A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations

Guan, H. R.; Zhang, Jing; Ma, Qin; Liu, Min; Wu, Fayun; Su, Yanjun; Hu, Tianyu; Wang, Rui; Yang, Qiuli; Sun, Xiuhong; Li, Yumei; Jin, Shichao

doi:10.1109/tgrs.2019.2953654

Cited by 32 publications

(9 citation statements)

References 57 publications

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“…Automated point cloud alignment in forests has been carried out using proven approaches. Some of these approaches depend on the geometric features and parameters of the forest (tree/stem locations, tree height, or DBH) [21,22,36,37], while others use keypoints from canopy analysis by performing mean-shift segmentation [20]. Our method is similar to the methods based on canopy analysis; however, the previous methods required multi-TLS scan data for reasonable tree crown extraction.…”

Section: Discussionmentioning

confidence: 99%

“…The main limitation of this research is that it primarily depends on the accuracy of the extracted tree locations. Guan et al [37] assumed a common fixed forest geometry between the segmented trees in the point cloud from the UAV, BLS, and TLS and introduced an approach for their co-registration, based on the angle and area matching of the Triangular Irregular Networks (TINs) created between the tree locations in the forest plot. Finally, fine registration was performed via the Iterative Closest Point (ICP) algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

Fekry

Yao

Cao

et al. 2021

IJGI

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A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn–Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

Fekry

Yao

Cao

et al. 2021

IJGI

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“…Due to the changes in lidar sensors, viewing angles, vegetation conditions, and so on, lidar data collected at different times and from different platforms can hardly be directly compared [319]. The registration error of multitemporal lidar data may further increase the difficulty of detecting ecosystem changes [41]. Finding a method that can be used to compare lidar data across platforms and time is urgently needed.…”

Section: Moving Toward the Era Of Big Datamentioning

confidence: 99%

Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective

Guo

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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“…A probabilistic method is developed by Dai et al, (2019) to coregister point cloud data from ALS and TLS by extracting the modes of tree crowns and identifying the correspondence by probability density function. Guan et al 2019) utilized the tree locations and common fixed forest geometry to register UAV, Backpack and TLS based on TIN matching and then applied ICP for fine registration.…”

Section: Related Workmentioning

confidence: 99%

Automated Uav Lidar Strip Alignment in Forested Areas Using Density-Based Canopy Clustering

Fekry

Yao

Cao

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Recently, LiDAR point cloud data acquired by Unmanned Aerial Vehicles (UAVs) are used in many scientific disciplines and like the former photogrammetric techniques these data are usually collected in overlapping strips. Generation of comprehensive models of the scanned areas requires these strips to be aligned together which is a challenging process due to the multi sensor scanning system including the scanning sensor, the GNSS receiver and the IMU sensor. The main errors result from the inaccurate GNSS locations and flight path shifts as well as failure of the GNSS signals in complex urban or forest environments. For that reasons, the development of an automatic feature-dependant method in urban areas or individual tree-based in forest areas where there are no distinct features for strip adjustment in these environments become a must. This research work focuses on automated co-registration/alignment multiple point cloud strips of forested areas acquired from UAV LiDAR (or referred to as ULS) lack of artificial ground control. The main limitations of ULS data of forests are the relatively low sampling density of near ground areas and stem nullity due to the top-view scanning mode of ULS. To obviate this, this work explicates the tree crowns shape to identify the key points required for co-registration by applying a density based clustering algorithm (DBSCAN) to the tree crowns and models resulting clusters with Gaussian mixture models by learning the best parameters using maximum likelihood estimation to define the key points. A feature vector is assigned to each point by quantifying its angular and linear relationship with respect to the local system origin. Next, the similarity score matrix is computed by a fixed geometric relationship between the distance and angle similarity. Then, the maximum weight matching problem is solved for the similarity score to gain point-to-point correspondence. Finally, the optimal 2D rigid transformation parameters (one rotation and two translations without scale factor ) are obtained using permutations to try out for all possible paired combinations and count the number of inlier points satisfying a tolerance of planimetric deviation after alignment within a user defined threshold. The results of two test forest plots with different tree species and ULS point densities show a mean planimetric enhancement from 1.79 m to 0.22 ± 0.13 m for plot one and from 2.33 ± 0.53 m to 0.61 ± 0.21 m.

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A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations

Cited by 32 publications

References 57 publications

Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective

Automated Uav Lidar Strip Alignment in Forested Areas Using Density-Based Canopy Clustering

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