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Calculates a user defined set of landscape fragmentation indices for patch types identified in a raster*Object.

FragStatsCalc.Rd

Calculates a user defined set of landscape fragmentation indices for patch types identified in a raster*Object.

Usage

FragStatsCalc(
  inputRasterFiles = NULL,
  latlon = NULL,
  polyName = NULL,
  studysite = NULL,
  FragStats = "all",
  ncores = 1,
  saveCSV = FALSE
)

Arguments

inputRasterFiles

A raster-Object. Fragmentation statistics area calculated seperatly for every layer.

latlon

logical: Indicates whether or not the inputForestMap object is based on a geographic coordinate system or is projected to a planar coordinate system. In the former case, area is approximated by raster::area.

polyName

charachter of length 1. Indicates the column in the data frame of the studysite object to uniquely identify the features of interest. The function will fail if there is no unique identification of the polygons

studysite

An object of type sf with a given number of polygons defining the areas of interest. Forest area statistics will be calculated for features in the studysite object.

FragStats

Character vector of forest fragmentation parameters to be calculated. Currently available statistics are:

  • n.patches the number of patches of a particular patch type or in a class

  • total.area the sum of the areas (m2) of all patches of the corresponding patch type

  • prop.landscape the proportion of the total lanscape represented by this class

  • patch.density the numbers of patches of the corresponding patch type divided by total landscape area

  • total.edge the total edge length of a particular patch type

  • edge.density edge length on a per unit area basis that facilitates comparison among landscapes of varying size

  • mean.patch.area average area of patches

  • mean.frac.dim.index mean of fractal dimension index

  • lanscape.division.index based on the cumulative patch area distribution and is interpreted as the probability that two randomly chosen pixels in the landscape are not situated in the same patch

  • patch.cohesion.index measures the physical connectedness of the corresponding patch type

  • landscape.shape.index a standardized measure of total edge or edge density that adjusts for the size of the landscape

  • largest.patch.index largest patch index quantifies the percentage of total landscape area comprised by the largest patch

  • sd.patch.areastandard deviation of patch areas

  • min.patch.area the minimum patch area of the total patch areas

  • max.patch.area the maximum patch area of the total patch areas

  • perimeter.area.frac.dim perimeter-area fractal dimension equals 2 divided by the slope of regression line obtained by regressing the logarithm of patch area (m2) against the logarithm of patch perimeter (m)

  • mean.perim.area.ratio the mean of the ratio patch perimeter. The perimeter-area ratio is equal to the ratio of the patch perimeter (m) to area (m2)

  • sd.perim.area.ratio standard deviation of the ratio patch perimeter

  • max.perim.area.ratio maximum perimeter area ratio

  • mean.shape.index mean of shape index

  • sd.shape.index standard deviation of shape index

  • min.shape.index the minimum shape index

  • max.shape.index the maximum shape index

  • sd.frac.dim.index standard deviation of fractal dimension index

  • min.frac.dim.index the minimum fractal dimension index

  • max.frac.dim.index the maximum fractal dimension index

  • total.core.areathe sum of the core areas of the patches (m2)

  • prop.landscape.core proportional landscape core

  • mean.patch.core.area mean patch core area

  • sd.patch.core.area standard deviation of patch core area

  • min.patch.core.area the minimum patch core area

  • max.patch.core.area the maximum patch core area

  • prop.like.adjacencies calculated from the adjacency matrix, which shows the frequency with which different pairs of patch types (including like adjacencies between the same patch type) appear side-by-side on the map (measures the degree of aggregation of patch types)

  • aggregation.index computed simply as an area-weighted mean class aggregation index, where each class is weighted by its proportional area in the landscape

  • izesplitting.index based on the cumulative patch area distribution and is interpreted as the effective mesh number, or number of patches with a constant patch size when the landscape is subdivided into S patches, where S is the value of the splitting index

  • effective.mesh.size equals 1 divided by the total landscape area (m2) multiplied by the sum of patch area (m2) squared, summed across all patches in the landscape

ncores

The number of cores to use, i.e. at most how many child processes will be run simultaneously.

saveCSV

Default is FALSE. You can specify a directory on your local machine where the results area saved in .csv format. The features will be identified by the column specified in polyName.

Value

An sf-object with it's dataframe ammended by the specified fragmentation statistics with one column for every layer in inputRasterFiles representing observation years. Additionally trend statistics and the difference between the first and last observation are automatically calculated for every single parameter based on trend:sens.slope.

Author

Darius Görgen (MapTailor Geospatial Consulting GbR) info@maptailor.net
Maintainer: MAPME-Initiative contact@mapme-initiative.org
Contact Person: Dr. Johannes Schielein
Copyright: MAPME-Initiative
License: GPL-3

Examples

library(sf)
library(raster)
library(mapme.forest)

aoi = st_read(system.file("extdata", "aoi_polys.gpkg", package = "mapme.forest"))
#> Reading layer `aoi_polys' from data source 
#>   `/home/runner/work/_temp/Library/mapme.forest/extdata/aoi_polys.gpkg' 
#>   using driver `GPKG'
#> Simple feature collection with 8 features and 1 field
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 107.1291 ymin: 16.59741 xmax: 107.2502 ymax: 16.6916
#> Geodetic CRS:  WGS 84
yearlyRaster = stack(system.file("extdata", "pkgTest_yearlyCover.tif",
                                 package = "mapme.forest"))
names(yearlyRaster) = paste("y_", as.character(2000:2018))

