baranja {plotKML} R Documentation

## 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). 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

### Note

Consider using the 30 m resolution grid (see `bargrid`) as the target resolution (output maps).

Tomislav Hengl

### Examples

```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)
```

[Package plotKML version 0.5-8 Index]