GIS Portfolio

To show off all I have learned during my GIS Graduate Certificate program, I created an online portfolio. Click here to check it out. 

The portfolio includes my resume highlighting my GIS experience as well as examples of work. I tried to highlight different skills I learned during coursework and also included several examples of projects from my internship. I have maps highlighting my work in cartography, suitability modeling, land classification, LiDAR analysis, least-cost analysis, python, in-field data collection, ArcGIS online, and database creation. Some of my examples from the internship illustration my application of GIS in the field of archaeology including GPR, site documentation, historic landscape analysis, and community-based projects.

Here is a short video tour of my portfolio where I discuss a database I created with open data and my favorite map.

In creating the portfolio, I've been able to reflect on all of the things I've learn over the year, and it's pretty impressive! I know I've done a lot of coursework, but to see how I've actually applied it at work through my internship has been even more inciteful. Simple tasks that seemed so arduous before I started this program I can now do without even thinking about it. I'm excited to continue my GIS journey and have already started a list of projects to pursue when I'm done with coursework and have a little more freetime to play. 

GIS Day

 For my GIS Day Event, I wrote a blog for the organization I work for. GIS is such an integral part of archaeology these days, but I'm guessing a lot of the public don't really understand exactly what GIS is and how it's utilized. I got to chat with my awesome colleague and fellow GIS nerd about her experiences with GIS. While I've done a lot with her (or more truthfully, watch her do cool GIS things while I offered thoughts on the project outcomes without entirely understand what she was doing), I never knew about her journey into the field. Below is the blog text. Click here to see it with all the images on the original blog.

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Every November, GIS Day is a celebration of the innovative applications of GIS! GIS, short for geographic information systems, are databases that help you store, visualize, and analyze spatial data (data with locational information). Today, most of this work is done through digital databases that can incorporate mobile apps for data collection and user interfaces online for viewing and sharing data. GIS is a field in and of itself, but it also an important toolkit that has been incorporated into many fields of study, industry, and commercial ventures. I (Emily Jane) have recently been working on a graduate certificate in GIS to do more with archaeological and heritage data at FPAN. But I'm by no means the only person using these skills in our organization! To celebrate, I thought I'd sit down with our resident expert, HMS Florida Database manager Kassie Kemp, to talk about all the cool ways GIS helps us do our work. 

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 Emily Jane (EmJay): Happy GIS Day, Kassie! 

 Kassie (KK): Happy GIS Day! Welcome to the GIS fold. 

 EmJay: I'm excited to be inducted into such a cool group! I thought we could chat a little bit about GIS and how we use it at FPAN. But before we get into all of that, I just wanted to start by asking what got you into GIS in the first place? 

 KK: It was a suggested class in my undergraduate program. I had heard about GIS but had no idea what it was. It ended up being the greatest class ever! GIS combines all the things I love: data, computer processing, and archaeology. The class wasn't specific to archaeology but it was one of the applications that the class covered. At that time, there weren't a lot of places offering GIS to archaeology students at the undergraduate level, so I was very fortunate to learn about it so early in my career. 

EmJay: That's awesome. I decided to learn GIS after working so much with you on our projects at work. I guess my entry into the world of GIS was through the data collection we do in the field. And then I'd come back to office to watch you turn it into something beautiful and magical. What is your favorite thing about GIS?  

 KK: Hahaha. Oh yes, GIS is all about visualization. Archaeology itself is a lot about visuals as well, and so that visualization in GIS can help us look at the data across a site itself, or at clusters of sites across states or regions. We can learn a lot about past cultures by looking at where things were. With all of the digital GIS programs, we can do this so easily today. And the analysis tools in the software can help us do higher levels of data analysis, which can even give us even more insights! As part of my field school, we actually mapped in our test units into GIS. Archaeology is destructive by nature, so GIS provided a way to archive and document the test units we had excavated. This allows others to examine and reexamine the sites that are no longer there. 

 EmJay: I agree. So much of archaeology is about understanding the context of artifacts, features, and sites. And a huge part of this context is that locational data. I can't imagine how much more difficult all of this was before the digital programs. So tell me more about how you use GIS at FPAN. 

 KK: I do so many things in GIS at FPAN! A lot of centers around recording and documenting resources. The first step in this it to check to see if a site is recorded in a given location, or to see if a recorded site is in the correct location. I take the data my colleagues collect in the field, or information collected by our Heritage Monitoring Scouts, to check and update the locations of site they've visited. GIS makes this very easy because I can see where all of the sites are on a basemap of the real world.

