phenology {PHENTHAUproc}

Calculate phenological events

Description

Using daily mean or min and max temperature data, the function calculates the temperature-dependent development stages of OPM or the bud stages (bud swelling and leaf unfolding) of its host tree Quercus robur.

The default settings correspond to the model described by Halbig et al. 2024. Additional parametrizations are provided but have not yet been tested.

Halbig et al. 2024 It follows 4 different steps:

• a) Calculating and summing up cold days or frost days. (Cold days are defined as days with a mean temperature below ldt (lower development threshold), while frost days are all days with a min temperature below ldt). Hatch dependent development stages need a hatch raster (hatch happened 1 or not 0) for each day

• b) Calculating degree days with the single sine method of Baskerville & Emin, 1969 or simple summing up tmean temperatures over ldt.

• c) Calculating the needed sum of effective temperatures for the development stage

• d) Comparing degree days with the needed sum of effective temperatures

Usage

phenology(
  x,
  model,
  parametrisation = NULL,
  year = NULL,
  hatch = NULL,
  return_date = TRUE,
  ...
)

Arguments

x

SpatRaster list/dataframe (tmean, tmax, tmin) - numeric - with time attribute/date column

model

name of model - character

parametrisation

name of parametrisation - character

year

year for prognosis - numeric

hatch

SpatRaster - logical - with time attribute TRUE/FALSE hatch/no_hatch

return_date

returns start of phenological event with time serial number

...

parameter to change default values. (i.e. ldt = 3.5)

Value

If return_date is TRUE returns single layered SpatRaster with time serial number (first occurence of phenological event). If return_date is FALSE returns a one layer per day SpatRaster type logical with phenological event occurred/not TRUE/FALSE.

Author(s)

Bachfischer Lorenz, Department of Forest Protection FVA (2024) [e-mail]

References

Halbig et al. 2014: Halbig, P., Stelzer, A. S., Baier, P., Pennerstorfer, J., Delb, H., & Schopf, A. (2024). PHENTHAUproc–An early warning and decision support system for hazard assessment and control of oak processionary moth (Thaumetopoea processionea). Forest Ecology and Management, 552, 121525 Baskerville & Emin 1969: Baskerville, G. L., & Emin, P. (1969). Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology, 50(3), 514-517. (doi:10.2307/1933912) Menzel 1997: Menzel, A. (1997). Phänologie von Waldbäumen unter sich ändernden Klimabedingungen: Auswertung der Beobachtungen in den internationalen phänologischen Gärten und Möglichkeiten der Modellierung von Phänodaten. Frank.

See Also

Other Main: get_legend(), mortality(), parameter(), phenthau()

Examples

## SpatRaster
srl <- load_test()

# Calculating bud swelling for our raster example
budswelling <- phenology(srl, "budswelling", "quercus_robur_clone256_type1", year = 2020)