spatialze_lsm()
with directions
argumentcalculate_lsm
(all metrics: more than 5 times faster with 70% less memory
allocation for augusta_nlcd
; larger increases were found for smaller data)
and window_lsm
(a single metric: more than 6 times faster for augusta_nlcd
;
larger increases were found for smaller data)calculate_lsm
extras_df
object that lists which extras are needed by
each metrictibble::tibble()
with tibble::new_tibble(list())
in most functions.
This change is partially responsible for improvements of the window_lsm
speedraster_to_points
with get_points
in several places.
The get_points
function is based on the column and row numbers multiplied by
the resolution, not actual coordinates.table
with (faster) tabulate
in lsm_p_core
prepare_extras
,
get_area_patches
, get_class_patches
, get_complexity
, get_enn_patch
,
get_points
, and get_perimeter_patch
window_lsm
behaviour for situations with NAs values and non-square windowsterra
and sf
instead of raster
and sp
as underlying frameworksshow_*
functions to avoid ggplot2
warningshow_correlation
points_as_mat()
helper functionextract_lsm
returned an no-needed warning messagergeos
dependencyget_patches
returns a unique patch id for all classeswindow_lsm()
get_boundaries()
for matrix input and return_raster = TRUE
rel_mut_inf
to list_lsm()
ggplot2
versionlsm_l_ai
if class with only one cell existslsm_c_lsi
, lsm_c_nlsi
, lsm_l_lsi
not using cell surfaceslsm_l_relmutinf
to calculate relative mutual informationto_disk = TRUE
for spatialize_lsm
and matrix_to_raster
get_circumscribingcircle()
lsm_p_gyrate
has an argument to force the cell centroid to be within patchget_nearestneighbour()
can now return ID of neighbouring patchget_boundaries()
allows now to specify edge depthget_boundaries()
can return the patch id for edge cellsget_centroid()
returns the coordinates of each patch centroidsample_lsm()
that adds NA if class is not present in sample plotlsm_p_circle()
when whole landscape contains only one patchextract_lsm()
that directions argument was not passed onunpad_raster
to remove padding around rasterget_boundaries
to consider landscape boundarysample_lsm()
sample_lsm()
sample_lsm()
can use SpatialPolygonsDataFramecalculate_correlation()
returns a tibble with all correlations between metricsscale_sample()
allows to sample landscape metrics in buffer with increasing sizescale_window()
allows calculate selected metrics in moving windows over the provided landscape.show_correlation()
can take result from calculate_correlation()
sample_lsm()
returns a warning if percentage_inside
< 90%sample_lsm()
and extract_lsm()
can now be used with sample_ids (rather than just 1...n)lsm_c_ai()
if only one class and NA values were presentshow_correlation()
that first col was lostsample_lsm()
and extract_lsm()
to forward arguments to
calculate_lsm()
sample_lsm()
is now comparable between squares and circleswindow_lsm()
that some arguments were not passed on (resolution and points)extract_lsm()
and window_lsm()
that allowed metric subset was wrongshow_correlation()
list_lsm()
allows to return all BUT the selected metricssample_lsm()
can now use SpatialPolygons to sample metricssample_lsm()
can now handle SpatialLines to sample metricssample_lsm()
automatically detects provided data typeextract_lsm()
can now handle SpatialLines to extract metricslsm_l_ent()
and thus lsm_l_condent()
and lsm_l_mutinf()
.calculate_lsm()
/extract_lsm()
/sample_lsm()
/spatialize_lsm()
/window_lsm()
can print progresscat()
with message()
calculate_lsm()
returns an error message if selected metrics do not existconstruct_buffer()
can now return a matrix with coords instead of polygonscalculate_lsm()
checks the input data before calculating metricsget_lsm()
is now called spatialize_lsm()
moving_window()
to calculate metrics within a moving windowget_lsm()
to raster in which each cell has patch metric valuecheck_landscape()
if NA values were presentpad_raster()
that pad_raster_cells > 1 did not workcalculate_lsm()
calculating class area where resolution was lostdplyr
check_landscape()
check_landscape()
pad_raster()
is now typestable and always returns a listpad_raster()
can now return also a RasterLayer (and not just a matrix as)sample_lsm()
that occured when metrics where selected using what
argumentlsm_p_core()
if only one patch is presentlsm_p_circle()
if only one cell is present in classlsm_p_hyrate()
if only one cell is present in classget_adjacencies()
that checks if neighbourhood
was specified correctly did not work properlysample_lsm()
now returns a tibble including an extra column if return_raster = TRUE
(and not a nested tibble as before)list_lsm()
, calculate_lsm()
and `sample_lsm()
return_plots
to return_raster
in sample_lsm()
check_landscape
to make sure your landscapes are feasible for landscapemetricsraster_to_points()
to get also NA cells (not possible with raster::rasterToPoints
)get_boundaries()
to get only boundary cellsget_unique_values()
that shows all uniques labels in a classlist_lsm()
function to print available metricsshow_lsm()
function to vizualize patch level metricslsm_l_rpr
: Typo in internal function, used landscapemetrics::landscape instead of user inputshow_()
* functions that na.value color is identicalget_
-functions can now take matrix as input and also return a matrixcalculate_lsm()
now uses list_lsm()
. This allows more options to specify metrics to calculatelsm_abbreviations_names
show_()
-functions don't throw warningsshow_()
functionsextract_lsm()
now uses list_lsm()
. This allows more options to specify metrics to calculatercpp_get_coocurrence_matrix()
can now handle large rasters and is fasterlsm_p_circle()
and get_circumscribingcircle()
now consideres different x- & y-resolutionswhat
arguments of all show_()
-functions now are named class
for consistency (so all what
arguments deal with metrics)what
arguments of get_patches()
is now named class
for consistency (so all what
arguments deal with metrics)calculate_metrics
is now calculate_lsm
show_cores
, a function to plot the core area of patchesshow_patches
now also shows labelled class facets (option what
)sample_lsm
, a function to sample metrics around buffered pointsextract_lsm
, a function to extract landscape metrics for spatial coordinatespurrr
package and replaced them by lapply
calculate_lsm
has the option progress
consider_boundary
is available for all core metricsedge_depth
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