Plot KML Manual

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Package ‘plotKML’
January 9, 2019
Version 0.5-9
Date 2019-01-04
Title Visualization of Spatial and Spatio-Temporal Objects in Google
Earth
Maintainer Tomislav Hengl 
Depends R (>= 2.13.0)
Imports methods, tools, utils, XML, sp, raster, rgdal, spacetime,
colorspace, plotrix, dismo, aqp, pixmap, plyr, stringr,
colorRamps, scales, gstat, zoo, RColorBrewer, RSAGA, classInt
Suggests adehabitatLT, maptools, fossil, spcosa, rjson, animation,
spatstat, RCurl, rgbif, Hmisc, GSIF, uuid, intervals, reshape,
gdalUtils, snowfall, parallel
Description Writes sp-class, spacetime-class, raster-class and similar spatial and spatiotemporal objects to KML following some basic cartographic rules.
License GPL
URL http://plotkml.r-forge.r-project.org/
LazyLoad yes
RoxygenNote 6.0.1
NeedsCompilation no
Author Tomislav Hengl [cre, aut],
Pierre Roudier [ctb],
Dylan Beaudette [ctb],
Edzer Pebesma [ctb],
Michael Blaschek [ctb]

R topics documented:
plotKML-package .
aesthetics . . . . .
baranja . . . . . . .
bigfoot . . . . . . .
check_projection .
col2kml . . . . . .
count.GridTopology
display.pal . . . . .

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. 3
. 4
. 4
. 6
. 8
. 9
. 10
. 11

R topics documented:

2
eberg . . . . . . . . . . . . . .
fmd . . . . . . . . . . . . . . .
geopath . . . . . . . . . . . . .
getCRS-methods . . . . . . . .
getWikiMedia.ImageInfo . . . .
gpxbtour . . . . . . . . . . . . .
grid2poly . . . . . . . . . . . .
HRprec08 . . . . . . . . . . . .
HRtemp08 . . . . . . . . . . . .
kml-methods . . . . . . . . . .
kml.tiles . . . . . . . . . . . . .
kml_compress . . . . . . . . . .
kml_description . . . . . . . . .
kml_layer-methods . . . . . . .
kml_layer.Raster . . . . . . . .
kml_layer.RasterBrick . . . . .
kml_layer.SoilProfileCollection .
kml_layer.SpatialLines . . . . .
kml_layer.SpatialPhotoOverlay .
kml_layer.SpatialPixels . . . . .
kml_layer.SpatialPoints . . . . .
kml_layer.SpatialPolygons . . .
kml_layer.STIDF . . . . . . . .
kml_layer.STTDF . . . . . . . .
kml_legend.bar . . . . . . . . .
kml_legend.whitening . . . . .
kml_metadata-methods . . . . .
kml_open . . . . . . . . . . . .
kml_screen . . . . . . . . . . .
LST . . . . . . . . . . . . . . .
makeCOLLADA . . . . . . . .
metadata2SLD-methods . . . .
metadata2SLD.SpatialPixels . .
normalizeFilename . . . . . . .
northcumbria . . . . . . . . . .
plotKML-method . . . . . . . .
plotKML.env . . . . . . . . . .
plotKML.GDALobj . . . . . . .
RasterBrickSimulations-class . .
RasterBrickTimeSeries-class . .
readGPX . . . . . . . . . . . .
readKML.GBIFdensity . . . . .
reproject . . . . . . . . . . . . .
SAGA_pal . . . . . . . . . . . .
sp.palette-class . . . . . . . . .
SpatialMaxEntOutput-class . . .
SpatialMetadata-class . . . . . .
SpatialPhotoOverlay-class . . .
SpatialPredictions-class . . . . .
SpatialSamplingPattern-class . .
SpatialVectorsSimulations-class
spMetadata-methods . . . . . .

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plotKML-package

3

spPhoto . . . . . . . .
vect2rast . . . . . . . .
vect2rast.SpatialPoints
whitening . . . . . . .
worldgrids_pal . . . .

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Index

85
88
90
91
92
94

plotKML-package

Visualization of spatial and spatio-temporal objects in Google Earth

Description
A suite of functions for converting 2D and 3D spatio-temporal (sp, raster and spacetime package
classes) objects into KML or KMZ documents for use in Google Earth.
Details
Package:
Type:
URL:
License:
LazyLoad:

plotKML
Package
http://plotkml.r-forge.r-project.org/
GPL
yes

Note
This package has been developed as a part of the Global Soil Information Facilities project, which
is run jointly by the ISRIC Institute and collaborators. ISRIC is a non-profit organization with a
mandate to serve the international community as custodian of global soil information and to increase
awareness and understanding of the role of soils in major global issues.
Author(s)
Tomislav Hengl (), Pierre Roudier (),
Dylan Beaudette (), Edzer Pebesma ()
References
• KML documentation (http://code.google.com/apis/kml/documentation/)
• Google Earth Outreach project (http://earth.google.com/outreach/tutorials.html)
• Hengl, T., Roudier, P., Beaudette, D. and Pebesma, E. (2015) plotKML: Scientific Visualization of Spatio-Temporal Data. Journal of Statistical Software, 63(5): 1–25.

4

baranja

aesthetics

Plotting aesthetics parameters

Description
Parses various object parameters / columns to KML aesthetics: size of the icons, fill color, labels,
altitude, width, . . .
Usage
kml_aes(obj, ...)
Arguments
obj

space-time object for plotting

...

other arguments

Details
Valid aesthetics: colour = "black", fill = "white", shape, whitening, alpha, width = 1,
labels, altitude = 0, size, balloon = FALSE. Specific features (target variables and the connected hot-spots) can be emphasized by using two or three graphical parameters for the same variable. See plotKML package homepage / vignette for more examples.
Author(s)
Pierre Roudier
See Also
kml-methods

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). 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)

baranja

5

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).
Author(s)
Tomislav Hengl
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.
• http://geomorphometry.org/content/baranja-hill
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,

6

bigfoot
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)

bigfoot

Bigfoot reports (USA)

Description
2984 observations of bigfoot (with attached dates). The field occurrence records have been obtained
from the BigFoot Research Organization (BFRO) website. The BFRO reports generally consist of
a description of the event and where it occurred, plus the quality classification. Similar data set
has been used by Lozier et al. (2009) to demonstrate possible miss-interpretations of the results
of species distribution modeling. The maps in the USAWgrids data set represent typical gridded
environmental covariates used for species distribution modeling.
Usage
data(bigfoot)

bigfoot

7

Format
The bigfoot data frame contains the following columns:
Lon a numeric vector; x-coordinate / longitude in the WGS84 system
Lat a numeric vector; y-coordinate / latitude in the WGS84 system
NAME name assigned by the observer (usually referent month / year)
DATE ’POSIXct’ class vector
TYPE confidence levels; according to the BFRO website: "Class A" reports involve clear sightings
in circumstances where misinterpretation or misidentification of other animals can be ruled
out with greater confidence; "Class B" and "Class C" reports are less credible.
The USAWgrids data frame (46,018 pixels; Washington, Oregon, Nevada and California state) contains the following columns:
globedem a numeric vector; elevations from the ETOPO1 Global Relief Model
nlights03 an integer vector; lights at night image for 2003 (Version 2 DMSP-OLS Nighttime
Lights Time Series)
sroads a numeric vector; distance to main roads and railroads (National Atlas of the United States)
gcarb a numeric vector; Global Biomass Carbon Map (New IPCC Tier-1 Global Biomass Carbon
Map for the Year 2000)
dTRI a numeric vector; density of pollutant releases (North American Pollutant Releases and Transfers database)
twi a numeric vector; Topographic Wetness Index based on the globedem
states an integer vector; USA states
globcov land cover classes based on the MERIS FR images (GlobCover Land Cover version V2.2)
s1 a numeric vector; x-coordinates in the Albers equal-area projection system
s2 a numeric vector; y-coordinates in the Albers equal-area projection system
Note
According to the Time.com, a team of a dozen-plus experts from as far afield as Canada and Sweden
have proclaimed themselves 95 percent certain of the mythical animal’s existence on Kemerovo
region territory some 3,000 kilometers east of Moscow (announced at the Tashtagol conference in
2011).
Author(s)
Tomislav Hengl
References
• Lozier, J.D., Aniello, P., Hickerson, M.J., (2009) Predicting the distribution of Sasquatch in
western North America: anything goes with ecological niche modelling. Journal of Biogeography, 36(9):1623-1627.
• BigFoot Research Organization (http://www.bfro.net)

8

check_projection

Examples
## Not run: # Load the BFRO records:
library(sp)
data(bigfoot)
aea.prj <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96
+x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"
library(sp)
coordinates(bigfoot) <- ~Lon+Lat
proj4string(bigfoot) <- CRS("+proj=latlon +datum=WGS84")
library(rgdal)
bigfoot.aea <- spTransform(bigfoot, CRS(aea.prj))
# Load the covariates:
data(USAWgrids)
gridded(USAWgrids) <- ~s1+s2
proj4string(USAWgrids) <- CRS(aea.prj)
# Visualize data:
data(SAGA_pal)
pnts <- list("sp.points", bigfoot.aea, pch="+", col="yellow")
spplot(USAWgrids[2], col.regions=rev(SAGA_pal[[3]]), sp.layout=pnts)
## End(Not run)

check_projection

Extracts the proj4 parameters and checks if the projection matches the
referent CRS

Description
Function parse_proj4 gets the proj4 string from a space-time object and check_projection
checks if the input projection is compatible with the referent projection system. The referent system
is by default the longlat projection with WGS84 datum (KML-compatible coordinates).
Usage
check_projection(obj, control = TRUE,
ref_CRS = get("ref_CRS", envir = plotKML.opts))
Arguments
obj

object of class Spatial* or Raster*

control

logical; if TRUE, a logical value is returned, if FALSE, an error is thrown if the
test failed

ref_CRS

the referent coordinate system.

Details
A cartographic projection is KML compatible if: (a) geographical coordinates are used, and (b) if
they relate to the WGS84 ellispoid ("+proj=longlat +datum=WGS84"). You can also set your own
local referent projection system by specifiying plotKML.env(ref_CRS = ...).

col2kml

9

Warning
obj needs to have a proper proj4 string (CRS), otherwise check_projection will not run. If the
geodetic datum is defined via the +towgs, consider converting the coordinates manually i.e. by
using the spTransform or reproject method.
Author(s)
Pierre Roudier, Tomislav Hengl, and Dylan Beaudette
References
• WGS84 (http://spatialreference.org/ref/epsg/4326/)
See Also
reproject, rgdal::CRS-class
Examples
data(eberg)
library(sp)
library(rgdal)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
check_projection(eberg)
# not yet ready for export to KML;
parse_proj4(proj4string(eberg))
eberg.geo <- reproject(eberg)
check_projection(eberg.geo)
# ... now ready for export

col2kml

Convert a color strings to the KML format

Description
Converts some common color formats (internal R colors, hexadecimal format, Munsell color codes)
color to KML format.
Usage
col2kml(colour)
Arguments
colour

R color string

Value
KML-formatted color as #aabbggrr where aa=alpha (00 to ff), bb=blue (00 to ff), gg=green (00 to
ff), rr=red (00 to ff).

10

count.GridTopology

Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
See Also
aqp::munsell2rgb
Examples
col2kml("white")
col2kml(colors()[2])
hex2kml(rgb(1,1,1))
x <- munsell2kml("10YR", "2", "4")
kml2hex(x)

count.GridTopology

Counts the number of occurrences of a list of vector object over a
GridTopology

Description
Counts the number of occurrences of a vector object over a "GridTopology" for a list of vector
objects (usually multiple realizations of the same process).
Usage
count.GridTopology(x, vectL, ...)
Arguments
x

object of type "GridTopology"

vectL

list of vectors of class "SpatialPoint*", "SpatialLines*" or "SpatialPolygons*"
(equiprobable realizations of the same process)

...

(optional) arguments passed to the lower level functions

Author(s)
Tomislav Hengl
See Also
SpatialVectorsSimulations-class, vect2rast

display.pal

display.pal

11

Display a color palette

Description
Plots a color palette in a new window.
Usage
display.pal(pal, sel=1:length(pal), names=FALSE)
Arguments
pal

list; each palette a vector of HEX-formated colors

sel

integer; selection of palettes to plot

names

logical; specifies whether to print also the class names

Details
The internal palettes available in plotKML typically consists of 20 elements. If class names are
requrested (names=TRUE) than only one palette will be plotted.
Author(s)
Tomislav Hengl and Pierre Roudier
See Also
SAGA_pal, R_pal, worldgrids_pal
Examples
# SAGA GIS palette (http://saga-gis.org/en/about/software.html)
data(SAGA_pal)
names(SAGA_pal)
## Not run: # display palettes:
display.pal(pal=SAGA_pal, sel=c(1,2,7,8,10,11,17,18,19,21,22))
dev.off()
data(worldgrids_pal)
worldgrids_pal[["globcov"]]
display.pal(pal=worldgrids_pal, sel=c(5), names = TRUE)
dev.off()
# make icons (http://www.statmethods.net/advgraphs/parameters.html):
for(i in 0:25){
png(filename=paste("icon", i, ".png", sep=""), width=45, height=45,
bg="transparent", pointsize=16)
par(mar=c(0,0,0,0))
plot(x=1, y=1, axes=FALSE, xlab='', ylab='', pch=i, cex=4, lwd=2)
dev.off()
}
## End(Not run)

12

eberg

eberg

Ebergotzen — soil mapping case study

Description
Ebergötzen is 10 by 10 km study area in the vicinity of the city of Göttingen in Central Germany.
This area has been extensively surveyed over the years, mainly for the purposes of developing
operational digital soil mapping techniques (Gehrt and Böhner, 2001), and has been used by the
SAGA GIS development team to demonstrate various processing steps.
eberg table contains 3670 observations (augers) of soil textures at five depths (0–10, 10–30, 30–
50, 50–70, and 70–90), and field records of soil types according to the German soil classification
system. eberg_grid contains gridded maps at 100 m resolution that can be used as covariates
for spatial prediction of soil variables. eberg_grid25 contains grids at finer resolution (25 m).
eberg_zones is a polygon map showing the distribution of parent material (Silt and sand, Sandy
material, Clayey derivats, Clay and loess). eberg_contours shows contour lines derived from the
25 m DEM of the area using 10 m equidistance.
Usage
data(eberg)
Format
The eberg data frame (irregular points) contains the following columns:
ID universal identifier
soiltype a vector containing factors; soil classes according to the German soil classification
system: "A" (Auenboden), "B" (Braunerde), "D" (Pelosol), "G" (Gley), "Ha" (Moor), "Hw"
(HMoor), "K" (Kolluvisol), "L" (Parabraunerde), "N" (Ranker), "Q" (Regosol), "R" (Rendzina), "S" (Pseudogley), "Z" (Pararendzina)
TAXGRSC a vector containing factors; full soil class names according to the German soil classification system (see soiltype column)
X a numeric vector; x-coordinate (m) in DHDN / Gauss-Krueger zone 3 (German coordinate system)
Y a numeric vector; y-coordinate (m) in DHDN / Gauss-Krueger zone 3 (German coordinate system)
UHDICM_* a numeric vector; upper horizon depth in cm per horizon
LHDICM_* a numeric vector; lower horizon depth in cm per horizon
SNDMHT_* a numeric vector; sand content estimated by hand per horizon (0-100 percent); see Adhoc-AG Boden (2005) for more details
SLTMHT_* a numeric vector; silt content estimated by hand per horizon (0-100 percent)
CLYMHT_* a numeric vector; clay content estimated by hand per horizon (0-100 percent)
The eberg_grid data frame (regular grid at 100 m resolution) contains the following columns:
PRMGEO6 a vector containing factors, parent material classes from the geological map (mapping
units)
DEMSRT6 a numeric vector; elevation values from the SRTM DEM
TWISRT6 a numeric vector; Topographic Wetness Index derived using the SAGA algorithm

eberg

13

TIRAST6 a numeric vector; Thermal Infrared (TIR) reflection values from the ASTER L1 image
band 14 (2010-06-05T10:26:50Z) obtained via the NASA’s GloVis browser
LNCCOR6 a vector containing factors; Corine Land Cover 2006 classes
x a numeric vector; x-coordinate (m) in DHDN / Gauss-Krueger zone 3 (German coordinate system)
y a numeric vector; y-coordinate (m) in DHDN / Gauss-Krueger zone 3 (German coordinate system)
The eberg_grid25 data frame (regular grid at 25 m resolution) contains the following columns:
DEMTOPx a numeric vector; elevation values from the topographic map
HBTSOLx a vector containing factors; main soil type according to the German soil classification
system (see column "soiltype" above) estimated per crop field
TWITOPx a numeric vector; Topographic Wetness Index derived using the SAGA algorithm
NVILANx a numeric vector; NDVI image derived using the Landsat image from the Image 2000
project
x a numeric vector; x-coordinate (m) in DHDN / Gauss-Krueger zone 3 (German coordinate system)
y a numeric vector; y-coordinate (m) in DHDN / Gauss-Krueger zone 3 (German coordinate system)
Note
Texture by hand method can be used to determine the content of soil earth fractions only to an
accuracy of ±5–10% (Skaggs et al. 2001). A surveyor distinguishes to which of the 32 texture
classes a soil samples belongs to, and then estimates the content of fractions; e.g. texture class St2
has 10% clay, 25% silt and 65% sand (Ad-hoc-AG Boden, 2005).
Author(s)
The Ebergötzen dataset is courtesy of Gehrt Ernst (), the
State Authority for Mining, Energy and Geology, Hannover, Germany and Olaf Conrad, University
of Hamburg (). The original data set has been prepared for
this exercise by Tomislav Hengl ().
References
• Ad-hoc-AG Boden, (2005) Bodenkundliche Kartieranleitung. 5th Ed, Bundesanstalt für
Geowissenschaften und Rohstoffe und Niedersaechsisches Landesamt für Bodenforshung,
Hannover, p. 423.
• Böhner, J., McCloy, K. R. and Strobl, J. (Eds), (2006) SAGA — Analysis and Modelling Applications. Göttinger Geographische Abhandlungen, Heft 115. Verlag Erich Goltze GmbH,
Göttingen, 117 pp.
• Gehrt, E., Böhner, J., (2001) Vom punkt zur flache — probleme des ’upscaling’ in der bodenkartierung. In: Diskussionsforum Bodenwissenschaften: Vom Bohrstock zum Bildschirm.
FH, Osnabrück, pp. 17-34.
• Skaggs, T. H., Arya, L. M., Shouse, P. J., Mohanty, B. P., (2001) Estimating Particle-Size
Distribution from Limited Soil Texture Data. Soil Science Society of America Journal 65 (4):
1038-1044.
• http://geomorphometry.org/content/ebergotzen

14

fmd

Examples
data(eberg)
data(eberg_grid)
data(eberg_zones)
data(eberg_contours)
library(sp)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
# visualize the maps:
data(SAGA_pal)
l.sp <- list("sp.lines", eberg_contours, col="black")
## Not run:
spplot(eberg_grid["DEMSRT6"], col.regions = SAGA_pal[[1]], sp.layout=l.sp)
spplot(eberg_zones, sp.layout=list("sp.points", eberg, col="black", pch="+"))
## End(Not run)

fmd

2001 food-and-mouth epidemic, north Cumbria (UK)

Description
This data set gives the spatial locations and reported times of food-and-mouth disease in north
Cumbria (UK), 2001. It is of no scientific value, as it deliberately excludes confidential information
on farms at risk in the study-region. It is included in the package purely as an illustrative example.
Usage
data(fmd)
Format
A matrix containing (x,y,t) coordinates of the 648 observations.
Author(s)
Edith Gabriel 
References
Diggle, P., Rowlingson, B. and Su, T. (2005). Point process methodology for on-line spatiotemporal disease surveillance. Environmetrics, 16, 423–34.
See Also
northcumbria for boundaries of the county of north Cumbria.

geopath

geopath

15

Geopath — shortest trajectory line between two geographic locations

Description
Derives a SpatialLines class object showing the shortest path between the two geographic locations
and based on the Haversine Formula for Great Circle distance.
Usage
geopath(lon1, lon2, lat1, lat2, ID, n.points, print.geo = FALSE)
Arguments
lon1

longitude coordinate of the first point

lon2

longitude coordinate of the second point

lat1

latitude coordinate of the first point

lat2

latitude coordinate of the second point

ID

(optional) point ID character

n.points

number of intermediate points

print.geo

prints the distance and bearing

Details
Number of points between the start and end point is derived using a simple formula:
round(sqrt(distc)/sqrt(2), 0)
where distc is the Great Circle Distance.
Value
Bearing is expressed in degrees from north. Distance is expressed in kilometers (Great Circle
Distance).
Author(s)
Tomislav Hengl
References
• fossil package (https://CRAN.R-project.org/package=fossil)
• Haversine formula from Math Forums (http://mathforum.org/dr.math/)
See Also
kml_layer.SpatialLines, kml_layer.STTDF, fossil::earth.bear

