Package 'gimms' reference manual

Title:	Download and Process GIMMS NDVI3g Data
Description:	This is a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread 'raster' package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.
Authors:	Florian Detsch [cre, aut]
Maintainer:	Florian Detsch <[email protected]>
License:	MIT + file LICENSE
Version:	1.2.2
Built:	2024-11-11 06:00:22 UTC
Source:	https://github.com/environmentalinformatics-marburg/gimms

Download and Process GIMMS NDVI3g Data

Description

Download and Process GIMMS NDVI3g Data

Details

This is a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread raster package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon Raster* images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.

Author(s)

Florian Detsch

References

Pinzon, JE & Tucker, CJ (2014). A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series. Remote Sensing, 6(8), 6929-6960, doi:10.3390/rs6086929.

The National Center for Atmospheric Research (2018). A Big Earth Data Platform for Three Poles. Global GIMMS NDVI3g v1 dataset (1981-2015). Available online at http://poles.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88/ (accessed on 2021-04-15).

Bale Mountains NDVI3g.v1

Description

Bale Mountains NDVI3g.v1.

Format

raster::RasterStack

Details

This dataset contains NDVI3g.v1 observations for the Bale Mountains National Park, southern Ethiopia (Jul 1981 to Dec 2015).

Download GIMMS NDVI3g Data

Description

Download GIMMS NDVI3g data, optionally for a given period of time. NDVI3g.v1 (NetCDF, until end 2015) is available from the "poles" and "ecocast" servers, whereas NDVI3g.v0 (ENVI binary, until end 2013) is "ecocast" and "nasanex" exclusive.

Usage

## S4 method for signature 'dateORmissing,dateORmissing'
downloadGimms(
  x,
  y,
  version = 1L,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  server = c("poles", "nasanex", "ecocast"),
  ...
)

## S4 method for signature 'numericORmissing,numericORmissing'
downloadGimms(
  x,
  y,
  version = 1L,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  server = c("poles", "nasanex", "ecocast"),
  ...
)

## S4 method for signature 'character,ANY'
downloadGimms(
  x,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  ...
)

## S4 method for signature 'missing,missing'
downloadGimms(
  version = 1L,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  server = c("poles", "nasanex", "ecocast"),
  ...
)
## S4 method for signature 'dateORmissing,dateORmissing'
downloadGimms(
  x,
  y,
  version = 1L,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  server = c("poles", "nasanex", "ecocast"),
  ...
)

## S4 method for signature 'numericORmissing,numericORmissing'
downloadGimms(
  x,
  y,
  version = 1L,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  server = c("poles", "nasanex", "ecocast"),
  ...
)

## S4 method for signature 'character,ANY'
downloadGimms(
  x,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  ...
)

## S4 method for signature 'missing,missing'
downloadGimms(
  version = 1L,
  dsn = getwd(),
  overwrite = FALSE,
  quiet = TRUE,
  mode = "wb",
  cores = 1L,
  server = c("poles", "nasanex", "ecocast"),
  ...
)

Arguments

`x`	Start time for data download as either `Date` object (e.g., `as.Date("2000-01-01")`) or `numeric` year (e.g., `2000`). Alternatively, a `character` vector of online filepaths to download created from `updateInventory`. If `missing`, all files available online are being downloaded.
`y`	End time for data download as either `Date` object or `numeric` year. Ignored if 'x' is a `character` object or missing.
`version`	`integer` (or any other convertible class), defaults to `1L`. Specifies desired GIMMS NDVI3g product version, see 'Details' in `updateInventory`. Ignored if 'x' is a `character` object.
`dsn`	`character`, defaults to the current working directory. Target folder for file download.
`overwrite`	`logical`, defaults to `FALSE`. If `TRUE`, identically named files in 'dsn' will be overwritten.
`quiet`	`logical`. If `TRUE` (default), console output is reduced.
`mode`	`character`. See `download.file`.
`cores`	`integer`, defaults to `1L`. Number of cores used for parallel processing. Note that a fast internet connection is required in order for parallelization to take effect.
`server`	See `updateInventory`.
`...`	Further arguments passed to `download.file`, e.g. 'method'.

