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Geodata Acquisition: Mobile GNSS Data Acquisition II

782 words·4 mins·
Adian Dawuda
Master’s student in Applied Geoinformatics focused on earth observation and spatial data science

Located in central Europe, the city of Salzburg is home to many different species of trees. This project aims to analyze the distribution of tree species within the city. To obtain data for this analysis, the location, species, and selected attributes of trees are manually collected in the field using Esri Field Maps. The data acquisition is conducted in groups of two. However, for the analysis of the results the data collected by all groups of the course is used.

Methods #

The attributes to be collected for each tree are the location, species, height, diameter, and picture (optional). To store the data, a custom layer in an ArcGIS web map is created ( This web map can be accessed and contributed to through the Esri Field Maps smartphone application. For the collection of data, a shared web map of the same design is used ( Figure 1 shows the view from the Field Maps app when adding new data.

Field Maps
Figure 1: Field Maps App

The height of the trees is calculated using the Three-angle method described by (Nagel, 2011). For this, the angles from the observer to the base of the tree, to a marker at 2m height, and to the top of the tree are measured. With these three values, the height of the tree can be determined (Figure 2). This methodology is implemented in the Baumhöhenmesser app for Android smartphones (Nagel, 2011).

Three-angle method
Figure 2: Three-angle method (Nagel, 2011)

To calculate the diameter of a tree, the circumference can be divided by đťś‹ (Pi). Therefore, only the circumference is needed to determine the diameter. We implement this formula into the ArcGIS web map layer storing the tree data. The circumference of the trees is measured at the tree base using a tape measure.

Results & Discussion #

Figure 3 shows the location of the 25 samples collected by our group. The attributes of the collected samples can be seen in Figure 4. Our group took a photo of each measured tree. These photos can be viewed in the shared web map using the Field Maps app. Figure 5 shows the individual locations and Figure 6 is a heatmap of the 212 samples collected by all members of the course (as of 09.05.23). Table 1 lists the average and range of the Diameter and Height attributes per species.

our samples
Figure 3: Samples collected by our group
our samples
Figure 4: Attribute table of samples collected by our group
all samples
Figure 5: All captured samples
Figure 6: Heatmap of all captured samples
Table 1: Average and range of diameter and height per species
Diameter (cm) Height (m)
Fagus Sylvatica (Beech) 36.9, 39.5, 2–108 18.4, 15.5, 4–50
Abies Alba (Fir) 21.1, 14.5, 1–63 18.8, 13, 7–80
Pinus Silvestris (Pine) 54, 38, 6–311 18.7, 18, 4–55
Picea Abies (Spruce 45.7, 39, 3–167 18,16,4–50
Other 36.8, 22, 3–241 15.5, 14, 3–48
Key: mean, median, range

The accuracy of the captured data may be influenced by a variety of factors that include:

Device limitations

  • Mobile phone GNSS sensor → Not the best accuracy
  • Tape measure for circumference → Good accuracy. However, in reality, the diameter varies as trees are not perfectly round
  • Mobile phone used to measure angles → Not highly precise

Human limitations

  • Rough measurement of markers and angles for height measurements
  • Not always completely certain of tree species

Particularly the location of the trees’ accuracy can be influenced by many factors such as the satellite constellation, measuring device (mobile phone), atmospheric conditions, multipath effects, or obstructions between the sensor and satellites. During the data acquisition phase, we encountered some difficulties due to terrain and were not able to measure multiple trees due to their position on a very steep slope next to a creek. Besides restrictions imposed by the terrain, we were also not able to capture some trees positioned on private property. Despite lessening motivation towards the end of the acquisition phase, we were able to reach our goal of at least 20 samples captured over multiple areas. The more samples are available, the better the data represents the entire population (all trees in Salzburg). As our group only collected 25 samples in total, it makes sense to use the samples captured by all members of the course for future analysis. The data distribution and data type of attributes affect the types of statistical analyses able to be performed on the data (Peck & Devore, 2011) and should be taken into account. The likely present effects of convenience sampling should also be kept in mind in case of future analysis.

References #