result = FragStatsCalc(inputRasterFiles = yearlyRaster,
                       studysite = aoi[1,],
                       FragStats = c("n.patches", "total.area",
                                     "prop.landscape", "patch.density"),
                       latlon = TRUE,
                       polyName = "id",
                       ncores = 1,
                       saveCSV = FALSE)
str(result)
#> Classes ‘sf’ and 'data.frame':	1 obs. of  90 variables:
#>  $ id                    : int 1
#>  $ n.patches_Dif         : num 15
#>  $ n.patches_Sig         : num 0.019
#>  $ n.patches_Slope       : num 0.938
#>  $ n.patches_y_.2000     : num 1
#>  $ n.patches_y_.2001     : num 1
#>  $ n.patches_y_.2002     : num 1
#>  $ n.patches_y_.2003     : num 1
#>  $ n.patches_y_.2004     : num 1
#>  $ n.patches_y_.2005     : num 5
#>  $ n.patches_y_.2006     : num 16
#>  $ n.patches_y_.2007     : num 16
#>  $ n.patches_y_.2008     : num 19
#>  $ n.patches_y_.2009     : num 20
#>  $ n.patches_y_.2010     : num 20
#>  $ n.patches_y_.2011     : num 20
#>  $ n.patches_y_.2012     : num 15
#>  $ n.patches_y_.2013     : num 17
#>  $ n.patches_y_.2014     : num 16
#>  $ n.patches_y_.2015     : num 15
#>  $ n.patches_y_.2016     : num 16
#>  $ n.patches_y_.2017     : num 16
#>  $ n.patches_y_.2018     : num 16
#>  $ patch.density_Dif     : num 1.88e-05
#>  $ patch.density_Sig     : num 0.019
#>  $ patch.density_Slope   : num 1.18e-06
#>  $ patch.density_y_.2000 : num 1.26e-06
#>  $ patch.density_y_.2001 : num 1.26e-06
#>  $ patch.density_y_.2002 : num 1.26e-06
#>  $ patch.density_y_.2003 : num 1.26e-06
#>  $ patch.density_y_.2004 : num 1.26e-06
#>  $ patch.density_y_.2005 : num 6.28e-06
#>  $ patch.density_y_.2006 : num 2.01e-05
#>  $ patch.density_y_.2007 : num 2.01e-05
#>  $ patch.density_y_.2008 : num 2.39e-05
#>  $ patch.density_y_.2009 : num 2.51e-05
#>  $ patch.density_y_.2010 : num 2.51e-05
#>  $ patch.density_y_.2011 : num 2.51e-05
#>  $ patch.density_y_.2012 : num 1.88e-05
#>  $ patch.density_y_.2013 : num 2.14e-05
#>  $ patch.density_y_.2014 : num 2.01e-05
#>  $ patch.density_y_.2015 : num 1.88e-05
#>  $ patch.density_y_.2016 : num 2.01e-05
#>  $ patch.density_y_.2017 : num 2.01e-05
#>  $ patch.density_y_.2018 : num 2.01e-05
#>  $ prop.landscape_Dif    : num -0.942
#>  $ prop.landscape_Sig    : num 2.84e-08
#>  $ prop.landscape_Slope  : num -0.0513
#>  $ prop.landscape_y_.2000: num 0.991
#>  $ prop.landscape_y_.2001: num 0.991
#>  $ prop.landscape_y_.2002: num 0.991
#>  $ prop.landscape_y_.2003: num 0.991
#>  $ prop.landscape_y_.2004: num 0.99
#>  $ prop.landscape_y_.2005: num 0.699
#>  $ prop.landscape_y_.2006: num 0.247
#>  $ prop.landscape_y_.2007: num 0.247
#>  $ prop.landscape_y_.2008: num 0.213
#>  $ prop.landscape_y_.2009: num 0.207
#>  $ prop.landscape_y_.2010: num 0.207
#>  $ prop.landscape_y_.2011: num 0.207
#>  $ prop.landscape_y_.2012: num 0.127
#>  $ prop.landscape_y_.2013: num 0.0671
#>  $ prop.landscape_y_.2014: num 0.0531
#>  $ prop.landscape_y_.2015: num 0.0494
#>  $ prop.landscape_y_.2016: num 0.0485
#>  $ prop.landscape_y_.2017: num 0.0485
#>  $ prop.landscape_y_.2018: num 0.0485
#>  $ total.area_Dif        : num -750111
#>  $ total.area_Sig        : num 2.84e-08
#>  $ total.area_Slope      : num -40807
#>  $ total.area_y_.2000    : num 788692
#>  $ total.area_y_.2001    : num 788692
#>  $ total.area_y_.2002    : num 788692
#>  $ total.area_y_.2003    : num 788692
#>  $ total.area_y_.2004    : num 787950
#>  $ total.area_y_.2005    : num 556462
#>  $ total.area_y_.2006    : num 196616
#>  $ total.area_y_.2007    : num 196616
#>  $ total.area_y_.2008    : num 169906
#>  $ total.area_y_.2009    : num 164712
#>  $ total.area_y_.2010    : num 164712
#>  $ total.area_y_.2011    : num 164712
#>  $ total.area_y_.2012    : num 100905
#>  $ total.area_y_.2013    : num 53420
#>  $ total.area_y_.2014    : num 42291
#>  $ total.area_y_.2015    : num 39323
#>  $ total.area_y_.2016    : num 38581
#>  $ total.area_y_.2017    : num 38581
#>  $ total.area_y_.2018    : num 38581
#>  $ geometry              :sfc_POLYGON of length 1; first list element: List of 1
#>   ..$ : num [1:5, 1:2] 107 107 107 107 107 ...
#>   ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
#>  - attr(*, "sf_column")= chr "geometry"
#>  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
#>   ..- attr(*, "names")= chr [1:89] "id" "n.patches_Dif" "n.patches_Sig" "n.patches_Slope" ...