Package 'ecochange' reference manual

Title:	Integrating Ecosystem Remote Sensing Products to Derive EBV Indicators
Description:	Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that document biodiversity change. Freely available ecosystem remote sensing products (ERSP) are downloaded and integrated with data for national or regional domains to derive indicators for EBV in the class ecosystem structure (Pereira et al., 2013) <doi:10.1126/science.1229931>, including horizontal ecosystem extents, fragmentation, and information-theory indices. To process ERSP, users must provide a polygon or geographic administrative data map. Downloadable ERSP include Global Surface Water (Peckel et al., 2016) <doi:10.1038/nature20584>, Forest Change (Hansen et al., 2013) <doi:10.1126/science.1244693>, and Continuous Tree Cover data (Sexton et al., 2013) <doi:10.1080/17538947.2013.786146>.
Authors:	Wilson Lara Henao [aut, cre] , Victor Gutierrez-Velez [aut] , Ivan Gonzalez [ctb] , Maria C. Londono [ctb]
Maintainer:	Wilson Lara Henao <[email protected]>
License:	GPL-3
Version:	2.9.3.2
Built:	2024-11-09 04:08:08 UTC
Source:	https://github.com/cran/ecochange

EBV Stats

Description

This function is a wrapper of cellStats used to compute statistics for spatial indicators in the EBV class ecosystem structure. To derive the spatial indicators see functions echanges and sampleIndicator

Usage

EBVstats(ccp, stats, 
    ...)EBVstats(ccp, stats, 
    ...)

Arguments

`ccp`	`echanges`, or `RasterStack` or `NULL`. If `NULL` then `NULL` is returned.
`stats`	`character`. vector of `stats` defined in `cellStats`. If missing then six summary statistics, including `'mean'`, `'sd'`, `'min'`, `'max'`, are computed.
`...`	Additional arguments in `cellStats`

Value

tibble.

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

Examples

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) are computed by
## processing the 'amazon' brick:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'EBVstats' is used to compute ecosystem statistics
st_amazon <- EBVstats(def)

## A plot of the 'st_amazon' object
plot.EBVstats(st_amazon,
               cex = 1.5,
               xlab = 'Year',
               ylab = 'Canopy cover (%)',
               main = 'Ecosystem changes',
               sub = 'Northern Amazon',
               fill = 'Layer')
## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) are computed by
## processing the 'amazon' brick:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'EBVstats' is used to compute ecosystem statistics
st_amazon <- EBVstats(def)

## A plot of the 'st_amazon' object
plot.EBVstats(st_amazon,
               cex = 1.5,
               xlab = 'Year',
               ylab = 'Canopy cover (%)',
               main = 'Ecosystem changes',
               sub = 'Northern Amazon',
               fill = 'Layer')

Ecosystem changes

Description

This function produces ecosystem-change maps by masking cell values in a layer of ecosystem changes over a target set of ecosystem variables. The function also allows focusing the ecosystem-change analysis on a species distribution range.

Usage

echanges(ps, eco = names(ps[[1:(nlayers(ps) - 
    1)]]), change = names(ps[[(nlayers(ps))]]), 
    sp_dist, eco_range = c(1, 
        100), change_vals = 1:19, 
    sp_dist_range = c(1, 
        1), spread = TRUE, 
    get_unaffected = TRUE, 
    binary_output = FALSE, 
    noDataValue = 0, 
    mc.cores = round(detectCores() * 
        0.6, 0), ...)echanges(ps, eco = names(ps[[1:(nlayers(ps) - 
    1)]]), change = names(ps[[(nlayers(ps))]]), 
    sp_dist, eco_range = c(1, 
        100), change_vals = 1:19, 
    sp_dist_range = c(1, 
        1), spread = TRUE, 
    get_unaffected = TRUE, 
    binary_output = FALSE, 
    noDataValue = 0, 
    mc.cores = round(detectCores() * 
        0.6, 0), ...)

