,
Dominique Lamonica [aut, cre],
Arthur Bailly [aut],
Arthur Pere [aut],
Ariane Tanguy [aut],
Jerome Chave [dtc],
Ted Feldpausch [dtc],
Philippe Verley [ctb],
Camille Piponiot [aut],
Bruno Hérault [aut]| Title: | Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests |
|---|---|
| Description: | Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, (3) construct height-diameter models, (4) manage tree and plot coordinates, (5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty. To cite 'BIOMASS', please use citation("BIOMASS"). See more in the article of Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>. |
| Authors: | Maxime Réjou-Méchain [aut, dtc],
Guillaume Cornu [aut] ,
Dominique Lamonica [aut, cre],
Arthur Bailly [aut],
Arthur Pere [aut],
Ariane Tanguy [aut],
Jerome Chave [dtc],
Ted Feldpausch [dtc],
Philippe Verley [ctb],
Camille Piponiot [aut],
Bruno Hérault [aut] |
| Maintainer: | Dominique Lamonica <[email protected]> |
| License: | GPL-2 |
| Version: | 2.2.3 |
| Built: | 2025-02-20 17:21:49 UTC |
| Source: | https://github.com/umr-amap/biomass |
Propagation of the errors throughout the steps needed to compute AGB or AGC.
AGBmonteCarlo( D, WD = NULL, errWD = NULL, H = NULL, errH = NULL, HDmodel = NULL, coord = NULL, Dpropag = NULL, n = 1000, Carbon = FALSE, Dlim = NULL, plot = NULL )AGBmonteCarlo( D, WD = NULL, errWD = NULL, H = NULL, errH = NULL, HDmodel = NULL, coord = NULL, Dpropag = NULL, n = 1000, Carbon = FALSE, Dlim = NULL, plot = NULL )
D |
Vector of tree diameters (in cm) |
WD |
Vector of wood density estimates (in g/cm3) |
errWD |
Vector of error associated to the wood density estimates (should be of the same size as |
H |
(option 1) Vector of tree heights (in m). If set, |
errH |
(if |
HDmodel |
(option 2) Model used to estimate tree height from tree diameter (output from |
coord |
(option 3) Coordinates of the site(s), either a vector giving a single site (e.g. c(longitude, latitude)) or a matrix/dataframe with two columns (e.g. cbind(longitude, latitude)). The coordinates are used to predict height-diameter allometry with bioclimatic variables. |
Dpropag |
This variable can take three kind of values, indicating how to propagate the errors on diameter measurements:
a single numerical value or a vector of the same size as |
n |
Number of iterations. Cannot be smaller than 50 or larger than 1000. By default |
Carbon |
(logical) Whether or not the propagation should be done up to the carbon value (FALSE by default). |
Dlim |
(optional) Minimum diameter (in cm) for which above ground biomass should be calculated (all diameter below
|
plot |
(optional) Plot ID, must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models. |
See Rejou-Mechain et al. (2017) for all details on the error propagation procedure.
Returns a list with (if Carbon is FALSE):
meanAGB: Mean stand AGB value following the error propagation
medAGB: Median stand AGB value following the error propagation
sdAGB: Standard deviation of the stand AGB value following the error propagation
credibilityAGB: Credibility interval at 95\
AGB_simu: Matrix with the AGB of the trees (rows) times the n iterations (columns)
Maxime REJOU-MECHAIN, Bruno HERAULT, Camille PIPONIOT, Ariane TANGUY, Arthur PERE
Chave, J. et al. (2004). Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1443), 409-420.
Rejou-Mechain et al. (2017). BIOMASS: An R Package for estimating above-ground biomass and its uncertainty in tropical forests. Methods in Ecology and Evolution, 8 (9), 1163-1167.
# Load a database data(NouraguesHD) data(NouraguesTrees) # Modelling height-diameter relationship HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2") # Retrieving wood density values NouraguesWD <- getWoodDensity(NouraguesTrees$Genus, NouraguesTrees$Species, stand = NouraguesTrees$Plot ) # Propagating errors with a standard error for Wood density resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, HDmodel = HDmodel ) # If only the coordinates are available coord <- c(-52.683213,4.083024 ) resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, coord = coord ) # Propagating errors with a standard error in wood density in all plots at once NouraguesTrees$meanWD <- NouraguesWD$meanWD NouraguesTrees$sdWD <- NouraguesWD$sdWD resultMC <- by( NouraguesTrees, NouraguesTrees$Plot, function(x) AGBmonteCarlo( D = x$D, WD = x$meanWD, errWD = x$sdWD, HDmodel = HDmodel, Dpropag = "chave2004" ) ) meanAGBperplot <- unlist(sapply(resultMC, "[", 1)) credperplot <- sapply(resultMC, "[", 4)# Load a database data(NouraguesHD) data(NouraguesTrees) # Modelling height-diameter relationship HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2") # Retrieving wood density values NouraguesWD <- getWoodDensity(NouraguesTrees$Genus, NouraguesTrees$Species, stand = NouraguesTrees$Plot ) # Propagating errors with a standard error for Wood density resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, HDmodel = HDmodel ) # If only the coordinates are available coord <- c(-52.683213,4.083024 ) resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, coord = coord ) # Propagating errors with a standard error in wood density in all plots at once NouraguesTrees$meanWD <- NouraguesWD$meanWD NouraguesTrees$sdWD <- NouraguesWD$sdWD resultMC <- by( NouraguesTrees, NouraguesTrees$Plot, function(x) AGBmonteCarlo( D = x$D, WD = x$meanWD, errWD = x$sdWD, HDmodel = HDmodel, Dpropag = "chave2004" ) ) meanAGBperplot <- unlist(sapply(resultMC, "[", 1)) credperplot <- sapply(resultMC, "[", 4)
Function to attribute the trees on each subplot, the trees that are at the exterior of the subplot will be marked as NA
attributeTree(xy, plot, coordAbs)attributeTree(xy, plot, coordAbs)
xy |
The coordinates of the trees for each plot |
plot |
The label of the plot (same length as the number of rows of |
coordAbs |
Output of the function |
A vector with the code of the subplot for each trees, the code will be plot_X_Y. X and Y are the coordinate
where the tree is inside the plot in regards to the corresponding subplot.
