R/sta-package.R

In sta: Seasonal Trend Analysis for Time Series Imagery in R

#' Statistical Trend Analysis (STA) for Time Series of Satellite Imagery
#' 
#' STA applies the Mann-Kendall test for trend to the so-called \emph{shape parameters} 
#' of periodic time series. STA estimates shape parameters via harmonic regression.
#' STA can handle \code{numeric} time series and \code{RasterStack} of satellite images.
#' 
#' Shape parameters is the term used in vegetation monitoring to refer to the amplitudes
#' and phase angles resulting from fitting a harmonic regression model to time series of 
#' vegetation indices derived from satellite images. Regardless of its origin, STA can be
#' applied to any periodic time series which makes this package potentially useful to
#' other disciplines such as hydrology, climatology and econometrics.
#' 
#' @details 
#' 
#' With \code{\link{sta}} (the main function of this package) it is possible to perform
#' the Mann-Kendall test for trend on time series of the three most commonly used 
#' shape parameters: \emph{mean}, \emph{annual} and \emph{semiannual}. These parameters
#' are the estimated amplitude coefficients of the aforementioned harmonic regresion
#' model. This function allows parallel processing to handle large satellite time series
#' imagery.
#' 
#' STA includes the following graphical methods:
#' \itemize{
#'    \item \code{\link{plot.staNumeric}}: generic plot displaying \code{\link{sta}}'s output
#'    for numeric time series.
#'    
#'    \item \code{\link{plot.staMatrix}}: maps of \code{\link[mapview]{mapview-class}} displaying
#'    \code{\link{sta}}'s output for \code{RasterStack}.
#' }
#' 
#' STA include the following datasets:
#' \itemize{
#'     \item \code{\link{marismas}}: numeric vector containing 10-day Composite NDMI values
#'     from 2000 to 2018.
#'     
#'     \item \code{\link{ndmi}}: \code{RasterStack} containing 612 spatial subsets of 10-day
#'     Composite NDMI images acquired from 2001 to 2017.
#' }
#' 
#' @name sta-package
#' @author Tecuapetla-Gómez, I. \email{itecuapetla@@conabio.gob.mx}
#' 
#' @keywords package
#' @references Eastman, R., Sangermano, F., Ghimire, B., Zhu, H., Chen, H., Neeti, N., Cai, Y., Machado, E.,  Crema, S. (2009).
#' \emph{Seasonal trend analysis of image time series}, International Journal of Remote Sensing
#' \bold{30(10)}, 2721--2726.
#' @docType package
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