View source: R/add_min_mesh_constraint.R
add_min_mesh_constraint | R Documentation |
Add constraint to a restoration problem (restopt_problem()
) object
to specify the minimum effective mesh size of a solution.
add_min_mesh_constraint(problem, min_mesh, precision = 4, unit = "ha")
problem |
|
min_mesh |
|
precision |
|
unit |
|
The effective mesh size (MESH) is a measure of landscape fragmentation based on the probability that two randomly chosen points are located in the same patch (Jaeger, 2000). Maximizing it in the context of restoration favours fewer and larger patches.
An updated restoration problem (restopt_problem()
) object.
Jaeger, J. A. G. (2000). Landscape division, splitting index, and effective mesh size: New measures of landscape fragmentation. Landscape Ecology, 15(2), 115‑130. https://doi.org/10.1023/A:1008129329289
set_max_mesh_objective
Other constraints:
add_available_areas_constraint()
,
add_compactness_constraint()
,
add_components_constraint()
,
add_connected_constraint()
,
add_locked_out_constraint()
,
add_min_iic_constraint()
,
add_nb_patches_constraint()
,
add_no_new_patch_constraint()
,
add_restorable_constraint()
## Not run:
# load data
habitat_data <- rast(
system.file("extdata", "habitat_hi_res.tif", package = "restoptr")
)
# create problem
p <- restopt_problem(
existing_habitat = habitat_data,
aggregation_factor = 16,
habitat_threshold = 0.7
) %>%
add_restorable_constraint(
min_restore = 200,
max_restore = 300,
) %>%
add_min_mesh_constraint(min_mesh = 2500, unit = "ha")
# plot preprocessed data
plot(rast(list(p$data$existing_habitat, p$data$restorable_habitat)), nc = 2)
# print problem
print(p)
# Solve problem
s <- solve(p)
# plot solution
plot(s)
## End(Not run)
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