Arguments

`ps`	`RasterStack` or `SpatialPolygonsDataFrame`. Stack of spatial data, including the target ecosystem variables, a layer of changes, and an alternative layer of a species distribution range. This argument can also be a polygon geometry used to integrate such spatial data via implementation of `rsp2ebv`; see the ellipsis term below.
`eco`	`character`. Regular expression matching names of a subset of layers representing the target ecosystem variables. Default matches names of the first `1:(n-1)` layers in `ps`.
`change`	`character`. Name of the layer of ecosystem changes. Default matches the name of the last layer in `ps`.
`sp_dist`	`character`. Name of an alternative layer representing a species distribution range. If missing then this argument is ignored.
`eco_range`	`numeric`. Range of values in the target ecosystem variable.
`change_vals`	`numeric`. Vector of values in the layer of ecosystem changes.
`sp_dist_range`	`numeric`. Range of values in the alternative layer of species. distribution range. This argument is ignored if `sp_dist` is missing.
`spread`	`logical`. Spread representation of ecosystem changes. Users do not need to `change` this argument. It is used by other rouines to fastening computation of ecosystem horizontal extents. If `FALSE` then the function mask cell values in the target ecosystem variables over over the layer of ecosystem changes. Default `TRUE`.
`get_unaffected`	`logical`. Extract unaffected areas. If `FALSE` then pixel values of the ecological variable across the changed areas are extracted. Default `TRUE`.
`binary_output`	`logical`. Produce binary outputs (masks). If `FALSE` then ranges of values of the ecological variable are maintained. Default `FALSE`.
`noDataValue`	`numeric`. Output NoDataValue. Default `0`.
`mc.cores`	`numeric`. The number of cores. Default uses around 60 percent of the CPU capacity.
`...`	If `ps` is a polygon then additional arguments in `rsp2ebv`.

Value

Class echanges.

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

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.

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.

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.

Pereira, H.M., Ferrier, S., Walters, M., Geller, G.N., Jongman, R.H.G., Scholes, R.J., Bruford, M.W., Brummitt, N., Butchart, S.H.M., Cardoso, A.C. and Coops, N.C., 2013. Essential biodiversity

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. variables. Science, 339(6117), pp.277-278.

Examples

## 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 <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Method 'plot.echanges' allows comparing rasters using a common scale bar:
plot.echanges(def)
## 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 <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Method 'plot.echanges' allows comparing rasters using a common scale bar:
plot.echanges(def)

Gauge Biodiversity Indicator

Description

This function processes ecosystem-change maps from echanges to calculate biodiversity indicators, including ecosystem extent, entropy, fractal dimension, among others. To sample the indicators across fixed-size grids see sampleIndicator.

Usage

gaugeIndicator(ps, ..., 
    metric = "area_ha", 
    smp_lsm = list(), 
    mc.cores = round(detectCores() * 
        0.6, 0))gaugeIndicator(ps, ..., 
    metric = "area_ha", 
    smp_lsm = list(), 
    mc.cores = round(detectCores() * 
        0.6, 0))

Arguments

`ps`	`SpatialPolygonsDataFrame` or `RasterStack`. Polygon geometry used to produce ecosystem-change maps via the implementation of `echanges` or the stack of ecosystem-change maps.
`...`	If `ps` is a `polygon` then additional arguments in `echanges` or `rsp2ebv`.
`metric`	`character`. The name of an indicator. Default `'area_ha'` computes ecosystem areas (ha) at class level. See the argument `'metric'` in `list_lsm` to implement other metrics.
`smp_lsm`	`list`. List of arguments in `calculate_lsm`. This argument is ignored when `metric = 'area_ha'`.
`mc.cores`	`numeric`. The number of cores. Default uses around 60 percent of the cores.

Details

Coordinate reference system of the spatial units must have metric units UTM. Performance in the computation of ecosystem extents is optimized via the implementation of the function tabuleRaster. Indicators other than ecosystem extents are calculated implementing calculate_lsm.

Value

Class Indicator.

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

References

Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K., & Nowosad, J. (2019). landscapemetrics: an open source R tool to calculate landscape metrics. Ecography, 42(10), 1648-1657.

