Spatialize trees and forest stand metrics with BIOMASS

Overview

BIOMASS enables users to manage their plots by:

calculating the projected/geographic coordinates of the plot’s corners and the trees from the relative coordinates (or local coordinates, i.e. those of the field)
visualising the plots
validating plot’s corners and tree coordinates with LiDAR data
dividing plots into subplots
summarising any tree metric at subplot level

Required data

Two data frames are required to perform the analysis. One for the corner of the plot(s), and one for the trees, which contains at least their coordinates.

The corner data frame must contains at least:
- the names of the corresponding plots if there are several plots (below referred as to the ‘Plot’ column)
- the coordinates of the plot’s corners in the geographic or projected coordinate system (the GPS coordinates, below referred as to the ‘Xutm’ and ‘Yutm’ columns)
- the coordinates of the plot’s corners in the plot’s relative coordinate system (the local or field coordinates, below referred as to the ‘Xfield’ and ‘Yfield’ columns)

In this vignette, for educational purpose, we will not use only one but two datasets of corner coordinates, derived from permanent plots in the Nouragues forest (French Guiana):

NouraguesPlot201 which contains simulated corner coordinates of one plot with repeated GPS measurements of each corner:

data("NouraguesPlot201")

kable(head(NouraguesPlot201), digits = 5, row.names = FALSE, caption = "Head of NouraguesPlot201")

Head of NouraguesPlot201
Site	Plot	Xutm	Yutm	Long	Lat
Petit_Plateau	201	313002.7	451723.1	-52.68451	4.08504
Petit_Plateau	201	313007.5	451720.5	-52.68446	4.08502
Petit_Plateau	201	313008.7	451723.4	-52.68445	4.08504
Petit_Plateau	201	313002.8	451725.7	-52.68451	4.08506
Petit_Plateau	201	313005.8	451722.1	-52.68448	4.08503
Petit_Plateau	201	313010.5	451736.0	-52.68444	4.08516

NouraguesCoords which contains corner coordinates of four plots with a single GPS measurement of each corner, also derived from Nouragues forest:

data("NouraguesCoords")

kable(head(NouraguesCoords), digits = 5, row.names = FALSE, caption = "Head of NouraguesCoords")

Head of NouraguesCoords
Site	Plot	Xfield	Yfield	Xutm	Yutm	Long	Lat
Petit_Plateau	201	0	0	313007.9	451717.2	-52.68446	4.08499
Petit_Plateau	201	0	100	313095.8	451669.5	-52.68367	4.08456
Petit_Plateau	201	100	0	312960.2	451629.3	-52.68489	4.08419
Petit_Plateau	201	100	100	313048.0	451581.6	-52.68410	4.08376
Petit_Plateau	204	0	300	313271.5	451574.0	-52.68208	4.08370
Petit_Plateau	204	0	400	313359.4	451526.3	-52.68129	4.08327

The data frame including the tree coordinates, must contain at least:
- the name of the plots if there are several plots
- the tree coordinates in the plot’s relative coordinate system (the local/field one)
- the desired information about trees, such as diameter, wood density, height, AGB, etc. (see BIOMASS vignette)

data("NouraguesTrees")

kable(head(NouraguesTrees), digits = 3, row.names = FALSE, caption = "Head of the table trees")

Head of the table trees
Site	Plot	Xfield	Yfield	Family	Genus	Species	D
Petit_Plateau	201	0.0	31.5	Burseraceae	Protium	surinamense	11.0
Petit_Plateau	201	0.1	75.2	Anacardiaceae	Tapirira	guianensis	74.4
Petit_Plateau	201	0.2	27.6	Lecythidaceae	Indet.Lecythidaceae	Indet.	25.4
Petit_Plateau	201	-4.0	67.5	Euphorbiaceae	Conceveiba	guyanensis	10.0
Petit_Plateau	201	0.3	39.9	Burseraceae	Protium	altissimum	18.9
Petit_Plateau	201	-3.5	41.5	Euphorbiaceae	Mabea	speciosa	10.0

This dataset is also derived from the 2012 Nouragues forest dataset, but for educational purpose, some virtual trees with erroneous coordinates have been added in the data.

