Accuracy of tree stem circumference estimation using close range photogrammetry: Does point-based stem disk thickness matter?

Akpo, Hospice A.; Atindogbé, Gilbert; Obiakara, Maxwell C.; Gbedolo, Madaï; Laly, Finagnon G.; Lejeune, Philippe; Fonton, Noël

doi:10.1016/j.tfp.2020.100019

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…This fits the trend identified in the literature review, where DBH was also the most commonly used measurement used to validate sensor performance when capturing structural forest attributes (n = 19, 86.4%). In some studies that used single-stem capture approaches, mea-surements of DBH were supplemented or replaced with PBH [39,[57][58][59]. The rationale behind this choice is that stem perimeter measurements better accommodate for irregularities across the surface of the stem and thus give a better estimation of stem size.…”

Section: Discussionmentioning

confidence: 99%

“…DBH was used as a measurement in six of the individual stem CRP publications (Table 2), with root mean square error (RMSE) values ranging between 0.37 cm and 1.71 cm when compared to measurements acquired with manual tools. PBH was used as an alternative to DBH in two of the identified publications [39,57], and used alongside DBH in another two [58,59]. PBH measurements derived from SfM had a reported RMSE between 0.25 cm and 1.87 cm when compared to manual measurements.…”

Section: Terrestrial Close Range Photogrammetry Literature Analysismentioning

confidence: 99%

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The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

McGlade

Wallace

Reinke

et al. 2022

Forests

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Limitations with benchmark light detection and ranging (LiDAR) technologies in forestry have prompted the exploration of handheld or wearable low-cost 3D sensors (<2000 USD). These sensors are now being integrated into consumer devices, such as the Apple iPad Pro 2020. This study was aimed at determining future research recommendations to promote the adoption of terrestrial low-cost technologies within forest measurement tasks. We reviewed the current literature surrounding the application of low-cost 3D remote sensing (RS) technologies. We also surveyed forestry professionals to determine what inventory metrics were considered important and/or difficult to capture using conventional methods. The current research focus regarding inventory metrics captured by low-cost sensors aligns with the metrics identified as important by survey respondents. Based on the literature review and survey, a suite of research directions are proposed to democratise the access to and development of low-cost 3D for forestry: (1) the development of methods for integrating standalone colour and depth (RGB-D) sensors into handheld or wearable devices; (2) the development of a sensor-agnostic method for determining the optimal capture procedures with low-cost RS technologies in forestry settings; (3) the development of simultaneous localisation and mapping (SLAM) algorithms designed for forestry environments; and (4) the exploration of plot-scale forestry captures that utilise low-cost devices at both terrestrial and airborne scales.

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Section: Discussionmentioning

confidence: 99%

Section: Terrestrial Close Range Photogrammetry Literature Analysismentioning

confidence: 99%

The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

McGlade

Wallace

Reinke

et al. 2022

Forests

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“…We found that there are a few related studies on modeling point clouds from tree images based on the QSM method [28]. Both the LiDAR point clouds and the photogrammetry point clouds are composed of a series of coordinate points, and the point clouds contain data in three directions: X axis, Y axis, and Z axis [44,45]. We used AdQSM to reconstruct TP point clouds.…”

Section: Forest Inventory Datamentioning

confidence: 99%

“…In this paper, the commercial software Pix4D mapper 4.5.2 (Pix4D company, Prilly, Switzerland, www.pix4d.com (accessed on 30 July 2021)) was used to automatically realize the SfM process in 30 min, which generated a 3D point cloud of the plot from the disordered 2D photo by searching for feature points. In the photo-matching process, the Scale Invariable Feature Transformation (SIFT) algorithm matched data based on the feature points between the stereo pairs [28,44]. The model is optimized by bundle block adjustment and nonlinear least squares algorithm [46].…”

Section: D Point Cloud Generation Based On Photosmentioning

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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“…Most of the studies that evaluated CRP generally assessed its accuracy in extracting geometrical measurements. For instance, in the area of forestry, several studies [23][24][25][26] evaluated the accuracy of CRP in estimating tree attributes (e.g., tree radius, circumference, and height) extracted from point clouds generated from digital compact camera images. Similarly, in the area of engineering, some studies assessed CRP according to its accuracy in estimating models' volumes [22,27,28] and deformation monitoring [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Photogrammetry Assessment in Comparison with a Compact Camera for Construction Management Applications

Saif

Alshibani

2022

Applied Sciences

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Close-range photogrammetry (CRP) has proven to be a remarkable and affordable technique for data modeling and measurements extraction in construction management applications. Nevertheless, it is important to aim for making CRP more accessible by using smartphones on-site directly without a pre-calibration procedure. This study evaluated the potential of smartphones as data acquisition tools in comparison with compact cameras based on the quality and accuracy of their photogrammetric results in extracting geometrical measurements (i.e., surface area and volume). Two concrete specimens of regular shapes (i.e., beam and cylinder) along with an irregular-shaped sand pile were used to conduct this study. The datasets of both cameras were analyzed and compared based on lens distortions, image residuals, and projections multiplicity. Furthermore, the photogrammetric models were compared according to various quality criteria, processing time, and memory utilization. Though both cameras were not pre-calibrated, they both provided highly accurate geometrical estimations. The volumetric estimation error ranged from 0.37% to 2.33% for the compact camera and 0.67% to 3.19% for the smartphone. For surface area estimations, the error ranged from 0.44% to 0.91% for the compact camera and 0.50% to 1.89% for the smartphone. Additionally, the smartphone data required less processing time and memory usage with higher applicability compared with the compact camera. The implication of these findings is that they provide professionals in construction management with an assessment of a more direct and cost-effective 3D data acquisition tool with a good understanding of its reliability. Moreover, the assessment methodology and comparison criteria presented in this study can assist future research in conducting similar studies for different capturing devices in construction management applications. The findings of this study are limited to small quantification applications. Therefore, it is recommended to conduct further research that assesses smartphones as a photogrammetric data acquisition tool for larger construction elements or tracking ongoing construction activities that involve measurements estimation.

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Accuracy of tree stem circumference estimation using close range photogrammetry: Does point-based stem disk thickness matter?

Cited by 5 publications

References 19 publications

The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

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

Smartphone-Based Photogrammetry Assessment in Comparison with a Compact Camera for Construction Management Applications

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