Value

A character vector of local filepaths.

Examples

## Not run: 
tmp <- tempdir()

## 'Date' method
gimms_files_date <- downloadGimms(x = as.Date("2000-01-01"),
                                  y = as.Date("2000-12-31"),
                                  dsn = tmp)

## 'numeric' method (i.e., particular years)
gimms_files_year <- downloadGimms(x = 2000,
                                  y = 2002,
                                  dsn = tmp)

## 'character' method (i.e., particular files)
ecocast <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
gimms_files_char <- readRDS(ecocast)
gimms_files_char <- downloadGimms(x = gimms_files_char[1:6],
                                  dsn = tmp)

## 'missing' method (i.e., entire collection)
gimms_files_full <- downloadGimms(dsn = tmp)

## End(Not run)

## Not run: 
tmp <- tempdir()

## 'Date' method
gimms_files_date <- downloadGimms(x = as.Date("2000-01-01"),
                                  y = as.Date("2000-12-31"),
                                  dsn = tmp)

## 'numeric' method (i.e., particular years)
gimms_files_year <- downloadGimms(x = 2000,
                                  y = 2002,
                                  dsn = tmp)

## 'character' method (i.e., particular files)
ecocast <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
gimms_files_char <- readRDS(ecocast)
gimms_files_char <- downloadGimms(x = gimms_files_char[1:6],
                                  dsn = tmp)

## 'missing' method (i.e., entire collection)
gimms_files_full <- downloadGimms(dsn = tmp)

## End(Not run)

Kilimanjaro NDVI3g.v0

Description

Kilimanjaro NDVI3g.v0.

Format

raster::RasterStack

Details

This dataset contains NDVI3g.v0 observations for the Kilimanjaro region, northern Tanzania (Jul 1981 to Dec 2013).

Calculate Monthly Composite Images

Description

Based on a user-defined function, e.g. max for maximum value composites (MVC), aggregate half-monthly GIMMS data sets to monthly composites.

Usage

## S4 method for signature 'RasterStackBrick'
monthlyComposite(x, indices, fun = max, cores = 1L, filename = "", ...)

## S4 method for signature 'character'
monthlyComposite(
  x,
  version = 1L,
  pos1 = ifelse(version == 1, 15L, 4L),
  pos2 = ifelse(version == 1, 23L, 8L),
  fun = max,
  cores = 1L,
  filename = "",
  ...
)
## S4 method for signature 'RasterStackBrick'
monthlyComposite(x, indices, fun = max, cores = 1L, filename = "", ...)

## S4 method for signature 'character'
monthlyComposite(
  x,
  version = 1L,
  pos1 = ifelse(version == 1, 15L, 4L),
  pos2 = ifelse(version == 1, 23L, 8L),
  fun = max,
  cores = 1L,
  filename = "",
  ...
)

Arguments

`x`	Multi-layered `Raster*` object or `character` vector of filenames. If the latter applies and 'pos1', 'pos2' are not specified, the function will try to retrieve monthly indices from `monthlyIndices`. Note that the specification of NDVI3g.v0 ENVI binary files is hereby not allowed and in such a case, `rasterizeGimms` should be run beforehand.
`indices`	`numeric`. Indices to denote layers or files from identical months.
`fun`	`function`. Used to calculate monthly composite layers, defaults to `max`. Note that a separate 'na.rm' argument is passed down to `stackApply` via '...' and hence should not be included here.
`cores`	`integer`. Number of cores for parallel computing.
`filename`	`character`. Optional output filename passed to `stackApply`.
`...`	Further arguments passed to `stackApply` (i.e., 'na.rm') and its underlying `writeRaster` call.
`version`	`integer` (or any other class convertible to `integer`). Specifies GIMMS NDVI3g product version, see 'Details' in `updateInventory`.
`pos1`, `pos2`	`integer`. If 'x' is a vector of filenames, the first and last element of the date string to build monthly indices from. Defaults to the 'version'-specific NDVI3g naming convention, see `monthlyIndices`.

Value

If length(x) == 2, a single RasterLayer object, else a RasterStack object with monthly composite layers.

