A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds

Dai, Wei; Wang, Jinhu; Shaker, Ahmed

doi:10.1016/j.isprsjprs.2018.08.010

Cited by 127 publications

(94 citation statements)

References 35 publications

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“…In addition to monochromatic wavelength lidar points, the algorithm was also employed for multispectral airborne lidar data by Dai et al [28], who firstly segmented the trees only in the spatial domain, then the SVM was used to detect those which had been undersegmented, which were then refined by a second round of mean shift segmentation, considering the multispectral domain. The cylindrical kernel followed the same design as in [24], apart from an extra weight on higher points in the kernel, which guided the kernel to move upwards.…”

Section: Related Workmentioning

confidence: 99%

“…The kernel can be easily expanded into 3D, so the mean can be calculated directly from the 3D points. It has shown promising results for different types of tree conditions, such as multi-layered temperate [24] and tropical forests [26], mixed-species urban trees [27], and boreal coniferous forest [28]. However, there are a few factors to be considered, such as the kernel shape, size, and weight, to better implement it to segment trees.As an in depth analysis of the kernel function and weighting are inadequate from the literature, this paper aims at providing a detailed assessment of the mean shift algorithm for ITD from airborne lidar data to clarify the influence of the variations to the performance.…”

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Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

et al. 2019

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Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements. and angle in the form of 3D points [9,10]. They are usually mounted on airplanes for a large coverage while maintaining a good (cm) level of accuracy. The point density is dependent upon a few factors, e.g., scanner measurement rate and scanning mechanism, flight height and speed, swath width, and strip overlaps, hence it may vary from less than 1 point per m 2 to more than 50 points per m 2 . But in general, the maximum point density is getting higher with the development of airborne laser scanners.Early studies have mostly focused on the characteristics at stand-level, such as canopy cover and height, from airborne lidar data, due to limited point density [11][12][13]. Now the point density is high enough to capture a sufficient number of points on each individual tree, so that individual tree detection or delineation (ITD), including tree location, size, shape and number, has drawn considerable attention [5,[14][15][16]. Vertical distribution, above ground biomass and other secondary properties, can be derived from those accurate delineation parameters. Therefore, ALS has been increasingly used for precise forest mapping and monitoring at landscape or regional scale [10].Although ITD from airborne lidar is an important research topic for forest studies, it still remains as a challenge due to the complexity and heterogeneity of the forest structure and its composition. The main difficulty of ITD is tree segmentation, a step to segment the overall points into clusters that represent individual trees. There are two main strategies for tree segmentation: Raster-based and point-based [17,18]. Earlier methods mostly adopted the first strategy, converting the 3D point clouds into canopy height models (CHMs), a raster image, then detecting tree tops using 2D image processing techniques such as local maxima, region growing and watershed [5]. The second strategy segments the trees based directly on 3D points [14,19]. Exam...

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Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

et al. 2019

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“…Current tree segmentation approaches are primarily based on user-defined algorithms that describe the appearance of trees in a hierarchical sequence of rules. These rule-based approaches rely on combinations of shape features (Gomes et al, 2018), template matching (Dai et al, 2018), network analysis (Williams et al, 2019), and watershed routines (Silva et al, 2016) that are applied to either LIDAR point clouds or RGB photogrammetric imagery (Brieger et al, 2019). By describing the parameters that define an individual tree, unsupervised algorithms attempt to match these rules when predicting unlabeled data.…”

Section: Introductionmentioning

confidence: 99%

“…Within the field of computer vision, there has been a broad shift away from rule-based approaches (i.e., user-designed features) towards approaches that learn features from data using deep learning neural networks (Agarwal et al, 2018). There have been few attempts to use learned features in tree detection (Dai et al, 2018) due to the need for a large amounts of labeled training data, which is often difficult or impossible to collect in ecological contexts. Overall, generalization of deep learning algorithms across applications in airborne remote sensing remains a challenging task (Zhu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Marconi

Bohlman

Zare

et al. 2019

Preprint

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11Tree detection is a fundamental task in remote sensing for forestry and ecosystem 12 ecology applications. While many individual tree segmentation algorithms have been 13 proposed, the development and testing of these algorithms is typically site specific, with 14 few methods evaluated against data from multiple forest types simultaneously. This 15 makes it difficult to determine the generalization of proposed approaches, and limits tree 16 detection at broad scales. Using data from the National Ecological Observatory Network 17 we extend a recently developed semi-supervised deep learning algorithm to include 18 data from a range of forest types, determine whether information from one forest can be 19 used for tree detection in other forests, and explore the potential for building a universal 20 tree detection algorithm. We find that the deep learning approach works well for 21 Discovery Initiative through grant GBMF4563 to E.P. White and by the National Science 464 Foundation through grant 1926542 to E.P. White, S.

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Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

Xie

Wang

et al. 2023

Ecology and Evolution

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Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m 2 . Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R match ) of tree tops is up to 0.92, the coefficient of determination (R 2 ) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

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A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds

Cited by 127 publications

References 35 publications

Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

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