This lab introduced key visual interpretation skills used in remote sensing. Each map explores different visual elements—tone, texture, shape, size, shadow, pattern, association, and color—to identify geographic features using aerial imagery.
Figure 1. Identification of Tone and Texture in Aerial Photography
This map illustrates how variations in tone (brightness) and texture (surface roughness) reveal distinct land cover types such as vegetation, water, and urban areas. Differences in tone and texture help interpret the landscape even without attribute data or scale.
Figure 2. Identifying Geographic Features Using Shape, Size, Shadow, Pattern, and Association
This layout demonstrates how visual cues—such as shape, shadow length, repeating patterns, and spatial associations—assist in recognizing man-made and natural features. Examples include identifying buildings by shape, trees by shadow, and neighborhoods by pattern.
Figure 3. Feature Identification Using True Color Imagery
This true color image shows features as they appear to the human eye, making it easier to distinguish forests, water bodies, and developed land. It emphasizes how true color composites provide realistic visual context for aerial interpretation.
Through these three exercises, I learned how visual interpretation techniques transform raw aerial imagery into meaningful geographic information. Recognizing features through tone, texture, and spatial relationships is an essential foundation for remote sensing analysis.
This week marks the start of a new adventure — GIS 4045: Photo Interpretation and Remote Sensing! I’m really excited to explore how satellite imagery and aerial photos can reveal patterns that aren’t visible from the ground. Coming from an emergency management background, I’m especially interested in how remote sensing supports disaster response, from mapping hurricane damage to monitoring flood zones.
As someone who loves traveling and scuba diving, I think it’ll be fascinating to see how these same tools are used to study coastlines, coral reefs, and environmental change here in Florida and around the world. Looking forward to sharpening my “spatial eye” and seeing the planet in a whole new way!
This project focused on analyzing the environmental and community impacts of the Florida Power & Light (FPL) Bobwhite–Manatee Transmission Line. Using GIS tools, I evaluated conservation lands, wetlands, uplands, and parcel data within the study corridor to understand potential environmental sensitivities. The project built on skills from earlier modules and combined spatial analysis with cartographic design to communicate complex spatial relationships through clear visual maps.
Throughout this project, I used several ArcGIS Pro tools and workflows:
Clip, Select by Location, and Intersect to define environmental impacts.
Calculate Geometry and Summary Tables to quantify affected lands.
Layout View for creating final maps with unified color schemes, legends, and scale bars.
Attribute queries and labeling to differentiate between wetlands and uplands.
I also incorporated data from multiple authoritative sources, including:
Florida Department of Education (School Locations)
U.S. Census Bureau TIGER/Line Shapefiles (Parcels, Roads, Boundaries)
FGDL Conservation Lands Dataset
NWI Wetland and Upland Polygons
💡 Learnings
This project reinforced the importance of data organization, projection management, and clear cartographic communication. I learned how essential it is to track coordinate systems and attribute structures before beginning analysis. Working through multiple data layers helped strengthen my understanding of overlay analysis and how different GIS operations can produce unique insights depending on the sequence of tools applied.
😣 Frustrations and Fixes
Not everything went smoothly! Some of the main challenges included:
Difficulty adding neatlines to maps—this option was unexpectedly unavailable in the Insert menu.
Trouble summarizing multiple environmental datasets together in one statistical table.
Confusion over source citations, since not all datasets included clear attribution metadata.
Despite these obstacles, persistence and experimentation paid off. By testing alternate symbology, refining selections, and verifying projection settings, I was able to complete a cohesive analysis and presentation.
🎤 Reflection
This project demonstrated how GIS supports environmental planning and infrastructure assessment, particularly when balancing community needs with ecological sensitivity. The ability to visualize overlaps between human development and conservation priorities makes GIS a powerful decision-making tool in both academic and professional contexts.
Blog Title: Ten Hours, One Misplaced Campus, and a Lesson in Georeferencing
I’ll be honest — I spent ten hours trying to georeference the UWF SP1 image before realizing I was aligning it to the completely wrong part of campus. Ten. Whole. Hours. I zoomed, stretched, and cursed at Null Island (that mysterious spot off the coast of Africa where all unreferenced rasters go to die), wondering why the roads never quite matched. Only after an embarrassingly long stare at the campus map did it hit me: I’d been forcing the image to fit a section it didn’t belong to. Lesson learned — sometimes it’s not your control points that are off, it’s your entire frame of reference.
