openSTARS (version 1.2.2)

import_data: Import data into 'GRASS.'

Description

This function loads a DEM (digital elevation model) and sites data (both required) into the 'GRASS' session. Optionally, prediction sites and streams data can be loaded and the streams may be corrected by snapping to prevent lose ends. Likewise, potential predictor maps (raster or vector format) can be loaded.

Usage

import_data(
  dem,
  band = 1,
  sites,
  streams = NULL,
  snap_streams = FALSE,
  pred_sites = NULL,
  predictor_raster = NULL,
  predictor_r_names = NULL,
  predictor_vector = NULL,
  predictor_v_names = NULL
)

Arguments

dem

character; path to DEM (digital elevation model) raster file.

band

integer (optional); defines which band of the dem to use

sites

character string or object; path to sites vector file (ESRI shape) or sp or sf data object.

streams

character string or object (optional); path to network vector file (ESRI shape) or sp or sf data object. If available this can be burnt into the DEM in derive_streams

snap_streams

boolean (optional); snap line ends. If TRUE line ends of the streams are snapped to the next feature if they are unconnected with threshold of 10 m using 'GRASS' function v.clean.

pred_sites

character string vector or object(s) (optional); path(s) to prediction sites vector files (ESRI shape) or sp or sf data object. Different formats (i.e. path and objects) must not be mixed; more than one sf or sp object must be provided as a list, not concatenated with c.

predictor_raster

character vector (optional); paths to raster data to import as predictors.

predictor_r_names

character string vector (optional); names for potential predictor variables in raster format; if not provided perdictor_raster is used.

predictor_vector

character string vector of object(s) (optional); path(s) to vector data (ESRI shape) or sp or sf object names to import as predictors. Different formats (i.e. path and objects) must not be mixed; more than one sf or sp object must be provided as a list, not concatenated with c.

predictor_v_names

character vector (optional); names for potential predictor variables in vector format ; if not provided perdictor_vector is used.

Value

Nothing, the data is loaded into the 'GRASS' session (mapset PERMANENT). The DEM is stored as raster 'dem', sites as vector 'sites_o', prediction sites as vector using the original file names with an appended '_o' (without extension), streams as vector 'streams_o' in the 'GRASS' location. Additionally, predictor raster map(s) can be read in and are stored in 'GRASS' using either the original file names (without extension) or using the names provides in predictor_r_names. The latter option may be useful if ArcGIS grid data (typically stored as 'grid_name/w001001.adf') are used. Likewise, predictor vector maps can be read in from Esri Shape file (given as the full file path) or as sf or sp objects. Potential predictor data can also be read in later, e.g. using GRASS commands v.import or r.in.gdal (see examples below).

Details

All vector data (sites, streams and potential predictors) is imported into the current location using v.import. Hence, if the projections does not match to the one of the DEM (which was used to specify the location in setup_grass_environment) the maps are projected and imported on the fly. All raster data are not transformed but it is assumed that they have the same projection as the current location. Hence, it is important to make sure that they all have indeed the same projection (and same cell size) and that the correct one is set in setup_grass_environment. If this condition is not met, the raster data should be preprocessed before importing. Use check_projection to compare the projection of a raster data set and the one of the current location (i.e the one of the dem).

Examples

# NOT RUN {
dem_path <- system.file("extdata", "nc", "elev_ned_30m.tif", package = "openSTARS")
if(.Platform$OS.type == "windows"){
  grass_program_path = "c:/Program Files/GRASS GIS 7.6"
  } else {
  grass_program_path = "/usr/lib/grass78/"
  }

setup_grass_environment(dem = dem_path, 
                        gisBase = grass_program_path,      
                        remove_GISRC = TRUE,
                        override = TRUE
                        )
gmeta()

# Load files into GRASS
dem_path <- system.file("extdata", "nc", "elev_ned_30m.tif", package = "openSTARS")
sites_path <- system.file("extdata", "nc", "sites_nc.shp", package = "openSTARS")
streams_path <- system.file("extdata", "nc", "streams.shp", package = "openSTARS")
preds_v_path <- system.file("extdata", "nc", "pointsources.shp", package = "openSTARS")
preds_r_path <- system.file("extdata", "nc", "landuse_r.tif", package = "openSTARS")
                 
import_data(dem = dem_path, sites = sites_path, streams = streams_path,
            predictor_vector = preds_v_path, predictor_raster = preds_r_path)

# Plot data
library(sp)
dem <- readRAST("dem", ignore.stderr = TRUE, plugin = FALSE)
sites_orig <-  readVECT("sites_o", ignore.stderr = TRUE)
lu <- readRAST("landuse_r", ignore.stderr = TRUE, plugin = FALSE)
# import additional vector data
fp <-  system.file("extdata", "nc", "pointsources.shp", package = "openSTARS")
execGRASS("v.import", flags = c("overwrite", "quiet"),
parameters = list(
  input = fp,
  output =  "psources",
  extent = "region"),  # to import into current regien
  intern = TRUE, ignore.stderr = TRUE)
  
#plot(dem, col = terrain.colors(20))
plot(dem, col = grey.colors(20))
points(sites_orig, pch = 4)
ps <- readVECT("psources")
points(ps, bg = "red", pch = 21, col = "grey", cex = 1.5)

# plot landuse data
library(raster)
par(mfcol = c(1,1), mar = c(5,4,4,2))
op <- par()
par(xpd = FALSE)
plot(raster(lu), legend = FALSE, xaxt = "n", yaxt = "n", bty = "n",
     col = c("red", "goldenrod", "green", "forestgreen","darkgreen", "blue", "lightblue"))
par(xpd = TRUE)
legend("bottom", cex = 0.75,
       legend = c("developed", "agriculture", "herbaceous", 
                  "shrubland", "forest", "water", "sediment"),
       fill = c("red", "goldenrod", "green", 
                "forestgreen","darkgreen", "blue", "lightblue"),
       horiz = TRUE, inset = -0.175)
par <- op
# }