Examples

data("bale3g.v1")

## select layers from 1981 only
fls <- system.file("extdata/inventory_ecv1.rds", package = "gimms")
fls <- readRDS(fls)[1]
rst <- bale3g.v1[[1:12]]

## aggregate to monthly mvc layers
mvc <- monthlyComposite(rst, indices = monthlyIndices(fls))

data("bale3g.v1")

## select layers from 1981 only
fls <- system.file("extdata/inventory_ecv1.rds", package = "gimms")
fls <- readRDS(fls)[1]
rst <- bale3g.v1[[1:12]]

## aggregate to monthly mvc layers
mvc <- monthlyComposite(rst, indices = monthlyIndices(fls))

Create Monthly Indices from NDVI3g Files

Description

Create numeric monthly indices from (local or online) GIMMS NDVI3g filenames as input for the monthlyComposite function.

Usage

monthlyIndices(
  x,
  version = 1L,
  pos1 = ifelse(version == 1, 15L, 4L),
  pos2 = ifelse(version == 1, 23L, 8L),
  timestamp = FALSE,
  ...
)
monthlyIndices(
  x,
  version = 1L,
  pos1 = ifelse(version == 1, 15L, 4L),
  pos2 = ifelse(version == 1, 23L, 8L),
  timestamp = FALSE,
  ...
)

Arguments

`x`	`character`. Vector of (local or online) filenames.
`version`	`integer` (or any other class convertible to `integer`). Specifies GIMMS NDVI3g product version, see 'Details' in `updateInventory`.
`pos1`, `pos2`	`numeric`. The first and last element of the date string in 'x', defaults to the GIMMS naming convention of the specified product version.
`timestamp`	`logical`, defaults to `FALSE`. If `TRUE`, an actual `Date` object is returned rather than a `numeric` vector of indices.
`...`	Currently not used.

Value

A numeric vector with unique monthly indices or, if timestamp = TRUE, the actual timestamps as Date objects.

Examples

## NDVI3g.v1
gimms_files_v1 <- readRDS(system.file("extdata", "inventory_ecv1.rds", package = "gimms"))

monthlyIndices(gimms_files_v1[1], version = 1)                   # indices
monthlyIndices(gimms_files_v1[1], version = 1, timestamp = TRUE) # dates

## Similarly, NDVI3g.v0
gimms_files_v0 <- readRDS(system.file("extdata", "inventory_ecv0.rds", package = "gimms"))
gimms_files_v0 <- rearrangeFiles(gimms_files_v0) # reorder by date

monthlyIndices(gimms_files_v0[1:12], version = 0)
monthlyIndices(gimms_files_v0[1:12], version = 0, timestamp = TRUE)

## NDVI3g.v1
gimms_files_v1 <- readRDS(system.file("extdata", "inventory_ecv1.rds", package = "gimms"))

monthlyIndices(gimms_files_v1[1], version = 1)                   # indices
monthlyIndices(gimms_files_v1[1], version = 1, timestamp = TRUE) # dates

## Similarly, NDVI3g.v0
gimms_files_v0 <- readRDS(system.file("extdata", "inventory_ecv0.rds", package = "gimms"))
gimms_files_v0 <- rearrangeFiles(gimms_files_v0) # reorder by date

monthlyIndices(gimms_files_v0[1:12], version = 0)
monthlyIndices(gimms_files_v0[1:12], version = 0, timestamp = TRUE)

Create Traditional GIMMS NDVI3g File Names

Description

Reformat the names of local NDVI3g.v1 files according to "traditional" NDVI3g.v0 naming convention.

Usage

oldNaming(x, suffix = "")
oldNaming(x, suffix = "")

Arguments

`x`	`character`. GIMMS NDVI3g.v1 file names to reformat, see 'Details'.
`suffix`	`character`. Optional suffix to append to the resulting file names, e.g. `".tif"` for GeoTiff files (see also `writeFormats`).

Details

The satellite number, which was an essential constituent of the NDVI3g.v0 file naming convention (see 'References'), has been removed from NDVI3g.v1 file names. However, it can easily be accessed via ncvar_get given that the file has previously been downloaded. If an NDVI3g.v1 file specified to 'x' is not available locally, by contrast, the satellite number is retrieved from the built-in NDVI3g.v0 file inventory (until end 2013; see 'Examples') and newer files are tagged with "XX" indicating missing information.