Once I finally lined up the right buildings, everything clicked. Using the Georeferencing tools in ArcGIS Pro, I matched the unknown rasters (uwf_n.jpg and uwf_s1.jpg) to known vector data — roads, buildings, and the Eagles Nest feature. Through this, I learned the delicate dance between Root Mean Square Error (RMSE) and actual visual accuracy. A low RMSE feels satisfying, but if the image doesn’t look right, it isn’t right. Precision means nothing if your buildings are swimming in the bay.
After georeferencing, we moved into editing and digitizing. Creating new polygons for campus buildings and tracing new road segments taught me how essential snapping and attribute management are for clean, logical data. It’s oddly satisfying to see your newly drawn Gym building sitting perfectly atop the raster you just anchored to reality. (And yes, saving edits — manually — is a must. Auto-save doesn’t exist here to save you from yourself.)
The geoprocessing tools came next, with the Multiple Ring Buffer (MRB) tool taking center stage. By buffering 330 and 660 feet around the Eagles Nest, we mapped the FWC’s conservation zones — a reminder that GIS isn’t just about pixels and points, but protecting habitats through spatial awareness.
Finally, the lab ventured into 3D mapping, hyperlinking data (like the eagle nest photo stored on Google Drive), and visualizing layers in a scene that felt almost tangible. Seeing those buffers rise in a 3D environment made the entire process — the frustration, the misalignment, the rediscovery — feel worth it.
In the end, this lab wasn’t just about georeferencing or buffers. It was about patience, perspective, and realizing that accuracy in GIS depends as much on critical thinking as it does on technical skill. And next time, before spending another ten hours georeferencing, I’ll double-check that I’m even on the right part of campus.
ArcGIS Field Maps, Data Sharing, and Projections: A Two-Part Journey in GIS 4043
This week’s lab was a double feature — part hands-on data collection with ArcGIS Field Maps and part deep dive into the world of map projections. The two labs fit together beautifully: the first focused on collecting and sharing spatial data in real time, while the second emphasized understanding and managing the coordinate systems that underlie all GIS work. Below, I’ll walk through my process, what I learned, and where I found the “aha!” moments along the way.
📱 Part 1: ArcGIS Field Maps & Data Sharing
The first part of the lab centered on creating an empty feature class, configuring it with domains, sharing it as a web layer, and then collecting data in the field using the ArcGIS Field Maps mobile app
ArcGIS Field Maps
Setting Up the Data Structure
I began by creating a new geodatabase in ArcGIS Pro and setting up a coded value domain for the “Condition” attribute. This step felt very procedural — define the domain, set up the fields, match the data types, and attach the domain to the correct field — but it paid off later. As I noted in my process summary, planning and organization really mattered here. Because I set up the domains carefully, everything worked smoothly when I started collecting data. No error messages, no mismatched fields — just smooth data entry.
Symbolizing for the Field
Before heading out, I customized the symbology for the Condition field (Excellent, Fair, Poor) using Unique Values and bright, field-friendly colors. This step seems minor, but when you’re outside trying to squint at your phone screen in the sun, clear symbols make a difference.
Sharing and Collecting
Next, I connected to the UWF ArcGIS Online organization, published my feature class as a hosted editable feature layer, and configured the web map for use in Field Maps. I also enabled attachments so I could include photos directly in the attribute data, which is especially useful for post-disaster assessments.
Out in the field, I collected several features, each with a condition value, notes, and a photo. Because my domains were set up correctly, the mobile experience was seamless. I could easily select the right category, take a quick photo, and submit the point.
Why This Matters: Hurricane Response
One of the reflection questions asked how ArcGIS Field Maps could help after a hurricane. The answer is clear: this technology allows for rapid, coordinated, real-time damage assessments. Crews could collect data on flooded areas, downed power lines, blocked roads, and infrastructure damage with GPS precision and attach photos and notes to each feature. Data would sync immediately to ArcGIS Online, allowing emergency operations centers to visualize damage as it happens. Even if cell service is down, offline maps can keep the work going until connectivity is restored. It’s a textbook example of how mobile GIS enhances situational awareness and speeds up recovery planning.
Sharing in Multiple Formats
To finish, I shared my data in three different formats:
ArcGIS Online Map – fully interactive and editable within a browser.
KML File for Google Earth – great for visualization and public sharing.
Map Package (MPK) – useful for other ArcGIS Pro users who need the full dataset and map.
Each format has different strengths and requirements.
Image coming soon. Blogger does not want to upload them today.
