GIS Internship - GIS Portfolio

I felt that the best way to showcase my GIS portfolio was through a personal website, so I used WIX to set up a website. I will admit creating a website from scratch is not really in my wheelhouse. Thankfully, my wife, who has experience in marketing, was willing to share her expertise and help me create a nice design and color scheme. She also helped me come up with the title of page “REIMagine with Jackson." I decided to set up my website with a home page that you could use to navigate to other pages. The other pages included in my website are an about me page, a GIS projects page, a publications page, and a contact me page. 

The about me page features a brief description of my background and then links my most recent CV and GIS resume. The GIS projects page highlights my favorite GIS projects from the GIS certification and my research projects. The publications page links directly to research articles that I helped author. Last, the contact me page has my contact information and provides an opportunity for people to reach out to me. 

Since my professional interests extend beyond GIS, I wanted to use a media format that would allow me to go back and edit/update my portfolio. As I continue to work on the website, I plan to showcase more about my research endeavors from my time at UWF and from my professional career. 

Check out the website using this link!

GIS Internship - GIS Day Celebration 2025

To celebrate GIS Day, I posted on my LinkedIn about a project that inspired me to continue learning new GIS skills. The project involved updating the signage on the UWF nature trails. This is probably one of my favorite projects because it involved collaborating with so many people at the university and in the community. Many of the relationships created throughout the project have continued to this day. This project is also part of the reason that I ended up in the certification program. Here is a link if you want to check out the post! I have also attached a picture of the signage.

Unfortunately, I was unable to attend the EPA GIS user group GIS Day celebration this year, but after attending a recent environmental symposium and local user group meeting, I look forward to attending GIS Day events in the future and connecting with other GIS professionals!

Module 5: Supervised/Unsupervised Classification

    For this week's lab, I used different classification methods to classify land use/land cover of different areas. Throughout the lab, I explored ways to gather information that feeds into the classification (supervised) process and ways to avoid spectral confusion during the classification process. The selection of the bands used in the classification plays a big role in the quality of the output. Last, I learned how to interpret some of the output generated during the classification process such as the distance file.

    For exercise 5, I was given a raster of Germantown, MD, and I was asked to classify the image using a supervised classification. Initially, I had difficulty properly classifying the roads. Personally, I think an image with a higher spatial resolution would have been better for classifying the roads, but after removing some of my initial road signatures that I made and adding more agriculture and deciduous forest signatures, I was classifying the roads better. The final product is not perfect. Some of the agricultural areas could probably be better classified, but this output is much better than the initial output that I had. The distance file indicates that most uncertainty in the classification is around some of the rural areas. Below is an image of my classification output for Germantown, MD.

Module 4: Multispectral Data Interpretation

    In this week’s lab assignment, I learned about techniques that are used to identify features in ERDAS and ArcGIS Pro using multispectral imagery. Then, I utilized those techniques to identify features based on their spectral signature.

    In exercise 1, I explored accessing and downloading Landsat satellite imagery from Glovis. Then in exercise 2, I utilized high pass and low pass filters in ERDAS to understand detailed and broad patterns in imagery. Next I learned about application of the focal statistics tool in ArcGIS Pro and compared output from ERDAS and ArcGIS Pro.

    In exercise 3, I accessed the histogram of imagery in ERDAS and ArcGIS Pro to look at the distribution of pixel values with a layer of multispectral imagery. I learned how to interpret the histogram graphic and alter breakpoints in both programs. Continuing to Exercise 4, I explored how altering the band combinations affected the visibility of certain features. In particular, I was interested in understanding what band combinations made logging roads more visible. I personally found that the TM False Natural Color band Combination (Red:5, Green: 4, and Blue: 3) made the roads standout the best. The roads appeared pink which allowed them to stand out against the largely green background (forest).

    For exercise 5, I used the NDVI tool to calculate the normalized differential vegetation index (NDVI) using multispectral imagery of Washington state. Then, I compared the NDVI layer values for different features in the image. Next, in exercise 6 I utilized the metadata to identify important information about the layers such as the data type, maximum/minimum pixel values, and the pixel size (image resolution). Understanding this information helps me make inferences about the broad scale trends in an image (e.g. Does the image feature mostly urban areas? forested areas? water?).

    Last, in exercise 7 I applied many of the skills learned in the previous lab exercises and to identify features based on their spectral signatures. Altering the multispectral bands and knowing what to look for in the output based on histogram plots is an extremely valuable skill set. Using band combinations that I did not necessarily explore in the previous lab exercises helped me create maps that highlighted the features of interest more clearly. Below is one of the maps that I made for the assignment. By using a custom band combination (R: band 2, G: band 3, B: band 6), the snow caps are highlighted as a bright yellow which sharply juxtaposes the blue/purple features in the background.

