Cut Sentinel-2 time series into separate cycles, detecting dates of cuts and peaks.

cut_cycles(
  ts,
  n_cycles = Inf,
  min_win = 60,
  min_peakvalue = 0.1,
  max_dropvalue = 0.6,
  max_groundvalue = 0.2,
  ground_buffer = 10,
  value_type = "relative",
  min_relh = 0.15,
  relevance = 0,
  newyearday = "01-01",
  weight_metric = "integral"
)

Arguments

ts

Time series in s2ts format (generated using fill_s2ts()).

n_cycles

(optional) Maximum number of cycles to be detected in one year (default: Inf, meaning that all the identified cycles are kept). A cycle overlapping the new year's day (argument newyearday) is assigned to the year in which the date of maximum value falls.

min_win

(optional) Minimum time window between two consecutive maxima / minima to consider a separate cycle.

min_peakvalue

(optional) Minimum value to consider a cycle peak.

max_dropvalue

(optional) Maximum value to consider a cycle drop (breakpoint).

max_groundvalue

(optional) Maximum value to identify a ground plain (window without cycles).

ground_buffer

(optional) n. of days of beginning / ending of grounds to be included in previous / next seasons.

value_type

(optional) Character: if "relative" (default), values set with arguments min_peakval and max_dropval are relative values (normalised to 0-1 range among IDs); if "absolute", absolute values are considered.

min_relh

(optional) Numeric: minimum relative difference between the maximum and each of the two minima to consider a separate cycle. Default is 0.15.

relevance

(optional) Numeric: threshold used to consider local minima as relevant, according to Meroni et al. (2021) (see for reference). This is an alternative criterion with respect to min_relh (nevertheless they can be used together, too). Default is 0, meaning that this criterion is not used by default.

newyearday

(optional) day to be considered as new year's day, used to assign cycles to the proper year. It can be an object of class Date (in which case the year is ignored) or a character value in the form mm-dd. In case it is July 1 or higher, cycles whose maximum value is falling in the last part of the year are assigned to the subsequent year; otherwise, cycles whose maximum value is falling in the first part of the year are assigned to the previous year. Default is January 1 (all cycles are assigned the the year in which their maximum value falls).

weight_metric

(optional) Criterion used to assign a weight value to each seasons (used by subsequent functions: "integral" (default: sum of values among the cycle), "length" (length of the cycle) or "maxval" (maximum value reached in the cycle).

Value

A data table with the following fields:

  • id: the time series ID (see s2ts);

  • year: the year assigned to each cycle;

  • cycle: the cycle ID (progressive integer within each year);

  • begin: the date of the begin of the cycle;

  • end: the date of the end of the cycle;

  • maxval: the date of the maximum value of the cycle;

  • weight: the value of the metric used for ranking seasons.

Note

The steps used to discriminate seasons are partially based on the method exposed in Meroni et al. (2021) (doi: 10.1016/j.rse.2020.112232 ). The methodology will be documented in future.

Author

Luigi Ranghetti, PhD (2021) luigi@ranghetti.info

Examples

# Load input data data("ts_filled") # Cut seasons with standard parameters dt_cycles <- cut_cycles(ts_filled) dt_cycles
#> id year cycle begin end maxval weight #> 1: 1 2020 1 2020-04-27 2020-10-15 2020-07-13 88.71541 #> 2: 2 2020 1 2020-01-04 2020-07-16 2020-04-21 92.78070 #> 3: 2 2020 2 2020-07-16 2020-10-16 2020-08-08 13.91427
# Plot the TS highlighting the extracted cycles plot(ts_filled, pheno = dt_cycles, plot_dates = TRUE)
# Cut cycles considering separate cycles only if the maximum NDVI is > 0.7 dt_cycles_2 <- cut_cycles( ts_filled, min_win = 120, # exclude cycles shorter than 4 months min_peakvalue = 0.7, # exclude cycles with NDVI of peak < 0.7 value_type = "absolute", # 0.7 is the absolute NDVI value, not relative newyearday = "10-01" # consider a year from 1st October to 30th September ) plot(ts_filled, pheno = dt_cycles_2)