Description Usage Format Note Author(s) References Examples
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; doi: 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).
1 |
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
Consider using the 30 m resolution grid (see bargrid
) as the target resolution (output maps).
Tomislav Hengl
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. doi: 10.5194/hess-14-1153-2010
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | 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)
## End(Not run)
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