Arthur PERE
# Trees relative coordinates xy <- data.frame(x = runif(200, min = 0, max = 200), y = runif(200, min = 0, max = 200)) # cut the plot in multiple part coord <- data.frame(X = rep(c(0, 200, 0, 200), 2), Y = rep(c(0, 0, 200, 200), 2)) coord[1:4, ] <- coord[1:4, ] + 5000 coord[5:8, ] <- coord[5:8, ] + 6000 corner <- rep(c(1, 2, 4, 3), 2) plot <- rep(c("plot1", "plot2"), each = 4) cut <- cutPlot(coord, plot, corner, gridsize = 100, dimX = 200, dimY = 200) # Assign a plot to 200 trees plot <- rep(c("plot1", "plot2"), 100) # attribute trees to subplots attributeTree(xy, plot, cut)# Trees relative coordinates xy <- data.frame(x = runif(200, min = 0, max = 200), y = runif(200, min = 0, max = 200)) # cut the plot in multiple part coord <- data.frame(X = rep(c(0, 200, 0, 200), 2), Y = rep(c(0, 0, 200, 200), 2)) coord[1:4, ] <- coord[1:4, ] + 5000 coord[5:8, ] <- coord[5:8, ] + 6000 corner <- rep(c(1, 2, 4, 3), 2) plot <- rep(c("plot1", "plot2"), each = 4) cut <- cutPlot(coord, plot, corner, gridsize = 100, dimX = 200, dimY = 200) # Assign a plot to 200 trees plot <- rep(c("plot1", "plot2"), 100) # attribute trees to subplots attributeTree(xy, plot, cut)
Attribute GPS coordinates to trees
attributeTreeCoord(xy, plot, dim, coordAbs)attributeTreeCoord(xy, plot, dim, coordAbs)
xy |
The relative coordinates of the trees within each plot |
plot |
The label of the plot (same length as the number of rows of |
dim |
The dimension of the plot (either one value if the plot is a square or a vector if a rectangle) |
coordAbs |
The result of the function |
A data frame with two columns:
- Xproj: The X coordinates in the absolute coordinate system
- Yproj: The Y coordinates in the absolute coordinate system
# Trees relative coordinates xy <- data.frame(x = runif(200, min = 0, max = 200), y = runif(200, min = 0, max = 200)) # cut the plot in multiple part coord <- data.frame(X = rep(c(0, 200, 0, 200), 2), Y = rep(c(0, 0, 200, 200), 2)) coord[1:4, ] <- coord[1:4, ] + 5000 coord[5:8, ] <- coord[5:8, ] + 6000 corner <- rep(c(1, 2, 4, 3), 2) Forestplot <- rep(c("plot1", "plot2"), each = 4) Outcut <- cutPlot(coord, Forestplot, corner, gridsize = 100, dimX = 200, dimY = 200) # Assign a plot to 200 trees Forestplot <- rep(c("plot1", "plot2"), 100) # attribute trees to subplots attributeTreeCoord(xy, Forestplot, dim =100,coordAbs = Outcut)# Trees relative coordinates xy <- data.frame(x = runif(200, min = 0, max = 200), y = runif(200, min = 0, max = 200)) # cut the plot in multiple part coord <- data.frame(X = rep(c(0, 200, 0, 200), 2), Y = rep(c(0, 0, 200, 200), 2)) coord[1:4, ] <- coord[1:4, ] + 5000 coord[5:8, ] <- coord[5:8, ] + 6000 corner <- rep(c(1, 2, 4, 3), 2) Forestplot <- rep(c("plot1", "plot2"), each = 4) Outcut <- cutPlot(coord, Forestplot, corner, gridsize = 100, dimX = 200, dimY = 200) # Assign a plot to 200 trees Forestplot <- rep(c("plot1", "plot2"), 100) # attribute trees to subplots attributeTreeCoord(xy, Forestplot, dim =100,coordAbs = Outcut)
Apply a generalized bilinear interpolation to convert any coordinates from one original coordinate system to another, using the plot's 4 corner coordinates of both system.
bilinear_interpolation( coord, from_corner_coord, to_corner_coord, ordered_corner = F )bilinear_interpolation( coord, from_corner_coord, to_corner_coord, ordered_corner = F )
coord |
a matrix or data.frame : coordinates to be transformed, with X and Y corresponding to the first two columns |
from_corner_coord |
a matrix or data.frame : corner coordinates of the rectangular plot in the original coordinate system, with X and Y corresponding to the first two columns |
to_corner_coord |
a matrix or data.frame : corner coordinates of the plot in the coordinate system to be projected, with the same line order as from_corner_coord and , with X and Y corresponding to the first two columns |
ordered_corner |
a logical, if TRUE : indicating that from_corner_coord and to_corner_coord rows are sorted in correct order (clockwise or counter-clockwise) |
The plot represented by the 4 coordinates in from_corner_coord must have 4 right angles, i.e. a rectangular (or square) plot.
When ordered_corner = FALSE, the function automatically reassigns corners in a counter-clockwise order.
a data.frame containing the converted coordinates
Arthur Bailly
C. -C. Wei and C. -H. Chen, "Generalized Bilinear Interpolation of Motion Vectors for Quad-Tree Mesh," 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Harbin, China, 2008, pp. 635-638, doi: 10.1109/IIH-MSP.2008.283.
from_corner_coord <- expand.grid(X = c(0, 100), Y = c(0, 50)) rot_mat <- matrix(c(cos(-pi/6),sin(-pi/6),-sin(-pi/6),cos(-pi/6)),nrow=2) to_corner_coord <- as.matrix(from_corner_coord) %*% rot_mat to_corner_coord <- sweep(to_corner_coord, 2, c(50,100), FUN = "+") coord <- expand.grid(X = seq(0,100,10), Y = seq(0,50,5)) projCoord = bilinear_interpolation(coord = coord, from_corner_coord = from_corner_coord, to_corner_coord = to_corner_coord)from_corner_coord <- expand.grid(X = c(0, 100), Y = c(0, 50)) rot_mat <- matrix(c(cos(-pi/6),sin(-pi/6),-sin(-pi/6),cos(-pi/6)),nrow=2) to_corner_coord <- as.matrix(from_corner_coord) %*% rot_mat to_corner_coord <- sweep(to_corner_coord, 2, c(50,100), FUN = "+") coord <- expand.grid(X = seq(0,100,10), Y = seq(0,50,5)) projCoord = bilinear_interpolation(coord = coord, from_corner_coord = from_corner_coord, to_corner_coord = to_corner_coord)
Function that return a possibly cached file, transparently downloading it if missing
cacheManager(nameFile)cacheManager(nameFile)
nameFile |
character. file to resolve cached path. |
file path of the resolved cached file.
Cache path discovery protocol
BIOMASS.cache option set to an existing folder
existing user data folder rappdirs::user_data_dir()
On Linux : ~/.local/share/R/BIOMASS
On Mac OS X : ~/Library/Application Support/R/BIOMASS
On Windows 7 up to 10 : C:\\Users\\<username>\\AppData\\Local\\R\\BIOMASS
On Windows XP : C:\\Documents and Settings\\<username>\\Data\\R\\BIOMASS
fallback to R session tempdir
Parameters are similar to that of file.path function
cachePath(...)cachePath(...)
... |
character vectors. Elements of the subpath of cache path |
A character vector of normalized file path with a source attribute holding a hint to cache path source ("option", "data", "temp")
Cache path discovery protocol
BIOMASS.cache option set to an existing folder
existing user data folder rappdirs::user_data_dir()
On Linux : ~/.local/share/R/BIOMASS
On Mac OS X : ~/Library/Application Support/R/BIOMASS
On Windows 7 up to 10 : C:\\Users\\<username>\\AppData\\Local\\R\\BIOMASS
On Windows XP : C:\\Documents and Settings\\<username>\\Data\\R\\BIOMASS
fallback to R session tempdir
Quality check of plot corner and tree coordinates.
check_plot_coord( corner_data, proj_coord = NULL, longlat = NULL, rel_coord, trust_GPS_corners, draw_plot = TRUE, tree_data = NULL, tree_coords = NULL, max_dist = 10, rm_outliers = TRUE, plot_ID = NULL, tree_plot_ID = NULL, ref_raster = NULL, prop_tree = NULL, ask = T )check_plot_coord( corner_data, proj_coord = NULL, longlat = NULL, rel_coord, trust_GPS_corners, draw_plot = TRUE, tree_data = NULL, tree_coords = NULL, max_dist = 10, rm_outliers = TRUE, plot_ID = NULL, tree_plot_ID = NULL, ref_raster = NULL, prop_tree = NULL, ask = T )
corner_data |
A data frame, data frame extension, containing the plot corner coordinates. |
proj_coord |
(optional, if longlat is not supplied) A character vector of length 2, specifying the column names (resp. x, y) of the corner projected coordinates. |
longlat |
(optional, if proj_coord is not supplied) A character vector of length 2 specifying the column names of the corner geographic coordinates (long,lat). |
rel_coord |
A character vector of length 2 specifying the column names (resp. x, y) of the corner relative coordinates (that of the field, ie, the local ones). |
trust_GPS_corners |
A logical indicating whether or not you trust the GPS coordinates of the plot's corners. See details. |
draw_plot |
A logical indicating if the plot design should be displayed and returned. |
tree_data |
A data frame, data frame extension, containing the relative coordinates (field/local coordinates) of the trees and optional other tree metrics. |
tree_coords |
A character vector specifying the column names of the tree relative coordinates. |
max_dist |
If dealing with repeated measurements of each corner : the maximum distance (in meters) above which GPS measurements should be considered outliers (default 15 m). |
rm_outliers |
If TRUE and dealing with repeated measurements of each corner, then outliers are removed from the coordinate calculation of the referenced corners. |
plot_ID |
If dealing with multiple plots : a character indicating the variable name for corner plot IDs in corner_data. |
tree_plot_ID |
If dealing with multiple plots : a character indicating the variable name for tree plot IDs in tree_data. |
ref_raster |
A SpatRaster object from terra package, typically a chm raster created from LiDAR data. |
prop_tree |
The column name variable of tree_data for which the tree visualization will be proportional. |
ask |
If TRUE and dealing with multiple plots, then prompt user before displaying each plot. |
If trust_GPS_corners is TRUE, corner coordinates in the projected coordinate system are averaging by corner (if multiple measures) and outlier corners are identified sequentially using these averages and the max_dist argument. Then, projected coordinates of the trees are calculated from the local coordinates using a bilinear interpolation that follows the correspondence of the corners between these two coordinate systems. Be aware that this projection only works if the plot, in the relative coordinates system, is rectangular (ie, has 4 right angles).
