Module 2- Photo Interpretation and Remote Sensing- LULC Classification and Ground Truthing

    This weeks lecture/lab focused on the USGS Land Use Land Cover Classification System and ground truthing. It was a very interesting, but time consuming project. I could have easily spent 3x as long on this lab as I did, maybe more, but the lab said to spend close to 4 hours which I had already gone over. Every time I looked at the picture more features stood out and I would use different context clues, parking lot size, surrounding buildings, etc. to try to figure out what I was looking at. I would have loved to have gone more into level 3 classification as there were some specific features I was able to pick out but that would have been beyond the scope of the project. Going into this program I had absolutely 0 exposure to GIS and it's been stunning to me how quickly the time goes and how much work goes into each project/map. It's made me stop and think when I'm just perusing the internet and stumble upon a map clearly made with GIS.

    After we created our classification data on an image of Pascagoula, MS, we created random sample points and use ground truthing via Google Maps to determine the accuracy of our maps. I wasn't surprised that out of 30 random points, my 2 errors were in the residential and commercial fields. These were the hardest to distinguish for me. There are obvious features that would be classified as commercial- business with large parking lots, and even schools and churches, but then there are those buildings that are smaller or used to be homes that were rezoned, or buildings surrounded by homes on all sides although they themselves are commercial that are confusing. Water, trees, and even industrial areas are a lot easier to identify. I feel like I got a good grasp on this classification system with this hands-on experience.

Map depicting LULC in Pascagoula, MS, and random sample areas used for ground truthing with accuracy.

Module 1- Photo Interpretation and Remote Sensing- Visual Interpretation

 This week was my first introduction to remote sensing and visual image interpretation. We got hands-on experience interpreting image elements from an aerial photograph. For the first map created, we were working with a black and white image to identify tone (on a scale ranging from very light to very dark) and texture (on a scale ranging from very fine to very coarse). I found myself second guessing my choices a lot trying to get it “perfect”. I definitely see how this part of remote sensing is more of an “art” than a "science".

                  Map deliverable 1 depicts a range of tone and textures visible on an aerial photograph

The second map was focused on using Shape/Size, Pattern, Shadow, and Association image elements to identify features in a photo. For some of the features I feel like I used multiple elements to come to my conclusions about the nature of the object I was viewing but I chose the element I felt best reflected my process.

Map 2 deliverable depicts several identified features categorized by which image element was used to identify them

Introduction to GIS- Final Project

If someone told me I spent 30 hours on this project I would not be surprised. I made a lot of mistakes but learned a lot. I really wanted to get it finish before my kids went on fall break so I spent up to 6 hours a day working on it to try to get it done in one week. It really was a little of everything that we learned throughout the semester. I think my biggest takeaways were:

    1. How to use StoryMaps. I wasn't sure if I'd use this method versus a PowerPoint. I really wanted to since I hadn't done it before and I'm all about learning new programs when I can. I found it very intuitive and easy to use.

2. Make ONE layer and use the field mode to differentiate. I knew how to do this. I don't know why but when I dove into the project I was a little overwhelmed and not quite sure what I was doing. I told myself "just take a tiny step forward, what is the first step you need to take" and that led to me trying to build a map of the conservation lands and wetlands within the corridor. I knew to use select by attributes and I just got a little click-happy with the "export features to new layer" button. I should have used the "set field to..." option under the field calculator more often to keep my layers together. I was drowning in map layers!

3. You can't perform a union on point data. There may be a workaround. But when I went to perform a union on a layer I made the above mistake with I got the notification that my features had to be polygons to run that tool.

4. The amount of things I can do with python are seemingly infinite. While trying to correct mistake #2, I discovered a youtube video that helped me reclass one of my layers using python after a union. There are so many options! Definitely looking forward to learning what other tools I have at my disposal with coding.

The link to my StoryMap is: StoryMap

I also created a transcript to go along with my StoryMap with significantly more detail: Transcript

Module 6- Introduction to GIS- Georeferencing

     This week’s lab focused on georeferencing and working with rasters. We made two map layouts. For the first map we used the georeferencing pane to practice georeferencing two rasters of the UWF campus and a historic picture onto the road and building layers we had already added. We would mark points on our unreferenced map and match them to points on our referenced data layers. It was interesting to see the map adjust and resize itself between each point. We digitized a building and a road onto one of the images. I found the building difficult to digitize and think that is something I can work on improving. We then built a multi-ring buffer to show the bald eagle's nest easement located on campus.

    For the second part we turned our first map into a 3D scene. I found working with the rasters to make a map layout more frustrating than previous maps, some of the options that were available to me were not available in raster format. In particular, I had trouble with labels. I wanted to label my North Campus and South Campus rasters in my first map but ended up adding a text box instead of a traditional label. I also ran into trouble with labeling the 3D scene, I was trying to offset labels on my 3D buildings and road but this option was whited out and not available in 3D format. I also struggled to get my roads to label until I moved them up into the 3D pane. It seems like working with 3D and 2D data in the same map layout caused complications.

     Now that I have established some of the basics of creating a map I find myself interested in learning ways to make a map visually appealing. I spend so much time on completing the labs and learning the fundamentals that I haven't had a lot of extra time to study well made maps and I'll admit it wasn't something I did too often before taking this course. Hopefully I can find some time between semesters to dig into this topic some more.

