Introduction to GDAL

The geospatial data abstraction library (GDAL, www.gdal.org) is an open source library for translating geospatial data between different formats. It is the primary intermediary by which all open-source data analysis and GIS software is able to interact with ArcGIS datasets. For example, one can translate ArcGIS binary grids into GRASS rasters or import them as an SpatialGridDataFrame object in R. GDAL proper supports raster data types, but it has been effectively merged with another library, OGR, that supports vector data conversion.

There are some file formats that are not directly translatable by GDAL, notably ESRI proprietary Smart Data Compression (SDC) files. The GDAL website provides a list of supported vector formats and raster formats. ESRI binary grids, coverages, and personal geodatabases can be read but not written.

Since GDAL is a library, it is meant for developers who write code that reference the library, rather than for end users who actually translate datasets. The library API is in C, but there are bindings for R, Perl, Python, VB6, Ruby, Java and C#. The GDAL package does come with a few commandline utilities for working with geospatial data: GDAL Utilites. Among those tools that I commonly use

gdalinfo

Raster information

gdal_translate.py

Convert rasters between formats

gdal_merge.py

Merge Tiled rasters into one

gdal2tiles.py

Makes a Google Maps- or Google Earth-compatible set of rasters

Binary distributions of GDAL are available for most platforms are available here, and can be installed without using the command line.

In the next post, we'll look at the R bindings for GDAL in the RGDAL package built by Tim Keitt at the University of Texas, and I will show some examples of working with raster datasets in R.

Extracting Raster Values from Points in R and GRASS

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.

gr.asc

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.

pt.shp

a point shapefile.

You also need the maptools and sp packages.

The Code

library(maptools) # autoloads sp gr <- readAsciiGrid("www.unc.edu/~toddjobe/data/gr.asc") pt <- readShapePoints("www.unc.edu/~toddjobe/data/pt.shp") overlay(gr, pt)

coordinates gr.asc 1497 (569292, 1224170) 6094.6080 539 (567718, 1227840) 7964.5331 1023 (564565, 1225810) -293.6599 663 (562462, 1227260) -5351.7297 69 (563675, 1229710) -2923.9394 716 (563062, 1227180) -4241.8255 339 (567636, 1228780) 4781.6488 509 (561722, 1227870) -3958.7312 805 (560981, 1226690) 139.9091 155 (560884, 1229220) -2719.7353

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.

v.db.addcol map=pt columns="grval double precision" v.what.rast vector=pt raster=gr column=grval