16

getCRS-methods

Examples
library(fossil)
ams.ny <- geopath(lon1=4.892222, lon2=-74.005973, lat1=52.373056, lat2=40.714353,
print.geo=TRUE)
# write to a file:
kml(ams.ny)

getCRS-methods

Methods to get the proj4 string

Description
Gets the proj4 string from a object of type "Spatial" or "Raster".
Usage
## S4 method for signature 'Spatial'
getCRS(obj)
## S4 method for signature 'Raster'
getCRS(obj)
Arguments
obj

object of type "Spatial" or "Raster"

Details
For more details about the PROJ.4 parameters refer to the https://proj4.org/usage/projections.
html.
Author(s)
Tomislav Hengl and Pierre Roudier
See Also
sp::CRS, raster::raster, check_projection
Examples
data(eberg_grid)
library(sp)
coordinates(eberg_grid) <- ~x+y
gridded(eberg_grid) <- TRUE
library(rgdal)
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
library(raster)
r <- raster(eberg_grid[1])
getCRS(r)
r.ll <- reproject(r)
getCRS(r.ll)

getWikiMedia.ImageInfo

17

getWikiMedia.ImageInfo
Gets EXIF information

Description
getWikiMedia.ImageInfo function fetches the EXIF (Exchangeable image file format) data via the
Wikimedia API for any donated image. The resulting EXIF data (named list) can then be further
used to construct an object of class "SpatialPhotoOverlay", which can be parsed to KML.
Usage
getWikiMedia.ImageInfo(imagename,
APIsource = "https://commons.wikimedia.org/w/api.php",
module = "imageinfo",
details = c("url", "metadata", "size", "extlinks"), testURL = TRUE)
Arguments
imagename

Wikimedia commons unique image title

APIsource

location of the API service

module

default module

details

detailed parameters of interest

testURL

logical; species if the program should first test whether the image exist at all
(recommended)

Details
Although this is often not visible in picture editing programs, almost any image uploaded to Wikimedia contains usefull EXIF metadata. However, it is highly recommended that you insert the some
important tags in the image header yourself, by using e.g. the EXIF tool (courtesy of Phil Harvey),
before uploading the files to Wikimedia. The getWikiMedia.ImageInfo function assumes that all
required metadata has already been entered by the user before the upload, hence no further changes
in the metadata will be possible. Examples of how to embed EXIF tags into an image file are available here.
To geocode an uploaded image consider adding:
{{location|lat deg|lat min|lat sec|NS|long deg|long min|long sec|EW}}
tag to the file description, in which case getWikiMedia.ImageInfo will automatically look for the
attached coordinates via the external links. For practical purposes and because the image properties
information determined by the Wikimedia system can are more reliable, the function will rewrite
some important EXIF metadata (image width and height) using the actual values determined by
Wikimedia server.
For a list of modules and parameters that can be used via getWikiMedia.ImageInfo, please refer
to Wikimedia API manual.
Author(s)
Tomislav Hengl

18

gpxbtour

References
• Wikimedia API (http://www.mediawiki.org/wiki/API)
• EXIF tool (http://www.sno.phy.queensu.ca/~phil/exiftool/)
• EXIF Tags (http://www.sno.phy.queensu.ca/~phil/exiftool/TagNames/EXIF.html)
See Also
spPhoto, Rexif::getExifPy
Examples
## Not run: # Photo taken using a GPS-enabled camera:
imagename = "Africa_Museum_Nijmegen.jpg"
x <- getWikiMedia.ImageInfo(imagename)
# Get the GPS info:
x$metadata[grep(names(x$metadata), pattern="GPS")]
# prints the complete list of metadata tags;
## End(Not run)

gpxbtour

GPS log of a bike tour

Description
GPS log of a bike tour from Wageningen (the Netherlands) to Münster (Germany). The table
contains 3228 records of GPS locations, speed and elevation.
Usage
data(gpxbtour)
Format
The data frame contains the following columns:
lon longitude (x-coordinate)
lat latitude (y-coordinate)
ele GPS-estimated elevation in m
speed GPS-estimated speed in km per hour
time XML Schema time
Note
The log was produced using the GlobalSat GH-615 GPS watch. The original data log (trackpoints)
was first saved to GPX exchange format (http://www.topografix.com/gpx.asp) and then imported to R using the XML package and formatted to a data frame.
Author(s)
Tomislav Hengl

grid2poly

19

Examples
## Not run: ## load the data:
data(gpxbtour)
library(sp)
## format the time column:
gpxbtour$ctime <- as.POSIXct(gpxbtour$time, format="%Y-%m-%dT%H:%M:%SZ")
coordinates(gpxbtour) <- ~lon+lat
proj4string(gpxbtour) <- CRS("+proj=longlat +datum=WGS84")
## convert to a STTDF class:
library(spacetime)
library(adehabitatLT)
gpx.ltraj <- as.ltraj(coordinates(gpxbtour), gpxbtour$ctime, id = "th")
gpx.st <- as(gpx.ltraj, "STTDF")
## Google maps plot:
library(RgoogleMaps)
llc <- c(mean(gpx.st@sp@bbox[2,]), mean(gpx.st@sp@bbox[1,]))
MyMap <- GetMap.bbox(center=llc, zoom=8, destfile="map.png")
PlotOnStaticMap(MyMap, lat=gpx.st@sp@coords[,2], lon=gpx.st@sp@coords[,1],
FUN=lines, col="black", lwd=4)
## End(Not run)

grid2poly

Converts a gridded map to a polygon map

Description
Converts a "SpatialGridDataFrame" object to a polygon map with each available grid node represented with a polygon. To allow further export to KML, grid2poly will, by default, convert any
projected coordinates to the lat-lon system (geographic coordinates in the WGS84 system).
Usage
grid2poly(obj, var.name = names(obj)[1], reproject = TRUE,
method = c("sp", "raster", "RSAGA")[1], tmp.file = TRUE,
saga_lib = "shapes_grid", saga_module = 3, silent = FALSE, ...)
Arguments
obj

"SpatialGridDataFrame" object

var.name

target variable column name

reproject

logical; reproject coordinates to lat lon system?

method

decide to convert grids to polygons either using "sp", "raster" or "RSAGA"
packages

tmp.file

logical; specify whether to create a temporary file, or to actually write to the
workding directory (in the case of SAGA GIS is used to convert grids)

saga_lib

string; SAGA GIS library name

saga_module

SAGA GIS module number; see ?rsaga_get_modules for more details

silent

logical; specifies whether to print the SAGA GIS output

...

additional arguments that can be parsed to the rasterToPolygons command

20

HRprec08

Details
grid2poly is not recommended for large grids (»10e4 pixels). Consider splitting large input grids
into tiles before running grid2poly. For converting large grids to polygons consider using SAGA
GIS (method = "RSAGA") instead of using the default sp method.
Author(s)
Tomislav Hengl
See Also
vect2rast, raster::rasterToPolygons
Examples
data(eberg_grid)
library(sp)
coordinates(eberg_grid) <- ~x+y
gridded(eberg_grid) <- TRUE
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
data(SAGA_pal)
## Not run: # compare various methods:
system.time(dem_poly <- grid2poly(eberg_grid, "DEMSRT6", method =
system.time(dem_poly <- grid2poly(eberg_grid, "DEMSRT6", method =
system.time(dem_poly <- grid2poly(eberg_grid, "DEMSRT6", method =
## plotting large polygons in R -> not a good idea
# spplot(dem_poly, col.regions = SAGA_pal[[1]])
## visualize the data in Google Earth:
kml(dem_poly, colour_scale = SAGA_pal[[1]], colour = DEMSRT6, kmz

"raster"))
"sp"))
"RSAGA"))

= TRUE)

## End(Not run)

HRprec08

Daily precipitation for Croatia for year 2008

Description
The daily measurements of precipitation (rain gauges) for year 2008 kindly contributed by the Croatian National Meteorological Service. HRprec08 contains 175,059 measurements of precipitation
sums (489 stations by 365 days).
Usage
data(HRprec08)
Format
The HRprec08 data frames contain the following columns:
NAME name of the meteorological station
Lon a numeric vector; x-coordiante / longitude in the WGS84 system
Lat a numeric vector; y-coordinate / latitude in the WGS84 system
DATE ’Date’ class vector
PREC daily cummulative precipitation in mm (precipitation from the day before)

HRtemp08

21

Note
The precipitation estimates in mm (HRprec08) are collected in a bottle within the rain gauge and
readings are usually manual by an observer at 7 a.m. The precipitation collected in the morning
refer to the precipitation for previous 24 hours. To project coordinates we suggest using the UTM
zone 33N system as this coordinate system was used to prepare the gridded predictors.
Author(s)
Tomislav Hengl and Melita Percec Tadic
References
• Testik, F.Y. and Gebremichael, M. Eds (2011) Rainfall: State of the Science. Geophysical
monograph series, Vol. 191, 287 p.
• Zaninovic K., Gajic-Capka, M., Percec Tadic, M. et al., (2010) Klimatski atlas Hrvatske /
Climate atlas of Croatia 1961-1990., 1971-2000. Zagreb, Croatian National Meteorological
Service, 200 p.
• AGGM book datasets (http://spatial-analyst.net/book/HRclim2008)
See Also
HRtemp08
Examples
data(HRprec08)
library(sp)
## Not run: # subset:
prec.2008.05.01 <- HRprec08[HRprec08$DATE=="2008-05-01",]
coordinates(prec.2008.05.01) <- ~Lon+Lat
proj4string(prec.2008.05.01) <- CRS("+proj=lonlat +datum=WGS84")
# write to KML:
shape = "http://plotkml.r-forge.r-project.org/circle.png"
data(SAGA_pal)
kml(prec.2008.05.01, size = PREC, shape = shape, colour = PREC,
colour_scale = SAGA_pal[[9]], labels = PREC)
## End(Not run)

HRtemp08

Daily temperatures for Croatia for year 2008

Description
The daily measurements of temperature (thermometers) for year 2008 kindly contributed by the
Croatian National Meteorological Service. HRtemp08 contains 56,608 measurements of temperature
(159 stations by 365 days).
Usage
data(HRtemp08)

22

HRtemp08

Format
The HRtemp08 data frames contain the following columns:
NAME name of the meteorological station
Lon a numeric vector; x-coordiante / longitude in the WGS84 system
Lat a numeric vector; y-coordinate / latitude in the WGS84 system
DATE ’Date’ class vector
TEMP daily temperature measurements in degree C
Note
The precision of the temperature readings in HRtemp08 is tenth of degree C. On most climatological
stations temperature is measured three times a day, at 7 a.m., 1 p.m. and 9 p.m. The daily mean can
be calculated as a weighted average.
Author(s)
Tomislav Hengl, Melita Percec Tadic and Benedikt Gräler
References
• Hengl, T., Heuvelink, G.B.M., Percec Tadic, M., Pebesma, E., (2011) Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theoretical and Applied
Climatology, 107(1-2): 265-277.
• AGGM book datasets (http://spatial-analyst.net/book/HRclim2008)
See Also
HRprec08
Examples
data(HRtemp08)
## Not run:
## examples from: http://dx.doi.org/10.1007/s00704-011-0464-2
library(spacetime)
library(gstat)
library(sp)
sp <- SpatialPoints(HRtemp08[,c("Lon","Lat")])
proj4string(sp) <- CRS("+proj=longlat +datum=WGS84")
HRtemp08.st <- STIDF(sp, time = HRtemp08$DATE-.5,
data = HRtemp08[,c("NAME","TEMP")],
endTime = as.POSIXct(HRtemp08$DATE+.5))
## Country borders:
con0 <- url("http://www.gadm.org/data/rda/HRV_adm1.RData")
load(con0)
stplot(HRtemp08.st[,"2008-07-02::2008-07-03","TEMP"],
na.rm=TRUE, col.regions=SAGA_pal[[1]],
sp.layout=list("sp.polygons", gadm))
## Load covariates:
con <- url("http://plotkml.r-forge.r-project.org/HRgrid1km.rda")
load(con)

HRtemp08
str(HRgrid1km)
sel.s <- c("HRdem","HRdsea","HRtwi","Lat","Lon")
## Prepare static covariates:
begin <- as.Date("2008-01-01")
endTime <- as.POSIXct(as.Date("2008-12-31"))
sp.grid <- as(HRgrid1km, "SpatialPixels")
HRgrid1km.st0 <- STFDF(sp.grid, time=begin,
data=HRgrid1km@data[,sel.s], endTime=endTime)
## Prepare dynamic covariates:
sel.d <- which(!names(HRgrid1km) %in% sel.s)
dates <- sapply(names(HRgrid1km)[sel.d],
function(x){strsplit(x, "LST")[[1]][2]}
)
dates <- as.Date(dates, format="%Y_%m_%d")
## Sort values of MODIS LST bands:
m <- data.frame(MODIS.LST = as.vector(unlist(HRgrid1km@data[,sel.d])))
## >10M values!
## Create an object of type STFDF:
HRgrid1km.stD <- STFDF(sp.grid, time=dates-4, data=m,
endTime=as.POSIXct(dates+4))
## Overlay in space and time:
HRtemp08.stxy <- spTransform(HRtemp08.st, CRS(proj4string(HRgrid1km)))
ov.s <- over(HRtemp08.stxy, HRgrid1km.st0)
ov.d <- over(HRtemp08.stxy, HRgrid1km.stD)
## Prepare the regression matrix:
regm <- do.call(cbind, list(HRtemp08.stxy@data, ov.s, ov.d))
## Estimate cumulative days:
regm$cday <- floor(unclass(HRtemp08.stxy@endTime)/86400-.5)
str(regm)
## Plot a single station:
scatter.smooth(regm$cday[regm$NAME=="Zavian"],
regm$TEMP[regm$NAME=="Zavian"],
xlab="Cumulative days",
ylab="Mean daily temperature (\260C)",
ylim=c(-12,28), main="GL039 (Zavi\236an)",
col="grey")
## Run PCA so we can filter missing pixels in the MODIS images:
pca <- prcomp(~HRdem+HRdsea+Lat+Lon+HRtwi+MODIS.LST,
data=regm, scale.=TRUE)
selc <- c("TEMP","Lon","Lat","cday")
regm.pca <- cbind( regm[-pca$na.action, selc],
as.data.frame(pca$x))
## Fit a spatio-temporal regression model:
theta <- min(regm.pca$cday)
lm.HRtemp08 <- lm(TEMP~PC1+PC2+PC3+PC4+PC5+PC6
+cos((cday-theta)*pi/180), data=regm.pca)
summary(lm.HRtemp08)
## Prediction locations -> focus on Istria:
data(LST)
gridded(LST) <- ~lon+lat
proj4string(LST) <- CRS("+proj=longlat +datum=WGS84")
LST.xy <- reproject(LST[1], proj4string(HRgrid1km))
LST.xy <- as(LST.xy, "SpatialPixels")
## targeted dates:
t.dates <- as.Date(c("2008-02-01","2008-05-01","2008-08-01"),

23

24

kml-methods
format="%Y-%m-%d")
LST.st <- STF(geometry(LST.xy), time=t.dates)
## get values of covariates:
ov.s.IS <- over(LST.st, HRgrid1km.st0)
ov.d.IS <- over(LST.st, HRgrid1km.stD)
LST.stdf <- STFDF(geometry(LST.xy), time=t.dates,
data=cbind(ov.s.IS, ov.d.IS))
## predict Principal Components:
LST.pca <- as.data.frame(predict(pca, LST.stdf@data))
LST.stdf@data[,paste0("PC",1:6)] <- LST.pca
cday.l <- as.vector(sapply(
floor(unclass(LST.stdf@endTime)/86400-.5),
rep, nrow(LST.xy@coords)))
LST.stdf@data[,"cday"] <- cday.l
stplot(LST.stdf[,,"PC1"], col.regions=SAGA_pal[[1]])
stplot(LST.stdf[,,"PC2"], col.regions=SAGA_pal[[1]])
## Predict spatio-temporal regression:
LST.stdf@data[,"TEMP.reg"] <- predict(lm.HRtemp08,
newdata=LST.stdf@data)
## Plot predictions:
gadm.ll <- as(spTransform(gadm,
CRS(proj4string(HRgrid1km))), "SpatialLines")
stplot(LST.stdf[,,"TEMP.reg"], col.regions=SAGA_pal[[1]],
sp.layout=list( list("sp.lines", gadm.ll),
list("sp.points", HRtemp08.stxy, col="black", pch=19) )
)
## End(Not run)

kml-methods

Write to a KML file

Description
Writes any Spatial* object (from the sp package) or Raster* object (from the raster package)
to a KML file via the plotKML.fileIO environment. Various aesthetics parameters can be set via
colour, alpha, size, shape arguments. Their availability depends on the class of the object to
plot.
Usage
## S4 method for signature 'Raster'
kml(obj, folder.name, file.name, kmz, ...)
## S4 method for signature 'Spatial'
kml(obj, folder.name, file.name, kmz, ...)
## S4 method for signature 'STIDF'
kml(obj, folder.name, file.name, kmz, ...)
## S4 method for signature 'SoilProfileCollection'
kml(obj, folder.name, file.name, kmz, ...)
## S4 method for signature 'SpatialPhotoOverlay'
kml(obj, folder.name, file.name, kmz, ...)

kml-methods

25

Arguments
obj

object inheriting from the Spatial* or the Raster* classes

folder.name

character; folder name in the KML file

file.name

character; output KML file name

kmz

logical; specief whether to compress the KML file

...

additional aesthetics arguments (see details below)

Details
To kml you can also pass folder.name, file.name (output file name *.kml), overwrite (logical;
overwrites the existing file) and kmz (logical; specifies whether to compress the kml file) arguments.
Gridded objects (objects of class "SpatialGridDataFrame" or "RasterLayer" require at least one
aesthetics parameter to run, usually the colour.)

Value
A KML file. By default parses the object name and adds a ".kml" extension.

Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette

See Also
kml_open, kml_aes, kml_close, kml_compress

Examples
# Plotting a SpatialPointsDataFrame object
library(rgdal)
data(eberg)
eberg <- eberg[runif(nrow(eberg))<.1,]
library(sp)
library(rgdal)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## Not run: # Simple plot
kml(eberg, file = "eberg-0.kml")
# Plot using aesthetics
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
kml(eberg, colour = SNDMHT_A, size = CLYMHT_A,
alpha = 0.75, file = "eberg-1.kml", shape=shape)
## End(Not run)

26

kml.tiles

kml.tiles

Write vector object as tiled KML

Description
Writes vector object as tiled KML. Suitable for plotting large vectors i.e. large spatial data sets.
Usage
kml.tiles(obj, folder.name, file.name,
block.x, kml.logo, cpus, home.url=".", desc=NULL,
open.kml=TRUE, return.list=FALSE, ...)
Arguments
obj

"SpatialPoints*" or "SpatialLines*" or "SpatialPolygons*"; vector layer

folder.name

character; KML folder name

file.name

character; output KML file name

block.x

numeric; size of block in decimal degrees (geographical coordinates)

kml.logo

character; optional project logo file (PNG)

cpus

integer; specifies number of CPUs to be used by the snowfall package to speed
things up

home.url

character; optional web-directory where the PNGs will be stored

desc

character; optional layer description

open.kml

logical; specifies whether to open the KML file after writing

return.list

logical; specifies whether to return list of tiled objects

...

(optional) aesthetics arguments (see aesthetics)

Value
Returns a list of KML files.
Note
This operation can be time-consuming for processing very large vectors. To speed up writing of
KMLs, use the snowfall package.
Author(s)
Tomislav Hengl
See Also
plotKML, plotKML.GDALobj

kml_compress

27

Examples
## Not run:
library(sp)
library(snowfall)
library(GSIF)
library(rgdal)
data(eberg)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## plot using tiles:
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
tiles.p <- kml.tiles(eberg["SNDMHT_A"], block.x=0.05,
size=0.8, z.lim=c(20,50), colour=SNDMHT_A, shape=shape,
labels=SNDMHT_A, return.list=TRUE)
## Returns a list of tiles
data(eberg_contours)
tiles.l <- kml.tiles(eberg_contours, block.x=0.05,
colour=Z, z.lim=range(eberg_contours$Z),
colour_scale=SAGA_pal[[1]], return.list=TRUE)
## End(Not run)

kml_compress

Compress a KML file with auxiliary files

Description
Compresses the KML file toghether with the auxiliary files (images, models, textures) using the
default ZIP program.
Usage
kml_compress(file.name, zip = "", files = "", rm = FALSE, ...)
Arguments
file.name
zip
files
rm
...

KML file name
(optional) location of an external ZIP program
a character vector specifying the list of auxiliary files
logical; specify whether to remove temporary files
other kml arguments

Details
The KMZ file can carry the model files (.dae), textures and ground overlay images. For practical
purposes, we recommend that you, instead of compressing the images together with the KML file,
consider serving the ground overlay images via a server i.e. as network links.
If no internal ZIP program exists, the function looks for the system ZIP program:
Sys.getenv("R_ZIPCMD", "zip")
External ZIP program can also be specified via the zip argument.