O'Connor, B., Secades, C., Penner, J., Sonnenschein, R., Skidmore, A., Burgess, N. D., & Hutton, J. M. (2015). Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote sensing in ecology and conservation, 1(1), 19-28.

Pereira, H.M., Ferrier, S., Walters, M., Geller, G.N., Jongman, R.H.G., Scholes, R.J., Bruford, M.W., Brummitt, N., Butchart, S.H.M., Cardoso, A.C. and Coops, N.C., 2013. Essential biodiversity variables. Science, 339(6117), pp.277-278.

Skidmore, A. K., & Pettorelli, N. (2015). Agree on biodiversity metrics to track from space: Ecologists and space agencies must forge a global monitoring strategy. Nature, 523(7561), 403-406.

Examples

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'gaugeIndicator' is used to compute ecosystem areas
## (default):
am_areas <- gaugeIndicator(def,
                           mc.cores = 2)

plot.Indicator(am_areas)

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'gaugeIndicator' is used to compute ecosystem areas
## (default):
am_areas <- gaugeIndicator(def,
                           mc.cores = 2)

plot.Indicator(am_areas)

Get Geographic Adminitrative Unit

Description

This function can retrieve Geographic Administrative Data Maps (GADM).

Usage

getGADM(unit.nm = NULL, 
    level = 2, country = "COL", 
    ext = "json", path = tempdir())getGADM(unit.nm = NULL, 
    level = 2, country = "COL", 
    ext = "json", path = tempdir())

Arguments

`unit.nm`	`character` or `NULL`. Name of Geographic Administrative Data Map (e.g., municipality), or the name of such an unit plus its corresponding higher-level unit (e.g., department/state). If `NULL` then a list of administrative subdivisions is printed.
`level`	`numeric`. A number between zero and two, indicating any of the levels of administrative subdivisions (`0=country`, `1=first administrative subdivision`, and `2=second administrative subdivision`).
`country`	`character`. `ISO` code specifying a `country`. Default `'COL'`.
`ext`	`character`. File extension of the retrieved data file. Default `'json'`.
`path`	`character`. Path name indicating where the unit will be stored. Default stores the data in a temporary directory.

Value

SpatialPolygonsDataFrame or character vector of GADM units..

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre] (<https://orcid.org/0000-0003-3527-1380>), Victor Gutierrez-Velez [aut] (<https://orcid.org/0000-0003-1338-2020>), Ivan Gonzalez [ctb] (<https://orcid.org/0000-0002-0313-398X>), Maria C. Londono [ctb] (<https://orcid.org/0000-0002-2317-5503>)

References

https://gadm.org/

Examples

## Printing municipalities of Colombia:

     
         muni <- getGADM(NA)
         head(muni)
     

## Printing municipalities of Colombia:

     
         muni <- getGADM(NA)
         head(muni)

Get remote sensing product

Description

This function processes the extent of a predefined region of interest (polygon geometry or GADM unit) to download ecosystem remote sensing products (ERSP). Downloadable ERSP include Global Surface Water, Forest Change, and Continuous Tree Cover data. See listGP.

Usage

getrsp(roi = NULL, ..., 
    lyrs = NULL, path, 
    rewrite.pass = FALSE, 
    verify.web = FALSE, 
    mc.cores = round(detectCores() * 
        0.6, 0))getrsp(roi = NULL, ..., 
    lyrs = NULL, path, 
    rewrite.pass = FALSE, 
    verify.web = FALSE, 
    mc.cores = round(detectCores() * 
        0.6, 0))

Arguments

`roi`	`SpatialPolygonsDataFrame`; or `sf`; or `character`; or `NULL`. Region of interest. This can be either 1) a polygon geometry; or 2) the name of a `GADM` unit (see `getGADM`); or 3) a `NULL` value. Default `NULL` makes the function to print a list of `GADM` units.
`...`	If `roi` is a `GADM` unit then additional arguments in `getGADM`.
`lyrs`	`character`. Remote-sensing products. Default `NULL` makes the function to print a list of Downloadable data, see `listGP`.
`path`	`character`. Path name indicating where the variables are stored. If missing then a folder named as `'ecochange'` created in a current temporary directory is used.
`rewrite.pass`	`logical`. Rewrite password. Only valid to download new NASA Earth data, see details section.
`verify.web`	`logical`. Verify in the web whether the `URL`s used to download the `rsp` are available. See `getOption('webs')`. Default `FALSE`.
`mc.cores`	`numeric`. The number of cores. Default uses around 60 percent of the cores.

