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Forest fires pose a significant threat to forest carbon storage and sinks, yet they also play a crucial role in the natural dynamics of boreal forests. Accurate quantification of biomass changes resulting from forest fires is essential for damage assessment and controlled burning evaluation. This study utilized terrestrial laser scanning (TLS) to quantify changes in ground vegetation resulting from low-intensity surface fires. TLS data were collected before and after controlled burnings at eight one-hectare test sites in Scots pine (Pinus sylvestris L.) dominated boreal forests in Finland. A surface differencing-based method was developed to identify areas exposed to fire. Validation, based on visual interpretation of 1 × 1 m surface patches (n = 320), showed a recall, precision, and F1-score of 0.9 for the accuracy of identifying burned surfaces. The developed method allowed the assessment of the magnitude of fire-induced vegetation changes within the test sites. The proportions of burned 1 × 1 m areas within the test sites varied between 51–96%. Total volumetric change in ground vegetation was on average –1200 m³ ha-1, with burning reducing the vegetation volume by 1700 m³ ha-1 and vegetation growth increasing it by 500 m³ ha-1. Substantial variations in the volumetric changes within and between the test sites were detected, highlighting the complex dynamics of surface fires, and emphasizing the importance of having observations from multiple sites. This study demonstrates that bitemporal TLS measurements provide a robust means for characterizing fire-induced changes, facilitating the assessment of the impact of surface fires on forest ecosystems.

Abstract Tree functional traits together with processes such as forest regeneration, growth, and mortality affect forest and tree structure. Forest management inherently impacts these processes. Moreover, forest structure, biodiversity, resilience, and carbon uptake can be sustained and enhanced with forest management activities. To assess structural complexity of individual trees, comprehensive and quantitative measures are needed, and they are often lacking for current forest management practices. Here, we utilized 3D information from individual Scots pine (Pinus sylvestris L.) trees obtained with terrestrial laser scanning to, first, assess effects of forest management on structural complexity of individual trees and, second, understand relationship between several tree attributes and structural complexity. We studied structural complexity of individual trees represented by a single scale‐independent metric called “box dimension.” This study aimed at identifying drivers affecting structural complexity of individual Scots pine trees in boreal forest conditions. The results showed that thinning increased structural complexity of individual Scots pine trees. Furthermore, we found a relationship between structural complexity and stem and crown size and shape as well as tree growth. Thus, it can be concluded that forest management affected structural complexity of individual Scots pine trees in managed boreal forests, and stem, crown, and growth attributes were identified as drivers of it.

Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape and also about the quality of branches affecting the shape of a crown is, however, still limited. Thus, the aim was to study the crown structure of individual Scots pine trees in forest stands with varying stem densities due to past forest management practices. Furthermore, we wanted to understand how crown and stem attributes and also tree growth affect stem area at the height of maximum crown diameter (SAHMC), which could be used as a proxy for tree growth potential. We used terrestrial laser scanning (TLS) to generate attributes characterizing crown size and shape. The results showed that increasing stem density decreased Scots pine crown size. TLS provided more detailed attributes for crown characterization compared with traditional field measurements. Furthermore, decreasing stem density increased SAHMC, and strong relationships (Spearman’s correlations > 0.5) were found between SAHMC and crown and stem size and also stem growth. Thus, this study provided quantitative and more comprehensive characterization of Scots pine crowns and their growth potential. The combination of a traditional growth and yield study design and 3D characterization of crown architecture and growth potential can open up new research possibilities.

Thinning is a forest management activity that regulates the competition between the trees within a forest. However, the effect of different thinning treatments on competition is largely unexplored, especially because of the difficulty in measuring crown characteristics. This study aimed to investigate how different type and intensity thinning treatments affect the stem- and crown-based competition of trees based on terrestrial laser scanning (TLS) point clouds. The research was conducted in three study sites in southern Finland where the Scots pine (Pinus sylvestris L.) is the dominant tree species. Nine rectangular sample plots of varying sizes (1000 m2 to 1200 m2) were established within each study site, resulting in 27 sample plots in total. The experimental design of each study site included two levels of thinning intensities and three thinning types, resulting in six different thinning treatments. To assess the competition between the trees, six distance-dependent competition indices were computed for each tree. The indices were based on diameter at breast height (DBH) (CIDBH), height (CIH), maximum crown diameter (CIMCD), crown projection area (CICA), crown volume (CICV), and crown surface area (CICS). The results showed that for both moderate and intensive intensities, the competition decrease was 45.5–82.5% for thinning from below, 15.6–73.6% for thinning from above, and 12.8–66.8% for systematic thinning when compared with control plots. In most cases, the crown- and stem-based metrics were affected by thinning treatments significantly when compared with control plots at a 95% confidence interval. Moreover, moderate from-below and from-above thinning showed no statistical difference with each other in both crown- and stem-based competition indices except for CIDBH (p-value ≤ 0.05). Our results confirm the great potential of TLS point clouds in quantifying stem- and crown-based competition between trees, which could be beneficial for enhancing ecological knowledge on how trees grow in response to competition.

