SOS
EOS

• Simplest method to determine SOS and EOS.
• Threshold is arbitrarily set at a certain level
  (e.g. 0.09, 0.17, 0.3 etc).

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Thresholds

• Measure is easy to apply.
• However, across the conterminous US, NDVI
  threshold can vary from 0.08 to 0.40.
• Thus, it is inconsistent when applied towards
  large areas.

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Thresholds based on NDVI ratios

• First, translate NDVI to a ratio based on the
  annual minimum and maximum
• NDVIratio (NDVI-NDVImin) /
• (NDVImax-NDVImin)

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• 50 is the most often used threshold.
• The increase in greenness is believed to be most
  rapid at this threshold.

• Some believe that rapid growth is more important
  than first leaf occurrence or bud burst.
• Lower likelihood of soil vegetation confusion
  than at lower thresholds.

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50 Threshold (Seasonal Mid-point)
(White et al., mean day 124, May 4th)
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Derivatives

• What is a derivative?
• What is the slope of this line?
• Why?

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Local Derivative

• Derivative is calculated based on 3 composites.
• (Week 3 Week 1) / (difference in days)
• SOS day where derivative is highest
• EOS day where derivative is lowest

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Smoothing Algorithms

• Autoregressive moving average
• Fourier analysis

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Autoregressive moving average

• Frequently used method developed in the early
  1990s by Dr. Brad Reed.
• Works similarly as the thresholds method, however
  the threshold is established by a moving average.
• What is a moving average?

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Autoregressive moving average

• You take the average of a certain number of time
  periods.
• Each time period you shift one over.

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Autoregressive moving average

• The time lag used to calculate the forward and
  backward looking curves is arbitrarily chosen.
• Brad Reed (1994) used a time lag of 9 composites.
• In case of shorter seasons (semi-arid Africa)
  shorter time lags have been used (2 months or 4
  composites).

Archibald and Scholes (2007).
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Archibald and Scholes (2007).
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Limitations of the moving average method

• How would the moving average curve look in case
  of a major disturbance?
• The method does not work in case of multi-peak
  growing seasons.
• There are no clear criteria regarding the
  selection of the delay time.

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Fourier Analysis

• Fourier analysis approximates complicated curves
  with a sum of sinusoidal waves at multiple
  frequencies.
• The more components are included the more the sum
  approximates the signal.

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• In phenological studies
• Amplitude variability of productivity
• Phase measures the timing of the peak.

de Beurs and Henebry, 2008
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Limitations of Fourier Analysis

• The Fourier composites do not necessarily have an
  ecological interpretation.
• This approach is only useful for a study region
  that you know really well.
• The method requires long time series, with
  observations that are equally spaced.
• Missing values (clouds!) have to be filled.
• NDVI signals are typically not exactly
  sinusoidal, so it is necessary to fit several
  terms.

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Complicated adjustments

• High order annual splines with roughness
  dampening.

Hermance et al. 2007
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Bradley et al 2007
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Model fit

• Models based on growing degree days
• Logistic Models
• Gaussian Local Functions

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Growing Degree Days

• Development rate of plants and insects is
  temperature dependent.
• A plant develops quicker at a higher temperature.
• Daily temperature readings can be used to
  calculate growing degree-days
• Growing degree days are a measure of accumulated
  heat.
• Idea was first introduced in 1735 by Reaumur.

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Growing Degree Days

• Accumulated temperature is now recognized as the
  main factor influencing year-to-year variation in
  phenology.
• Photoperiod alone, without the interaction with
  temperature, cannot explain the annual
  variability of phenology at a given location.
• Photoperiod is the same in each year.

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Intercept NDVI at the start of the observed
growing season. Start of Season First
composite included in the best model. NDVI peak
height NDVI at peak NDVI. Green-up period
(DOY) Translated from accumulated relative
humidity, the number of days necessary to reach
peak NDVI

• Intercept a
• Green-up period
• peak position (hum)
• NDVI peak height

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Logistic Models

• Straightforward logistic model
• a and b are empirical coefficients that are
  associated with the timing and rate of change in
  EVI.

• cd combined give the potential maximum value
• d presents the minimum value (the background EVI
  value).
• This model can be approximated with numerical
  methods such as Levenberg-Marquardt

Zhang, 2004
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Onset_Greenness_Increase days since 1 January
2000 16-bit Onset_Greenness_Maximum days since
1 January 2000 16-bit Onset_Greenness_Decrease
days since 1 January 2000 16-bit
Onset_Greenness_Minimum days since 1 January
2000 16-bit NBAR_EVI_Onset_Greenness_Minimum
NBAR EVI value 16-bit unsigned
NBAR_EVI_Onset_Greenness_Maximum NBAR EVI value
16-bit unsigned NBAR_EVI_Area NBAR EVI
area 16-bit unsigned
MODIS/Terra Land Cover Dynamics Yearly L3 Global
1km SIN Grid MOD12Q2
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Gaussian Local Functions
c1 and c2 base parameters determine the
intercept and the amplitude of the curves,
respectively. a1 the timing of the maximum
(measured in time units).
The upper part of the equation is fitted to the
right half of the time series. The lower part of
the equation fits to the left half of the time
series. a2 and a4 the width of the curves a3
and a5 the flatness (or kurtosis) of the curves
Jönsson and Eklundh, 2002 and Jönsson and
Eklundh, 2004
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Gaussian Local Functions

• Applied in a program called TIMESAT

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http//accweb.nascom.nasa.gov/data/

• MODIS phenology for the North American Carbon
  Program
• Annual phenology data based on
• NDVI, EVI, LAI or FPAR
• Spatial resolution 250m or 500m

Phenology data include greenup date, browndown
date, length of growing season, minimum NDVI,
date of peak NDVI, peak NDVI, seasonal amplitude,
greenup rate, browndown rate, seasonal integrated
NDVI, maximum NDVI during the year, quality
control map, land cover map.