Details

Downloads of Continuous Tree Cover data require user authentication through the NASA Earth data Login. To obtain a NASA Earth data Login account, please visit: https://urs.earthdata.nasa.gov/users/new.

Value

Path names of the remote sensing products just retrieved, or character vectors suggesting GADM units/Global Products that can be used to download ERSP (see NULL defaults in arguments 'roi' and 'lyrs').

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

References

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.

Examples

## Polygon of the Colombian municipality of Cartagena del Chaira:
    load(system.file('cchaira_roi.RData',package = 'ecochange'))

## A Global Surface Water layer ('seasonality') which covers the
## extent of the polygon is retrieved:

 
 rsp_cchaira <- getrsp(cchaira_roi,
   lyrs = 'seasonality', mc.cores = 2, path = tempdir())
 file.exists(rsp_cchaira)
 
## Polygon of the Colombian municipality of Cartagena del Chaira:
    load(system.file('cchaira_roi.RData',package = 'ecochange'))

## A Global Surface Water layer ('seasonality') which covers the
## extent of the polygon is retrieved:

 
 rsp_cchaira <- getrsp(cchaira_roi,
   lyrs = 'seasonality', mc.cores = 2, path = tempdir())
 file.exists(rsp_cchaira)

Get WRS

Description

This function processes regions of interest (a polygon geometry or GADM unit) to find corresponding Landsat Path/Row World Reference System (WRS) polygons. This function is internally implemented by getrsp

Usage

getWRS(roi = NULL, path = tempdir(), 
    ...)getWRS(roi = NULL, path = tempdir(), 
    ...)

Arguments

`roi`	`SpatialPolygonsDataFrame`; or `character`; or `NULL`. Region of interest. This can be whether 1) a polygon geometry; or 2) the name of a `GADM` unit (see `getGADM`); or 3) a `NULL` value. Default `NULL` makes the function to print a list of `GADM` units.
`path`	`character`. Path name indicating where the `WRS` data are processed.
`...`	Additional arguments in `getGADM`.

Value

SpatialPolygonsDataFrame, or set of GADM units.

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

Examples

load(system.file('cchaira_roi.RData',package = 'ecochange'))
 
 wrs_cchaira <- getWRS(cchaira_roi)
     plot(wrs_cchaira)
 
load(system.file('cchaira_roi.RData',package = 'ecochange'))
 
 wrs_cchaira <- getWRS(cchaira_roi)
     plot(wrs_cchaira)

List of global products

Description

This function prints information about ecosystem remote sensing products that can be downloaded with getrsp.

Usage

listGP(layer = TRUE, 
    Algorithm = TRUE, 
    author = TRUE, funs = FALSE, 
    api.code = FALSE)listGP(layer = TRUE, 
    Algorithm = TRUE, 
    author = TRUE, funs = FALSE, 
    api.code = FALSE)

Arguments

`layer`	`character`. Add column `'layer'` to the data.
`Algorithm`	`character`. Add column `'Algorithm'` to the data.
`author`	`character`. Add column `'author'` to the data.
`funs`	`character`. Add column `'funs'` to the data.
`api.code`	`character`. Add column `'api.code'` to the data.

Value

tibble.

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

References

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.

Examples

lst <- listGP()

lst <- listGP()

Visualize EBVstats objects

Description

Plots for objects from EBVstats are printed.

Usage

## S3 method for class 'EBVstats'
plot(x, y, ...)## S3 method for class 'EBVstats'
plot(x, y, ...)

Arguments

x

tibble. Data set of statistics such as that produced by EBVstats.

y

character. Color scale. If missing then grDevices::terrain.colors(n), where n is the number of layers, is implemented.