If trust_GPS_corners is FALSE, corner coordinates in the projected coordinate system are calculated by a procrust analysis that preserves the shape and dimensions of the plot in the local coordinate system. Outlier corners are also identified sequentially and projected coordinates of the trees are calculated by applying the resulting procrust analysis.
If longlat is supplied instead of proj_coord, the function will first convert the long/lat coordinates into UTM coordinates. An error may result if the parcel is located right between two UTM zones. In this case, the user has to convert himself his long/lat coordinates into any projected coordinates which have the same dimension than his local coordinates (in meters most of the time).
If longlat and proj_coord are supplied, only longitude/latitude coordinates will be considered.
When ref_raster is supplied, this raster is cropped for every plot contained in corner_data.
Returns a list including :
corner_coord: a data frame containing the projected coordinates (x_proj and y_proj) and the relative coordinates (x_rel and y_rel) of the 4 corners of the plot
polygon: a sf object containing plot's polygon(s)
tree_data: if tree_data is supplied in the arguments of the function, a data frame corresponding to tree_data for which the projected coordinates of the trees (x_proj and y_proj) are added, and also a variable telling if the trees are inside the plot (is_in_plot). The name of the relative tree coordinates are also standardised and renamed to (x_rel and y_rel).
outliers: a data frame containing the projected coordinates and the row number of GPS measurements considered outliers
plot_design: if draw_plot is TRUE, a ggplot object corresponding to the design of the plot
UTM_code: if longlat is supplied, a character containing the UTM code of the GPS coordinates
Arthur PERE, Maxime REJOU-MECHAIN, Arthur BAILLY
Arthur BAILLY, Arthur PERE, Maxime REJOU-MECHAIN
# One plot with repeated measurements of each corner data("NouraguesPlot201") check_plot201 <- check_plot_coord( corner_data = NouraguesPlot201, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE) check_plot201$corner_coord check_plot201$plot_design # 4 plots with one measurement of each corner data("NouraguesCoords") check_plots <- check_plot_coord( corner_data = NouraguesCoords, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, plot_ID = "Plot", draw_plot = FALSE) check_plots$corner_coord check_plots$plot_design # Displaying the associated CHM raster and representing trees proportionally to their diameter plot_204_coords <- NouraguesCoords[NouraguesCoords$Plot==204,] data("NouraguesTrees") plot_204_trees <- NouraguesTrees[NouraguesTrees$Plot == 204, ] nouragues_raster <- terra::rast( system.file("extdata", "NouraguesRaster.tif", package = "BIOMASS", mustWork = TRUE) ) check_plot_204 <- check_plot_coord( corner_data = plot_204_coords, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE, tree_data = plot_204_trees, tree_coords = c("Xfield","Yfield"), ref_raster = nouragues_raster, prop_tree = "D" ) check_plot_204$plot_design# One plot with repeated measurements of each corner data("NouraguesPlot201") check_plot201 <- check_plot_coord( corner_data = NouraguesPlot201, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE) check_plot201$corner_coord check_plot201$plot_design # 4 plots with one measurement of each corner data("NouraguesCoords") check_plots <- check_plot_coord( corner_data = NouraguesCoords, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, plot_ID = "Plot", draw_plot = FALSE) check_plots$corner_coord check_plots$plot_design # Displaying the associated CHM raster and representing trees proportionally to their diameter plot_204_coords <- NouraguesCoords[NouraguesCoords$Plot==204,] data("NouraguesTrees") plot_204_trees <- NouraguesTrees[NouraguesTrees$Plot == 204, ] nouragues_raster <- terra::rast( system.file("extdata", "NouraguesRaster.tif", package = "BIOMASS", mustWork = TRUE) ) check_plot_204 <- check_plot_coord( corner_data = plot_204_coords, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE, tree_data = plot_204_trees, tree_coords = c("Xfield","Yfield"), ref_raster = nouragues_raster, prop_tree = "D" ) check_plot_204$plot_design
It will refuse to clear or remove a custom cache folder set using BIOMASS.cache option as we don't know whether this folder contains other possibly valuable files apart from our cached files.
clearCache(remove = FALSE)clearCache(remove = FALSE)
remove |
logical. If TRUE cache folder will be removed too (not only content) resulting in deactivating cache as a side effect |
No return value, called for side effects
This function uses Chave et al. 2014's pantropical models to estimate the above ground biomass of tropical trees.
computeAGB(D, WD, H = NULL, coord = NULL, Dlim = NULL)computeAGB(D, WD, H = NULL, coord = NULL, Dlim = NULL)
D |
Tree diameter (in cm), either a vector or a single value. |
WD |
Wood density (in g/cm3), either a vector or a single value. If not available, see |
H |
(optional) Tree height (H in m), either a vector or a single value. If not available, see |
coord |
(optional) Coordinates of the site(s), either a vector giving a single site
(e.g. c(longitude, latitude)) or a matrix/dataframe with two columns (e.g. cbind(longitude, latitude)).
The coordinates are used to account for variation in height-diameter relationship thanks to an environmental
proxy (parameter E in Chave et al. 2014). Compulsory if tree heights |
Dlim |
(optional) Minimum diameter (in cm) for which aboveground biomass should be calculated
(all diameter below |
This function uses two different ways of computing the above ground biomass of a tree:
If tree height data are available, the AGB is computed thanks to the following equation (Eq. 4 in Chave et al., 2014):
If no tree height data is available, the AGB is computed thanks to the site coordinates with the following equation, slightly modified from Eq. 7 in Chave et al., 2014 (see Réjou-Méchain et al. 2017):
where E is a measure of environmental stress estimated from the site coordinates (coord).
The function returns the AGB in Mg (or ton) as a single value or a vector.
Maxime REJOU-MECHAIN, Ariane TANGUY, Arthur PERE
Chave et al. (2014) Improved allometric models to estimate the aboveground biomass of tropical trees, Global Change Biology, 20 (10), 3177-3190
# Create variables D <- 10:99 WD <- runif(length(D), min = 0.1, max = 1) H <- D^(2 / 3) # If you have height data AGB <- computeAGB(D, WD, H) # If you do not have height data and a single site lat <- 4.08 long <- -52.68 coord <- c(long, lat) AGB <- computeAGB(D, WD, coord = coord) # If you do not have height data and several sites (here three) lat <- c(rep(4.08, 30), rep(3.98, 30), rep(4.12, 30)) long <- c(rep(-52.68, 30), rep(-53.12, 30), rep(-53.29, 30)) coord <- cbind(long, lat) AGB <- computeAGB(D, WD, coord = coord)# Create variables D <- 10:99 WD <- runif(length(D), min = 0.1, max = 1) H <- D^(2 / 3) # If you have height data AGB <- computeAGB(D, WD, H) # If you do not have height data and a single site lat <- 4.08 long <- -52.68 coord <- c(long, lat) AGB <- computeAGB(D, WD, coord = coord) # If you do not have height data and several sites (here three) lat <- c(rep(4.08, 30), rep(3.98, 30), rep(4.12, 30)) long <- c(rep(-52.68, 30), rep(-53.12, 30), rep(-53.29, 30)) coord <- cbind(long, lat) AGB <- computeAGB(D, WD, coord = coord)
Extract the Feldpausch et al. (2012)'s regions using local coordinates.
computeFeldRegion(coord, level = c("region"))computeFeldRegion(coord, level = c("region"))
coord |
Coordinates of the site(s), a matrix/dataframe with two columns (e.g. cbind(longitude, latitude)) (see examples). |
level |
a string or a vector of string, the length must match the number of rows of the parameter coord. This parameter gives the scale at which Feldpausch regions should be assigned. There are tree levels:
|
The function returns a vector with the Feldpausch et al. (2012)'s regions that can be
incorporated in the retrieveH function.