Checking plot’s coordinates

Two situations may occur:

The GPS coordinates of the plot corners are considered very accurate or enough measurements have been made to be confident in the accuracy of their average. In this case, the shape of the plot measured on the field will follow the GPS coordinates of the plot corners when projected into the projected/geographic coordinate system. See 3.1.1
Too few measurements of the GPS coordinates of plot corners have been collected and/or are not reliable. In this case, the shape of the plot measured on the field is considered to be accurate and the GPS corner coordinates will be recalculated to fit the shape and dimensions of the plot. See 3.1.2

In both cases, the use of the check_plot_coord() function is recommended as a first step.

Checking the corners of the plot

The check_plot_coord() function handles both situations using the trust_GPS_corners argument (= TRUE or FALSE).

You can give either geographical coordinates with the ‘longlat’ argument or another projected coordinates with the ‘proj_coord’ argument for the corner coordinates.

If we rely on the GPS coordinates of the corners:

When only 1 GPS measurement by corner has been recorded with a high degree of accuracy (by a geometer, for example), or if you already have averaged your measurements by yourself, you can supply these 4 GPS coordinates to the function.
When enough coordinates have been recorded for each corner (for more information, see the CEOS good practices protocol, section A.1.3.1 ), coordinates will be averaged by corner, resulting in 4 reference coordinates. The function can also detect and remove GPS outliers using the ‘rm_outliers’ and ‘max_dist’ arguments.

check_plot_trust_GPS <- check_plot_coord(
  corner_data = NouraguesPlot201,
  longlat = c("Long", "Lat"),  # or proj_coord = c("Xutm", "Yutm"), 
  rel_coord = c("Xfield", "Yfield"),
  trust_GPS_corners = T,
  draw_plot = TRUE,
  max_dist = 10, rm_outliers = TRUE )

The two blue arrows represent the origin of the plot’s relative coordinate system.

If we rely on the shape of the plot measured on the field:

Let’s degrade the data to mimic the case where we only have 8 unreliable GPS coordinates.

degraded_corner_coord <- NouraguesPlot201[c(1:2,11:12,21:22,31:32),]

check_plot_trust_field <- check_plot_coord(
  corner_data = degraded_corner_coord,
  longlat = c("Long", "Lat"),  # or proj_coord = c("Xutm", "Yutm"), 
  rel_coord = c("Xfield", "Yfield"),
  trust_GPS_corners = FALSE,
  draw_plot = TRUE, rm_outliers = FALSE)
#> Warning:  Be carefull, you may have GNSS measurement outliers. 
#>  Removing them may improve the georeferencing of your plot (see rm_outliers and max_dist arguments).

We can see that the corners of the plot do not match the GPS measurements. In fact, they correspond to the best compromise between the shape and dimensions of the plot and the GPS measurements.

Recovering reference corner coordinates and the associated polygon(s)

Reference corner coordinates are returned by the function via the $corner_coord output, with standardised column names for future data processing.

kable(check_plot_trust_GPS$corner_coord, row.names = FALSE, caption = "Reference corner coordinates")

Reference corner coordinates
x_rel	y_rel	x_proj	y_proj
0	0	313005.7	451723.2
100	0	312956.9	451630.2
100	100	313050.2	451582.6
0	100	313100.5	451665.9

The associated polygon is returned via the $polygon output and can be saved into a shapefile as follows:

sf::st_write(check_plot_trust_GPS$polygon, "your_directory/plot201.shp")

For full details, the $outlier_corners output returns all the information about GPS outliers found by the function, and the $UTM_code output returns the UTM code calculated by the function if geographic coordinates have been provided.