Georeferenced raster images on map depicting Eagle's nest location on the UWF campus

                    3D scene of the UWF campus highlighting digitized building and road

Module 5- Geographic Information Systems- XY Point Data and Geocoding

     This week we learned how to clean and convert data in Microsoft Excel to make it more favorable for processing in ArcGIS Pro as well as to geocode addresses. With the intention of mapping known Eagle’s nest locations in Santa Rosa County, Florida, we started by using Microsoft Excel to convert latitude and longitude of the known locations from Degrees, Minutes, Seconds (DMS) into the more favorable format for the program, Decimal Degrees. We then uploaded this location data to ArcGIS to create a map and projected this data to the State Plane coordinate system.

    Our next project was geocoding the schools located in Manatee County, Florida. We obtained the Florida Department of Education’s data with locations of all schools in the county. We then cleaned it in Excel to prepare it for ArcGIS. We ended up with a CSV file with the necessary address, county, zip code located in their own columns. This data was uploaded into ArcGIS along with the US Census Bureau’s County and roads files for Manatee. We projected our data to State Plane Florida West and added our data as a table to the map. To geocode the addresses, we set up an address locator and then used the Geocode Table function to locate the schools on the map. We then rematched the addresses that were unable to be located. I had about 7 addresses that were unable to be found. I used Google Maps and available data to locate the schools and then manually searched the location on the map and dropped the pin in the accurate location. I ran into some challenges with my data at this point. Some of the addresses given were PO boxes and others were the address to the main office of the school system not the actual physical location of the school. I made several mistakes that cost more time in the long run trying to move points and change addresses. Even though the purpose of the lesson was to geocode, I wanted to make sure I was habitual about making sure my data and maps were as accurate and possible.

Instead of creating a map layer, this time we uploaded our map as a web map to ArcGIS Online. My final geocoded map is located here: Manatee County Web Map

Module 4- Geographic Information Systems- Vector Analysis

     This week we learned the ins and outs of running a Spatial Query as well as creating and working with buffers for multiple map features. We focused on creating a map displaying campsite options in De Soto National Forest near Hattiesburg, Mississippi, based on combined buffer zones within 150 meters of lakes or 500 meters of rivers as well as within 300 meters of a road. We used the union tool to only show areas where the water and road buffers overlapped.

    We took the resulting map and made our map layout. This part was challenging for me, the instructions were to include an inset map showing the location of the National Forest within Mississippi. Up until now I have either been given data or instructed on where to download it, but this time it was up to me to source my own data. I wasn’t even sure where to begin. I searched Mississippi’s state GIS website as a start but it looks like they’re in the middle of transferring data to a new system so it took me awhile to find the correct website, in the mean time I found the US Forest Service’s GIS website and downloaded the National Forest Land Units data file and added it to a new map. While gave me a visual of all the national forests in the US, I wanted my information to be specific to Mississippi, unfortunately there was no way in particular to specify state- the closest option was the headquarters of the National Forest which appeared to be located in the capital of the state the forest was located in, so I ran a query to select for forests where headquarters was in Jackson, Mississippi. I quickly realized I really needed a map with the Mississippi boundary so first I went and downloaded the National Boundary Dataset from USGS but it wasn’t quite what I needed so I went back to Mississippi GIS to try to find a boundary map. Success! In their MARIS database I was able to download the Mississippi County Boundaries. At this point I had a map with the Mississippi counties as well as the Mississippi National Forests. I also downloaded and added a cities layer from the MARIS database. The cities were polygons so I converted them to points. I then ran spatial queries to narrow down my Mississippi map to only show the city of Hattiesburg and the De Soto National Forest. After all of this it was just a matter of adding the features I wanted on my map.

    For the campsite map, I chose to display the possible campsites based on 5 different area sizes. I chose to customize the ranges because all of the other options (equal area, jenks, etc. Gave odd numbers. Basically I wanted to be able to easily see extremely small areas and extremely large and some in the middle but I wanted a round number for display purposes. I chose lighter colors for the smaller areas and increasingly darkening colors as the area got larger. I didn't want to choose colors in the exact same family because to me that makes it harder to differentiate between say a light light yellow and simply a light yellow when they aren't right next to each other.

    I attempted to label road names to my main map of possible sites, but it was very crowded looking, so then I tried to run a spatial query to narrow down to only the longest roads, but they were among those that didn’t have names listed in the attributes. I finally settled on running the intersect tool to isolate roads that intersected with my selected campsite zones for labeling. I thought this was necessary for people viewing the map to be able to orient themselves and find the locations of the possible sites. 

Here is my final map!

Map of possible campsite locations within the De Soto National Forest

Module 3- Geographic Information Systems- Projections

     This week’s lab exercise consisted of two parts. For the first part we familiarized ourselves with Field Maps and learned how to set up our map and app for collecting field data, as well as practiced collecting data offline with the app. After collecting the data, we transformed and viewed the data several ways, online through ESRI, creating a map package that could be viewed in ArcGIS Pro and uploading and viewing data in Google Earth.

Screenshot illustrating crosswalk locations in Headland, Alabama on Google Earth as well as the crosswalk condition.

    For the second part of the lab we learned about map projections, how different projections account for distortion and practiced making and viewing the same map with multiple projections to compare them. After we created 3 maps with 3 different projections, we made a map layout comparing the projections and showing how the area of 4 different counties located throughout the state was altered based on the projection used.

Comparison of 3 projections of the State of Florida, showing area variances of selected counties.

    

    This lab assignment gave me a chance to make a map layout based on information given in past assignments, without step-by-step guidance given in this one. It was exciting to see the pieces come together and notice how much more comfortable I'm becoming with the program.