Arthur PERE
Feldpausch, T.R., et al. (2012). Tree height integrated into pantropical forest biomass estimates. Biogeosciences, 9, 3381–3403.
#' # One study site lat <- 4.08 long <- -52.68 coord <- cbind(long, lat) FeldRegion <- computeFeldRegion(coord) # Several study sites (here three sites) long <- c(-52.68, -51.12, -53.11) lat <- c(4.08, 3.98, 4.12) coord <- cbind(long, lat) FeldRegion <- computeFeldRegion(coord)#' # One study site lat <- 4.08 long <- -52.68 coord <- cbind(long, lat) FeldRegion <- computeFeldRegion(coord) # Several study sites (here three sites) long <- c(-52.68, -51.12, -53.11) lat <- c(4.08, 3.98, 4.12) coord <- cbind(long, lat) FeldRegion <- computeFeldRegion(coord)
This function builds the most probable GPS coordinates of the plot corners from multiple GPS measurements.
correctCoordGPS( longlat = NULL, projCoord = NULL, coordRel, rangeX, rangeY, maxDist = 15, drawPlot = FALSE, rmOutliers = TRUE )correctCoordGPS( longlat = NULL, projCoord = NULL, coordRel, rangeX, rangeY, maxDist = 15, drawPlot = FALSE, rmOutliers = TRUE )
longlat |
(optional) data frame with the coordinate in longitude latitude (eg. cbind(longitude, latitude)). |
projCoord |
(optional) data frame with the projected coordinate in X Y |
coordRel |
data frame with the relative coordinate in the same order than the longlat or projCoord |
rangeX |
a vector of length 2 giving the range for plot relative X coordinates |
rangeY |
a vector of length 2 giving the range for plot relative Y coordinates |
maxDist |
a numeric giving the maximum distance above which GPS measurements should be considered as outliers (by default 15 m) |
drawPlot |
a logical if you want to display a graphical representation |
rmOutliers |
a logical if you want to remove the outliers from coordinates calculation |
GPS coordinates should be either given in longitude latitude (longlat) or in projected coordinates (projCoord)
If there are no outliers or rmOutliers = TRUE, a list with:
cornerCoords: a data.frame with the coordinates of the corners
correctedCoord: a data.frame with the adjusted coordinates given as input
polygon: a spatial polygon
outliers: index of coordinates lines considered as outliers, if any
codeUTM: the UTM code of the coordinates if the parameter longlat is set
Arthur PERE, Maxime REJOU-MECHAIN
projCoord <- data.frame( X = c( runif(5, min = 9, max = 11), runif(5, min = 8, max = 12), runif(5, min = 80, max = 120), runif(5, min = 90, max = 110) ), Y = c( runif(5, min = 9, max = 11), runif(5, min = 80, max = 120), runif(5, min = 8, max = 12), runif(5, min = 90, max = 110) ) ) projCoord <- projCoord + 1000 coordRel <- data.frame( X = c(rep(0, 10), rep(100, 10)), Y = c(rep(c(rep(0, 5), rep(100, 5)), 2)) ) aa <- correctCoordGPS( projCoord = projCoord, coordRel = coordRel, rangeX = c(0, 100), rangeY = c(0, 100) ) bb <- correctCoordGPS( projCoord = projCoord, coordRel = coordRel, rangeX = c(0, 100), rangeY = c(0, 100), rmOutliers = TRUE ) correctCoordGPS( projCoord = projCoord, coordRel = coordRel, rangeX = c(0, 100), rangeY = c(0, 100), drawPlot = TRUE )projCoord <- data.frame( X = c( runif(5, min = 9, max = 11), runif(5, min = 8, max = 12), runif(5, min = 80, max = 120), runif(5, min = 90, max = 110) ), Y = c( runif(5, min = 9, max = 11), runif(5, min = 80, max = 120), runif(5, min = 8, max = 12), runif(5, min = 90, max = 110) ) ) projCoord <- projCoord + 1000 coordRel <- data.frame( X = c(rep(0, 10), rep(100, 10)), Y = c(rep(c(rep(0, 5), rep(100, 5)), 2)) ) aa <- correctCoordGPS( projCoord = projCoord, coordRel = coordRel, rangeX = c(0, 100), rangeY = c(0, 100) ) bb <- correctCoordGPS( projCoord = projCoord, coordRel = coordRel, rangeX = c(0, 100), rangeY = c(0, 100), rmOutliers = TRUE ) correctCoordGPS( projCoord = projCoord, coordRel = coordRel, rangeX = c(0, 100), rangeY = c(0, 100), drawPlot = TRUE )
This function corrects typos for a given taxonomic name using the Taxonomic Name Resolution Service (TNRS).
correctTaxo( genus, species = NULL, score = 0.5, useCache = FALSE, verbose = TRUE, accepted = FALSE )correctTaxo( genus, species = NULL, score = 0.5, useCache = FALSE, verbose = TRUE, accepted = FALSE )
genus |
Vector of genera to be checked. Alternatively, the whole species name (genus + species) or (genus + species + author) may be given (see example). |
species |
(optional) Vector of species to be checked (same size as the genus vector). |
score |
Score of the matching ( see https://tnrs.biendata.org/instructions ) below which corrections are discarded. |
useCache |
logical. Whether or not use a cache to reduce online search of taxa names (NULL means use cache but clear it first) |
verbose |
logical. If TRUE various messages are displayed during process |
accepted |
logical. If TRUE accepted names will be returned instead of matched names. Cache will not be used as synonymy changes over time. |
This function create a file named correctTaxo.log (see Localisation), this file have the memory of all the previous requests, as to avoid the replication of time-consuming server requests.
By default, names are queried in batches of 500, with a 0.5s delay between each query. These values can be modified using options:
options(BIOMASS.batch_size=500) for batch size (max 1000), options(BIOMASS.wait_delay=0.5) for delay (in seconds).
The function returns a dataframe with the corrected (or not) genera and species.
Cache path discovery protocol
BIOMASS.cache option set to an existing folder
existing user data folder rappdirs::user_data_dir()
On Linux : ~/.local/share/R/BIOMASS
On Mac OS X : ~/Library/Application Support/R/BIOMASS
On Windows 7 up to 10 : C:\\Users\\<username>\\AppData\\Local\\R\\BIOMASS
On Windows XP : C:\\Documents and Settings\\<username>\\Data\\R\\BIOMASS
fallback to R session tempdir
Ariane TANGUY, Arthur PERE, Maxime REJOU-MECHAIN, Guillaume CORNU
Boyle, B. et al. (2013). The taxonomic name resolution service: An online tool for automated standardization of plant names. BMC bioinformatics, 14, 1. doi:10.1186/1471-2105-14-16
## Not run: correctTaxo(genus = "Astrocarium", species = "standleanum") correctTaxo(genus = "Astrocarium standleanum") ## End(Not run)## Not run: correctTaxo(genus = "Astrocarium", species = "standleanum") correctTaxo(genus = "Astrocarium standleanum") ## End(Not run)
Permanent cache is located by default in user data dir.
createCache(path = NULL)createCache(path = NULL)
path |
Use a custom path to host cache |
You can provide a custom path (that will be defined as a BIOMASS.cache option) but clearCache function will refuse to operate on it for security reasons.
