Detecting and quantifying standing dead tree structural loss with reconstructed tree models using voxelized terrestrial lidar data

Putman, E.; Popescu, Sorin C.; Eriksson, Marian; Zhou, Tan; Klockow, Paul A.; Vogel, Jason G.; Moore, Georgianne W.

doi:10.1016/j.rse.2018.02.028

Cited by 19 publications

(14 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TLS data have also been successfully applied for the detection and quantification of the structural loss on standing dead trees. The approach proposed by Putman et al [93] makes use of the TreeVolX algorithm, a voxel-based methodology developed by Putman and Popescu [94] which segments the voxelized point cloud by layer in order to identify stem or branch sections. When identified, these sections are then filled with further voxels, recreating a solid voxel model.…”

Section: Terrestrial Laser Scanningmentioning

confidence: 99%

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

2018

View full text Add to dashboard Cite

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future.

show abstract

Section: Terrestrial Laser Scanningmentioning

confidence: 99%

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

2018

View full text Add to dashboard Cite

show abstract

“…The branch shifting during the year could be related to the branch length [50]. This observation using an RTM approach in a mixed broadleaf forest confirmed the sagging of a branch and on the opposite branch stub rising after losing its terminal part, simply as the total branch weight decreased.…”

mentioning

confidence: 61%

“…While branch movement has been detected on live plants [51] or live trees [52,53] and has been described by changes in turgor at the molecular level of plant or tree components, in this study, we detected branch movements of dead trees. In contrast, dead branch movement has been observed to be related to the decay process with the loss of branch terminals [50]. Excluding root interconnection between living and dead trees, dead branch movement is not a physiological process but physical one.…”

mentioning

confidence: 99%

UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

et al. 2020

View full text Add to dashboard Cite

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.

show abstract

“…The occlusion of forest trees is determined by various factors such as leaf area index, the location of the scanner, scanning parameter setting [74], brand of the scanner [75] and tree Our results suggest that for the three experimental trees, the CSLN changed as the search radius r 1 increased, and the highest segmentation accuracy was obtained when r 1 was one-quarter of the average width of the leaves of each tree crown. In addition, the best selected value of the input parameter MaxR was approximately half of the average leaf width for the crowns of the different tree species.…”

Section: Recommendationsmentioning

confidence: 83%

Section: Recommendationsmentioning

confidence: 99%

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

Cao

Liu

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Leaf attribute estimation is crucial for understanding photosynthesis, respiration, transpiration, and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests. Terrestrial laser scanning (TLS) has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns. In this paper, we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features (i.e., leaf area and leaf angle distributions (leaf azimuthal angle and leaf inclination angle)) from single leaves. To this end, we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions; meanwhile, four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface. We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters. Finally, the leaf attributes (leaf area and leaf angle distributions) were calculated and assessed by analysing the segmented single-leaf point cloud. To validate the final results, the actual leaf area, leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities. In addition, a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm. The results demonstrated that the segmentation accuracy of Ehretia macrophylla (94.0%) was higher than that of crape myrtle (90.6%) and Fatsia japonica (88.8%). The segmentation accuracy of Fatsia japonica was the lowest of the three experimental trees. In addition, the single-leaf area estimation accuracy for Ehretia macrophylla (95.39%) was still the highest among the three experimental trees, and the single-leaf area estimation accuracy for crape myrtle (91.92%) was lower than that for Ehretia macrophylla (95.39%) and Fatsia japonica (92.48%). Third, the method proposed in this paper provided accurate leaf inclination and azimuthal angles for the three experimental trees (Ehretia macrophylla: leaf inclination angle: R 2 = 0.908, RMSE = 6.806° and leaf azimuth angle: R 2 = 0.981, RMSE = 7.680°; crape myrtle: leaf inclination angle: R 2 = 0.901, RMSE = 8.365° and leaf azimuth angle: R 2 = 0.938, RMSE = 7.573°; Fatsia japonica: leaf inclination angle: R 2 = 0.849, RMSE = 6.158° and leaf azimuth angle: R 2 = 0.947, RMSE = 3.946°). The results indicate that the proposed method is effective and operational for providing accurate, detailed information on single leaves and vegetation structure from scanned data. This capability facilitates improvements in applications such as the estimation of leaf area, leaf angle distribution and biomass.

show abstract

Detecting and quantifying standing dead tree structural loss with reconstructed tree models using voxelized terrestrial lidar data

Cited by 19 publications

References 35 publications

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

Contact Info

Product

Resources

About