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%).

Strong wind disturbances can affect large forested areas and often occur irregularly within a forest. Due to this, identifying damaged sites and estimating the extent of these losses are crucial for the harvesting management of salvage logging. Furthermore, the location should be surveyed as soon as possible after the disturbance to prevent the degradation of fallen trees. A fixed-wing type of unmanned aircraft system (UAS) with a compact digital camera was used in this study. The imagery was acquired on approximately 200 hectares where five large windthrow areas had occurred. The objective of the study was to determine the location of the windthrow areas using a semi-automatic approach based on the UAS imagery, and on the combination of UAS imagery with airborne laser scanning (ALS). The results were compared with reference data measured by global navigation satellite system (GNSS) devices. At the same time, windthrow areas were derived from Landsat imagery to investigate whether the UAS imagery would have significantly more accurate results. GNSS measurements and Landsat imagery are currently used in forestry on an operational level. The salvage logging was estimated for each forest stand based on the estimated areas and volume per hectare obtained from the forest management plan. The results from the UAS (25.09 ha) and the combined UAS/ALS (25.56 ha) methods were statistically similar to the reference GNSS measurements (25.39 ha). The result from Landsat, at 19.8 ha, was not statistically similar to the reference GNSS measurements or to the UAS and UAS/ALS methods. The estimate of salvage logging for the whole area, from UAS imagery and the forest management plan, overestimated the actual salvage logging measured by foresters by 4.93% (525 m³), when only the most represented tree species were considered. The UAS/ALS combination improved the preliminary results of determining windthrow areas which lead to decreased editing time for all operators. The UAS imagery shows potential for application to early-stage surveys of windthrow areas in forests. The advantages of this method are that it provides the ability to conduct flights immediately after the disturbance, the foresters do not need to walk within the affected areas which decreases the risk of injury, and allows flights to be conducted on cloudy days. The orthomosaic of the windthrow areas, as a by-product of data processing in combination with forest maps and forest road maps, can be used as a tool to plan salvage logging.

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.

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.