This vignette gives a quick example of a simple workflow to produce discrete lidar point clouds from TROLL simulation and corresponding height canopy models.

Setup

We will use rcontroll with a previous simulation (TROLLv3_output) as a starting point for new simulations.

suppressMessages(library(rcontroll))
data("TROLLv3_output")

Lidar simulation

The troll function include a lidar input to specify the details of the lidar simulation that can be easily generated using the generate_lidar function. Parameters for the lidar simulation includes mean and standard deviation of pulse density, the laser attenuation factor, the transmittance of laser after hit, and the iteration for point cloud generation (see ?generate_lidar).

sim <- troll(
  name = "test",
  global = update_parameters(TROLLv3_output, nbiter = 12 * 1),
  species = TROLLv3_output@inputs$species,
  climate = TROLLv3_output@inputs$climate,
  daily = TROLLv3_output@inputs$daily,
  forest = get_forest(TROLLv3_output),
  lidar = generate_lidar(mean_beam_pc = 10, iter_pointcloud_generation = 11),
  verbose = FALSE
)

Printing the output rcontroll inform the lidar simulation status:

sim
#> Object of class : trollsim 
#> Name : test 
#> Path :  
#> In memory : TRUE 
#> Extended : TRUE 
#> From data : FALSE 
#> Lidar simulation : TRUE 
#> Random : FALSE 
#> 
#> 2D discrete network: horizontal step =  1 m, one tree per 1 m^2 
#> Number of sites      :  100 x 100 
#> Number of iterations :  12 
#> Duration of timestep :  30.41667 days 
#> Number of Species    :  45

The point cloud can be accessed in a list in the attribute @las:

sim@las
#> [[1]]
#> class        : LAS (v1.2 format 0)
#> memory       : 5 Mb 
#> extent       : 0, 100, 0, 100 (xmin, xmax, ymin, ymax)
#> coord. ref.  : NA 
#> area         : 10016 units²
#> points       : 145.3 thousand points
#> density      : 14.5 points/units²
#> density      : 9.61 pulses/units²

lidR::plot(sim@las[[1]])

lidR include a plot method for las objects with a nice 3D interactive plot.

rcontroll includes a get_chm method to extract the canopy height model from the point cloud as a raster:

get_chm(sim)
#> $test
#> class       : SpatRaster 
#> dimensions  : 102, 102, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : -1, 101, -1, 101  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        : canopy_height 
#> min value   :       0.00000 
#> max value   :      33.93667

rcontroll includes in the autoplot method an automatic plot of the canopy height model when choosing what = "lidar" (if the lidar simulations as been activated obviously):

rcontroll::autoplot(sim, what = "lidar")

Prefer to use rcontroll::autoplot instead of autoplot alone to avoid namespace conflict with ggplot2.

Lidar stack

Similarly, the stack function include a lidar input to specify the details of the lidar simulation that can be easily generated using the generate_lidar function. Here we test the difference between a simulation with mean pulse density of 10 (10pc) and 20 (20pc):

sim_stack <- stack(
  name = "teststack",
  simulations = c("10pc", "20pc"),
  global = update_parameters(TROLLv3_output, nbiter = 12 * 1),
  species = TROLLv3_output@inputs$species,
  climate = TROLLv3_output@inputs$climate,
  daily = TROLLv3_output@inputs$daily,
  forest = get_forest(TROLLv3_output),
  lidar = list(
    "10pc" = generate_lidar(
      mean_beam_pc = 10,
      iter_pointcloud_generation = 11
    ),
    "20pc" = generate_lidar(
      mean_beam_pc = 10,
      iter_pointcloud_generation = 11
    )
  ) %>% dplyr::bind_rows(.id = "simulation"),
  verbose = FALSE,
  cores = 2,
  thin = c(1, 5, 10)
)
#> 
  |                                                                            
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  |======================================================================| 100%

Printing the output rcontroll inform the lidar simulation status:

sim_stack
#> Object of class : trollstack 
#> Name : teststack 
#> Path :  
#> In memory : TRUE 
#> Number of simulations : 2 
#> Extended : TRUE 
#> From data : FALSE 
#> Lidar simulation : TRUE 
#> Random : FALSE 
#> 
#> 2D discrete network: horizontal step =  1 m, one tree per 1 m^2 
#> Number of sites      :  100 x 100 
#> Number of iterations :  12 
#> Duration of timestep :  30.41667 days 
#> Number of Species    :  45

The point clouds can be accessed in a list in the attribute @las:

sim_stack@las
#> $`10pc`
#> class        : LAS (v1.2 format 0)
#> memory       : 5 Mb 
#> extent       : 0, 100, 0, 100 (xmin, xmax, ymin, ymax)
#> coord. ref.  : NA 
#> area         : 10016 units²
#> points       : 145.3 thousand points
#> density      : 14.5 points/units²
#> density      : 9.61 pulses/units²
#> 
#> $`20pc`
#> class        : LAS (v1.2 format 0)
#> memory       : 5 Mb 
#> extent       : 0, 100, 0, 100 (xmin, xmax, ymin, ymax)
#> coord. ref.  : NA 
#> area         : 10016 units²
#> points       : 145.3 thousand points
#> density      : 14.5 points/units²
#> density      : 9.61 pulses/units²

lidR::plot(sim_stack@las[[2]])

rcontroll includes a get_chm method to extract the canopy height model from the point clouds as a raster:

get_chm(sim_stack)
#> $`10pc`
#> class       : SpatRaster 
#> dimensions  : 102, 102, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : -1, 101, -1, 101  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        : canopy_height 
#> min value   :       0.00000 
#> max value   :      33.93667 
#> 
#> $`20pc`
#> class       : SpatRaster 
#> dimensions  : 102, 102, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : -1, 101, -1, 101  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        : canopy_height 
#> min value   :       0.00000 
#> max value   :      33.93667

rcontroll includes in the autoplot method an automatic plot of the canopy height model when choosing what = "lidar" (if the lidar simulations as been activated obviously):

rcontroll::autoplot(sim_stack, what = "lidar")

Lidar simulation using TROLL

Setup

Lidar simulation

Lidar stack