Managing uncertainty is certainly an interesting topic to discuss and is very relevant for all areas of GIS (not just in using QGIS). It is worthy of a whole session in itself and I am happy to answer any specific questions you have.
The important thing is to be “certain of the uncertainty” ! - there are a lot of aspects to “uncertainty” and spatial precision and accuracy are just two aspects. If dealing with information that others have collected, or even using maps produced by national agencies, knowing what the “error margin” is very important (for cartographic reasons, commercial and national maps will have features removed, merged, simplified or shifted in location - and all this will dependant on the scale of the map being used.
Using correctly scaled data for your particular task is a key thing to remember.
in sampling riverine macroinvertebrates, both spatial and temporal uncertainties apply. As the situation on the ground in rivers changes rapidly and weather patterns changes seasonally. Yet our data does not provide the actual representative of macroinvertebrates population found in the river. Perhaps we should take our samples on precise location, date, time and on the same weather.
Uncertainty may arise in my data due to false sightings of species and identification uncertainty.
Population estimation to decide animal density per kilometre might be the example of uncertainty. Routine monitoring and finding where they usually gather might help.
The biggest source of uncertainty in my data is temporal uncertainty. This is because survey flights are not always conducted at the exact same time of day (although we try to) and the weather is inevitably not exactly the same for every flight (we count more elephants when it is cool and cloudy than when it is hot and sunny). Therefore, this uncertainty is inevitable even though we try to limit the scope of it. When writing flight reports, we always take into account this uncertainty by adding notes to indicate if it was hot or cold, sunny or cloudy, and morning or evening, etc. We reduce this source of uncertainty by trying to fly the surveys at the same time of day, at the same time of the year, and with the same exact transect lines, but of course the weather is a somewhat unpredictable variable so this is not always possible.
In the data I collecte there are always uncertainties. With GPS, if I sit still and tracking on, the track can sometimes be quite far away, though it clusters around my position. There are also uncertainties due to how GPS data are collected, for example by someone in a boat while an observer is towed in the water on manta tow surveys, where the track of the boat and the surveyor might not be quite the same, as both try to follow the reef crest as best they can.Even when using the same datum as the base map, sometimes the coordinates when plotted look as through what we have mapped is on land when it is in the sea or vice-versa. When delimiting an area ( e.g seagrass bed) there is almost always uncertainty about exactly where the boundary between one ecosystem and another.should be. When making reports, we had to emplain this (this was when I has ArcView). When using free satellite data, there is often uncertainty due to the resolution of the data compared to the (often finer) scale of what we are looking at. Uncertainties on pixcel allocation can be reduced using ground truthing data.
Sorry, explain not emplain… typing in the gloaming…
With satellite imagery, it is likely that only a high-level of detail is available, and borders between zones or areas may not be distinguishable. These uncertainties aren’t inevitable, and can be taken into account by collecting extra data and including this when map-making.
Uncertainties in my data exist in the form of thematic uncertainty where
the distinction between patches of vegetated seagrass beds and unvegetated,
as well as between different species of seagrasses, may be blurred or difficult to
define. Temporal and spatial uncertainties exist in the sense that the flow of water in
rivers and estuaries are ever changing with different seasons and weather patterns
causing the presence and number of microplastics to change with them.