fixes for CRAN

Envirometrix · Oct 7, 2021 · 8d817cd · 8d817cd
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,39 +1,39 @@
-Package: plotKML
-Version: 0.8-1
-Date: 2021-04-12
-Title: Visualization of Spatial and Spatio-Temporal Objects in Google Earth
-Author: Tomislav Hengl [cre, aut], Andrea Gilardi [ctb], Pierre Roudier [ctb], Dylan Beaudette [ctb], Edzer Pebesma [ctb], Michael Blaschek [ctb]
-Authors@R: c(person("Tomislav", "Hengl", role = c("cre", "aut"),
-        email = "[email protected]"),
-    person("Andrea", "Gilardi", role = "ctb"),
-    person("Pierre", "Roudier", role = "ctb"),
-    person("Dylan", "Beaudette", role = "ctb"),
-    person("Edzer", "Pebesma", role = "ctb"),
-    person("Michael", "Blaschek", role = "ctb"))
-Maintainer: Tomislav Hengl <[email protected]>
-Depends: R (>= 3.5.0)
-Imports: methods, tools, utils, XML, landmap, sp, raster, rgdal, aqp, gstat, spacetime, colorspace, plotrix, dismo, pixmap, plyr, stringr, colorRamps, scales, zoo, RColorBrewer, RSAGA, classInt, sf, stars
-Suggests: 
-    adehabitatLT,
-    maptools,
-    fossil,
-    rjson,
-    animation,
-    spatstat,
-    spatstat.linnet,
-    spatstat.geom,
-    RCurl,
-    rgbif,
-    Hmisc,
-    uuid,
-    intervals,
-    reshape,
-    gdalUtils,
-    snowfall,
-    parallel,
-    tinytex,
-    testthat
-Description: Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.
-License: GPL
-URL: https://github.com/Envirometrix/plotKML
-LazyLoad: yes 
+Package: plotKML
+Version: 0.8-2
+Date: 2021-10-06
+Title: Visualization of Spatial and Spatio-Temporal Objects in Google Earth
+Author: Tomislav Hengl [cre, aut], Andrea Gilardi [ctb], Pierre Roudier [ctb], Dylan Beaudette [ctb], Edzer Pebesma [ctb], Michael Blaschek [ctb]
+Authors@R: c(person("Tomislav", "Hengl", role = c("cre", "aut"),
+        email = "[email protected]"),
+    person("Andrea", "Gilardi", role = "ctb"),
+    person("Pierre", "Roudier", role = "ctb"),
+    person("Dylan", "Beaudette", role = "ctb"),
+    person("Edzer", "Pebesma", role = "ctb"),
+    person("Michael", "Blaschek", role = "ctb"))
+Maintainer: Tomislav Hengl <[email protected]>
+Depends: R (>= 3.5.0)
+Imports: methods, tools, utils, XML, landmap, sp, raster, rgdal, aqp, gstat, spacetime, colorspace, plotrix, dismo, pixmap, plyr, stringr, colorRamps, scales, zoo, RColorBrewer, RSAGA, classInt, sf, stars
+Suggests: 
+    adehabitatLT,
+    maptools,
+    fossil,
+    rjson,
+    animation,
+    spatstat,
+    spatstat.linnet,
+    spatstat.geom,
+    RCurl,
+    rgbif,
+    Hmisc,
+    uuid,
+    intervals,
+    reshape,
+    gdalUtils,
+    snowfall,
+    parallel,
+    tinytex,
+    testthat
+Description: Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.
