Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) systems are effective ways to capture forest structures from top and side views, respectively. The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation. Conventional registration methods based on geometric features (e.g., points, lines, and planes) are likely to fail due to the irregular natural point distributions of forest point clouds. Currently, automatic registration methods for forest point clouds typically rely on tree attributes (such as tree position and stem diameter). However, these methods are often unsuitable for forests with diverse compositions, complex terrains, irregular tree layouts, and insufficient common trees. In this study, an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives, which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%. A new evaluation method for registration accuracy evaluation is proposed, where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans, which are then compared to reference parameters obtained using artificial spherical targets. Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors, or control points in both TLS and ULS data that are difficult to collect. The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations. The proposed method was tested on 12 plots with diverse stem densities, tree species, and altitudes located in a mountain forest. A total of 124 TLS scans were successfully registered to ULS data. The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method, with average rotation errors of 2.03 and 2.06 mrad, and average translation errors of 7.63 and 6.51 cm, respectively. The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.

Terrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accuracy of automated individual tree segmentation. In this study, we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects. Firstly, region growing and point compensation algorithms are used to determine the location of tree roots. Secondly, the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor (KNN). Thirdly, neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption. Finally, a graph describing connectivity between a point and its neighbors is constructed, and it is utilized to complete individual tree segmentation based on the shortest path algorithm. The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo, Finland, and it reached the highest mean accuracy of 77.5%, higher than previous studies on tree detection. We also extracted and validated the tree structure attributes using manual segmentation reference values. The RMSE, RMSE%, bias, and bias% of tree height, crown base height, crown projection area, crown surface area, and crown volume were used to evaluate the segmentation accuracy, respectively. Overall, the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.

Ground filtering is a fundamental step in airborne LiDAR data processing toward a variety of applications. However, existing algorithms remain tremendously challenging in complex environments, e.g. steep hillsides, ridges, valleys, discontinuities, and numerous objects. We presented a new ground filtering algorithm that can handle various landscapes. First, the multi-view window is developed to increase the number of ground seeds on the various terrains. Second, multi-resolution hierarchical cloth simulation is used to rapidly construct the high-resolution reference terrain, and bidirectional internal force operation is proposed to improve the accuracy of reference terrain by smoothing the spikes in cloth. Finally, ground and non-ground points are classified based on the height differences between points and the reference terrain. The proposed algorithm was validated not only in the International Society for Photogrammetry and Remote Sensing (ISPRS) but also karst datasets, where particularly complex environments is contained. Results showed that the proposed algorithm outperformed the existing algorithms, with the lowest average total error of 3.85% and the highest average kappa coefficient of 87.75%. Moreover, the proposed algorithm can completely preserve complex terrain, e.g. extremely steep hillsides, and sharp ridges. This study had great potential to provide a useful tool for LiDAR data processing.

Information about trees in forest is essential for the assessment of the quantity and the quality of forest ecosystem services. Recently, Deep Learning (DL) methods were regarded as a new cornerstone of algorithm development. Semantic annotations of 3D forest scenes are fundamental for DL algorithm developments. Its necessity has become more urgent as DL is data-driven and requires large amount of training and verification data. However, high-quality annotated forest datasets are still rare, as trees comprise of irregular structures and small components and pose significantly greater challenges even for manual recognition in comparison with artificial objects. This paper introduces a new open point cloud dataset ForestSemantic for forest semantic studies at both individual tree- and plot-levels. The dataset is based on TLS data with different forest conditions. Manual annotation was carried out to a level of detail 4, i.e., until all visible branches. Semantic information is provided at both plot- and tree-levels, as well as at both object- and point-level. Thus, the dataset supports both instance and semantic studies, such as objects detection and segmentation and classification at both tree- and plot-levels. In addition, the dataset also provides comprehensive structural tree traits as reference for further methodological development and verification. This dataset is expected to facilitate research in new dimensions and benchmarks of different systems and solutions. A few examples are demonstrated in this paper to unveil the potentials of the dataset for various applications. In future, it is also possible to simulate other types of point clouds by down-sampling and deforming, and to transfer the dataset for training and verification of other close-range sensing systems, as the dataset was generated using TLS point clouds that represent the highest spatial resolution and geometric accuracy in all close-range point clouds.

Forest is one of the most challenging environments to be recorded in a three-dimensional (3D) digitized geometrical representation, because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions. Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors. In practice, the ideal short-baseline observations, i.e., the dense collection mode, is rarely feasible, considering the low accessibility in forest environments and the commonly limited labor and time resources. The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations, are therefore more preferable and commonly applied. Nevertheless, the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets. Until now, a robust automated registration solution that is independent of special hardware requirements has still been missing. That is, the registration accuracy is still far from the required level, and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration. This paper proposes a discrete overlap search (DOS) method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds. The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level. An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels. The performance of the proposed method was evaluated using various accuracy criteria, as well as based on data acquired from different hardware, platforms, viewing perspectives, and at different points of time. The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions. In the terrestrial-aerial registration, data sets were collected from different sensors and at different points of time with scene changes, and a registration accuracy at the raw data geometric accuracy level was achieved. These results represent the highest automated registration accuracy and the strictest evaluation so far. The proposed method is applicable in multiple scenarios, such as 1) the global positioning of individual under-canopy observations, which is one of the main challenges in applying terrestrial observations lacking a global context, 2) the fusion of point clouds acquired from terrestrial and aerial perspectives, which is required in order to achieve a complete forest observation, 3) mobile mapping using a new stop-and-go approach, which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach. Furthermore, this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter- and object-level error estimates; it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.