Value

A character vector of file names following the "traditional" NDVI3g.v0 naming convention.

References

http://nasanex.s3.amazonaws.com/AVHRR/GIMMS/3G/00READMEgeo.txt (accessed on 2021-04-15).

Examples

fls <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
fls <- basename(readRDS(fls))

oldNaming(fls[64:65], suffix = ".tif") # adds .tif extension
oldNaming(fls[65:66])                  # tags newer files with 'XX'

fls <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
fls <- basename(readRDS(fls))

oldNaming(fls[64:65], suffix = ".tif") # adds .tif extension
oldNaming(fls[65:66])                  # tags newer files with 'XX'

Perform Quality Control on GIMMS NDVI3g Data

Description

Perform quality control on GIMMS NDVI3g data based on the companion flag information. This is merely a wrapper around overlay and, since quality control is readily available through rasterizeGimms, it is strongly recommended to use the latter function for this purpose.

Usage

## S4 method for signature 'RasterStackBrick'
qualityControl(x, keep = NULL, filename = "", ...)
## S4 method for signature 'RasterStackBrick'
qualityControl(x, keep = NULL, filename = "", ...)

Arguments

`x`	A single 2-layered `RasterStack` object (NDVI and flags).
`keep`	`integer`. Accepted flag values (see 'Details').
`filename`	`character`. Optional output filename.
`...`	Further arguments passed to `writeRaster`.

Details

If 'keep' is missing, the function will automatically skip quality control and return the input object.

Value

A quality-controlled 'RasterLayer' object.

Examples

## Not run: 
tmp <- tempdir()

## Download NDVI3g.v1 sample data
ecocast <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
gimms_files <- downloadGimms(readRDS(ecocast)[1], dsn = tmp)

## Import data as 'Raster*' objects
ndvi <- raster::raster(gimms_files, varname = "ndvi")
ndvi[ndvi[] %in% c(-32768, -3000)] <- NA
ndvi <- ndvi / 1e4

flag <- floor(raster::raster(gimms_files, varname = "percentile") / 2e3)

## Perform quality control and visualize
to_check <- stack(ndvi[[1]], flag[[1]])
qcl <- qualityControl(to_check, keep = 0)

plot(qcl)

## End(Not run)

## see also 'Examples' section in ?rasterizeGimms for automated quality check

## Not run: 
tmp <- tempdir()

## Download NDVI3g.v1 sample data
ecocast <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
gimms_files <- downloadGimms(readRDS(ecocast)[1], dsn = tmp)

## Import data as 'Raster*' objects
ndvi <- raster::raster(gimms_files, varname = "ndvi")
ndvi[ndvi[] %in% c(-32768, -3000)] <- NA
ndvi <- ndvi / 1e4

flag <- floor(raster::raster(gimms_files, varname = "percentile") / 2e3)

## Perform quality control and visualize
to_check <- stack(ndvi[[1]], flag[[1]])
qcl <- qualityControl(to_check, keep = 0)

plot(qcl)

## End(Not run)

## see also 'Examples' section in ?rasterizeGimms for automated quality check

Rasterize GIMMS NDVI3g Data

Description

Import GIMMS NDVI3g (binary or NetCDF) data into R as Raster* objects.

Usage

rasterizeGimms(
  x,
  ext = NULL,
  snap = "out",
  keep = NULL,
  split = FALSE,
  cores = 1L,
  filename = "",
  ...
)
rasterizeGimms(
  x,
  ext = NULL,
  snap = "out",
  keep = NULL,
  split = FALSE,
  cores = 1L,
  filename = "",
  ...
)

Arguments

`x`	`character`. Vector of local filepaths. Note that product versions must not be mixed, i.e. 'x' should represent files originating from either NDVI3g.v1 or NDVI3g.v0 only.
`ext`	`Extent`, or any object from which an `Extent` can be extracted, see `crop`.
`snap`	`character`, defaults to "out". Other available options are "in" and "near", see `crop`.
`keep`	`integer`. Flag values of NDVI3g pixels to spare during quality control. Pixels with non-included flag values are set to `NA`. If not specified (i.e., `NULL`; default), quality control is skipped.
`split`	`logical`, defaults to `FALSE`. If `TRUE`, a `list` of `RasterStack` objects (of `length(x)`) is returned rather than a single `RasterStack`.
`cores`	`integer`. Number of cores for parallel computing.
`filename`	`character`. Optional output filename. If specified, this must be of the same length as 'x'.
`...`	Further arguments passed to `writeRaster`.

