High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, pose normalization, three-dimensional (3D) reconstruction, and trait estimation, is proposed. First, a handheld laser scanner is used to obtain the legume seed point clouds in batches. Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. Third, a coordinate rotation method based on PCA and the table normal is proposed to conduct pose normalization. Fourth, a fast symmetry-based 3D reconstruction method is built to reconstruct a 3D model of the single seed, and the Poisson surface reconstruction method is used for surface reconstruction. Finally, 34 traits, including 11 morphological traits, 11 scale factors, and 12 shape factors, are automatically calculated. A total of 2500 samples of five kinds of legume seeds are measured. Experimental results show that the average accuracies of scanning and segmentation are 99.52% and 100%, respectively. The overall average reconstruction error is 0.014 mm. The average morphological trait measurement accuracy is submillimeter, and the average relative percentage error is within 3%. The proposed method provides a feasible method of batch data acquisition and processing, which will facilitate the automation in high-throughput legume seed phenotyping.

Point cloud registration is a necessary prerequisite for conducting precise, large-scale forest surveys and management. This paper focuses on providing a systematic overview and summary of the work on forest point cloud registration over the past 20 years. The developmental process of forest point cloud registration methods, spanning from the early reliance on manual markers to the subsequent evolution towards automatic registration based on feature matching, and then to the advanced technology based on deep learning were reviewed. Furthermore, the paper offered detailed discussions on the registration between different point cloud platforms: ground platforms, between ground platforms and aerial platforms, and between aerial platforms. Additionally, the paper delved into mainstream datasets and evaluation metrics in the domain of forest point cloud registration. Finally, the paper summarized the current state of research in this area, highlighted challenges, and provided future research outlooks. This review aims to provide researchers with a comprehensive understanding of forest point cloud registration, and to promote the advancement of point cloud technology, hopefully inspiring further applications in the field.

Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and the TerraSAR-X Add-on for Digital Elevation Measurement (TerraSAR-X/TanDEM-X), Sentinel-2A, and Shuttle Radar Topography Mission (SRTM) data were used, and various feature combinations were established in conjunction with measurements from Light Detection and Ranging (LiDAR). Classification and regression tree (CART), gradient-boosting decision tree (GBDT), random forest (RF), and support vector machine (SVM) algorithms were employed to estimate forest height in the study area. Independent validation on the basis of LiDAR forest height samples showed the following results: (1) Regarding feature combinations, the combination of coherence and decorrelation of volume scattering provided by TerraSAR-X/TanDEM-X data outperformed the combination of backscatter coefficient and local incidence angle, as well as the combination of coherence, decorrelation of volume scattering, backscatter coefficient, and local incidence angle. The best results (R² = 0.67, RMSE = 2.89 m) were achieved with the combination of coherence and decorrelation of volume scattering using the GBDT and RF algorithms. (2) In terms of machine learning methods, the GBDT algorithm proved suitable for estimating forest height. The most effective approach for forest height mapping involved combining the GBDT algorithm with coherence, decorrelation of volume scattering, and a small amount of LiDAR forest height data, used as training data.

The registration of panoramic images and mobile light detection and ranging (LiDAR) data is quite challenging because different imaging mechanisms and viewing angle differences generate significant geometric and radiation distortions between the two multimodal data sources. To address this problem, we propose a registration method for panoramic images and mobile LiDAR data based on the hybrid geometric structure index feature of phase. We use the initial GPS/IMU to transform the mobile LiDAR data into an intensity map and align the two images to complete registration. Firstly, a novel feature descriptor called a hybrid geometric structure index of phase (HGIFP) is built to capture the structural information of the images. Then, a set of corresponding feature points is obtained from the two images using the constructed feature descriptor combined with a robust false-match elimination algorithm. The average pixel distance of the corresponding feature points is used as the error function. Finally, in order to complete the accurate registration of the mobile LiDAR data and panoramic images and improve computational efficiency, we propose the assumption of local motion invariance of 3D–2D corresponding feature points and minimize the error function through multiple reprojections to achieve the best registration parameters. The experimental results show that the method in this paper can complete the registration of panoramic images and the mobile LiDAR data under a rotation error within 12° and a translation error within 2 m. After registration, the average error of rotation is about 0.15°, and the average error of translation is about 1.27 cm. Moreover, it achieves a registration accuracy of less than 3 pixels in all cases, which outperforms the current five state-of-the-art methods, demonstrating its superior registration performance.

The current status as well as the potential absorption capability of the forest carbon sinks in terrestrial ecosystems are in urgent need of further studies. The ground observation-based methods are labor-intensive, and the resulting statistics from samples are difficult to evaluate, while inversion methods based on remote sensing data lack theoretical explanation and universality. This paper proposes a pixel-level, multi-scale, high-precision Explicit Forest carbon stock Model (EFM) that is universal and adaptive. First, four key variables were used in the construction of the EFM: remote sensing image resolution, forest canopy density, terrain slope, and forest height; Second, simulated forest scene were generated based on the growth characteristics of individual trees, and EFM parameters were solved and analyzed by these simulated pixels; Third, the EFM was tested at various scales and forest saturation levels to verify its accuracy, robustness, and applicability, the simulation and real-life experiments show that the correlation coefficient is greater than 0.98 and the relative error is about 20%. The EFM solves the problem that the existing methods are lack in theoretical interpretation and universal applicability, thus can be used to map forest carbon stocks at high resolution and large scale and even monitor forest carbon dynamics at global scale.