GIS4035 - Module 2 - LULC Classification and Ground Truthing

 

For this lab, I created a land use/land cover (LULC) map using the USGS Anderson Classification System (Levels I and II), which breaks land types into standardized categories such as Residential (11), Commercial (12), Industrial (13), and Mixed Forest (43).

Digitizing and Mapping

I digitized features at a working scale of about 1:5,000, which offered a good balance between precision and efficiency. To stay consistent, I used a minimum mapping unit (MMU) of 0.5 acre, merging or ignoring smaller polygons unless they were clearly distinct—like a small commercial lot surrounded by forest and water.

Tools like Trace and Align Features helped me keep clean boundaries, although I still ended up with some minor gaps and overlaps. I used Snapping and Vertex Editing throughout the process for precision. This reinforced how detail-oriented digitizing must be—something I also see daily in my internship at the Santa Rosa County Property Appraiser’s Office, where topology accuracy is critical for parcel mapping.

Ground Truthing and Accuracy Assessment

To test the accuracy of my map, I created an Extent layer covering all land areas and used the Create Random Points tool to generate 30 sample locations. Each site was verified using Google Earth and Street View, serving as a stand-in for field (in-situ) verification.

For each point, I compared my original LULC code with what I observed in imagery. Out of the 30 sites, 25 were correctly classified, giving my map an overall accuracy of about 83%. The Residential (11) category proved most accurate, while most errors came from Commercial (12) areas that turned out to be residential. One Industrial (13) site was actually commercial, and another Residential (11) was better classified as Mixed Forest (43).

This lab used the In-Situ Data Collection accuracy method—verifying classifications against high-resolution imagery rather than field visits. The process emphasized the importance of attention to detail, scale management, and classification consistency.

Ultimately, it highlighted how careful interpretation and ground verification are essential to producing reliable, real-world GIS data.