Moving window
window_lsm(landscape, window, level, metric, name, type, what, progress, ...)# S3 method for RasterLayer
window_lsm(
landscape,
window,
level = "landscape",
metric = NULL,
name = NULL,
type = NULL,
what = NULL,
progress = FALSE,
...
)
# S3 method for RasterStack
window_lsm(
landscape,
window,
level = "landscape",
metric = NULL,
name = NULL,
type = NULL,
what = NULL,
progress = FALSE,
...
)
# S3 method for RasterBrick
window_lsm(
landscape,
window,
level = "landscape",
metric = NULL,
name = NULL,
type = NULL,
what = NULL,
progress = FALSE,
...
)
# S3 method for stars
window_lsm(
landscape,
window,
level = "landscape",
metric = NULL,
name = NULL,
type = NULL,
what = NULL,
progress = FALSE,
...
)
# S3 method for list
window_lsm(
landscape,
window,
level = "landscape",
metric = NULL,
name = NULL,
type = NULL,
what = NULL,
progress = FALSE,
...
)
Raster* Layer, Stack, Brick or a list of rasterLayers.
Moving window matrix.
Level of metrics. Either 'patch', 'class' or 'landscape' (or vector with combination).
Abbreviation of metrics (e.g. 'area').
Full name of metrics (e.g. 'core area')
Type according to FRAGSTATS grouping (e.g. 'aggregation metrics').
Selected level of metrics: either "patch", "class" or "landscape".
It is also possible to specify functions as a vector of strings, e.g. what = c("lsm_c_ca", "lsm_l_ta")
.
Print progress report.
Arguments passed on to calculate_lsm()
.
list
The function calculates for each focal cell the selected landscape metrics (currently only landscape level
metrics are allowed) for a local neighbourhood. The neighbourhood can be specified using a matrix. For more
details, see ?raster::focal()
. The result will be a RasterLayer
in which each focal cell includes
the value of its neighbourhood and thereby allows to show gradients and variability in the landscape (Hagen-Zanker 2016).
To be type stable, the acutally result is always a nested list (first level for RasterStack
layers, second level
for selected landscape metrics).
Fletcher, R., Fortin, M.-J. 2018. Spatial Ecology and Conservation Modeling: Applications with R. Springer International Publishing. 523 pages
Hagen-Zanker, A. (2016). A computational framework for generalized moving windows and its application to landscape pattern analysis. International journal of applied earth observation and geoinformation, 44, 205-216.
McGarigal, K., Cushman, S.A., and Ene E. 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following website: http://www.umass.edu/landeco/research/fragstats/fragstats.html
# NOT RUN {
window <- matrix(1, nrow = 5,ncol = 5)
window_lsm(landscape, window = window, what = c("lsm_l_pr", "lsm_l_joinent"))
window_lsm(landscape_stack, window = window, what = c("lsm_l_pr", "lsm_l_joinent"))
# }
# NOT RUN {
# }
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