baytsSpatial: Function to run bayts on mulitple raster bricks

In jreiche/bayts: Probabalistic approach to combine multiple time series and Bayesian probability updating for near real-time forest change detection

Description

Implements bayts function on multiple time series rasterBrick object(s). Time information is provided as an extra object and the time series can be regular or irregular. Information describing F and NF distributions is provided for each time series. See (bayts) for more details.

Usage

baytsSpatial(bL = list(NULL, ...), datesL = list(NULL, ...),
  pdfL = list(NULL, ...), bwf = c(0.1, 0.9), chi = 0.9, PNFmin = 0.5,
  start = NULL, end = NULL, pptype = "irregular", out_file = NULL,
  mc.cores = 1)

Arguments

bL	list of raster bricks. Raster bricks need to have the same extent and spatial resolution.
datesL	list of time vector of the format: "2014-10-07".
pdfL	list of "pdf" object(s) describing F and NF distributions (see calcPNF). "pdf" object: pdf[1] = pdf type F, pdf[2] = pdf type NF, pdf[3] and pdf[4] = pdf parameter describing F, pdf[5] and pdf[6] = pdf parameter describing NF. pdf types supported: Gaussian or Weibull.
bwf	block weighting function to truncate the NF probability; Default=c(0.1,0.9); (c(0,1) = no truncation)
chi	threshold of Pchange at which the change is confirmed; Default=0.5
PNFmin	threshold of pNF above which the first observation is flagged; Default=0.5
start	start date of monitoring period. Default=NULL (start of input time series).
end	end date of monitoring period. Default=NULL (end of input time series)
pptype	character. Type of preprocessing to be applied to individual time series vectors. The two options are 'irregular' and '16-days'. See bfastts for more details.
mc.cores	numeric. number of cores to be used for the job. See mc.calc for more details (default = 1)
outfile	output file

Value

A rasterBrick with 5 layers: (1) flag: time at which unconfirmed change got flagged; (2) change.flagged: time at which confirmed change got flagged; (3) change.confirmed: time at which change is confirmed; (4) Pflag: Probabilty of change for unconfirmed flagged changes; (5) Pchange.confirmed: Probabilty of change for confirmed changes.

Author(s)

Johannes Reiche (Wageningen University)

References

Reiche et al. (2015): A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection. Remote Sensing. 7(5), 4973-4996; doi:10.3390/rs70504973

Examples

#TBD

jreiche/bayts documentation built on Feb. 3, 2021, 1:12 a.m.