R/geoTS-package.R

In geoTS: Methods for Handling and Analyzing Time Series of Satellite Images

#' Methods for Handling and Analyzing Time Series of Satellite Images
#' 
#' We provide tools for handling time series of satellite images as well
#' as some statistical methods for spatio-temporal analysis
#' 
#' @name geoTS-package
#' @author Tecuapetla-Gomez, I. \email{itecuapetla@@conabio.gob.mx}
#' 
#' @section Tools for handling time series of satellite images:
#' \code{\link{transfer_bin_raster}} transfers data from images originally
#' recorded in a binary format to images in any of the formats
#' allowed by the \code{\link[raster]{raster-package}}. Similarly, 
#' \code{\link{transfer_raster_RData}} extracts the entries of
#' images originally recorded in \code{\link{tiff}} format, virtually stores them 
#' in an \code{\link{array}} object and, finally, this array is saved in an \code{RData} file.
#' \code{\link{split_replace}} allows us to split \code{Raster*} objects, which can
#' be arguably large, into smaller chunks. These chunks can be saved
#' in any of the formats allowed by \code{\link[raster]{writeRaster}}. Often, satellite
#' images come with missing values (or fill values assigned by other computer
#' programs), \code{\link{split_replace}} allows to replace these values by
#' values of users' convenience; see also \code{\link{reclassify}}. 
#' \code{\link{raster_intersect_sp}} allows us to obtain data in the intersection of
#' \code{Raster*} and \code{SpatialPolygonsDataFrame} objects.
#' 
#' @section Methods for analyzing time series of satellite images:
#' \code{\link{haRmonics}} allows us to fit harmonic regression models
#' to numeric vectors; the method \code{hants} is based on \cite{Roerink et al. (2000)}
#' whereas the method \code{harmR} is based on \cite{Jakubauskas et al. (2001)}.
#' The \code{wls_harmR} is the weighted least squares method which requires pre-estimation
#' of heteroscedastic variance; \code{\link{hetervar}} allows for heteroscedastic variance
#' estimation for numeric vectors extracted from time series of satellite imagery. 
#' 
#' geoTS include the following datasets:
#' \itemize{
#'     \item \code{\link[geoTS]{master}}: \code{RasterLayer} with a land mask of eastern Yucatan 
#'     Peninsula, Mexico.
#'     
#'     \item \code{\link{MOD13Q1_NDVI_2000129_009}}: A spatial subset of NDVI measurements
#'     taken over the eastern Yucatan Peninsula, Mexico in 2000.
#'     
#'     \item \code{\link{MOD13Q1_NDVI_Mohinora}}: \code{RasterStack} containing 23 spatial 
#'     subsets of 16-day NDVI images of \bold{Cerro Mohinora} acquired in 2001.
#'     
#'     \item \code{\link{shp_mohinora}}: \code{SpatialPolygonsDataFrame} delimiting
#'     the smallest Protected Area of Flora and Fauna in Mexico (\bold{Cerro Mohinora}).
#' }
#' 
#' @keywords package
#' 
#' @references Roerink, G.J., Menenti, M., Verhoef, W. (2000).
#' \emph{Reconstructing clodfree NDVI composites using Fourier analysis of time series}, 
#' Int. J. Remote Sensing, \bold{21(9)}, 1911--1917.
#' 
#' @references Jakubauskas, M., Legates, D., Kastens, J. (2001).
#' \emph{Harmonic analysis of time-series AVHRR NDVI data},
#' Photogrammetric Engineering and Remote Sensing, \bold{67(4)}, 461--470.
#'   
#' @references The Matlab implementation of HANTS can be found 
#' \href{https://nl.mathworks.com/matlabcentral/fileexchange/38841-matlab-implementation-of-harmonic-analysis-of-time-series-hants}{here}. 
#' @docType package
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