Visualising and retrieving projected tree coordinates

Tree coordinates are usually measured in the plot’s relative coordinate system. To project them in the projected system, you can supply their relative coordinates using the tree_data and tree_coords arguments.

plot201Trees <- NouraguesTrees[NouraguesTrees$Plot==201,]

check_plot_trust_GPS <- check_plot_coord(
  corner_data = NouraguesPlot201,
  longlat = c("Long", "Lat"), rel_coord = c("Xfield", "Yfield"),
  trust_GPS_corners = TRUE,
  tree_data = plot201Trees, tree_coords = c("Xfield","Yfield"))
#> Warning in check_corner_fct(.SD):  Be careful, one or more trees are not inside the plot defined by rel_coord (see is_in_plot column of tree_data output)

The projected coordinates of the trees are added to the tree data-frame and returned by the output $tree_data (columns x_proj and y_proj).

plot201Trees[c("Xutm","Yutm")] <- check_plot_trust_GPS$tree_data[c("x_proj","y_proj")]

kable(head(check_plot_trust_GPS$tree_data[,-c(5,6,7)]), digits = 3, row.names = FALSE, caption = "Head of the $tree_data output")

Head of the $tree_data output
Site	Plot	x_rel	y_rel	D	x_proj	y_proj	is_in_plot
Petit_Plateau	201	0.0	31.5	11.0	313035.6	451705.1	TRUE
Petit_Plateau	201	0.1	75.2	74.4	313077.0	451680.0	TRUE
Petit_Plateau	201	0.2	27.6	25.4	313031.8	451707.2	TRUE
Petit_Plateau	201	-4.0	67.5	10.0	313071.7	451688.0	FALSE
Petit_Plateau	201	0.3	39.9	18.9	313043.4	451700.1	TRUE
Petit_Plateau	201	-3.5	41.5	10.0	313046.8	451702.5	FALSE

The output of the function also standardises the names of the relative tree coordinates (to x_rel and y_rel) and adds the is_in_plot column, indicating if a tree is in the plot or not.

You can also access and modify the plot via the $plot_design output which is a ggplot object. For example, to change the plot title:

plot_to_change <- check_plot_trust_GPS$plot_design
plot_to_change <- plot_to_change + ggtitle("A custom title")
plot_to_change

If you provided longitude and latitude corner coordinates, you can retrieve the GPS coordinates of the trees in a longitude/latitude format using this code:

tree_GPS_coord <- as.data.frame( proj4::project(check_plot_trust_GPS$tree_data[c("x_proj","y_proj")], proj = check_plot_trust_GPS$UTM_code$UTM_code, inverse = TRUE) )

Integrating LiDAR data

If you have LiDAR data in raster format (typically a CHM raster) that you want to compare with a tree metric, this can be done with the ref_raster and the prop_tree arguments.

# Load internal CHM raster
nouraguesRaster <- terra::rast(system.file("extdata", "NouraguesRaster.tif",package = "BIOMASS", mustWork = TRUE))

check_plot_trust_GPS <- check_plot_coord(
  corner_data = NouraguesPlot201,
  longlat = c("Long", "Lat"), rel_coord = c("Xfield", "Yfield"),
  trust_GPS_corners = TRUE,
  tree_data = plot201Trees, tree_coords = c("Xfield","Yfield"), prop_tree = "D", # here the treediameter
  ref_raster = nouraguesRaster)
#> Warning in check_corner_fct(.SD):  Be careful, one or more trees are not inside the plot defined by rel_coord (see is_in_plot column of tree_data output)

Checking multiple plots at once

When corner_data and tree_data contain several plots, you have to supply the column names containing the plots IDs of the corners and the trees via the plot_ID and tree_plot_ID arguments:

multiple_checks <- check_plot_coord(
  corner_data = NouraguesCoords, # NouraguesCoords contains 4 plots
  proj_coord = c("Xutm", "Yutm"), rel_coord = c("Xfield", "Yfield"),
  trust_GPS_corners = TRUE, 
  plot_ID = "Plot",
  tree_data = NouraguesTrees, tree_coords = c("Xfield","Yfield"), 
  prop_tree = "D", tree_plot_ID = "Plot",
  ref_raster = nouraguesRaster, ask = FALSE)
#> Warning in check_corner_fct(.SD): In plot 201 : Be careful, one or more trees are not inside the plot defined by rel_coord (see is_in_plot column of tree_data output)
#> Warning in check_corner_fct(.SD): In plot 213 : Be careful, one or more trees are not inside the plot defined by rel_coord (see is_in_plot column of tree_data output)
#> Warning in check_corner_fct(.SD): In plot 223 : Be careful, one or more trees are not inside the plot defined by rel_coord (see is_in_plot column of tree_data output)

