calcPNF: Calculating conditional non-forest probabilities

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

Description

Calculating conditional non-forest probability (PNF). Probabilities are calculated based on pdfs for F and NF (derived from F and NF distributions). (Guassian and Weibull pdf types are supported)

Usage

calcPNF(ts, pdf, bwf = c(0, 1))

Arguments

ts	single value, vector or time series object of class "ts"
pdf	"pdf" object describing F and NF distributions. "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. See Example for details on how to create a "pdf" object.
bwf	block weighting function to truncate the NF probability (default = c(0,1) = no truncation)

Value

single value, vector or time series object of class "ts"

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

# NDVI time series
ndvi <- c(0.5,0.7)

# create pdf (Probability density functions) object for forest and non-forest
pdfType <- c("gaussian","gaussian")
pdfF <-  c(0.85, 0.1) #mean and sd
pdfNF <- c(0.3, 0.2)  #mean and sd
pdf <- c(pdfType,pdfF,pdfNF)

# calculate conditional non-forest probabilities
calcPNF(ndvi,pdf)

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