Home > Uncategorized > rasterVis to the rescue

rasterVis to the rescue

Programmers like Oscar Perpiñán Lamigueiro are the reason I love R!  Oscar is the maintainer of the rasterVis package and it in this post I’ll explain why it is must have package for anyone working with the raster package in R.  My latest project is focused on the NOAA’s Climate Reference Network. The details can be found here.  Over a year ago I wrote a package call CRN designed to download and organize the data from the Climate Reference Network and I wanted to update and put some finishing touches on that package. In the course of that I decided it would be a cool idea to build a detailed metadata package for the stations.

The CRN is a rather like a gold standard of climate data collection. Triple redundant sensors, 5 minute data, hourly data, daily and monthly. The sites have all been selected using rigorous standards and as the system has matured the data availability rates are close to 100%. Here are just of few of the projects I have in mind.

1. Doing a proper estimate of the US temperature using just the CRN stations. One person has already  done this incorrectly ( or sub optimally ) so, I’m planning on approaching the problem with Kriging. For this I won’t use the Berkeley Earth code because I want to do something in R, using gstat perhaps.

2. Looking at LST  around the CRN sites. There is also a really cool new package in R called Modis and I really want to do an interesting project to test that package out. The goal here would be to fetch the correct Modis tiles for Land Surface Temperature, and then look at the Air temperatures, soil temperatures ( CRN collects those ) 

3. Landcover study. I’ve been reading a bunch of papers by Marc Imhoff on  SUHI and the relationship between SUHI and impervious surfaces, tree cover, etc. and Marc’s team has done some really cool work on the relationship between SUHI and the particular biome that a city is embedded in. In some forthcoming work they also look at the effects of city size and city shape on SUHI. A a few points in their papers they have hinted at SUHI effects for even small urban developments with areas around 1 sq km. I’d like to understand that better.

 To pull off all these projects I will need some rather detailed metadata, more detailed than I’ve ever worked with before. For starters  I needed a station list for the CRN and USRCRN stations. There are any number of ways of getting this data, but I drop a note to Matt Menne of NOAA and he directed me to Michael Palecki who was kind enough to send me a csv file of stations. The thing I was looking for was the most accurate Latitude/Longitude data I could find, and Michael supplied it. 

Next task was collecting metadata for the stations and assembling a variety of tables. I won’t document that work here, but just to give you an idea of what I’m putting together For every station I have the following :  1) elevation,slope and aspect  from 30 meter DEM.  2) distance from the sea coast, distance from the nearest body of water, and percentage of the “land” that is covered in water over a broad range of radii from the site. 3  various landcover datasets. 4. Impervious Area. 5. Biome. 6 Anthrom. 7 Populations from various sources and at various levels of accuarcy. 8 Housing. 9. Nightlights ( stable and radiance calibrated ). 10 economic activity.  and I plan to get LST and probably Modis Albedo.

So where does Oscar and rasterVis come in?  Specifically in this case Oscar came to the rescue for a plotting problem I was having with landcover data.

Using the  2006 NLCD 30 meter land cover dataset the first thing I do is save off  “patches”  of landcover for  roughly 1 degree around the site. The 30 meter dataset is huge and things go much easier if I just create  small data sets for each station.

In code that looks like this


LcName  <-  “G:\\NLCD\\nlcd2006_landcover_4-20-11_se5.img”
LC           <-  raster(LcName)

Inv <- read.table(“Core.tab”,stringsAsFactors =FALSE)

coordinates(Inv) <- c(“Lon”,”Lat”)
proj4string(Inv)   <- CRS(“+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0” )
Inv <-  spTransform(Inv, CRS(projection(LC)))


for(station in 1:nrow(Inv)){
     Lon <- coordinates(Inv)[station,”Lon”]
     Lat <- coordinates(Inv)[station,”Lat”]
     if(Lon >= xmin(LC) & Lon <= xmax(LC) & Lat >= ymin(LC) & Lat <= ymax(LC)){
         e <- extent(Lon-110000,Lon+110000,Lat-110000,Lat+110000)
         patch <- crop(LC,e)
         fname <-file.path(“NLCD2006″,paste(Inv$Id[station],”NLCD06.grd”,sep=””),fsep=.Platform$file.sep)
         writeRaster(patch,filename = fname)
}else {
    print(“Alaska or HI”)


Pretty simple. For 2006 Alaska and Hawaii are not in the dataset so we just skip them. The output of this is a small grid with the station in the center and 30 meter landcover data for 110,000 meters in all directions. That’s the data I will be working with.

