## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(dynamicSDM) ## ----check Google, eval = FALSE----------------------------------------------- # library(rgee) # rgee::ee_check() # # library(googledrive) # googledrive::drive_user() # #user.email<-"your_google_email_here" ## ----create directories------------------------------------------------------- variablenames <- c("eight_sum_prec", "year_sum_prec", "grass_crop_percentage") project_directory <- file.path(tempdir(), "dynamicSDM_vignette") # project_directory<-"your_path_here" dir.create(project_directory, showWarnings = FALSE) projection_directories <- file.path(project_directory, "projection") dir.create(projection_directories, showWarnings = FALSE) projectionrasters <- file.path(project_directory, "projectionrasters") dir.create(projectionrasters, showWarnings = FALSE) projection_covariates <- file.path(project_directory, "projectioncovariates") dir.create(projection_covariates, showWarnings = FALSE) projection_GIF <- file.path(project_directory, "projection_GIF") dir.create(projection_GIF, showWarnings = FALSE) ## ----example-dynamic_proj_dates----------------------------------------------- # 5 day intervals dynamic_proj_dates(startdate = "2018-01-01", enddate = "2018-12-01", interval = 5, interval.level = "day") # 2 week intervals dynamic_proj_dates(startdate = "2018-01-01", enddate = "2018-12-01", interval = 2, interval.level = "week") ## ----case study dynamic_proj_dates-------------------------------------------- projectiondates <- dynamic_proj_dates(startdate = "2018-01-01", enddate = "2018-12-01", interval = 3, interval.level = "month") ## ----example-extract_dynamic_raster, eval = FALSE----------------------------- # data("sample_extent_data") # # extract_dynamic_raster(dates = projectiondates, # datasetname = "UCSB-CHG/CHIRPS/DAILY", # bandname = "precipitation", # user.email = user.email, # spatial.res.metres = 5566, # GEE.math.fun = "sum", # temporal.direction = "prior", # temporal.res = 56, # spatial.ext = sample_extent_data, # varname = variablenames[1], # save.directory = projectionrasters) # # extract_dynamic_raster(dates = projectiondates, # datasetname = "UCSB-CHG/CHIRPS/DAILY", # bandname = "precipitation", # user.email = user.email, # spatial.res.metres = 5566, # GEE.math.fun = "sum", # temporal.direction = "prior", # temporal.res = 364, # spatial.ext = sample_extent_data, # varname = variablenames[2], # save.directory = projectionrasters) # # matrix<-dynamicSDM::get_moving_window(radial.distance = 10000, # spatial.res.degrees = 0.05, # spatial.ext = sample_extent_data) # # extract_buffered_raster(dates=projectiondates, # datasetname = "MODIS/006/MCD12Q1", # bandname = "LC_Type5", # spatial.res.metres = 500, # GEE.math.fun = "sum", # moving.window.matrix = matrix, # user.email = user.email, # categories = c(6,7), # agg.factor = 12, # spatial.ext = sample_extent_data, # varname = variablenames[3], # save.directory = projectionrasters) ## ----example-dynamic_proj_covariates, eval = FALSE---------------------------- # dynamic_proj_covariates(dates = projectiondates, # varnames = variablenames, # local.directory = projectionrasters, # spatial.ext = sample_extent_data, # spatial.mask = sample_extent_data, # spatial.res.degrees = 0.05, # resample.method = c("bilinear","bilinear","ngb"), # cov.file.type = "csv", # prj="+proj=longlat +datum=WGS84", # save.directory = projection_covariates) ## ----example-dynamic_proj, eval = FALSE--------------------------------------- # #sample_brt_models<- readRDS(paste0(project_directory, "/fitted_quelea_SDMs.rds")) # data("sample_explan_data") # sample_explan_data$weights <- (1 - sample_explan_data$REL_SAMP_EFFORT) # # sample_brt_models <- brt_fit(sample_explan_data, # response.col = "presence.absence", # varnames = variablenames, # block.col = "BLOCK.CATS", # weights.col = "weights", # distribution = "bernoulli") # # dynamic_proj(dates = projectiondates, # projection.method = c("proportional"), # local.directory = projection_covariates, # cov.file.type = "csv", # sdm.mod = sample_brt_models, # spatial.mask = sample_extent_data, # save.directory = projection_directories) ## ----plot projections, eval = FALSE------------------------------------------- # terra::plot(terra::rast(list.files(projection_directories)[1])) # terra::plot(terra::rast(list.files(projection_directories)[2])) # terra::plot(terra::rast(list.files(projection_directories)[3])) # terra::plot(terra::rast(list.files(projection_directories)[4])) ## ----example-dynamic_proj_GIF, eval = FALSE----------------------------------- # # cols1<- c("#F0F0F0","#40863A","#FBF357","#ED8E07","#cc0000","#660000") # border.countries<- c('South Africa', 'Botswana','Lesotho', 'Swaziland','Mozambique','Namibia' # ,'Zimbabwe','Angola','Zambia','Malawi') # # dynamic_proj_GIF( # dates = projectiondates, # projection.type = "proportional", # local.directory = projection_directories, # save.directory = projection_GIF, # width = 7, # height = 5, # colour.palette.custom = cols1, # borders = TRUE, # border.regions = border.countries, # border.colour = "grey50", # legend.max = 1, # legend.min = 0, # legend.name = "Distribution\n suitability", # file.name = "RBQ_proportional_GIF") #