28

kml_description

Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
References
• KMZ description (http://code.google.com/apis/kml/documentation/)
See Also
kml-methods, kml_open
Examples
data(eberg)
eberg <- eberg[runif(nrow(eberg))<.1,]
library(sp)
library(rgdal)
library(raster)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
kml_open("eberg.kml")
kml_layer(eberg, colour = CLYMHT_A)
kml_close("eberg.kml")
# compress:
kml_compress("eberg.kml")

kml_description

Generate a table description from a data frame

Description
Converts a two-column data frame to a table in HTML format. This can then parsed to a KML file
as the layer description.
Usage
kml_description(x, iframe = NULL, caption = "Object summary",
fix.enc = TRUE, cwidth = 150, twidth = 300,
delim.sign = "_", asText = FALSE)
Arguments
x

object of class "data.frame" with two columns

iframe

(optional) iframe content

caption

character; table caption

fix.enc

logical; specify whether to fix encoding

cwidth

numeric; first column width

twidth

numeric; table width

delim.sign

character; delimiter sign

asText

logical; specifies whether to return the formatted table as text or XML

kml_layer-methods

29

Author(s)
Tomislav Hengl
See Also
kml-methods

kml_layer-methods

Write objects to a KML connection

Description
Writes any Spatial* object (from the sp package), spatio-temporal object (from the ST-class
package) or Raster* object (from the raster package) to a KML file (connection) as a separate
layer. Various aesthetics, i.e. ways to represent target variables, can be set via colour, transparency,
size, width, shape arguments. Their availability depends on the class of the object to plot.
Usage
kml_layer(obj, ...)
Arguments
obj

object inheriting from the Spatial* or the Raster* classes

...

additional aesthetics arguments; see details for each kml_layer function and the
kml_aes function

Value
An XML object that can be further parsed to a KML file (via an open connection).
Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
See Also
kml_layer.SpatialPoints, kml_layer.Raster, kml_layer.SpatialLines, kml_layer.SpatialPolygons,
kml_layer.STIDF, kml_layer.STTDF, kml_layer.SoilProfileCollection, kml-methods, kml_open,
kml_close
Examples
library(rgdal)
data(eberg_grid)
library(sp)
library(raster)
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
data(SAGA_pal)
data(R_pal)
## Not run: # Plot two layers one after the other:

30

kml_layer.Raster
kml_open("eberg_grids.kml")
kml_layer(eberg_grid, colour=DEMSRT6, colour_scale=R_pal[["terrain_colors"]])
kml_layer(eberg_grid, colour=TWISRT6, colour_scale=SAGA_pal[[1]])
kml_close("eberg_grids.kml")
# print the result:
library(XML)
xmlRoot(xmlTreeParse("eberg_grids.kml"))[["Document"]]
## End(Not run)

kml_layer.Raster

Writes raster objects to KML

Description
Writes rasters to PNG images and makes a KML code (ground overlays). Works with "RasterLayer"
and "RasterStack" class objects. Target attributes can be specified using aesthetics arguments (e.g.
"colour").
Usage
kml_layer.Raster(obj, subfolder.name = paste(class(obj)), plot.legend = TRUE,
metadata = NULL, raster_name,
png.width = ncol(obj), png.height = nrow(obj),
min.png.width = 800, TimeSpan.begin, TimeSpan.end,
layer.name, png.type, ...)
Arguments
obj

object of class "RasterLayer", "SpatialPixelsDataFrame" or "SpatialGridDataFrame"

subfolder.name character; optional subfolder name
plot.legend

logical; specify whether a map legend should be generated automatically

metadata

(optional) specify the metadata object

raster_name

(optional) specify the output file name (PNG)

png.width

(optional) width of the PNG file

png.height

(optional) height of the PNG file

min.png.width

(optional) minimum width of the PNG file

TimeSpan.begin object of class "POSIXct"; (optional) begin of the sampling period
TimeSpan.end

object of class "POSIXct"; (optional) end of the sampling period

layer.name

character; optional layer name

png.type

character; PNG type

...

additional aesthetics arguments

Details
Google Earth does not properly handle a 24-bit PNG file which has a single transparent color
(read more at Google Earth Help). To force transparency, plotKML will try to set it using the
-matte -transparent "#FFFFFF" option in the ImageMagick convert program (ImageMagick
needs to be installed separately and located using plotKML.env()). On some Unix run machines
the png.type argument has to be set manually to avoid producing empty PNGs.

kml_layer.RasterBrick

31

Author(s)
Tomislav Hengl, Pierre Roudier and Dylan Beaudette
See Also
kml-methods, kml_open, kml_layer.RasterBrick, plotKML-method
Examples
data(eberg_grid)
library(sp)
coordinates(eberg_grid) <- ~x+y
gridded(eberg_grid) <- TRUE
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
data(SAGA_pal)
library(raster)
r <- raster(eberg_grid["TWISRT6"])
## Not run: # KML plot with a single raster:
kml(r, colour_scale = SAGA_pal[[1]], colour = TWISRT6)
## End(Not run)

kml_layer.RasterBrick Export a time series of images to KML

Description
Writes a series of images to PNGs and uses them to create ground overlays. Works only with
"RasterBrick" class objects with time dimension specified via the "@zvalue".
Usage
kml_layer.RasterBrick(obj, plot.legend = TRUE, dtime = "", tz = "GMT",
z.lim = c(min(minValue(obj), na.rm=TRUE), max(maxValue(obj), na.rm=TRUE)),
colour_scale = get("colour_scale_numeric", envir = plotKML.opts),
home_url = get("home_url", envir = plotKML.opts),
metadata = NULL, html.table = NULL,
altitudeMode = "clampToGround", balloon = FALSE,
png.width, png.height, min.png.width = 800, png.type, ...)
Arguments
obj

object of class "RasterBrick" (e.g. a time series of images)

plot.legend

logical; specify whether a map legend should be generated automatically

dtime

temporal support (point or block) expressed in seconds

tz

referent time zone

z.lim

upper and lower limits (unique for all maps in the time series); the function by
default uses the absolute minimum and maximum in values

colour_scale

color palette; by default uses the color scale for numeric variables

home_url

(optional) URL directory / location of the images

32

kml_layer.SoilProfileCollection
metadata

(optional) the metadata object

html.table

(optional) the description block (html)

altitudeMode

character; the default altitudeMode

balloon

logical; specifies whether to display balloon for each element

png.width

(optional) width of the PNG files

png.height

(optional) height of the PNG files

min.png.width

(optional) minimum width of the PNG file

png.type

character; PNG type

...

additional arguments (see aesthetics)

Details
This method is recommended for visualization of numeric bands representing the same variable i.e.
time series of images. To export a stack of images of different type see kml_layer.Raster. If the
"@zvalue" slot is empty, dates will be added by subtracting days from the current day with 1–day
increments.
Author(s)
Tomislav Hengl
See Also
kml-methods, kml_open, kml_layer.Raster, plotKML-method

kml_layer.SoilProfileCollection
Writes a list of soil profiles to KML

Description
Writes object of type "SoilProfileCollection" (a number of soil profiles with site and horizon
data) to KML. Several attributes such as horizontal and vertical exaggeration can be passed via
arguments.
Usage
kml_layer.SoilProfileCollection(obj,
var.name, var.min = 0, var.scale,
site_names = profile_id(obj),
method = c("soil_block", "depth_function")[1],
block.size = 100,
color.name, z.scale = 1, x.min, max.depth = 300,
plot.points = TRUE,
LabelScale = get("LabelScale", envir = plotKML.opts) * 0.7,
IconColor = "#ff0000ff",
shape = paste(get("home_url", envir = plotKML.opts),
"circlesquare.png", sep = ""),
outline = TRUE, visibility = TRUE, extrude = TRUE, tessellate = TRUE,

kml_layer.SoilProfileCollection

33

altitudeMode = "relativeToGround", camera.distance = 0.01,
tilt = 90, heading = 0, roll = 0,
metadata = NULL, html.table = NULL, plot.scalebar = TRUE,
scalebar = paste(get("home_url", envir = plotKML.opts),
"soilprofile_scalebar.png", sep = ""),
... )
Arguments
obj

object of class "SoilProfileCollection" (package aqp)

var.name

target column name in the horizons slot

var.min

smallest value

var.scale

exaggeration in vertical dimension

site_names

site names as listed in the site table

method

visualization type (soil block or depth-function)

block.size

(optional) size of the block of land

color.name

(optional) column name carrying the color information for each horizon

z.scale

exaggeration in horizontal direction

x.min

offset in longitude direction (in decimal degrees)

max.depth

maximum height/depht of a profile in cm

plot.points

logical; specifies whether to plot horizon centres with attribute values

LabelScale

numeric; specifies size of the labels for each horizon

IconColor

colors for the labels for each horizon

shape

default icon for Google placemarks

outline

logical; specifies whether to draw outline for the soil-depth functions (or simply
a line)

visibility

logical; specifies whether to make the layer visible

extrude

logical; specifies whether to extrude horizon centers

tessellate

logical; specifies whether to tessellate polygons

altitudeMode
by default relativeToGround
camera.distance
distance from a profile in arc degrees
tilt

angle between the direction of the LookAt position and the normal to the surface
of the earth

heading

orientation towards north

roll

rotation about the y axis

metadata

(optional) spatial metadata for the input object

html.table

(optional) tabular content (attributes) for each horizon

plot.scalebar

logical; specifies whether to plot a scale bar next to the profile plot

scalebar

default icon for the scale bar

...

additional style arguments

34

kml_layer.SoilProfileCollection

Details
Horizon depths are typically expressed in cm, hence the default exaggeration factor (z.scale) is
10. It is highly recommended to turn off the terrain layer in Google Earth, otherwise Google Earth
will deform the plots in areas of high relief.
Note
The spatial exaggeration needs to be used because often the detail in the background imagery in
Google Earth is limited to a spatial accuracy of 2–20 m, hence there is no point of zooming into
objects of size of few meters. These exaggeration factors were selected empirically and will need
to be adjusted as the detail in the background imagery increases.
Author(s)
Tomislav Hengl, Dylan Beaudette and Pierre Roudier
References
• Algorithms for Quantitative Pedology (https://CRAN.r-project.org/package=aqp)
See Also
kml_layer.SpatialPhotoOverlay, plotKML-method
Examples
## Not run: ## install.packages("aqp", repos="http://R-Forge.R-project.org")
library(aqp)
library(fossil)
library(plyr)
data(ca630)
## Promote to SoilProfileCollection
ca <- join(ca630$lab, ca630$site, type='inner')
depths(ca) <- pedon_key ~ hzn_top + hzn_bot
## extract site data
site(ca) <- ~ mlra + ssa + lon + lat + cntrl_depth_to_top + cntrl_depth_to_bot + sampled_taxon_name
# generate SpatialPoints
library(sp)
coordinates(ca) <- ~ lon + lat
## assign CRS data
proj4string(ca) <- "+proj=longlat +datum=NAD83"
## plot changes in base saturation by sum of cations method (pH 8.2):
kml(ca, method = "depth_function", file.name = "ca_bs_8_2.kml",
var.name="bs_8.2", balloon = TRUE)
## plot changes in cation exchange capacity by sum of cations method (pH 8.2):
kml(ca, file.name = "ca_CEC8_2.kml", var.name="CEC8.2", IconColor = "#ff009000")
## plot soil profile as 'block':
kml(ca, file.name = "ca_CEC8_2_block.kml", var.name="CEC8.2", balloon = TRUE)
## End(Not run)

kml_layer.SpatialLines

35

kml_layer.SpatialLines
Writes spatial lines to KML

Description
Writes object of class "SpatialLines*" to KML with a possibility to parse attribute variables using
several aesthetics arguments.
Usage
kml_layer.SpatialLines(obj, subfolder.name = paste(class(obj)),
extrude = FALSE, z.scale = 1, metadata = NULL,
html.table = NULL, TimeSpan.begin = "", TimeSpan.end = "", ...)
Arguments
obj

object of class "SpatialLines*"

subfolder.name character; optional subfolder name
extrude

logical; specifies whether to connect the LinearRing to the ground

z.scale

vertical exaggeration

metadata

(optional) specify the metadata object

html.table

optional description block (html) for each GPS point (vertices)

TimeSpan.begin (optional) beginning of the referent time period
TimeSpan.end

(optional) end of the referent time period

...

additional style arguments (see aesthetics)

Details
Only colour and width (aesthetics) are recommended when visualizing SpatialLines* objects.
TimeSpan.begin and TimeSpan.end are optional TimeStamp vectors in the format:
yyyy-mm-ddThh:mm:sszzzzzz
Use the same time values for both TimeSpan.begin and TimeSpan.end if the measurements refer
to a single moment in time. TimeSpan.begin and TimeSpan.end can be either a single value or a
vector of values.
Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
See Also
kml-methods, kml_open, kml_layer.SpatialPolygons, plotKML-method

36

kml_layer.SpatialPhotoOverlay

Examples
library(rgdal)
library(sp)
data(eberg_contours)
data(SAGA_pal)
names(eberg_contours)
# KML plot with elevations used as 'colour' argument:
kml(eberg_contours, colour_scale = SAGA_pal[[1]], colour = Z, kmz = TRUE)

kml_layer.SpatialPhotoOverlay
Exports objects of type SpatialPhotoOverlay to KML

Description
Writes object of type SpatialPhotoOverlay to KML together with a COLLADA 3D model file (optional).
Usage
kml_layer.SpatialPhotoOverlay(obj, method = c("PhotoOverlay", "monolith")[1],
PhotoOverlay.shape = obj@PhotoOverlay$shape, href = obj@filename,
coords, dae.name = "", heading = obj@PhotoOverlay$heading,
tilt = obj@PhotoOverlay$tilt, roll = obj@PhotoOverlay$roll,
near = obj@PhotoOverlay$near, range = obj@PhotoOverlay$range,
leftFov = obj@PhotoOverlay$leftFov, rightFov = obj@PhotoOverlay$rightFov,
bottomFov = obj@PhotoOverlay$bottomFov, topFov = obj@PhotoOverlay$topFov,
altitudeMode = "clampToGround", block.size = 100, max.depth = 300,
scale.x = 1, scale.y = 1, scale.z = 1, refreshMode = "once",
html.table = NULL, ... )
Arguments
obj

object of class "SpatialPhotoOverlay" (a photograph with spatial coordinates, metadata and orientation)

method
visualization type: either "PhotoOverlay" or "monolith"
PhotoOverlay.shape
PhotoOverlay shape value (KML)
href

location of the image file

coords

(optional) 3D coordinates of the trapesoid corners

dae.name

(optional) COLLADA 3D model file name (without the extension)

heading

a PhotoOverlay argument; direction (azimuth) of the camera, in degrees

tilt

a PhotoOverlay argument; rotation, in degrees, of the camera around the X axis

roll

a PhotoOverlay argument; rotation, in degrees, of the camera around the Z axis

near

a PhotoOverlay argument; measurement in meters along the viewing direction
from the camera viewpoint to the PhotoOverlay shape

range

a PhotoOverlay argument; distance in meters from the point specified by , , and  to the LookAt position

kml_layer.SpatialPhotoOverlay

37

leftFov

a PhotoOverlay argument; angle, in degrees, between the camera’s viewing direction and the left side of the view volume

rightFov

a PhotoOverlay argument; angle, in degrees, between the camera’s viewing direction and the right side of the view volume

bottomFov

a PhotoOverlay argument; angle, in degrees, between the camera’s viewing direction and the bottom side of the view volume

topFov

a PhotoOverlay argument; angle, in degrees, between the camera’s viewing direction and the top side of the view volume

altitudeMode

altitude mode

block.size

width of the block (100 m by default)

max.depth

300 m by default

scale.x

exaggeration in X dimension (COLLADA rectangle)

scale.y

exaggeration in Y dimension (COLLADA rectangle)

scale.z

exaggeration in Z dimension (COLLADA rectangle)

refreshMode

refresh mode for the COLLADA object

html.table

(optional) specify the description block (html) for each point

...

other additional arguments

Details
The default widht and height (100 m and 300 m) were selected based on empirical testing (level of
detail in the background imagery in Google Earth). User specified coordinates can be passed via
the cords argument. For more info see makeCOLLADA.rectangle.
Author(s)
Tomislav Hengl
References
• KML Reference (http://code.google.com/apis/kml/documentation/kmlreference.html)
• COLLADA Reference (https://www.khronos.org/collada/)
See Also
spPhoto, getWikiMedia.ImageInfo
Examples
## Not run: # display spatially referenced photograph in Google Earth:
imagename = "Soil_monolith.jpg"
x1 <- getWikiMedia.ImageInfo(imagename)
sm <- spPhoto(filename = x1$url$url, exif.info = x1$metadata)
kml_open("sm.kml")
kml_layer(sm, method="monolith")
kml_close("sm.kml")
kml_compress("sm.kml", files="Soil_monolith_jpg.dae")
## End(Not run)

38

kml_layer.SpatialPixels

kml_layer.SpatialPixels
Writes SpatialPixels or SpatialGrid objects to KML

Description
Writes sp classes "SpatialGrid" or "SpatialPixels" to PNG images and makes a KML document (ground overlays). Target attributes can be specified using aesthetics arguments (e.g. "colour").
Usage
kml_layer.SpatialPixels(obj, subfolder.name = paste(class(obj)), raster_name,
plot.legend = TRUE, metadata = NULL,
png.width = gridparameters(obj)[1,"cells.dim"],
png.height = gridparameters(obj)[2,"cells.dim"],
min.png.width = 800, TimeSpan.begin, TimeSpan.end,
layer.name, png.type, ...)
Arguments
obj

object of class "RasterLayer", "SpatialPixelsDataFrame" or "SpatialGridDataFrame"

subfolder.name character; optional subfolder name
plot.legend

logical; specify whether a map legend should be generated automatically

metadata

(optional) specify the metadata object

raster_name

(optional) specify the output file name (PNG)

png.width

(optional) width of the PNG file

png.height

(optional) height of the PNG file

min.png.width

(optional) minimum width of the PNG file

TimeSpan.begin object of class "POSIXct"; (optional) begin of the sampling period
TimeSpan.end

object of class "POSIXct"; (optional) end of the sampling period

layer.name

character; optional layer name

png.type

character; PNG type

...

additional aesthetics arguments

Details
Google Earth does not properly handle a 24-bit PNG file which has a single transparent color
(read more at Google Earth Help). To force transparency, plotKML will try to set it using the
-matte -transparent "#FFFFFF" option in the ImageMagick convert program (ImageMagick
needs to be installed separately and located using plotKML.env()). The PNG export uses the
’cairographics’, which will never use a palette and normally creates a larger 32-bit ARGB file, but
then always allows transparancy. On some Unix run machines the png.type argument has to be set
manually to avoid producing empty PNGs.
Author(s)
Tomislav Hengl, Pierre Roudier and Dylan Beaudette

kml_layer.SpatialPoints

39

See Also
kml-methods, kml_open, kml_layer.Raster, plotKML-method
Examples
data(eberg_grid)
library(sp)
library(rgdal)
library(raster)
coordinates(eberg_grid) <- ~x+y
gridded(eberg_grid) <- TRUE
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
data(SAGA_pal)
## Not run: ## KML plot with a single raster:
kml(eberg_grid, colour_scale = SAGA_pal[[1]], colour = TWISRT6)
## make a larger image:
kml(eberg_grid, colour_scale = SAGA_pal[[1]], colour = TWISRT6,
png.width = 600, png.height = 600)
## End(Not run)

kml_layer.SpatialPoints
Writes spatial points to KML

Description
Writes object of class "SpatialPoints*" to KML with a possibility to parse attribute variables
using several aesthetics arguments.
Usage
kml_layer.SpatialPoints(obj, subfolder.name = paste(class(obj)),
extrude = TRUE, z.scale = 1,
LabelScale = get("LabelScale", envir = plotKML.opts),
metadata = NULL, html.table = NULL, TimeSpan.begin = "",
TimeSpan.end = "", points_names, ...)
Arguments
obj
subfolder.name
extrude
z.scale
LabelScale
metadata
html.table
TimeSpan.begin
TimeSpan.end
points_names
...

object of class "SpatialPoints*"
character; optional subfolder name
logical; specifies whether to connect the point to the ground with a line
numeric; exaggeration in vertical dimension
numeric; scale factor for size of labels
(optional) specify the metadata object
(optional) specify the description block (html) for each point
(optional) beginning of the referent time period
(optional) end of the referent time period
character; forces the point labels (size of the character vector must equal the
number of the points)
additional style arguments (see aesthetics)

40

kml_layer.SpatialPoints

Details
TimeSpan.begin and TimeSpan.end are optional TimeStamp vectors:
yyyy-mm-ddThh:mm:sszzzzzz
For observations at point support (a single moment in time), use the same time values for both
TimeSpan.begin and TimeSpan.end. TimeSpan.begin and TimeSpan.end can be either a single
value or a vector of values.
Optional aesthetics arguments are shapes (icons), colour, sizes, altitude (if not a 3D object; variable to be used to specify altitude above ground), altitudeMode (altitude mode type
(clampToGround, relativeToGround or absolute). Although this function can be used to plot
over five variables, more than three aesthetics arguments is not recommended (e.g. limit to size and
colour).
Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
See Also
kml_layer.STTDF, plotKML-method
Examples
data(eberg)
data(SAGA_pal)
library(sp)
library(rgdal)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
names(eberg)
# subset to 10 percent:
eberg <- eberg[runif(nrow(eberg))<.1,]
## Not run: # plot the measured CLAY content:
kml(eberg, labels = CLYMHT_A)
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
# color only:
kml(eberg, shape = shape, colour = SLTMHT_A, labels = "", colour_scale = SAGA_pal[[1]])
# two variables at the same time:
kml(eberg, shape = shape, size = CLYMHT_A, colour = SLTMHT_A, labels = "")
# two aesthetics elements are effective in emphasizing hot-spots:
kml(eberg, shape = shape, altitude = CLYMHT_A*10, extrude = TRUE,
colour = CLYMHT_A, labels = CLYMHT_A, kmz = TRUE)
## End(Not run)
## example of how plotKML is programmed:
data(HRtemp08)
HRtemp08[1,]
library(XML)
p1 = newXMLNode("Placemark")
begin <- format(HRtemp08[1,"DATE"]-.5, "%Y-%m-%dT%H:%M:%SZ")
end <- format(HRtemp08[1,"DATE"]+.5, "%Y-%m-%dT%H:%M:%SZ")
txt <- sprintf('%s%s%s

kml_layer.SpatialPolygons

41

%.4f,%.4f,%.0f', HRtemp08[1,"NAME"],
begin, end, HRtemp08[1,"Lon"], HRtemp08[1,"Lat"], 0)
parseXMLAndAdd(txt, parent=p1)
p1

kml_layer.SpatialPolygons
Writes spatial polygons to KML

Description
Writes object of class "SpatialPolygons*" to KML with a possibility to parse attribute variables
using several aesthetics arguments.