The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 × 32 m. The ALS crown metrics of width (WD), projection area (A2D), volume (V), and surface area (A3D) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Δ), especially for the 3D crown metrics of V and A3D, with Cohen’s D values of 1.09–1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (rΔ) and showed statistically significant differences with Norway spruce and birch in terms of rΔWD, rΔA2D, rΔV, and rΔA3D at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in rΔWD, rΔV, and rΔA3D (p-value < 0.0001). However, the amount of rΔ variability that could be explained by the species was only 2–5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales.

Structural complexity of trees is related to various ecological processes and ecosystem services. To support management for complexity, there is a need to assess the level of structural complexity objectively. The fractal-based box dimension (Db) provides a holistic measure of the structural complexity of individual trees. This study aimed to compare the structural complexity of Scots pine (Pinus sylvestris L.) trees assessed with Db that was generated with point cloud data from terrestrial laser scanning (TLS) and aerial imagery acquired with an unmanned aerial vehicle (UAV). UAV imagery was converted into point clouds with structure from motion (SfM) and dense matching techniques. TLS and UAV measured Db-values were found to differ from each other significantly (TLS: 1.51 ± 0.11, UAV: 1.59 ± 0.15). UAV measured Db-values were 5% higher, and the range was wider (TLS: 0.81–1.81, UAV: 0.23–1.88). The divergence between TLS and UAV measurements was found to be explained by the differences in the number and distribution of the points and the differences in the estimated tree heights and number of boxes in the Db-method. The average point density was 15 times higher with TLS than with UAV (TLS: 494,000, UAV 32,000 points/tree), and TLS received more points below the midpoint of tree heights (65% below, 35% above), while UAV did the opposite (22% below, 78% above). Compared to the field measurements, UAV underestimated tree heights more than TLS (TLS: 34 cm, UAV: 54 cm), resulting in more boxes of Db-method being needed (4–64%, depending on the box size). Forest structure (two thinning intensities, three thinning types, and a control group) significantly affected the variation of both TLS and UAV measured Db-values. Still, the divergence between the two approaches remained in all treatments. However, TLS and UAV measured Db-values were consistent, and the correlation between them was 75%.

Climate change is increasing pest insects’ ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the mapping of trees that were in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicles (UAV)-based imagery collected in spring and fall in four study areas in Helsinki, Finland. We used the Random Forest machine learning to classify trees based on their symptoms during both occasions. Our approach achieved an overall classification accuracy of 78.2% and 84.5% for healthy, declined and dead trees for spring and fall datasets, respectively. The results suggest that fall or the end of summer provides the most accurate tree vitality classification results. We also investigated the transferability of Random Forest classifiers between different areas, resulting in overall classification accuracies ranging from 59.3% to 84.7%. The findings of this study indicate that multispectral UAV-based imagery is capable of classifying tree decline in Norway spruce trees during a bark beetle infestation.

Wood density is well known to vary between tree species as well as within and between trees of a certain species depending on the growing environment causing uncertainties in forest biomass and carbon storage estimation. This has created a need to develop novel methodologies to obtain wood density information over multiple tree communities, landscapes, and ecoregions. Therefore, the aim of this study was to evaluate the dependencies between structural characteristics of Scots pine (Pinus sylvestris L.) tree communities and internal wood property (i.e., mean wood density and ring width) variations at breast height. Terrestrial laser scanning was used to derive the structural characteristics of even-aged Scots pine dominated forests with varying silvicultural treatments. Pearson’s correlations and linear mixed effect models were used to evaluate the interactions. The results show that varying silvicultural treatments did not have a statistically significant effect on the mean wood density. A notably stronger effect was observed between the structural characteristics and the mean ring width within varying treatments. It can be concluded that single time terrestrial laser scanning is capable of capturing the variability of structural characteristics and their interactions with mean ring width within different silvicultural treatments but not the variation of mean wood density.

