Edson Silva-Júnior 2026-09-29
Package to calculate Canopy Openness Index (COI) from canopy images
require("devtools")
devtools::install_github("Edbbioeco/coiR")coiR package is downloaded now, so we can library it.
Additionally, we library the following packages:
terra: import images files as rasters;
purrr: create loops for multiple operations;
ggplot2: create elegant graphs for imported images;
tidyterra: visualize RGB raster
library(coiR)
library(terra)
library(purrr)
library(ggplot2)
library(tidyterra)Now, we need to import our data Images may be shotten photos, as
.png, .jpg or .jpeg files. first, we import package exemple data, using
coiR::coir_data() function, and import them using
terra::rast() function for every image, throught a loop
with purrr::map() function. Our images
(images) are setted as a list class object.
images <- purrr::map(coiR::data_coir(),
~.x |> terra::rast())
images[[1]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source : imagem1.png
names : imagem1_1, imagem1_2, imagem1_3, imagem1_4
[[2]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source : imagem2.png
names : imagem2_1, imagem2_2, imagem2_3, imagem2_4
[[3]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source : imagem3.png
names : imagem3_1, imagem3_2, imagem3_3, imagem3_4
[[4]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source : imagem4.png
names : imagem4_1, imagem4_2, imagem4_3, imagem4_4Next, lets visualize every image, using a purrr::map()
loop, through ggplot and
tidyterra::geom_spatraster_rgb() function.
purrr::map(images,
purrr::in_parallel(
~ggplot() +
tidyterra::geom_spatraster_rgb(data = .x) +
theme_void()
),
.progress = TRUE)[[1]]
[[2]]
[[3]]
[[4]]
For our exemples, lets work in two ways: run for a single and run for multiple images. Lets set first image as a single image.
single_image <- images[[1]]Usely, canopy images are shotten photos, square images. For our
analysis, we need to crop images into circles. We use
coiR::coir_crop() |function.
single_image |>
coiR::coir_crop()
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
varname : imagem1
names : imagem1_1, imagem1_2, imagem1_3, imagem1_4
min values : 4, 25, 33, 29
max values : 243, 241, 246, 255And we also can analyse multiple images from an one shot, using
purrr::map() loop.
purrr::map(images, coiR::coir_crop)
[[1]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
varname : imagem1
names : imagem1_1, imagem1_2, imagem1_3, imagem1_4
min values : 4, 25, 33, 29
max values : 243, 241, 246, 255
[[2]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
varname : imagem2
names : imagem2_1, imagem2_2, imagem2_3, imagem2_4
min values : 2, 21, 21, 29
max values : 255, 255, 255, 255
[[3]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
varname : imagem3
names : imagem3_1, imagem3_2, imagem3_3, imagem3_4
min values : 3, 19, 7, 29
max values : 255, 255, 255, 255
[[4]]
class : SpatRaster
size : 800, 800, 4 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
varname : imagem4
names : imagem4_1, imagem4_2, imagem4_3, imagem4_4
min values : 1, 13, 1, 30
max values : 255, 255, 255, 255Our next step is to binarize our images. We use
coiR::coir_binarize() function. To avoid replot canopy
image, we use plot = FALSE argument at
coiR::coir_crop() function. To facilite our analysis, lets
use pipe (|>) to conect functions output.
single_image |>
coiR::coir_crop(plot = FALSE) |>
coiR::coir_binarize()
class : SpatRaster
size : 800, 800, 1 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
name : variavel
min value : 0
max value : 1As previously made, we can binarize multiple images, making a
function in our purrr::map() loop.
purrr::map(images,
purrr::in_parallel(
~coiR::coir_crop(data = .x,
plot = FALSE) |>
coiR::coir_binarize()
),
.progress = TRUE)
[[1]]
class : SpatRaster
size : 800, 800, 1 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
name : variavel
min value : 0
max value : 1
[[2]]
class : SpatRaster
size : 800, 800, 1 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
name : variavel
min value : 0
max value : 1
[[3]]
class : SpatRaster
size : 800, 800, 1 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
name : variavel
min value : 0
max value : 1
[[4]]
class : SpatRaster
size : 800, 800, 1 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 0, 800, 0, 800 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
name : variavel
min value : 0
max value : 1Finally, we get COI index. We use coiR::coir_index() for
get that value. To avoid replot cropped and binarized images, we set
plot = FALSE for both coiR::coir_crop() and
coiR::coir_binarize() functions.
single_image |>
coiR::coir_crop(plot = FALSE) |>
coiR::coir_binarize(plot = FALSE) |>
coiR::coir_index()[1] 0.5As previously made, we can also do for multiple images, making a
function in purrr::map_dbl() function.
purrr::map_dbl(images,
purrr::in_parallel(
~coiR::coir_crop(data = .x,
plot = FALSE) |>
coiR::coir_binarize(plot = FALSE) |>
coiR::coir_index()
),
.progress = TRUE)[1] 0.50 0.52 0.28 0.29Finally, we make a data rame with those values, using
purrr::imap_dfr(), to get image names.
df_index <- purrr::imap_dfr(
images,
purrr::in_parallel(
\(imagem, nome){
index <- imagem |>
coiR::coir_crop(plot = FALSE) |>
coiR::coir_binarize(plot = FALSE) |>
coiR::coir_index(round = 3)
tibble::tibble(id = nome,
Index = index)
}
),
.progress = TRUE)
df_index# A tibble: 4 × 2
id Index
<int> <dbl>
1 1 0.501
2 2 0.518
3 3 0.281
4 4 0.29