GIS For Archaeology: Module 2

For the first lab assignments for Applications in Archaeology, we worked with data related to the Great Chicago Fire of 1871, comparing the city just prior the fire and approximately 20 years later. The map below illustrates the boundaries of the city in both 1869 and 1890, along with the origin and extent of the fire, landmarks constructed prior to the fire, and landmarks constructed after the fire but before 1890. This allows us to see both the expansion of the city and the concentrated construction of new landmarks within the damaged area and its surrounding wards.

I didn't have enough time this week to make the map look as good as I would have liked (especially those labels--yikes, ArcMap is bad at automatically labeling!), but I think it turned out okay, albeit unimpressive. The data for this week was very interesting, even though the lab itself was mostly a review of things I've done before.

GIS Portfolio

Below is the link to my GIS portfolio showcasing some of my projects and assignments from the last few semesters. I'm considering this a work in progress; since I haven't yet taken the final two advanced courses for the GIS certificate, and I'm doing GIS work outside of class as well, I will be able to add more examples in the future, or replace these with better ones. I will probably make a website at some point, too. In the meantime, here is the "paper" version.

The second link is to an audio file with my [awkward] answers to two mock interview questions. I get kind of self-conscious about recording myself talking to no one rather than to an actual person, but I didn't want to write out my answers beforehand because that seemed like cheating!

Portfolio
Interview Questions

GIS Day!

As part of the internship seminar, we were to celebrate GIS Day (which is technically in November, but that doesn't mean we can't observe it at another time of year) by sharing GIS with others. Conveniently, I had already planned to attend the Middle Atlantic Archaeological Conference in Virginia Beach last weekend, where I gave a presentation about some of the work I'm doing for my internship project--specifically, georeferencing several historic maps of the region where our archaeological site is located. Unfortunately, since I'm still in the process of georeferencing, we haven't been able to do much with them in GIS yet, but I spoke about the benefits of eventually being able to overlay them with other GIS data and use them to digitize features like historic road networks and the locations of other plantations, mills, stores, and other sites that are now long gone, all in the interest of helping to contextualize our site.

The conference is typically attended by professional archaeologists in academic, government, and private sector positions, along with many students. My presentation was in a session organized around a regional theme, but the following day I was able to attend another session that was all about applying GIS to archaeology. All of the papers in that session were great, but one that I found particularly interesting (and important) was about creating detailed predictive models to assess the risk sea level rise poses to coastal sites and estimate how long it will take for those sites to be destroyed. All in all, it was a great conference. It's exciting to hear what other people are working on and that they're interested in my work, too.

Photo Interpretation and Remote Sensing Module 10

This module's focus was the last method of image classification, supervised classification, which involves identifying known examples of different LULC classes so that the software can associate a spectral signature with each one and classify the remaining pixels based on their statistical similarity to the training classes. It's more work upfront than unsupervised classification, but there's the advantage of not having to go back and figure out what the classes are after the fact.

This was another fun lab assignment, but also kind of frustrating. Despite several attempts, I had a very hard time establishing a unique spectral signature for the road class, and as you might be able to see in my map below, even the final output has some confusion between the road and urban classes, and possibly also road and grass in some places.

You can also see from the distance output image (in which brighter areas correspond to areas that are more different from the established spectral signatures and thus more likely to end up misclassified) that there might be some issues with some of the agricultural areas and along the edges of certain features like the lakes. If this map were for a real-world application, those areas might need to be ground truthed or compared with other imagery to see if they need to be reclassified.

Photo Interpretation and Remote Sensing Module 9

This week we're back to land use land cover classification from aerial imagery, this time using automated methods. In this lab, we learned how to perform unsupervised classification in both ArcMap and ERDAS Imagine, culminating in a classified image of the UWF campus. For this assignment, the image was automatically classified into 50 different classes based on pixel characteristics, then manually simplified into five classes by identifying the broad LULC category each of the original 50 classes. My output is below; I ended up with a significant portion of the image falling into the "Mixed" class, which consists of pixels whose automatically assigned spectral class spanned multiple LULC classes (most of the confusion seemed to be between grass and buildings/roads--when I started out by assigning classes to the grass category, I suddenly had a number of green roofs!). 

Photo Interpretation and Remote Sensing Module 8

Another fun lab this week! After learning about thermal energy, how to work with thermal infrared imagery in both ArcMap and Erdas (as well as how to compile multiple image bands into a single image in both programs), and some applications for thermal image analysis, such as identifying forest fires, we were asked to use the thermal band in one of the lab images to identify a feature of our choice.

I looked at the image from Ecuador using a panchromatic display of the thermal band in comparison to a true color composite and a panchromatic display of the near-infrared band. There were several things in the image that intrigued me (some of which I actually wasn't able to identify with certainty), but I eventually noticed this pair of bridges that only popped out at me when looking at the thermal band--in the visible and near-infrared bands, the one on the left is subtle and the one on the right is almost invisible, because their coloring isn't that far off from the water and both pavement and water absorb strongly in the near-infrared. But pavement certainly gives off thermal energy after warming up in the sun!

Anyway, I thought it was really interesting, because I wasn't expecting such a stark difference in what seems like it should be a prominent feature. There are certainly benefits to comparing multiple image bands.

Photo Interpretation and Remote Sensing Module 7

Lots of imagery and maps this week! We learned about working with multispectral imagery in both ERDAS Imagine and ArcMap. For the lab assignment, we were given a Landsat Image and asked to identify three features based on clues about their reflectance in different bands of the image, i.e. at different wavelengths. Then we had to create composite images using band combinations that would make those features stand out.

Here are mine:

One issue I had was that the first image (water bodies) looked very washed out once I brought it into ArcMap and I couldn't figure out how to fix it, although I was able to do some histogram correction using the Image Analysis window, just not to get it looking as good as it did in Imagine. What's puzzling is that it didn't happen with the other two, so if it's related to creating the image subset, I don't know why it only caused a problem with one. Aside from that, I enjoyed this lab!