Terrestrial laser scanning (TLS) can provide accurate and detailed three-dimensional (3D) structure information of the forest understory. Segmenting individual trees from disordered, discrete, and high-density TLS point clouds is the premise for obtaining accurate individual tree structure parameters of forest understory, pest control and fine modeling. In this study, we propose a bottom-up method to segment individual trees from TLS forest data based on density-based spatial clustering of applications with noise (DBSCAN). In addition, we also improve the DBSCAN based on the distance distribution matrix (DDM) to automatically and adaptively determine the optimal cluster number and the corresponding input parameters. Firstly, the proposed method is based on the improved DBSCAN to detect the trunks and obtain the initial clustering results. Then, the Hough circle fitting method is used to modify the trunk detection results. Finally, individual tree segmentation is realized based on regional growth layer-by-layer clustering. In this paper, we use TLS multi-station scanning data from Chinese artificial forest and German mixed forest, and then evaluate the efficiency of the method from three aspects: overall segmentation, trunk detection and small tree segmentation. Furthermore, the proposed method is compared with three existing individual tree segmentation methods. The results show that the total recall, precision, and F1-score of the proposed method are 90.84%, 95.38% and 0.93, respectively. Compared with traditional DBSCAN, recall, accuracy and F1-score are increased by 6.96%, 4.14% and 0.06, respectively. The individual tree segmentation result of the proposed method is comparable to those of the existing methods, and the optimal parameters can be automatically extracted and the small trees under tall trees can be accurately segmented.

Background: Measuring stem diameter (SD) is a crucial foundation for forest resource management, but current methods require expert personnel and are time-consuming and costly. In this study, we proposed a novel device and method for automatic SD measurement using an image sensor and a laser module. Firstly, the laser module generated a spot on the tree stem that could be used as reference information for measuring SD. Secondly, an end-to-end model was performed to identify the trunk contour in the panchromatic image from the image sensor. Finally, SD was calculated from the linear relationship between the trunk contour and the spot diameter in pixels.Results: We conducted SD measurements in three natural scenarios with different land cover types: transitional woodland/shrub, mixed forest, and green urban area. The SD values varied from 2.00 cm to 89.00 cm across these scenarios. Compared with the field tape measurements, the SD data measured by our method showed high consistency in different natural scenarios. The absolute mean error was 0.36 cm and the root mean square error was 0.45 cm. Our integrated device is low cost, portable, and without the assistance of a tripod. Compared to most studies, our method demonstrated better versatility and exhibited higher performance.Conclusion: Our method achieved the automatic, efficient and accurate measurement of SD in natural scenarios. In the future, the device will be further explored to be integrated into autonomous mobile robots for more scenarios.

Chest X-ray (CXR) imaging is essential for diagnosing thoracic diseases, and computer-aided diagnosis (CAD) systems have made substantial progress in automating the interpretation of CXR images. However, some existing methods often overemphasize local features while neglecting global context, limiting their ability to capture the broader pathological landscape. Moreover, most methods fail to model label correlations, leading to insufficient utilization of prior knowledge. To address these limitations, we propose a novel multi-label CXR image classification framework, termed the Label Masking-enhanced Residual Attention Network (LMeRAN). Specifically, LMeRAN introduces an original label-specific residual attention to capture disease-relevant information effectively. By integrating multi-head self-attention with average pooling, the model dynamically assigns higher weights to critical lesion areas while retaining global contextual features. In addition, LMeRAN employs a label mask training strategy, enabling the model to learn complex label dependencies from partially available label information. Experiments conducted on the large-scale public dataset ChestX-ray14 demonstrate that LMeRAN achieves the highest mean AUC value of 0.825, resulting in an increase of 3.1% to 8.0% over several advanced baselines. To enhance interpretability, we also visualize the lesion regions relied upon by the model for classification, providing clearer insights into the model’s decision-making process.

Background: Stem diameter (SD) measuring is a crucial foundation for forest resource management. SD measuring in various scenarios necessitates the involvement of expert personnel. As a result, the adoption of automatic measurement methods is greatly expected. In this study, we built a device with an integrated image sensor and laser module to measure the SD of trees of varied diameters. The spot generated by the laser module on the tree stem can be utilized as reference information for measuring height and SD with this device. An end-to-end model is performed to identify the trunk in the panchromatic image, then SD is calculated from the linear relationship between the trunk contour and the spot diameter in pixels. Results: We measured in three scenarios with different land cover types, i.e., transitional woodland/shrub, mixed forest, and green urban area. The overall SD ranges from 2.00 cm to 89.00 cm. To investigate the differences in measurements in different natural scenarios, we analyzed the accuracy of the device for measuring trees of different diameters. The results showed a high consistency between our method and the tape measure measurements. The absolute mean error was 0.36 cm and the root mean square error was 0.45 cm. Compared to most studies, our method demonstrates better versatility and exhibits higher performance. Our integrated device is low cost, portable, and without the assistance of a tripod. Conclusion: Our method achieves the objective of automated measurements and is experimentally proven to possess the potential of efficient and accurate measurement of SD in natural scenarios. Although some limitations remain at present, the device can be employed as a means of forest inventory. In the future, the device can be further explored to be integrated into autonomous mobile robots for more scenarios.