error in creating raster files for multiple tiles

[taeyoonlee87]

Hi, I am trying to create raster files and a map (images) after applying the regression model.

I define a function, f, including a regression model I would like to apply.

When I run my code with a single file, it runs without critical errors.

But when I ran it for multiple las files (in this case, I used two las files), it failed and indicated error messages.

Here is the part of the code that I encountered error.

routine <- function(chunk){ 
  las <- readLAS(chunk)
  if (is.empty(las)) return(NULL)
  prediction <- pixel_metrics(lasnorm, ~f(X,Y, Z, Intensity, ReturnNumber,Classification), 5) # predicting model mapping 
  plot(prediction, col = height.colors(50)) 
 }  

opt_output_files(lasnorm) <- "E:/Lidar/R/dtm/vol_{XLEFT}_{YBOTTOM}"
catalog_apply(lasnorm, routine)

and this is error message:

Processing [=================================================] 100% (2/2) eta:  0s
Processing [=======================>-------------------------]  50% (1/2) eta:  2m
Error: Trying to write an object of class list but this type is not supported.

Or, I got this error either.

50% (1/2) eta:  2m
Error: [writeRaster] file exists. You can use 'overwrite=TRUE' to overwrite it.

I changed the direction in opt_output_files and removed plot(prediction, col = height.colors(50)) but it did not helpful.

Can you please advise how I create raster files?

[Jean-Romain]

I need a minimal reproducible example

[taeyoonlee87]

I can share my code with you.

library(lidR)
library(rgl)
library(raster)
library(sp)
library(sf)
library(terra)
library(rgdal)
library(future)
#library(rLiDAR)

####DEFINE YOUR WORKING DIRECTORY
setwd("E:/Lidar_2022/R/trials")

las_cat<-readLAScatalog("E:/Lidar_2022/R/trials")

###Creat DTM 
opt_output_files(las_cat) <- "E:/Lidar_2022/R/trials/trial_dtm/dtm_{XLEFT}_{YBOTTOM}"
dtm <- rasterize_terrain(las_cat, res = 1, algorithm = tin(), keep_lowest = FALSE)
 ##normalize - to substract ground elevation from each point / need las data and DTM
opt_output_files(las_cat) <-"E:/Lidar_2022/R/trials/trial_dtm/norm_{XLEFT}_{YBOTTOM}" 
lasnorm <- normalize_height(las_cat, dtm)  

f=function(X,Y, Z, Intensity, ReturnNumber,Classification) {
  ans = stdmetrics(X,Y,Z,Intensity,ReturnNumber,Classification,dz=1, zmin=0)
  ans = ans[c("zmean", "zsd", "pzabove2","zq5","zq15","zq35","zq40","zq45","zq95","zpcum6","zpcum7","isd","ipcumzq10", 
              "ipcumzq30","ipcumzq90","p1th","p2th")]
  volume = -16.18216 + 0.1314547 * ans$zmean + 0.5586847 * ans$zsd + 0.05094551 * ans$pzabove2 - 0.8545914 * ans$zq5 - 3.435632 * ans$zq15 + 0.05012992 * ans$zq35 + 0.07849599 * ans$zq40 + 0.003158031 * ans$zq45 +5.12619*10^(-5) * ans$zq95 - 0.004623234 * ans$zpcum6 - 0.01141505 * ans$zpcum7 + 6.68966*10^(-5) * ans$isd + 0.03436324 * ans$ipcumzq10 + 0.02746617 * ans$ipcumzq30 + 0.2301962 * ans$ipcumzq90 - 0.06276224 * ans$p1th + 0.0969937 * ans$p2th
  lambda = 0.3030303
  
  return(list((lambda*volume+1)^(1/lambda)))
}

routine <- function(chunk){ 
  las <- readLAS(chunk)
  if (is.empty(las)) return(NULL)
prediction <- pixel_metrics(lasnorm, ~f(X,Y, Z, Intensity, ReturnNumber,Classification), 5) # predicting model mapping 
plot(prediction, col = height.colors(50)) # some plotting
 }  

opt_output_files(lasnorm) <- "E:/Lidar_2022/R/trials/trial_dtm/vol_{XLEFT}_{YBOTTOM}"
catalog_apply(lasnorm, routine)

[Jean-Romain]

The code is inccorect. I see an undefined variable lasnorm in routine(). I guess your code can be written

las_cat<-readLAScatalog("E:/Lidar_2022/R/trials")

###Creat DTM 
opt_output_files(las_cat) <- "E:/Lidar_2022/R/trials/trial_dtm/dtm_{XLEFT}_{YBOTTOM}"
dtm <- rasterize_terrain(las_cat, res = 1, algorithm = tin(), keep_lowest = FALSE)
 ##normalize - to substract ground elevation from each point / need las data and DTM
opt_output_files(las_cat) <-"E:/Lidar_2022/R/trials/trial_dtm/norm_{XLEFT}_{YBOTTOM}" 
lasnorm <- normalize_height(las_cat, dtm)  

f=function(X,Y, Z, Intensity, ReturnNumber,Classification) {
  ans = stdmetrics(X,Y,Z,Intensity,ReturnNumber,Classification,dz=1, zmin=0)
  ans = ans[c("zmean", "zsd", "pzabove2","zq5","zq15","zq35","zq40","zq45","zq95","zpcum6","zpcum7","isd","ipcumzq10", 
              "ipcumzq30","ipcumzq90","p1th","p2th")]
  volume = -16.18216 + 0.1314547 * ans$zmean + 0.5586847 * ans$zsd + 0.05094551 * ans$pzabove2 - 0.8545914 * ans$zq5 - 3.435632 * ans$zq15 + 0.05012992 * ans$zq35 + 0.07849599 * ans$zq40 + 0.003158031 * ans$zq45 +5.12619*10^(-5) * ans$zq95 - 0.004623234 * ans$zpcum6 - 0.01141505 * ans$zpcum7 + 6.68966*10^(-5) * ans$isd + 0.03436324 * ans$ipcumzq10 + 0.02746617 * ans$ipcumzq30 + 0.2301962 * ans$ipcumzq90 - 0.06276224 * ans$p1th + 0.0969937 * ans$p2th
  lambda = 0.3030303
  
  return(list((lambda*volume+1)^(1/lambda)))
}

prediction <- pixel_metrics(lasnorm, ~f(X,Y, Z, Intensity, ReturnNumber,Classification), 5)

[taeyoonlee87]

I really appreciate it!