# LNRS decision-making - Luke Bentley require(terra) require(sf) require(dplyr) require(ecoservR) require(rmapshaper) library(combinat) require(gtools) # There will be tiebreakers everywhere. Where multiple habs are available, first option will be based on a tiebreaker, # second option will also be based on a tiebreaker, and in some instances the two habitats recommended could be # done as a mosaic. No more than two habitats. Need to give options to stakeholders. Tiebreakers may be based on a species # there that requires habitat, or expert layer suggesting habitat is necessary, or multiple benefits or BNG distinctiveness. # The order is important of the decisions. The first decision condition which matches for a particular pixel will be selected, so # the most important decisions/habitats need to come first. # The script will produce two outputs (selection one and selection two). These could be used individually, but in most cases where there # are different habitats for the two selections, a mosaic could be created. The polygons would just need to be overlayed to see # which habitats are the best and where they are. # Load Local authorities to run model LA <- st_read('C://Users/nsplben1/OneDrive - Liverpool John Moores University/LJMU_ecoservR_projects/LCR_opportunityLB/Generalisation to select important areas/data/LCR_LA.gpkg') # Load constrained expert layers #species_files <- list.files('decision-making/correct_constrained_expert_layers/', pattern = '.gpkg$', full.names = T) #for (i in species_files){ # indiv_file <- st_read(i) %>% st_geometry() %>% st_as_sf() # message(i) # name <- stringr::str_sub(basename(i), 1, -6) # name <- stringr::str_replace_all(name, " ", "_") # message(name) # assign(name, indiv_file) #} # Load basemap to aid in decision-making #mm <- readRDS('data/Habitat_Map/Tom - basemap files/Integrated_basemap.RDS') # Farm habitat to aid in DM #mm_farm <- mm %>% dplyr::filter(HABCODE == 'c1') %>% st_geometry() %>% st_as_sf() #st_geometry(mm_farm) <- 'x' # Load flood mitigation and water quality opps to also aid in decision-making #flood_mitig_opps <- rast('data/ecosystem_services/opportunities/flood_mitigation.tif') %>% as.polygons() %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% checkgeometry('POLYGON') %>% st_set_crs(27700) #st_geometry(flood_mitig_opps) <- 'x' #water_quality_opps <- rast('data/ecosystem_services/opportunities/water_quality.tif') %>% as.polygons() %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% checkgeometry('POLYGON') %>% st_set_crs(27700) #st_geometry(water_quality_opps) <- 'x' ## Expert layers list #expert_layers <- list(flood_mitig_opps, water_quality_opps, mm_farm, Buglife_Blines_LC, clippedAncient_Woodland_100m, clippedbats_100, clippedblack_poplar_100, # clippedBrownfield_100m, clippedClay_Cliffs_100m, clippedCoastal_100m, clippedEstuary_100m, # clippedFarmland_100m, clippedgrey_seal_100, clippedground_nesting_birds_100m, clippedharbour_porpoise_100, # clippedhedgehog_100, clippedhedgerow_100m, clippednatterjack_100, clippedred_squirrel_100, # clippedriver_brooks_streams_100m, clippedurban_100m, clippedwetland_100m, clippedwillow_tit_100, # clippedwoodland_100m, coastal_cba_desigs, EA_FloodMapForPlanningRiversAndSeaFloodZone2, ewco_biodiversity_priority_habitat_network, # farm_adjacent_water, grassland_1km, grassland_cba_desigs, heathland_cba_desigs, # hedge_opps, mosaic_cba_desigs, Peat_soils_NatMa, Risk_of_Flooding_from_Rivers_and_Se, # Sandy_soils_NatMa, wetland_cba_desigs, woodland_cba_desigs, # WWNP_Runoff_Attenuation_Features_3_3_percent_AE) #expert_layer_names <- c('flood_mitig_opps', 'water_quality_opps', 'mm_farm', 'Buglife_Blines_LC', 'clippedAncient_Woodland_100m', 'clippedbats_100', 'clippedblack_poplar_100', # 'clippedBrownfield_100m', 'clippedClay_Cliffs_100m', 'clippedCoastal_100m', 'clippedEstuary_100m', # 'clippedFarmland_100m', 'clippedgrey_seal_100', 'clippedground_nesting_birds_100m', 'clippedharbour_porpoise_100', # 'clippedhedgehog_100', 'clippedhedgerow_100m', 'clippednatterjack_100', 'clippedred_squirrel_100', # 'clippedriver_brooks_streams_100m', 'clippedurban_100m', 'clippedwetland_100m', 'clippedwillow_tit_100', # 'clippedwoodland_100m', 'coastal_cba_desigs', 'EA_FloodMapForPlanningRiversAndSeaFloodZone2', 'ewco_biodiversity_priority_habitat_network', # 'farm_adjacent_water', 'grassland_1km', 'grassland_cba_desigs', 'heathland_cba_desigs', # 'hedge_opps', 'mosaic_cba_desigs', 'Peat_soils_NatMa', 'Risk_of_Flooding_from_Rivers_and_Se', # 'Sandy_soils_NatMa', 'wetland_cba_desigs', 'woodland_cba_desigs', # 'WWNP_Runoff_Attenuation_Features_3_3_percent_AE') #expert_layers <- setNames(expert_layers, expert_layer_names) # Combine into one layer to make the analysis quicker. All dissolved to also make things quicker #expert_layers <- lapply(names(expert_layers), function(name) { # message(1) # layer <- expert_layers[[name]] %>% st_union() %>% st_as_sf() # layer$layer_name <- name # Add the name as a new column # return(layer) #}) #combined_expert_layers <- do.call(rbind, expert_layers) #st_write(combined_expert_layers, 'decision-making/combined_expert_layers.gpkg') # Tile up expert layer for faster processing #expert_layer <- st_read('decision-making/combined_expert_layers.gpkg') #studyArea <- st_read('C://Users/nsplben1/OneDrive - Liverpool John Moores University (1)/LJMU_ecoservR_projects/LCR_NEIRF/data/boundaries/LiverpoolCityRegion.shp') #ecoservR_1_km_grid <- st_read('C://Users/nsplben1/OneDrive - Liverpool John Moores University (1)/LJMU_ecoservR_projects/00_Data_library_national/OSGB_Grids/Shapefile/OSGB_Grid_1km.shp') #grid_tiles_covered_by_polygons_index <- st_intersects(studyArea, ecoservR_1_km_grid) #ecoservR_1_km_grid <- ecoservR_1_km_grid[unlist(unique(grid_tiles_covered_by_polygons_index)),] #tiled_expert <- list() #for (i in 1:891){ # message(i) # expert_layer_tile <- st_intersection(expert_layer, ecoservR_1_km_grid[i, ]$geometry) %>% st_make_valid() %>% ecoservR::checkgeometry('POLYGON') # name <- ecoservR_1_km_grid[i,]$PLAN_NO # tiled_expert[[name]] <- expert_layer_tile #} #saveRDS(tiled_expert, 'decision-making/tiled_expert.RDS') # NB all expert layers will just be polygons with no habitat information (this comes from the title), # except for the stakeholder expert layer which must have a hab column) # Load all constrained multiple benefit layers #multiple_benefits <- rast(list.files('C://Users/NSPLBEN1/OneDrive - Liverpool John Moores University/General - LCR_LNRS Team/decision-making/multiple_benefits/', pattern = '.tif$', full.names = TRUE)) # Mask to correct LA for (n in c('St. Helens District (B)', 'Sefton District (B)', 'Halton (B)')){ message(n) tiled_expert <- readRDS('C://Users/NSPLBEN1/OneDrive - Liverpool John Moores University/General - LCR_LNRS Team/decision-making/tiled_expert.RDS') multiple_benefits <- rast(list.files('C://Users/NSPLBEN1/OneDrive - Liverpool John Moores University/General - LCR_LNRS Team/decision-making/multiple_benefits/', pattern = '.tif$', full.names = TRUE)) multiple_benefits <- crop(multiple_benefits, dplyr::filter(LA, Name == n)) %>% mask(dplyr::filter(LA, Name == n)) multiple_benefits <- classify(multiple_benefits, cbind(NA, 99999)) habs <- c('grassland', 'urban', 'wetland', 'woodland') # Create dataframes with MBs and location of pixels multiple_benefits_df <- as.data.frame(multiple_benefits) multiple_benefits_coords <- as.data.frame(xyFromCell(multiple_benefits, 1:ncell(multiple_benefits))) colnames(multiple_benefits_df) <- habs # Load tiles that expert layers were split into studyArea <- st_read('C://Users/nsplben1/OneDrive - Liverpool John Moores University/LJMU_ecoservR_projects/LCR_NEIRF/data/boundaries/LiverpoolCityRegion.shp') ecoservR_1_km_grid <- st_read('C://Users/nsplben1/OneDrive - Liverpool John Moores University/LJMU_ecoservR_projects/00_Data_library_national/OSGB_Grids/Shapefile//OSGB_Grid_1km/OSGB_Grid_1km.shp') grid_tiles_covered_by_polygons_index <- st_intersects(studyArea, ecoservR_1_km_grid) ecoservR_1_km_grid <- ecoservR_1_km_grid[unlist(unique(grid_tiles_covered_by_polygons_index)),] multiple_benefits_coords$tile_no <- NA alluvium_soils <- st_read('C://Users/NSPLBEN1/OneDrive - Liverpool John Moores University/General - LCR_LNRS Team/decision-making/expert_layers/alluvium_soils/alluvium_soils.shp') tiled_alluv <- list() for (i in 1:891){ message(i) alluv_tile <- st_intersection(alluvium_soils, ecoservR_1_km_grid[i, ]$geometry) %>% st_make_valid() %>% st_cast('POLYGON') name <- ecoservR_1_km_grid[i,]$PLAN_NO tiled_alluv[[name]] <- alluv_tile } # Add herptiles (This needs to be tiled to a 1km grid using the grid below) herptiles <- st_read('C://Users/NSPLBEN1/OneDrive - Liverpool John Moores University/General - LCR_LNRS Team/decision-making/expert_layers/clippedherptiles 100.gpkg') tiled_herp <- list() for (i in 1:891){ message(i) herp_tile <- st_intersection(herptiles, ecoservR_1_km_grid[i, ]$geometry) %>% st_make_valid() %>% st_cast('POLYGON') name <- ecoservR_1_km_grid[i,]$PLAN_NO tiled_herp[[name]] <- herp_tile } alluvium_soils <- tiled_alluv herptiles <- tiled_herp # Create template raster on which opportunities will be mapped template_rasters <- lapply(1:33, function(i) rast(multiple_benefits[[1]])) template_rasters <- rast(template_rasters) names(template_rasters) <- c('wet_woodland_willow_tit_expertprioritysp', 'black_poplar_expertprioritysp', 'dune_natterjack_expertprioritysp', 'woodland_bats_expertspassem', 'woodland_expertspassem', 'woodland_red_squirrel_expertprioritysp', 'peat_sensitive_farming_expert', 'herptiles_wetland_expertspassem', 'herptiles_wet_grassland_expertspassem', 'herptiles_grassland_expertspassem', 'ground_nesting_birds_farmland_expertspassem', 'ground_nesting_birds_expertspassem', 'wetland_expertspassem', 'hedgerow_expertspassem', 'grassland_expertspassem', 'urban_bats_expertspassem', 'urban_hedgehog_expertspassem', 'urban_green_wall_trees_esurban', 'farmland_expert', 'mosaic_expert','grassland_expert', 'woodland_expert','wetland_expert', 'hedgerow_expert', 'woodland_network', 'grassland_network', 'wetland_network', 'wetland_es', 'grassland_es', 'wetland_bng', 'grassland_bng', 'woodland_bng', 'woodland_es') # Loop through every pixel to decide which habitat it should have habitat_all <- {} for (i in 1:nrow(multiple_benefits_coords)){ message(paste0(i, ' of ',length(multiple_benefits_df$grassland))) # Get point out of raster pixel multbens <- multiple_benefits_df[i,] # Set as 99999 so that we know which are constrained regions where decision-making won't be applied and assign no habitat if (multbens$grassland == 99999) {habitat <- c('no_habitat', 'no_habitat') } else { # Get names of expert layers the point lies on point <- st_sfc(st_point(c(multiple_benefits_coords[i,]$x, multiple_benefits_coords[i,]$y)), crs = 27700) %>% st_as_sf() tile <- st_intersection(ecoservR_1_km_grid, point) tile_no <- tile$PLAN_NO #stakeholder_opps <- st_intersection(stakeholder_expert_layer_fake, point) if (nrow(tile) == 0) { expert_layer_opps <- NULL alluvium <- NULL herps <- NULL #Select correct tile first and then get intersection so process is fast. } else { expert_layer_tile <- tiled_expert[[tile_no]] expert_layer_opps <- st_intersection(expert_layer_tile, point) alluvium <- alluvium_soils[[tile_no]] alluvium <- st_intersection(alluvium, point) herps <- herptiles[[tile_no]] herps <- st_intersection(herps, point) # Expert layers expert_layer_opps <- expert_layer_opps$layer_name } # Placeholder in case no habitat gets put there habitat <- c('no_habitat', 'no_habitat') # If the pixel lies on a stakeholder expert polygon, make that habitat no questions asked. Need to get the stakeholder layer # into format with polygons and habitat required called something #if (nrow(stakeholder_opps) > 0){ # habitat <- c(stakeholder_opps$hab, stakeholder_opps$hab) # Not included as being done by Andrew # Now focus on important priority species that may need habitat creation. Black poplar, hedgehog, natterjack toad, red squirrel, willow tit # Where a species overlaps with the correct network, create habitat for that species. # Black poplar where on flood zones or in wetland network create wet wood # Willow tit on flood zones or wetland network if ('clippedwillow_tit_100' %in% expert_layer_opps && ('EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps || multbens$wetland > 0)) { # Wet woodland on woodland network if (multbens$woodland > 0){ habitat <- c('wet_woodland_willow_tit_expertprioritysp')} } else if ('clippedblack_poplar_100' %in% expert_layer_opps && ('EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps || multbens$wetland > 0) && nrow(herps) == 0 && !