Value

If split = TRUE, a list of NDVI3g RasterStack objects corresponding to the files specified in 'x'; else a single NDVI3g RasterStack object.

Examples

## Not run: 
tmp <- tempdir()

## Download NDVI3g.v1 sample data
gimms_files <- downloadGimms(x = as.Date("2000-01-01"),
                             y = as.Date("2000-12-31"),
                             dsn = tmp)

## Extent for clipping
shp <- getData("GADM", country = "DEU", level = 0, path = tmp)

## Rasterize without quality control
gimms_raster <- rasterizeGimms(x = gimms_files,
                               ext = shp) # clipping
plot(gimms_raster[[1]])
lines(shp)

## Rasterize with quality control
gimms_rasterq <- rasterizeGimms(x = gimms_files,
                                ext = shp, # clipping
                                keep = 0)  # quality control
plot(gimms_rasterq[[1]])
lines(shp)

## End(Not run)

## Not run: 
tmp <- tempdir()

## Download NDVI3g.v1 sample data
gimms_files <- downloadGimms(x = as.Date("2000-01-01"),
                             y = as.Date("2000-12-31"),
                             dsn = tmp)

## Extent for clipping
shp <- getData("GADM", country = "DEU", level = 0, path = tmp)

## Rasterize without quality control
gimms_raster <- rasterizeGimms(x = gimms_files,
                               ext = shp) # clipping
plot(gimms_raster[[1]])
lines(shp)

## Rasterize with quality control
gimms_rasterq <- rasterizeGimms(x = gimms_files,
                                ext = shp, # clipping
                                keep = 0)  # quality control
plot(gimms_rasterq[[1]])
lines(shp)

## End(Not run)

Rearrange GIMMS NDVI3g.v0 Files

Description

Rearrange local GIMMS NDVI3g.v0 files in ascending order of time. Since the naming convention has significantly changed towards NDVI3g.v1, such a measure should only be relevant for older file formats.

Usage

rearrangeFiles(
  x,
  dsn = getwd(),
  pattern = "^geo.*.VI3g$",
  pos = c(4, 6, 11),
  ...
)
rearrangeFiles(
  x,
  dsn = getwd(),
  pattern = "^geo.*.VI3g$",
  pos = c(4, 6, 11),
  ...
)

Arguments

`x`	`character`. Vector of local filepaths. If missing, 'dsn' will be searched for available files via pattern matching.
`dsn`	`character`, defaults to the current working directory. Path to look for GIMMS-related data if 'x' is missing.
`pattern`	`character`, defaults to `"^geo.*.VI3g$"` for standard NDVI3g.v0 files. A regular expression passed to `list.files`.
`pos`	`integer`, defaults to `c(4, 6, 11)` for standard NDVI3g.v0 files. The start positions of year, month and part of the month ('a' or 'b') in the target GIMMS files.
`...`	Further arguments passed to `list.files`.

Value

A character vector of filepaths arranged in ascending order of time.

Compute (Pre-Whitened) Kendall's $\tau$

Description

Apply the Mann-Kendall trend test (Mann, 1945) to a series of observations and return Kendall's $\tau$ (Kendall, 1938) based on a predefined significance level. In contrast to other readily available implementations, it is up to the user to decide whether or not to apply pre-whitening as described in the zyp package vignette (Bronaugh and Werner, 2013).

