When I try to run band math the result is always an image of a color and the values min and max very different from the one predicted. I did not find any question here that showed this problem. I worked out this way
r.stack <- stack("path to raster file"))
I use resampling instead of crop to cut out the white edges that were in the original images
prj <- "+proj=utm +zone=23 +south +datum=WGS84 +units=m"
r <- raster(res=11.47, ext=extent(c(301496, 323919, 9888968, 9913982)), crs=prj, vals=NA
r.stack <- resample(r.stack, r)
After that the images have this configuration:
> class : RasterBrick
> dimensions : 2181, 1955, 4263855, 4 (nrow, ncol, ncell, nlayers)
> resolution : 11.47, 11.47 (x, y)
> extent : 301496, 323919.8, 9888966, 9913982 (xmin, xmax, ymin, ymax)
>coord. ref. : +proj=utm +zone=23 +south +datum=WGS84 +units=m +ellps=WGS84 +towgs84=0,0,0
>data source : in memory
>names : l.1, l.2, l.3, l.4
>min values : -36.12217, -45.12768, -46.30455, -35.26328
>max values : 10.567671, 4.050200, 3.878345, 11.613799
and than use the function below for calc
f <- function(x){
(x[[2]])/(x[[1]])
}
s <- r.stack[[c(1,2)]]
r2 <- calc(s, f)
and I also run overlay whit the fun
f <- function(x,y){
y/x
}
r2 <- overlay(r.stack[[1]], r.stack[[2]], fun= f)
Any of the methods result in a image of one value
Am I missing some steps?
Here is your code with some example data (without that it is hard to answer questions). I have simplified one function, a bit, but the results are the same.
library(raster)
b <- brick(system.file("external/rlogo.grd", package="raster"))
b <- b/10 + 1
f <- function(x){ x[2]/ x[1] }
s <- b[[c(1,2)]]
r1 <- calc(s, f)
f <- function(x,y){ y / x }
r2 <- overlay(b[[1]], b[[2]], fun= f)
Or simply
r3 <- b[[2]] / b[[1]]
r3
#class : RasterLayer
#dimensions : 77, 101, 7777 (nrow, ncol, ncell)
#resolution : 1, 1 (x, y)
#extent : 0, 101, 0, 77 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=merc +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#data source : in memory
#names : layer
#values : 0.7692308, 1.7 (min, max)
r1 and r2 are the same.
The reason that you get a "single color" is because most values are near 1, but there are a few big outliers; probably because of a division by a number between -1 and 1? This might illustrate it:
q <- quantile(r3, c(0.1, 0.9))
d <- clamp(r3, q[1], q[2])
plot(d)
And look at the extremes
i <- which.max(r3)
b[i][,2:1]