('clippedground_nesting_birds_100m' %in% expert_layer_opps)){ # No trees on peat if (!('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('black_poplar_expertprioritysp')} # Natterjack where on sandy soil dune type habitat for Natterjack toad } else if ('clippednatterjack_100' %in% expert_layer_opps && 'Sandy_soils_NatMa' %in% expert_layer_opps){ habitat <- c('dune_natterjack_expertprioritysp') # Bat richness and in the woodland network create bat trees } else if (multbens$woodland > 0 && 'clippedbats_100' %in% expert_layer_opps && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('woodland_bats_expertspassem') # Red squirrel where on ewco woodland priority and woodland network create red squirrel woodland } else if ('clippedred_squirrel_100' %in% expert_layer_opps && ('ewco_biodiversity_priority_habitat_network' %in% expert_layer_opps || multbens$woodland > 0)){ habitat <- c('woodland_red_squirrel_expertprioritysp') # Natterjack toad is disjunct so don't need to worry about multiple priority species. # Black poplar, some overlap with red squirrel # Black poplar and red squirrel } else if ('clippedblack_poplar_100' %in% expert_layer_opps && 'clippedred_squirrel' %in% expert_layer_opps && (multbens$wetland > 0 || 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps) && nrow(herps) == 0 && !('clippedground_nesting_birds_100m' %in% expert_layer_opps) && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('black_poplar_expertprioritysp') } else if ('clippedblack_poplar_100' %in% expert_layer_opps && 'clippedred_squirrel' %in% expert_layer_opps && multbens$wetland == 0 && 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('woodland_red_squirrel_expertprioritysp') # After important species, focus on peat-sensitive farming } else if ('mm_farm' %in% expert_layer_opps && 'Peat_soils_NatMa' %in% expert_layer_opps){ habitat <- c('peat_sensitive_farming_expert') # Focus next on high richness regions of species assemblages where there aren't individual priority species to consider. # Herptiles next on wetland } else if (nrow(herps) > 0 && (multbens$wetland > 0 || 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps)){ habitat <- c('herptiles_wetland_expertspassem') # On wet grassland } else if (nrow(herps) > 0 && multbens$grassland > 0 && nrow(alluvium) > 0 && 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps){ habitat <- c('herptiles_wet_grassland_expertspassem') # On grassland } else if (nrow(herps) > 0 && multbens$grassland > 0 && 'Sandy_soils_NatMa' %in% expert_layer_opps){ habitat <- c('herptiles_grassland_expertspassem') # Farmland/ground-nesting - prioritise farmland for nesting birds } else if (('clippedFarmland_100m' %in% expert_layer_opps || 'clippedground_nesting_birds_100m' %in% expert_layer_opps) && 'mm_farm' %in% expert_layer_opps){ habitat <- c('ground_nesting_birds_farmland_expertspassem') # General protection of ground nesting birds } else if ('clippedground_nesting_birds_100m' %in% expert_layer_opps){ habitat <- c('ground_nesting_birds_expertspassem') # Estuary or wetland - if on estuary or wetland and on the wetland network and the EA flood zones. Put wetland as the first option # as it's easier to make. But also noting the possibility of estuary. } else if (('clippedwetland_100m' %in% expert_layer_opps) && (multbens$wetland > 0 || 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps) ){ habitat <- c('wetland_expertspassem') # Hedgerow opportunities - where hedgerow species and hedgerow opportunities } else if ('clippedhedgerow_100m' %in% expert_layer_opps && 'hedge_opps' %in% expert_layer_opps){ habitat <- c('hedgerow_expertspassem') # Grassland opps areas of high grassland diversity } else if (multbens$grassland > 0 && 'grassland_1km' %in% expert_layer_opps && 'Sandy_soils_NatMa' %in% expert_layer_opps){ habitat <- c('grassland_expertspassem') # Ancient woodland richness and in the woodland network and ewco woodland regions with no other networks, create woodland } else if (('clippedAncient_Woodland_100m' %in% expert_layer_opps ||'clippedwoodland_100m' %in% expert_layer_opps) && multbens$woodland > 0 && 'ewco_biodiversity_priority_habitat_network' %in% expert_layer_opps && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('woodland_expertspassem') # Focus on urban opportunities. (4 out of 4) opps and it's an opportunity. Also hedgehog opps here } else if (multbens$urban > 0 && 'clippedbats_100' %in% expert_layer_opps){ habitat <- c('urban_bats_expertspassem') } else if (multbens$urban > 0 && 'clippedhedgehog_100' %in% expert_layer_opps){ habitat <- c('urban_hedgehog_expertspassem') } else if (multbens$grassland == 0 && multbens$wetland == 0 && multbens$woodland == 0 && multbens$urban > 3){ habitat <- c('urban_green_wall_trees_esurban') # Stop chemical runoff from farmland. Only where no species-related habitat creation } else if ('water_quality_opps' %in% expert_layer_opps && (multbens$wetland > 0 || 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps) && 'farm_adjacent_water' %in% expert_layer_opps){ habitat <- c('farmland_expert') # Add hedgerows where in the woodland network and possible hedgerow opps } else if ('hedge_opps' %in% expert_layer_opps && multbens$woodland > 0){ habitat <- c('hedgerow_expert') # Wetland in runoff attenuation features as long as in wetland network or flood layer } else if ('WWNP_Runoff_Attenuation_Features_3_3_percent_AE' %in% expert_layer_opps){ habitat <- c('wetland_expert') # LCR designated sites - could