Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

Chen, Shilin; Zhong-ke, Feng; Chen, Panpan; Khan, Tauheed Ullah; Lian, Yining

doi:10.3390/f10110936

Cited by 20 publications

(8 citation statements)

References 50 publications

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“…The high level of correlation and RMSE we found between field-measured and our optimal MLS-derived DBH (R 2 = 0.99, RMSE = 1.72 cm/5.4%) conforms with the correlations reported in other SLAM-based MLS studies (R 2 = 0.99, RMSE = 1.11-2.9 cm/3.4-23%) [24,58]. Furthermore, our results fall within the upper end of studies focused more generally on TLS (R 2 range of 0.93-0.99 and RMSE = 1.13-3.37 cm/5.4-13.4%) [24,26,[58][59][60][61]. MLS-derived DBH displayed a negative bias of ~1 cm, which was most pronounced in trees of larger diameter.…”

Section: Tree Metrics 421 Dbhsupporting

confidence: 92%

Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping

et al. 2022

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Phenotyping has been a reality for aiding the selection of optimal crops for specific environments for decades in various horticultural industries. However, until recently, phenotyping was less accessible to tree breeders due to the size of the crop, the length of the rotation and the difficulty in acquiring detailed measurements. With the advent of affordable and non-destructive technologies, such as mobile laser scanners (MLS), phenotyping of mature forests is now becoming practical. Despite the potential of MLS technology, few studies included detailed assessments of its accuracy in mature plantations. In this study, we assessed a novel, high-density MLS operated below canopy for its ability to derive phenotypic measurements from mature Pinus radiata. MLS data were co-registered with above-canopy UAV laser scanner (ULS) data and imported to a pipeline that segments individual trees from the point cloud before extracting tree-level metrics. The metrics studied include tree height, diameter at breast height (DBH), stem volume and whorl characteristics. MLS-derived tree metrics were compared to field measurements and metrics derived from ULS alone. Our pipeline was able to segment individual trees with a success rate of 90.3%. We also observed strong agreement between field measurements and MLS-derived DBH (R2 = 0.99, RMSE = 5.4%) and stem volume (R2 = 0.99, RMSE = 10.16%). Additionally, we proposed a new variable height method for deriving DBH to avoid swelling, with an overall accuracy of 52% for identifying the correct method for where to take the diameter measurement. A key finding of this study was that MLS data acquired from below the canopy was able to derive canopy heights with a level of accuracy comparable to a high-end ULS scanner (R2 = 0.94, RMSE = 3.02%), negating the need for capturing above-canopy data to obtain accurate canopy height models. Overall, the findings of this study demonstrate that even in mature forests, MLS technology holds strong potential for advancing forest phenotyping and tree measurement.

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Section: Tree Metrics 421 Dbhsupporting

confidence: 92%

Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping

et al. 2022

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“…For a pine woodland test site in France, least-squares circle fitting was reported to be the most accurate method (RMSE = 0.019 m) for estimating DBH when compared to least squares cylinder fitting and circular Hough transformation [85]. Similarly, Calders et al [40] and Chen et al [167] obtained R 2 values of 0.97 when using least squares circle fitting for DBH estimation with a multi-scan TLS scan with 5 positions in a eucalypt open forest and boreal forest respectively. Yurtseven et al [148] obtained an R 2 value of 0.99 with a multi-scan with 8 positions in a Mediterranean forest employing randomised hough transformation with least square regression.…”

Section: Vegetation Parameters Extracted From Tls Datamentioning

confidence: 84%

Remote Sensing of Savannas and Woodlands

2021

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“…QSM method does not presuppose the tree structure and rely on the finite tree structure parameters to reconstruct the 3D tree model [62]. This is important because QSM can not only monitor natural gradual changes in biomass, but also can monitor sudden changes caused by storm damage, harvesting, fire or disease, which is essential for formulating effective forest management strategies [65]. Our research focuses particularly on the rapid and accurate determination of high-value trees' trunk attributes [66,67].…”

Section: Limitations and Application Potentialmentioning

confidence: 99%

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Dong

Fan

Zhou

et al. 2021

Forests

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The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

Cited by 20 publications

References 50 publications

Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping

Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping

Remote Sensing of Savannas and Woodlands

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

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