+License: GPL
+URL: https://github.com/Envirometrix/plotKML
+LazyLoad: yes 
diff --git a/man/baranja.Rd b/man/baranja.Rd
@@ -1,91 +1,91 @@
-\name{baranja}
-\docType{data}
-\encoding{latin1}
-\alias{barxyz}
-\alias{bargrid}
-\alias{barstr}
-\title{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 \href{http://geomorphometry.org/book}{Hengl and Reuter, 2008}; Hengl et al., 2010; \doi{10.5194/hess-14-1153-2010}). Object \code{barxyz} contains 6370 precise observations of elevations (from field survey and digitized from the stereo images); \code{bargrid} contains \emph{observed} probabilities of streams (digitized from the 1:5000 topo map); \code{barstr} contains 100 simulated stream networks (\code{"SpatialLines"}) using \code{barxyz} point data as input (see examples below).}
-\usage{data(bargrid)}
-\format{
-The \code{bargrid} data frame (regular grid at 30 m intervals) contains the following columns:
-  \describe{
-	\item{\code{p.obs}}{observed probability of stream (0-1)}
-	\item{\code{x}}{a numeric vector; x-coordinate (m) in the MGI / Balkans zone 6 }
-	\item{\code{y}}{a numeric vector; y-coordinate (m) in the MGI / Balkans zone 6 }
-}
-}
-\author{ Tomislav Hengl }
-\references{
-\itemize{
-\item Hengl, T., Reuter, H.I. (eds), (2008) \href{http://geomorphometry.org/book}{Geomorphometry: Concepts, Software, Applications}. Developments in Soil Science, vol. 33, Elsevier, 772 p.
-\item Hengl, T., Heuvelink, G. B. M., van Loon, E. E., (2010) On the uncertainty of stream networks derived from elevation data: the error propagation approach. Hydrology and Earth System Sciences, 14:1153-1165. \doi{10.5194/hess-14-1153-2010}
-\item \url{http://geomorphometry.org/content/baranja-hill}
-}
-}
-\note{Consider using the 30 m resolution grid (see \code{bargrid}) as the target resolution (output maps). 
-}
-\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
-\dontrun{## Example with simulated streams:
-data(R_pal)
-library(rgdal)
-library(RSAGA)
-pnt = list("sp.points", barxyz, col="black", pch="+")
-spplot(bargrid[1], sp.layout=pnt, 
-  col.regions = R_pal[["blue_grey_red"]])
-## Deriving stream networks using geostatistical simulations:
-Z.ovgm <- vgm(psill=1831, model="Mat", range=1051, nugget=0, kappa=1.2)
-sel <- runif(length(barxyz$Z))<.2
-N.sim <- 5
-## geostatistical simulations:
-DEM.sim <- krige(Z~1, barxyz[sel,], bargrid, model=Z.ovgm, nmax=20, 
-   nsim=N.sim, debug.level=-1)
-## Note: this operation can be time consuming
-
-stream.list <- list(rep(NA, N.sim))
-## derive stream networks in SAGA GIS:
-for (i in 1:N.sim) {
-  writeGDAL(DEM.sim[i], paste("DEM", i, ".sdat", sep=""), 
-     drivername = "SAGA", mvFlag = -99999)
-  ## filter the spurious sinks:
-  rsaga.fill.sinks(in.dem=paste("DEM", i, ".sgrd", sep=""), 
-     out.dem="DEMflt.sgrd", check.module.exists = FALSE)
-  ## extract the channel network SAGA GIS:
-  rsaga.geoprocessor(lib="ta_channels", module=0, 
-    param=list(ELEVATION="DEMflt.sgrd", 
-    CHNLNTWRK=paste("channels", i, ".sgrd", sep=""), 
-    CHNLROUTE="channel_route.sgrd", 
-    SHAPES="channels.shp", 
-    INIT_GRID="DEMflt.sgrd", 
-    DIV_CELLS=3, MINLEN=40),
-    check.module.exists = FALSE, 
-    show.output.on.console=FALSE)
-  stream.list[[i]] <- readOGR("channels.shp", "channels", 
-    verbose=FALSE)
-  proj4string(stream.list[[i]]) <- barxyz@proj4string
-}
-# plot all derived streams at top of each other:
-streams.plot <- as.list(rep(NA, N.sim))
-for(i in 1:N.sim){
-  streams.plot[[i]] <- list("sp.lines", stream.list[[i]])
-}
-spplot(DEM.sim[1], col.regions=grey(seq(0.4,1,0.025)), scales=list(draw=T), 
-sp.layout=streams.plot)
-}
-}
-\keyword{datasets}
+\name{baranja}
+\docType{data}
+\encoding{latin1}
+\alias{barxyz}
+\alias{bargrid}
+\alias{barstr}
+\title{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 \href{https://geomorphometry.org/geomorphometry-concepts-software-applications/}{Hengl and Reuter, 2008}; Hengl et al., 2010; \doi{10.5194/hess-14-1153-2010}). Object \code{barxyz} contains 6370 precise observations of elevations (from field survey and digitized from the stereo images); \code{bargrid} contains \emph{observed} probabilities of streams (digitized from the 1:5000 topo map); \code{barstr} contains 100 simulated stream networks (\code{"SpatialLines"}) using \code{barxyz} point data as input (see examples below).}
+\usage{data(bargrid)}
+\format{
+The \code{bargrid} data frame (regular grid at 30 m intervals) contains the following columns:
+  \describe{
+	\item{\code{p.obs}}{observed probability of stream (0-1)}
+	\item{\code{x}}{a numeric vector; x-coordinate (m) in the MGI / Balkans zone 6 }
+	\item{\code{y}}{a numeric vector; y-coordinate (m) in the MGI / Balkans zone 6 }
+}
+}
+\author{ Tomislav Hengl }
+\references{
+\itemize{
+\item Hengl, T., Reuter, H.I. (eds), (2008) \href{https://geomorphometry.org/geomorphometry-concepts-software-applications/}{Geomorphometry: Concepts, Software, Applications}. Developments in Soil Science, vol. 33, Elsevier, 772 p.
