oGFC: Obtain Global Forest Change data
In EhrmannS/rasterTools: Obtain and process earth observation data

Description Usage Arguments Details Value Functions References See Also Examples

Obtain data from the 'Global Forest Change' dataset (paper).

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oGFC(mask = NULL, years = NULL, keepRaw = FALSE)

downloadGFC(file = NULL, localPath = NULL)

`mask`	[`geom` \| `Spatial*` \| `sf` \| `raster`] spatial object of which the extent is the area of interest.
`years`	[`integerish(.)`] year(s) for which GFC data should be extracted; see Details.
`keepRaw`	[`logical(1)`] should the raw data be retained (`TRUE`), or should only the derived data be returned (`FALSE`, default)?
`file`	[`character(1)`] the name of the file to download.
`localPath`	[`character(1)`] the local path where files are supposed to be stored (i.e. on your harddisc).

The object provided in mask is treated as a single mask, irrespective of that object consisting of only one or several features. The extent comprising all features (point(s), line(s), polygon(s)) is used as area of interest. This is in contrast to obtain, where a mask may consist of several features, each of which are treated as seperate mask.

The GFC dataset is based on a time-series analysis of Landsat images characterizing forest extent and change.

A problem with the GFC dataset is that the gain-layer is calculated for the overall period from 2000 to 2014, while the loss-layer contains the loss events on a yearly basis. Hence, to find the true value per year for a raster-cell may not be straightforward. In oGFC a yearly value is derived by removing all loss events up to the year in question from the year 2000-layer and subsequently adding the gain-layer. Gain events are in nature rather diffuse and happen progressively and relatively slowely throughout time. A raster cell, which was marked as "forest absent" in 2000 and which had a positive gain-value by 2014, "became tree" within this time-frame (i.e. became over 5 m tall). This event, "becoming tree", presumably does not mean that the vegetation in this raster cell grew to a tall and mature forest. Much rather it would have some hight between 5 m and what a tree in the given region can grow in that short time. To get a more accurate estimation of the forest cover - particularly in very dynamic landscapes - it might be wise to weigh the forest cover with some sort of productivity and/or macroclimate dataset, because more suitable sites result in faster growth of trees.

A RasterStack of gfc data.

downloadGFC: function to download data related to the GFC dataset

Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 846–850.

Other obtain operators (Global): oESALC, oMODIS, oWCLIM

## Not run: 

require(magrittr)

myGFC <- oGFC(mask = rtGeoms$mask,
              years = c(2002, 2006, 2010, 2014))
visualise(raster = myGFC$treecover_2002, trace = TRUE)

# get the (updated) bibliography
reference(style = "bibtex")

# the gfc tiles
gfcWindow <- data.frame(x = c(-180, 180),
                        y = c(-60, 80))
tiles_gfc <- gs_tiles(window = gfcWindow, cells = c(36, 14),
                       crs = projs$longlat)
visualise(geom = tiles_gfc)

## End(Not run)