...

Graphical arguments:

cex: adjustment of sizes for most text values,
xlab, and ylab: titles for the x and y axes,
main: a text of the main title,
sub: a text for the sub title,
labels: a string or numeric sequence for the x-axis labels,
fill: a text for the legend title

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

Examples

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) are computed by
## processing the 'amazon' brick:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'EBVstats' is used to compute ecosystem statistics
st_amazon <- EBVstats(def)

## A plot of the 'st_amazon' object
plot.EBVstats(st_amazon,
               cex = 1.5,
               xlab = 'Year',
               ylab = 'Canopy cover (%)',
               main = 'Ecosystem changes',
               sub = 'Municipality: Cartagena del Chaira',
               fill = 'Layer')

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) are computed by
## processing the 'amazon' brick:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'EBVstats' is used to compute ecosystem statistics
st_amazon <- EBVstats(def)

## A plot of the 'st_amazon' object
plot.EBVstats(st_amazon,
               cex = 1.5,
               xlab = 'Year',
               ylab = 'Canopy cover (%)',
               main = 'Ecosystem changes',
               sub = 'Municipality: Cartagena del Chaira',
               fill = 'Layer')

Visualize ecosystem changes

Description

This function can display level and box plots for objects from rsp2ebv, echanges, or sampleIndicator.

Usage

## S3 method for class 'echanges'
plot(x, y, ...)## S3 method for class 'echanges'
plot(x, y, ...)

Arguments

x

Raster*, or echanges. RasterStack object or ecoystem-change representation.

y

character. A color palette. If this is missing or the suggest viridis is not installed then terrain.colors is implemented.

...

Graphical arguments:

type: what type of plot should be drawn: "p" for level plots (default), or "b" for box plots,
cex: adjustment of sizes for most text values. If missing then cex = 1; if a main title is specified then it is increased 1.4*cex,
xlab, and ylab: titles for the x and y axes,
main: a text of the main title,
labels: a string or numeric sequence for the panel titles

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

Examples

## 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 <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

plot.echanges(def)
## 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 <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

plot.echanges(def)

Visualize Indicator objects

Description

Plots for objects from gaugeIndicator are produced.

Usage

## S3 method for class 'Indicator'
plot(x, y, 
    ...)## S3 method for class 'Indicator'
plot(x, y, 
    ...)

Arguments

x

tibble. Data set of indicators such as that produced by gaugeIndicator.

y

character. A color palette. If this is missing or the suggest viridis is not installed then terrain.colors is implemented.

...

Graphical arguments:

type: what type of plot should be drawn: "s" for stacked bar plots (default), or "b" for box plots,
cex: adjustment of sizes for most text values,
xlab, and ylab: titles for the x and y axes,
main: a text of the main title,
sub: a text for the sub title,
labels: a string or numeric sequence for the x-axis labels,
fill: a text for the legend title

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

Examples

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) are computed by
## processing the 'amazon' brick:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'gaugeIndicator' is used to compute ecosystem areas
## (default metric = 'area_ha'):
am_areas <- gaugeIndicator(def,
                         mc.cores = 2)

## A plot of the 'am_areas' object
plot.Indicator(am_areas,
               cex = 1.5,
               xlab = 'Year',
               ylab = 'Area (ha)',
               main = 'Ecosystem changes',
               sub = 'Northern amazon',
               fill = 'Forest cover (%)')

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) are computed by
## processing the 'amazon' brick:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)

## Function 'gaugeIndicator' is used to compute ecosystem areas
## (default metric = 'area_ha'):
am_areas <- gaugeIndicator(def,
                         mc.cores = 2)

## A plot of the 'am_areas' object
plot.Indicator(am_areas,
               cex = 1.5,
               xlab = 'Year',
               ylab = 'Area (ha)',
               main = 'Ecosystem changes',
               sub = 'Northern amazon',
               fill = 'Forest cover (%)')

Integrate remote sensing products

Description

This function integrates ecosystem remote sensing products and produces raster-data sections with the cell values enclosed in a region of interest.

