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I want to calculate the percentage area of habitat suitability of a species that overlaps with protected area polygons. I do not know the R language very well, but here is what I have so far.

These are the attributes of the area of habitat suitability derived from a maxent prediction:

class      : RasterLayer 
dimensions : 6480, 8520, 55209600  (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333  (x, y)
extent     : -103, -32, -36, 18  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +ellps=WGS84

of the protected areas:

Simple feature collection with 5667 features and 2 fields (with 8 geometries empty)
geometry type:  GEOMETRY
dimension:      XY
bbox:           xmin: -118.6344 ymin: -59.85538 xmax: -25.29094 ymax: 32.48333
CRS:            +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0

Does someone know a way to calculate the percentage area of habitat suitability that overlaps with protected area polygons?

Sorry, I really do not know so much about how to work with these data. I hope I gave all the relevant information.

I would appreciate any input.

crocsandturtles
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  • I would separate these as into two questions. I don't think you'll get an answer for the second question - it looks like something went awry when you imported the data. I assume the data does not have the large circles if you open in a GIS? – Tim Assal Apr 10 '20 at 15:08

1 Answers1

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To answer your first question, you should be able to use zonal statistics to calculate the area of potential habitat found in protected areas using the spatialEco package:

zonal.stats(x, y, stats = c("min", "mean", "max"))
#x = Polygon object of class SpatialPolygonsDataFrame
#y = rasterLayer object of class raster

https://www.rdocumentation.org/packages/spatialEco/versions/1.3-0/topics/zonal.stats

Here is a reproducible example from the spatialEco package that first calculates the percentage of pixels in each polygon >= a threshold value and second calculates the sum of pixels in each polygon >= the threshold value used to reclassify the input raster. You might be interested in both avenues for your work. 

library(spatialEco)    
library(raster)
library(sp)                                                                          

# here the fxn will calculate the percentage of cells >= 0.5
# percent x >= p function
pct <- function(x, p=0.50, na.rm = FALSE) {
  if ( length(x[x >= p]) < 1 )  return(0) 
  if ( length(x[x >= p]) == length(x) ) return(1) 
  else return( length(x[x >= p]) / length(x) ) 
}

# create some example data
p <- raster(nrow=10, ncol=10)
p[] <- runif(ncell(p)) * 10
p <- rasterToPolygons(p, fun=function(x){x > 9})
r <- raster(nrow=100, ncol=100)
r[] <- runif(ncell(r)) 
plot(r)
plot(p, add=TRUE, lwd=4) 

# run zonal statistics using pct functions  
z.pct <- zonal.stats(x=p, y=r, stats = "pct")
z.pct

#Alternatively, reclassify the raster based on a threshold
r.c<-reclassify(r, c(-Inf, 0.5, 0, 0.5, Inf, 1)) #all values >0.5 reclassified to 1
plot(r.c)
plot(p, add=TRUE, lwd=4) #add poly to the plot

# run zonal stats and calculate sum of cells in each poly
z.sum <- zonal.stats(x=p, y=r.c, stats = "sum")
z.sum
Tim Assal
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  • Thank you very much for your response! So far it worked, but I do not quite understand the output and how I can calculate the overlap from it. And I think I have to subset the raster so that only pixels of value 1 are considered in the statistics. I would be very grateful for further input. – crocsandturtles Apr 11 '20 at 13:53
  • I modified my answer @crocsandturtles with a reproducible example that should answer your question. – Tim Assal Apr 11 '20 at 18:50
  • Great, I understand it now. Works perfectly! Thank you very much! – crocsandturtles Apr 15 '20 at 06:38