+\item Hengl, T., Heuvelink, G. B. M., van Loon, E. E., (2010) On the uncertainty of stream networks derived from elevation data: the error propagation approach. Hydrology and Earth System Sciences, 14:1153-1165. \doi{10.5194/hess-14-1153-2010}
+\item \url{https://geomorphometry.org/baranja-hill/}
+}
+}
+\note{Consider using the 30 m resolution grid (see \code{bargrid}) as the target resolution (output maps). 
+}
+\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
+\dontrun{## Example with simulated streams:
+data(R_pal)
+library(rgdal)
+library(RSAGA)
+pnt = list("sp.points", barxyz, col="black", pch="+")
+spplot(bargrid[1], sp.layout=pnt, 
+  col.regions = R_pal[["blue_grey_red"]])
+## Deriving stream networks using geostatistical simulations:
+Z.ovgm <- vgm(psill=1831, model="Mat", range=1051, nugget=0, kappa=1.2)
+sel <- runif(length(barxyz$Z))<.2
+N.sim <- 5
+## geostatistical simulations:
+DEM.sim <- krige(Z~1, barxyz[sel,], bargrid, model=Z.ovgm, nmax=20, 
+   nsim=N.sim, debug.level=-1)
+## Note: this operation can be time consuming
+
+stream.list <- list(rep(NA, N.sim))
+## derive stream networks in SAGA GIS:
+for (i in 1:N.sim) {
+  writeGDAL(DEM.sim[i], paste("DEM", i, ".sdat", sep=""), 
+     drivername = "SAGA", mvFlag = -99999)
+  ## filter the spurious sinks:
+  rsaga.fill.sinks(in.dem=paste("DEM", i, ".sgrd", sep=""), 
+     out.dem="DEMflt.sgrd", check.module.exists = FALSE)
+  ## extract the channel network SAGA GIS:
+  rsaga.geoprocessor(lib="ta_channels", module=0, 
+    param=list(ELEVATION="DEMflt.sgrd", 
+    CHNLNTWRK=paste("channels", i, ".sgrd", sep=""), 
+    CHNLROUTE="channel_route.sgrd", 
+    SHAPES="channels.shp", 
+    INIT_GRID="DEMflt.sgrd", 
+    DIV_CELLS=3, MINLEN=40),
+    check.module.exists = FALSE, 
+    show.output.on.console=FALSE)
+  stream.list[[i]] <- readOGR("channels.shp", "channels", 
+    verbose=FALSE)
+  proj4string(stream.list[[i]]) <- barxyz@proj4string
+}
+# plot all derived streams at top of each other:
+streams.plot <- as.list(rep(NA, N.sim))
+for(i in 1:N.sim){
+  streams.plot[[i]] <- list("sp.lines", stream.list[[i]])
+}
+spplot(DEM.sim[1], col.regions=grey(seq(0.4,1,0.025)), scales=list(draw=T), 
+sp.layout=streams.plot)
+}
+}
+\keyword{datasets}