No return value, called for side effects
This function divides a plot (or several plots) in subplots and returns the coordinates of the grid. These coordinates are calculated by a bilinear interpolation with the projected corner coordinates as references.
cutPlot(projCoord, plot, cornerNum, gridsize = 100, dimX = 200, dimY = 200)cutPlot(projCoord, plot, cornerNum, gridsize = 100, dimX = 200, dimY = 200)
projCoord |
A data frame containing the projected coordinates of plot corners, with X and Y on the first and second column respectively |
plot |
A vector indicating the plot codes |
cornerNum |
A vector with corners numbered from 1 to 4 for each plot, numbering must be in clockwise direction |
gridsize |
The size of the subplots |
dimX |
A vector indicating the size of the plot on the X axis, in meters and in the relative coordinates system (if a single value is supplied, it will be replicated for all plots) |
dimY |
A vector indicating the size of the plot on the Y axis, in meters and in the relative coordinates system (if a single value is supplied, it will be replicated for all plots) |
Returns a data-frame containing as many rows as there are corners corresponding to the subplots, and the following columns :
plot: The plot code
subplot: The automatically generated subplot code
XRel: The relative coordinates on the X axis (defined by corners 1->4)
YRel: The relative coordinates on the Y axis (defined by corners 1->2)
XAbs: The absolute (projected) X coordinates
YAbs: The absolute (projected) Y coordinates
Arthur PERE
coord <- data.frame(X = c(0, 200, 0, 200), Y = c(0, 0, 200, 200)) + 5000 cornerNum <- c(1, 2, 4, 3) plot <- rep("plot1", 4) cut <- cutPlot(coord, plot, cornerNum, gridsize = 100, dimX = 200, dimY = 200) # plot the result plot(coord, main = "example", xlim = c(4900, 5300), ylim = c(4900, 5300), asp = 1) text(coord, labels = cornerNum, pos = 1) points(cut$XAbs, cut$YAbs, pch = "+") legend("bottomright", legend = c("orignal", "cut"), pch = c("o", "+"))coord <- data.frame(X = c(0, 200, 0, 200), Y = c(0, 0, 200, 200)) + 5000 cornerNum <- c(1, 2, 4, 3) plot <- rep("plot1", 4) cut <- cutPlot(coord, plot, cornerNum, gridsize = 100, dimX = 200, dimY = 200) # plot the result plot(coord, main = "example", xlim = c(4900, 5300), ylim = c(4900, 5300), asp = 1) text(coord, labels = cornerNum, pos = 1) points(cut$XAbs, cut$YAbs, pch = "+") legend("bottomright", legend = c("orignal", "cut"), pch = c("o", "+"))
This function divides a plot (or several plots) into subplots in the relative coordinates system, and returns the coordinates of subplot corners.
divide_plot( corner_data, rel_coord, proj_coord = NULL, grid_size, tree_data = NULL, tree_coords = NULL, corner_plot_ID = NULL, tree_plot_ID = NULL, grid_tol = 0.1, centred_grid = F )divide_plot( corner_data, rel_coord, proj_coord = NULL, grid_size, tree_data = NULL, tree_coords = NULL, corner_plot_ID = NULL, tree_plot_ID = NULL, grid_tol = 0.1, centred_grid = F )
corner_data |
A data frame, data frame extension, containing the plot corner coordinates. Typically, the output |
rel_coord |
A character vector of length 2, specifying the column names (resp. x, y) of the corner relative coordinates. |
proj_coord |
A character vector of length 2, specifying the column names (resp. x, y) of the corner projected coordinates. |
grid_size |
A vector indicating the dimensions of grid cells (resp. X and Y dimensions). If only one value is given, grid cells will be considered as squares. |
tree_data |
A data frame containing tree relative coordinates and other optional tree metrics (one row per tree). |
tree_coords |
A character vector of length 2, specifying the column names of the relative coordinates of the trees. |
corner_plot_ID |
If dealing with multiple plots : a vector indicating plot IDs for corners. |
tree_plot_ID |
If dealing with multiple plots : a vector indicating tree plot IDs. |
grid_tol |
A numeric between (0;1) corresponding to the percentage of the plot area allowed to be excluded from the plot division (when grid_size doesn't match exactly plot dimensions). |
centred_grid |
When grid_size doesn't match exactly plot dimensions, a logical indicating if the subplot grid should be centered on the plot. |
If corner coordinates in the projected coordinate system are supplied (proj_coord), projected coordinates of subplot corners are calculated by a bilinear interpolation in relation with relative coordinates of plot corners. Be aware that this bilinear interpolation only works if the plot in the relative coordinates system is rectangular (ie, has 4 right angles).
If tree_data isn't supplied, returns a data-frame containing as many rows as there are corners corresponding to the subplots, and the following columns :
corner_plot_ID: If dealing with multiple plots : the plot code
subplot_ID: The automatically generated subplot code, using the following rule : subplot_X_Y
x_rel and y_rel : the relative X-axis and Y-axis coordinates of subplots corners.
x_proj and y_proj : if proj_coord is supplied, the projected X-axis and Y-axis coordinates of subplots corners
If tree_data is supplied, returns a list containing :
the previous data-frame
the tree_data data-frame with the subplot_ID of each tree in the last column
Arthur PERE, Arthur BAILLY
# One plot with repeated measurements of each corner data("NouraguesPlot201") check_plot201 <- check_plot_coord( corner_data = NouraguesPlot201, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE) subplots_201 <- divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), proj_coord = c("x_proj","y_proj"), grid_size = 50) subplots_201 # Assigning trees to subplots data("NouraguesTrees") plot201_trees <- NouraguesTrees[NouraguesTrees$Plot == 201,] subplots_201 <- suppressWarnings( divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), proj_coord = c("x_proj","y_proj"), grid_size = 50, tree_data = plot201_trees, tree_coords = c("Xfield","Yfield"))) head(subplots_201$sub_corner_coord) head(subplots_201$tree_data) # When grid dimensions don't fit perfectly plot dimensions divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), grid_size = c(41,41), grid_tol = 0.4, centred_grid = TRUE) # Dealing with multiple plots data("NouraguesCoords") nouragues_subplots <- suppressWarnings( divide_plot( corner_data = NouraguesCoords, rel_coord = c("Xfield","Yfield"), proj_coord = c("Xutm","Yutm"), corner_plot_ID = "Plot", grid_size = 50, tree_data = NouraguesTrees, tree_coords = c("Xfield","Yfield"), tree_plot_ID = "Plot")) head(nouragues_subplots$sub_corner_coord) head(nouragues_subplots$tree_data)# One plot with repeated measurements of each corner data("NouraguesPlot201") check_plot201 <- check_plot_coord( corner_data = NouraguesPlot201, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE) subplots_201 <- divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), proj_coord = c("x_proj","y_proj"), grid_size = 50) subplots_201 # Assigning trees to subplots data("NouraguesTrees") plot201_trees <- NouraguesTrees[NouraguesTrees$Plot == 201,] subplots_201 <- suppressWarnings( divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), proj_coord = c("x_proj","y_proj"), grid_size = 50, tree_data = plot201_trees, tree_coords = c("Xfield","Yfield"))) head(subplots_201$sub_corner_coord) head(subplots_201$tree_data) # When grid dimensions don't fit perfectly plot dimensions divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), grid_size = c(41,41), grid_tol = 0.4, centred_grid = TRUE) # Dealing with multiple plots data("NouraguesCoords") nouragues_subplots <- suppressWarnings( divide_plot( corner_data = NouraguesCoords, rel_coord = c("Xfield","Yfield"), proj_coord = c("Xutm","Yutm"), corner_plot_ID = "Plot", grid_size = 50, tree_data = NouraguesTrees, tree_coords = c("Xfield","Yfield"), tree_plot_ID = "Plot")) head(nouragues_subplots$sub_corner_coord) head(nouragues_subplots$tree_data)
From given genus, the function finds the APG III family, and optionally the order, from the genusFamily database and the apgFamilies dataset
getTaxonomy(genus, findOrder = FALSE)getTaxonomy(genus, findOrder = FALSE)
genus |
Vector of genus names |
findOrder |
(Boolean) If |
Data frame with the order (if findOrder is TRUE), family and genus.