Terrestrial laser scanning (TLS) accompanied by quantitative tree-modeling algorithms can potentially acquire branching data non-destructively from a forest environment and aid the development and calibration of allometric crown biomass and wood quality equations for species and geographical regions with inadequate models. However, TLS’s coverage in capturing individual branches still lacks evaluation. We acquired TLS data from 158 Scots pine (Pinus sylvestris L.) trees and investigated the performance of a quantitative branch detection and modeling approach for extracting key branching parameters, namely the number of branches, branch diameter (bd) and branch insertion angle (bα) in various crown sections. We used manual point cloud measurements as references. The accuracy of quantitative branch detections decreased significantly above the live crown base height, principally due to the increasing scanner distance as opposed to occlusion effects caused by the foliage. bd was generally underestimated, when comparing to the manual reference, while bα was estimated accurately: tree-specific biases were 0.89 cm and 1.98°, respectively. Our results indicate that full branching structure remains challenging to capture by TLS alone. Nevertheless, the retrievable branching parameters are potential inputs into allometric biomass and wood quality equations.

State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter >15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67° (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2° (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

Information on forest trafficability (i.e. carrying capacity of the forest floor) is required before harvesting operations in Southern Boreal forest conditions. It describes the seasons when harvesting operations may take place without causing substantial damage to the forest soil using standard logging machinery. The available trafficability information have been based on subjective observations made during the wood procurement planning. For supporting forest operations, an open access map product has been developed to provide information on trafficability of forests. The forest stands are distributed into classes that characterize different harvesting seasons based on topographic wetness index, amount of vegetation, ground water height and ditch depth. The main goal of this case study was to evaluate the information of the static forest trafficability map in relation to the detected rutting within logging tracks measured in the field. The analysis concentrated on thinning stands since the effect of rutting is significant on the growth of the remaining trees. The results showed that the static trafficability map provided reliable and slightly conservative estimation of the forest trafficability. The majority (91.7%) of the evaluated stands were harvested without causing significant damage if harvesting was timed correctly compared to the trafficability information. However, it should be pointed out that the weather history at small scale, the skills of a driver, and effects of used machinery are not considered in the map product although they can have a considerable impact on the rutting.

Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99–1.22 cm for diameter at breast height (Δdbh), 44.14–55.49 cm2 for basal area (Δg), and 1.91–4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60–1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔDg), 0.81–2.26 m for changes in basal area-weighted mean height (ΔHg), 1.40–2.34 m2/ha for changes in mean basal area (ΔG), and 74–193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

We evaluated a smartphone app (TRESTIMATM) for forest sample plot measurements. The app interprets imagery collected from the sample plots using the camera in the smartphone and then estimates forest inventory attributes, including species-specific basal areas (G) as well as the diameter (DgM) and height (HgM) of basal area median trees. The estimates from the smartphone app were compared to forest inventory attributes derived from tree-wise measurements using calipers and a Vertex height measurement device. The data consist of 2169 measured trees from 25 sample plots (32 m × 32 m), dominated by Scots pine and Norway spruce from southern Finland. The root-mean-square errors (RMSEs) in the basal area varied from 19.7% to 29.3% and the biases from 11.4% to 18.4% depending on the number of images per sample plot and image shooting location. DgM measurement bias varied from −1.4% to 3.1% and RMSE from 5.2% to 11.6% depending on the tree species. Respectively, HgM bias varied from 5.0% to 8.3% and RMSE 10.0% to 13.6%. In general, four images captured toward the center of the plot provided more accurate results than four images captured away from the plot center. Increasing the number of captured images per plot to the analyses yielded only marginal improvement to the results.

The requirements for up-to-date tree data in city parks and forests are increasing, and an important question is how to keep the digital databases current for various applications. Traditional map-updating procedures, such as visual interpretation of digital aerial images or field measurements using tachymeters, are either inaccurate or expensive. Recently, the development of laser-scanning technology has opened new opportunities for tree mapping and attributes updating. For a detailed measurement and attributes update of urban trees, we tested the use of a multisource single-tree inventory (MS-STI) for heterogeneous urban forest conditions. MS-STI requires an existing tree map as input information in addition to airborne laser-scanning (ALS) data. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System (GNSS). Tree attributes were either measured from ALS or predicted by using metrics extracted from ALS data. Stem diameter-at-breast height (DBH) was predicted and compared to the field measures, and tree height and crown area were directly measured from ALS data at the two different urban-forest areas. The results indicate that MS-STI can be used for updating urban-forest attributes. The accuracies of DBH estimations were improved compared to the existing attribute information in the city of Helsinki’s urban-tree register. In addition, important attributes, such as tree height and crown dimensions, were extracted from ALS and added as attributes to the urban-tree register.

