I was recently asked for help with a project that involves correlating occurrences of marine animals found at the surface with satellite measurements of surface chlorophyll. Being a physical oceanographer, I’m not too familiar with the details of such data sets (though I did previously help someone read in MODIS netcdf with the oce package), but saw it as a nice opportunity to learn a bit about a different data set, but also to gain some new data processing and plotting skills.

## MODIS Chla data

The MODIS chlorophyll data are provided by NASA through the OceanColor WEB site, which provides various manual ways of downloading binary files (e.g. hdf and netCDF) files. For the present application, which potentially required approximately 400 or so images, this wasn’t a very appealing option.

A quick google search turned up two very relevant (and fantastic looking!) packages, the spnc package and the obpgcrawler package (both authored by Ben Tupper from the Bigelow Laboratory). spnc provides some simplified methods for dealing with “spatial” datasets and netCDF files, and the obpgcrawler provides an interface for programmatically downloading various datasets from the NASA Ocean Biology Processing Group (including MODIS!).

### Installing spnc and obpgcrawler

As the packages are not on CRAN (yet?), they have to be installed using the devtools package (and of course all its dependencies, etc). The obpgcrawler package also depends on another non-CRAN package, called threddscrawler which must be installed first. Note that due to an issue with OpenDAP support for the ncdf4 package on windows, the below only works on Linux or OSX.

To install the packages, do:

There are some great examples provided on the Github pages, from which I built on to accomplish what I needed. The below example is pulled straight from the obpgcrawler page, to download a subset of the most recent MODIS data and plot it as a “raster” image (more on that later).

## The animal data

The animal data consists of a data frame containing: a date of observation, a longitude, and a latitude. To mimic the data set, I’ll just create a single random point and time in the North Atlantic:

Now, we can extract a chlorophyll value from the location, using the extract() function:

## Things to figure out (sp plots, rasters, projections, etc)

The world of “spatial” objects (e.g. through the sp package), and things that derive from them, is a new one for me. For example, in the oce package, we have developed methods for plotting matrices (e.g. imagep()) and geographical data (e.g. mapPlot(), mapImage(), etc) that differ from the GIS-like approach contained in the world of spatial analyses in R. I have long desired to learn more about this “other” world, and so have taken this opportunity with MODIS data to do so.

### Projected rasters and lon/lat labels

The neat thing about raster objects is that they contain the coordinate projection information. For example, the coordinate system for the MODIS data that we downloaded can be seen with:

For those used to doing projected maps in oce, this string should be familiar as a proj4 string, which specifies that the coordinate system is simply “longlat” (i.e. not projected). To change the projection of the raster, we can use the projectRaster() function to update it to a new reference, e.g. polar stereographic centred on -20 degrees W:

Now, if we use spplot() again, we get a raster that is plotted in a projected coordinate:

There are some things I haven’t figured out yet, particularly how to plot nice graticules (e.g. grid lines for constant latitude and longitude), since if the above is plotted as before with the scales= argument, what is plotted are the projected coordinates:

It looks like the graticules package will be helpful for this, but it still doesn’t appear to be non-trivial. See also here for some other good-looking examples.

### Extract a matrix from raster to use imagep()

One solution (at least for making maps), would be to extract the matrix data from the raster along with the longitude and latitude vectors. This would then allow for plotting in a projection using mapImage() from the oce package as I’m used to. Let’s try and pull stuff out of the raster object r:

Note that using as.matrix() on a raster should extract the matrix values of the object, however in this case it extracts a vector with all the values. So, we need to reshape it:

Ok! Now we’re getting somewhere! Let’s try a projected version:

Awesome!