extract mean value of raster with buffer condition on second layer/attribute - r

Question

I have this stars object (could be also formatted to raster):
stars object with 2 dimensions and 2 attributes
attribute(s):
LST_mean elevation
Min. :14.98 Min. :296.0
1st Qu.:16.89 1st Qu.:346.9
Median :17.64 Median :389.3
Mean :17.52 Mean :389.2
3rd Qu.:18.18 3rd Qu.:428.3
Max. :20.11 Max. :521.6
dimension(s):
from to offset delta refsys point values
x 71 83 4387654 860.241 DHDN / 3-degree Gauss-Kru... FALSE NULL [x]
y 33 41 5598885 -860.241 DHDN / 3-degree Gauss-Kru... FALSE NULL [y]
Which has 2 attributes (layers in the case of raster): temperature and elevation.
Using temperature, I would like to select the pixels that fall within a buffer and return the mean, only for the pixels whose difference in elevation with the considered one everytime is less than 90 meters.
Any ideas how to do this?
Calculating the averages of the pixels that fall within the buffer is very easy, but I couldn't find a way to set any condition on them.
I will be immensly grateful for your help and suggestions. Approaches using other packages than satrs are also very welcome :)

Answer 1

Please see below a solution using terra. The code uses terra::extract to create two corresponding lists:
The pixel values
The surrounding buffer values
Subsequently the values are processed pairwise, using mapply, with a function similar to the one you suggested.
It's the first time I'm using terra but seems like terra::extract is much faster than raster::extract, therefore this solution may be feasible even for a large raster.
Creating sample data:
library(sf)
library(terra)
r = rast(ncol = ncol(volcano), nrow = nrow(volcano), xmin = 0, xmax = ncol(volcano), ymin = 0, ymax = nrow(volcano))
values(r) = volcano
s = r
s[] = rnorm(ncell(s))
r = c(r, s)
crs(r) = ""
plot(r)
Calculating buffers:
pnt = as.points(r, values = FALSE)
pol = buffer(pnt, 10)
Extracting raster values from points:
x = extract(r, pnt)
head(x)
## ID lyr.1 lyr.1
## [1,] 1 100 -0.03525223
## [2,] 2 100 0.31525467
## [3,] 3 101 0.94054608
## [4,] 4 101 0.37209238
## [5,] 5 101 -0.38388234
## [6,] 6 101 -0.03120593
Extracting raster values from buffers:
y = extract(r, pol)
head(y)
## ID lyr.1 lyr.1
## [1,] 1 100 -0.03525223
## [2,] 1 100 0.31525467
## [3,] 1 101 0.94054608
## [4,] 1 101 0.37209238
## [5,] 1 101 -0.38388234
## [6,] 1 101 -0.03120593
Now, the extracted values can be processed sequentially using mapply.
First, we convert the objects to lists:
x = as.data.frame(x)
x = split(x, x$ID)
y = as.data.frame(y)
y = split(y, y$ID)
Next, we use mapply to make the necessary calculation, each time
considering the current focal point value x and surrounding buffer
values y:
f = function(x, y) {
d = abs(x[, 2] - y[, 2]) ## differences
values = y[, 3] ## values
mean(values[d < 5], na.rm = TRUE) ## Mean of subset
}
result = mapply(f, x, y)
Finally, putting the results back into the raster template:
u = r[[1]]
values(u) = result
plot(u)