ENVS 421: GIS IV: Advanced GIS Applications
Lab 1 – Map Digitization and Georeferencing
Lab 2 – Remote Sensing in ArcGIS Pro
Lab 2 Practical Exam
Lab 3 – Watershed Analysis
In this lab, we used elevation data to model where streams should be (based on what we know about the topography) and delineate sub-watersheds and catchments. My task was to produce a Model Builder model that other analysts can easily run using their own data, along with a guide to using the model and interpreting the output files. In the last section of the lab, I modeled connectivity along the streams using a geometric network dataset.
Lab 4 – Watershed Crossings Assessment
Forest roads potentially impact freshwater fish habitat through the rerouting of water, the triggering of shallow rapid landslides, and the delivery of fine sediments to the stream. Fine sediments can wash down from forest road impacted streams into fish spawning streams and clog up the coarser spawning ground substrates, severely impacting the survival of incubating fish. Additionally, fine sediments can fill in fish habitat rearing pools, resulting in shallower depths, higher water temperatures and less habitat area for juvenile rearing in the summer. The impacts make forest roads a potential factor limiting fish production throughout the Pacific Northwest.
Forest road density and fish stream crossing density are two metrics that can serve as proxies of the potential for forest roads to impact streams. There is more fine sediment produced as road densities increase and there is a higher probability of road sediment entering streams as road/stream crossings increase.
In this lab I completed a GIS assessment of potential forest road conditions in the Stillaguamish River watershed, sub-watersheds, and catchments. Specifically, I will calculated road density as miles of forest road per square miles of area for watershed, sub-watersheds and catchments, and I calculated road crossing density as number of road crossings per miles of fish bearing streams within a watershed, sub-watersheds and catchments. Upon completion I created an ArcGIS Story Map to tell the story of my GIS Assessment of potential forest road impacts in the Stillaguamish watershed.
Lab 5 – Primary Data Collection
The objective for this lab was to design both a data collection system and a data storage system for primary spatial data collection. My professor provided each student in the class with 15 survey point locations that were selected randomly across Western Washington University’s campus. We were to visit each of the survey points and collect data on land cover, moisture, noise, and aesthetics. Much of the data was highly variable and dependent on the individual collecting it.
We were tasked with creating a field data collection form that allowed us to efficiently collect data on paper/Excel/Collector for the each category. The points were given to us in an Excel file and we had to import them into Arc in order to create a field map. Once we created the field map, we would visit the points, collect the data, and then enter the data into our collection form and into our attribute table.
This exercise was the first part of a geostatistical analysis that you will see in my Lab 6 post where we aggregate the entire classes data together to map two variables, campus safety and campus noise.
Lab 6 – Data Management and Information Creation
This lab is a continuation of Lab 5 “Primary Data Collection” my objective was to compile all of the point feature classes collected by my class into a single point feature class in a single database and to create interpolated surfaces from the combined points for noise, and safety. We were all given 32 geodatabases with 32 feature classes of 15 survey points with matching attributes. The goal was to populate a geodatabase with empty feature classes would serve as the container for the final combined 480 points. The geodatabase and empty feature class had the same exact domains and attributes that I used in Lab 5.
The best way I found to get the data from each of the individual 32 geodatabases was through the use of an iterator in Model Builder. I used the “Iterate Feature Classes” tool and loaded the folder the 32 geodatabases were stored in into the iterator. The iterator will visit each of the 32 geodatabases inside of the folder and look for feature classes inside of each of the geodatabases. I then added the “Append” tool to my model. If each of my classmates designed our geodatabases and feature classes exactly the way we were directed to in the Geodatabase section of Lab 5 then all of the feature classes would append perfectly, points and attributes, into the feature class I was appending to. Any attribute that has not been formatted correctly will be Null in the feature class I was appending to. Meaning, the points will append, but you will see Null values in the cells for that particular attribute.
After cleaning up some of the data that was incorrectly formatted all of the attributes were populated in the final feature class of points. I was then tasked with researching different interpolation methods inside of the Geostatistical Analyst toolbox. After making a decision on which interpolation method to use I was to make two maps .One of the maps is for noise surface and the other map is for safety surface which, again, were highly variable and dependent on the individual who collected the data.