A topology-based approach to individual tree segmentation from airborne LiDAR data

Xu, Xin; Iuricich, Federico; Floriani, Leila De

doi:10.1007/s10707-023-00487-4

Cited by 4 publications

(5 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the above fitting surface, it was not necessary to correct the elevation of all second-level grids, and the correction range was defined by the fitting and interpolation elevation. The fitting elevation was calculated using equation (6). In the firstlevel grid we used equations ( 8) and ( 9) to calculate the AE and MSE between the fitting and interpolation elevations of the grid nodes.…”

Section: Strategy Obtaining Robust Reference Surfacesmentioning

confidence: 99%

“…With the emergence of unmanned aerial vehicle photogrammetry and airborne lidar, their scope of application has further expanded [3]. But at the same time, it has also caused a negative effect of large amounts of data and difficulty in processing them [4][5][6]. Particularly in terrain observation, the advantage of aerial measurement allows experimenters to easily obtain measurement data for complex terrains (such as gullies and mountainous regions), which was previously difficult to achieve.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

Li,

Wang,

Sun

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The appearance of unmanned aerial vehicle photogrammetry and airborne lidar makes it possible to obtain measurement data for complex terrains such as gullies and mountainous regions. However, extracting ground points from these abundant and massive measurement datasets is challenging. In traditional extractions, their essence is to determine the surfaces that can describe the terrain from the seed points in the grid and use them as the basis for separating non-ground points. For effective extraction, this study proposes a multisource elevations strategy (MES) obtaining robust seed points and reference surfaces. First, two-level extended grids were constructed as the basic units. Then, to select more robust values between measurement and interpolation elevations, an elevation-determination rule was established for seed points. After, based fitting and interpolation elevations of grid nodes, the correction range is determined and the elevation is corrected for reference surfaces. In two representative complex terrain areas, when non-ground points were marked as seed points, the MES effectively reduced the phenomenon of seed points moving away from the ground. Reference surfaces can also accurately represent the global change trend and local elevation of the ground in areas where the terrain changes rapidly. This strategy provides a new thinking for ground point extraction from point cloud.

show abstract

Section: Strategy Obtaining Robust Reference Surfacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

Li,

Wang,

Sun

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In addition, although NEWFOR has provided the field measurement data such as tree locations and tree heights, they do not perfectly match with the actual point clouds. Previous research efforts have shown that there will be more significant errors within the given field measurement data for a sample plot characterized by low overall point cloud density but high trunk density [5]. To ensure more reliable validation results, we have obtained the reference data (location, height, and crown diameter) by manually segmenting the individual trees in the open source software CloudCompare (https://www.cloudcompare.org/ (accessed on 5 February, 2024)).…”

Section: Datasetsmentioning

confidence: 99%

“…However, with the expansion of human activity and the influence of climate change, forests are under increasingly serious threats. In light of these challenges, ensuring enhanced protection and efficient management of forest resources has become of paramount importance [5,6]. The traditional forest resources inventory method highly depends on site surveying [7,8].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Individual Tree Points by Combining Marker-Controlled Watershed Segmentation and Spectral Clustering Optimization

Liu,

Chen,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892.

show abstract

“…The segmentation result was refined in a horizontal plane based on three tree-crown features. Xu et al [28] applied Laplacian smoothing and watershed segmentation directly to the point cloud to derive tree clusters and created two segmentations based on treetops and tree bottoms (trunks) within each cluster. Finally, individual trees were identified by intersecting the two segmentations.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Individual Tree Detection in Broadleaf Forests Based on UAV-LiDAR Data and Multistage 3D Structure Analysis

Deng,

Jing,

Zhao

2024

Forests

View full text Add to dashboard Cite

Individual tree detection and segmentation in broadleaf forests have always been great challenges due to the overlapping crowns, irregular crown shapes, and multiple peaks in large crowns. Unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) is a powerful tool for acquiring high-density point clouds that can be used for both trunk detection and crown segmentation. A hybrid method that combines trunk detection and crown segmentation is proposed to detect individual trees in broadleaf forests based on UAV-LiDAR data. A trunk point distribution indicator-based approach is first applied to detect potential trunk positions. The treetops extracted from a canopy height model (CHM) and the crown segments obtained by applying a marker-controlled watershed segmentation to the CHM are used to identify potentially false trunk positions. Finally, the three-dimensional structures of trunks and branches are analyzed at each potentially false trunk position to distinguish between true and false trunk positions. The method was evaluated on three plots in subtropical urban broadleaf forests with varying proportions of evergreen trees. The F-score in three plots ranged from 0.723 to 0.829, which are higher values than the F-scores derived by a treetop detection method (0.518–0.588) and a point cloud-based individual tree segmentation method (0.479–0.514). The influences of the CHM resolution (0.25 and 0.1 m) and the data acquisition season (leaf-off and leaf-on) on the final individual tree detection result were also evaluated. The results indicated that using the CHM with a 0.25 m resolution resulted in under-segmentation of crowns and higher F-scores. The data acquisition season had a small influence on the individual tree detection result when using the hybrid method. The proposed hybrid method needs to specify parameters based on prior knowledge of the forest. In addition, the hybrid method was evaluated in small-scale urban broadleaf forests. Further research should evaluate the hybrid method in natural forests over large areas, which differ in forest structures compared to urban forests.

show abstract

A topology-based approach to individual tree segmentation from airborne LiDAR data

Cited by 4 publications

References 52 publications

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

Segmentation of Individual Tree Points by Combining Marker-Controlled Watershed Segmentation and Spectral Clustering Optimization

A Hybrid Method for Individual Tree Detection in Broadleaf Forests Based on UAV-LiDAR Data and Multistage 3D Structure Analysis

Contact Info

Product

Resources

About