Usage
kml_layer.SpatialPolygons(obj, subfolder.name = paste(class(obj)),
extrude = TRUE, tessellate = FALSE,
outline = TRUE, plot.labpt = FALSE, z.scale = 1,
LabelScale = get("LabelScale", envir = plotKML.opts),
metadata = NULL, html.table = NULL, TimeSpan.begin = "",
TimeSpan.end = "", colorMode = "normal", ...)
Arguments
obj

object of class "SpatialPolygons*"

subfolder.name character; optional subfolder name
extrude

logical; specifies whether to connect the point to the ground with a line

tessellate

logical; specifies whether to connect the LinearRing to the ground

outline

logical; specifies whether to outline the polygon

plot.labpt

logical; specifies whether to add the label point (polygon centre)

z.scale

numeric; exaggeration in vertical dimension

LabelScale

numeric; scale factor for size of labels

metadata

(optional) specify the metadata object

html.table

optional description block (html) for each GPS point (vertices)

TimeSpan.begin (optional) beginning of the referent time period
TimeSpan.end

(optional) end of the referent time period

colorMode

(optional) KML color mode (normal or random)

...

additional style arguments (see aesthetics)

42

kml_layer.STIDF

Details
Label points are be default not plotted. We recommend adding the legend to attribute maps instead.
Transparency can be set by using the alpha argument.
TimeSpan.begin and TimeSpan.end are optional TimeStamp vectors:
yyyy-mm-ddThh:mm:sszzzzzz
Use the same time values for both TimeSpan.begin and TimeSpan.end if the measurements refer
to a single moment in time. TimeSpan.begin and TimeSpan.end can be either a single value or a
vector of values.
Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
See Also
kml_layer.SpatialLines, kml_layer.STIDF, , plotKML-method
Examples
library(rgdal)
library(sp)
data(eberg_zones)
names(eberg_zones)
## visualize zones using random colors:
kml(eberg_zones, colorMode = "random")
## with labels:
kml(eberg_zones, colour = ZONES, plot.labpt = TRUE,
labels = ZONES, kmz = TRUE, balloon=TRUE)

kml_layer.STIDF

Write irregular spatio-temporal observations (points, lines and polygons) to KML

Description
Writes an object of class "STIDF" (unstructured/irregular spatio-temporal data) to a KML file with
a possibility to parse attribute variables using several aesthetics arguments.
Usage
kml_layer.STIDF(obj, dtime, ...)
Arguments
obj

space-time object of class "STIDF" (spatio-temporal irregular data frame) or
class "STFDF" (spatio-temporal full data frame)

dtime

temporal support (point or block) expressed in seconds

...

additional arguments that can be passed to the kml_layer.Spatial method

kml_layer.STIDF

43

Details
An object of class "STIDF" contains a slot of type "Spatial*", which is parsed via the kml_layer
method depending on the type of spatial object (points, lines, polygons). The dateTime is defined as:
yyyy-mm-ddThh:mm:sszzzzzz
where T is the separator between the date and the time, and the time zone is either Z (for UTC) or
zzzzzz, which represents ±hh:mm in relation to UTC. For more info on how Time Stamps work
see https://developers.google.com/kml/documentation/kml_tut. If the time is measured at
block support, then:
  
tags will be inserted. Temporal support for any spacetime class, if not specified by the user, is
determined as a difference between the "time" (indicating begin time) and "endTime" slots.
Author(s)
Tomislav Hengl and Benedikt Graeler
References
• Pebesma, E. (2012) Classes and Methods for Spatio-Temporal Data in R. Journal of Statistical
Software. 51(7): 1-30.
• spacetime package (https://CRAN.R-project.org/package=spacetime)
See Also
kml_layer.STTDF, plotKML-method
Examples
## Not run:
data(HRtemp08)
# format the time column:
HRtemp08$ctime <- as.POSIXct(HRtemp08$DATE, format="%Y-%m-%dT%H:%M:%SZ")
# create a STIDF object:
library(spacetime)
sp <- SpatialPoints(HRtemp08[,c("Lon","Lat")])
proj4string(sp) <- CRS("+proj=longlat +datum=WGS84")
HRtemp08.st <- STIDF(sp, time = HRtemp08$ctime, data = HRtemp08[,c("NAME","TEMP")])
# write to a KML file:
HRtemp08_jan <- HRtemp08.st[1:500]
shape <- "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
kml(HRtemp08_jan, dtime = 24*3600, colour = TEMP, shape = shape, labels = "", kmz=TRUE)
## North Carolina SIDS data set:
library(maptools)
fname <- system.file("shapes/sids.shp", package="maptools")[1]
nc <- readShapePoly(fname, proj4string=CRS("+proj=longlat +datum=NAD27"))

44

kml_layer.STTDF
time <- as.POSIXct(strptime(c(rep("1974-01-01", length(nc)),
rep("1979-01-01", length(nc))), format="%Y-%m-%d"), tz = "GMT")
data <- data.frame(BIR = c(nc$BIR74, nc$BIR79), NWBIR = c(nc$NWBIR74, nc$NWBIR79),
SID = c(nc$SID74, nc$SID79))
# copy polygons:
nc.poly <- rep(slot(nc, "polygons"), 2)
# fix the polygon IDs:
for(i in 1:length(row.names(data))) { nc.poly[[i]]@ID = row.names(data)[i] }
sp <- SpatialPolygons(nc.poly, proj4string=CRS("+proj=longlat +datum=NAD27"))
# create a STIDF object:
nct <- STIDF(sp, time = time, data = data)
# write to a KML file:
kml(nct, colour = SID)
## End(Not run)

kml_layer.STTDF

Write a space-time trajectory to KML

Description
Writes an object of class "STTDF" to a KML file with a possibility to parse attribute variables using
several aesthetics arguments.
Usage
kml_layer.STTDF(obj, id.name = names(obj@data)[which(names(obj@data)== "burst")],
dtime, extrude = FALSE,
start.icon = paste(get("home_url", envir = plotKML.opts),
"3Dballyellow.png", sep = ""),
end.icon = paste(get("home_url", envir = plotKML.opts),
"golfhole.png", sep = ""),
LabelScale = 0.8 * get("LabelScale", envir = plotKML.opts), z.scale = 1,
metadata = NULL, html.table = NULL, ... )
Arguments
obj

space-time object of class "STTDF" (spatio-temporal irregular data.frames trajectory)

id.name

trajectory ID column name

dtime

temporal support size (in seconds)

extrude

logical; extrude GPS vertices?

start.icon

start icon name (3Dballyellow.png)

end.icon

destination icon name (golfhole.png)

LabelScale

the default size of icons

z.scale

vertical exaggeration

metadata

(optional) specify the metadata object

html.table

optional description block (html) for each GPS point (vertices)

...

other optional arguments

kml_legend.bar

45

Details
The dateTime is defined as yyyy-mm-ddThh:mm:sszzzzzz, where T is the separator between the
date and the time, and the time zone is either Z (for UTC) or zzzzzz, which represents ±hh:mm
in relation to UTC. For more info on how Time Stamps work see https://developers.google.
com/kml/documentation/kml_tut. If the time is measured at block support, then:
  
tags will be inserted. Temporal support for any spacetime class, if not specified by the user, is
determined as a difference between the "time" (indicating begin time) and "endTime" slots.
Author(s)
Tomislav Hengl
References
•
• Pebesma, E. (2012) Classes and Methods for Spatio-Temporal Data in R. Journal of Statistical
Software. 51(7): 1-30.
• spacetime package (https://CRAN.R-project.org/package=spacetime)
See Also
readGPX, plotKML-method
kml_legend.bar

Generates a legend bar (PNG file)

Description
Produces a PNG file that can be used as a screen overlay — legend bar for numeric and factor type
variables.
Usage
kml_legend.bar(x, width, height, pointsize = 14, legend.file, legend.pal,
z.lim = range(x, na.rm=TRUE, finite=TRUE), factor.labels, png.type = "cairo-png")
Arguments
x
width
height
pointsize
legend.file
legend.pal
z.lim
factor.labels
png.type

numeric or factor-type vector
numeric; (optional) width of image in pixels
numeric; (optional) height of image in pixels
numeric; point size for the plot
PNG file name
character; color palette
numeric; lower and upper limits
character; class names if applicable
character; PNG type

46

kml_legend.whitening

Details
When exporting raster layers to KML the legend bar is generated by default. If the width and height
are not provided, the function will try to estimate them automatically.
Author(s)
Tomislav Hengl, Pierre Roudier, and Dylan Beaudette
See Also
grDevices::png, kml-methods, kml_layer

kml_legend.whitening

Whitening legend (PNG)

Description
Produces a PNG file that can be used in KML plots (visualization of uncertainty).
Usage
kml_legend.whitening(legend.res = 0.01, width = 120, height = 300, pointsize = 14,
x.lim, e.lim, leg.asp = 0.3 * width/height,
legend.file = "whitening_legend.png",
matte = FALSE, png.type = "cairo-png")
Arguments
legend.res

numeric; resolution on a 0-1 scale

width

integer; image width

height

integer; image height

pointsize

integer; point size in units for text

x.lim

numeric; upper and lower limits for target variable

e.lim

numeric; upper and lower limits for the normalized error

leg.asp

numeric; legend aspect

legend.file

character; output PNG file name

matte

logical; specify whether to fix transparency using ImageMagick

png.type

character; PNG type

Details
The output PNG file shows a 2D legend with values on the vertical axis and uncertainty on the
horizontal axis. Whitening is only valid with Hue-Saturation-Intensity system where Hue’s are
used to represent values of the target variable, so that the amount of white color can be linearly used
to represent uncertainty (i.e. whitening can not be used with different color palettes; or at least we
do not recommend this).

kml_metadata-methods

47

Note
Google Earth does not properly handle a 24-bit PNG file which has a single transparent color. In
order to force transparency in the output PNG, the function with try using ImageMagick convert
function. ImageMagick needs to be installed separately and located using plotKML.env().
Author(s)
Tomislav Hengl
References
• Hengl, T., Heuvelink, G.M.B., Stein, A., (2004) A generic framework for spatial prediction of
soil variables based on regression-kriging. Geoderma 122 (1-2): 75-93.
• Hengl, T., (2003) Visualisation of uncertainty using the HSI colour model: computations with
colours. 7th International Conference on GeoComputation (CD-ROM), p. 8.
See Also
whitening
Examples
## Not run: # create the 2D legend for whitening (PNG file):
kml_legend.whitening(x.lim=c(5,20), e.lim=c(.6,1))
## End(Not run)

kml_metadata-methods

Add metadata table to the active layer

Description
Adds a selection of metadata to the description box of an active layer.
Usage
## S4 method for signature 'SpatialMetadata'
kml_metadata(obj, cwidth = 150, twidth = 500, asText = FALSE)
Arguments
obj

object of class "SpatialMetadata"

cwidth

html column width for the field names

twidth

html total table width

asText

logical; return the output as XML or characters

48

kml_open

Details
The kml_metadata function, by default, prints out only a number of selected metadata fields:
1. "Citation_title",
2. "Abstract",
3. "Object_Count",
4. "Beginning_Date",
5. "Ending_Date",
6. "Data_Order_URL",
7. "Other_Citation_Details",
8. "Citation_URL",
9. "Data_Set_Credit",
10. "Data_Distributing_Organization",
11. "Format_Information_Content",
12. "Native_Data_Set_Environment"
See data(mdnames) for a complete list of metadata fields.
Author(s)
Tomislav Hengl
See Also
spMetadata

kml_open

Open / close a KML file connection

Description
Opens a KML file in write mode and initiates the KML header. The same file connection is further
accessible by other kml_*() functions such as kml_layer() and kml_close(). kml_View tries to
open the produced file using the default application.
Usage
kml_open(file.name, folder.name = file.name, kml_open = TRUE,
kml_visibility = TRUE, overwrite = TRUE, use.Google_gx = FALSE,
kml_xsd = get("kml_xsd", envir = plotKML.opts),
xmlns = get("kml_url", envir = plotKML.opts),
xmlns_gx = get("kml_gx", envir = plotKML.opts))

kml_screen

49

Arguments
file.name

KML file name

folder.name

character string; KML folder name

kml_open

logical; specify whether to open the folder by default

kml_visibility logical; specify whether to make the whole folder visible
overwrite

logical; if TRUE, "name" will be overwritten if it exists

use.Google_gx

logical; specify whether to use the Google’s extended schema

kml_xsd

URL of the KML scheme to be used

xmlns

URL of the OGC KML standard

xmlns_gx

URL of the extended standard

...

other arguments

Details
These lower level functions can be used to create customized multi-layered KML files. See plotKML
package homepage / manual for more examples.
Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
See Also
plotKML-method, kml_layer, kml-methods

kml_screen

Add a screen overlay

Description
Adds an image file (map legend or logo) as screen overlay. The same file connection is further accessible by other kml_*() functions such as kml_layer() and kml_close(). This allows creation
of customized multi-layered KML files.
Usage
kml_screen(image.file, sname = "",
position = c("UL","ML","LL","BC","LR","MR","UR","TC")[1],
overlayXY, screenXY, xyunits = c("fraction", "pixels", "insetPixels")[1],
rotation = 0, size = c(0,0) )

50

kml_screen

Arguments
image.file

image file to be used for screen overlay

sname

screen overlay name

position

one of the nine standard positions

overlayXY

manually specified tie point on the overlay image e.g. 'x="0" y="1"'

screenXY

manually specified matching tie point on the scren e.g. 'x="0" y="1"'

xyunits

values of the XY units (("pixels", "fraction", or "insetPixels")

rotation

(optional) rotation in degrees clock-wise

size

size correction in x and y direction

Details
If nothing else is specified the function looks for some of the nine typical positions: "UL" (upper
left), "ML" (middle left), "LL" (lower left), "BC" (bottom centre), "LR" (lower right), "MR" (middle
right), "UR" (upper right), and "TC" (top centre). The x and y values can be specified in three
different ways: as pixels ("pixels"), as fractions of the image ("fraction"), or as inset pixels
("insetPixels") — an offset in pixels from the upper right corner of the image.
Note
The function, by default, calculates with fractions. If you change the xyunits type, all other elements need to be expressed in the same units.
Author(s)
Tomislav Hengl
References
• KML Reference (http://code.google.com/apis/kml/documentation/)
See Also
kml-methods
Examples
library(rgdal)
library(sp)
data(eberg_zones)
## Not run: # add logo in the top-center:
kml_open("eberg_screen.kml")
kml_layer(eberg_zones)
logo = "http://meta.isric.org/images/ISRIC_right.png"
kml_screen(image.file = logo, position = "TC", sname = "ISRIC logo")
kml_close("eberg_screen.kml")
kml_compress("eberg_screen.kml")
## End(Not run)

LST

51

LST

Time series of MODIS LST images

Description
LST contains a spatial sub-sample (Istra region in Croatia) of 46 time series of MODIS LST images
(estimated Land Surface Temperature in degrees C) at 1 km resolution. The temporal support size
of these images is 8-days.
Usage
data(LST)
Format
The LST data frame contains the following layers:
LST2008_01_01 8-day MODIS LST mosaick for period 2007-12-29 to 2008-01-04
LST2008_01_09 8-day MODIS LST mosaick for period 2008-01-05 to 2008-01-13
... subsequent bands
lon a numeric vector; x-coordinate (m) in the WGS84 system
lat a numeric vector; y-coordinate (m) in the WGS84 system
Note
Time series of 46 day-time and night-time 8-day composite LST images (MOD11A2 product bands
1 and 5) was obtained from the NASA’s FTP server (https://ladsweb.modaps.eosdis.nasa.
gov/). The original 8-day composite images were created by patching together images from a period of ±4 days, so that the proportion of clouds can be reduced to a minimum. The "zvalue"
slot in the "RasterBrick" object can be used as the dateTime column expressed as:
yyyy-mm-ddThh:mm:sszzzzzz
where T is the separator between the date and the time, and the time zone is either Z (for UTC) or
zzzzzz, which represents ±hh:mm in relation to UTC.
Author(s)
Tomislav Hengl and Melita Percec Tadic
References
• Hengl, T., Heuvelink, G.B.M., Percec Tadic, M., Pebesma, E., (2011) Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theoretical and Applied
Climatology, 107(1-2): 265-277.
• MODIS products (https://lpdaac.usgs.gov/products/modis_products_table)

52

makeCOLLADA

makeCOLLADA

Generate a COLLADA file representing the 3D model of a rectangle

Description
Produces a COLLADA file representing the 3D model of a rectangle with the image specifies via
href wrapped over the surface (as texture fill). This allows free rotation of any rectangular image
in the 3D space.
Usage
makeCOLLADA.rectangle(coords, filename, href, DateTime,
up_axis = "Z_UP", authoring_tool = "plotKML",
technique_profile = "GOOGLEEARTH",
double_sided = TRUE)
Arguments
coords

a matrix defining the rectangle: 4 points with X, Z and Y coordinates (P1 —
upper right, P2 — upper left, P3 — lower right, P4 — lower left)

filename

output filename with *.dae extension

href

location of the image used for wrapping (texture fill)

DateTime

creation / update time (system time)

up_axis

specify which axis is errected

authoring_tool specify authoring tool
technique_profile
specify technique profile
double_sided

logical; specify whether to drape image on both sides

Details
COLLADA is managed by the nonprofit technology consortium, the Khronos Group. You can also
simply drag and drop a COLLADA (.dae) file on top of the virtual Earth.
Author(s)
Tomislav Hengl
References
• COLLADA Schema (https://www.khronos.org/collada/)
See Also
kml_layer.SpatialPhotoOverlay

metadata2SLD-methods

53

Examples
## Not run: # image previously uploaded to Wikimedia commons:
imagename = "Soil_monolith.jpg"
x1 <- getWikiMedia.ImageInfo(imagename)
sm <- spPhoto(filename = x1$url$url, exif.info = x1$metadata)
kml(sm, method="monolith")
xmlTreeParse("Soil_monolith_jpg.dae")
## End(Not run)

metadata2SLD-methods

Methods to create a Styled Layer Description (SLD) file

Description
Creates a Styled Layer Description (SLD) file, that can be attached to a spatial layer contributed to
GeoServer. It writes the "sp.pallete" object (legend entries, titles and colors) to an external file.

Usage
## S4 method for signature 'SpatialMetadata'
metadata2SLD(obj, ...)

Arguments
obj

object of class "SpatialMetadata"

...

other arguments

Details
The structure of the SLD file is determined by the object class (Point, Polygon, SpatialPixels).

