FragStatsCalc: Calculates a user defined set of landscape fragmentation...

In mapme-initiative/mapme.forest: Package to Support Project Evaluation and Planning in the Forest Sector

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

Description

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(s)

Darius Görgen (MapTailor Geospatial Consulting GbR) [e-mail]
Maintainer: MAPME-Initiative [e-mail]
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"))
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)

mapme-initiative/mapme.forest documentation built on April 15, 2022, 4:50 a.m.