Ariane TANGUY, Arthur PERE, Maxime REJOU-MECHAIN
# Find the Family of the Aphelandra genus getTaxonomy("Aphelandra") # ... and the order getTaxonomy("Aphelandra", findOrder = TRUE)# Find the Family of the Aphelandra genus getTaxonomy("Aphelandra") # ... and the order getTaxonomy("Aphelandra", findOrder = TRUE)
The function estimates the wood density (WD) of the trees from their taxonomy or from their congeners using the global wood density database (Chave et al. 2009, Zanne et al. 2009) or any additional dataset. The WD can either be attributed to an individual at a species, genus, family or stand level.
getWoodDensity( genus, species, stand = NULL, family = NULL, region = "World", addWoodDensityData = NULL, verbose = TRUE )getWoodDensity( genus, species, stand = NULL, family = NULL, region = "World", addWoodDensityData = NULL, verbose = TRUE )
genus |
Vector of genus names |
species |
Vector of species names |
stand |
(optional) Vector with the corresponding stands of your data. If set, the missing wood densities at the genus level will be attributed at stand level. If not, the value attributed will be the mean of the whole tree dataset. |
family |
(optional) Vector of families. If set, the missing wood densities at the genus level will be attributed at family level if available. |
region |
Region (or vector of region) of interest of your sample. By default, Region is set to 'World', but you can restrict the WD estimates to a single region :
|
addWoodDensityData |
A dataframe containing additional wood density data to be combined with the global wood density database. The dataframe should be organized in a dataframe with three (or four) columns: "genus","species","wd", the fourth column "family" is optional. |
verbose |
A logical, give some statistic with the database |
The function assigns to each taxon a species- or genus- level average if at least one wood density value at the genus level is available for that taxon in the reference database. If not, the mean wood density of the family (if set) or of the stand (if set) is given.
The function also provides an estimate of the error associated with the wood density estimate (i.e. a standard deviation): a mean standard deviation value is given to the tree at the appropriate taxonomic level using the sd_10 dataset.
Returns a dataframe containing the following information:
family: (if set) Family
genus: Genus
species: Species
meanWD (g/cm^3): Mean wood density
sdWD (g/cm^3): Standard deviation of the wood density that can be used in error propagation
(see sd_10 and AGBmonteCarlo())
levelWD: Level at which wood density has been calculated. Can be species, genus, family,
dataset (mean of the entire dataset) or, if stand is set, the name of the stand (mean of the current stand)
nInd: Number of individuals taken into account to compute the mean wood density
Maxime REJOU-MECHAIN, Arthur PERE, Ariane TANGUY
Chave, J., et al. Towards a worldwide wood economics spectrum. Ecology letters 12.4 (2009): 351-366. Zanne, A. E., et al. Global wood density database. Dryad. Identifier: http://hdl. handle. net/10255/dryad 235 (2009).
# Load a data set data(NouraguesTrees) # Compute the Wood Density up to the genus level and give the mean wood density of the dataset WD <- getWoodDensity( genus = NouraguesTrees$Genus, species = NouraguesTrees$Species ) # Compute the Wood Density up to the genus level and then give the mean wood density per stand WD <- getWoodDensity( genus = NouraguesTrees$Genus, species = NouraguesTrees$Species, stand = NouraguesTrees$plotId ) # Compute the Wood Density up to the family level and then give the mean wood density per stand WD <- getWoodDensity( family = NouraguesTrees$family, genus = NouraguesTrees$Genus, species = NouraguesTrees$Species, stand = NouraguesTrees$plotId ) str(WD)# Load a data set data(NouraguesTrees) # Compute the Wood Density up to the genus level and give the mean wood density of the dataset WD <- getWoodDensity( genus = NouraguesTrees$Genus, species = NouraguesTrees$Species ) # Compute the Wood Density up to the genus level and then give the mean wood density per stand WD <- getWoodDensity( genus = NouraguesTrees$Genus, species = NouraguesTrees$Species, stand = NouraguesTrees$plotId ) # Compute the Wood Density up to the family level and then give the mean wood density per stand WD <- getWoodDensity( family = NouraguesTrees$family, genus = NouraguesTrees$Genus, species = NouraguesTrees$Species, stand = NouraguesTrees$plotId ) str(WD)
Methods used for modeling height-diameter relationship
loglogFunction(data, method) michaelisFunction(data, weight = NULL) weibullFunction(data, weight = NULL)loglogFunction(data, method) michaelisFunction(data, weight = NULL) weibullFunction(data, weight = NULL)
data |
Dataset with the informations of height (H) and diameter (D) |
method |
In the case of the loglogFunction, the model is to be chosen between log1, log2 or log3. |
weight |
(optional) Vector indicating observation weights in the model. |
These functions model the relationship between tree height (H) and diameter (D). loglogFunction Compute two types of log model (log and log2) to predict H from D. The model can be:
log 1: (equivalent to a power model)
log 2:
michaelisFunction Construct a Michaelis Menten model of the form:
(A and B are the model parameters to be estimated)
weibullFunction Construct a three parameter Weibull model of the form:
(a, b, c are the model parameters to be estimated)
All the functions give an output similar to the one given by stats::lm(), obtained for
michaelisFunction and weibullFunction from minpack.lm::nlsLM).
Result of a model (lm object)
Result of a model (nlsM object)
Result of a model (nlsM object)
Maxime REJOU-MECHAIN, Ariane TANGUY
Michaelis, L., & Menten, M. L. (1913). Die kinetik der invertinwirkung. Biochem. z, 49(333-369), 352. Weibull, W. (1951). Wide applicability. Journal of applied mechanics, 103. Baskerville, G. L. (1972). Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research, 2(1), 49-53.
Translate the long lat coordinate in UTM coordinate
latlong2UTM(coord)latlong2UTM(coord)
coord |
Coordinates of the site(s), a matrix/dataframe with two columns (e.g. cbind(longitude, latitude)) (see examples). |
a data frame with :
long: The longitude of the entry
lat: The latitude of the entry
codeUTM: The code proj for UTM
X: The X UTM coordinate
Y: The Y UTM coordinate
long <- c(-52.68, -51.12, -53.11) lat <- c(4.08, 3.98, 4.12) coord <- cbind(long, lat) UTMcoord <- latlong2UTM(coord)long <- c(-52.68, -51.12, -53.11) lat <- c(4.08, 3.98, 4.12) coord <- cbind(long, lat) UTMcoord <- latlong2UTM(coord)
This function fits and compares (optional) height-diameter models.
modelHD(D, H, method = NULL, useWeight = FALSE, drawGraph = FALSE, plot = NULL)modelHD(D, H, method = NULL, useWeight = FALSE, drawGraph = FALSE, plot = NULL)
D |
Vector with diameter measurements (in cm). NA values are accepted but a minimum of 10 valid entries (i.e. having a corresponding height in H) is required. |
H |
Vector with total height measurements (in m). NA values are accepted but a minimum of 10 valid entries (i.e. having a corresponding diameter in D) is required. |
method |
Method used to fit the relationship. To be chosen between:
If |
useWeight |
If weight is |
drawGraph |
If |
plot |
(optional) Plot ID, must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models. |
All the back transformations for log-log models are done using the Baskerville correction (,
where RSE is the Residual Standard Error).
If plot is NULL or has a single value, a single list is returned. If there is more than one plot,
multiple embedded lists are returned with plots as the list names.
