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Ground filtering is one of the essential steps for processing airborne light detection and ranging data in forestry applications. However, the performance of existing methods is still limited in forested areas due to the complex terrain and dense vegetation. To overcome this limitation, we proposed an improved surface-based filter based on multi-directional narrow window and cloth simulation. The innovations mainly involve two aspects as follows: (1) sufficient and uniformly distributed ground seeds are identified by merging the lowest points and line segments from the point clouds within a multi-directional narrow window; (2) complete and accurate ground points are extracted using a cyclic scheme that includes incorrect ground point elimination using the internal force adjustment of cloth simulation, terrain reconstruction with moving least-squares plane fitting, and ground point extraction based on progressively refined terrain. The proposed method was tested in five forested sites with various terrain characteristics and vegetation distributions. Experimental results showed that the proposed method could accurately separate ground points from non-ground points in different forested environments, with the average kappa coefficient of 88.51% and total error of 4.22%. Moreover, the comparative experiments proved that the proposed method performed better than the classical methods involving the slope-based, mathematical morphology-based and surface-based methods. [ABSTRACT FROM AUTHOR]

Background: The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods: We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results: The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion: The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications. [ABSTRACT FROM AUTHOR]

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users. [ABSTRACT FROM AUTHOR]

Building roof extraction from airborne laser scanning point clouds is significant for building modeling. The common method adopts a bottom-up strategy which requires a ground filtering process first, and the subsequent process of region growing based on a single seed point easily causes oversegmentation problem. This paper proposes a novel method to extract roofs. A top-down strategy based on cloth simulation is first used to detect seed point sets with semantic information; then, the roof seed points are extracted instead of a single seed point for region-growing segmentation. The proposed method is validated by three point cloud datasets that contain different types of roof and building footprints. The results show that the top-down strategy directly extracts roof seed point sets, most roofs are extracted by the region-growing algorithm based on the seed point set, and the total errors of roof extraction in the test areas are 0.65%, 1.07%, and 1.45%. The proposed method simplifies the workflow of roof extraction, reduces oversegmentation, and determines roofs in advance based on the semantic seed point set, which suggests a practical solution for rapid roof extraction. • A top-down strategy is presented for extracting roofs from airborne LiDAR point clouds. • The proposed strategy avoids ground filtering and simplifies the workflow of roof extraction. • The cloth simulation algorithm detects seed points. • The combination of roughness and connect-component labeling determines roof seed sets. • The proposed seed point set-based region-growing method reduces oversegmentation. [ABSTRACT FROM AUTHOR]