stfit (version 0.99.9)

stfit_landsat: STFIT for Landsat data

Description

This function is used for Landsat data imputation, which includes five steps: mean estimation, outlier detection, temporal effect estimation, spatial effect estimation and imputation. In real application, one can use this as a template to create a five steps imputation procedure depending on the real data structure.

Usage

stfit_landsat(
  year,
  doy,
  mat,
  img.nrow,
  img.ncol,
  doyeval = 1:365,
  h.tcov = 100,
  h.tsigma2 = 300,
  h.scov = 2,
  h.ssigma2 = 2,
  nnr = 10,
  outlier.action = c("keep", "remove"),
  outlier.tol = 0.2,
  intermediate.save = TRUE,
  intermediate.dir = "./intermediate_output/",
  use.intermediate.result = TRUE,
  teff = TRUE,
  seff = TRUE,
  doy.break = NULL,
  cycle = FALSE,
  t.grid = NULL,
  t.grid.num = 50,
  clipRange = c(0, 1800),
  clipMethod = "nnr",
  var.est = FALSE
)

Value

List of length 4 with entries:

• imat: imputed matrix of mat

• smat: standard error matrix of the same size as mat

• idx: a list of image indexes

  • idx.allmissing: completely missing image indexes,

  • idx.partialmissing: partially observed image indexes,

  • idx.fullyobserved: fully observed image indexes,

  • idx.outlier: outlier image indexes.

• outlier: a list of image outliers information

  • outidx: image index with outlier pixels,

  • outpct: percentage of outlier pixels corresponding to outidx,

  • outlst: a list of the same length as outidx, with each list the missing pixel index.

Arguments

year

vecotr of year

doy

vecotr of DOY (day of the year)

mat

a numeric matrix. Each row contains a row stacked image pixel values.

img.nrow

number of rows of the image

img.ncol

number of columns of the image

doyeval

a vector of DOY on which to get the mean and temporal imputation

h.tcov

bandwidth for temporal covariance estimation

h.tsigma2

bandwith for temporal variance estimation

h.scov

bandwidth for spatial covariance estimation

h.ssigma2

bandwidth for spatial variance estimation

nnr

maximum number of nearest neighbor pixels to use for spatial covariance estimation

outlier.action

"keep" to keep outliers; "remove" to replace outliers with imputed values

outlier.tol

The threshold to use to define outlier image. Default is 0.2, i.e. images with more than 20% outlier pixels are treated as outlier image.

intermediate.save

TRUE or FASLE; whether to save the intermediate results including mean, temporal effect and spacial effect imputation resutls. The intermediate results can be useful to avoid duplicating the computation for some imputation steps.

intermediate.dir

directory where to save the intermediate results

use.intermediate.result

whether to use the intermediate results in the 'intermediate.dir' folder. Default is TRUE.

teff

TRUE or FALSE, wheter to calculate the temporal effect. Default is TRUE.

seff

TRUE or FALSE, wheter to calculate the spatial effect. Default is TRUE.

doy.break

a vector of break points for doy where the spatial effect are estimated seperately on each interval. Default is NULL, i.e. the spatial effect is assumed to be the same over doy.

cycle

TRUE or FALSE. When doy.break is specified, whether to combine the first doy.break interval and the last doy.break together for spatial effect estimation.

t.grid

a vector of grid points on which to calculate the temporal covariance function

t.grid.num

number of grid points to use for temporal covariance estimation. Ignored if t.grid is given.

clipRange

passed to meanEst function

clipMethod

passed to meanEst function

var.est

Whether to estimate the variance of the temporal and spatial effects. Default is FALSE.

Examples

# \donttest{
library(doParallel)
library(raster)
library(rasterVis)
library(RColorBrewer)
dfB = landsat106[landsat106$year >= 2000,]
matB = as.matrix(dfB[,-c(1:2)])
year = dfB$year
doy = dfB$doy
if(require(doParallel))
  registerDoParallel(1)
res <- stfit_landsat(year, doy, matB, 31, 31, nnr=30,
use.intermediate.result = FALSE, intermediate.save = FALSE, var.est = TRUE)
## visualize the imputed results
idx = c(res$idx$idx.allmissing[150], res$idx$idx.partialmissing[c(30, 60, 90)])
rst_list = list()
for(i in 1:length(idx)){
  rst_list[(i-1)*3+1] = raster(matrix(matB[idx[i],], 31))
  rst_list[(i-1)*3+2] = raster(matrix(res$imat[idx[i],], 31))
  rst_list[(i-1)*3+3] = raster(matrix(res$sdmat[idx[i],], 31))
}
s = stack(rst_list)
levelplot(s, index.cond=list(c(seq(1, 12, 3), seq(2, 12, 3), seq(3, 12, 3))),
          par.setting = rasterTheme(panel.background=list(col="black"),
                                    region = brewer.pal(9, 'YlOrRd')),
          names.attr = c(rbind(paste0("Original ", idx), 
                               paste0("Imputed ", idx),
                               paste0("Std. Error ", idx))),
          layout = c(4,3))

# }