Non-rectangular inventory zone"
In SamsaRaLight: Simulate Tree Light Transmission Using Ray-Tracing

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(SamsaRaLight)
library(dplyr)

Introduction

In previous tutorials, inventories were assumed to be simple, axis-aligned rectangles. In practice, however, forest inventories are often more complex, and may not be necessary rectangle depending on the inventory protocol.

In this vignette, we illustrate the case of an non-rectangular inventory. The sampled area is defined by an irregular polygon (e.g. a combination of circular plots), filled around by virtual trees.

Context and data

We now consider the example inventory Cloture20, stored in the package as SamsaRaLight::data_cloture20.

This inventory was collected by Gauthier Ligot in Wallonia, Belgium, as part of the CLOTURE project, which investigates the effect of fences on beech and oak plots with varying beech proportions. In this case, the plot is primarily made up of beech trees (Fagus sylvatica). The crowns are described with full asymmetric shapes "8E". The inventory protocol is complex and resulted in non-rectangular inventory zones formed from multiple circular areas. Thus, the core polygon is defined by 94 vertices:

str(SamsaRaLight::data_cloture20$core_polygon)

Virtual stand with complex core polygon

First, we can create the virtual stand by setting the pre-defined core polygon stored in the package. Representing the inventory as a rectangle in this case would be inconsistent with the field protocol. Therefore, the polygon is preserved inside a larger virtual stand with modify_polygon = "none" and the virtual stand is filled around the inventory zone with fill_around = TRUE.

stand_cloture_filled <- SamsaRaLight::create_sl_stand(
  trees_inv = SamsaRaLight::data_cloture20$trees,
  cell_size = 5,

  latitude = SamsaRaLight::data_cloture20$info$latitude,
  slope    = SamsaRaLight::data_cloture20$info$slope,
  aspect  = SamsaRaLight::data_cloture20$info$aspect,
  north2x = SamsaRaLight::data_cloture20$info$north2x,

  core_polygon_df = SamsaRaLight::data_cloture20$core_polygon,
  modify_polygon = "none",
  fill_around = TRUE
)

plot(stand_cloture_filled)

summary(stand_cloture_filled)

Run SamsaraLight

Finally, the radiation data and the simulation run are done as usual:

data_radiations_cloture <- SamsaRaLight::get_monthly_radiations(
  latitude  = SamsaRaLight::data_cloture20$info$latitude,
  longitude = SamsaRaLight::data_cloture20$info$longitude
)

output_cloture_filled <- SamsaRaLight::run_sl(
  sl_stand = stand_cloture_filled,
  monthly_radiations = data_radiations_cloture
)