TROLL is coded in C++ and it typically simulates hundreds of thousands of
individuals over hundreds of years. The rcontroll R package is a wrapper of
TROLL. rcontroll includes functions that generate inputs for simulations
and run simulations. Finally, it is possible to analyse the TROLL outputs
through tables, figures, and maps taking advantage of other R visualisation
packages. rcontroll also offers the possibility to generate a virtual LIDAR
point cloud that corresponds to a snapshot of the simulated forest.
As stated above, three types of input data are needed for a typical TROLL
simulation: (i) climate data, (ii) plant functional traits, (iii) global
model parameters. Pre-simulation functions include global parameters
definition (generate_parameters function) and climate data generation
(generate_climate function). rcontroll also includes default data for
species and climate inputs for a typical French Guiana rainforest site. The
purpose of the generate_climate function with the help of the corresponding
vignette is to create TROLL climate inputs from ERA5-Land (Muñoz-Sabater et
al. 2021), a global climatic reanalysis dataset that is freely available. The
ERA5-Land climate reanalysis is available at 9 km spatial resolution and
hourly temporal resolution since 1950, and daily or monthly means are
available and their uncertainties reported. Therefore, rcontroll users only
need to input the species-specific trait data to run TROLL simulations,
irrespective of the site. TROLL was originally developed for tropical and
subtropical forests, so certain assumptions must be critically examined when
applying it outside the tropics. The input files can be used to start a
TROLL simulation run within the rcontroll environment (see below), or
saved so that the TROLL simulation can be started as a command line tool.
The default option is to run a TROLL simulation using the troll function
of the rcontroll package, which currently calls version 3.1.7 of TROLL
using the Rcpp package (Eddelbuettel & François 2011). The output is stored
in a trollsim R class. For multiple runs, users can rely on the stack
function, and the output is stored in the trollstack class. Both trollsim
and trollstack values can be accessed using object attributes in the form
of simple R objects (with @ in R). They consist of eight simulation
attributes: (1) name, (2) path to saved files, (3) parameters, (4) inputs,
(5) log, (6) initial and final state, (7) ecosystem output metrics, and (8)
species output metrics. The initial and final states are represented by a
table with the spatial position, size and other relevant traits of all trees
at the start and end of the simulation. The ecosystem and species metrics are
summaries of ecosystem processes and states, such as net primary production
and aboveground biomass, and they are documented at species level and
aggregated over the entire stand. Simulations can be saved using a
user-defined path when run and later loaded as a simple simulation
(load_output function) or a stack of simulations (load_stack function).
TROLL also has the capacity of generating point clouds from virtual aerial
lidar scannings of simulated forest scenes. Within each cubic metre voxel of
the simulated stand, points are generated probabilistically, with the
probability depending both on the amount of light reaching the particular
voxel and the amount of leaf matter intercepting light within the voxel.
Extinction and interception of light are based on the Beer-Lambert law, but
an effective extinction factor is used to account for differences between the
near-infrared and visible light. The definition of the lidar parameters
(generate_lidar function) is optional but allows the user to add a virtual
aerial lidar scan for a time step of the TROLL simulation. When this option
is enabled, the cloud of points from simulated aerial lidar scans are stored
as LAS using the R package lidR (Roussel et al., 2020) as a ninth attribute
of the trollsim and trollstack objects.
rcontroll includes functions to manipulate simulation outputs. Simulation
outputs can be retrieved directly from the trollsim or trollstackobjects
and summarised or plotted in the R environment with the print, summary
and autoplot functions. The get_chm function allows users to retrieve
canopy height models from aerial lidar point clouds. In addition, a
rcontroll function is available to visualise TROLL simulations as an
animated figure (autogif function).
version 3.1.6