If model is not null (model comparison), returns a list :
input: list of the data used to construct the model (list(H, D))
model: outputs of the model (same outputs as given by stats::lm(), stats::nls())
RSE: Residual Standard Error of the model
RSElog: Residual Standard Error of the log model (NULL if other model)
residuals: Residuals of the model
coefficients: Coefficients of the model
R.squared: of the model
formula: Formula of the model
method: Name of the method used to construct the model
predicted: Predicted height values
fitPlot: a ggplot object containing the model fitting plot
If the parameter model is null, the function return a plot with all the methods for comparison, the function also returns a data.frame with:
method: The method that had been used to construct the plot
RSE: Residual Standard Error of the model
RSElog: Residual Standard Error of the log model (NULL if other model)
Average_bias: The average bias for the model
Maxime REJOU-MECHAIN, Arthur PERE, Ariane TANGUY, Arthur Bailly
# Load a data set data(NouraguesHD) # Fit H-D models for the Nouragues dataset HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, drawGraph = TRUE) # For a chosen model HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2", drawGraph = TRUE) # Using weights HDmodel <- modelHD( D = NouraguesHD$D, H = NouraguesHD$H, method = "log2", useWeight = TRUE, drawGraph = TRUE) # With multiple stands (plots) HDmodel <- modelHD( D = NouraguesHD$D, H = NouraguesHD$H, method = "log2", useWeight = TRUE, plot = NouraguesHD$plotId, drawGraph = TRUE)# Load a data set data(NouraguesHD) # Fit H-D models for the Nouragues dataset HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, drawGraph = TRUE) # For a chosen model HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2", drawGraph = TRUE) # Using weights HDmodel <- modelHD( D = NouraguesHD$D, H = NouraguesHD$H, method = "log2", useWeight = TRUE, drawGraph = TRUE) # With multiple stands (plots) HDmodel <- modelHD( D = NouraguesHD$D, H = NouraguesHD$H, method = "log2", useWeight = TRUE, plot = NouraguesHD$plotId, drawGraph = TRUE)
Dataset containing the corner coordinates of 4 plots of ‘Petit Plateau’ in Nouragues forest (French Guiana).
data(NouraguesCoords)data(NouraguesCoords)
A data frame with 16 observations (GPS measurements) of the 8 following variables :
Site: Name of the site set up in the Nouragues forest
Plot: Plot ID of the site
Xfield: Corner location on the x-axis in the local coordinate system (defined by the 4 corners of the plot)
Yfield: Corner location on the y-axis in the local coordinate system
Xutm: Corner location on the x-axis in the UTM coordinate system
Yutm: Corner location on the y-axis in the UTM coordinate system
Long: Corner longitude coordinate
Lat: Corner latitude coordinate
Jaouen, Gaëlle, 2023, "Nouragues forest permanent plots details", doi:10.18167/DVN1/HXKS4E, CIRAD Dataverse, V2
data(NouraguesCoords) str(NouraguesCoords)data(NouraguesCoords) str(NouraguesCoords)
Dataset from two 1-ha plots from the Nouragues forest (French Guiana)
data("NouraguesHD")data("NouraguesHD")
A data frame with 1051 observations on the following variables :
plotId: Names of the plots
genus: Genus
species: Species
D: Diameter (cm)
H: Height (m)
lat: Latitude
long: Longitude
Réjou-Méchain, M. et al. (2015). Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest Remote Sensing of Environment, 169, 93-101.
data(NouraguesHD) str(NouraguesHD)data(NouraguesHD) str(NouraguesHD)
Simulated corner coordinates of Nouragues 'Petit plateau' plot 201. The original coordinates have been modified to make the plot non-squared, and 10 repeated measurements of each corner have been simulated adding a random error to x and y coordinates.
data(NouraguesPlot201)data(NouraguesPlot201)
A data frame with 40 (simulated GPS measurements) of the 8 following variables :
Site: Name of the site set up in the Nouragues forest
Plot: Plot ID of the site
Xfield: Corner location on the x-axis in the local coordinate system (defined by the 4 corners of the plot)
Yfield: Corner location on the y-axis in the local coordinate system
Xutm: Corner location on the x-axis in the UTM coordinate system
Yutm: Corner location on the y-axis in the UTM coordinate system
Long: Corner longitude coordinate
Lat: Corner latitude coordinate
Jaouen, Gaëlle, 2023, "Nouragues forest permanent plots details", doi:10.18167/DVN1/HXKS4E, CIRAD Dataverse, V2
data(NouraguesPlot201) str(NouraguesPlot201)data(NouraguesPlot201) str(NouraguesPlot201)
This dataset contains 4 of the 12 plots of ‘Petit Plateau’ permanent plots fifth census, 2012, Nouragues forestTree dataset (French Guiana). For educational purposes, some virtual trees have been added in the dataset. Dead trees have been removed.
data(NouraguesTrees)data(NouraguesTrees)
A data frame with 2050 observations (trees) of the 8 following variables :
site: Name of the site set up in the Nouragues forest
plot: Plot ID
Xfield: Tree location on the x-axis in the local coordinate system (defined by the 4 corners of the plot)
Yfield: Tree location on the y-axis in the local coordinate system
family: Tree family
genus: Tree genus
species: Tree species
D: Tree diameter (in cm)
‘Petit Plateau’ permanent plots fifth census, 2012, Nouragues forest, https://doi.org/10.18167/DVN1/TZ1RL9, CIRAD Dataverse, V1
data(NouraguesTrees) str(NouraguesTrees)data(NouraguesTrees) str(NouraguesTrees)
Get the UTM coordinates from the latitude and longitude of the corners of a plot. The function also assign a number to the corners in a clockwise or counterclockwise way, with the number 1 for the XY origin. Corner numbering is done as followed:
axis X: the corner 1 to the corner 2
axis Y: the corner 1 to the corner 4
numberCorner(longlat = NULL, projCoord = NULL, plot, origin, clockWise)numberCorner(longlat = NULL, projCoord = NULL, plot, origin, clockWise)
longlat |
(optional) data frame with the coordinates in longitude latitude (eg. cbind(longitude, latitude)). |
projCoord |
(optional) data frame with the projected coordinates in X Y |
plot |
A vector of codes (names) of the plots |
origin |
A logical vector with TRUE corresponding of the origin of the axis of each plot. |
clockWise |
A logical, whether the numbering should be done in a clockwise (TRUE) or counterclockwise (FALSE) way. |
A data frame with:
plot: The code of the plot
X: The coordinates X in UTM
Y: The coordinates Y in UTM
corner: The corner numbers
Arthur PERE, Maxime REJOU-MECHAIN
coord <- data.frame(X = c(0, 200, 0, 200), Y = c(0, 0, 200, 200)) + 5000 plot <- rep("plot1", 4) origin <- c(FALSE, FALSE, TRUE, FALSE) # if you turn clock wise corner <- numberCorner(projCoord = coord, plot = plot, origin = origin, clockWise = TRUE) # Plot the plot plot(coord, asp = 1) text(coord, labels = corner$corner, pos = 1) # Using a counterclockwise way corner <- numberCorner(projCoord = coord, plot = plot, origin = origin, clockWise = FALSE) # Plot the plot plot(coord, asp = 1) text(coord, labels = corner$corner, pos = 1)coord <- data.frame(X = c(0, 200, 0, 200), Y = c(0, 0, 200, 200)) + 5000 plot <- rep("plot1", 4) origin <- c(FALSE, FALSE, TRUE, FALSE) # if you turn clock wise corner <- numberCorner(projCoord = coord, plot = plot, origin = origin, clockWise = TRUE) # Plot the plot plot(coord, asp = 1) text(coord, labels = corner$corner, pos = 1) # Using a counterclockwise way corner <- numberCorner(projCoord = coord, plot = plot, origin = origin, clockWise = FALSE) # Plot the plot plot(coord, asp = 1) text(coord, labels = corner$corner, pos = 1)
The function predicts height from diameter based on a fitted model.
predictHeight(D, model, err = FALSE, plot = NULL)predictHeight(D, model, err = FALSE, plot = NULL)
D |
Vector of diameter (in cm). |
model |
A height-diameter model output by the function |
err |
If |
plot |
(optional) Plot ID, must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models. |
In the case where the error is FALSE and the model is a log-log model, we use the
Baskerville correction, a bias correction factor used to get unbiased backtransformation values.
Returns a vector of total tree height (in m).
