Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths: A simulation study

Forsman, Mona; Börlin, Niclas; Olofsson, Kenneth; Reese, Heather; Holmgren, Johan

doi:10.1016/j.isprsjprs.2017.11.013

Cited by 27 publications

(21 citation statements)

References 21 publications

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“…The RMSE of Figure 12a was 1.13 cm, the RMSE of Figure 12b was 0.93 cm, and the RMSE of Figure 12c was 0.92 cm while the overall fitting effect was better and followed the same pattern. This method of extracting DBH through nonlinear fitting of a point cloud model is an improvement to the simple circle fitting method [9,35,36,54] and the fitting accuracy is also improved. In addition, the difficulty and accuracy of the extraction of the DBH extraction were related to the topography of the observed stand.…”

Section: Discussionmentioning

confidence: 99%

“…Since the point density of the point cloud was sufficiently large, the diameter at any height of the tree can be measured by a very thin incision with a height interval of 20 mm (for example, DBH extraction selected all points between 1.29 m and 1.31 m above the ground point, as shown in Figure 8). Due to the irregularity of stem growth, we couldn't simply use the method of the fitting circle to extract DBH [35,36]. To accurately extract the tree's DBH, based on the plane coordinates of the point cloud data of the cutting ring, we used the least square method to precisely fit it into a twodimensional ellipse.…”

Section: Extraction Of Tree Dbhmentioning

confidence: 99%

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Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

et al. 2018

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Enriching forest resource inventory is important to ensure the sustainable management of forest ecosystems. Obtaining forest inventory data from the field has always been difficult, laborious, time consuming, and expensive. Advances in integrating photogrammetry and computer vision have helped researchers develop some numeric algorithms and methods that can turn 2D (images) into 3D (point clouds) and are highly applicable to forestry. This paper aimed to develop a new, highly accurate methodology that extracts sample plot parameters based on continuous terrestrial photogrammetry. For this purpose, we designed and implemented a terrestrial observation instrument combining real-time kinematic (RTK) and charge-coupled device (CCD) continuous photography. Then, according to the set observation plan, three independent experimental plots were continuously photographed and the 3D point cloud of the plot was generated. From this 3D point cloud, the tree position coordinates, tree DBHs, tree heights, and other plot characteristics of the forest were extracted. The plot characteristics obtained from the 3D point cloud were compared with the measurement data obtained from the field to check the accuracy of our methodology. We obtained the position coordinates of the trees with the positioning accuracy (RMSE) of 0.162 m to 0.201 m. The relative root mean square error (rRMSE) of the trunk diameter measurements was 3.07% to 4.51%, which met the accuracy requirements of traditional forestry surveys. The hypsometrical measurements were due to the occlusion of the forest canopy and the estimated rRMSE was 11.26% to 11.91%, which is still good reference data. Furthermore, these image-based point cloud data also have portable observation instruments, low data collection costs, high field measurement efficiency, automatic data processing, and they can directly extract tree geographic location information, which may be interesting and important for certain applications such as the protection of registered famous trees. For forest inventory, continuous terrestrial photogrammetry with its unique advantages is a solution that deserves future attention in the field of tree detection and ecological construction.

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Section: Discussionmentioning

confidence: 99%

Section: Extraction Of Tree Dbhmentioning

confidence: 99%

Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

et al. 2018

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“…Stem diameters could be overestimated if laser returns from branches are included in the circle fit. Also, a large beam width (footprint) could cause overestimations, according to a simulation study, because the system could report the location of a return to be in front of the tree stem surface if the beam hits near the edge of the tree stem [19]. In this study, laser returns near the stem edges were removed if a poor circle estimate was obtained and a circle was estimated a second time.…”

Section: Discussionmentioning

confidence: 99%

Mobile Laser Scanning for Estimating Tree Stem Diameter Using Segmentation and Tree Spine Calibration

et al. 2019

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Mobile laser scanning (MLS) could make forest inventories more efficient, by using algorithms that automatically derive tree stem center positions and stem diameters. In this work we present a novel method for calibration of the position for laser returns based on tree spines derived from laser data. A first calibration of positions was made for sequential laser scans and further calibrations of laser returns were possible after segmentation, in which laser returns were associated to individual tree stems. The segmentation made it possible to model tree stem spines (i.e., center line of tree stems). Assumptions of coherent tree spine positions were used for correcting laser return positions on the tree stems, thereby improving estimation of stem profiles (i.e., stem diameters at different heights from the ground level). The method was validated on six 20-m radius field plots. Stem diameters were estimated with a Root-Mean-Square-Error (RMSE) of 1 cm for safely linked trees (maximum link distance of 0.5 m) and with a restriction of a minimum amount of data from height intervals for supporting circle estimates. The accuracy was high for plot level estimates of basal area-weighted mean stem diameter (relative RMSE 3.4%) and basal area (relative RMSE 8.5%) because of little influence of small trees (i.e., aggregation of individual trees). The spine calibration made it possible to derive 3D stem profiles also from 3D laser data calculated from sensor positions with large errors because of disturbed below canopy signals from global navigation satellite systems.

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“…The highest MBE and MAE have been denoted in the EB measured at a high incident angle. Consequently, the point cloud quality is affected by the scanning geometry, when increased incidence angles and enhanced distances from the scanner occured [33,34]. While the bias may be corrected, the lack of precision remains.…”

Section: Meanmentioning

confidence: 99%

“…This was the case for S 3 , where 440 missing hits were found. The distortion will be especially pronounced if part of the beam falls outside the target, the likelihood of which additionally depends on the angular resolution [33]. Therefore, the combination of the perturbating factors can possibly increase the MBE and MAE, especially in objects of smaller dimension such as the EB.…”

Section: Meanmentioning

confidence: 99%

Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR

et al. 2019

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Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 m), and applied in an apple orchard to obtain the 3D structural parameters of the trees (n = 224). The analysis of a metal box which considered the height of four sides resulted in a mean absolute error (MAE) of 8.18 mm with a bias (MBE) of 2.75 mm, representing a root mean square error (RMSE) of 1.63% due to gaps in the point cloud and increased incident angle with enhanced distance between laser aperture and the object. A methodology based on a bivariate point density histogram is proposed to estimate the stem position of each tree. The cylindrical boundary was projected around the estimated stem positions to segment each individual tree. Subsequently, height, stem diameter, and volume of the segmented tree point clouds were estimated and compared with manual measurements. The estimated stem position of each tree was defined using a real time kinematic global navigation satellite system, (RTK-GNSS) resulting in an MAE and MBE of 33.7 mm and 36.5 mm, respectively. The coefficient of determination (R2) considering manual measurements and estimated data from the segmented point clouds appeared high with, respectively, R2 and RMSE of 0.87 and 5.71% for height, 0.88 and 2.23% for stem diameter, as well as 0.77 and 4.64% for canopy volume. Since a certain error for the height and volume measured manually can be assumed, the LiDAR approach provides an alternative to manual readings with the advantage of getting tree individual data of the entire orchard.

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Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths: A simulation study

Cited by 27 publications

References 21 publications

Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

Mobile Laser Scanning for Estimating Tree Stem Diameter Using Segmentation and Tree Spine Calibration

Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR

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