bayts: Bayesian approach to combine multiple time series for near...

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

Description

(i) Creates "bayts" time series data frame from multiple input time series and calculates time series of normalised conditional non-forest probabilities (PNF). (ii) Iterative Bayesian updating of the conditional probability of change (PChange) based on PNF and detection of change

Usage

bayts(tsL = list(NULL, ...), pdfL = list(NULL, ...), bwf = c(0.1, 0.9),
  chi = 0.9, PNFmin = 0.5, start = NULL, end = NULL)

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)
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)

Details

Short method description: First, the conditional probability for non-forest (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). Observations at time t (current observation) are flagged to be potentially changed in the case that the conditional NF probability (PNF) is larger than 0.5. For a flagged observation, the conditional probability of change (PChange) is computed by iterative Bayesian updating (calcPosterior), using the previous observation (t − 1), the current observation (t), as well as i upcoming observations (t + i) to confirm or reject a change event at t. A change is confirmed in case PChange exceeds a given threshold "chi". A detailed description is provided in Reiche et al. 2015 (Chapter 2.1.2 and 2.1.3).

Value

List of 7 output paramter. (1) bayts: "bayts" time series data frame (2) flag: time at which unconfirmed change got flagged; (3) change.flagged: time at which confirmed change got flagged; (4) change.confirmed: time at which change is confirmed; (5) oldflag: time of earlier flagged but not confirmed changes; (6) vchange: vector of time steps from time at which change got flagged until confirmation; (7) vflag: vector of time steps at which unconfirmed change is flaged

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

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