The stem diameter distribution, stem form and quality information must be measured as accurately as possible to optimize cutting. For a detailed measurement of the stands, we developed and demonstrated the use of a multisource single-tree inventory (MS-STI). The two major bottlenecks in the current airborne laser scanning (ALS)-based single-tree-level inventory, tree detection and tree species recognition, are avoided in MS-STI. In addition to airborne 3D data, such as ALS, MS-STI requires an existing tree map with tree species information as the input information. In operational forest management, tree mapping would be carried out after or during the first thinning. It should be highlighted that the tree map is a challenging prerequisite, but that the recent development in mobile 2D and 3D laser scanning indicates that the solution is within reach. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System. Predictors for tree quality attributes were extracted from ALS data or digital stereo imagery (DSI) and used in the nearest-neighbor estimation approach. Stem distribution was compiled by summing the predicted single-tree measures. The accuracy of the MS-STI was validated using harvester data (timber assortments) and field measures (stem diameter, tree height). RMSEs for tree height, diameter, saw log volume and pulpwood volume varied from 4.2% to 5.3%, from 10.9% to 19.9%, from 28.7% to 43.5% and from 125.1% to 134.3%, respectively. Stand-level saw log recoveries differed from −2.2% to 1.3% from the harvester measurements, as the respective differences in pulpwood recovery were between −3.0% and 10.6%. We conclude that MS-STI improves the predictions of stem-diameter distributions and provides accurate estimates for tree quality variables if an accurate tree map is available.

Large and comprehensive datasets, traditionally based on destructive stem analysis or other labor-intensive approaches, are commonly considered as a necessity in developing stem-volume equations. The aim here was to investigate how a decreasing number of sample trees affects parametrizing an existing taper curve equation and resultant stem-volume estimates. Furthermore, the potential of terrestrial laser scanning (TLS) in producing taper curves was examined. A TLS-based taper curve was derived for 246 Scots pines (Pinus sylvestris L.) from southern Finland to parametrize an existing taper curve equation. To assess sensitivity of the parametrization regarding sample size, the number of Scots pines included in the parametrization varied between full census and 1 Scots pine at a time. Root mean square error of stem-volume estimates remained ≤20.9% and the mean absolute difference was relatively constant (≤9.0%) between stem-volume estimates when the sample size included ≥46 Scots pines. Thus, it can be concluded that, with a rather small sample size, a taper curve equation can be re-parametrized for local conditions using point clouds from TLS to produce consistent stem-volume estimates.

Terrestrial laser scanning (TLS) has proven to accurately represent individual trees, while the use of TLS for plot-level forest characterization has been studied less. We used 91 sample plots to assess the feasibility of TLS in estimating plot-level forest inventory attributes, namely the stem number (N), basal area (G), and volume (V) as well as the basal area weighed mean diameter (Dg) and height (Hg). The effect of the sample plot size was investigated by using different-sized sample plots with a fixed scan set-up to also observe possible differences in the quality of point clouds. The Gini coefficient was used to measure the variation in tree size distribution at the plot-level to investigate the relationship between stand heterogeneity and the performance of the TLS-based method. Higher performances in tree detection and forest attribute estimation were recorded for sample plots with a low degree of tree size variation. The TLS-based approach captured 95% of the variation in Hg and V, 85% of the variation in Dg and G, and 67% of the variation in N. By increasing the sample plot size, the tree detection rate was decreased, and the accuracy of the estimates, especially G and N, decreased. This study emphasizes the feasibility of TLS-based approaches in plot-level forest inventories in varying southern boreal forest conditions.

Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.

Exact knowledge over tree growth is valuable information for decision makers when considering the purposes of sustainable forest management and planning or optimizing the use of timber, for example. Terrestrial laser scanning (TLS) can be used for measuring tree and forest attributes in very high detail. The study aims at characterizing changes in individual tree attributes (e.g., stem volume growth and taper) during a nine year-long study period in boreal forest conditions. TLS-based three-dimensional (3D) point cloud data were used for identifying and quantifying these changes. The results showed that observing changes in stem volume was possible from TLS point cloud data collected at two different time points. The average volume growth of sample trees was 0.226 m3 during the study period, and the mean relative change in stem volume was 65.0%. In addition, the results of a pairwise Student’s t-test gave strong support (p-value 0.0001) that the used method was able to detect tree growth within the nine-year period between 2008−2017. The findings of this study allow the further development of enhanced methods for TLS-based single tree and forest growth modeling and estimation, which can thus improve the accuracy of forest inventories and offer better tools for future decision-making processes.

Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 × 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation.

Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.