constrain this and map the boundaries if you want. Also useful to map to compare and see if there are any specific species that may # there and need habitat. # Mosaic has to be in a network. } else if ('mosaic_cba_desigs' %in% expert_layer_opps && sum(multbens$grassland, multbens$wetland, multbens$woodland) > 0){ habitat <- c('mosaic_expert') # Wetland has to be in wetland network or flood zones } else if ('wetland_cba_desigs' %in% expert_layer_opps && (multbens$wetland > 0 || 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps)){ habitat <- c('wetland_expert') # Heathland leaving out as heathland opps already covered by Andrew # Grassland has to be in grassland network and on Sandy soils } else if ('grassland_cba_desigs' %in% expert_layer_opps && multbens$grassland > 0 && 'Sandy_soils_NatMa' %in% expert_layer_opps){ habitat <- c('grassland_expert') # Woodland has to be in woodland network } else if ('woodland_cba_desigs' %in% expert_layer_opps && multbens$woodland > 0 && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('woodland_expert') # Grassland in B-lines and within the grassland network where sandy soils } else if ('Buglife_Blines_LC' %in% expert_layer_opps && multbens$grassland > 0){ habitat <- c('grassland_expert') # Create wet woodland in floodplain regions where in wetland and woodland networks } else if ('EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps && multbens$wetland > 0 && multbens$woodland > 0 && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('wetland_expert') # Lastly on the rest of the networks will select habitats with highest multiple benefits. Focus on regions with high multiple benefits # and ensure woodland doesn't dominate by deflating. } else if (sum(multbens$grassland, multbens$wetland, multbens$woodland) > 0){ # If only woodland with at least four benefits make woodland if (multbens$woodland > 3 && sum(multbens$wetland, multbens$grassland) == 0 && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('woodland_network') # If only grassland with at least one benefit make grassland } else if (multbens$grassland > 0 && sum(multbens$wetland, multbens$woodland) == 0 && 'Sandy_soils_NatMa' %in% expert_layer_opps){ habitat <- c('grassland_network') # If only wetland with at least one benefit make wetland } else if (multbens$wetland > 0 && sum(multbens$woodland, multbens$woodland) == 0 && 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps){ habitat <- c('wetland_network') # If wetland/grassland have at least one benefit } else if (multbens$wetland > 0 && multbens$grassland > 0 && multbens$woodland == 0 && 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps && (nrow(alluvium_soils) > 0 || 'Sandy_soils_NatMa' %in% expert_layer_opps)){ # wetland has more benefits make wetland first if (multbens$wetland > multbens$grassland){ habitat <- c('wetland_es') # If grassland has more benefits make grassland first } else if (multbens$grassland > multbens$wetland){ habitat <- c('grassland_es') # If equal wetland first because of higher BNG score } else if (multbens$grassland == multbens$wetland){ habitat <- c('wetland_bng') } # If wetland and woodland benefits } else if (multbens$wetland > 0 && multbens$grassland == 0 && multbens$woodland > 3 && 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps && !('Peat_soils_NatMa' %in% expert_layer_opps)){ # Both have MBs so wetland first (higher BNG score), but wet woodland definite option. habitat <- c('wetland_bng') } else if (multbens$grassland > 0 && multbens$woodland > 3 && multbens$wetland == 0 && !('Peat_soils_NatMa' %in% expert_layer_opps) && 'Sandy_soils_NatMa' %in% expert_layer_opps){ # If grassland at least one, grassland first (could also be mosaic) if (multbens$grassland > 0){ habitat <- c('grassland_bng') # If woodland at least four, woodland first (either or mosaic) } else if (multbens$woodland > 3){ habitat <- c('woodland_es') } # If all three have benefits } else if (multbens$grassland > 0 && multbens$woodland > 3 && multbens$wetland > 0 && 'EA_FloodMapForPlanningRiversAndSeaFloodZone2' %in% expert_layer_opps && !('Peat_soils_NatMa' %in% expert_layer_opps) && (nrow(alluvium_soils) > 0 || 'Sandy_soils_NatMa' %in% expert_layer_opps)){ # If equal, wetland then grassland as higher BNG for those two if (multbens$grassland == multbens$wetland && multbens$wetland == multbens$woodland){ habitat <- c('wetland_bng') # Grassland at least one grassland first (or mosaic) } else if (grassland > 0){ habitat <- c('grassland_bng') # Woodland at least four, woodland first (or mosaic) } else if (woodland > 3){ habitat <- c('woodland_es') } } # Plant some trees potentially where could be flooding, and no other interventions (not in network either) } else if ('flood_mitig_opps' %in% expert_layer_opps && !('Peat_soils_NatMa' %in% expert_layer_opps)){ habitat <- c('woodland_es') } } habitat_all <- rbind(habitat_all, habitat) } habitat_all <- data.frame(habitat_all) colnames(habitat_all) <- c('selection_one') write.csv(habitat_all, paste0('results/habitat_all_options_',n,'.csv')) } # Restart from here - creating polygons inters <- list.files('results', pattern = '.csv', full.names = TRUE) for (i in 1:6){ names <- c('Halton (B)', 'Knowsley District (B)', 'Liverpool District (B)', 'Sefton District (B)', 'St. Helens District (B)', 'Wirral District (B)') name <- names[i] message(name) habitat_all <- read.csv(inters[i]) habitat_all$X <- NULL habitat_all$NA. <- NULL LA <- st_read('C://Users/nsplben1/OneDrive - Liverpool John Moores University/LJMU_ecoservR_projects/LCR_opportunityLB/Generalisation to select important areas/data/LCR_LA.gpkg') multiple_benefits <- rast(list.files('C://Users/NSPLBEN1/OneDrive - Liverpool John Moores University/General - LCR_LNRS Team/decision-making/multiple_benefits/', pattern = '.tif$', full.names = TRUE)) multiple_benefits <- crop(multiple_benefits, dplyr::filter(LA, Name == name)) %>% mask(dplyr::filter(LA, Name == name)) #multiple_benefits <- classify(multiple_benefits, cbind(NA, 99999)) template_rasters <- lapply(1:33, function(i) rast(multiple_benefits[[1]])) template_rasters <- rast(template_rasters) names(template_rasters) <- c('wet_woodland_willow_tit_expertprioritysp', 'black_poplar_expertprioritysp', 'dune_natterjack_expertprioritysp', 'woodland_bats_expertspassem', 'woodland_expertspassem', 'woodland_red_squirrel_expertprioritysp', 'peat_sensitive_farming_expert', 'herptiles_wetland_expertspassem', 'herptiles_wet_grassland_expertspassem', 'herptiles_grassland_expertspassem', 'ground_nesting_birds_farmland_expertspassem', 'ground_nesting_birds_expertspassem', 'wetland_expertspassem', 'hedgerow_expertspassem', 'grassland_expertspassem', 'urban_bats_expertspassem', 'urban_hedgehog_expertspassem', 'urban_green_wall_trees_esurban', 'farmland_expert', 'mosaic_expert','grassland_expert', 'woodland_expert','wetland_expert', 'hedgerow_expert', 'woodland_network', 'grassland_network', 'wetland_network', 'wetland_es', 'grassland_es', 'wetland_bng', 'grassland_bng', 'woodland_bng', 'woodland_es') # Create zeroes and ones for the habitat type # Define replacement vectors replacements <- list( 'no_habitat' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'wet_woodland_willow_tit_expertprioritysp' = c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'black_poplar_expertprioritysp' = c(0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'dune_natterjack_expertprioritysp' = c(0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'woodland_bats_expertspassem' = c(0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'woodland_expertspassem' = c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'woodland_red_squirrel_expertprioritysp' = c(0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'peat_sensitive_farming_expert' = c(0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'herptiles_wetland_expertspassem' = c(0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'herptiles_wet_grassland_expertspassem' = c(0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'herptiles_grassland_expertspassem' = c(0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'ground_nesting_birds_farmland_expertspassem' = c(0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'ground_nesting_birds_expertspassem' = c(0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'wetland_expertspassem' = c(0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'hedgerow_expertspassem' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'grassland_expertspassem' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'urban_bats_expertspassem' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'urban_hedgehog_expertspassem' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'urban_green_wall_trees_esurban' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'farmland_expert' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 'mosaic_expert' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0), 'grassland_expert' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0), 'woodland_expert' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0), 'wetland_expert' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0), 'hedgerow_expert' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0), 'woodland_network' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0), 'grassland_network' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0), 'wetland_network' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0), 'wetland_es' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0), 'grassland_es' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0), 'wetland_bng' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0), 'grassland_bng' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0), 'woodland_bng' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0), 'woodland_es' = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1) ) # Replace values with lists (selection one) habitat_all <- habitat_all %>% mutate(selection_one = lapply(selection_one, function(x) { if (x %in% names(replacements)) { replacements[[x]] } else { NA # Or handle unexpected cases } })) # Replace values with lists (selection two) #habitat_all <- habitat_all %>% # mutate(selection_two = lapply(selection_two, function(x) { # if (x %in% names(replacements)) { # replacements[[x]] # } else { # NA # Or handle unexpected cases # } # })) # Selection one # Find the maximum length of the vectors max_len <- max(sapply(habitat_all$selection_one, length)) # Fill the matrix with NA for missing values and create a matrix habitats_selection_one <- do.call(rbind, lapply(habitat_all$selection_one, function(x) { c(x, rep(NA, max_len - length(x))) # Pad with NA for shorter vectors })) # Selection two # Find the maximum length of the vectors #max_len <- max(sapply(habitat_all$selection_one, length)) # Fill the matrix with NA for missing values and create a matrix #habitats_selection_two <- do.call(rbind, lapply(habitat_all$selection_two, function(x) { # c(x, rep(NA, max_len - length(x))) # Pad with NA for shorter vectors #})) # Selection one habitats_selection_one[habitats_selection_one == 0] <- NA colnames(habitats_selection_one) <- c('wet_woodland_willow_tit_expertprioritysp', 'black_poplar_expertprioritysp', 'dune_natterjack_expertprioritysp', 'woodland_bats_expertspassem', 'woodland_expertspassem', 'woodland_red_squirrel_expertprioritysp', 'peat_sensitive_farming_expert', 'herptiles_wetland_expertspassem', 'herptiles_wet_grassland_expertspassem', 'herptiles_grassland_expertspassem', 'ground_nesting_birds_farmland_expertspassem', 'ground_nesting_birds_expertspassem', 'wetland_expertspassem', 'hedgerow_expertspassem', 'grassland_expertspassem', 'urban_bats_expertspassem', 'urban_hedgehog_expertspassem', 'urban_green_wall_trees_esurban', 'farmland_expert', 'mosaic_expert','grassland_expert', 'woodland_expert','wetland_expert', 'hedgerow_expert', 'woodland_network', 'grassland_network', 'wetland_network', 'wetland_es', 'grassland_es', 'wetland_bng', 'grassland_bng', 'woodland_bng', 'woodland_es') # selection two #habitats_selection_two[habitats_selection_two == 0] <- NA #colnames(habitats_selection_two) <- c('wet_woodland_willow_tit', 'black_poplar', 'dune_natterjack', # 'woodland_bats', 'woodland', 'woodland_red_squirrel', 'peat_sensitive_farming', # 'herptiles_wetland', 'herptiles_wet_grassland', 'herptiles_grassland', # 'ground_nesting_birds_farmland', 'ground_nesting_birds', 'wetland', # 'hedgerow', 'grassland', 'urban_bats', 'urban_hedgehog', 'urban_green_wall_trees', # 'farmland', 'mosaic') # Create interventions/opportunities final_interventions_selection_one <- setValues(template_rasters, habitats_selection_one) #final_interventions_selection_two <- setValues(template_rasters, habitats_selection_two) # Start here when I go back to this on Friday after Grosvenor # Converting the different habitat types into polygons final_interventions_poly_wet_woodland_willow_tit_expertprioritysp_s1 <- as.polygons(final_interventions_selection_one$wet_woodland_willow_tit_expertprioritysp) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wet_woodland_willow_tit_expertprioritysp') #final_interventions_poly_wet_woodland_willow_tit_s2 <- as.polygons(final_interventions_selection_two$wet_woodland_willow_tit) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wet_woodland_willow_tit') final_interventions_poly_black_poplar_expertprioritysp_s1 <- as.polygons(final_interventions_selection_one$black_poplar_expertprioritysp) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'black_poplar_expertprioritysp') #final_interventions_poly_black_poplar_s2 <- as.polygons(final_interventions_selection_two$black_poplar) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'black_poplar') final_interventions_poly_dune_natterjack_expertprioritysp_s1 <- as.polygons(final_interventions_selection_one$dune_natterjack_expertprioritysp) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'dune_natterjack_expertprioritysp') #final_interventions_poly_dune_natterjack_s2 <- as.polygons(final_interventions_selection_two$dune_natterjack) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'dune_natterjack') final_interventions_poly_woodland_bats_expertspassem_expert_s1 <- as.polygons(final_interventions_selection_one$woodland_bats_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_bats_expertspassem') #final_interventions_poly_woodland_bats_s2 <- as.polygons(final_interventions_selection_two$woodland_bats) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_bats') final_interventions_poly_woodland_expertspassem_s1 <- as.polygons(final_interventions_selection_one$woodland_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_expertspassem') #final_interventions_poly_woodland_s2 <- as.polygons(final_interventions_selection_two$woodland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland') final_interventions_poly_woodland_red_squirrel_expertprioritysp_s1 <- as.polygons(final_interventions_selection_one$woodland_red_squirrel_expertprioritysp) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_red_squirrel_expertprioritysp') #final_interventions_poly_woodland_red_squirrel_s2 <- as.polygons(final_interventions_selection_two$woodland_red_squirrel) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_red_squirrel') final_interventions_poly_peat_sensitive_farming_expert_s1 <- as.polygons(final_interventions_selection_one$peat_sensitive_farming_expert) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'peat_sensitive_farming_expert') #final_interventions_poly_peat_sensitive_farming_s2 <- as.polygons(final_interventions_selection_two$peat_sensitive_farming) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'peat_sensitive_farming') final_interventions_poly_herptiles_wetland_expertspassem_s1 <- as.polygons(final_interventions_selection_one$herptiles_wetland_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_wetland_expertspassem') #final_interventions_poly_herptiles_wetland_s2 <- as.polygons(final_interventions_selection_two$herptiles_wetland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_wetland') final_interventions_poly_herptiles_wet_grassland_expertspassem_s1 <- as.polygons(final_interventions_selection_one$herptiles_wet_grassland_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_wet_grassland_expertspassem') #final_interventions_poly_herptiles_wet_grassland_s2 <- as.polygons(final_interventions_selection_two$herptiles_wet_grassland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_wet_grassland') final_interventions_poly_herptiles_grassland_expertspassem_s1 <- as.polygons(final_interventions_selection_one$herptiles_grassland_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_grassland_expertspassem') #final_interventions_poly_herptiles_grassland_s2 <- as.polygons(final_interventions_selection_two$herptiles_grassland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_grassland') final_interventions_poly_ground_nesting_birds_farmland_expertspassem_s1 <- as.polygons(final_interventions_selection_one$ground_nesting_birds_farmland_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'ground_nesting_birds_farmland_expertspassem') #final_interventions_poly_ground_nesting_birds_farmland_s2 <- as.polygons(final_interventions_selection_two$ground_nesting_birds_farmland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'ground_nesting_birds_farmland') final_interventions_poly_ground_nesting_birds_expertspassem_s1 <- as.polygons(final_interventions_selection_one$ground_nesting_birds_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'ground_nesting_birds_expertspassem') #final_interventions_poly_ground_nesting_birds_s2 <- as.polygons(final_interventions_selection_two$ground_nesting_birds) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'ground_nesting_birds') final_interventions_poly_wetland_expertspassem_s1 <- as.polygons(final_interventions_selection_one$wetland_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wetland_expertspassem') #final_interventions_poly_wetland_s2 <- as.polygons(final_interventions_selection_two$wetland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wetland') final_interventions_poly_hedgerow_expertspassem_s1 <- as.polygons(final_interventions_selection_one$hedgerow_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'hedgerow_expertspassem') #final_interventions_poly_hedgerow_s2 <- as.polygons(final_interventions_selection_two$hedgerow) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'hedgerow') final_interventions_poly_grassland_expertspassem_s1 <- as.polygons(final_interventions_selection_one$grassland_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland_expertspassem') #final_interventions_poly_grassland_s2 <- as.polygons(final_interventions_selection_two$grassland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland') final_interventions_poly_urban_bats_expertspassem_s1 <- as.polygons(final_interventions_selection_one$urban_bats_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_bats_expertspassem') #final_interventions_poly_urban_bats_s2 <- as.polygons(final_interventions_selection_two$urban_bats) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_bats') final_interventions_poly_urban_hedgehog_expertspassem_s1 <- as.polygons(final_interventions_selection_one$urban_hedgehog_expertspassem) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_hedgehog_expertspassem') #final_interventions_poly_urban_hedgehog_s2 <- as.polygons(final_interventions_selection_two$urban_hedgehog) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_hedgehog') final_interventions_poly_urban_green_wall_trees_esurban_s1 <- as.polygons(final_interventions_selection_one$urban_green_wall_trees_esurban) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_green_wall_trees_esurban') #final_interventions_poly_urban_green_wall_trees_s2 <- as.polygons(final_interventions_selection_two$urban_green_wall_trees) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_green_wall_trees') final_interventions_poly_farmland_expert_s1 <- as.polygons(final_interventions_selection_one$farmland_expert) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'farmland_expert') #final_interventions_poly_farmland_s2 <- as.polygons(final_interventions_selection_two$farmland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'farmland') final_interventions_poly_mosaic_expert_s1 <- as.polygons(final_interventions_selection_one$mosaic_expert) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic_expert') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_expert_s1 <- as.polygons(final_interventions_selection_one$grassland_expert) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland_expert') final_interventions_poly_woodland_expert_s1 <- as.polygons(final_interventions_selection_one$woodland_expert) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_expert') final_interventions_poly_wetland_expert_s1 <- as.polygons(final_interventions_selection_one$wetland_expert) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wetland_expert') final_interventions_poly_hedgerow_expert_s1 <- as.polygons(final_interventions_selection_one$hedgerow_expert) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'hedgerow_expert') final_interventions_poly_woodland_network_s1 <- as.polygons(final_interventions_selection_one$woodland_network) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_network') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_network_s1 <- as.polygons(final_interventions_selection_one$grassland_network) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland_network') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_wetland_network_s1 <- as.polygons(final_interventions_selection_one$wetland_network) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wetland_network') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_wetland_es_s1 <- as.polygons(final_interventions_selection_one$wetland_es) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wetland_es') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_es_s1 <- as.polygons(final_interventions_selection_one$grassland_es) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland_es') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_woodland_es_s1 <- as.polygons(final_interventions_selection_one$woodland_es) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_es') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_woodland_bng_s1 <- as.polygons(final_interventions_selection_one$woodland_bng) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_bng') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_bng_s1 <- as.polygons(final_interventions_selection_one$grassland_bng) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland_bng') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_wetland_bng_s1 <- as.polygons(final_interventions_selection_one$grassland_bng) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland_bng') #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') # Combining all polygons final_interventions_poly_s1 <- rbind(final_interventions_poly_wet_woodland_willow_tit_expertprioritysp_s1, #final_interventions_poly_wet_woodland_willow_tit_s2 <- as.polygons(final_interventions_selection_two$wet_woodland_willow_tit) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wet_woodland_willow_tit') final_interventions_poly_black_poplar_expertprioritysp_s1, #final_interventions_poly_black_poplar_s2 <- as.polygons(final_interventions_selection_two$black_poplar) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'black_poplar') final_interventions_poly_dune_natterjack_expertprioritysp_s1, #final_interventions_poly_dune_natterjack_s2 <- as.polygons(final_interventions_selection_two$dune_natterjack) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'dune_natterjack') final_interventions_poly_woodland_bats_expertspassem_expert_s1, #final_interventions_poly_woodland_bats_s2 <- as.polygons(final_interventions_selection_two$woodland_bats) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_bats') final_interventions_poly_woodland_expertspassem_s1, #final_interventions_poly_woodland_s2 <- as.polygons(final_interventions_selection_two$woodland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland') final_interventions_poly_woodland_red_squirrel_expertprioritysp_s1, #final_interventions_poly_woodland_red_squirrel_s2 <- as.polygons(final_interventions_selection_two$woodland_red_squirrel) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'woodland_red_squirrel') final_interventions_poly_peat_sensitive_farming_expert_s1, #final_interventions_poly_peat_sensitive_farming_s2 <- as.polygons(final_interventions_selection_two$peat_sensitive_farming) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'peat_sensitive_farming') final_interventions_poly_herptiles_wetland_expertspassem_s1, #final_interventions_poly_herptiles_wetland_s2 <- as.polygons(final_interventions_selection_two$herptiles_wetland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_wetland') final_interventions_poly_herptiles_wet_grassland_expertspassem_s1, #final_interventions_poly_herptiles_wet_grassland_s2 <- as.polygons(final_interventions_selection_two$herptiles_wet_grassland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_wet_grassland') final_interventions_poly_herptiles_grassland_expertspassem_s1, #final_interventions_poly_herptiles_grassland_s2 <- as.polygons(final_interventions_selection_two$herptiles_grassland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'herptiles_grassland') final_interventions_poly_ground_nesting_birds_farmland_expertspassem_s1, #final_interventions_poly_ground_nesting_birds_farmland_s2 <- as.polygons(final_interventions_selection_two$ground_nesting_birds_farmland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'ground_nesting_birds_farmland') final_interventions_poly_ground_nesting_birds_expertspassem_s1, #final_interventions_poly_ground_nesting_birds_s2 <- as.polygons(final_interventions_selection_two$ground_nesting_birds) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'ground_nesting_birds') final_interventions_poly_wetland_expertspassem_s1, #final_interventions_poly_wetland_s2 <- as.polygons(final_interventions_selection_two$wetland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'wetland') final_interventions_poly_hedgerow_expertspassem_s1, #final_interventions_poly_hedgerow_s2 <- as.polygons(final_interventions_selection_two$hedgerow) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'hedgerow') final_interventions_poly_grassland_expertspassem_s1, #final_interventions_poly_grassland_s2 <- as.polygons(final_interventions_selection_two$grassland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'grassland') final_interventions_poly_urban_bats_expertspassem_s1, #final_interventions_poly_urban_bats_s2 <- as.polygons(final_interventions_selection_two$urban_bats) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_bats') final_interventions_poly_urban_hedgehog_expertspassem_s1, #final_interventions_poly_urban_hedgehog_s2 <- as.polygons(final_interventions_selection_two$urban_hedgehog) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_hedgehog') final_interventions_poly_urban_green_wall_trees_esurban_s1, #final_interventions_poly_urban_green_wall_trees_s2 <- as.polygons(final_interventions_selection_two$urban_green_wall_trees) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'urban_green_wall_trees') final_interventions_poly_farmland_expert_s1, #final_interventions_poly_farmland_s2 <- as.polygons(final_interventions_selection_two$farmland) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'farmland') final_interventions_poly_mosaic_expert_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_expert_s1, final_interventions_poly_woodland_expert_s1, final_interventions_poly_wetland_expert_s1, final_interventions_poly_hedgerow_expert_s1, final_interventions_poly_woodland_network_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_network_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_wetland_network_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_wetland_es_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_es_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_woodland_es_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_woodland_bng_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_grassland_bng_s1, #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') final_interventions_poly_wetland_bng_s1 #final_interventions_poly_mosaic_s2 <- as.polygons(final_interventions_selection_two$mosaic) %>% st_as_sf() %>% st_geometry() %>% st_as_sf() %>% mutate(hab_type = 'mosaic') ) %>% st_set_crs(27700) #final_interventions_poly_s2 <- rbind(final_interventions_poly_wet_woodland_willow_tit_s2, # final_interventions_poly_black_poplar_s2, # final_interventions_poly_dune_natterjack_s2, # final_interventions_poly_woodland_bats_s2, # final_interventions_poly_woodland_s2, ## final_interventions_poly_woodland_red_squirrel_s2, # final_interventions_poly_peat_sensitive_farming_s2, ## final_interventions_poly_herptiles_wetland_s2, # final_interventions_poly_herptiles_wet_grassland_s2, # final_interventions_poly_herptiles_grassland_s2, # final_interventions_poly_ground_nesting_birds_farmland_s2, # final_interventions_poly_ground_nesting_birds_s2, # final_interventions_poly_wetland_s2, ## final_interventions_poly_hedgerow_s2, # final_interventions_poly_grassland_s2, # final_interventions_poly_urban_bats_s2, # final_interventions_poly_urban_hedgehog_s2, # final_interventions_poly_urban_green_wall_trees_s2, # final_interventions_poly_farmland_s2, # final_interventions_poly_mosaic_s2) %>% st_set_crs(27700) # # Add type column to the selection final_interventions_poly_s1 <- final_interventions_poly_s1 %>% dplyr::mutate(type = stringr::str_extract(hab_type, "[^_]+$")) # Saving separately for s1 and s2 st_write(final_interventions_poly_s1, paste0('C://Users/NSPLBEN1/OneDrive - Liverpool John Moores University/Documents - Natural Capital Lab at LJMU Team/Decision tool paper/results/',name,'_poly.gpkg'), append = F) #st_write(final_interventions_poly_s2, paste0('decision-making/outputs/final_interventions_LNRS_s2.gpkg'), append = F) }