Using adehabitat and splancs to achieve point-in-polygon analysis on raster?

Hope you had a good time in the field! Is it spring in France?

Yes, though a little cloudy. And it is certainly colder than in South 
Africa!

Thanks for the very helpful reply! I will try it right now! I need to
present at a conference in early April and would like to get these
results before that!
Just three quick questions before you go to enjoy the Easter weekend:

- how do I get the values out of 'jj' (## jj contains the values of
elevation inside the home range of the animals) to calculate mean,
variance, etc, across all the points in 'jj'? I assume 'jj' is of class
'kasc'. In my case it might be 'asc' as I will have only a single home
range at the time (one animal).

Just a small correction to my previous e-mail. If you creates jj with:

jj<-lapply(foo, function(x) x*el)

jj is not a map of class "kasc". To convert jj to the class "kasc", copy the
attributes of the map "foo" to jj with getkascattr:

jj<-getkascattr(foo, jj)
image(jj)

And now, jj is map of class "kasc", i.e. a data.frame with each column
corresponding to a map. All the pixels located inside the home ranges
are coded by numeric values in the data frame, and pixels located outside
the home ranges are coded by NA. So you can get the pixels values with
something like:

oo<-lapply(jj, function(x) x[!is.na(x)])

oo is a list with one component per animal. Each component corresponds to a
vector of pixels inside the animals home range. It is quite easy after 
that to get
values such as average, mean, median, etc.:

lapply(oo, summary)
lapply(oo, mean)
lapply(oo, sd)
etc.

- can I get the points out of the 'kasc' object in a way that I can
still run spatial statistics on them? I would like to calculate the
nugget and range for the NDVI values within the home range.

I never performed such spatial statistics with home ranges but this
could certainly be done... IMHO, the best way to achieve is also to get
the coordinates of the kept pixels when computing the vector oo:

xyc<-getXYcoords(jj)
## xyc contains the coordinates of the pixels of the maps
## it is a list with two components, the vectors of coordinates
## of the pixels of the maps
## compute the x values
xc <- rep(xyc$x, times=length(xyc$y))
yc <- rep(xyc$y, each=length(xyc$x))
xyc<-data.frame(x=xc,y=yc)

lixyc<-lapply(1:ncol(jj), function(x) xyc[!is.na(jj[,x]),])

## So that each point in oo is associated to its coordinates
## in lixyc
## for example
plot(lixyc[[1]], col=grey(oo[[1]]/300), pch=15, cex=4, asp=1)
plot(lixyc[[2]], col=grey(oo[[2]]/300), pch=15, cex=4, asp=1)
plot(lixyc[[3]], col=grey(oo[[3]]/300), pch=15, cex=4, asp=1)

Then you have to compute a variogram. I never used functions
allowing such analyses, but it seems that the package nlme has a function
Variogram...

- do you know how to convert a polygon shapefile into the same class as
produced by the command kernelUD? Alternatively I could import a raster
version of the polygon shape file, convert it to the correct format, and
repeat the spatial join method you are suggesting for the home ranges!

I'm sorry, I do not have any easy solution to this problem. The function 
kernelUD
returns a *raster* map of class "asc", a central class in adehabitat. I 
don't know how
to rasterize a shapefile in R (the library shapefiles or Maptools, which 
deal with
shapefiles do not allow such operations). Maybe the simpler way would be to
rasterize the shapefile with a GIS...

Hope this helps,


Clem.

Thanks again,

Sander.


Cl?ment Calenge wrote:

Dear Sander,

Sorry for my late answer, I was in the field.

I send this email to you directly, because it concerns the link 
between Adehabitat and Splancs.

I have two sources of polygons:
- polygon shape files with areas of interest;
- polygons calculated using the home range methods in Adehabitat.
For the moment, each polygon source will only contain a single polygon.
I have one source of points: a grid representing NDVI values. I read 
the grid using the import.asc function in Adehabitat.
I would like to use both polygon sources to select the points in the 
NDVI raster which are inside the relevant polygons. Then I want to 
run several statistics (mean, standard deviation, etc) on the NDVI 
values of these points.

The solution depends on what you call "polygon". The function 
mcp.rast() is intended to "rasterize"
a polygon on a raster grid of class "asc" or "kasc". The polygon is 
here stored as a data frame with
two columns (the X and Y coordinates of the polygon). The function 
hr.rast() can be used if you have
an object of class "area", i.e. a data.frame with three columns (the 
X and Y coordinates of the polygon,
and a third column defining the ID of the polygon).

Now, it seems that  you work with more complex type of polygons, e.g. 
kernel estimations of home ranges
(where each animal has an home range, and each home range can be made 
of several polygons). In such cases,
the "Spatial join" operation can be more complex. I detail below an 
example of "spatial join" with a kernel
estimation of wild boar home ranges. Note, that in this case, you do 
not have to compute the contour of
the estimation. Just copy and paste the lines below on R:

## loads and display the data
library(adehabitat)
data(puechabon)

## keep only the elevation
el<-getkasc(puechabon$kasc,1)
image(el)

## estimate a kernel home-range
kud<-kernelUD(puechabon$locs[,c("X","Y")], puechabon$locs$Name, grid=el)
## kud contains a density
## note that the grid containing the values of the elevation is used 
for the estimation

vud<-getvolumeUD(kud)
image(vud)
## vud contains the limits of the home ranges estimated at different 
levels

toto<-lapply(vud, function(x) x$UD)
## toto is the list of maps of Utilization Distribution
tutu<-lapply(toto, function(x) {x[x<95]<-1; x[x>95]<-NA; return(x)})
## tutu is a list of raster maps containing 1 if the pixel is inside
## the 95% home range and NA otherwise

foo<-as.kasc(tutu)
image(foo)
## foo is a kasc object

jj=lapply(foo, function(x) x*el)
image(jj)
## jj contains the values of elevation inside the home range of the 
animals

The last method that is available for home-range estimation of 
animals home ranges
is the Nearest Neighbor Convex hull method of Getz and Wilmers 
(2004). The function
NNCH.rast() can be used to "rasterize" the home range estimation in 
the same way, and then
you can multiply the resulting maps with your NDVI in the same way.
Hope this helps,


Cl?ment.