Author(s)
Tomislav Hengl

See Also
metadata2SLD.SpatialPixels, spMetadata

54

metadata2SLD.SpatialPixels

metadata2SLD.SpatialPixels
Writes a Styled Layer Description (SLD) file

Description
Writes a Styled Layer Description (SLD) file, that can be attached to a spatial layer contributed to
GeoServer.
Usage
metadata2SLD.SpatialPixels(obj,
Format_Information_Content = xmlValue(obj@xml[["//formcont"]]),
obj.name = normalizeFilename(deparse(substitute(obj))),
sld.file = set.file.extension(obj.name, ".sld"),
Citation_title = xmlValue(obj@xml[["//title"]]),
ColorMap_type = "intervals", opacity = 1,
brw.trg = 'Greys', target.var, ...)
Arguments
obj
object of class "SpatialMetadata"
Format_Information_Content
character; class of the object to be written to SLD file
obj.name

character; name of the layer

sld.file

character; name of the output file

Citation_title character; title of the layer
ColorMap_type

character; type of the colorMap see http://docs.geoserver.org

opacity

logical; specifies the opacity

brw.trg

character; color scheme according to www.colorbrewer2.org; default to ’Greys’

target.var

character; target variable used to calculate the class-intervals

...

additional arguments

Author(s)
Tomislav Hengl
See Also
spMetadata
Examples
## Not run: # generate missing metadata
data(eberg_grid)
library(sp)
coordinates(eberg_grid) <- ~x+y
gridded(eberg_grid) <- TRUE
proj4string(eberg_grid) <- CRS("+init=epsg:31467")

normalizeFilename

55

# with localy prepared metadata file:
eberg_TWI <- as(eberg_grid["TWISRT6"], "SpatialPixelsDataFrame")
eberg.md <- spMetadata(eberg_TWI, Target_variable="TWISRT6")
# export to SLD format:
metadata2SLD(eberg.md, "eberg_TWI.sld")
## End(Not run)

normalizeFilename

Normalize filename string

Description
Remove all reserved characters from the file name.
Usage
normalizeFilename(x, form = c("default", "8.3")[1],
fix.encoding = TRUE, sub.sign = "_")
Arguments
x

input character

form

target format (standard or the short 8.3 file name)

fix.encoding

logical; specifies whether to fix the encoding

sub.sign

substitution symbol

Details
This function removes all reserved characters: (less than), (greater than), (colon), (double quote),
(forward slash), (backslash), (vertical bar or pipe), (question mark), (asterisk), and empty spaces,
from the file name. This is important when writing a list of objects to an external file (e.g. KML)
as it prevents from creating erroneous file names.
Author(s)
Tomislav Hengl
See Also
utils::shortPathName, RSAGA:set.file.extension
Examples
normalizeFilename("name[%].txt")
normalizeFilename("name .txt")

56

plotKML-method

northcumbria

Polygon boundary of north Cumbria

Description
This data set gives the boundary of the county of north Cumbria (UK).
Usage
data(northcumbria)
Format
A matrix containing (x,y) coordinates of the boundary.
Author(s)
Edith Gabriel 
See Also
fmd for the space-time pattern of food-and-mouth disease in this county in 2001.

plotKML-method

Methods for plotting results of spatial analysis in Google Earth

Description
The method writes inputs and outputs of spatial analysis (a list of point, gridded and/or polygon
data usually) to KML and opens the KML file in Google Earth (or any other default package used
to view KML/KMZ files).
Usage
## S4 method for signature 'SpatialPointsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
size, colour, points_names,
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialLinesDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPolygonsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),

plotKML-method

57

colour, plot.labpt, labels, metadata = NULL,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPixelsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'SpatialGridDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'RasterLayer'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'SpatialPhotoOverlay'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
dae.name, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SoilProfileCollection'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
var.name, metadata = NULL, kmz = get("kmz", envir = plotKML.opts),
open.kml = TRUE, ...)
## S4 method for signature 'STIDF'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
points_names, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'STFDF'
plotKML(obj, ...)
## S4 method for signature 'STSDF'
plotKML(obj, ...)
## S4 method for signature 'STTDF'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, start.icon = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'RasterBrickTimeSeries'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
pngwidth = 680, pngheight = 180, pngpointsize = 14,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'RasterBrickSimulations'
plotKML(obj,

58

plotKML-method
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
obj.summary = TRUE,
pngwidth = 680, pngheight = 200, pngpointsize = 14,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialMaxEntOutput'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
html.file = obj@maxent@html,
iframe.width = 800, iframe.height = 800, pngwidth = 280,
pngheight = 280, pngpointsize = 14, colour,
shape = "http://plotkml.r-forge.r-project.org/icon17.png",
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE,
TimeSpan.begin = obj@TimeSpan.begin, TimeSpan.end = obj@TimeSpan.end, ...)
## S4 method for signature 'SpatialPredictions'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""), colour,
grid2poly = FALSE, obj.summary = TRUE, plot.svar = FALSE,
pngwidth = 210, pngheight = 580, pngpointsize = 14,
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialSamplingPattern'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialVectorsSimulations'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""), colour,
grid2poly = FALSE, obj.summary = TRUE, plot.svar = FALSE,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'list'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env=parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
size = NULL, colour, points_names = "",
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
plot.labpt = TRUE, labels = "", metadata = NULL,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)

Arguments
obj

input object of specific class; either some sp, or raster or spacetime package
class object, or plotKML composite objects containing both inputs and outputs
of analysis

folder.name

character; folder name in the KML file

file.name

character; output KML file name

size

for point objects for plotting (see aesthetics)

colour

colour variable for plotting (see aesthetics)

plotKML-method

59

points_names

vector of characters that can be used as labels

shape

character; icons used for plotting (see aesthetics)

raster_name

(optional) specify the output file name (PNG)

var.name

target variable name (only valid for visualization of "SoilProfileCollection"
class data

metadata

(optional) the metadata object

plot.labpt

logical; specifies whether to plot centroids for polygon data

labels

character vector; list of labels that will attached to the centroids

start.icon

icon for the start position (for trajectory data)

dae.name

output DAE file name

html.file

specify the location of the html file containing report data (if the input object is
of class "SpatialMaxEntOutput")

iframe.width

integer; width of the screen for iframe

iframe.height

integer; height of the screen for iframe

TimeSpan.begin object of class "POSIXct"; begin of the sampling period
TimeSpan.end

object of class "POSIXct"; end of the sampling period

pngwidth

integer; width of the PNG plot (screen image)

pngheight

integer; height of the PNG plot (screen image)

pngpointsize

integer; text size in the PNG plot (screen image)

grid2poly

logical; specifies whether to convert gridded object to polygons

obj.summary

logical; specifies whether to print the object summary

plot.svar

logical; specifies whether to plot the model uncertainty

kmz

logical; specifies whether to compress the output KML file

open.kml

logical; specifies whether to directly open the output KML file (i.e. in Google
Earth)

...

(optional) arguments passed to the lower level functions

Details

This is a generic function to plot various spatial and spatio-temporal R objects that contain both
inputs and outputs of spatial analysis. The resulting plots (referred to as ‘views’) are expected to be
cartographically complete as they should contain legends, and data and model descriptions. In principle, plotKML works with both simple spatial objects, and complex objects such as "SpatialPredictions",
"SpatialVectorsSimulations", "RasterBrickSimulations", "RasterBrickTimeSeries", "SpatialMaxEntOutput
and similar. To further customize visualizations consider combining the lower level functions
kml_open, kml_close, kml_compress, kml_screen into your own plotKML() method.
All ST-classes are coerced to the STIDF format and hence use the plotKML method for STIDFs.
Note
To prepare a list of objects of class "SpatialPointsDataFrame", "SpatialLinesDataFrame",
"SpatialPolygonsDataFrame", or "SpatialPixelsDataFrame" consider using the GSIF::tile
function. Writting large spatial objects via plotKML can be time consuming. Please refer to the
package manual for more information.

60

plotKML-method

See Also
SpatialPredictions-class, SpatialVectorsSimulations-class, RasterBrickSimulations-class,
RasterBrickTimeSeries-class, SpatialMaxEntOutput-class, SpatialSamplingPattern-class
Examples
plotKML.env(kmz = FALSE)
## -------------- SpatialPointsDataFrame --------- ##
library(sp)
library(rgdal)
data(eberg)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## subset to 20 percent:
eberg <- eberg[runif(nrow(eberg))<.1,]
## Not run: ## bubble type plot:
plotKML(eberg["CLYMHT_A"])
plotKML(eberg["CLYMHT_A"], colour_scale=rep("#FFFF00", 2), points_names="")
## End(Not run)
## plot points with a legend:
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
kml_open("eberg_CLYMHT_A.kml")
kml_layer(eberg["CLYMHT_A"], colour=CLYMHT_A, z.lim=c(20,60),
colour_scale=SAGA_pal[[1]], shape=shape, points_names="")
kml_legend.bar(x=eberg$CLYMHT_A, legend.file="kml_legend.png",
legend.pal=SAGA_pal[[1]], z.lim=c(20,60))
kml_screen(image.file="kml_legend.png")
kml_close("eberg_CLYMHT_A.kml")
## -------------- SpatialLinesDataFrame --------- ##
data(eberg_contours)
## Not run:
plotKML(eberg_contours)
## plot contour lines with actual altitudes:
plotKML(eberg_contours, colour=Z, altitude=Z)
## End(Not run)
## -------------- SpatialPolygonsDataFrame --------- ##
data(eberg_zones)
## Not run:
plotKML(eberg_zones["ZONES"])
## add altitude:
zmin = 230
plotKML(eberg_zones["ZONES"], altitude=zmin+runif(length(eberg_zones))*500)
## End(Not run)
## -------------- SpatialPixelsDataFrame --------- ##
library(rgdal)
library(raster)
data(eberg_grid)
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
TWI <- reproject(eberg_grid["TWISRT6"])

plotKML-method
data(SAGA_pal)
## Not run: ## set limits manually (increase resolution):
plotKML(TWI, colour_scale = SAGA_pal[[1]])
plotKML(TWI, z.lim=c(12,20), colour_scale = SAGA_pal[[1]])
## End(Not run)
## categorical data:
eberg_grid$LNCCOR6 <- as.factor(paste(eberg_grid$LNCCOR6))
levels(eberg_grid$LNCCOR6)
data(worldgrids_pal)
## attr(worldgrids_pal["corine2k"][[1]], "names")
pal = as.character(worldgrids_pal["corine2k"][[1]][c(1,11,13,14,16,17,18)])
LNCCOR6 <- reproject(eberg_grid["LNCCOR6"])
## Not run:
plotKML(LNCCOR6, colour_scale=pal)
## End(Not run)
## -------------- SpatialPhotoOverlay --------- ##
## Not run:
library(RCurl)
imagename = "Soil_monolith.jpg"
urlExists = url.exists("http://commons.wikimedia.org")
if(urlExists){
x1 <- getWikiMedia.ImageInfo(imagename)
sm <- spPhoto(filename = x1$url$url, exif.info = x1$metadata)
# str(sm)
plotKML(sm)
}
## End(Not run)
## -------------- SoilProfileCollection --------- ##
library(aqp)
library(plyr)
## sample profile from Nigeria:
lon = 3.90; lat = 7.50; id = "ISRIC:NG0017"; FAO1988 = "LXp"
top = c(0, 18, 36, 65, 87, 127)
bottom = c(18, 36, 65, 87, 127, 181)
ORCDRC = c(18.4, 4.4, 3.6, 3.6, 3.2, 1.2)
hue = c("7.5YR", "7.5YR", "2.5YR", "5YR", "5YR", "10YR")
value = c(3, 4, 5, 5, 5, 7); chroma = c(2, 4, 6, 8, 4, 3)
## prepare a SoilProfileCollection:
prof1 <- join(data.frame(id, top, bottom, ORCDRC, hue, value, chroma),
data.frame(id, lon, lat, FAO1988), type='inner')
prof1$soil_color <- with(prof1, munsell2rgb(hue, value, chroma))
depths(prof1) <- id ~ top + bottom
site(prof1) <- ~ lon + lat + FAO1988
coordinates(prof1) <- ~ lon + lat
proj4string(prof1) <- CRS("+proj=longlat +datum=WGS84")
prof1
## Not run:
plotKML(prof1, var.name="ORCDRC", color.name="soil_color")
## End(Not run)
## -------------- STIDF --------- ##

61

62

plotKML-method
library(sp)
library(spacetime)
## daily temperatures for Croatia:
data(HRtemp08)
## format the time column:
HRtemp08$ctime <- as.POSIXct(HRtemp08$DATE, format="%Y-%m-%dT%H:%M:%SZ")
## create a STIDF object:
sp <- SpatialPoints(HRtemp08[,c("Lon","Lat")])
proj4string(sp) <- CRS("+proj=longlat +datum=WGS84")
HRtemp08.st <- STIDF(sp, time = HRtemp08$ctime, data = HRtemp08[,c("NAME","TEMP")])
## subset to first 500 records:
HRtemp08_jan <- HRtemp08.st[1:500]
str(HRtemp08_jan)
## Not run:
plotKML(HRtemp08_jan[,,"TEMP"], LabelScale = .4)
## End(Not run)
## foot-and-mouth disease data:
data(fmd)
fmd0 <- data.frame(fmd)
coordinates(fmd0) <- c("X", "Y")
proj4string(fmd0) <- CRS("+init=epsg:27700")
fmd_sp <- as(fmd0, "SpatialPoints")
dates <- as.Date("2001-02-18")+fmd0$ReportedDay
library(spacetime)
fmd_ST <- STIDF(fmd_sp, dates, data.frame(ReportedDay=fmd0$ReportedDay))
data(SAGA_pal)
## Not run:
plotKML(fmd_ST, colour_scale=SAGA_pal[[1]])
## End(Not run)
## -------------- STFDF --------- ##
## results of krigeST:
library(gstat)
library(sp)
library(spacetime)
library(raster)
## define space-time variogram
sumMetricVgm <- vgmST("sumMetric",
space=vgm( 4.4, "Lin", 196.6, 3),
time =vgm( 2.2, "Lin",
1.1, 2),
joint=vgm(34.6, "Exp", 136.6, 12),
stAni=51.7)
## example from the gstat package:
data(air)
rural = STFDF(stations, dates, data.frame(PM10 = as.vector(air)))
rr <- rural[,"2005-06-01/2005-06-03"]
rr <- as(rr,"STSDF")
x1 <- seq(from=6,to=15,by=1)
x2 <- seq(from=48,to=55,by=1)
DE_gridded <- SpatialPoints(cbind(rep(x1,length(x2)), rep(x2,each=length(x1))),
proj4string=CRS(proj4string(rr@sp)))
gridded(DE_gridded) <- TRUE
DE_pred <- STF(sp=as(DE_gridded,"SpatialPoints"), time=rr@time)

plotKML-method

63

DE_kriged <- krigeST(PM10~1, data=rr, newdata=DE_pred,
modelList=sumMetricVgm)
gridded(DE_kriged@sp) <- TRUE
stplot(DE_kriged)
## plot in Google Earth:
z.lim = range(DE_kriged@data, na.rm=TRUE)
## Not run:
plotKML(DE_kriged, z.lim=z.lim)
## add observations points:
plotKML(rr, z.lim=z.lim)
## End(Not run)
## -------------- STTDF --------- ##
## Not run:
library(fossil)
library(spacetime)
library(adehabitatLT)
data(gpxbtour)
## format the time column:
gpxbtour$ctime <- as.POSIXct(gpxbtour$time, format="%Y-%m-%dT%H:%M:%SZ")
coordinates(gpxbtour) <- ~lon+lat
proj4string(gpxbtour) <- CRS("+proj=longlat +datum=WGS84")
xy <- as.list(data.frame(t(coordinates(gpxbtour))))
gpxbtour$dist.km <- sapply(xy, function(x) {
deg.dist(long1=x[1], lat1=x[2], long2=xy[[1]][1], lat2=xy[[1]][2])
} )
## convert to a STTDF class:
gpx.ltraj <- as.ltraj(coordinates(gpxbtour), gpxbtour$ctime, id = "th")
gpx.st <- as(gpx.ltraj, "STTDF")
gpx.st$speed <- gpxbtour$speed
gpx.st@sp@proj4string <- CRS("+proj=longlat +datum=WGS84")
str(gpx.st)
plotKML(gpx.st, colour="speed")
## End(Not run)
## -------------- Spatial Metadata --------- ##
## Not run:
eberg.md <- spMetadata(eberg, xml.file=system.file("eberg.xml", package="plotKML"),
Target_variable="SNDMHT_A", Citation_title="Ebergotzen profiles")
plotKML(eberg[1:100,"CLYMHT_A"], metadata=eberg.md)
## End(Not run)
## -------------- RasterBrickTimeSeries --------- ##
library(raster)
library(sp)
data(LST)
gridded(LST) <- ~lon+lat
proj4string(LST) <- CRS("+proj=longlat +datum=WGS84")
dates <- sapply(strsplit(names(LST), "LST"), function(x){x[[2]]})
datesf <- format(as.Date(dates, "%Y_%m_%d"), "%Y-%m-%dT%H:%M:%SZ")
## begin / end dates +/- 4 days:
TimeSpan.begin = as.POSIXct(unclass(as.POSIXct(datesf))-4*24*60*60, origin="1970-01-01")
TimeSpan.end = as.POSIXct(unclass(as.POSIXct(datesf))+4*24*60*60, origin="1970-01-01")
## pick climatic stations in the area:

64

plotKML-method
pnts <- HRtemp08[which(HRtemp08$NAME=="Pazin")[1],]
pnts <- rbind(pnts, HRtemp08[which(HRtemp08$NAME=="Crni Lug - NP Risnjak")[1],])
pnts <- rbind(pnts, HRtemp08[which(HRtemp08$NAME=="Cres")[1],])
coordinates(pnts) <- ~Lon + Lat
proj4string(pnts) <- CRS("+proj=longlat +datum=WGS84")
## get the dates from the file names:
LST_ll <- brick(LST[1:5])
LST_ll@title = "Time series of MODIS Land Surface Temperature images"
LST.ts <- new("RasterBrickTimeSeries", variable = "LST", sampled = pnts,
rasters = LST_ll, TimeSpan.begin = TimeSpan.begin[1:5],
TimeSpan.end = TimeSpan.end[1:5])
data(SAGA_pal)
## Not run: ## plot MODIS images in Google Earth:
plotKML(LST.ts, colour_scale=SAGA_pal[[1]])
## End(Not run)
## -------------- Spatial Predictions --------- ##
library(sp)
library(rgdal)
library(gstat)
data(meuse)
coordinates(meuse) <- ~x+y
proj4string(meuse) <- CRS("+init=epsg:28992")
## load grids:
data(meuse.grid)
gridded(meuse.grid) <- ~x+y
proj4string(meuse.grid) <- CRS("+init=epsg:28992")
## Not run: ## fit a model:
library(GSIF)
omm <- fit.gstatModel(observations = meuse, formulaString = om~dist,
family = gaussian(log), covariates = meuse.grid)
## produce SpatialPredictions:
om.rk <- predict(omm, predictionLocations = meuse.grid)
## plot the whole geostatical mapping project in Google Earth:
plotKML(om.rk, colour_scale = SAGA_pal[[1]])
## plot each cell as polygon:
plotKML(om.rk, colour_scale = SAGA_pal[[1]], grid2poly = TRUE)
## End(Not run)
## -------------- SpatialSamplingPattern --------- ##
## Not run:
library(spcosa)
library(sp)
## read a polygon map:
shpFarmsum <- readOGR(dsn = system.file("maps", package = "spcosa"),
layer = "farmsum")
## stratify `Farmsum' into 50 strata
myStratification <- stratify(shpFarmsum, nStrata = 50)
## sample two sampling units per stratum
mySamplingPattern <- spsample(myStratification, n = 2)
## attach the correct proj4 string:
library(RCurl)
urlExists = url.exists("http://spatialreference.org/ref/sr-org/6781/proj4/")
if(urlExists){
nl.rd <- getURL("http://spatialreference.org/ref/sr-org/6781/proj4/")

plotKML-method

}

proj4string(mySamplingPattern@sample) <- CRS(nl.rd)
# prepare spatial domain (polygons):
sp.domain <- as(myStratification@cells, "SpatialPolygons")
sp.domain <- SpatialPolygonsDataFrame(sp.domain,
data.frame(ID=as.factor(myStratification@stratumId)), match.ID = FALSE)
proj4string(sp.domain) <- CRS(nl.rd)
# create new object:
mySamplingPattern.ssp <- new("SpatialSamplingPattern",
method = class(mySamplingPattern), pattern = mySamplingPattern@sample,
sp.domain = sp.domain)
# the same plot now in Google Earth:
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
plotKML(mySamplingPattern.ssp, shape = shape)

## End(Not run)
## -------------- RasterBrickSimulations --------- ##
## Not run:
library(sp)
library(gstat)
data(barxyz)
## define the projection system:
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)
data(bargrid)
coordinates(bargrid) <- ~x+y
gridded(bargrid) <- TRUE
proj4string(bargrid) <- CRS(prj)
## fit a variogram and generate simulations:
Z.ovgm <- vgm(psill=1352, model="Mat", range=650, nugget=0, kappa=1.2)
sel <- runif(length(barxyz$Z))<.2
## Note: this operation can be time consuming
sims <- krige(Z~1, barxyz[sel,], bargrid, model=Z.ovgm, nmax=20,
nsim=10, debug.level=-1)
## specify the cross-section:
t1 <- Line(matrix(c(bargrid@bbox[1,1], bargrid@bbox[1,2], 5073012, 5073012), ncol=2))
transect <- SpatialLines(list(Lines(list(t1), ID="t")), CRS(prj))
## glue to a RasterBrickSimulations object:
library(raster)
bardem_sims <- new("RasterBrickSimulations", variable = "elevations",
sampled = transect, realizations = brick(sims))
## plot the whole project and open in Google Earth:
data(R_pal)
plotKML(bardem_sims, colour_scale = R_pal[[4]])
## End(Not run)
## -------------- SpatialVectorsSimulations --------- ##
## Not run:
data(barstr)
data(bargrid)
library(sp)
coordinates(bargrid) <- ~ x+y

