SamsaraLightLoader and RCapsis

SamsaraLightLoader is a module of the Capsis platform that allows to test the SamsaraLight library. From inventory data (spatialized trees) and weather data, this module allows to estimate the amount of light that is intercepted by the trees or that is transmitted to different points in the understory.

RCapsis is an R package allowing to use Capsis and Java objects directly from R. https://sourceforge.net/projects/rcapsis.capsisbridge.p/

Objectives

The objective of this document is to illustrate the use of these two tools. The approach proposed here allows to estimate the light intercepted by the trees of different plots.

Steps

Step 1 : Load the packages and connect to capsis

library(RCapsis)
## Loading required package: J4R
## Welcome to J4R!
## Please, make sure that Java (version 8 or later) is installed on your computer.
## For more information, visit https://sourceforge.net/p/repiceasource/wiki/J4R/ .
library(tidyverse)
## -- Attaching packages ------------------------------------------------------------------------------------------------------ tidyverse 1.3.0 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.2
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## Warning: package 'ggplot2' was built under R version 4.0.5
## -- Conflicts --------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
path="c:/Eclipse_Workspace/capsis4/"

setCapsisPath(path = path)
connectToCapsis()
## Starting local Java server...
## [1] TRUE

Step 2 : Initialize a script of SamsaraLightLoader

A script has been implmented in SamsaraLightLoader. This script is initialized providing the adresses of four directories containing the inventory files, weather files or directories where output will be written.

tree_dir <- createJavaObject("java.io.File", "c:/Data_G_current/J4R/20210630_lilo/tree")
meteo_dir <- createJavaObject("java.io.File", "c:/Data_G_current/J4R/20210630_lilo/meteo")
output_dir <- createJavaObject("java.io.File", "c:/Data_G_current/J4R/20210630_lilo/output")
export_dir <- createJavaObject("java.io.File", "c:/Data_G_current/J4R/20210630_lilo/export")
export <- createJavaObject("samsaralightloader.myscripts.LiloExportTreeLight", tree_dir, meteo_dir, output_dir, export_dir)

Step 3 : Run the simulation

The next step run the computation for each combination of inventory and weather file.

export$doAnalysis() #takes time : it runs the simulation.

Step 4 : Explore the results

The results can be store in a csv file or imported as R objects.

export$writeResults() #write a csv file
results<-export$getResults()
n_items <- results$size()
indexes <- as.integer(0:(n_items-1))
items<-results$get(indexes)
capsis.output<-tibble(site=items$site[indexes],
       meteo=items$meteo[indexes],
       id = items$id[indexes],
       species = items$species[indexes],
       dbh = items$dbh[indexes],
       e = items$e[indexes],
       epot = items$epot[indexes]
       )
head(capsis.output)
## # A tibble: 6 x 7
##   site       meteo    id species       dbh       e    epot
##   <chr>      <chr> <int> <chr>       <dbl>   <dbl>   <dbl>
## 1 inventory1 meteo    98 pseudotsuga  63.0 214049. 521837.
## 2 inventory1 meteo    45 pseudotsuga  66.5 158742. 497859.
## 3 inventory1 meteo   140 pseudotsuga  41.7  45256. 149044.
## 4 inventory1 meteo    47 pseudotsuga  50.9  40937. 328474.
## 5 inventory1 meteo    87 pseudotsuga  59.8 141559. 339908.
## 6 inventory1 meteo    56 pseudotsuga  60.5 162038. 593316.
ggplot(data=capsis.output, aes(x=dbh,y=e))+facet_wrap(~site)+geom_point()+stat_smooth()+ylab("Intercept light (MJ)")+xlab("dbh (cm)")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Step 5 : shut down Java client

shutdownClient()
## Closing connections and removing sockets...
## Done.