plotKML (version 0.8-3)

baranja: Baranja hill case study

Description

Baranja hill is a 4 by 4 km large study area in the Baranja region, eastern Croatia (corresponds to a size of an aerial photograph). This data set has been extensively used to describe various DEM modelling and analysis steps (see Hengl and Reuter, 2008; Hengl et al., 2010; 10.5194/hess-14-1153-2010). Object barxyz contains 6370 precise observations of elevations (from field survey and digitized from the stereo images); bargrid contains observed probabilities of streams (digitized from the 1:5000 topo map); barstr contains 100 simulated stream networks ("SpatialLines") using barxyz point data as input (see examples below).

Usage

data(bargrid)

Format

The bargrid data frame (regular grid at 30 m intervals) contains the following columns:

p.obs

observed probability of stream (0-1)

x

a numeric vector; x-coordinate (m) in the MGI / Balkans zone 6

y

a numeric vector; y-coordinate (m) in the MGI / Balkans zone 6

References

• Hengl, T., Reuter, H.I. (eds), (2008) Geomorphometry: Concepts, Software, Applications. Developments in Soil Science, vol. 33, Elsevier, 772 p.

• Hengl, T., Heuvelink, G. B. M., van Loon, E. E., (2010) On the uncertainty of stream networks derived from elevation data: the error propagation approach. Hydrology and Earth System Sciences, 14:1153-1165. 10.5194/hess-14-1153-2010

• https://geomorphometry.org/baranja-hill/

Examples

# NOT RUN {
library(sp)
library(gstat)
## sampled elevations:
data(barxyz)
prj = "+proj=tmerc +lat_0=0 +lon_0=18 +k=0.9999 +x_0=6500000 +y_0=0 +ellps=bessel +units=m 
+towgs84=550.499,164.116,475.142,5.80967,2.07902,-11.62386,0.99999445824"
coordinates(barxyz) <- ~x+y
proj4string(barxyz) <- CRS(prj)
## grids:
data(bargrid)
data(barstr)
coordinates(bargrid) <- ~x+y
gridded(bargrid) <- TRUE
proj4string(bargrid) <- barxyz@proj4string
bargrid@grid
# }
# NOT RUN {
## Example with simulated streams:
data(R_pal)
library(rgdal)
library(RSAGA)
pnt = list("sp.points", barxyz, col="black", pch="+")
spplot(bargrid[1], sp.layout=pnt, 
  col.regions = R_pal[["blue_grey_red"]])
## Deriving stream networks using geostatistical simulations:
Z.ovgm <- vgm(psill=1831, model="Mat", range=1051, nugget=0, kappa=1.2)
sel <- runif(length(barxyz$Z))<.2
N.sim <- 5
## geostatistical simulations:
DEM.sim <- krige(Z~1, barxyz[sel,], bargrid, model=Z.ovgm, nmax=20, 
   nsim=N.sim, debug.level=-1)
## Note: this operation can be time consuming

stream.list <- list(rep(NA, N.sim))
## derive stream networks in SAGA GIS:
for (i in 1:N.sim) {
  writeGDAL(DEM.sim[i], paste("DEM", i, ".sdat", sep=""), 
     drivername = "SAGA", mvFlag = -99999)
  ## filter the spurious sinks:
  rsaga.fill.sinks(in.dem=paste("DEM", i, ".sgrd", sep=""), 
     out.dem="DEMflt.sgrd", check.module.exists = FALSE)
  ## extract the channel network SAGA GIS:
  rsaga.geoprocessor(lib="ta_channels", module=0, 
    param=list(ELEVATION="DEMflt.sgrd", 
    CHNLNTWRK=paste("channels", i, ".sgrd", sep=""), 
    CHNLROUTE="channel_route.sgrd", 
    SHAPES="channels.shp", 
    INIT_GRID="DEMflt.sgrd", 
    DIV_CELLS=3, MINLEN=40),
    check.module.exists = FALSE, 
    show.output.on.console=FALSE)
  stream.list[[i]] <- readOGR("channels.shp", "channels", 
    verbose=FALSE)
  proj4string(stream.list[[i]]) <- barxyz@proj4string
}
# plot all derived streams at top of each other:
streams.plot <- as.list(rep(NA, N.sim))
for(i in 1:N.sim){
  streams.plot[[i]] <- list("sp.lines", stream.list[[i]])
}
spplot(DEM.sim[1], col.regions=grey(seq(0.4,1,0.025)), scales=list(draw=T), 
sp.layout=streams.plot)
# }