Usage

rsp2ebv(ps = NULL, ..., 
    lyrs = NULL, path, 
    sr, ofr = c(30, 30), 
    mc.cores = round(detectCores() * 
        0.6, 0))rsp2ebv(ps = NULL, ..., 
    lyrs = NULL, path, 
    sr, ofr = c(30, 30), 
    mc.cores = round(detectCores() * 
        0.6, 0))

Arguments

`ps`	`SpatialPolygonsDataFrame`; or `sf`; or `character`; or `NULL`. Region of interest. This can be whether 1) a polygon geometry; or 2) the name of a `GADM` unit (see `getGADM`); or 3) a `NULL` value. Default `NULL` makes the function to print a list of `GADM` units.
`...`	Additional arguments in `getGADM` and `getrsp`.
`lyrs`	`character`. Remote-sensing products. Default `NULL` makes the function to print a list of Downloadable data, see `listGP`.
`path`	`character`. Path name indicating where the variables are stored. If missing then a folder named as `'ecochange'` created in a current temporary directory is used.
`sr`	`character`. `PROJ.4` description of the target coordinate reference system. If missing then the target layers are projected to metric system `UTM`.
`ofr`	`numeric`. `c(xres,yres)`. Output file resolution (in target georeferenced units). Default `c(30,30)` m2.
`mc.cores`	`numeric`. The number of cores. Default uses around 60 percent of the cores.

Details

This function implements 'sf::gdal_utils' so it assumes the user's machine has a valid GDAL installation.

Value

Class echanges.

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

References

Skidmore, A. K., & Pettorelli, N. (2015). Agree on biodiversity metrics to track from space: Ecologists and space agencies must forge a global monitoring strategy. Nature, 523(7561), 403-406.

Examples

## A Global Surface Water layer ('seasonality') covering the extent of a
## Colombian municipality Cartagena del Chaira is formated into an
## spatial EBV:
        load(system.file('cchaira_roi.RData',package = 'ecochange'))

 
 rsp_cchaira <- getrsp(cchaira_roi,
   lyrs = 'seasonality', mc.cores = 2, path = tempdir())

 file.exists(rsp_cchaira)

 season_cchaira <- rsp2ebv(cchaira_roi,
                               lyrs = 'seasonality', path = tempdir())
 
## A Global Surface Water layer ('seasonality') covering the extent of a
## Colombian municipality Cartagena del Chaira is formated into an
## spatial EBV:
        load(system.file('cchaira_roi.RData',package = 'ecochange'))

 
 rsp_cchaira <- getrsp(cchaira_roi,
   lyrs = 'seasonality', mc.cores = 2, path = tempdir())

 file.exists(rsp_cchaira)

 season_cchaira <- rsp2ebv(cchaira_roi,
                               lyrs = 'seasonality', path = tempdir())

Sample Biodiversity indicator

Description

This function divides into fixed-size grids each of the scenes of a stack of ecosystem-spatial data and samples a biodiversity indicator by every grid. To compute biodiversity indicators at the class and landscape levels, see gaugeIndicator

Usage

sampleIndicator(ps = NULL, 
    ..., metric = "condent", 
    classes = 5, min = 1, 
    max = 100, side, 
    smp_lsm = list(level = "landscape"), 
    mc.cores = round(detectCores() * 
        0.6, 0))sampleIndicator(ps = NULL, 
    ..., metric = "condent", 
    classes = 5, min = 1, 
    max = 100, side, 
    smp_lsm = list(level = "landscape"), 
    mc.cores = round(detectCores() * 
        0.6, 0))

