Set an objective to maximize effective mesh size

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_pus_objective(), set_min_restore_objective(), set_no_objective()

Examples

# \donttest{
# 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)
#> ----------------------------------------------------------------- 
#>                          Restopt                          
#> ----------------------------------------------------------------- 
#> original habitat:     habitat_hi_res.tif 
#> aggregation factor:   16 
#> habitat threshold:    0.7 
#> existing habitat:     in memory 
#> restorable habitat:   in memory 
#> ----------------------------------------------------------------- 
#> objective:            Maximize effective mesh size 
#> ----------------------------------------------------------------- 
#> constraints:          
#>   -  restorable (min_restore = 5, max_restore = 5, min_proportion = 1, unit = ha) 
#>   -  locked out (data = in memory) 
#> ----------------------------------------------------------------- 
#> settings: 
#>   - precision = 4
#>   - time_limit = 1
#>   - nb_solutions = 1
#>   - optimality_gap = 0
#>   - solution_name_prefix = Solution  
#> ----------------------------------------------------------------- 

# solve problem
s <- solve(p)
#> Note: The current solution is the best that the solver could find within the time limit. However, the solver had not enough to prove whether it is optimal or not. Consider increasing the time limit if you need a better solution (solving time = 1 s)

# plot solution
plot(s)

# }