In this last chapter, we will tackle the challenge of labelling your tag!
This chapter assumes that you are familiar with the overall process of GeoPressureR presented in the Basic tutorial, as well as the concept of pressurepath and the use of GeopressureViz.
Labelling your tracks is imperative because GeoPressureR requires highly precise and well-defined pressure timeseries of a fixed/constant location both horizontally (geographical: +/- 10-50km) and vertically (altitude: +/- 2m).
The procedure involves labelling each datapoint (1) with the flight label when the bird is in active migratory flight and (2) with the discard label for pressure datapoints that should be discarded from the matching exercise. The overall objective is to create a pressure timeseries for each stationary period where the bird can be assumed to remain at the same location and elevation during the entire period.
Labelling flight defines stationary periods and flight duration. A stationary period is a period during which the bird is considered static relative to the size of the grid (~10-50km). The start and end of the stationary period is used to define the pressure timeseries to be matched. Having an accurate flight duration is critical to correctly estimate the distance traveled by the bird between two stationary periods.
Labelling discard eliminates vertical (altitudinal) movements of the bird. The algorithm matching the pressure timeseries is sensitive to small pressure variations of a few hPa, such that even altitudinal movements of a couple of meters can throw off the estimation map for short stationary periods. Since the reanalysis data to be matched is provided at a single pressure level, we must discard all data points corresponding to a different elevation.
Each species’ migration behaviour is so specific that manual editing remains the fastest option. Indeed, small changes in pressure and activity can correspond to either local movement or slow migration. Expertise on your bird’s expected migration style will be essential to correctly label your tracks. As you label, you will learn how the bird is moving (e.g. long continuous high altitude flights, short flights over multiple days, alternation between short migration flights and stopovers, etc.). Manual editing also gives a sense of the uncertainty of your labelling, which is useful to interpret your results.
Acceleration data can significantly improve our understanding of bird activity and movement. One of its main strength is to refine short stationary periods or flights at the end of the night, when birds tend to flight low. In addition, acceleration is typically recorded at a higher temporal resolution (5min), which can refine flight duration and thus the movement model when building the trajectory.
When acceleration data is available, use the flight label on the acceleration timeseries, and the discard label on the pressure timeseries.
In the absence of acceleration data, both labels are applied to the pressure timeseries.
Let’s see an example using acceleration data with 18LX.
tag <- tag_create("18LX",
crop_start = "2017-06-20",
crop_end = "2018-05-02"
)✔ Read './data/raw-tag/18LX/18LX_20180725.pressure'✔ Read './data/raw-tag/18LX/18LX_20180725.glf'✔ Read './data/raw-tag/18LX/18LX_20180725.acceleration'✔ Read './data/raw-tag/18LX/18LX_20180725.temperature'Acceleration can be used to initialize the flight label automatically. tag_label_auto() first classifies low and high activity using a k-mean clustering, and then identifies and labels long periods of high activity (e.g., lasting more than 30 minutes) as flights.
tag <- tag_label_auto(tag, min_duration = 30)
plot(tag, type = "acceleration")