I’m big fan of the NASA Earth Observatory website run by the Goddard Space Flight Center. Their Image of the Day series showcases stunning imagery and applications of remote sensing every day, in the style of the long-running Astronomy Picture of the Day. They also hold a monthly “puzzler” competition, where they post a satellite image without any annotations and ask readers to identify the location and why it’s interesting. For the December puzzler, they posted the following image:
The scene depicts a valley in a cold climate, with some kind of body of water (or other liquid?) at center. The sinuous shape suggests glacial activity, though this conflicts with the lack of snow and ice. Notably, no vegetation is visible anywhere, making it look very similar to a HiRISE image of Mars. I then thought that this could be the McMurdo Dry Valleys in Antarctica. In college, I saw a number of presentations from professors and graduate students who had conducted research in the Dry Valleys because of the similarities to Martian climate. After only a few minutes of hunting around on Google Maps, I found the location. But what was in the center of the image that looked like a lake?
The Wikipedia page for the McMurdo Dry Valleys lists a number of lakes. I clicked on the link for Don Juan Pond, which was noted to be the most saline of all, and bingo: the image was a perfect match. I quickly wrote up a description and submitted it in the comments section. Lo and behold, I was the first commenter and won the puzzler!
As it turned out, the research paper that inspired the puzzler was coauthored by Brown researchers Jay Dickson and Jim Head. Perhaps it was the memory of one of their presentations that made me think of the Dry Valleys. Either way, what a small world.
I frequently use the HydroSHEDS dataset from the USGS for projects requiring a DEM outside of the United States. HydroSHEDS is a contrived acronym for Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales, meaning the digital elevation model comes from the Shuttle Radar Topography Mission (SRTM). SRTM mounted an InSAR array on Space Shuttle Endeavour during STS-99 in 2000 to collect elevation data from latitudes 56°S to 60°N. Although SRTM has less global coverage and less spatial resolution than the ASTER GDEM, SRTM has better vertical accuracy, making it more attractive for hydrologic applications like HydroSHEDS.
The 90 meter (or 3 arc second) gridded data products from HydroSHEDS are distributed in tiles that are 5 degrees by 5 degrees in size. This includes a void-filled DEM, a hydrologically conditioned DEM, and a flow direction grid. For reasons unknown, there is no index map of the 5 degree tiles available on the USGS website. Since it’s difficult to determine exactly which tiles fall in a given extent, I decided to make the map I needed:
Each tile in this map is labeled with the concatenated tile name representing the coordinates of the lower-left corner of each tile. For example, the tile for Rhode Island is “n40w075” with a lower left corner at 40°N latitude and 75°W longitude.
The fact that the tiles are measured in degrees means that this is an excellent case for using the plate carrée equirectangular projection, which has horizontal and vertical units of degrees. This is, of course, equivalent to what ArcMap shows when setting a data frame’s coordinate system to a “geographic” projection.
For convenience, you can also download the index in shapefile or GeoJSON format.
For the third year running, I returned to Brown to give a guest lecture about using Python in GIS for GEOL1320: Introduction to Geographic Information Systems for Environmental Applications. I’ve used ArcPython extensively in my work at Cadmus, and it’s exciting and heartening to be invited to lecture to GIS novices about the topic.
Nonetheless, the lecture is a challenge. I don’t have any formal experience teaching undergraduates. Moreover, it’s practically impossible to teach a programming language (even one as intuitive as Python) in an 80 minute time slot, let alone its nuanced GIS applications. Thus my strategy has been to think of the lecture as an icebreaker: a way to take away the barriers and scare-factor associated with getting started with Python. After a brief introduction, I showcased a few real-world examples of how I’ve used ArcPython in my work. Then the whole class worked through a live demo, doing a fairly simple task—adding fields to a feature class—using progressively more complex Python commands. Eventually, we even packed the final tool into a Python custom toolbox connected to a separate .py script file.
This year was definitely the most successful yet. The fact that students typed the commands themselves in a lab-format class made for a more engaging and effective class versus students watching me type in a lecture-format class. I hope these GIS novices got a good sense of what is possible with ArcPython. My slides are attached below.