Module 3: Introduction to ERDAS Imagine

    In this week's lab, I learned about ERDAS Imagine and some of its capabilities. Most of part A was spent orienting myself to the feature and functions with ERDAS Imagine. I have not used this program before, so it was all very new to me. The navigation of images was somewhat similar to ArcGIS Pro but there were a few minor differences. I was able to follow the lab instructions for the most part. Once I got to the tm_subset creation and updated the area values, I did have an issue saving the tm_subset with the updated attributes. It does not seem like ERDAS Imagine likes to overwrite files. I ended up having to repeat the steps and create a new tm_subset. This time I elected to remove the image and save it when the pop up window appeared. This seemed to avoid the overwriting issue that I had initially. I then created a map in ArcGIS that displayed the tm_subset file created in ERDAS imagine, and it listed the area values calculated. Below is a map of the tm_subset file that I created.

    In part B of the lab, I explored the information stored in the metadata windows in ERDAS Imagine for a multispectral image. After exploring the metadata, I opened four view windows and compared four images with different spatial resolutions. The images with higher resolutions allowed me to see cars or groups of cars in a parking lot, but for the images with lower resolution, I was not able to distinguish the cars in the parking lot. Objects smaller than the pixel size of the image are not distinguishable. Next, I looked at four images with different radiometric resolutions and compared their appearance based on their resolution. As the radiometric resolution increased, the detail of the images became more and more apparent. Other resolution considerations include temporal and spectral resolution which deal with the time between images and the range of wavelengths/bands of EMR collected, respectively. Last, I used ERDAS to calculate the percent area of each soil type, and I learned how to select soil types based on criteria. The criteria function worked similarly to the select by attributes function in ArcGIS Pro. Once I selected the areas with higher erosion potential, I inverted the selection and hid the unwanted areas. 

Module 2: Land Use/Land Cover and Ground Truthing

    During this week's lab, I learned about land use/land cover (LULC) classification, and I accessed the accuracy of LULC layers using ex situ ground truthing methods. 

    For the first part of the lab assignment, I created an LULC dataset using satellite imagery of Pascagoula, MS. During the process, I realized how tedious the process can be, but I did learn a few tricks that saved me a significant amount of time. The first tip is the use of the clip function when modifying the features of a feature class. Before mapping each class take time to determine the major LULC classes. Map those areas first as large areas (don't exclude areas that differ from the primary LULC class). Once you have the large area mapped. Go through and map the areas that differ from the main LULC class of the area. Once you have the smaller areas mapped, you can select them and clip the larger area so they become separated. Another helpful tip was to use the snapping function when drawing the polygons. This ensures that the polygons directly border one another. Preventing a gap between the polygons would be nearly impossible without this function. Lastly, it is important to determine how invested you want to be with your classification. The map that I generated is somewhat coarse since I was only classify to Level II LULC classification. Higher classifications will require greater time to classify, so make sure that you take the time to consider the pros and cons of an LULC product that it is more detailed. 

    For the second part of the lab assignment, I explored ground truthing and assessing the accuracy of an LULC layer. I did this by randomly generating points throughout the study area. I then visited those points in google maps and used the satellite imagery and the street view to determine the true classification of the point. Next, I compared the observed classification and the mapped classification and noted any differences. I used this information to assess the overall accuracy of my LULC layer. Ultimately, I determined that my LULC layer had an accuracy of 80% which is not bad. The error for some classes was greater than others (e.g. cropland). A random stratified sampling method would have provided more insight into the accuracy for each class.

Below is a copy of my map displaying the LULC classes and my accuracy assessment.

Module 1: Visual Interpretation

    In the first lab assignment for Photo Interpretation and Remote Sensing, I explored different attributes and how they may be useful for visual interpretation of satellite imagery. The lab was split into three exercises that explored different aspects of imagery.

    In exercise 1, I learned about identifying tone and texture in imagery. I identified representative areas for categories of tone (very light, light, medium, dark, and very dark) and texture (very fine, fine, mottled, coarse, and very coarse). The areas within each of the categories were relative to the image that I was provided. The exercise helped me better understand the concepts of tone and texture and how that could be useful for visual identification.

    In exercise 2, I identified features in an image of a beach based on shape/size, shadow, pattern, and association. Shape/size seemed like obvious attributes for identifying features whereas shadow, pattern and association were not attributes that I had used frequently. The resolution of the image made it difficult to identify some features based on their shadow, but in the end, I was able to identify a water tower, sign, and utility pole. The other categories were a bit easier.

    Last, I explored how different imagery a different perspective of color in exercise 3. I used images of visible light (“true color”) and infrared (false color IR”) to understand how different features/colors appear with different imagery. All in all, this lab was helpful for understanding the basic concepts used in visual interpretation. I look forward to learning about automated techniques that employ some of these attributes.