The goal of multilandr is to provide a clean platform for typical landscape-scale analysis. The package builds on several spatial-oriented R packages to provide a useful tool to develop and inspect landscapes at multiple spatial scales. The main functionality allows to calculate landscape metrics within a multi-scale approach. Among other practical capabilities, the package provides several utility functions to: (i) plot landscapes at different spatial scales; (ii) visualize correlations between different metrics; (iii) filter landscapes that fulfill with certain pre-defined conditions regarding their metrics; and (iv) generate optimized gradients for a given landscape metric.
multilandr supports several spatial objects from widespread spatial-oriented R packages as main inputs in their functions, including objects from packages terra, raster, and sf.
You can install the development version of multilandr from GitHub with:
# install.packages("devtools")
devtools::install_github("phuais/multilandr")
A common landscape-scale study comprises the evaluation of a variable or process (e.g. species richness or any ecological response) along a set of landscapes varying in one or more attributes (e.g. the amount of natural habitat). Usually, this evaluation is made through the definition of several spatial-scales. Multi-scale landscapes can be defined as concentric circular buffers around a central point (each of the evaluated sites). There are plenty of examples for this experimental design in the literature, such as De Camargo et al. (2018), Melo et al., (2017) or Verga et al. (2021).
multilandr provides tools to generate landscapes at different spatial scales and calculate landscape metrics at each spatial scale.
We will first generate an object of class ‘MultiLand’. For this, we need a vector of points depicting the location of the sites, around which landscapes of different sizes will be generated (buffers).
library(multilandr)
# Loads points
elchaco_sites <- terra::vect(system.file("extdata", "elchaco_sites.shp",
package = "multilandr"))
We also need at least one raster layer. This will typically represent different land covers within the extent of the vector layer of points. Other types of raster layers can be inputted. In this example, we also input a raster layer containing NDVI values within the extent of analysis.
# Loads main raster of land covers
elchaco <- terra::rast(system.file("extdata", "elchaco.tif",
package = "multilandr"))
# Loads extra raster of NDVI values
elchaco_ndvi <- terra::rast(system.file("extdata", "elchaco_ndvi.tif",
package = "multilandr"))
We can define representative names for the land covers of the main raster layer (here, a total of six):
cl_names <- c(1, "Forest",
2, "Grassland",
3, "Crops",
4, "Pastures",
5, "Water",
6, "Urban")
We are now in conditions of generating a ‘MultiLand’ object with the
function mland()
. We also need to specify the spatial scales to be
evaluated, through the argument radii
. Here we define five increasing
scales, from 1000 m until 5000 m, by 1000 m steps.
# Creates 'MultiLand' object by loading points, the main raster and an extra raster.
ernesdesign <- mland(points_vect = elchaco_sites,
land_rast = elchaco,
radii = seq(1000, 5000, 1000),
classnames = list(cl_names),
site_ref = "name",
ext_rast = elchaco_ndvi,
layer_names = c("landuse", "NDVI"))
#> Loading layers
#> Generating buffers
#> Generating intersections
The argument site_ref
receives a string with the name of the attribute
of the vector layer of points, that contains the information about the
identity of each point (i.e. the name of each site). We can also specify
a name for the raster layers in argument layer_names
, which could be
useful in further analyses.
Let’s see the output for the generated object:
# Returns basic information about the object
ernesdesign
#> class : MultiLand
#> On the fly : FALSE
#> Rasterlayers : 1
#> n classes : 6
#> Ext. rasterlayers: 1
#> n points : 15
#> Site reference : "name"
#> Radii (m) : 1000 2000 3000 4000 5000
Basic information about the object is provided, including the number of
main raster layers (and classes for each one), the number of extra
raster layers, the number of points and the evaluated spatial scales.
The object itself contains the vector layer of points, and the
intersections between the required spatial scales and the inputted
raster layers. These are circular buffers around each point. Further
exploration of the object can be made through ernesdesign@
.
By default, when mland()
generates an object of class ‘MultiLand’, it
generates the intersections between the buffers (defined by the
specified spatial scales) and the provided raster layers. However, this
might not be desirable when a great amount of points is being evaluated
(hundreds or thousands of sites), mainly due to memory issues.
Therefore, mland()
can be ran with the argument on_the_fly = TRUE
.
If this is the case, intersections will not be generated in this step,
and will be generated when needed by other functions of the package. For
more information, see the help page for the function, and the provided
example.
An object of class ‘MultiLand’ can be saved for further use in another R
session, through mland_save()
. An object of this class can be loaded
in a fresh R session through mland_load()
.
mland_save(ernesdesign)
Multi-scale landscapes can be plotted through mland_plot()
. Only those
landscapes generated by the intersections with one raster layer can be
plotted at one time. Here, we plot all the multi-scale landscapes around
the site "Peje"
and, by default, the main raster layer containing the
land covers is plotted.
mland_plot(ernesdesign, points = "Peje", title = "sitename")
Visualizing spatial data in the R environment can be uncomfortable.