Another huge part of what I do is map making. Turning this circle representing a site into a map that can convey information about where and what the site is. A lot of the maps go into the Florida Master Site File (FMSF) with the forms for sites we're newly recording or updating. But I also make maps for public programs, presentations, and publications. 

In addition to creating and updating data, I also manage known information. The HMS Florida Monitoring Database is all map based. We are constantly updating it with new information from the FMSF and adding new tools and layers like sea level rise projections, historic maps, and aerial imagery. 

 And sometimes I get to do fun projects like mapping shorelines and comparing the data collected through time to see changes through time like erosion. Or I use old maps and aerials to help us relocate sites. This often involves georeferencing the imagery and then pulling spatial information from it.  

EmJay: We do so much with GIS! I think back to when we had less of these tools and I don't know how we accomplished what we did. One of the major things I've been using it for is to attach the locational data to GPR surveys. I collect the location of grid in the field using our database and then georeference the final slices from the survey to this grids. This allows us to go back out in the field and pinpoint any anomalies we found.

Thanks again for chatting GIS with me! I look forward to working with you on whatever cool project we think up next! 

 KK: Anytime! I do too! Words and images by Emily Jane Murray and Kassie Kemp, FPAN Staff.

Unsupervised and Supervised Classification

In this week's lab, we worked on completing unsupervised and supervised classifications of aerial imagery. Unsupervised classifications are pretty much when the software tries to figure things out on it's own. To create this, I just told the software how many classes to find and then had to sort through them to recode them into the appropriate classes I desired. The imagery we worked on feature a portion of UWF's campus. It was a bit tricky as some of the vegetation was similar in color to the buildings. It might have been easier if the imagery was collected in the spring or summer when it was a little less brown.

Supervised classifications are completed by giving the software some examples of classes and then letting it figure out which pixels fall into which class. For this classification, I found spectral signatures as examples of each class. These are created by either drawing an area of interest (AOI) by hand in a known area or using a pixel at a specific coordinate and expanding on this using a grow tool. We also had to figure out the best band combination to highlight the differences between the classes. Needless to say, I feel like the supervised classification worked much better, but it did take a little more time and knowledge on the front end. 

Here is the supervised classification I created this week, exploring the land use of Germantown, Maryland.

Spatial Enhancement, Multispectral Data, and Band Indices

This week, we learned about image preprocessing and spatial enhancements to aid in interpreting data. We completed several exercises to learn about the various aspects of this and then completed a final exercise to put it all together and create 3 maps as deliverables for the assignment. 

The first few execises focused on obtaining and prepping the data. First, we learned how to download data. In this, we used Glovis to download Level 1 products, which have been terrain corrected, orthorectified and georeferenced, and radiometrically calibrated. Level 2 generally also would include atmospheric corrections. The next step included formatting the files, importing the imagery as a tiff and saving them as image files in ERDAS Imagine.

Then we moved on to the pre-processing. We applied basic filters in Imagine including low passes for broader patterns, and high passes and sharpening for finer details. We also looked at how to use the Focal Statistics tool in ArcGIS for similar filtering. Next we explored histograms in both Imagine and ArcGIS to understand how to find spikes and manipulate breakpoints in the data. Then we explored how to change the bands to explore different visualizations of the imagery as well as how to create band indices to enhance the appearances of certain features. Finally, we looked at the Metadata and used the Inquiry Cursor to examine specific pixels.

For final exercise, we put all of these skills to use to examine three features within an image from Northwest Washington state. We had to examine the historgrams to look at areas with spikes, then explore the imagery to figure out where these pixels were in the data. We looked at individual bands in grey scale as well as explored the various band combinations to see which helped visualize the features best. Finally, I used the Inquiry Cursor to make sure I had the correct pixels as noted in the histogram based on there brightness. After identifying the areas, I used the Inquiry Box and Subset tools to create a new file that contained only the feature data. I then brought these into ArcGIS to create maps for each.

Feature 1 was identified as a spike of dark pixels in Layer 4 of the data. It turned out to be the rivers. I visualized this using False Natural Color as this emphasized the contrast between the river and the landscape.

Feature 2 was identified as bright pixels in layers 1-4 and dark pixels in layers 5-6. This feature turned out to be the snow on the mountains. I used the False Color IR to visualize this as this gave the best contrast between the snow and the surrounding ground cover.

Feature 3 included areas of water that looked much lighter in layers 1-3 but was not visible in layers 4-6. I used True Color to visualize this as it only used the layers that have the lighter pixels visible. This had the best combination of RGB to show the area.