Maxime REJOU-MECHAIN, Ariane TANGUY, Arthur PERE
Do a procrust analysis. X is the target matrix, Y is the matrix we want to fit to the target. This function returns a translation vector and a rotation matrix After the procrust problem you must do the rotation before the translation. Warning : The order of the value on both matrix is important
procrust(X, Y)procrust(X, Y)
X |
the target matrix |
Y |
the matrix we want to fit to the target |
A list with the translation vector and the matrix of rotation
Arthur PERE
From the diameter and either i) a model, ii) the coordinates of the plot or iii) the region, this function gives an estimate of the total tree height.
retrieveH(D, model = NULL, coord = NULL, region = NULL, plot = NULL)retrieveH(D, model = NULL, coord = NULL, region = NULL, plot = NULL)
D |
Vector of diameters. |
model |
A model output by the function |
coord |
Coordinates of the site(s), either a vector (e.g. c(longitude, latitude)) or a matrix/dataframe with two columns (e.g. cbind(longitude, latitude)). |
region |
Area of your dataset to estimate tree height thanks to Weibull-H region-, continent-specific and pantropical models proposed by Feldpausch et al. (2012). To be chosen between:
|
plot |
(optional) Plot ID, must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models. |
Returns a list with:
H: Height predicted by the model
RSE Residual Standard Error of the model, or a vector of those for each plot
Ariane TANGUY, Maxime REJOU-MECHAIN, Arthur PERE
Feldpausch et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences (2012): 3381-3403.
Chave et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Global change biology 20.10 (2014): 3177-3190.
# Load a database data(NouraguesHD) model <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2") # If any height model is available H <- retrieveH(D = NouraguesHD$D, model = model) # If the only data available are the coordinates of your spot n <- length(NouraguesHD$D) coord <- cbind(long = rep(-52.68, n), lat = rep(4.08, n)) H <- retrieveH(D = NouraguesHD$D, coord = coord) # If the only data available is the region of your spot H <- retrieveH(D = NouraguesHD$D, region = "GuianaShield")# Load a database data(NouraguesHD) model <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2") # If any height model is available H <- retrieveH(D = NouraguesHD$D, model = model) # If the only data available are the coordinates of your spot n <- length(NouraguesHD$D) coord <- cbind(long = rep(-52.68, n), lat = rep(4.08, n)) H <- retrieveH(D = NouraguesHD$D, coord = coord) # If the only data available is the region of your spot H <- retrieveH(D = NouraguesHD$D, region = "GuianaShield")
After applying the divide_plot() function, this function summarises with any defined function the desired tree metric by sub-plot and displays the plot representation.
subplot_summary( subplots, value = NULL, draw_plot = TRUE, per_ha = TRUE, fun = sum, ... )subplot_summary( subplots, value = NULL, draw_plot = TRUE, per_ha = TRUE, fun = sum, ... )
subplots |
output of the |
value |
a character indicating the column in subplots$tree_data (a tree metric) to be summarised |
draw_plot |
a logical indicating whether the plot design should be displayed |
per_ha |
a logical indicating whether the metric summary should be per hectare |
fun |
the function to be applied |
... |
optional arguments to fun |
a list containg the following elements :
tree_summary : a summary of the metric per subplot
polygon : an sf object : simple feature collection of the subplot's polygon
plot_design : a ggplot object (or a list of ggplot objects) that can easily be modified
Arthur Bailly
# One plot with repeated measurements of each corner data("NouraguesPlot201") data("NouraguesTrees") check_plot201 <- check_plot_coord( corner_data = NouraguesPlot201, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE) subplots_201 <- suppressWarnings( divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), proj_coord = c("x_proj","y_proj"), grid_size = 50, tree_data = NouraguesTrees[NouraguesTrees$Plot == 201,], tree_coords = c("Xfield","Yfield"))) # Sum summary (by default) of diameter subplots_201_sum <- subplot_summary(subplots_201 , value = "D", draw_plot = FALSE) subplots_201_sum$tree_summary subplots_201_sum$polygon subplots_201_sum$plot_design # 9th quantile summary (for example) of diameter subplots_201_quant <- subplot_summary(subplots_201 , value = "D", draw_plot = FALSE, fun = quantile, probs=0.9) # Dealing with multiple plots data("NouraguesCoords") nouragues_subplots <- suppressWarnings( divide_plot( corner_data = NouraguesCoords, rel_coord = c("Xfield","Yfield"), proj_coord = c("Xutm","Yutm"), corner_plot_ID = "Plot", grid_size = 50, tree_data = NouraguesTrees, tree_coords = c("Xfield","Yfield"), tree_plot_ID = "Plot")) # Sum summary (by default) nouragues_sum <- subplot_summary(nouragues_subplots , value = "D", draw_plot = FALSE) nouragues_sum$tree_summary subplots_201_sum$plot_design# One plot with repeated measurements of each corner data("NouraguesPlot201") data("NouraguesTrees") check_plot201 <- check_plot_coord( corner_data = NouraguesPlot201, proj_coord = c("Xutm","Yutm"), rel_coord = c("Xfield","Yfield"), trust_GPS_corners = TRUE, draw_plot = FALSE) subplots_201 <- suppressWarnings( divide_plot( corner_data = check_plot201$corner_coord, rel_coord = c("x_rel","y_rel"), proj_coord = c("x_proj","y_proj"), grid_size = 50, tree_data = NouraguesTrees[NouraguesTrees$Plot == 201,], tree_coords = c("Xfield","Yfield"))) # Sum summary (by default) of diameter subplots_201_sum <- subplot_summary(subplots_201 , value = "D", draw_plot = FALSE) subplots_201_sum$tree_summary subplots_201_sum$polygon subplots_201_sum$plot_design # 9th quantile summary (for example) of diameter subplots_201_quant <- subplot_summary(subplots_201 , value = "D", draw_plot = FALSE, fun = quantile, probs=0.9) # Dealing with multiple plots data("NouraguesCoords") nouragues_subplots <- suppressWarnings( divide_plot( corner_data = NouraguesCoords, rel_coord = c("Xfield","Yfield"), proj_coord = c("Xutm","Yutm"), corner_plot_ID = "Plot", grid_size = 50, tree_data = NouraguesTrees, tree_coords = c("Xfield","Yfield"), tree_plot_ID = "Plot")) # Sum summary (by default) nouragues_sum <- subplot_summary(nouragues_subplots , value = "D", draw_plot = FALSE) nouragues_sum$tree_summary subplots_201_sum$plot_design
This function summarizes the matrix AGB_val given by the function AGBmonteCarlo() by plot.
summaryByPlot(AGB_val, plot, drawPlot = FALSE)summaryByPlot(AGB_val, plot, drawPlot = FALSE)
AGB_val |
Matrix resulting from the |
plot |
Vector corresponding to the plots code (plots ID) |
drawPlot |
A logic indicating whether the graphic should be displayed or not |
If some trees belong to an unknown plot (i.e. NA value in the plot arguments), their AGB values are randomly assigned to a plot at each iteration of the AGB monte Carlo approach.
a data frame where:
plot: the code of the plot
AGB: AGB value at the plot level
Cred_2.5: the 2.5\
Cred_97.5: the 97.5\
# Load a database data(NouraguesHD) data(NouraguesTrees) # Modelling height-diameter relationship HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2") # Retrieving wood density values NouraguesWD <- getWoodDensity(NouraguesTrees$Genus, NouraguesTrees$Species, stand = NouraguesTrees$plotId) # Propagating errors resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, HDmodel = HDmodel ) # The summary by plot summaryByPlot(AGB_val = resultMC$AGB_simu, plot = NouraguesTrees$Plot)# Load a database data(NouraguesHD) data(NouraguesTrees) # Modelling height-diameter relationship HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2") # Retrieving wood density values NouraguesWD <- getWoodDensity(NouraguesTrees$Genus, NouraguesTrees$Species, stand = NouraguesTrees$plotId) # Propagating errors resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, HDmodel = HDmodel ) # The summary by plot summaryByPlot(AGB_val = resultMC$AGB_simu, plot = NouraguesTrees$Plot)