The next step is to calculate a variety of statistics on this “patch” and smaller and smaller versions of the patch, so we get the land cover stats at various radii from the site, from 110km  down to 30 meters. Working with the 1 degree “patch” makes this work go very quickly.  raster has a function called “freq” which counts the frequency of values in the entire raster. [ As I write this I wonder if “freq” can be passed as  a function to the raster  “extract” function as a buffer option] 

In addition to creating 1 degree patches for every site I also create 1km patches which makes a nice size for display. That’s where rasterVis comes in.

Before Oscar solved my problem here is an example of what my plot looked like. Note this is at 500 meters



There are a bunch of problems here. First and foremost I could not get a consistent coloring for these plots. The issue is that plot() in raster was considering my data to be integers rather than factors and if land cover types were missing my colors scheme changed from plot to plot.

Oscar fixed all that by suggesting that I use rasterVis  and the  levelplot function.  His code dropped right in and after finding the color scheme in rgb for NLCD  the same plot now looks like this





I’ve got some work to do to switch back to the official legend descriptions but the maps now use the rgb from the standard and every map is consistently covered.  In the next installment I’ll post up Oscars code.


Here is what the Impervious surface data looks like ( Needs a grey scale of 0-100% )





To the naked eye it looks like this





Categories: Uncategorized
  1. Jonathan Kennel
    September 26, 2012 at 8:07 PM

    Hello Steven,

    Nice post! While lattice tends to be my preference, I think you can do this without using rasterVis and just using the raster::plot function. Here is an example which does what I believe you are trying to do (the majority of this code is just building the rasters which you already have):


    R1 <- raster(nrows=10, ncols=10)
    R2 <- R1

    IND1 <- c('a','f')
    VAL1 <- sample(IND1,100,replace=TRUE)

    IND2 <- c('b','c','d')
    VAL2 <- sample(IND2,100,replace=TRUE)

    LEVELS <- sort(unique(c(VAL1,VAL2)))

    R1 <- setValues(R1,as.numeric(factor(VAL1,LEVELS)))
    R2 <- setValues(R2,as.numeric(factor(VAL2,LEVELS)))

    B1 <- brick(R1,R2)

    BREAKS <- 0:length(LEVELS)
    axis.args=list(at=1:length(LEVELS)-0.5, labels=LEVELS ))

    axis.args=list(at=1:length(LEVELS)-0.5, labels=LEVELS ))

    axis.args=list(at=1:length(LEVELS)-0.5, labels=LEVELS ))


    • Jonathan Kennel
      September 26, 2012 at 9:21 PM

      Or using spplot with the previous raster brick:

      at = 0:length(LEVELS)+0.5,
      colorkey = list(labels=list(labels=LEVELS)))


    • Steven Mosher
      September 26, 2012 at 11:03 PM

      Thanks Jonathan I will give that a try. Oscar has made some changes into rasterVis that he wants me to test so as soon as I finish that I’ll try your approach

  2. Evan Englund
    September 28, 2012 at 2:11 AM

    Hello Steven,

    I’m a retired geologist/geostatistician with experience using gstat for kriging and conditional simulation in R. I’m probably best known for having developed a PC geostatics software package called Geo-EAS that was very widely used in the late DOS era. I’d like to volunteer my assistance with your CRN kriging project. In addition to the standard kriging estimation, I recommend using multiple conditional simulations on at least a couple of years of data in order to get the best possible estimates of estimation error – both for local estimates and for the overall mean. I’d be happy to collaborate actively on this, but at the least, I can provide you with a rigorous and constructive technical review. Let me know if you are interested.


  3. September 29, 2012 at 8:53 PM

    I’d also suggest you check out the fields package, which I’ve been using. In addition to kriging, it has thin-plate splines which from my initial reading may be a better choice than kriging. (Both Krig and Tps create the same output structure, which is a clever idea on the author’s part.)

  4. November 18, 2015 at 1:37 AM

    Hi Steve. Thanks for the informative post. I’ve never worked with raster before, so I was wondering what would be the advantages of working with raster compared to using a SpatialPolygonDataframe and using ggplot to map the data?

  1. May 6, 2013 at 11:44 AM
  2. May 21, 2013 at 6:07 AM

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