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Set an objective to maximize effective mesh size

Source: R/set_max_mesh_objective.R
set_max_mesh_objective.Rd

Specify that a restoration problem (restopt_problem()) should maximize effective mesh size.

Usage

set_max_mesh_objective(problem)

Arguments

problem

restopt_problem() Restoration problem object.

Value

An updated restoration problem (restopt_problem()) object.

Details

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.

References

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

See also

Other objectives: set_max_iic_objective(), set_max_nb_pus_objective(), set_max_restore_objective(), set_min_nb_patches_objective(), set_min_nb_pus_objective(), set_min_restore_objective(), set_no_objective()

Examples

if (FALSE) { # \dontrun{
# load data
habitat_data <- rast(
  system.file("extdata", "habitat_hi_res.tif", package = "restoptr")
)

locked_out_data <- rast(
 system.file("extdata", "locked_out.tif", package = "restoptr")
)

# plot data
plot(rast(list(habitat_data, locked_out_data)), nc = 2)

# create problem with locked out constraints
p <- restopt_problem(
    existing_habitat = habitat_data,
    aggregation_factor = 16,
    habitat_threshold = 0.7
  ) %>%
  set_max_mesh_objective() %>%
  add_restorable_constraint(
    min_restore = 5,
    max_restore = 5,
  ) %>%
  add_locked_out_constraint(data = locked_out_data) %>%
  add_settings(time_limit = 1)

# print problem
print(p)

# solve problem
s <- solve(p)

# plot solution
plot(s)
} # }