Usage

## S4 method for signature 'numeric'
significantTau(
  x,
  p = 0.001,
  prewhitening = TRUE,
  method = c("yuepilon", "zhang"),
  df = FALSE
)

## S4 method for signature 'RasterStackBrick'
significantTau(
  x,
  p = 0.001,
  prewhitening = TRUE,
  method = c("yuepilon", "zhang"),
  filename = "",
  ...
)
## S4 method for signature 'numeric'
significantTau(
  x,
  p = 0.001,
  prewhitening = TRUE,
  method = c("yuepilon", "zhang"),
  df = FALSE
)

## S4 method for signature 'RasterStackBrick'
significantTau(
  x,
  p = 0.001,
  prewhitening = TRUE,
  method = c("yuepilon", "zhang"),
  filename = "",
  ...
)

Arguments

`x`	Either a `Raster*` object or a `numeric` vector.
`p`	`numeric`, defaults to `0.001`. Significance level to be tested.
`prewhitening`	`logical`. If `TRUE` (default), pre-whitening is applied prior to the Mann-Kendall trend test.
`method`	`character`. The prewhitening method to apply, see `zyp.trend.vector`.
`df`	`logical`, defaults to `FALSE`. If `TRUE`, a `data.frame` holding the value of Kendall's $\tau$ and the referring significance level.
`filename`	`character`. Optional output filename.
`...`	Further arguments passed to `writeRaster`.

Details

If available, the function will automatically use open multi-core clusters for parallel processing (see beginCluster and Examples).

Value

numeric input: If df = FALSE (default), a single numeric or logical (i.e., NA) depending on whether or not 'p' was exceeded; else a data.frame with Kendall's $\tau$ and the corresponding significance level.
RasterStackBrick input: A RasterLayer with values of Kendall's $\tau$ . Values exceeding the specified 'p' are discarded.

References

Kendall, M.G. (1938). A new measure of rank correlation. Biometrika 30(1/2), 81-93, doi: 10.2307/2332226.

Mann, H.B. (1945). Nonparametric tests against trend. Econometrica 13(3), 245-259, doi: 10.2307/1907187.

Zhang, X., Vincent, L.A., Hogg, W.D. and A. Niitsoo (2000). Temperature and Precipitation Trends in Canada during the 20th Century. Atmosphere-Ocean 38(3), 395-429, doi:10.1080/07055900.2000.9649654.

Yue, S., Pilon, P., Phinney, B. and G. Cavadias (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes 16, 1807-1829,doi:10.1002/hyp.1095.

Examples

## Example taken from ?Kendall::MannKendall
library(Kendall)
data(PrecipGL)
plot(PrecipGL)

## Mann-Kendall trend test without pre-whitening
x <- as.numeric(PrecipGL)
significantTau(x, p = 0.001, prewhitening = FALSE, df = TRUE)

## Mann-Kendall trend test with pre-whitening
significantTau(x, p = 0.001, prewhitening = TRUE, df = TRUE)

#############################################################################
### use case: significant mann-kendall trends in ndvi3g.v0          #########
#############################################################################

## Not run: 
## Sample data from 1982 to 2013
data("kili3g.v0")
rst <- kili3g.v0[[13:nlayers(kili3g.v0)]]

## Remove seasonal signal
library(remote)
dsn <- deseason(rst, cycle.window = 24)

## Apply trend-free pre-whitened Mann-Kendall test (note that
## non-significant pixels are set to NA)
trd1 <- significantTau(dsn, p = 0.01, prewhitening = TRUE)
plot(trd1)

## Or, alternatively, use multi-core functionality
cores <- parallel::detectCores() - 1
if (require(snow)) {
  beginCluster(cores)
  trd2 <- significantTau(dsn, p = 0.01, prewhitening = TRUE)
  endCluster()
}

## End(Not run)

## Example taken from ?Kendall::MannKendall
library(Kendall)
data(PrecipGL)
plot(PrecipGL)

## Mann-Kendall trend test without pre-whitening
x <- as.numeric(PrecipGL)
significantTau(x, p = 0.001, prewhitening = FALSE, df = TRUE)

## Mann-Kendall trend test with pre-whitening
significantTau(x, p = 0.001, prewhitening = TRUE, df = TRUE)

#############################################################################
### use case: significant mann-kendall trends in ndvi3g.v0          #########
#############################################################################

