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Three-dimensional (3D) reconstruction of trees has always been a key task in precision forestry management and research. Due to the complex branch morphological structure of trees themselves and the occlusions from tree stems, branches and foliage, it is difficult to recreate a complete three-dimensional tree model from a two-dimensional image by conventional photogrammetric methods. In this study, based on tree images collected by various cameras in different ways, the Neural Radiance Fields (NeRF) method was used for individual tree dense reconstruction and the exported point cloud models are compared with point clouds derived from photogrammetric reconstruction and laser scanning methods. The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has a higher successful reconstruction rate, better reconstruction in the canopy area and requires less images as input. Compared with the photogrammetric dense reconstruction method, NeRF has significant advantages in reconstruction efficiency and is adaptable to complex scenes, but the generated point cloud tend to be noisy and of low resolution. The accuracy of tree structural parameters (tree height and diameter at breast height) extracted from the photogrammetric point cloud is still higher than those derived from the NeRF point cloud. The results of this study illustrate the great potential of the NeRF method for individual tree reconstruction, and it provides new ideas and research directions for 3D reconstruction and visualization of complex forest scenes. [ABSTRACT FROM AUTHOR]

Tree species identification is a critical component of forest resource monitoring, and timely and accurate acquisition of tree species information is the basis for sustainable forest management and resource assessment. Airborne hyperspectral images have rich spectral and spatial information and can detect subtle differences among tree species. To fully utilize the advantages of hyperspectral images, we propose a double-branch spatial–spectral joint network based on the SimAM attention mechanism for tree species classification. This method achieved high classification accuracy on three tree species datasets (93.31% OA value obtained in the TEF dataset, 95.7% in the Tiegang Reservoir dataset, and 98.82% in the Xiongan New Area dataset). The network consists of three parts: spectral branch, spatial branch, and feature fusion, and both branches make full use of the spatial–spectral information of pixels to avoid the loss of information. In addition, the SimAM attention mechanism is added to the feature fusion part of the network to refine the features to extract more critical features for high-precision tree species classification. To validate the robustness of the proposed method, we compared this method with other advanced classification methods through a series of experiments. The results show that: (1) Compared with traditional machine learning methods (SVM, RF) and other state-of-the-art deep learning methods, the proposed method achieved the highest classification accuracy in all three tree datasets. (2) Combining spatial and spectral information and incorporating the SimAM attention mechanism into the network can improve the classification accuracy of tree species, and the classification performance of the double-branch network is better than that of the single-branch network. (3) The proposed method obtains the highest accuracy under different training sample proportions, and does not change significantly with different training sample proportions, which are stable. This study demonstrates that high-precision tree species classification can be achieved using airborne hyperspectral images and the methods proposed in this study, which have great potential in investigating and monitoring forest resources. [ABSTRACT FROM AUTHOR]

Digital twins of forests (trees) are computational virtual recreations of forests (trees) in which the entity distributions and physical processes in real-world forests (trees) are duplicated. It is expected that conventional forest science and management can be undertaken in a digital twin of forests (trees) if the recreation of a real-world forest (tree) has accurate and comprehensive enough information. However, due to the various differences between the current tree model and the real tree, these envisioned digital twins of the forests (trees) stay a theoretical concept. In this study, we developed a processing strategy that partially integrated computational virtual measurement (CVM) process into the tree modeling workflow. Owing to the feature of CVM, partial tree skeleton reconstruction procedures were considered to have higher mechanical objectivity compared to conventional mathematical modeling methods. The reason was that we developed a novel method called virtual diameter tape (VDT), which could provide a certain percentage of modeling elements using CVM. Technically, VDT was able to virtually measure diameters and spatial distribution of cross-sectional area of trees, including the basal area, from point clouds. VDT simulated the physical scenario of diameter tapes, observing point clouds of trees. Diameter and the cross-sectional area of stem and branches were obtained by two consecutive physical measurement processes, one in the forest sample site and another in the virtual space. At the same time, VDT obtained better or a similar accuracy compared to the mathematical methods, i.e., Hough transform-based methods, using the same data sets. The root-mean-square deviation (RMSE) of retrieval of diameter at breast height (DBH) using VDT was 1.02 cm, while DBH obtained from three conventional methods varied from 1.29 cm to 1.73 cm. Based on VDT measurement results, tree skeleton reconstruction and actual forest scenario rendering of our sample plots were further implemented. Beyond the visual consistency, we believe that our work might be a small and solid step in the technological evolution from tree models to the digital twin of forests (trees). [ABSTRACT FROM AUTHOR]

Disturbances in forest ecosystems are expected to increase by the end of the twenty-first century. An understanding of these disturbed areas is critical to defining management measures to improve forest resilience. While some studies emphasize the importance of quick salvage logging, others emphasize the importance of the deadwood for biodiversity. Unmanned aerial vehicle (UAV) remote sensing is playing an important role to acquire information in these areas through the structure-from-motion (SfM) photogrammetry process. However, the technique faces challenges due to the fundamental principle of SfM photogrammetry as a passive optical method. In this study, we investigated a UAV video-based technology called full motion video (FMV) to identify fallen and snapped trees in a windthrow area. We compared the performance of FMV and an orthomosaic, created by the SfM photogrammetry process, to manually identify fallen and snapped trees, using a ground survey as a reference. The results showed that FMV was able to identify both types of damaged trees due to the ability of video to deliver better context awareness compared to the orthomosaic, although providing lower position accuracy. In addition to its processing being simpler, FMV technology showed great potential to support the interpretation of conventional UAV remote sensing analysis and ground surveys, providing forest managers with fast and reliable information about damaged trees in windthrow areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Airborne Laser Scanning (ALS) is a technology often used to study forest areas. The main area of application of ALS in forests is collecting data to determine the height of individual trees and entire stands, tree density and stand biomass. The content of the ALS data is also classified, i.e., registered objects are identified, including the species affiliation of individual trees. Important information for forest districts includes other parameters related to the structure and share of stands and the number of trees in the forest district. The main goal of this study was to propose the new ALS data processing methodology for detecting single trees in the Samławki Forest District. The idea of the proposed methodology is to indicate a free and accessible solution for any user (at least in Poland). This new ALS data processing methodology contributes to research on the use of ALS data in forest districts to maintain up-to-date and accurate stand statistics. This methodology was based on free data from the geoportal.gov.pl portal and free software, which allowed to minimize the costs of preparing data for the needs of forestry activities. In cooperation with the Samławki Forest District, the proposed methodology was used to detect the number and heights of trees for two forest addresses 13b and 30a, and then to calculate the volume of stands. As a result, the volume of the analyzed stands was calculated, obtaining values differing from the nominal ones included in the FMP (Forest Management Plan) by about 25% and 5%, respectively, for larch and oak. [ABSTRACT FROM AUTHOR]

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m − 2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management. [ABSTRACT FROM AUTHOR]