65

66

plotKML.env
gridded(bargrid) <- TRUE
## output topology:
cell.size = bargrid@grid@cellsize[1]
bbox = bargrid@bbox
nrows = round(abs(diff(bbox[1,])/cell.size), 0)
ncols = round(abs(diff(bbox[2,])/cell.size), 0)
gridT = GridTopology(cellcentre.offset=bbox[,1],
cellsize=c(cell.size,cell.size),
cells.dim=c(nrows, ncols))
bar_sum <- count.GridTopology(gridT, vectL=barstr[1:5])
## NOTE: this operation can be time consuming!
## plot the whole project and open in Google Earth:
plotKML(bar_sum)
## End(Not run)
## -------------- SpatialMaxEntOutput --------- ##
## Not run:
library(maptools)
library(rgdal)
data(bigfoot)
aea.prj <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96
+x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"
data(USAWgrids)
gridded(USAWgrids) <- ~s1+s2
proj4string(USAWgrids) <- CRS(aea.prj)
bbox <- spTransform(USAWgrids, CRS("+proj=longlat +datum=WGS84"))@bbox
sel = bigfoot$Lon > bbox[1,1] & bigfoot$Lon < bbox[1,2] &
bigfoot$Lat > bbox[2,1] & bigfoot$Lat < bbox[2,2]
bigfoot <- bigfoot[sel,]
coordinates(bigfoot) <- ~Lon+Lat
proj4string(bigfoot) <- CRS("+proj=longlat +datum=WGS84")
library(spatstat)
bigfoot.aea <- as.ppp(spTransform(bigfoot, CRS(aea.prj)))
## Load the covariates:
sel.grids <- c("globedem","nlights03","sdroads","gcarb","twi","globcov")
library(GSIF)
library(dismo)
## run MaxEnt analysis:
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')
if(file.exists(jar)){
bigfoot.smo <- MaxEnt(bigfoot.aea, USAWgrids[sel.grids])
icon = "http://plotkml.r-forge.r-project.org/bigfoot.png"
data(R_pal)
plotKML(bigfoot.smo, colour_scale = R_pal[["bpy_colors"]], shape = icon)
}
## End(Not run)

plotKML.env

plotKML specific environmental variables / paths

Description
Sets the environmental, package specific parameters and settings (URLs, names, default color
palettes and similar) that can be later on passed to other functions.

plotKML.env

67

Usage
plotKML.env(colour_scale_numeric = "", colour_scale_factor = "",
colour_scale_svar = "", ref_CRS, NAflag, icon, LabelScale, size_range,
license_url, metadata_sel, kmz, kml_xsd, kml_url, kml_gx, gpx_xsd,
fgdc_xsd, inspire_xsd, convert, gdalwarp, gdal_translate, python,
home_url, show.env = TRUE, silent = TRUE)
Arguments
colour_scale_numeric
default colour scheme for numeric variables
colour_scale_factor
default colour scheme for factor variables
colour_scale_svar
default colour scheme for model error (e.g. mapping error)
ref_CRS

the referent CRS proj4string ("+proj=longlat +datum=WGS84")

NAflag

the default missing value flag (usually "-99999")

icon

the default icon URL

LabelScale

the default scale factor for labels

size_range

the default size range

license_url

the default license URL

metadata_sel

a list of the default metadata fields for summary

kmz

logical; the default compression setting

kml_xsd

the default KML scheme URL

kml_url

the default KML format URL

kml_gx

the default extended KML scheme URL

gpx_xsd

the default GPX scheme URL

fgdc_xsd

the default metadata scheme URL

inspire_xsd

the default metadata scheme URL

convert

a path to ImageMagick convert program

gdalwarp

a path to gdalwarp program

gdal_translate a path to gdalwarp program
python

a path to Python program

home_url

the default location of all icons and auxiliary files

show.env

logical; specify whether to print all environmental parameters

silent

logical; specify whether to search for paths for external software

Details
The function will try to locate external software tools under either Windows or Unix platform and
then save the results to the plotKML.opts environment. plotKML-package does not look automatically for software paths (unless you specify this manually in your "Rprofile.site").
The external software tools are not required by default and most of operations in plotKML-package
can be run without using them. GDAL, SAGA GIS and Python are highly recommended, however,
for processing large data sets. The function paths looks for GDAL, ImageMagick, Python, SAGA
GIS, in the Windows Registry Hive, the Program Files directory or the usr/bin installation (Unix).

68

plotKML.GDALobj

Warning
Under Linux OS you need to install GDAL binaries by using e.g.:
sudo apt-get install gdal-bin
Note
To further customize the plotKML options, consider putting:
library(plotKML); plotKML.env(..., show.env = FALSE)
in your "/etc/Rprofile.site".
Author(s)
Tomislav Hengl, Dylan Beaudette
References
• ImageMagick (http://imagemagick.org)
• GDAL (http://gdal.org)
• SAGA GIS (http://www.saga-gis.org)
• Python (http://python.org)
Examples
## Not run: ## look for paths:
library(gdalUtils)
pts <- paths()
pts
plotKML.env(silent = FALSE)
gdalwarp <- get("gdalwarp", envir = plotKML.opts)
## if missing you need to install it!
system(paste(gdalwarp, "--help-general"))
system(paste(gdalwarp, "--formats"), intern = TRUE)
## End(Not run)
plotKML.env(show.env = FALSE)
get("home_url", envir = plotKML.opts)

plotKML.GDALobj

Write tiled objects to KML

Description
Write tiled objects to KML. Suitable for plotting large rasters i.e. large spatial data sets.
Usage
plotKML.GDALobj(obj, file.name, block.x, tiles=NULL,
tiles.sel=NULL, altitude=0, altitudeMode="relativeToGround", colour_scale,
z.lim=NULL, breaks.lst=NULL, kml.logo, overwrite=TRUE, cpus,
home.url=".", desc=NULL, open.kml=TRUE, CRS=attr(obj, "projection"),
plot.legend=TRUE)

plotKML.GDALobj

69

Arguments
obj

"GDALobj" object i.e. a pointer to a spatial layer

file.name

character; output KML file name

block.x

numeric; size of block in meters or corresponding mapping units

tiles

data.frame; tiling definition generated using GSIF::tile

tiles.sel

integer; selection of tiles to be plotted

altitude

numeric; altitude of the ground overlay

altitudeMode

character; either "absolute", "relativeToGround" or "clampToGround"

colour_scale

character; color palette

z.lim

numeric; upper lower boundaries

breaks.lst

numeric; optional break lines (must be of size length(colour_scale)+1)

kml.logo

character; optional project logo file (PNG)

overwrite

logical; specifies whether to overwrite PNGs if available

cpus

integer; specifies number of CPUs to be used by the snowfall package to speed
things up

home.url

character; optional web-directory where the PNGs will be stored

desc

character; optional layer description

open.kml

logical; specifies whether to open the KML file after writing

CRS

character; projection string (if missing)

plot.legend

logical; indicate whether to plot summary legend

Value
Returns a list of KML files.
Note
This operation can be time-consuming for processing very large rasters e.g. more than 10,000 by
10,000 pixels. To speed up writing of KMLs, use the snowfall package.
Author(s)
Tomislav Hengl
See Also
plotKML, kml.tiles
Examples
## Not run:
library(sp)
library(snowfall)
library(GSIF)
library(rgdal)
fn = system.file("pictures/SP27GTIF.TIF",
package = "rgdal")
obj <- GDALinfo(fn)
tiles <- getSpatialTiles(obj, block.x=5000,

70

RasterBrickSimulations-class
return.SpatialPolygons = FALSE)
## plot using tiles:
plotKML.GDALobj(obj, tiles=tiles, z.lim=c(0,185))
## Even better ideas is to first reproject
## the large grid using 'gdalUtils::gdalwarp', then tile...
## End(Not run)

RasterBrickSimulations-class
A class for spatial simulations containing equiprobable gridded features

Description
A class containing input and output maps containing multiple realizations of the same feature.
Objects of this class can be directly visualized in Google Earth by using the plotKML-method.
Slots
variable: character; variable name
sampled: object of class "SpatialLines"; one or more lines (cross sections) that can be used to
visualize how the values change in space
realizations: object of class "RasterBrick"; multiple realizations of the same feature
Methods
plotKML signature(obj = "RasterBrickSimulations"): plots all objects in Google Earth
Author(s)
Tomislav Hengl
See Also
SpatialVectorsSimulations-class, RasterBrickTimeSeries-class, plotKML-method
Examples
## Not run: # load input data:
data(barxyz)
# define the projection system:
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"
library(sp)
coordinates(barxyz) <- ~x+y
proj4string(barxyz) <- CRS(prj)
data(bargrid)
coordinates(bargrid) <- ~x+y
gridded(bargrid) <- TRUE
proj4string(bargrid) <- CRS(prj)
# fit a variogram and generate simulations:

RasterBrickTimeSeries-class

71

library(gstat)
Z.ovgm <- vgm(psill=1352, model="Mat", range=650, nugget=0, kappa=1.2)
sel <- runif(length(barxyz$Z))<.2 # Note: this operation can be time consuming
sims <- krige(Z~1, barxyz[sel,], bargrid, model=Z.ovgm, nmax=20, nsim=10, debug.level=-1)
# specify the cross-section:
t1 <- Line(matrix(c(bargrid@bbox[1,1],bargrid@bbox[1,2],5073012,5073012), ncol=2))
transect <- SpatialLines(list(Lines(list(t1), ID="t")), CRS(prj))
# glue to a RasterBrickSimulations object:
bardem_sims <- new("RasterBrickSimulations", variable = "elevations",
sampled = transect, realizations = brick(sims))
# plot the whole project and open in Google Earth:
data(R_pal)
plotKML(bardem_sims, colour_scale = R_pal[[4]])
## End(Not run)

RasterBrickTimeSeries-class
A class for a time series of regular grids

Description
A class containing list of rasters, begin, end times and sample points to allow exploration of the values. Objects of this class can be directly visualized in Google Earth by using the plotKML-method.
Slots
variable: object of class "character"; variable name
sampled: object of class "SpatialPoints"; one or more points that can be used to visualize temporal changes in the target variable
rasters: object of class "RasterBrick"; a time-series of raster objects
TimeSpan.begin: object of class "POSIXct"; begin of sampling for each raster map
TimeSpan.end: object of class "POSIXct"; end of sampling for each raster map
Methods
plotKML signature(obj = "RasterBrickTimeSeries"): plots time-series of rasters in Google
Earth
Author(s)
Tomislav Hengl
See Also
RasterBrickSimulations-class, plotKML-method

72

readGPX

readGPX

Import GPX (GPS track) files

Description
Reads various elements from a *.gpx file — metadata, waypoints, tracks and routes — and converts
them to dataframes.
Usage
readGPX(gpx.file, metadata = TRUE, bounds = TRUE,
waypoints = TRUE, tracks = TRUE, routes = TRUE)
Arguments
gpx.file

location of the gpx.file

metadata

logical; species whether the metadata should be imported

bounds

logical; species whether the bounding box coordinates should be imported

waypoints

logical; species whether all waypoints should be imported

tracks

logical; species whether all tracks should be imported

routes

logical; species whether all routes should be imported

Details
Waypoint is a point of interest, or named feature on a map. Track is an ordered list of points
describing a path. Route is an ordered list of waypoints representing a series of turn points leading
to a destination.
Author(s)
Tomislav Hengl
References
• GPX data format (http://www.topografix.com/gpx.asp)
• XML tutorial (https://github.com/omegahat/XML)
See Also
rgdal::readOGR, kml_layer.STTDF
Examples
## Not run: # read GPX file from web:
fells_loop <- readGPX("http://www.topografix.com/fells_loop.gpx")
str(fells_loop)
## End(Not run)

readKML.GBIFdensity

73

readKML.GBIFdensity

Imports GBIF cell density records

Description
Read GBIF cell (1–degree) density record counts and converts them to a "raster" object.
Usage
readKML.GBIFdensity(kml.file, gbif.url = FALSE, silent = FALSE)
Arguments
kml.file

GBIF cell density file (local file or URL)

gbif.url

logical; species whether the cellid and taxon content information should be also
imported (usually not used)

silent

logical; species whether the progress bar should be printed

Details
This document contains data shared through the GBIF Network — see http://www.gbif.org/
occurrence for more information. GBIF records are constantly updated and every map derived
refers to a certain date indicated in the @zname Last update slot.
All usage of these data must be in accordance with the GBIF Data Use Agreement: https://www.
gbif.org/terms.
Author(s)
Tomislav Hengl
References
• GBIF cell density description (http://www.gbif.org/occurrence)
See Also
readGPX
Examples
## Not run: # reading taxon density maps:
kml.file <- "taxon-celldensity-2294100.kml"
# download.file(paste("http://data.gbif.org/occurrences/taxon/celldensity/", kml.file, sep=""),
# destfile=paste(getwd(), kml.file, sep=""))
# this will not run (you must first accept the data usage agreeent);
# instead, obtain the kml file via a web browser, and save it to the working directory:
r <- readKML.GBIFdensity(kml.file)
class(r)
summary(r)
image(r)
# add world borders:
library(maps)

74

reproject
country.m = map('world', plot=FALSE, fill=TRUE)
IDs <- sapply(strsplit(country.m$names, ":"), function(x) x[1])
library(maptools)
country <- as(map2SpatialPolygons(country.m, IDs=IDs), "SpatialLines")
lines(country)
# to import a list of files, use e.g.:
kml.list <- list(kml.file)
r.lst <- lapply(kml.list, readKML.GBIFdensity, silent = TRUE)
# mask out missing layers (empty KML files):
mask <- !sapply(r.lst, is.null)
r.lst <- brick(r.lst[mask])
## End(Not run)

reproject

Methods to reproject maps to a referent coordinate system (WGS84)

Description
This wrapper function reprojects any vector or raster spatial data to some referent coordinate system
(by default: geographic coordinates on the World Geodetic System of 1984 / WGS84 datum).
Usage
## S4 method for signature 'SpatialPoints'
reproject(obj, CRS, ...)
## S4 method for signature 'SpatialPolygons'
reproject(obj, CRS, ...)
## S4 method for signature 'SpatialLines'
reproject(obj, CRS, ...)
## S4 method for signature 'RasterLayer'
reproject(obj, CRS, program = "raster", tmp.file = TRUE,
NAflag, show.output.on.console = FALSE, method, ...)
## S4 method for signature 'SpatialGridDataFrame'
reproject(obj, CRS, tmp.file = TRUE, program = "raster",
NAflag, show.output.on.console = FALSE, ...)
## S4 method for signature 'SpatialPixelsDataFrame'
reproject(obj, CRS, tmp.file = TRUE, program = "raster",
NAflag, show.output.on.console = FALSE, ...)
## S4 method for signature 'RasterBrick'
reproject(obj, CRS)
## S4 method for signature 'RasterStack'
reproject(obj, CRS)
Arguments
obj

Spatial* or Raster* object

CRS

object of class "CRS"; proj4 string

program

reprojection engine; either raster package or GDAL

tmp.file

logical; specifies whether to create a temporary file or not

NAflag

character; missing value flag

reproject

75

show.output.on.console
logical; specifies whether to print the progress
method

character; resampling method e.g."bilinear"

...

arguments evaluated in the context of function projectRaster from the raster
package

Details
In the case of raster and/or gridded maps, by selecting program = "GDAL" gdalwarp functionality
will be initiated (otherwise it tries to reproject via the package raster). This requires that GDAL are
installed and located from R via paths().
Warning
obj needs to have a proper proj4 string (CRS), otherwise reproject will not run.
Author(s)
Pierre Roudier, Tomislav Hengl and Dylan Beaudette
References
• Raster package (https://CRAN.R-project.org/package=raster)
• GDAL (http://GDAL.org)
See Also
paths, projectRaster, spTransform, CRS-class
Examples
## example with vector data:
data(eberg)
library(sp)
library(rgdal)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
eberg.geo <- reproject(eberg)
## Not run: ## example with raster data:
data(eberg_grid25)
gridded(eberg_grid25) <- ~x+y
proj4string(eberg_grid25) <- CRS("+init=epsg:31467")
## reproject to geographical coords (can take few minutes!):
eberg_grid_ll <- reproject(eberg_grid25[1])
## much faster when using GDAL:
eberg_grid_ll2 <- reproject(eberg_grid25[1], program = "GDAL")
## optional: compare processing times:
system.time(eberg_grid_ll <- reproject(eberg_grid25[1]))
system.time(eberg_grid_ll2 <- reproject(eberg_grid25[1], program="GDAL"))
## End(Not run)

76

SAGA_pal

SAGA_pal

Colour palettes for numeric variables

Description
SAGA_pal contains 22 colour palettes imported from SAGA GIS (Conrad, 2007). R_pal 12 standard colour palettes used in R to visualize continuous and binary variables. Each colour palette
consists of 20 colours in the hexadecimal system. Use display.pal function to plot different sets
of palettes.
Usage
data(SAGA_pal)
data(R_pal)
Note
rainbow_75, heat colors, terrain_colors, topo_colors, and bpy_colors are the standard
color palettes used in R to visualize numeric/continuous variables. soc_pal, pH_pal, tex_pal,
BS_pal and CEC_pal palettes are suitable for visualization of soil variables (soil organic carbon,
pH, soil texture fractions, Base Saturation and Cation Exchange Capacity). blue_grey_red palette
is recommended for visualization of binary variables (values in the range 0-1), and grey_black is a
white-to-black type color palette that contains no white color (hence it will not confuse low values
with NA values in the PNG/GIF files).
Possibly the most used palettes for visualization of numeric variables are rev(rainbow(65)[1:48])
and SAGA_pal[[1]] (the SAGA GIS default palette). It is however worth mentioning that in the
data visualization literature (and the cartography literature in particular), the rainbow (sometimes
also called spectral) color ramp is generally recognized as a bad choice for visualization of sequential/continuous variables (Rogowitz and Treinish, 1998; Borland and Russell, 2007).
Author(s)
SAGA GIS has been created by the SAGA GIS development team (lead by J. Böhner and O.
Conrad, from the Institute of Geography, University of Hamburg, Germany). The colour palettes
have been exported from SAGA (as ".sprm" SAGA parameter files) and ported to R. All palletes
described here were prepared for R by Tomislav Hengl ().
References
• Conrad, O., (2007). SAGA — Entwurf, Funktionsumfang und Anwendung eines Systems
für Automatisierte Geowissenschaftliche Analysen. Electronic doctoral dissertation, University of Göttingen.
• Rogowitz, B.E., Treinish, L.A., (1998, December). Data visualization: the end of the rainbow.
Spectrum, IEEE, 35(12):52-59
• Borland, D. and Russell, M. T. II, (2007). Rainbow Color Map (Still) Considered Harmful.
Computer Graphics and Applications, IEEE, 27(2):14-17.
• https://cran.r-project.org/package=RColorBrewer
• https://cran.r-project.org/package=colorspace

sp.palette-class

77

See Also
worldgrids_pal, RColorBrewer::display.brewer.all
Examples
data(SAGA_pal)
data(R_pal)
## Not run: # visualize SAGA GIS palettes:
display.pal(pal=SAGA_pal, sel=c(1,2,7,8,10,11,17,18,19,21,22))
dev.off()
display.pal(R_pal)
names(R_pal)
dev.off()
## End(Not run)

sp.palette-class

A class for color palette

Description
A class for color palette that can be further used to create an object of class "SpatialMetadata".
Slots
bounds: object of class "numeric" or "character"; class boundaries
color: object of class "character"; contains HEX colors
icons: object of class "character"; (optional) contains symbols or URI to icons
names: object of class "character"; class names (optional)
type: object of class "character"; variable type
Note
Size of class boundaries (upper and lower) is 1 element larger then the size of colors and element
names.
Author(s)
Tomislav Hengl
See Also
spMetadata, metadata2SLD-methods

78

SpatialMaxEntOutput-class

SpatialMaxEntOutput-class
A class for outputs of analysis produced using the dismo package
(MaxEnt)

