createBayts: Creates "bayts" time series data frame for multiple input...

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

Description

Creates "bayts" time series data frame from multiple input time series and calculates time series of normalised conditional non-forest probabilities (PNF).

Usage

createBayts(tsL = list(NULL, ...), pdfL = list(NULL, ...), bwf = c(0.1,
  0.9))

Arguments

tsL	list of object(s) of class ts
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)

Details

Short method description: PNF is estimated for each individual time series observation, using the corresponding sensor specific probability density functions (pdfs) for forest (F) and non-forest (NF). In case of multiple observation at the same date, PNF is interatively updated using Bayesian probability updating (calcPosterior). A detailed description is provided in Reiche et al. 2015 (Chapter 2.1.2 and 2.1.3).

Value

"bayts" time seris data frame including: time series observation (ts1, ts2 ...), conditional non-forest probabilties (PNF), empty Flag (Flag) and empty Change probability (PChange).

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.