6

I want to calculate grid_metrics() for several fields of my point cloud, using several functions. I want to perform this in loops to make my code look cleaner.

Basically I want this:

# loop through fields
 for (name in c("field1", "field2", "field3")) {
    # loop through statistics
    for (type in list(c("mean", mean), c("sd", sd))) {
      # calculate gris metrics
      raster <- grid_metrics(point_cloud, ~type[[2]](name), res = 1)

However, this does not work. Using the statistic I specified works. However, whatever I tried, I did not manage to replace the field name with a variable containing the name. I always get the warning In mean.default(name) : argument is not numeric or logical: returning NA.

I found a workaround using rasterize(), but then these rasters have an offset to my other rasters created with grid_metrics() and I think it would be better to create all rasters consistently in the same way.

(Here the workaround:)

raster <- rasterize(data.frame(X=point_cloud@data$X, Y=point_cloud@data$Y),
                    raster_object, field=get(name, point_cloud@data), fun = type[[2]])

Can someone help me to replace the column name with a variable? Putting an eval() around the variable does not help either.

6

That sounds overly complex and computationally inefficient. Why don't you compute all your metrics at once?

mymetrics = function(f1,f2,f3) {
  list(
   avgf1 = mean(f1),
   sdf2 = sd(f1),
   avgf2 = mean(f2),
   sdf2 = sd(f2),
   avgf3 = mean(f3),
   sdf3 = sd(f3))
}


grid_metrics(point_cloud, ~mymetrics(field1, field2, field3), res = 1)

Anyway if you really prefer to build custom calls in a loop I would go for building an expression from a concatenated string.

name = "Intensity"
type = "mean"
expr = paste0("~", type, "(", name, ")")
expr = eval(parse(text = expr))
raster <- grid_metrics(point_cloud, expr, res = 20)
2
  • Thanks! I thought calculating the bands separately maybe takes longer, but spares my RAM more, since I only have 16GB. I guess I have to decide what's more important. – Zoe May 9 at 12:27
  • 2
    Computation of raster does not take a lot of ram actually. If you are concerned by memory you can always process by chunk with a LAScatalog – JRR May 9 at 12:46

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