Extracting Raster Values from Points in R and GRASS

July 26, 2010

(This article was first published on Computational Ecology, and kindly contributed to R-bloggers)

A common task in GIS analysis is to extract the value of a remotely
sensed environmental variable at a point location. For instance we
may wish to extract the elevation of a field plot from a digital
elevation model. The elevation data is a raster (i.e. grid) and the
plots are a point shapefile (or a simple text file of X, Y
locations). The command for doing this in ArcGIS is
ExtractValuesToPoints available in the Spatial Analyst package.
Situations may arise where ArcGIS is not the most efficient way of
extracting these values. So, here, I provide a brief overview of how to
extract raster values to points in R and GRASS.

Extract Values to Points in R

This is strikingly easy is R. My work usually requires more
statistical sophistication than is available in ArcGIS. As a
result, I have completely switched to doing the extraction in R. I
known I am going to end in R eventually, and it is easier to automate
than writing a long python script in ArcGIS.

Data required

For the purpose of this exercise. All the data must be have the
same spatial projection.

an ESRI ASCII grid. This could also be an ArcGIS
binary grid if you know how to use RGDAL. That perhaps will be
another post.
a point shapefile.

You also need the maptools and sp packages.

The Code

That is it. Fast, and easy.

Extracting Values in GRASS

Extracting raster values in GRASS is somewhat faster than in R, but
it takes a little bit more planning in that you have to explicitly
create the column that the raster values will go into.

Data Required

  • gr : A GRASS grid
  • pt : A GRASS point dataset

The Code

The basic flow of this is that you create an empty column in the
point dataset with the right data type (i.e. varchar(10) string
of length 10, double precision floating point numbers, int
integers). Then, fill the column with the raster values.

To leave a comment for the author, please follow the link and comment on their blog: Computational Ecology.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Dommino data lab

Quantide: statistical consulting and training




CRC R books series

Six Sigma Online Training

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)