Arguments

`ps`	`SpatialPolygonsDataFrame` or `RasterStack`. Polygon geometry used to produce ecosystem-change maps via the implementation of `echanges` or the stack of ecosystem-change maps.
`...`	If `ps` is a `polygon` then additional arguments in `echanges` or `rsp2ebv`.
`metric`	`character`. The name of an indicator other than ecosystem extent. This can be cohesion (`'cohesion'`), conditional entropy (`'condent'`), perimeter-area fractal dimension (`'pafrac'`), among others, see package `list_lsm`. Default `'condent'`.
`classes`	`numeric`; or `NULL`. Number of evenly spaced `classes` used to reclassify the layers. Default `5`. If `NULL` then the layers are not reclassified.
`min`	`numeric`. If `classes != NULL` then minimum cell value in the layers. Default `1`
`max`	`numeric`. If `classes != NULL` then maximum cell value in the layers. Default `100`
`side`	`numeric`. Side of the sampling grid (`m`). If missing the function tries to find a grid size the samples at least a grid with a non-NA value of the indicator.
`smp_lsm`	`List`. Additional arguments in `sample_lsm`
`mc.cores`	`numeric`. The number of cores. Default uses 60 percent of the cores.

Value

Class echanges

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

References

Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K., & Nowosad, J. (2019). landscapemetrics: an open source R tool to calculate landscape metrics. Ecography, 42(10), 1648-1657.

Skidmore, A. K., & Pettorelli, N. (2015). Agree on biodiversity metrics to track from space: Ecologists and space agencies must forge a global monitoring strategy. Nature, 523(7561), 403-406.

Examples


## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)


plot.echanges(amazon)

## Function 'sampleIndicator' is implemented to sample a metric of
## conditional entropy (default):

    def_condent <- sampleIndicator(def, side = 400, mc.cores = 2)

plot.echanges(def_condent, cex = 1.5)

## RasterBrick of structural Essential Biodiversity Variables
## covering the extent of a location in the northern Amazon basin
## (Colombia) is imported:
path. <- system.file('amazon.grd',package = 'ecochange')
amazon <- brick(path.)

## Changes in layers of tree-canopy cover (TC) in the 'amazon'
## brick are computed:
def <- echanges(amazon, eco = 'TC',
                change = 'lossyear',
                eco_range = c(1,80),
                get_unaffected = TRUE,
                binary_output = FALSE,
                mc.cores = 2)


plot.echanges(amazon)

## Function 'sampleIndicator' is implemented to sample a metric of
## conditional entropy (default):

    def_condent <- sampleIndicator(def, side = 400, mc.cores = 2)

plot.echanges(def_condent, cex = 1.5)

Fast tabulation of pixel values

Description

This function generates frequency tables for scenes in ecosystem remote sensing products by wrapping rasterDT. The function is mapped by gaugeIndicator to optimize computation of ecoystem extents.

Usage

tabuleRaster(layer = "", 
    del0 = TRUE, useNA = "no", 
    n256 = FALSE)tabuleRaster(layer = "", 
    del0 = TRUE, useNA = "no", 
    n256 = FALSE)

Arguments

`layer`	`character`. File path to an ERSP scene.
`del0`	`logical`. Remove the 0-count categories.
`useNA`	`logical`. Include NA values. This argument is passed to `rasterDT::freqDT`.
`n256`	`logical`. Do the raster contains less than 256 unique values?

Value

data.frame.

Author(s)

Wilson Lara Henao <[email protected]> [aut, cre], Victor Gutierrez-Velez [aut], Ivan Gonzalez [ctb], Maria C. Londono [ctb]

Examples

 
 tabuleRaster(raster(volcano), n256 = FALSE)
 
tabuleRaster(raster(volcano), n256 = FALSE)

Package 'ecochange'

Help Index

EBV Stats

Description

Usage

Arguments

Value

Author(s)

Examples

Ecosystem changes

Description

Usage

Arguments

Value

Author(s)

References

Examples

Gauge Biodiversity Indicator

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Get Geographic Adminitrative Unit

Description

Usage

Arguments

Value

Author(s)

References

Examples

Get remote sensing product

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Get WRS

Description

Usage

Arguments

Value

Author(s)

Examples

List of global products

Description

Usage

Arguments

Value

Author(s)

References

Examples

Visualize EBVstats objects

Description

Usage

Arguments

Author(s)

Examples

Visualize ecosystem changes

Description

Usage

Arguments

Author(s)

Examples

Visualize Indicator objects

Description

Usage

Arguments

Author(s)

Examples

Integrate remote sensing products

Description

Usage

Arguments