To obtain precise cartometric measurements of forests is always a challenge and high-resolution data from Unmanned Aerial Vehicle (UAV) is currently the quickest method. Generation of a fine quality orthomosaic of the acquired image series is a pre-requisite for full exploitation of such data. This study examines six of the most frequently used photogrammetric software for popular and inexpensive UAV systems. It is assumed that ground control points (GCPs) are not required. The average Root Mean Square Error (RMSE) for raw orthophoto was 1.24 m and around 0.2 m precision for both X and Y axes. Additionally, the accuracy of UAV internal GNSS receiver was checked on reference points which slightly exceeds 2 m RMSE. The range of accuracy and precision of orthomosaic are provided as a valuable reference for the use of low-cost UAV in forest inventory.

The GNSS (Global Navigation Satellite System) receivers are commonly used in forest management in order to determine objects coordinates, area or length assessment and many other tasks which need accurate positioning. Unfortunately, the forest structure strongly limits access to satellite signals, which makes the positioning accuracy much weak comparing to the open areas. The main reason for this issue is the multipath phenomenon of satellite signal. It causes radio waves reflections from surrounding obstacles so the signal do not reach directly to the GNSS receiver’s antenna. Around 50% of error in GNSS positioning in the forest is because of multipath effect. In this research study, an attempt was made to quantify the forest stand features that may influence the multipath variability. The ground truth data was collected in six Forest Districts located in different part of Poland. The total amount of data was processed for over 2,700 study inventory plots with performed GNSS measurements. On every plot over 25 forest metrics were calculated and over 25 minutes of raw GNSS observations (1500 epochs) were captured. The main goal of this study was to find the way of multipath quantification and search the relationship between multipath variability and forest structure. It was reported that forest stand merchantable volume is the most important factor which influence the multipath phenomenon. Even though the similar geodetic class GNSS receivers were used it was observed significant difference of multipath values in similar conditions.

Despite the growing impact of remote sensing technology in forest inventories globally, there is a continuous need for ground measurements on sample plots. Even though the newest volume assessment methodology requires fewer sample plots, the accuracy of ground-recorded data influences the final accuracy of forest stand modeling. Therefore, effective and economically justified tools are in the continuous interest of foresters. In the presented research, a consumer-grade light detection and ranging (LiDAR) sensor mounted on iPad was used for forest inventory sample plot data collection—including tree location and diameter breast height. In contrast to other similar research, feasibility and user-friendliness were also documented and emphasized. The study was conducted in 63 real sample plots used for the inventory of Polish forests. In total, 776 trees were scanned in 3 types of forest stands: pine, birch, and oak. The root mean square error was 0.28 m for tree locations and 0.06 m for diameter breast height. Various additional analyses were performed to describe the usage of an iPad in tree inventories. It was contended that low-cost LiDAR scanners might be successfully used in real forest conditions and can be considered a reliable and easy-to-implement tool in forest inventory measurements.

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.

Artificial canopy gaps (forest openings) are frequently used as an element of regeneration cutting. The development of regeneration in gaps can be controlled by selecting a relevant size and shape for the gap, which will regulate the radiation microclimate inside it. Based on the size and shape of a gap computer models can assess where solar radiation is decreased or eliminated by the surrounding canopy. The accuracy of such models to a large extent depends on how the modeled shape of a gap matches the actual shape of the gap. The aim of this study was to compare the results of modeling solar radiation availability by applying Solar Radiation Tools (SRT) that use a different digital surface model (DSM) for a description of the shape of a studied gap, with the results of the analysis of 27 hemispherical photographs. The three-dimensional gap shape was approximated with the use of simple geometrical prisms or airborne laser scanning (LiDAR) data. The impact of two variations of exposure (automatic and manual underexposure) and two variations of automatic thresholding on the congruence of SRT and Gap Light Analyzer (GLA) results were studied. Taking into account information on differences in height between trees surrounding the gap enhanced the results of modeling. The best results were obtained when the boundary of the gap base estimated from LiDAR was expanded in all directions by a value close to a mean radius of the crowns of surrounding trees. Modeling of radiation conditions on the gap floor based on LiDAR data by an SRT program is efficient and more time effective than taking hemispherical photographs. The proposed solution can be successfully applied as a trustworthy source of information about light conditions in gaps, which is needed for management decision-making in silviculture.