Intro to ERDAS Imagine and Digital Data

This week our lab assignments focused on learning the basics of electromagnetic radiation (EMR) and using ERDAS Imagine to work with this data. In the Part A of the lab, we calculated wavelength, frequency, and energy of EMR. The formulas we used included Maxwell's Wave Theory (C = λv) and the Planck Relation (Q = hv). The big take away on how EMR works is that longer waves have lower frequencies and less energy while shorter waves have higher frequencies and more energy. I think I managed not to get lost amongst the zeros and decimal places! 

After these calculations, we did a few simple activities in ERDAS Imagine to get us familiarized with the software. This included opening files, saving files, zooming and panning, changing the band settings for visualizations, calculating areas, and exporting a subset of data to create a map. Apparently Imagine can have some issues with map preparation, so we exported a raster and finished the map layout in ArcGIS Pro. I choose a subset of the data that included some lovely mountain ranges and had all pixels classified. Take a look at my map below!

In Part B of the lab, we did a deep dive into exploring raster data. We looked at three datasets to explore Metadata. The first was one file with multiple layers. In this exercise, we learned how to open the Metadata menu and what each part of the information means. In the second exercise, we looked at spatial resolution by comparing four images with various resolutions, from 2m up to 16m. The file with each pixel representing the smallest area (2m) had the highest resolution which means it had the most detail. The 16m image was super pixelated and hard to see what was being depicted! The third exercise involved looking at the radiometric resolution of four different images. We examined the max digital umber, the data type (how many bits), and how many wavelengths or bands each image had. While the difference in this resolution was less noticeable to the eye, the files with higher radiometric resolution have more information documented within the pixels so software can get more detail from the data.

The last exercise for Part B included using some simple tools to calculate and mask data. We learned how to change the colors of features in the Attribute Table, as well as how to add columns of data. We completed a simple task of calculating the area and the percentage of the total area for each soil type. Many of these tools were very similar to those in ArcGIS.

ERDAS seems like a pretty cool program to help with exploring and manipulating raster data. I'm excited to see what else we do with it!

Land Use/Land Cover Classification, Ground Truthing & Accuracy Assessment

This week we focused on land classification by creating our own classified map for a portion of Pascagoula, MS. We were provided an aerial image and had to create a polygon layer representing the different land use/land cover classifications using the Anderson system down to a level 2 classification. I accomplished this by creating a new featuring class and drawing polygons based on the pixels in the image. I used a few extra tools like the clipping tool to help ensure each area had it's own stand alone polygon with no overlapping shapes. I then noted which class the polygon was in the attribute table and visualized the different classes using a color code.

Behold: my land classification map in all it's glory!

To check the accuracy of our classification, I generated 30 random points across the area of the image. I copied the coordinates for each point and compared these to the locations in Google Maps. The resolution of the aerial imagery, combined with the street view feature, allowed for a better assessment of these points to see if my classification was correct. I calculate a simple accuracy of 80% of the points were classed correctly. 

The worst class was 43 Mixed Forest Land. All of these would have been better classed as 41 Deciduous Forest Land. When determining the classes originally, I decided to use the mixed class because I couldn't determine the type of trees. However, Google showed me mostly deciduous so all should really be reclassed as such. Many of the errors were the result of not drawing the boundary in the exact place, or lumping resources into the surrounding class (like forest into residential, or residential into commercial). I interpreted the instructions to not get too granular with the classifications, so my lumping proved to cause some errors!

In general, this was a really interesting exercise. When I started, I though it was going to be the most tedious thing ever - drawing all of these polygons! But the snapping tool made the entire process go really fast once I had a few polygons on the map. The randomly generated points for checking accuracy were interesting. It really became a bit of the luck of the draw if I ended up with a point that was inaccurate. I can see why folks debate how many points you need to check, and how to distribute these over the project area. And in the end, you may still miss something. But you can't check every pixel and coordinate!

Visual Interpretation

This week kicked off the first of my assignments for Remote Sensing and Aerial Photography. We conducted several exercises to get us thinking and practicing visual interpretation of aerial imagery. These included assessing the tone and texture of an image, identifying features using various methods, and comparing standard color imagery with false infrared color. Below are two of the maps I made.

In this exercise, we assessed an image to label various tones from very dark to very light, as well as various textures including very coarse to very fine. We selected areas representing each and created polygons to show this.

In this exercise, we inspected an image to find various features using their shape/size, shadow, pattern, or association to determine what the object is. We created points for the objects and labelled them to show what they are and how we determined this.