Multi-scale landscapes can be exported for further exploration in a GIS
software, through the function mland_export_gis()
.
mland_export_gis(ernesdesign, dir = "ernesdesign")
Once generated the ‘MultiLand’ object, this will serve as the main
output for further functions, the most important being
mland_metrics()
. This function calculates metrics for the generated
landscapes at each spatial scale. Landscape metrics are calculated
through the package landscapemetrics
(Hesselbarth et al. 2019), and
the function accepts the same arguments as the functions of this
package: level
, metric
, name
, type
or what
. A full description
of available metrics can be explored with metrics_list()
, which simply
outputs the metrics available by landscapemetrics
.
In this example, we are calculating the percentage of the landscape
("pland"
) and the number of patches ("np"
) for each one of the land
covers of the main raster (level = "class"
). In the argument
absence_values
, one can define which value should have a given metric
when the class (land cover) is not present in the landscape. Here, it
makes sense that this value should be zero for each metric. Note that,
if not defined, the value in the case of absence of the given land cover
will be NA
.
Calculation of other non-typical landscape metrics can be calculated,
specially for those raster layers with continuous values. Here, we
calculate the mean value and a user-defined calculation for the NDVI
values, inside argument ext_calc
. The 1
indicates that calculations
must be performed for the first extra raster layer contained within the
‘MultiLand’ object (in this example, the unique one).
# User-defined function: quotient between mean and standard deviation
mean_sd <- function(x){ mean(x)/sd(x) }
# Calculation of metrics
ed_metrics <- mland_metrics(ernesdesign, level = "class", metric = c("pland", "np"),
absence_values = list("pland" = 0, "np" = 0),
ext_calc = list(c(1, "mean"), c(1, "mean_sd")))
Basic information about the object can be explored by printing the object:
# Returns basic information about the object
ed_metrics
#> class : MultiLandMetrics
#> Number of layers : 1
#> Number of classes: 6
#> Number of points : 15
#> Radii (m) : 1000 2000 3000 4000 5000
#> Metrics
#> Landscape-level:
#> Class-level : np pland
#> Patch-level :
#> Extra calcs. : mean sd
Function mland_metrics
outputs an object of class ‘MultiLandMetrics’,
whose main object comprises a data frame with the value for each
required metric at each landscape and spatial scale.
head(ed_metrics@data)
#> rasterlayer layer_name point_id site radius level class classname
#> 1 1 landuse 1 Algarrobo 1000 class 1 Forest
#> 2 1 landuse 1 Algarrobo 1000 class 2 Grassland
#> 3 1 landuse 1 Algarrobo 1000 class 3 Crops
#> 4 1 landuse 1 Algarrobo 1000 class 4 Pastures
#> 5 1 landuse 1 Algarrobo 1000 class 5 Water
#> 6 1 landuse 1 Algarrobo 1000 class 6 Urban
#> patch_id metric value
#> 1 NA np 57
#> 2 NA np 4
#> 3 NA np 81
#> 4 NA np 14
#> 5 NA np 0
#> 6 NA np 0
Data frame with metric’s values can be binded with a data frame
containing a response variable of interest within the evaluated sites.
The function metrics_bind()
fulfills this purpose, by generating a
wide-format data frame, with one column per metric and spatial scale.
For more information and examples, explore the help page of the
function.
multilandr provides several other utility functions, specially when
analyzing multi-scale landscapes comprising a hundreds or thousands of
points. Correlations between metrics can be calculated with
metrics_corr()
, and pairwise plots can be visualized with
metrics_plots()
. Landscapes can be filtered according to predefined
values for given metrics with metrics_filter()
. In addition, the
function metrics_gradient()
generates an optimized gradient of
predefined size for the values of a given metric, given the spectrum of
landscapes with different metric values. This can be useful to select
those landscapes that would be evaluated in a more bounded experimental
design that could comprises, for instance, field sampling of sites.
Explore these functions in the help pages.
If you use multilandr for your research, use the following citation:
Huais, PY. (under review). multilandr: an R package for multi-scale landscape analysis. Landscape Ecology.
De Camargo, R. X., Boucher‐Lalonde, V., & Currie, D. J. (2018). At the landscape level, birds respond strongly to habitat amount but weakly to fragmentation. Diversity and Distributions, 24(5), 629-639.
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.
Melo, G. L., Sponchiado, J., Cáceres, N. C., & Fahrig, L. (2017). Testing the habitat amount hypothesis for South American small mammals. Biological Conservation, 209, 304-314.
Verga, E. G., Huais, P. Y., & Herrero, M. L. (2021). Population responses of pest birds across a forest cover gradient in the Chaco ecosystem. Forest Ecology and Management, 491, 119174.
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