Description
A class containing input and output data produced by running the maxent (Maximum Entropy)
species distribution modeling algorithm. Object of this class can be directly visualized in Google
Earth by using the plotKML-method.
Slots
sciname: object of class "character"; vector of species name compatible with the rgbif package;
usually latin "genus" and "species" name
occurrences: object of class "SpatialPoints"; occurrence-only records
TimeSpan.begin: object of class "POSIXct"; begin of the sampling period
TimeSpan.end: object of class "POSIXct"; end of the sampling period
maxent: object of class "MaxEnt" (species distribution model); produced as an output of the dismo::maxent
function or similar
sp.domain: object of class "Spatial" (ideally "SpatialPolygonsDataFrame" or "SpatialPixelsDataFrame");
assumed spatial domain that can be set by the user or it will be estimated by MaxEnt (see examples below)
predicted: object of class "RasterLayer"; contains results of prediction produced using the
MaxEnt software
Methods
plotKML signature(obj = "SpatialMaxEntOutput"): plots all MaxEnt output objects in
Google Earth
Note

MaxEnt requires the maxent.jar file to be in the ’java’ folder of the dismo package (see: system.file("java", packag
For more info refer to the dismo package documentation. Alternatively use the maxlike package
(Royle et al. 2012), which does not require Java.
Author(s)
Tomislav Hengl
References
• Hijmans, R.J, Elith, J., (2012) Species distribution modeling with R. CRAN, Vignette for the
dismo package, 72 p.
• Royle, J.A., Chandler, R.B., Yackulic, C. and J. D. Nichols. (2012) Likelihood analysis of
species occurrence probability from presence-only data for modelling species distributions.
Methods in Ecology and Evolution.
• dismo package (https://CRAN.R-project.org/package=dismo)
• maxlike package (https://CRAN.R-project.org/package=maxlike)
• rgbif package (https://CRAN.R-project.org/package=rgbif)

SpatialMetadata-class

79

See Also
plotKML-method, dismo::maxent, maxlike::maxlike, rgbif::taxonsearch

SpatialMetadata-class A class for spatial metadata

Description
A class containing spatial metadata in the Federal Geographic Data Committee (FGDC) Content
Standard for Digital Geospatial Metadata.
Slots
xml: object of class "XMLInternalDocument"; a metadata slot
field.names: object of class "character"; corresponding metadata column names
palette: object of class "sp.palette"; contains legend names and colors
sp: object of class "Spatial"; bounding box and projection system of the input object
Methods
summary signature(obj = "SpatialMetadata"): summarize object
GetPalette signature(obj = "SpatialMetadata"): get only the color slot
GetNames signature(obj = "SpatialMetadata"): get metadata field names
Author(s)
Tomislav Hengl and Michael Blaschek
See Also
spMetadata, metadata2SLD-methods
Examples
## Not run:
data(eberg)
library(sp)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
names(eberg)
# add metadata:
eberg.md <- spMetadata(eberg, xml.file=system.file("eberg.xml", package="plotKML"),
Target_variable="SNDMHT_A")
p <- GetPalette(eberg.md)
str(p)
x <- summary(eberg.md)
str(x)
## End(Not run)

80

SpatialPredictions-class

SpatialPhotoOverlay-class
A class for Spatial PhotoOverlay

Description
A class for spatial photographs (spatially and geometrically defined) that can be plotted in Google
Earth.
Slots
filename object of class "character"; URI of the filename location (typically a URL)
pixmap object of class "pixmapRGB"; RGB bands of a bitmapped images
exif.info object of class "list"; EXIF photo metadata
PhotoOverlay object of class "list"; list of the camera geometry parameters (KML specifications)
sp object of class "SpatialPoints"; location of the camera
Extends
Class "pixmapRGB".
Methods
summary signature(obj = "SpatialMetadata"): summarize object
Author(s)
Tomislav Hengl
See Also
plotKML-method, spPhoto
SpatialPredictions-class
A class for spatial predictions produced using gstat package

Description
A class containing input and output maps generated through the process of geostatistical mapping.
Object of this class can be directly visualized in Google Earth by using the plotKML-method.
Slots
variable: object of class "character"; variable name
observed: object of class "SpatialPointsDataFrame" (must be 2D); see sp::SpatialPointsDataFrame
regModel.summary: contains the summary of the regression model
vgmModel: object of class "data.frame"; contains the variogram parameters passed from gstat
predicted: object of class "SpatialPixelsDataFrame"; see sp::SpatialPixelsDataFrame
validation: object of class "SpatialPointsDataFrame" containing results of validation

SpatialSamplingPattern-class

81

Methods
plot signature(x = "SpatialPredictions"): spatial predictions, regression model (observed
vs predicted), original variogram and variogram for residuals
plotKML signature(obj = "SpatialPredictions"): plots all objects in Google Earth
summary signature(obj = "SpatialPredictions"): summarize object by showing the mapping accuracy (cross-validation) and the amount of variation explained by the model
Note
"SpatialPredictions" saves results of predictions for a single target variable, which can be of
type numeric or factor. Multiple variables can be combined into a list.
Author(s)
Tomislav Hengl
References
• Hengl, T. (2009) A Practical Guide to Geostatistical Mapping, 2nd Edt. University of Amsterdam, www.lulu.com, 291 p.
• Hengl, T., Nikolic, M., MacMillan, R.A., (2012) Mapping efficiency and information content.
International Journal of Applied Earth Observation and Geoinformation, special issue Spatial
Statistics Conference.
See Also
plotKML-method, GSIF::fit.gstatModel, gstat::gstat-class, RasterBrickSimulations-class

SpatialSamplingPattern-class
A class for spatial samples produced using various spsample methods

Description
A class containing input and output objects generated by some sampling optimisation algorithm.
Objects of this type can be directly visualized in Google Earth by using the plotKML-method.
Slots
method: object of class "character"; sampling optimisation method
pattern: object of class "SpatialPoints"; sampling points
sp.domain: object of class "SpatialPolygonsDataFrame"; spatial domain / strata
Methods
plotKML signature(obj = "SpatialSamplingPattern"): plots generated sampling plan in
Google Earth
Author(s)
Tomislav Hengl

82

SpatialVectorsSimulations-class

See Also
plotKML-method, spcosa::spsample, plotKML-method

SpatialVectorsSimulations-class
A class for spatial simulations containing equiprobable line, point or
polygon features

Description
A class containing input and output maps generated as equiprobable simulations of the same discrete
object (for example multiple realizations of stream networks). Objects of this type can be directly
visualized in Google Earth by using the plotKML-method.
Slots
realizations: object of class "list"; multiple realizations of the same feature e.g. multiple
realizations of stream network
summaries: object of class "SpatialGridDataFrame"; summary measures
Methods
plotKML signature(obj = "SpatialVectorsSimulations"): plots simulated vector objects
and summaries (grids) in Google Earth
Author(s)
Tomislav Hengl
See Also
RasterBrickSimulations-class, plotKML-method
Examples
## load a list of equiprobable streams:
data(barstr)
data(bargrid)
library(sp)
coordinates(bargrid) <- ~ x+y
gridded(bargrid) <- TRUE
## output topology:
cell.size = bargrid@grid@cellsize[1]
bbox = bargrid@bbox
nrows = round(abs(diff(bbox[1,])/cell.size), 0)
ncols = round(abs(diff(bbox[2,])/cell.size), 0)
gridT = GridTopology(cellcentre.offset=bbox[,1], cellsize=c(cell.size,cell.size),
cells.dim=c(nrows, ncols))
## Not run: ## derive summaries (observed frequency and the entropy or error):
bar_sum <- count.GridTopology(gridT, vectL=barstr[1:5])
## NOTE: this operation can be time consuming!

spMetadata-methods

83

## plot the whole project and open in Google Earth:
plotKML(bar_sum, grid2poly = TRUE)
## End(Not run)

spMetadata-methods

Methods to generate spatial metadata

Description
The spMetadata function will try to generate missing metadata (bounding box, location info, session info, metadata creator info and similar) for any Spatial* object (from the sp package) or
Raster* object (from the raster package). The resulting object of class SpatialMetadata-class
can be used e.g. to generate a Layer description documents ( tag).
The read.metadata function reads the formatted metadata (.xml), prepared following e.g. the
Federal Geographic Data Committee (FGDC) Content Standard for Digital Geospatial Metadata or
INSPIRE standard, and converts them to a data frame.
Usage
## S4 method for signature 'RasterLayer'
spMetadata(obj, bounds, color, ... )
## S4 method for signature 'Spatial'
spMetadata(obj, xml.file, out.xml.file,
md.type = c("FGDC", "INSPIRE")[1],
generate.missing = TRUE, GoogleGeocode = FALSE,
signif.digit = 3, colour_scale, color = NULL, bounds,
legend_names, icons, validate.schema = FALSE, ...)
Arguments
obj

some "Spatial" or "Raster" class object with "data" slot

xml.file

character; optional input XML metadata file

out.xml.file

character; optional output XML metadata file

md.type
character; metadata standard FGDC or INSPIRE
generate.missing
logical; specifies whether to automatically generate missing fields
GoogleGeocode

logical; specifies whether the function should try to use GoogleGeocoding functionality to determine the location name

signif.digit

integer; the default number of significant digits (in the case of rounding)

colour_scale

the color scheme used to visualize this data

color

character; list of colors (rgb()) that can be passed instead of using the pallete

bounds

numeric vector; upper and lower bounds used for visualization

legend_names

character; legend names in the order of bounds

icons
character; file name or URL used for icons (if applicable)
validate.schema
logical; specifies whether to validate the schema using the xmlSchemaValidate
...

additional arguments to be passed e.g. via the metadata.env()

84

spMetadata-methods

Details
spMetadata tries to locate a metadata file in the working directory (it looks for a metadata file
with the same name as the object name). If no .xml file exists, it will load the template xml
file available in the system folder (e.g. system.file("FGDC.xml", package="plotKML") or
system.file("INSPIRE_ISO19139.xml", package="plotKML")). The FGDC.xml/INSPIRE_ISO19139.xml
files contain typical metadata entries with description and examples. For practical purposes, one
metadata object in plotKML can be associated with only one variable i.e. one column in the "data"
slot (the first column by default). To prepare a metadata xml file following the FGDC standard,
consider using e.g. the Tkme software: Another editor for formal metadata, by Peter N. Schweitzer
(U.S. Geological Survey). Before commiting the metadata file, try also running a validation test.
Before committing the metadata file following the INSPIRE standard, try running the INSPIRE
Geoportal Metadata Validator.
spMetadata tries to automatically generate the most usefull information, so that a user can easily
find out about the input data and procedures followed to generate the visualization (KML). Typical
metadata entries include e.g. (FGDC):
• metadata[["idinfo"]][["native"]] — Session info e.g.: Produced using R version 2.12.2
(2011-02-25) running on Windows 7 x64.
• metadata[["spdoinfo"]][["indspref"]] — Indirect spatial reference estimated using the
Google Maps API Web Services.
• metadata[["idinfo"]][["spdom"]][["bounding"]] — Bounding box in the WGS84 geographical coordinates estimated by reprojecting the original bounding box.
and for INSPIRE metadata:

• metadata[["fileIdentifier"]][["CharacterString"]] — Metadata file identifier (not
mandatory for INSPIRE-compl.) created by UUIDgenerate from package UUID (version 4
UUID).
• metadata[["dateStamp"]][["Date"]] — Metadata date stamp created using Sys.Date().
• metadata[["identificationInfo"]][["MD_DataIdentification"]] [["extent"]][["EX_Extent"]][["ge
— Bounding box in the WGS84 geographical coordinates estimated by reprojecting the original bounding box.
By default, plotKML uses the Creative Commons license, but this can be adjusted by setting the
Use_Constraints argument.
Author(s)
Tomislav Hengl and Michael Blaschek
References
• The Federal Geographic Data Committee, (2006) FGDC Don’t Duck Metadata — A short
reference guide for writing quality metadata. Vers. 1, http://www.fgdc.gov/metadata/
documents/MetadataQuickGuide.pdf
• Content Standard for Digital Geospatial Metadata (http://www.fgdc.gov/metadata/csdgm/)
• Tkme metadata editor (http://geology.usgs.gov/tools/metadata/tools/doc/tkme.html)
• INSPIRE, INS MD, Commission Regulation (EC) No 1205/2008 of 3 December 2008 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards
metadata (Text with EEA relevance). See also Corrigendum to INSPIRE Metadata Regulation.
• INSPIRE, INS MDTG, (2013) INSPIRE Metadata Implementing Rules: Technical Guidelines
based on EN ISO 19115 and EN ISO 19119, v1.3

spPhoto

85

See Also
kml_metadata, SpatialMetadata-class, sp::Spatial, kml_open
Examples
## Not run:
library(sp)
library(uuid)
library(rjson)
## read metadata from the system file:
x <- read.metadata(system.file("FGDC.xml", package="plotKML"))
str(x)
## generate missing metadata
data(eberg)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## no metadata file specified:
eberg.md <- spMetadata(eberg["SNDMHT_A"])
## this generates some metadata automatically e.g.:
xmlRoot(eberg.md@xml)[["eainfo"]][["detailed"]][["attr"]]
## combine with localy prepared metadata file:
eberg.md <- spMetadata(eberg["SNDMHT_A"],
xml.file=system.file("eberg.xml", package="plotKML"))
## Additional metadat entries can be added by using e.g.:
eberg.md <- spMetadata(eberg["SNDMHT_A"],
md.type="INSPIRE",
CI_Citation_title = 'Ebergotzen data set',
CI_Online_resource_URL = 'http://geomorphometry.org/content/ebergotzen')
## the same using the FGDC template:
eberg.md <- spMetadata(eberg["SNDMHT_A"],
Citation_title = 'Ebergotzen data set',
Citation_URL = 'http://geomorphometry.org/content/ebergotzen')
## Complete list of names:
mdnames <- read.csv(system.file("mdnames.csv", package="plotKML"))
mdnames$field.names
## these can be assigned to the "metadata" environment by using:
metadata.env(CI_Citation_title = 'Ebergotzen data set')
get("CI_Citation_title", metadata)
## write data and metadata to a file:
library(rgdal)
writeOGR(eberg["SNDMHT_A"], "eberg_SAND.shp", ".", "ESRI Shapefile")
saveXML(eberg.md@xml, "eberg_SAND.xml")
## export to SLD format:
metadata2SLD(eberg.md, "eberg.sld")
## plot the layer with the metadata:
kml(eberg, file.name = "eberg_md.kml", colour = SNDMHT_A, metadata = eberg.md, kmz = TRUE)
## End(Not run)

spPhoto

Generate an object of class "SpatialPhotoOverlay"

86

spPhoto

Description
spPhoto function can be used to wrap pixel map (pixmapRGB), EXIF (Exchangeable Image File
format) data, spatial location information (standing point), and PhotoOverlay (geometry) parameters to create an object of class "SpatialPhotoOverlay". This object can then be parsed to KML
and visualized using Google Earth.
Usage
spPhoto(filename, obj, pixmap, exif.info = NULL, ImageWidth = 0,
ImageHeight = 0, bands = rep(rep(1, ImageHeight*ImageWidth), 3),
bbox = c(0, 0, 3/36000*ImageWidth, 3/36000*ImageHeight),
DateTime = "", ExposureTime = "", FocalLength = "50 mm",
Flash = "No Flash", rotation = 0, leftFov = -30, rightFov = 30,
bottomFov = -30, topFov = 30, near = 50,
shape = c("rectangle", "cylinder", "sphere")[1], range = 1000, tilt = 90,
heading = 0, roll = 0, test.filename = TRUE)
Arguments
filename

file name with extension (ideally an URL)

obj

object of class "SpatialPoints" (requires a single point object)

pixmap

object of class "pixmapRGB" (see package pixmap)

exif.info

named list containing all available EXIF metadata

ImageWidth

(optional) image width in pixels

ImageHeight

(optional) image height in pixels

bands

(optional) RGB bands as vectors (see pixmap::pixmapRGB)

bbox

(optional) bounding box coordinates (by default 1 pixel is about 1 m in arc degrees)

DateTime

(optional) usually available from the camera EXIF data

ExposureTime

(optional) usually available from the camera EXIF data

FocalLength

(optional) usually available from the camera EXIF data

Flash

(optional) usually available from the camera EXIF data

rotation

(optional) rotation angle in 0–90 degrees

leftFov

(optional) angle, in degrees, between the camera’s viewing direction and the left
side of the view volume (-180 – 0)

rightFov

(optional) angle, in degrees, between the camera’s viewing direction and the
right side of the view volume (0 – 180)

bottomFov

(optional) angle, in degrees, between the camera’s viewing direction and the
bottom side of the view volume (-90 – 0)

topFov

(optional) angle, in degrees, between the camera’s viewing direction and the top
side of the view volume (0 – 90)

near

(optional) measurement in meters along the viewing direction from the camera
viewpoint to the PhotoOverlay shape

shape

(optional) shape type — rectangle (standard photograph), cylinder (for panoramas), or sphere (for spherical panoramas)

range

(optional) distance from the camera to the placemark

spPhoto

87

tilt

(optional) rotation, in degrees, of the camera around the X axis

heading

(optional) direction (azimuth) of the camera, in degrees (0 – 360)

roll

(optional) rotation about the y axis, in degrees (0 – 180)

test.filename

logical; species whether a test should be first performed that the file name really
exists (recommended)

Details
The most effective way to import a field photograph to SpatialPhotoOverlay for parsing to KML
is to: (a) use the EXIF tool (courtesy of Phil Harvey) to add any important tags in the image file,
(b) once you’ve added all important tags, you can upload your image either to a local installation of
Mediawiki or to a public portal such as the Wikimedia Commons, (c) enter the missing information
if necessary and add an image description. Once the image is on the server, you only need to record
its unique name and then read all metadata from the Wikimedia server following the examples
below.
You can also consider importing images to R by using the pixmap package, and reading the technical
information via e.g. the exif package. If the image is taken using a GPS enabled camera, by getting
the EXIF metadata you can generate the complete SpatialPhotoOverlay object with minimum
user interaction. Otherwise, you need to at least specify: creation date, file name, and location of
the focal point of the camera (e.g. by creating "SpatialPoints" object).
Value
Returns an object of class "SpatialPhotoOverlay":
filename

URL location of the original image

pixmap

optional; local copy of the image ("pixmapRGB" class)

exif.info

list of EXIF metadata

PhotoOverlay

list of the camera geometry parameters (KML specifications)

sp

location of the camera ("SpatialPoints" class)

Note
The spPhoto function will try to automatically fix the aspect ratio of the ViewVolume settings
(leftFov, rightFov, bottomFov, topFov), and based on the original aspect ratio as specified in
the EXIF data. This might not work for all images, in which case you will have to manually adjust
those parameters.
Dimension of 3/36000*ImageWidth in decimal degrees is about 10 m in nature (3-arc seconds is
about 100 m, depending on the latitude).
Author(s)
Tomislav Hengl
References
• EXIF tool (http://www.sno.phy.queensu.ca/~phil/exiftool/)
• Wikimedia API (http://www.mediawiki.org/wiki/API)
See Also
getWikiMedia.ImageInfo, pixmap::pixmapRGB, spMetadata

88

vect2rast

Examples
## Not run: # two examples with images on Wikimedia Commons
# (1) soil monolith (manually entered coordinates):
imagename = "Soil_monolith.jpg"
# import EXIF data using the Wikimedia API:
x1 <- getWikiMedia.ImageInfo(imagename)
# create a SpatialPhotoOverlay:
sm <- spPhoto(filename = x1$url$url, exif.info = x1$metadata)
# plot it in Google Earth
kml(sm, method="monolith", kmz=TRUE)
# (2) PhotoOverlay (geotagged photo):
imagename = "Africa_Museum_Nijmegen.jpg"
x2 <- getWikiMedia.ImageInfo(imagename)
af <- spPhoto(filename = x2$url$url, exif.info = x2$metadata)
kml(af)
## End(Not run)

vect2rast

Convert points, lines and/or polygons to rasters

Description
Converts any "SpatialPoints*", "SpatialLines*", or "SpatialPolygons*" object to a raster
map, and (optional) writes it to an external file (GDAL-supported formats; writes to SAGA GIS
format by default).
Usage
## S4 method for signature 'SpatialPoints'
vect2rast(obj, fname = names(obj)[1], cell.size, bbox,
file.name, silent = FALSE, method = c("raster", "SAGA")[1],
FIELD = 0, MULTIPLE = 1, LINE_TYPE = 0, GRID_TYPE = 2, ... )
## S4 method for signature 'SpatialLines'
vect2rast(obj, fname = names(obj)[1], cell.size, bbox,
file.name, silent = FALSE, method = c("raster", "SAGA")[1],
FIELD = 0, MULTIPLE = 1, LINE_TYPE = 1, GRID_TYPE = 2, ... )
## S4 method for signature 'SpatialPolygons'
vect2rast(obj, fname = names(obj)[1], cell.size, bbox,
file.name, silent = FALSE, method = c("raster", "SAGA")[1],
FIELD = 0, MULTIPLE = 0, LINE_TYPE = 1, GRID_TYPE = 2, ... )
Arguments
obj

Spatial-PointsDataFrame,-LinesDataFrame or -PolygonsDataFrame object

fname

character; target variable

cell.size

numeric; output cell size

bbox

matrix; output bounding box

file.name

character; (optional) output file name

silent

logical; specifies whether to print the output

vect2rast

89

method

character; output rasterization engine (either raster package or SAGA GIS)

