ecochange: deforest – R documentation

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deforest

Deforest EBV

Description

This function can extract/subtract deforested areas from essential biodiversity variables (ebv) while focusing the analysis on a predefined area of occupancy.

Usage

deforest(stk, ebv, loss = "lossyear", 
    aoo, ebv.vals, loss.vals, 
    incremental = TRUE, 
    remnant.areas = TRUE, 
    keep.ebv = FALSE, 
    mc.cores = round(detectCores() * 
        0.6, 0))

Arguments

`stk`	`Raster*`. Stack of `ebv` such as that produced by `rsp2ebv` containing a `'lossyear'` layer.
`ebv`	`character`. Name of the target layers in `stk`.
`loss`	`character`. Name of the `'lossyear'` layer. Only for the case that it has a different name in `stk`. Default `'lossyear'`.
`aoo`	`character`. Name of an optional binary raster in `stk` used to focus the analysis on a specific Area of Occupancy.
`ebv.vals`	`numeric`. Cell values in `ebv`. If missing then the whole range of values is processed
`loss.vals`	`numeric`. Cell values in `loss`. If missing then the function try to extract the values from the target layers in `ebv`. Otherwise the whole range of values in `loss` is processed.
`incremental`	`logical`. Develop `incremental` instead of discrete masking along the `ebv.vals`. Default `TRUE`.
`remnant.areas`	`logical`. Extract from `ebv` remnant areas instead of deforested areas . Default `TRUE`.
`keep.ebv`	`logical`. Keep in the extracted areas the corresponding cell values in `ebv`. Default `FALSE` produces binary masks: `c(0,1)`.
`mc.cores`	`numeric`. The number of cores. Default uses around 60 percent CPU capacity.

Value

RasterBrick.

Author(s)

Wilson Lara Henao <wilarhen@temple.edu> [aut, cre], Victor Gutierrez-Velez [aut]

References

Jetz, W., McGeoch, M. A., Guralnick, R., Ferrier, S., Beck, J., Costello, M. J., ... & Meyer, C. (2019). Essential biodiversity variables for mapping and monitoring species populations. Nature Ecology & Evolution, 3(4), 539-551.

Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418-422.

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., ... & Kommareddy, A. (2013). High-resolution global maps of 21st-century forest cover change. science, 342(6160), 850-853.

Sexton, J. O., Song, X. P., Feng, M., Noojipady, P., Anand, A., Huang, C., ... & Townshend, J. R. (2013). Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. International Journal of Digital Earth, 6(5), 427-448.

Examples

## Warnings from GDAL/PROJ are suppressed.

    ## Brick with structural Essential Biodiversity Variables covering the
    ## extent of a location in the northern Amazon basin (Colombia):
    path. <- system.file('amazon.grd',package = 'ecochange')
    amazon <- suppressWarnings(brick(path.))
    
    ## Tree-cover layers in the 'amazon' brick are both formatted and
    ## deforested:
    suppressWarnings(
        def <- deforest(amazon, names(amazon)[grepl('TC', names(amazon))],
                        ebv.vals = 0:100,
                        remnant.areas = TRUE, keep.ebv = TRUE, mc.cores = 2)
    )
    
    ## Function 'plotebv' allows comparing rasters using a common scale bar:
    suppressWarnings(
    plotebv(def)
)

ecochange

Integrating Ecological Remote Sensing Data to Derive EBV Metrics

v1.3

GPL-3

Authors

Wilson Lara Henao [aut, cre], Victor Gutierrez-Velez [aut]

Initial release

2020-10-06