Be aware that by default, the function will ask you to type Enter between each plot (argument ‘ask = TRUE’).

Dividing plots

Dividing plots into several sub-plots is performed using the divide_plot() function. This function takes the relative coordinates of the 4 corners of the plot to divide it into a grid. Be aware that the plot must be rectangular in the plot’s relative coordinates system, i.e. have 4 right angles:

subplots <- divide_plot(
  corner_data = check_plot_trust_GPS$corner_coord,
  rel_coord = c("x_rel","y_rel"),
  proj_coord = c("x_proj","y_proj"),
  grid_size = 25 # or c(25,25)
  )

kable(head(subplots, 10), digits = 1, row.names = FALSE, caption = "Head of the divide_plot() returns")

Head of the divide_plot() returns
subplot_ID	x_rel	y_rel	x_proj	y_proj
subplot_0_0	0	0	313005.7	451723.2
subplot_0_0	25	0	312993.5	451699.9
subplot_0_0	25	25	313017.1	451686.2
subplot_0_0	0	25	313029.4	451708.9
subplot_1_0	25	0	312993.5	451699.9
subplot_1_0	50	0	312981.3	451676.7
subplot_1_0	50	25	313004.8	451663.6
subplot_1_0	25	25	313017.1	451686.2
subplot_2_0	50	0	312981.3	451676.7
subplot_2_0	75	0	312969.1	451653.5

If you want to stay in the plot’s relative coordinate system, just set proj_coord = NULL.

The function also handles imperfect cuts with the arguments centred_grid and grid_tol. Here an example with a 40mx45m grid.

subplots <- divide_plot(
  corner_data = check_plot_trust_GPS$corner_coord, 
  rel_coord = c("x_rel","y_rel"), proj_coord = c("x_proj","y_proj"),
  grid_size = c(40,45), 
  centred_grid = TRUE, # centre the grid in the middle of the plot
  grid_tol = 0.3 # by default =0.1, ie, if more than 10% of the plot is not covered by the grid, it will returned an error
  )
#> Warning in divide_plot_fct(.SD, grid_size): 
#> The x-dimension of the plot is not a multiple of the x-dimension of the grid size
#> Warning in divide_plot_fct(.SD, grid_size): 
#> The y-dimension of the plot is not a multiple of the y-dimension of the grid size

For the purpose of summarising and representing subplots (coming in the next section), the function returns the coordinates of subplot corners but can also assign to each tree its subplot with the tree_data and tree_coords arguments:

# Add AGB predictions (calculated in Vignette BIOMASS) to plot201Trees
AGB_data <- readRDS("saved_data/NouraguesTreesAGB.rds")
plot201Trees <- merge(plot201Trees , AGB_data[c("Xfield","Yfield","D","AGB")])

subplots <- divide_plot(
  corner_data = check_plot_trust_GPS$corner_coord, 
  rel_coord = c("x_rel","y_rel"),
  proj_coord = c("x_proj","y_proj"),
  grid_size = 25, # or c(25,25)
  tree_data = plot201Trees, tree_coords = c("Xfield","Yfield")
  )
#> Warning in divide_plot(corner_data = check_plot_trust_GPS$corner_coord, : One
#> or more trees could not be assigned to a subplot (not in a subplot area)

The function now returns a list containing:

sub_corner_coord: coordinates of subplot corners as previously
tree_data: the tree data-frame with the subplot_ID added in last column

kable(head(subplots$tree_data[,-c(2,3,4)]), digits = 1, row.names = FALSE, caption = "Head of the divide_plot()$tree_data returns")

