algebra using rasters and dataframes - r

Question

I want to predict vegetation health using 2 remote sensing vegetation indices (VIs) for multiple tree-stands across multiple months. I previously approached this by using a for() loop to iterate through a list of multi-band rasters and calculate the two VIs for each raster (month) using a given equation. I then used raster::extract() to extract the pixels corresponding to each stand. However, I now would like to include some additional variables in my predictions of vegetation health, and am having trouble integrating them using the same method as they are simply columns in a dataframe and not rasters. I'm open to different ways to do this, I just can't think of any.
example:
#Part 1: Loading libraries and creating some sample data
library(sf)
library(raster)
library(terra)
#polygons to generate random points into
v <- vect(system.file("ex/lux.shp", package="terra"))
v <- v[c(1:12)]
v_sf <- st_as_sf(v) # Convert 'SpatVector' into 'sf' object
#5 rasters (months) with 5 bands each
r <- rast(system.file("ex/elev.tif", package="terra"))
r <- rep(r, 5) * 1:5
names(r) <- paste0("band", 1:5)
ras_list <- list(r,r,r,r,r)
#generating some points (10 forest stands)
pnts <- st_sample(v_sf, size = 10, type = "random")
pnts<- as_Spatial(pnts)
#Part 2: Loop to predict vegetation health using two VI variables
vis <- list() #empty list to store NDVI rasters
for (i in seq_along(ras_list)) {
b <- ras_list[[i]]
#vegetation health = 1.23 + (0.45 * VI1) - (0.67 * VI2)
vis[i] <- 1.23 + 0.45*((b[[4]] + b[[3]] - b[[1]]) / (b[[4]] + b[[3]])) - 0.67*(b[[1]] * b[[3]] - b[[4]])
}
#Part 3: Loop to extract pixel values for each forest stand
vi_vals <- list() #empty list to store extracted pixel values
for (i in 1:length(vis)) {
n <- raster(vis[[i]])
vi_vals[[i]] <- raster::extract(n, pnts, method = "bilinear")
}
This method works fine but as I mentioned above, I now need to repeat the same process using a new equation which incorporates variables that can't be calculated from a raster. These values are simply 3 columns in a dataframe that are identified by a stand ID.

Answer 1

Let's first simplify your example a bit
Example data
library(terra)
v <- vect(system.file("ex/lux.shp", package="terra"))
r <- rast(system.file("ex/elev.tif", package="terra"))
r <- rep(r, 5) * 1:5
names(r) <- paste0("b", 1:5)
ras_list <- list(r,r,r,r,r)
set.seed(1)
pnts <- spatSample(v, 10, "random")
values(pnts) = data.frame(id=10, a=5:14, b=3:12, d=6:15)
Compute VI and extract
vis <- list()
for (i in seq_along(ras_list)) {
b <- ras_list[[i]]
vis[[i]] <- 1.23 + 0.45*((b[[4]] + b[[3]] - b[[1]]) / (b[[4]] + b[[3]])) - 0.67*(b[[1]] * b[[3]] - b[[4]])
}
vis <- rast(vis)
names(vis) = paste0("set", 1:5)
vi_vals <- extract(vis, pnts, method = "bilinear")
And now you can do something with the tree parameters
out <- t(t(vi_vals[,-1])) * pnts$a + pnts$b / pnts$d
It would be more efficient to first extract the values and then apply the function
e <- list()
for (i in seq_along(ras_list)) {
x <- extract(ras_list[[i]], pnts, method="bilinear")[,-1]
e[[i]] = (1.23 + 0.45*((x$b4 + x$b3 - x$b1) / (x$b4 + x$b3)) - 0.67*(x$b1 * x$b3 - x$b4)) * pnts$a + pnts$b / pnts$d
}
e <- do.call(cbind, e)
The results are not exactly the same; I assume because of loss of decimal number precision in one or the other method.