Lab 6: Intro to QGIS

Lab Overview

In this lab we learned the basics of QGIS, an open-source and open-developed Geographic information system. QGIS is similar to platforms like ArcGIS, though the ability of it being open source means collaborators can contribute new plugins into the software with relative ease.

Exercise 1: Importing Data into QGIS

In setting up a sample interface, we saved a version of the source data to our personal drives, and imported the data into the map frame following add data, selecting according to the data type (i.e., vector or raster), and then specifying the root in the computer hard drive to the datafile. This specific dataset is for tree health at Brock University.

In this example of a lab from Brock University, we imported the dataset with the project files. Under the project menu, we then specified the map projection using the CRS active transformation to the NAD 93 UTM 17N zone. This zone is used typically for mapping south and central southern Ontario as an equal area projection within the Mercator system.

Exercise 2: Making Point Data from .CSV file

Here, we imported the delimited text layer for the trees into the QGIS data frame, and then selecting the file format of .csv allowed us to pick geographic information columns for mapping of the features. This is analogous to the process in Google Earth engine where, when importing a .csv layer, we select the corresponding columns for the latitude and longitude of the respective pixels in the interface. The resulting image, in both open-source GIS interfaces, allows for the representation of point data.

The resulting map layer has Brock University trees visualized in Red.

Figure 2: Brock Trees added to the MapAI interface in QGIS.

Problematically, the coordinates for X and Y (longitude and latitude respectively) were not in degrees minutes seconds (DMS), and were actually in decimal meters. So, we had to toggle and turn off the DMS format button under the add spatial reference pane.

Exercise 3: Buffer and Selections

We wanted to buffer trees within a radius of 20 meters to any road locations, so we went into the geoprocessing toolbox and buffered the road polylines by a 20 unit distance. Specifying meters, this distance was calculated and exported as a polygon file. Changing the transparency of the resulting buffer allowed us to see what trees were within this buffer.

Figure 3: Roads at Brock University buffered by a 20 meter polygon vector.

The select by location tool was used as a form of spatial intersection. The select by location tool uses an existing feature geometry as the “Location” to select other spatial features in a dataset. In ArcMap, this is referred to as the Intersect feature within the Spatial Analyst Toolbox.

We specified that only features entirely inside the feature collection would be included. This is easier with point-data like trees, but worth consideration when there are larger polygons where the boundaries to the focal area of intersection are not necessarily known. The trees in this intersection were exported as their own point data layer.

Figure 4: Export of Brock University trees within the road buffer

Alternatively, following the selection by attribute, we can use logical selection and operands to specify within the tree dataset, which points we want to select according to their attributes. Using the 'CanopyCond' attribute, we selected both critical and poor condition tree canopies for this study.

This feature is accessed within the attribute table, following the select by expression function where operators and field values for the aforementioned are specified.

One indicated, under feature visualization, we can select only selected features which will only show the features meeting the logical expression within the Map.AI.

Figure 5: Visualization of Brock Trees selected by condition of the tree canopy, and solely visualized within the MapAI

Exercise 4: Exporting A Map

Under layer properties, we selected Create New Map Layout to export the map features to a new map. We ensured to add a Scale Bar, map arrow, a legend for all visible map layers, and labels. In an author statement under the map legend we included our name as the Author, the date the map was created, the projection, and the data source. A map title was then added. We also added a frame to the map layer to define the boundaries of the map extent.

The resulting map was exported as a PNG and attached here.

Figure 6: Resulting Map for tree removal in the Saint Catherines, ON campus for Brock University, as generated in QGIS.

Exercise 5: Interpolating Point Data in QGIS

First we downloaded the data for shapefiles of Lake Arlington from TWDB. We then added the shapefiles following the import procedure we conducted in the Brock Lab. We added both the survey data and the lake boundary shapefile (Point and Polygon files, respectively. Imported into the map interface, these are the following files.

We then went into the QGIS plugins pane, and installed the interpolation plugin, and then under raster > Interpolation > interpolation we interpolated the point data for the elevation of the bathymetric readings into a raster layer for the bathymetry.

The resulting file is an elevation file for the height given the depth readings that were measured using sounding lines.

Figure 7: Point Data for Sounding Lines used to measure Lake depth in Arlington, VA.

We then used the clipper function to extract the discrete bathometry lines and make associated contour gradients for a bathymetry model. However, due to the differences in GIS software and the fact this is a tutorial using a QGIS version I can no longer download from the archive, using the current version of my computer meant I could not run the clipper feature, and the resulting layer kept giving me null values. I even tried the calculation in ArcGIS but the values were the same.