🧭 Part 2: Introduction to Projections
The second lab dove into coordinate systems and projections, a foundational GIS concept that often gets overlooked until something doesn’t line up correctly
Projections
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Downloading Data & Exploring Coordinate Systems
I started by downloading Florida county boundary data (projected in Albers Conical Equal Area) and adding it to a new ArcGIS Pro project. By examining the metadata and map properties, I confirmed that both the layer and the map were in the Albers projection. This step set the baseline for the next exercises.
Projecting to UTM and State Plane
Using the Project tool, I reprojected the county boundary layer twice:
First to UTM Zone 16N (NAD 1983), applying the appropriate transformation.
Then to State Plane Florida North (FIPS 0903), which is commonly used in Florida for local analyses.
I created separate map views for each projection — Albers, UTM, and State Plane — and compared them visually. There were subtle differences: the UTM version looked slightly compacted and rotated counter-clockwise compared to Albers. The State Plane projection aligned closely but is optimized for local precision.
Image coming soon. Blogger does not want to upload them today.
Quantifying the Differences
The lab then had me calculate the area of four counties (Alachua, Escambia, Miami-Dade, and Polk) in each projection. The results showed only tiny numerical differences — not visually noticeable, but present when you look at the attribute table. These small discrepancies highlight why projections matter for accurate spatial analysis, even if your map “looks right” on screen.
For statewide datasets, Albers is often preferred. For local engineering work, State Plane is typically more accurate.
✨ Key Takeaways
Preparation pays off: Careful domain setup in Field Maps prevented downstream issues.
Different formats serve different audiences: Map Packages are for ArcGIS Pro users, ArcGIS Online maps are interactive for organizations, and Google Earth KMLs are great for public sharing.
Projections matter: Even when differences are subtle, choosing the correct projection ensures spatial accuracy, especially for area calculations and overlay analysis.
Raster handling requires attention: Defining vs. projecting is a crucial distinction.
These two labs complemented each other well. The first showed how to collect and share data, and the second reminded me to handle that data responsibly in the spatial reference realm.
🗺️ Geocoding Schools in Manatee County: A Coordinate System Saga
This week’s lab was all about geocoding schools in Manatee County, FL — and let me just say, what started out sounding straightforward turned into a bit of a software scavenger hunt. The objective was clear enough: clean up a dataset of school addresses, download TIGER shapefiles, project everything to the correct coordinate system, create an address locator, and geocode. Easy… in theory.
📐 The Great Coordinate System Hunt
The lab called for NAD1983 (2011) HARN State Plane Florida West FIPS 0902 Feet, which makes total sense geographically and for road data. Unfortunately, ArcGIS Pro didn’t seem to agree. The projection wasn’t in the software by default, so I had to hunt it down, download it, and manually import it. This step felt more like a mini-quest than a lab task. Definitely not something you expect to spend 15 minutes Googling in the middle of a geocoding exercise.
📊 Table to Table… Or Not
The instructions referenced the trusty Table to Table tool, which—plot twist—doesn’t seem to exist in ArcGIS anymore. I thought I was losing my mind at first. After some trial and error (and muttering at my screen), I ended up using Excel to Table to bring the cleaned CSV into the project. It worked, but it definitely wasn’t the smooth, one-click experience the lab write-up implied.
📌 Create Locator: The Incomplete Puzzle
Creating the address locator was its own mini-nightmare. The screenshots and instructions felt incomplete—like trying to follow a recipe with half the ingredients missing. I kept thinking it was “just me,” until I checked the discussion board. Turns out… it was everyone. The field mapping, zip codes, and alias fields didn’t quite line up like the screenshots. Misery loves company, and apparently many of us love to do the lab the day before it’s due.
🧭 Geocoding & Rematching
Once the locator finally ran, the geocoding itself was mostly fine—except for the handful of unmatched addresses. Rematching those felt like being part GIS analyst, part detective. Between toggling layers, adding a hybrid basemap, and sometimes looking up schools on Google Maps, I eventually placed each stubborn point manually. It was a little tedious but also oddly satisfying once the last one snapped into place.
🌐 Sharing the Final Map
As a final step, I published my Schools_Geocoded layer as a web map, complete with a title, description, tags, and basemap. The link makes it easy to share my results with classmates and instructors without needing them to open ArcGIS Pro.
Despite the hiccups, this lab was a great exercise in flexibility and problem-solving. Not everything in ArcGIS works exactly like the videos or handouts, and sometimes you have to improvise. I also learned that I’m not alone in saving labs for the final day—and that the discussion posts can be just as useful as the official instructions.
Next time, I’ll (try to) start earlier. But hey, at least I came out with a properly geocoded Manatee County schools layer, a shareable web map, and a few new troubleshooting skills.
