All content presented below is entirely contributed by our students or individuals outside the University.
This space is intended for sharing practical (and sometimes hacky) mapping and data processing tips & tricks. Please note that these short videos are not meant to be complete instructional materials. Whenever we can, we will link other supporting content to provide more context to these tips & tricks.
Please also be aware that by making these videos, we are not endorsing any particular dataset or program over the other. As a data user and data producer, we need to be critical and assess each dataset/software as objectively and openly as possible.
We would like to hear your feedback on any of the videos below. We would also like to receive your tips-and-tricks video so we can all learn from you.
Point clouds, whether generated from laser scans or digital photogrammetry, are powerful remote sensing datasets that augment the measurement and visualization of static objects and spaces. Many Canadian cities have made their city-wide aerial laser scan (ALS) results publicly available as tiles of point clouds.
In this short video, Brandan demonstrates how to download and merge multiple point cloud tiles from the City of Winnipeg in Manitoba. Using CloudCompare, he also shows how to create and render a high-resolution cross-sectional drawing. This type of drawings can be highly informative for understanding the scale and arrangement of elements such as urban canopies, buildings, and even small urban furniture at centimeter-level resolution.
The point cloud for the City of Winnipeg can be downloaded from here.
CloudCompare is an open source software and can be accessed from here.
Contributed by Brandan Gatz
The Normalized Vegetation Difference Index (NDVI) is a common remote sensing product that uses R (red) and NIR (near-infrared) reflectance values to monitor photosynthetically active surfaces (e.g. vegetation).
In this short video, Matthew demonstrates how to use Google Earth Engine (GEE) to generate NDVI images using the data collected by the Landsat program.
All data and software in this video are free to use.
The GEE code can be downloaded from here. The current literature on NDVI can be accessed from here.
Contributed by Matthew Glowacki
Recently, Meta and the World Resources Institute released a global map of tree canopy height at a 1-meter resolution. This dataset is by far the most spatially detailed global canopy height model (CMH). Given its fine resolution and wide coverage, the entire image collection is well over 15-TB, a size that is not so practical for local studies.
In this short video, Yuhao demonstrates how to use Google Earth Engine (GEE) to only access a subset of this dataset, making it more manageable to users that only need a small area of interest.
All data and software in this video are free to use.
Please note that this is a very new dataset and its accuracy and precision are yet to be assessed. Here is a review published two days after the data release.
The GEE code can be downloaded from here. The methodology behind this dataset is published here. The code can also be modified to access other amazing datasets on GEE listed here.
Contributed by Yuhao Lu
An isochrone map highlights which areas you can reach within a given time or distance, from a set of origins. In this video, Simranpreet demonstrates how to create isochrone map using service area analysis to assess accessibility to a proposed National Urban Park in Winnipeg, considering walking and biking for various distances. ArcGIS Service Area Analysis uses the road network, shaping decisions regarding accessibility and entrances. These distance measurements are crucial for understanding how convenient park access is for everyone, ensuring inclusivity and equitable urban planning.
Please note that an Esri license is required to use this tool. QGIS (open source) equivalent can be found here.
Contributed by Simranpreet Kaur
The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure supported by governments from around the world, aiming at providing anyone, anywhere, open access to data about all types of fauna and flora on our planet.
In this short video, Reece demonstrates how to access, filter, and map datasets from GBIF directly in QGIS by using a plug-in called GBIF Occurrences.
All data and software in this video are open-access.
To know more about GBIF’s mission, you can watch this video.
Contributed by Reece Cullen
“Nothing is too small to know.
Nothing is too big to attempt.”