Abstract:The potential of close-range photogrammetry (CRP) to compete with terrestrial laser scanning (TLS) to produce dense and accurate point clouds has increased in recent years. The use of CRP for estimating tree diameter at breast height (DBH) has multiple advantages over TLS. For example, point clouds from CRP are similar to TLS, but hardware costs are significantly lower. However, a number of data collection issues need to be clarified before the use of CRP in forested areas is considered effective. In this paper we focused on different CRP data collection methods to estimate DBH. We present seven methods that differ in camera orientation, shooting mode, data collection path, and other important factors. The methods were tested on a research plot comprised of European beeches (Fagus sylvatica L.). The circle-fitting algorithm was used to estimate DBH. Four of the seven methods were capable of producing a dense point cloud. The tree detection rate varied from 49% to 81%. Estimates of DBH produced a root mean square error that varied from 4.41 cm to 5.98 cm. The most accurate method was achieved using a vertical camera orientation, stop-and-go shooting mode, and a path leading around the plot with two diagonal paths through the plot. This method also had the highest rate of tree detection (81%).

This study focuses on the horizontal and vertical accuracy of point-clouds based on unmanned aerial vehicle (UAV) imagery. The DJI Phantom 3 Professional unmanned aerial vehicle and Agisoft PhotoScan Professional software were used for the evaluation. Three test sites with differing conditions (canopy openness, slope, terrain complexity, etc.) were used for comparison. The accuracy evaluation was aimed on positions of points placed on the ground. This is often disregarded under forest conditions as it is not possible to photogrammetrically reconstruct terrain that is covered by a fully-closed forest canopy. Therefore, such a measurement can only be conducted when there are gaps in the canopy or under leaf-off conditions in the case of deciduous forests. The reported sub-decimetre horizontal accuracy and vertical accuracy lower than 20 cm have proven that the method is applicable for survey, inventory, and various other tasks in forests. An analysis of ground control point (GCP) quantity and configuration showed that the quantity had only a minor effect on the accuracy in cases of plots with~1-hectare area when using the aforementioned software. Therefore, methods increasing quality (precision, accuracy) of GCP positions should be preferred over the increase of quantity alone.

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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.

Global navigation satellite systems (GNSS) have a wide range of applications in forest industry, including forest inventory. In this study, the horizontal accuracy of 45 inventory plots in different forest environments and 5 inventory plots under open sky conditions were examined. The inventory plots were located using a mapping-grade GNSS receiver during leaf-on season in 2017. True coordinates of the plot centres were acquired using a survey-grade GNSS receiver during leaf-off season in 2018. A study was conducted across a range of forest conditions in the forest unit Vígľaš, which is located in Slovakia (Central Europe). Root mean square error of horizontal accuracies was 8.45 m in the plots under forest canopy and 6.61 m under open sky conditions. We note decreased positional errors in coniferous forests as well as in younger forests. However, results showed that there is no statistically significant effect of tree species composition and stand age on horizontal accuracy.

Slope failures are financially devastating natural hazards that contribute to land degradation in many areas throughout the world. The adaptation of civic technologies (Google Tango) in a field survey of landslides was examined. Data acquired from different resources and processed using different technologies were merged into a single model to concurrently demonstrate the interoperability and scalability of these data and the model quality. Reference control points were established using a survey‐grade Topcon Hiper SK global navigation satellite system receiver and a Topcon GPT 9003 M total station. An aerial survey was performed in an area of approximately 30,000 m2 using airborne laser scanning (9 points/m2) and aerial photogrammetry using a remotely piloted aircraft system (500 points/m2). The models suffered from data gaps in less visible areas, and micro‐scale landforms reflecting landslide activity were poorly visible. The missing details were supplied using data obtained from close‐range photogrammetry (9,132 m2; 92,300 points/m2) and a Lenovo Phab 2 Pro running Google Tango, which acquired detailed point clouds in near real‐time conditions (1,847 m2; 109,000 points/m2). Scans using the phablet provided point clouds with homogeneously dispersed data gaps, but the spatial accuracy was lower. However, the ergonomics of its field use and its low cost made it competitive with other technologies. The results confirmed that models based on point clouds acquired using different technologies allow the identification and measurement of micro‐scale landforms that may indicate landslide activity.

The compass measurement is one of the customary surveying methods that is used almost from the beginning of systematic geodesy and cartography. After the periods of the decreased use of this method, it was partially renewed by the ascension of the Field-Map technology that connects the compass measurement with the software tools for the environment mapping. It is currently mostly used for the forestry under-canopy mapping and other special tasks, where the current progressive technologies, especially photogrammetry and GNSS, can be used only with complication. The Field-Map set can include either the laser rangefinder Impulse LR 200 with MapStar Compass Module II compass, or TruPulse laser rangefinder that allows also the azimuth measurement. The research was aimed on the accuracy of mentioned devices. The measured lengths and azimuth were evaluated primarily. The evaluation of the field condition results showed the mean length error 0.07-0.09 m for Impulse rangefinder, while 0.25-0.29 m for TruPulse rangefinder. The mean azimuth error was around 0.5 degree for MapStar compass and above 3 degrees for TruPulse. After this basic evaluation the data were used for the computation of four compass traverses using various measurement and computation methods. The results showed, that the compass measurement, especially using the Impulse + MapStar set, can be still a suitable method for lower accuracy surveying, although effective only in specific conditions.

Structure-from-motion (SfM) in combination with multi-view stereo (MVS) represent techniques, which allow efficient generation of the point cloud from close-range photogrammetry (CRP) images of forest ground. Recent software products for the generation of digital terrain models (DTM) includes a wide range of interpolation methods. Previous studies showed different errors in elevations of DTMs interpolated with different methods. This study aims to analyze differences between the elevations of DTMs derived from CRP point cloud using different methods of interpolation. Six methods of interpolation included in modular system OPALS were tested in the study. In addition to simple methods of interpolation such as Snap or Moving average, more complex methods were used for interpolation of the DTMs elevations. For each method, 5 DTMs with resolution ranging from 1 to 20 cm were generated. Elevations of the DTMs were compared with the elevations of Global Navigation Satellite System (GNSS) surveyed check points. RMSE of DTMs elevations ranges from 3.4 cm to 16.2 cm. Differences between the elevations of DTMs interpolated using different methods and resolution were further investigated using one-way analysis of variance (ANOVA). The ANOVA rejected the statistical significance of the differences. Additionally, the spatial distribution of errors was analyzed. The analysis indicates that the interpolation of the extreme DTM values can be expected at the edges of the DTM when using the CRP images captured from single passing through the study site.