No abstract

The knowledge of tree characteristics, especially the shape of standing trees, is important for living tree volume estimation, the computation of a wide range of forest stand features, and the evaluation of stand stability. Nowadays, nondestructive and accurate approaches to data collection in the forest environment are required. Therefore, the implementation of accurate point cloud-based information in the field of forest inventory has become increasingly required. We evaluated the stem curves of the lower part of standing trees (diameters at heights of 0.3 m to 8 m). The experimental data were acquired from three point cloud datasets, which were created through different approaches to three-dimensional (3D) environment modeling (varying in terms of data acquisition and processing time, acquisition costs, and processing complexity): terrestrial laser scanning (TLS), close-range photogrammetry (CRP), and handheld mobile laser scanning (HMLS) with a simultaneous localization and mapping algorithm (SLAM). Diameter estimation errors varied across heights of cross sections and methods. The average root mean squared error (RMSE) of all cross sections for the specific methods was 1.03 cm (TLS), 1.26 cm (HMLS), and 1.90 cm (CRP). TLS and CRP reached the lowest RMSE at a height of 1.3 m, while for HMLS, it was at the height of 8 m. Our findings demonstrated that the accuracy of measurements of the standing tree stem curve was comparable for the usability of all three devices in forestry practices.

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.

Circle-fitting methods are commonly used to estimate diameter at breast height (DBH) of trees from horizontal cross-section of point clouds. In this paper, we addressed the problem of cross-section thickness optimization regarding DBH estimation bias and accuracy. DBH of 121 European beeches (Fagus sylvatica L.) and 43 Sessile oaks (Quercus petraea (Matt.) Liebl.) was estimated from cross-sections with thicknesses ranging from 1 to 100 cm. The impact of cross-section thickness on the bias, standard error, and accuracy of DBH estimation was statistically significant. However, the biases, standard errors, and accuracies of DBH estimation were not significantly different among 1–10-cm cross-sections, except for oak DBH estimation accuracy from an 8-cm cross-section. DBH estimations from 10–100-cm cross-sections were considerably different. These results provide insight to the influence of cross-section thickness on DBH estimation by circle-fitting methods, which is beneficial for point cloud data acquisition planning and processing. The optimal setting of cross-section thickness facilitates point cloud processing and DBH estimation by circle-fitting algorithms.

Abstract. Nowadays it is important to shift positional accuracy of object measurements under the forest canopy closer to the accuracy standards for land surveys due to the requirements in the field of ecosystem protection, sustainable forest management, property relations, and land register. Simultaneously, it is desirable to use the technology of environmental data acquisition which is not time consuming and cost demanding. Global Navigation Satellite Systems (GNSS) are the most used for positioning today. However, the usefulness and also the accuracy of the measurements with this technology depend on various factors (the strength of the GNSS signal, the geometric position of satellites, the multipath effect etc.). Based on the above mentioned facts, the usability of technology independent of GNSS indicates an ideal solution for positioning under the forest canopy. Several studies have studied the usability of Handheld Mobile Laser Scanners (HMLS) in complex environment. The goal of this paper was to verify a new data collection approach (HMLS with Simultaneous Localization and Mapping (SLAM) technology) for the forest environment practice. The main objective of our study was to reach a precision which complies with the accuracy standards for land surveys. The RMSE of derived positions from point cloud, produced by SLAM devices were 25.3 cm and 28.4 cm, for ZEB REVO and ZEB HORIZON, the handheld mobile laser SLAM scanners used in this study. ZEB HORIZON achieved twice as big accuracy of diameter of breast height (DBH) estimation as ZEB REVO.

Abstract. The positional accuracy derived from the outputs of carried integrated devices was evaluated in this study. For data collection, the inertial navigation system (INS) SPAN NovAtel and a handheld mobile laser scanner GeoSLAM ZEB Horizon which uses simultaneous localization and mapping technology (SLAM) were utilized for data collection. The accuracy was assessed on the set of reference objects located under the forest canopy, which were measured via a traditional field survey (the methods of geodesy). In the results of this study the high potential of the devices and the application of data collection methods into forestry practice were pointed out. In our research, when the horizontal position of artificial entities was evaluated the average RMSE of 0.26 m, and the average positional RMSE of the derived natural objects (trees) was 0.09 m, both extracted from SLAM. The horizontal positional accuracy of trajectories with RMSE of 9.93 m (INS) and 0.40 m (SLAM) were accomplished.

Aerial laser scanning technology has excellent potential in landscape management and forestry. Due to its specific characteristics, the application of this type of data is the subject of intensive research, with the search for new areas of application. This work aims to identify the boundaries of forest stands, and forest patches on non-forest land. The research objectives cover the diversity of conditions in the forest landscapes of Slovakia, with its high variability of tree species composition (coniferous, mixed, deciduous stands), age, height, and stand density. A semi-automatic procedure was designed and verified (consisting of the creation of a digital terrain model, a digital surface model, and the identification of peaks and contours of tree crowns), which allows after identification of homogeneous areas of forest stands and/or forest patches (areas covered with trees species canopy) with selected parameters (height, crown size, gap size), with high accuracy. The applicability of the proposed procedure increases the use of freely available ALS data (provided by the Office of Geodesy, Cartography, and Cadastre of the Slovak Republic) and freely distributable software tools (QGIS, CloudCompare).