openSTARS (version 1.1.0)

calc_attributes_edges: Calculate attributes of the edges.

Description

For each edge (i.e. stream segment) additional attributes (potential predictor variables) are derived based on given raster or vector maps.

Usage

calc_attributes_edges(input_raster = NULL, stat_rast = NULL,
  attr_name_rast = NULL, input_vector = NULL, stat_vect = NULL,
  attr_name_vect = NULL, round_dig = 2)

Arguments

input_raster

name(s) of raster map(s) to calculate attributes from.

stat_rast

name(s) giving the statistics to be calculated, from the raster maps, must be one of: "min", "max", "mean", "sum", "percent".

attr_name_rast

name(s) of new column names for the attribute(s) to be calculated. Attribute names must not be longer than 8 characters.

input_vector

name(s) of vector map(s) to calculate attributes from.

stat_vect

name(s) giving the statistics to be calculated from the vector maps, must be one of: "count" (for point data), "percent" (for polygon data).

attr_name_vect

name(s) of attribute column(s) to calculate the statistics from. For point data, results columns will have the same name, for polygon data, the results column names are determined by the content of this column.

round_dig

integer; number of digits to round results to. Can be a vector of different values or just one value for all attributes. #@param clean logical; should intermediate files be deleted

Value

Nothing. The function appends new columns to the 'edges' attribute table with column names given in attr_name_rast. For each attribute, two columns are appended: one giving the attribute for the rca of the edge ("attribute_name_e") and one for the attribute of the total catchment of the edge ("attribute_name_c").

Details

First, the subcatchments for all edges are calculated. Then these are intersected with the given raster and/or vector maps and the desired statistics are computed. This function must be run before computing approximate attribute values for sites calc_attributes_sites_approx.

For stat_rast = "percent" the input_raster must be coded as 1 and 0 (e.g., cells occupied by the land use under consideration and not). If the input_raster consists of percentages per cell (e.g., proportional land use of a certain type per cell) stat_rast = "mean" gives the overall proportion of this land use in the catchment.

For stat_vect = "percent" input_vector must contain polygons of e.g. different land use types. The column attr_name_vect would then give the code for the different land uses. Then, the percentage for each land use type in the catchment of the edge is calculated and given in separate columns with column names resampling the different categories given in column attr_name_vect

For stat_vect = "count" input_vector must contain points of e.g. waste water treatment plants. The column attr_name_vect gives the name of the column to hold the count value, e.g. nWWTP.

Both raster and vector maps to be used must be read in to the GRASS session, either in import_data or using the GRASS function r.in.rast or v.in.ogr (see examples).

Examples

# NOT RUN {
# Initiate GRASS session
if(.Platform$OS.type == "windows"){
  gisbase = "c:/Program Files/GRASS GIS 7.4.0"
  } else {
  gisbase = "/usr/lib/grass74/"
  }
initGRASS(gisBase = gisbase,
    home = tempdir(),
    override = TRUE)

# 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")
pred_path <- system.file("extdata", "nc", "landuse.shp", package = "openSTARS")
setup_grass_environment(dem = dem_path)
import_data(dem = dem_path, sites = sites_path, 
  predictor_vector = pred_path, predictor_v_names = "landuse")
gmeta()

# Derive streams from DEM
derive_streams(burn = 0, accum_threshold = 700, condition = TRUE, clean = TRUE)

# Check and correct complex junctions (there are no complex juctions in this 
# example date set)
cj <- check_compl_junctions()
if(cj){
  correct_compl_junctions()
}

# Prepare edges
calc_edges()

# Derive slope from the DEM as an example raster map to calculate attributes from
execGRASS("r.slope.aspect", flags = c("overwrite","quiet"),
parameters = list(
  elevation = "dem",
    slope = "slope"
    ))
    
# 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)
  
calc_attributes_edges(input_raster = "slope", stat_rast = "max", attr_name_rast = "maxSlo",
                     input_vector = c("landuse", "psources"), 
                     stat_vect = c("percent", "count"), attr_name_vect = c("landuse", "nps"))

# Plot data with maximum slope per edge as color ramp (steep slopes in red)
dem <- readRAST('dem', ignore.stderr = TRUE)
edges <- readVECT('edges', ignore.stderr = TRUE)
head(edges@data)
lu <- readVECT("landuse", ignore.stderr = TRUE)
plot(dem, col = gray(seq(0,1,length.out=20))) 
col <- adjustcolor(c("red", "green", "blue", "yellow"), alpha.f = 0.3)
plot(lu, add = TRUE, col = col[as.numeric(as.factor(lu$landuse))])
legend("topleft", col = col, pch = 15, legend = as.factor(sort(unique(lu$landuse))), 
  title = "landuse", ncol = 4)
mm <- range(c(edges$agri_c), na.rm = TRUE) 
b <- seq(from=mm[1],to=mm[2]+diff(mm)*0.01,length.out=10)
c_ramp <- colorRampPalette(c("blue", "red"))
cols <- c_ramp(length(b))[as.numeric(cut(edges$agri_c,breaks = b,right= FALSE))]
plot(edges, col = cols, add = TRUE, lwd = 2)
legend("topright", col = cols[c(1,length(cols))], lwd = 2, 
  legend = paste("precent agri", c(min(edges$agri_c), max(edges$agri_c))))
# }
# NOT RUN {
# }