The major uncertainty in my study is the temporal uncertainty. This is because the local population of my study subject, Great Egret are often supplemented by migratory population during the migratory season and yet, different population in tropic region tend to have seasonal movement in response to dry/wet season. I try to reduce the uncertainty by conducting my survey during non-migratory season to reduce uncertainty of counts cause by the migratory population but, the seasonal movement of some population might still generate some uncertainty in my study
Part of the uncertainty in satellites can come from lack of precision, misalignments in their trajectory and clouds that make measurements difficult. I think we can try to avoid them by choosing images of the precision we need or from different moments when the weather does not affect the measurement.
Since I am still a student and have yet do my own research but I will think some uncertainty I may face if I will be using camera trapping method and to know the home range of primates is the uncertainty to know the precise boundaries of home range and the movement of primate from one place to another. There are a lot yet to learn. Thank you for all the sharing and experience.
As Iām still a student I donāt have personal experience or project Iām working on this is all new to me but Iām learning a lot and according to the theory Iāve obtained Satellites can make mistakes when monitoring the weather. Lack of precision, misalignments in their course, and clouds that make observations difficult can all contribute to satellite error. Satellite errors can be avoided by selecting photographs with the precision we require or from various times when the weather has no bearing on the measurement.
Uncertainty is inevitable in all of cases because satellite precision at the time of sensing, many factors and variables can affect our results or simply the fact of differentiating the real boundaries in the study area. I could say the most common thing is working with approximate measurements, this can be improve by using the appropriate statistical models in order to get better results in our readings. One of the uncertainty cases in my field work would be āTemporal uncertaintyā due to marine environments variables are in constant changes through the time.
Uncertainties in our data are due to precision limitations of the measurement methods. These are currently inevitable, but are precise enough for current maps and management recommendations. We will have to increase frequency and number of measurements to reduce uncertainty.
I am collecting biological data for my study. I lead a team of two or three that monthly patrol the forest to collect all large mammal evidence in the forest and decribe the location where the sign is spotted.
- I think we might have both thematic and temporal uncertainties.
Thematic uncertainty may be link to vegetation description (difficulty to decide on the vegetation type where the sign is located), misapreciation of the nest categories (new, recent, old and very old)
Temporal uncertainty could be the GSP reading (geospatial coordinate of the location), nest counting or estimation of the popualtion size for a group of animals encoutered. - are these inevitable?
We can avoid or reduice the thematic uncertainty for our case by deeply review the criteria for each category
For Temporal uncertainty, we can reduice it by taking waypoint in the open area at the location. but with nests or group counting this can be avoid by searching or counting individual accurately - these incertainty can bias our results: under-estimating the population size, the range of a particular vegetation type, reduce the home range of a species, etc
I still have no data yet but my plan was to use map from Landsat or google map to identify all types of Landcover, then I will set up a plot for each land use. So I can have a coordinate of each land before I go to the field. The uncertainty I might have here is how to be sure that the plot are established in different land and not in the same land. For example I want to compare species from open-land derived agroforest and forest derived agroforest, or primary and degraded habitat?
I think you need to do the pilote trip to confirm different landcover type. This well helps you to be sure that what you consider in the Landsat image reflet the reallity on the ground. Good luck
Temporal uncertainties cause dolphin surveys are only done during the day and not at night. Think it could be reduced if I added acoustic recorders at multiple locations. But even that is gonna cause a spatial uncertainty hahaha.
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With our sightings data for individual animals the first bit of uncertainty is that the GPS point taken by the monitoring team will be the location they are, which will be slightly different from the location of the animal, anything from 5 to 50 metre difference. Doesnt present a huge issue for most general spatial surveys or mapping, however must be recognised if later this data is to be ground truth for a different study, such as vegetation preference by a certain individual animal. This would require ground truthing the vegetation, but we would not know the exact location to the microscale of the animal to do this.
Human error is something we are also very aware of which affects all of a our data. Issues in Identifying the animal, to miswriting GPS data, or using a different GPS format by accident. These are avoided and mitigated for by education, crosschecking and cross referencing, but can sneak into large datasets.
I think that some uncertainties I might encounter are the accuracy of points and the GPS data that I get from the local government agencies. they might be using different datum from me (UTMs or WGS 84). Iāve had problems with these in the past. The accuracy of their area maps might also be different as they may be using outdated imagery. this can affect my study by having points differ in actual location. these uncertainties can be mitigated by first checking what type of data the LGUās are using and appropriately methods for each data.
With my Honors thesis I guess the uncertainty would involve the spatial data of soil type. At this point I am still wrapping my head round the concepts so I may be misinterpreting.
In the dataset of public sightings available to me, the greatest uncertainty is the lack of coordinates, wich need to be manually inserted based on the description of the site. This will not be a major issue for visualizing the distribution at a regional level, but will prevent deeper analysis based on land use or habitat diversity.
Uncertainty with my project could be sampling of the correct species, where we had to test for the right species due to another species that looks practically identical to mine. Uncertainty is something that should be resolved as far as possible as there is definitely an impact on management and conservation practices.
In the data I would be working with, Iād be mapping the distribution of seagrasses. In South Africa, the area of seagrass meadows within different estuaries fluctuates regularly and at times, the meadows disappear completely. There is therefore uncertainty regarding how true a reflection distributional boundaries may be. To minimize this temporal uncertainty, surveys should be conducted regularly (at a high temporal resolution) and maps adjusted accordingly.
One source of uncertainty in my project to monitor kestrel nestbox usage is temporal uncertainty. The number/age/presence of kestrels in a nestbox will change over time, and the data we gather in one survey may not necessarily be up to date. We can reduce this by conducting surveys more regularly or perhaps by using camera traps or videos to film the kestrels constantly.
In my potential case, I would experience spatial uncertainty due to the constant movement of the areas utilised by baboon troops.
The use of VHF telemetry in the tracking of Southern Ground-hornbills in the mapping of their territories and seasonal habitat use within the territories is s subject to spatial uncertainty due to the terrain of study which may affect the reception of a signal from the transmitters.
One of the peculiar uncertainty experienced in satellite imagery for example landsat images is cloud cover which affect the visibiity of the image. This can be avoided by choosing a landsat image having a low cloud cover basically less than 10%.