Abstract. This paper deals with the use of airborne laser scanning data (ALS) in the process of the automatic delineation of forest and the generation of digital terrain models (DTM) in forested and non-forested areas. The study area where the procedures presented were examined is part of the University Forest Enterprise, Technical University in Zvolen (48˚ 37' N, 19˚ 04' E). A partial modification of existing solutions that iteratively takes into account the criteria of minimum area, height, width and crown coverage is presented within the forest delineation. At the same time this approach also evaluates the mutual distance of identified crowns and the presence of buildings. Compared with manually identified forest boundaries, the accuracy of the automated procedure in the study area reached the value of 93%. In the DTM generation, various alternative methods of interpolation and conversion were used, while ALS data from the summer and winter seasons were also available. The results showed that laser scanning in the area of interest provided systematically overestimated data for the DTM generation. The largest deviations of the DTM were found in terrains based on ALS data from the summer season, with a significant slope, regardless of the complexity of the afforestation structure (except for the youngest forest). In older stands and unforested areas, both with a moderate slope, the DTM accuracy achieved was in the range ±6-17 cm.
Smartphones with their capability to receive Global Navigation Satellite Systems (GNSS) signals can be currently considered the most common devices used for positioning tasks, including forestry applications. This study focuses on possible improvements related to two crucial changes implemented into Android smartphone positioning in the last 3 years – dual-frequency (L1/L5) GNSS receivers and the possibility of recording raw GNSS data. The study comprises three experiments: (1) real-time measurements of individual points, (2) real-time recording of trajectories, and (3) post-processing of raw GNSS data provided by the smartphone receiver. The real-time tests were conducted using final positions provided by the internal receiver, i.e. without further processing or averaging. The test on individual points has proven that the Xiaomi Mi8 smartphone with a multi-constellation, dual-frequency receiver was the only device whose accuracy was not significantly different from single-frequency mapping-grade receiver under any conditions. The horizontal accuracy of most devices was lower during leaf-on season (root mean square errors between 5.41 and 12.55 m) than during leaf-off season (4.10–11.44 m), and the accuracy was significantly better under open-area conditions (1.72–4.51 m) for all tested devices when compared with forest conditions. Results of the second experiment with track recording suggest that smartphone receivers are better suited for dynamic applications – the mean shift between reference and measured trajectories varied from 1.23 to 5.98 m under leaf-on conditions. Post-processing of the raw GNSS data in the third experiment brought very variable results. We achieved centimetre-level accuracy under open-area conditions; however, in forest, the accuracies varied from meters to tens of meters. Observed loss of the signal strength in the forest represented ~20 per cent of the open-area value. Overall, the multi-constellation, dual-frequency receiver provided more robust and accurate positional solutions compared with single-frequency smartphones. Applicability of the raw GNSS data must be further studied especially in forests, as the provided data are highly susceptible to multipath and other GNSS adverse effects.
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