Head of the divide_plot()$tree_data returns
x_rel	Plot	Family	Genus	Species	Xutm	Yutm	AGB	subplot_ID
-3.5	201	Euphorbiaceae	Mabea	speciosa	313046.8	451702.5	0.1	NA
-4.0	201	Euphorbiaceae	Conceveiba	guyanensis	313071.7	451688.0	0.0	NA
0.0	201	Burseraceae	Protium	surinamense	313035.6	451705.1	0.1	subplot_0_1
0.1	201	Anacardiaceae	Tapirira	guianensis	313077.0	451680.0	4.7	subplot_0_3
0.2	201	Lecythidaceae	Indet.Lecythidaceae	Indet.	313031.8	451707.2	0.6	subplot_0_1
0.3	201	Burseraceae	Protium	altissimum	313043.4	451700.1	0.2	subplot_0_1

Last but not least, the function can handle as many plots as you want, using the corner_plot_ID and tree_plot_ID arguments:

multiple_subplots <- divide_plot(
  corner_data = NouraguesCoords,
  rel_coord = c("Xfield","Yfield"), proj_coord = c("Xutm","Yutm"), corner_plot_ID = "Plot",
  grid_size = 25,
  tree_data = NouraguesTrees, tree_coords = c("Xfield","Yfield"), tree_plot_ID = "Plot"
)
#> Warning in divide_plot(corner_data = NouraguesCoords, rel_coord = c("Xfield", :
#> One or more trees could not be assigned to a subplot (not in a subplot area)

Summarising tree metrics at subplot level

Once you’ve applied the divide_plot() function with a non-null tree_data argument, you can summarise any tree metric at the subplot level with the subplot_summary() function.

subplot_metric <- subplot_summary(
  subplots = subplots,
  value = "AGB", # AGB was added before applying divide_plot()
  per_ha = TRUE)

By default, the function sums the metric per subplot and divides the result by the area of each subplot (to obtain a summary per hectare), but you can request any valid function using fun argument and choose between a raw or a per hectare summary using per_ha argument.

subplot_metric <- subplot_summary(
  subplots = subplots,
  value = "AGB",
  fun = quantile, probs = 0.5, # yes, it is the median
  per_ha = FALSE)

The output of the function is a list containing:

$tree_summary: a summary of the metric per subplot
$polygon: an object of the class sf containing a simple feature collection of the subplot’s polygon
$plot_design: a ggplot object that can easily be modified

The returned polygons can be saved into a shapefile like this:

# Set the CRS of the polygons
subplot_polygons <- sf::st_set_crs(
  subplot_metric$polygon ,
  value = "EPSG:2972") # EPSG:2972 (corresponding to UTM Zone 22N) is the UTM coordinate system of Nouragues

# Save the polygons in a shapefile
sf::st_write(subplot_polygons, "your_directory/subplots_201.shp")

And of course, the function can handle as many plots as provided in divide_plot():

multiple_subplot_metric <- subplot_summary(
  subplots = multiple_subplots,
  value = "D", fun = mean, per_ha = FALSE)

Customizing the ggplot

Here are some examples to custom the ggplot of the subplot_summary() function:

subplot_metric <- subplot_summary(subplots = subplots,
                                  value = "AGB")


custom_plot <- subplot_metric$plot_design
# Change the title and legend:
custom_plot + 
  labs(title = "Nouragues plot" , fill="Sum of AGB per hectare")

# Display trees with diameter as size and transparency (and a smaller legend on the right): 
custom_plot + 
  geom_point(data=plot201Trees, mapping = aes(x = Xutm, y = Yutm, size = D, alpha= D), shape=1,) +
  labs(fill = "Sum of AGB per hectare") +
  guides(alpha = guide_legend(title = "Diameter (cm)"),
         size = guide_legend(title = "Diameter (cm)")) + 
  theme(legend.position = "right", legend.key.size = unit(0.5, 'cm'))

Arthur Bailly

2025-02-20