This post covers how elevations for dragonfly observation points data for Vanuatu were sourced from a space shuttle mission in 2000.

For creatures that weigh way less than a gram, dragonflies have certainly kept me busy over the years.  We’ve already seen how my colleague, Milen, used some offline maps on his phone to collect observations in Vanuatu, as well as some mapping complications in Taveuni.  And there’s more to come as we work on mapping species diversity across the Pacific.

But now another installment in the continuing dragonfly story.  After Milen collected his observations in Vanuatu, he came back with a simple request: could he please have the elevations of all the observations?  My first reaction was, ummmm, sure, easy, but I haven’t got any elevation data for Vanuatu!  I haven’t even got any contours.  So what’s a spatial analyst to do?

Like most, my first stop was Google, but to no avail.  Unlike New Zealand, such data aren’t easily available.  I found a few sites, but no actual data I could use. has tons of data and lots for Oceania, but none for Vanuatu.  These sites had maps and some very nice pictures, but no actual data:

While these are raster images, they don’t have any real elevation data.  Then I remembered that, many years ago, there was a space shuttle mission that aimed to map elevations for 80% of the earth’s surface.  Can we get our hands on it?

Yes, we can.

The Shuttle Radar Topography Mission (SRTM) flew in 2000 and used radar to map elevation.  The shuttle Endeavour flew upside down with its cargo bay open.  Inside was the radar transmitter as well as a receiver.  At the end of a 60 metre mast was a second receiver.  The two receivers allowed the two responses to be reconciled with elevation at the land surface dropping out – this is called interferometry.  In a sense, it’s like our two eyes, slightly separated, that give us a sense of depth perception.

What originally came out of the data was a roughly 90 m resolution DEM of most of the earth’s surface (it varies with latitude) and freely available on the internet.  Milen was happy with a 90 m resolution, so let’s go to Vanuatu!

This site allows anyone to search for these data:

Three search options: Multiple Selection, Enable Mouse Drag and Input Coordinates.  I could see what I was after so I chose to click and drag:

This is probably a much larger area than I need but I’ve selected this just to be thorough.  I know my data are spread across several islands.

We should look at the observation data next to make sure we only grab what we need.

So here are my points, 92 in all spread across four islands.  Milen was busy!

I’ll need to ensure I get data that cover them all (actually, I’m just after three of the islands as Milen said not to worry about the northern most one).   Back in the SRTM search window, I got several results – here’s an example of one of the them:

So there’s a description at left, with file names and details, the Location is shown in the middle and an image of the actual coverage area at right.  As it turns out, this is one of the ones I need.  The southermost island I need, Aneityum, is on the tile just below.  I can download this file by clicking on “Data Download (HTTP)” button at the bottom and delivered to my Dowload folder is a zipped file of the data.  I do this for both tiles and, almost out of nowhere (well, from roughly 400 km above sea level), I’ve got the data I need:

Great – almost there.  Notice how there’s only half of the data for New Caledonia at lower left as well as the top half of Espiritu Santo – these are just the extents of the two tiles.  Also, these DEMs are TIF files, or to be more exact, GeoTiffs.  These are your standard image files but they have both position data built in and pixel values that relate to physical attributes rather than just colour, elevation in this case.  We can treat them as any other layer in ArcGIS.

Next step, I’d like to extract the elevation values for each point from the grid cell they are within.  Two ways to do this:

Both of these tools do the same thing – extract values from grid cells at points – the difference is the output.  The first tool creates a new spatial layer with the elevations added – sort of a vector to raster spatial join.  The second does that same but just outputs a table.  I could use either here, but since all Milen’s interested in is having the data in a table, I went the second route:

The input Features are my dragonfly observations and the Input rasters are my elevation grids.  I can save the table as a standalone DBF table or as a table in a geodatabase.  To finish things off, I’ll use the Table to Excel tool – this handy tool exports any table directly to Excel which saves a bit of work.  And here’s the final result – a spreadsheet I can send to Milen:

This spreadsheet has well over 50 columns so I’ve just showed the elevation column that was added by the tool.

So, to recap.  To get the elevations for a series of points, we downloaded some SRTM elevation data, used the Extract Values to Table tool, then exported that table to Excel, and job done.  Nice that we could make use of data collected by the shuttle to cover areas where there are no data.

As a sidenote, I used the same SRTM data to get the elevations in Taveuni, with slightly more work as we’ve seen.

Once again, those pesky dragonflies have kept me busy, but in a good way.  I just haven’t figured out how to get a charge code out of them.