Abstract
Global Navigation Satellite Systems (GNSS) are crucial elements used in forest inventories. Forest metrics modeling efficacy depends on the accuracy of determining sample plot locations by GNSS. As of 2021, the GNSS consists of 120 active satellites, ostensibly improving position acquisition in forest conditions. The main idea of this article was to evaluate GIS-class and geodetic class GNSS receivers on 33 control points located in the forest. The main assumptions were operating on four GNSS systems (GPS, GLONASS, Galileo, and BeiDou), keeping a continuous online connection to the network of reference stations, maintaining occupation time-limited to 60 epochs, and repeating all the measurements three times. Rapid static positioning was tested, as it compares the true performance of the four GNSS systems receivers. Statistical differences between the receivers were confirmed. The GIS-class receiver achieved an accuracy of 1.38 m and a precision of 1.29 m, while the geodetic class receiver reached 0.74 m and 0.91 m respectively. Even though the research was conducted under the same data capture conditions, the large variability of positioning results were found to be caused by cycle slips and the multipath effect.
Highlights
The global need for accurate information concerning forests’ impact on the dynamic development of precision forestry is immense [1]
Taking into account fast Global Navigation Satellite Systems (GNSS) development, which is progressing due to the new operational satellite segments like BeiDou or Galileo, [6] an important question should be asked: what is the present state of the possible accuracy of GNSS receivers? More than 25 years have passed since the first important research on GPS usage in forest environments was conducted by Deckert and Bolstad [7], who confirmed the accuracy of positioning on the level of 3.1 to 4.4 m
The average canopy openness was 6.7% and the 30 control points range from 3% to
Summary
The global need for accurate information concerning forests’ impact on the dynamic development of precision forestry is immense [1]. The importance and wide applicability of GNSS technology in forest practices [9,10] have led many researchers to seek further improvements, in accuracy and precision and in error identification and detection. The forest metrics, such as tree species, number of trees, diameter at breast height [11], basal area [12], the density of forest height, site condition and positioning mode [13] are only examples of factors that can influence the quality of measurements
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