Description Usage Arguments Details
View source: R/landscape_toxicIntensity.R
toxicIntendity function wrapping dispersal and exposure
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object |
sf or SpatialPolygonsDataFrame. A simple feature of class sf or SpatialPolygonsDataFrame |
sf |
sf. And object of class 'sf' on which exposure is computed from the previous list of raster by patch 'RasterStack_dispersal'. See sf for details. |
size_raster |
integer. Raster size (default = 2^10) |
tolerance_square |
numeric. Tolerance rate to test if an sf set is squared |
kernel |
string. Dispersion kernel, function name (default = NIG) |
kernel.options |
list. Parameters list for the kernel function |
loss |
numeric. Numeric vector to applied a loss on exposure cells. |
beta |
numeric. toxic adherence parameter between 0 and 1 (default = 0.4). |
nbr_cores |
integer. Parameters for parallel computing: the
number of cores to use, i.e. at most how many child processes
will be run simultaneously. Default is |
squared_frame |
sf. Select the sf to be considered as frame to rasterized. Default is 'NULL', and 'object' is used. |
quiet |
boolean. Set 'TRUE' to remove progress bar. |
The dispersal of contaminants is implemented by rastering the landscape and by computing the convolution between sources emissions and a dispersal kernel.
The dispersion kernel by default is Normal Inverse Gaussian kernel ("NIG" function).
Currently, two others are implemented "geometric" (with parameter a
) and "2Dt" kernels
(with parameters a
, b
, c1
, c2
).
Local intensity depends of beta
and alpha
parameters. Beta represents the toxic adherence between [0,1].
Alpha represents a list of parameters of the lost of toxic particules due to covariates (precipitation).
There are two configurations to integrate the loss in the function :
(i) simulating covariate (simulate=TRUE) or (ii) uploading covariate (simulate=FALSE).
The covariate is linked to the loss by a linear regression with paramaters minalpha, maxalpha, covariate_threshold.
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