Forest resource information has a hierarchical structure: individual tree attributes are summed at the plot level and then in turn, plot-level estimates are used to derive stand or large-area estimates of forest resources. Due to this hierarchy, it is imperative that individual tree attributes are measured with accuracy and precision. With the widespread use of different measurement tools, it is also important to understand the expected degree of precision associated with these measurements. The most prevalent tree attributes measured in the field are tree species, stem diameter-at-breast-height (dbh), and tree height. For dbh and height, the most commonly used measuring devices are calipers and clinometers, respectively. The aim of our study was to characterize the precision of individual tree dbh and height measurements in boreal forest conditions when using calipers and clinometers. The data consisted of 319 sample trees at a study area in Evo, southern Finland. The sample trees were measured independently by four trained mensurationists. The standard deviation in tree dbh and height measurements was 0.3 cm (1.5%) and 0.5 m (2.9%), respectively. Precision was also assessed by tree species and tree size classes; however, there were no statistically significant differences between the mensurationists for dbh or height measurements. Our study offers insights into the expected precision of tree dbh and height as measured with the most commonly used devices. These results are important when using sample plot data in forest inventory applications, especially now, at a time when new tree attribute measurement techniques based on remote sensing are being developed and compared to the conventional caliper and clinometer measurements.

Height models based on high-altitude aerial images provide a low-cost means of generating detailed 3D models of the forest canopy. In this study, the performance of these height models in the detection of individual trees was evaluated in a commercially managed boreal forest. Airborne digital stereo imagery (DSI) was captured from a flight altitude of 5 km with a ground sample distance of 50 cm and corresponds to regular national topographic airborne data capture programs operated in many countries. Tree tops were detected from smoothed canopy height models (CHM) using watershed segmentation. The relative amount of detected trees varied between 26% and 140%, and the RMSE of plot-level arithmetic mean height between 2.2 m and 3.1 m. Both the dominant tree species and the filter used for smoothing affected the results. Even though the spatial resolution of DSI-based CHM was sufficient, detecting individual trees from the data proved to be demanding because of the shading effect of the dominant trees and the limited amount of data from lower canopy levels and near the ground.

Yksi suomalaisen metsäpolitiikan keskeisistä tavoitteista on metsäalan yhteiskunnallisen hyväksyttävyyden lisääminen. Tästä lähtökohdasta kaikille alueellisten metsäneuvostojen jäsenille lähetettiin kysely, jonka tavoitteena oli selvittää, miten eri sidosryhmät ovat kokeneet osallistumisen alueellisen metsäohjelman (AMO) laadintaan. Tutkimuksen avulla haluttiin löytää osallistumisen onnistumisia ja ongelmia, selvittää tasapuolisuuden toteutumista metsäneuvostoissa, niiden jäsenten käsityksiä ohjelmatyön vaikuttavuudesta ja heidän sitoutumistaan ohjelman tavoitteisiin.

Alueelliset metsäohjelmat ovat kullekin metsäkeskusalueelle laadittavia strategia ohjelmia, joiden tarkoitus on kehittää metsien hoitoa ja käyttöä maakunnassa. Nykyään eri intressiryhmistä koostuvalla alueellisella metsäneuvostolla on merkittävä rooli ohjelmien laadinnassa ja seurannassa. Tutkimuksen tavoitteena oli selvittää metsäneuvoston jäsenten mielipiteitä hyvästä osallistamisprosessista. Aineisto kerättiin ja analysoitiin Q-menetelmällä, jonka tavoitteena on löytää tutkittavasta aiheesta erilaisia näkökulmia. Vastaajat olivat kolmen alueellisen metsä neuvoston (Keski-Suomi, Lounais-Suomi ja Pohjois-Pohjanmaa) jäseniä. Yhteensä 50 vastaukseen pohjautuneen monimuuttuja-analyysin avulla vastaajat jaettiin neljään ryhmään: käytännöllisiin konsensuksen hakijoihin, uudistajiin, asiantuntemussuuntautuneisiin sekä osallistumisskeptisiin. Kaikki ryhmät pitivät intressiryhmien yhteistyön parantamista ja eri tahojen asiantuntemuksen tuomista prosessiin tärkeinä asioina, mutta päätösvallan tasapuolinen jako ja ohjelmassa käsiteltävät teemat jakoivat ryhmien mielipiteitä. Seuraavia ohjelmia tarkistettaessa kannattaa keskittyä kaikkien tärkeänä pitämiin asioihin. Prosessiin liittyvät erimielisyydet taas kannattaa nostaa keskusteluun ohjelmatyötä käynnistettäessä ja sopia kutakin prosessia varten mahdollisimman selkeät yhteistyön pelisäännöt.