Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different types of forest were analyzed. The results showed that forest structure strongly affected the performance of all algorithms. Particularly, the success of tree detection was found to be dependent on tree density and clustering. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different forest types and may be of great value for future refinement of the single-tree detection algorithms.

Crown base height (CBH) is an important factor in relation to several characteristics of the tree stock. This paper introduces approaches for estimating tree-level CBH from airborne laser scanning (ALS) data that make use of features of computational geometry. For that purpose, the concepts of Delaunay triangulations and alpha shapes were applied and compared with approaches based on analysing return frequencies and predicting CBH by linear regression. These approaches were evaluated using test data on a total of 185 Scots pine trees, of which 136 were of sawlog size, that were detected and delineated from ALS data with a density of approximately 4 returns m^–2. The results suggest that variables based on the frequencies of crown returns within predefined height bins are the most accurate for estimating CBH. By combining the best CBH estimate with the estimated tree height in linear regression, a root mean squared error (RMSE) of 1.4 m (14%) was achieved when all study trees were considered. The estimation was generally less accurate for the trees smaller than those of sawlog size. Although the accuracy of estimating CBH is lower using the three-dimensional (3D) geometry approaches presented here, they are considered to have potential for further development.

Preharvest information on the quality of Scots pine (Pinus sylvestris) timber is required by the forest industry in Nordic countries, due to the strong association between the technical quality and product recovery of this species in particular. The objective of this study was to assess the accuracy of estimating external quality attributes and classifying the quality of mature Scots pine trees by terrestrial laser scanning (TLS). The tree quality was estimated using a random forest approach, based on both field and TLS measurements of stem diameters, tree height and branch heights. The relative root mean squared errors of the TLS measurements for tree height, diameter, diameter at 6 m and the lowest living and dead branch height were 7.1%, 5.9%, 8.9%, 9.6% and 42.9%, respectively. The highest errors of the branch heights were caused by the shadowing effect in the point cloud data. The quality classes were estimated accurately, based on both (field and TLS measured) tree attributes. Trees were classified with 95.0% and 83.6% accuracy into three operationally-important quality classes and with 87.1% and 76.4% accuracy into five classes using, field or TLS measurements, respectively. The obtained quality classification results were promising. The enhanced tree quality information could have a significant effect on planning forest management procedures, wood supply chains and optimizing the flow of raw materials. To fully integrate tree quality measurements in operational forestry, the methods used should be fully automated.

Remote sensing-based mapping of forest Ecosystem Service (ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties. This study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models. Prediction models based on Airborne Laser Scanning (ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses (berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97% of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies. Despite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m², i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.

Many studies have reported increased multi-functionality and financial profits due to a shift from even- to uneven-aged forest management. However, little is known (from long-term experiences or predictions) how alternative management systems could affect national-scale wood production and carbon storage, if adopted over very large areas. We analysed these effects using an area-based framework, in which multiple Markov chain models were used to simulate the development of forests according to different management systems. Classification of forests to wood availability categories was used to determine the system to be applied. We enhanced the framework to allow shifts between management systems that correspond to enforced or voluntary changes in forest use. Simulations of extensive shifts from conventional even-aged management to alternative silvicultural systems revealed interesting developmental patterns that cannot be directly deduced from studies that upscale from smaller areas. Our results show that the amount of carbon stored by Finnish forests can be increased by applying less intensive management systems, although this has trade-offs in terms of harvests and associated financial costs. The level of trade-offs differed depending on the type of forest that shifted between management systems and whether areas were also assumed to be completely set aside from forestry. These differences were further pronounced if the desired harvest levels and their allocation changed along with the management system. If the studied attributes were considered at the same relative scale and with equal weighting, the extensive shifts to alternative management systems exhibited the strongest impact on harvesting costs.