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Accurate estimation of tree position, diameter at breast height (DBH), and tree height measurements is an important task in forest inventory. Mobile Laser Scanning (MLS) is an important solution. However, the poor global navigation satellite system (GNSS) coverage under the canopy makes the MLS system unable to provide globally-consistent point cloud data, and thus, it cannot accurately estimate the forest attributes. SLAM could be an alternative for solutions dependent on GNSS. In this paper, a mobile phone with RGB-D SLAM was used to estimate tree position, DBH, and tree height in real-time. The main aims of this paper include (1) designing an algorithm to estimate the DBH and position of the tree using the point cloud from the time-of-flight (TOF) camera and camera pose; (2) designing an algorithm to measure tree height using the perspective projection principle of a camera and the camera pose; and (3) showing the measurement results to the observer using augmented reality (AR) technology to allow the observer to intuitively judge the accuracy of the measurement results and re-estimate the measurement results if needed. The device was tested in nine square plots with 12 m sides. The tree position estimations were unbiased and had a root mean square error (RMSE) of 0.12 m in both the x-axis and y-axis directions; the DBH estimations had a 0.33 cm (1.78%) BIAS and a 1.26 cm (6.39%) root mean square error (RMSE); the tree height estimations had a 0.15 m (1.08%) BIAS and a 1.11 m (7.43%) RMSE. The results showed that the mobile phone with RGB-D SLAM is a potential tool for obtaining accurate measurements of tree position, DBH, and tree height.

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Background: Spinal cord microglia plays a crucial role in the pathogenesis of neuropathic pain. However, the mechanisms underlying spinal microglial activation during neuropathic pain remain incompletely determined. Here, we investigated the role of Pellino1 (Peli1) and its interplay with spinal microglial activation in neuropathic pain. Methods: In this study, we examined the effects of Peli1 on pain hypersensitivity and spinal microglial activation after chronic constriction injury (CCI) of the sciatic nerve in mice. The molecular mechanisms involved in Peli1mediated hyperalgesia were determined by western blot, immunofluorescence, quantitative polymerase chain reaction (qPCR), and enzyme-linked immunosorbent assay (ELISA). We utilized immunoprecipitation to examine the ubiquitination of tumor necrosis factor receptor-associated factor 6 (TRAF6) following CCI. In addition, we explored the effect of Peli1 on BV2 microglial cells in response to lipopolysaccharide (LPS) challenge. Results: We found that CCI induced a significant increase in the levels of Peli1, which was present in the great majority of microglia in the spinal dorsal horn. Our results showed that spinal Peli1 contributed to the induction and maintenance of CCI-induced neuropathic pain. The biochemical data revealed that CCI-induced Peli1 in the spinal cord significantly increased mitogen-activated protein kinase (MAPK) phosphorylation, activated nuclear factor kappa B (NF-κB), and enhanced the production of proinflammatory cytokines, accompanied by spinal microglial activation. Peli1 additionally was able to promote K63-linked ubiquitination of TRAF6 in the ipsilateral spinal cord following CCI. Furthermore, we demonstrated that Peli1 in microglial cells significantly enhanced inflammatory reactions after LPS treatment. Conclusion: These results suggest that the upregulation of spinal Peli1 is essential for the pathogenesis of neuropathic pain via Peli1-dependent mobilization of spinal cord microglia, activation of MAPK/NF-κB signaling, and production of proinflammatory cytokines. Modulation of Peli1 may serve as a potential approach for the treatment of neuropathic pain.

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Enriching forest resource inventory is important to ensure the sustainable management of forest ecosystems. Obtaining forest inventory data from the field has always been difficult, laborious, time consuming, and expensive. Advances in integrating photogrammetry and computer vision have helped researchers develop some numeric algorithms and methods that can turn 2D (images) into 3D (point clouds) and are highly applicable to forestry. This paper aimed to develop a new, highly accurate methodology that extracts sample plot parameters based on continuous terrestrial photogrammetry. For this purpose, we designed and implemented a terrestrial observation instrument combining real-time kinematic (RTK) and charge-coupled device (CCD) continuous photography. Then, according to the set observation plan, three independent experimental plots were continuously photographed and the 3D point cloud of the plot was generated. From this 3D point cloud, the tree position coordinates, tree DBHs, tree heights, and other plot characteristics of the forest were extracted. The plot characteristics obtained from the 3D point cloud were compared with the measurement data obtained from the field to check the accuracy of our methodology. We obtained the position coordinates of the trees with the positioning accuracy (RMSE) of 0.162 m to 0.201 m. The relative root mean square error (rRMSE) of the trunk diameter measurements was 3.07% to 4.51%, which met the accuracy requirements of traditional forestry surveys. The hypsometrical measurements were due to the occlusion of the forest canopy and the estimated rRMSE was 11.26% to 11.91%, which is still good reference data. Furthermore, these image-based point cloud data also have portable observation instruments, low data collection costs, high field measurement efficiency, automatic data processing, and they can directly extract tree geographic location information, which may be interesting and important for certain applications such as the protection of registered famous trees. For forest inventory, continuous terrestrial photogrammetry with its unique advantages is a solution that deserves future attention in the field of tree detection and ecological construction.