## Not run: 
## Sample data from 1982 to 2013
data("kili3g.v0")
rst <- kili3g.v0[[13:nlayers(kili3g.v0)]]

## Remove seasonal signal
library(remote)
dsn <- deseason(rst, cycle.window = 24)

## Apply trend-free pre-whitened Mann-Kendall test (note that
## non-significant pixels are set to NA)
trd1 <- significantTau(dsn, p = 0.01, prewhitening = TRUE)
plot(trd1)

## Or, alternatively, use multi-core functionality
cores <- parallel::detectCores() - 1
if (require(snow)) {
  beginCluster(cores)
  trd2 <- significantTau(dsn, p = 0.01, prewhitening = TRUE)
  endCluster()
}

## End(Not run)

Update GIMMS NDVI3g File Inventory

Description

Download the latest version of the GIMMS NDVI3g file inventory from the National Center for Atmospheric Research, NASA Earth Exchange (NEX) Amazon AWS or Ames Ecological Forecasting Lab (ECOCAST). If the specified endpoint is not reachable (e.g., if there is no active internet connection), the latest local version of the file inventory is used.

Usage

updateInventory(
  server = c("poles", "nasanex", "ecocast"),
  version = 1L,
  quiet = FALSE
)
updateInventory(
  server = c("poles", "nasanex", "ecocast"),
  version = 1L,
  quiet = FALSE
)

Arguments

`server`	`character`. Specifies the remote server to use. Currently available options are `"poles"` (default), `"nasanex"` and `"ecocast"`.
`version`	`integer` (or any other convertible class), defaults to `1L`. Specifies desired GIMMS NDVI3g product version, see 'Details'. Currently ignored if `server != "ecocast"`.
`quiet`	`logical`, defaults to `FALSE`. If `TRUE`, console output is disabled.

Details

GIMMS NDVI3g.v1 is currently available from ECOCAST and The National Center for Atmospheric Research until end 2015 and comes in NetCDF (.nc4) format. In contrast, NDVI3g.v0 is available as ENVI binary imagery and available from ECOCAST (NASANEX) until end 2013 (2012) only.

Value

A character vector of online filepaths.

References

Examples

## Not run: 
updateInventory()
updateInventory(server = "nasanex", version = 0)

## End(Not run)

## note that local versions of the online file inventories are also available
ofl_ecv1 <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
readRDS(ofl_ecv1)

ofl_v0 <- system.file("extdata", "inventory_ecv0.rds", package = "gimms")
readRDS(ofl_v0)

ofl_plv1 <- system.file("extdata", "inventory_plv1.rds", package = "gimms")
readRDS(ofl_plv1)

## Not run: 
updateInventory()
updateInventory(server = "nasanex", version = 0)

## End(Not run)

## note that local versions of the online file inventories are also available
ofl_ecv1 <- system.file("extdata", "inventory_ecv1.rds", package = "gimms")
readRDS(ofl_ecv1)

ofl_v0 <- system.file("extdata", "inventory_ecv0.rds", package = "gimms")
readRDS(ofl_v0)

ofl_plv1 <- system.file("extdata", "inventory_plv1.rds", package = "gimms")
readRDS(ofl_plv1)

Package 'gimms'

Help Index

Download and Process GIMMS NDVI3g Data

Description

Details

Author(s)

References

Bale Mountains NDVI3g.v1

Description

Format

Details

Download GIMMS NDVI3g Data

Description

Usage

Arguments

Value

See Also

Examples

Kilimanjaro NDVI3g.v0

Description

Format

Details

Calculate Monthly Composite Images

Description

Usage

Arguments

Value

See Also

Examples

Create Monthly Indices from NDVI3g Files

Description

Usage

Arguments

Value

See Also

Examples

Create Traditional GIMMS NDVI3g File Names

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Perform Quality Control on GIMMS NDVI3g Data

Description

Usage

Arguments

Details

Value

See Also

Examples

Rasterize GIMMS NDVI3g Data

Description

Usage

Arguments

Value

See Also

Examples

Rearrange GIMMS NDVI3g.v0 Files

Description

Usage

Arguments

Value

See Also

Compute (Pre-Whitened) Kendall's τ\tauτ

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Update GIMMS NDVI3g File Inventory

Description

Usage

Arguments

Details

Compute (Pre-Whitened) Kendall's $\tau$