FIELD

integer; target column in the output shape file (see rsaga.get.usage("grid_gridding", 0))

MULTIPLE

integer; method for multiple values (see rsaga.get.usage("grid_gridding", 0))

LINE_TYPE

integer; method for lines (see rsaga.get.usage("grid_gridding", 0))

GRID_TYPE

integer; preferred target grid type (see rsaga.get.usage("grid_gridding", 0))

...

additional arguments that can be passed to the raster::rasterize command

Details
This function basically extends the rasterize function available in the raster package. The advantage of vect2rast, however, is that it requires no input from the user’s side i.e. it automatically
determines the grid cell size and the bounding box based on the properties of the input data set.
The grid cell size is estimated based on the density/size of features in the map (nndist function
in spatstat package): (a) in the case of "SpatialPoints" cell size is determined as half the mean
distance between the nearest points; (b) in the case of "SpatialLines" half cell size is determined
as half the mean distance between the lines; (c) in the case of polygon data cell size is determined as
half the median size (area) of polygons of interest. For more details see Hengl (2006). To process
larger vector maps consider using method="SAGA".
Value
Returns an object of type "SpatialGridDataFrame".
Author(s)
Tomislav Hengl
References
• Hengl T., (2006) Finding the right pixel size. Computers and Geosciences, 32(9): 1283-1298.
• Raster package (https://CRAN.R-project.org/package=raster)
• SpatStat package (http://www.spatstat.org)
See Also
vect2rast.SpatialPoints, raster::rasterize, spatstat::nndist
Examples
## Not run:
data(eberg)
library(sp)
library(maptools)
library(spatstat)
coordinates(eberg) <- ~X+Y
data(eberg_zones)
# point map:
x <- vect2rast(eberg, fname = "SNDMHT_A")
image(x)
# polygon map:
x <- vect2rast(eberg_zones)
image(x)
# for large data sets use SAGA GIS:

90

vect2rast.SpatialPoints
x <- vect2rast(eberg_zones, method = "SAGA")
## End(Not run)

vect2rast.SpatialPoints
Converts points to rasters

Description
Converts object of class "SpatialPoints*" to a raster map, and (optional) writes it to an external
file (GDAL-supported formats; it used the SAGA GIS format by default).
Usage
vect2rast.SpatialPoints(obj, fname = names(obj)[1], cell.size, bbox,
file.name, silent = FALSE, method = c("raster", "SAGA")[1], FIELD = 0,
MULTIPLE = 1, LINE_TYPE = 0, GRID_TYPE = 2, ... )
Arguments
obj

"SpatialPoints*" object

fname

target variable name in the "data" slot

cell.size

(optional) grid cell size in the output raster map

bbox

(optional) output bounding box (class "bbox") for cropping the data

file.name

(optional) file name to export the resulting raster map

silent

logical; specifies whether to print any output of processing

method

character; specifies the gridding method

FIELD

character; SAGA GIS argument attribute table field number

MULTIPLE

character; SAGA GIS argument method for multiple values — [0] first, [1] last,
[2] minimum, [3] maximum, [4] mean

LINE_TYPE

character; SAGA GIS argument method for rasterization — [0] thin, [1] thick

GRID_TYPE

character; SAGA GIS argument for coding type — [0] integer (1 byte), [1] integer (2 byte), [2] integer (4 byte), [3] floating point (4 byte), [4] floating point (8
byte)

...

additional arguments that can be passed to the raster::rasterize command

Value
Returns an object of type "SpatialGridDataFrame".
Author(s)
Tomislav Hengl
See Also
vect2rast

whitening

91

Examples
## Not run:
library(sp)
data(meuse)
coordinates(meuse) <- ~x+y
# point map:
x <- vect2rast(meuse, fname = "om")
data(SAGA_pal)
sp.p <- list("sp.points", meuse, pch="+", cex=1.5, col="black")
spplot(x, col.regions=SAGA_pal[[1]], sp.layout=sp.p)
## End(Not run)

whitening

whitening

Description
Derives a ‘whitenned’ color based on the Hue-Saturation-Intensity color model. This method can be
used to visualize uncertainty: the original color is leached proportionally to the uncertainty (white
color indicates maximum uncertainty).
Usage
whitening(z, zvar, zlim = c(min(z, na.rm=TRUE), max(z, na.rm=TRUE)),
elim = c(.4,1), global.var = var(z, na.rm=TRUE), col.type = "RGB")
Arguments
z

numeric; target variable (e.g. predicted values)

zvar

numeric; prediction error (variance)

zlim

upper and lower limits for target variable

elim

upper and lower limits for the normalized error

global.var

global variance (either estimated from the data or specified)

col.type

characted; "RGB" or "HEX"

Details
The HSI is a psychologically appealing color model for visualization of uncertainty: hue is used to
visualize values and whitening (paleness or leaching percentage) is used to visualize the uncertainty,
or in other words the map is incomplete in the areas of high uncertainty. Unlike standard legends
for continuous variables, this legend has two axis — one for value range and one for uncertainty
range (see also kml_legend.whitening).
The standard range for elim is 0.4 and 1.0 (maximum). This assumes that a satisfactory prediction
is when the model explains more than 85% of the total variation (normalized error = 40%). Otherwise, if the value of the normalized error get above 80%, the model accounts for less than 50% of
variability.
Whitening is of special interest for visualization of the prediction errors in geostatistics. Formulas
to derive the whitening color are explained in Hengl et al. (2004).

92

worldgrids_pal

Author(s)
Tomislav Hengl and Pierre Roudier
References
• Hengl, T., Heuvelink, G.M.B., Stein, A., (2004) A generic framework for spatial prediction of
soil variables based on regression-kriging. Geoderma 122 (1-2): 75-93.
• Hue-Saturation-Intensity color model (http://en.wikipedia.org/wiki/HSL_and_HSV)
See Also
kml_legend.whitening
Examples
whitening(z=15, zvar=5, zlim=c(10,20), global.var=7)
# significant color;
whitening(z=15, zvar=5, zlim=c(10,20), global.var=4)
# error exceeds global.var -> totally white;

worldgrids_pal

Standard global color palettes for factor variables

Description
A number of color palettes used to visualize various environmental categorical / factor variables:
land cover classes, water types, anthroms, soil types and similar. Each colour palette consists of a
variable number of colours (hexadecimal system). Factor levels names are attached as attributes to
the palette.
Usage
data(worldgrids_pal)
Format
The list contains:
anthroms Color palette used for the global map of anthroms (Ellis and Ramankutty, 2008).
bodemfgr A simplified color palette for soil types.
corine2k Color palette used in the Corine 2000 project for land cover classes (Büttner, et al.,
2002).
glc2000 Color palette used for the Global Land Cover 2000 mapping project (Global Land Cover
2000).
globcov Color palette used for the ENVISAT-based Global Land Cover map at resolution of 300
m (GlobCover Land Cover version V2.2).
gtkaart Color palette used for the Ground water levels map of the Netherlands (Gaast et al. 2005).
IGBP Color palette for 17 land cover classes defined by the International Geosphere Biosphere
Programme (IGBP).
lgn3 Color palette used for the Dutch land use map (Hazeu, 2005).
t250vlak Color palette used for the most general land use classes at scale 1:250k (TOP250NL).
watert Color palette used for the water types (generalized) in the Netherlands.

worldgrids_pal

93

Note
These colour palettes are only valid for factor-type variables. The names of classes used in the
legend can be obtained by loading the palette list.
Author(s)
Tomislav Hengl
References
• Bicheron, P. et al. (2008) GLOBCOVER: Products Description and Validation Report. MEDIAS France, Toulouse, 47 p.
• Büttner, G. et al. (2002) Corine Land Cover update 2000, Technical guidelines. EEA
(European Environment Agency), Kopenhagen.
• Ellis, E.C., Ramankutty, N. (2008) Putting people in the map: anthropogenic biomes of the
world. Frontiers in Ecology and the Environment, Vol. 6, No. 8, pp. 439-447.
• Fritz, S. et al. (2003) Harmonisation, mosaicing and production of the Global Land Cover
2000 database. JRC report EUR 20849 EN, Luxembourg, 41 p.
• Gaast, J.W.J. van der, H.R.J. Vroon en M. Pleijter, (2006) De grondwaterdynamiek in het
waterschap Regge en Dinkel. Wageningen, Alterra-rapport 1335.
• Hazeu, G.W., (2005) Landelijk Grondgebruiksbestand Nederland (LGN5). Vervaardiging,
nauwkeurigheid en gebruik. Wageningen, Alterra. Alterra-report 1213, 92 pp.
• Puijenbroek, P. van; Clement, J., (2008) Het oppervlaktewater getypeerd: de eerste Nederlandse watertypenkaart. Agro informatica 21(3): 21-25.
See Also
SAGA_pal, R_pal
Examples
data(worldgrids_pal)
## Not run: # globcov palette with class names:
display.pal(worldgrids_pal)
dev.off()
display.pal(worldgrids_pal, sel=5, names=TRUE)
## End(Not run)

Index
kml.tiles, 26
kml_layer-methods, 29
kml_layer.Raster, 30
kml_layer.RasterBrick, 31
kml_layer.SoilProfileCollection,
32
kml_layer.SpatialLines, 35
kml_layer.SpatialPhotoOverlay, 36
kml_layer.SpatialPixels, 38
kml_layer.SpatialPoints, 39
kml_layer.SpatialPolygons, 41
kml_layer.STIDF, 42
kml_layer.STTDF, 44
kml_legend.bar, 45
kml_open, 48
makeCOLLADA, 52
metadata2SLD-methods, 53
metadata2SLD.SpatialPixels, 54
plotKML-method, 56
plotKML-package, 3
plotKML.env, 66
plotKML.GDALobj, 68
readGPX, 72
readKML.GBIFdensity, 73
reproject, 74
spMetadata-methods, 83
spPhoto, 85
vect2rast, 88
vect2rast.SpatialPoints, 90
∗Topic utilities
kml_compress, 27
normalizeFilename, 55

∗Topic classes
RasterBrickSimulations-class, 70
RasterBrickTimeSeries-class, 71
sp.palette-class, 77
SpatialMaxEntOutput-class, 78
SpatialMetadata-class, 79
SpatialPhotoOverlay-class, 80
SpatialPredictions-class, 80
SpatialSamplingPattern-class, 81
SpatialVectorsSimulations-class,
82
∗Topic color
col2kml, 9
SAGA_pal, 76
worldgrids_pal, 92
∗Topic datasets
baranja, 4
bigfoot, 6
eberg, 12
fmd, 14
gpxbtour, 18
HRprec08, 20
HRtemp08, 21
LST, 51
northcumbria, 56
SAGA_pal, 76
∗Topic methods
getCRS-methods, 16
kml-methods, 24
kml_layer-methods, 29
kml_metadata-methods, 47
metadata2SLD-methods, 53
plotKML-method, 56
spMetadata-methods, 83
∗Topic spatial
aesthetics, 4
check_projection, 8
count.GridTopology, 10
display.pal, 11
geopath, 15
getCRS-methods, 16
grid2poly, 19
kml-methods, 24

aesthetics, 4, 26, 30, 35, 38, 39, 58, 59
baranja, 4
bargrid (baranja), 4
barstr (baranja), 4
barxyz (baranja), 4
bigfoot, 6
check_projection, 8, 16
col2kml, 9
color_palettes (SAGA_pal), 76
94

INDEX
count.GridTopology, 10
CRS-class, 75
display.pal, 11
eberg, 12
eberg_contours (eberg), 12
eberg_grid (eberg), 12
eberg_grid25 (eberg), 12
eberg_zones (eberg), 12
extractProjValue (check_projection), 8
fmd, 14, 56
geopath, 15
getCRS (getCRS-methods), 16
getCRS,Raster-method (getCRS-methods),
16
getCRS,Spatial-method (getCRS-methods),
16
getCRS-methods, 16
getCRS.Raster (getCRS-methods), 16
getCRS.Spatial (getCRS-methods), 16
GetNames (SpatialMetadata-class), 79
GetNames,SpatialMetadata-method
(SpatialMetadata-class), 79
GetPalette (SpatialMetadata-class), 79
GetPalette,SpatialMetadata-method
(SpatialMetadata-class), 79
getWikiMedia.ImageInfo, 17, 37, 87
gpxbtour, 18
grid2poly, 19
hex2kml (col2kml), 9
HRprec08, 20, 22
HRtemp08, 21, 21
kml (kml-methods), 24
kml,Raster-method (kml-methods), 24
kml,SoilProfileCollection-method
(kml-methods), 24
kml,Spatial-method (kml-methods), 24
kml,SpatialPhotoOverlay-method
(kml-methods), 24
kml,STIDF-method (kml-methods), 24
kml-methods, 24
kml.Spatial (kml-methods), 24
kml.tiles, 26, 69
kml2hex (col2kml), 9
kml_aes, 25
kml_aes (aesthetics), 4
kml_alpha (kml_layer-methods), 29
kml_altitude (kml_layer-methods), 29

95
kml_altitude_mode (kml_layer-methods),
29
kml_close, 25, 29, 59
kml_close (kml_open), 48
kml_colour (kml_layer-methods), 29
kml_compress, 25, 27, 59
kml_description, 28
kml_layer, 46, 49
kml_layer (kml_layer-methods), 29
kml_layer,RasterBrick-method
(kml_layer-methods), 29
kml_layer,RasterLayer-method
(kml_layer-methods), 29
kml_layer,RasterStack-method
(kml_layer-methods), 29
kml_layer,SoilProfileCollection-method
(kml_layer-methods), 29
kml_layer,SpatialGrid-method
(kml_layer-methods), 29
kml_layer,SpatialLines-method
(kml_layer-methods), 29
kml_layer,SpatialPhotoOverlay-method
(kml_layer-methods), 29
kml_layer,SpatialPixels-method
(kml_layer-methods), 29
kml_layer,SpatialPoints-method
(kml_layer-methods), 29
kml_layer,SpatialPolygons-method
(kml_layer-methods), 29
kml_layer,STFDF-method
(kml_layer-methods), 29
kml_layer,STIDF-method
(kml_layer-methods), 29
kml_layer,STSDF-method
(kml_layer-methods), 29
kml_layer,STTDF-method
(kml_layer-methods), 29
kml_layer-methods, 29
kml_layer.Raster, 29, 30, 32, 39
kml_layer.RasterBrick, 31, 31
kml_layer.SoilProfileCollection, 29, 32
kml_layer.SpatialLines, 15, 29, 35, 42
kml_layer.SpatialPhotoOverlay, 34, 36,
52
kml_layer.SpatialPixels, 38
kml_layer.SpatialPoints, 29, 39
kml_layer.SpatialPolygons, 29, 35, 41
kml_layer.STFDF (kml_layer.STIDF), 42
kml_layer.STIDF, 29, 42, 42
kml_layer.STTDF, 15, 29, 40, 43, 44, 72
kml_legend.bar, 45
kml_legend.whitening, 46, 91, 92

96
kml_metadata, 85
kml_metadata (kml_metadata-methods), 47
kml_metadata,SpatialMetadata-method
(kml_metadata-methods), 47
kml_metadata-methods, 47
kml_open, 25, 28, 29, 31, 32, 35, 39, 48, 59, 85
kml_screen, 49, 59
kml_shape (kml_layer-methods), 29
kml_size (kml_layer-methods), 29
kml_View (kml_open), 48
kml_width (aesthetics), 4
LST, 51
makeCOLLADA, 52
makeCOLLADA.rectangle, 37
metadata (spMetadata-methods), 83
metadata2SLD (metadata2SLD-methods), 53
metadata2SLD,SpatialMetadata-method
(metadata2SLD-methods), 53
metadata2SLD-methods, 53
metadata2SLD.Spatial
(metadata2SLD-methods), 53
metadata2SLD.SpatialPixels, 53, 54
munsell2kml (col2kml), 9
normalizeFilename, 55
northcumbria, 14, 56
parse_proj4 (check_projection), 8
paths, 75
paths (plotKML.env), 66
plot,SpatialPredictions,ANY-method
(SpatialPredictions-class), 80
plot.SpatialPredictions
(SpatialPredictions-class), 80
plotKML, 26, 69
plotKML (plotKML-method), 56
plotKML,list-method (plotKML-method), 56
plotKML,RasterBrickSimulations
(RasterBrickSimulations-class),
70
plotKML,RasterBrickSimulations-method
(plotKML-method), 56
plotKML,RasterBrickTimeSeries
(RasterBrickTimeSeries-class),
71
plotKML,RasterBrickTimeSeries-method
(plotKML-method), 56
plotKML,RasterLayer-method
(plotKML-method), 56
plotKML,SoilProfileCollection-method
(plotKML-method), 56

INDEX
plotKML,SpatialGridDataFrame-method
(plotKML-method), 56
plotKML,SpatialLinesDataFrame-method
(plotKML-method), 56
plotKML,SpatialMaxEntOutput-method
(plotKML-method), 56
plotKML,SpatialPhotoOverlay-method
(plotKML-method), 56
plotKML,SpatialPixelsDataFrame-method
(plotKML-method), 56
plotKML,SpatialPointsDataFrame-method
(plotKML-method), 56
plotKML,SpatialPolygonsDataFrame-method
(plotKML-method), 56
plotKML,SpatialPredictions-method
(plotKML-method), 56
plotKML,SpatialSamplingPattern
(SpatialSamplingPattern-class),
81
plotKML,SpatialSamplingPattern-method
(plotKML-method), 56
plotKML,SpatialVectorsSimulations
(SpatialVectorsSimulations-class),
82
plotKML,SpatialVectorsSimulations-method
(plotKML-method), 56
plotKML,STFDF-method (plotKML-method),
56
plotKML,STIDF-method (plotKML-method),
56
plotKML,STSDF-method (plotKML-method),
56
plotKML,STTDF-method (plotKML-method),
56
plotKML-method, 56
plotKML-package, 3, 67
plotKML.env, 66
plotKML.fileIO (kml-methods), 24
plotKML.GDALobj, 26, 68
plotKML.opts (plotKML.env), 66
projectRaster, 75
R_pal, 11, 93
R_pal (SAGA_pal), 76
RasterBrick (kml_layer.RasterBrick), 31
RasterBrickSimulations-class, 70
RasterBrickTimeSeries-class, 71
rasterize, 89
rasterize (vect2rast), 88
RasterLayer (kml_layer.Raster), 30
read.metadata (spMetadata-methods), 83
readGPX, 45, 72, 73
readKML.GBIFdensity, 73

INDEX
reproject, 9, 74
reproject,RasterBrick-method
(reproject), 74
reproject,RasterLayer-method
(reproject), 74
reproject,RasterStack-method
(reproject), 74
reproject,SpatialGridDataFrame-method
(reproject), 74
reproject,SpatialLines-method
(reproject), 74
reproject,SpatialPixelsDataFrame-method
(reproject), 74
reproject,SpatialPoints-method
(reproject), 74
reproject,SpatialPolygons-method
(reproject), 74
reproject.RasterBrick (reproject), 74
reproject.RasterLayer (reproject), 74
reproject.RasterStack (reproject), 74
reproject.SpatialGrid (reproject), 74
reproject.SpatialPoints (reproject), 74
SAGA_pal, 11, 76, 93
SoilProfileCollection
(kml_layer.SoilProfileCollection),
32
sp.palette-class, 77
SpatialLines (kml_layer.SpatialLines),
35
SpatialMaxEntOutput-class, 78
SpatialMetadata-class, 79
SpatialPhotoOverlay (spPhoto), 85
SpatialPhotoOverlay-class, 80
SpatialPixels
(kml_layer.SpatialPixels), 38
SpatialPoints
(kml_layer.SpatialPoints), 39
SpatialPolygons
(kml_layer.SpatialPolygons), 41
SpatialPredictions-class, 80
SpatialSamplingPattern-class, 81
SpatialVectorsSimulations-class, 82
spMetadata, 48, 53, 54, 77, 79, 87
spMetadata (spMetadata-methods), 83
spMetadata,RasterLayer-method
(spMetadata-methods), 83
spMetadata,Spatial-method
(spMetadata-methods), 83
spMetadata-methods, 83
spMetadata.Raster (spMetadata-methods),
83

97
spMetadata.Spatial
(spMetadata-methods), 83
spPhoto, 18, 37, 80, 85
spPhoto,SpatialPhotoOverlay
(SpatialPhotoOverlay-class), 80
spTransform, 75
STIDF (kml_layer.STIDF), 42
STTDF (kml_layer.STTDF), 44
summary,SpatialMetadata-method
(SpatialMetadata-class), 79
trajectory (gpxbtour), 18
USAWgrids (bigfoot), 6
vect2rast, 10, 20, 88, 90
vect2rast,SpatialLines-method
(vect2rast), 88
vect2rast,SpatialPoints-method
(vect2rast), 88
vect2rast,SpatialPolygons-method
(vect2rast), 88
vect2rast.SpatialLines (vect2rast), 88
vect2rast.SpatialPoints, 89, 90
vect2rast.SpatialPolygons (vect2rast),
88
whitening, 47, 91
worldgrids_pal, 11, 77, 92



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