Forest mapping and trunk parameter measurement on slope using a 3D-LIDAR

Seki, Saimon; Tsubouchi, Takashi; Saratat, Shigeru; Hara, Yoshitaka

doi:10.1109/sii.2017.8279242

Cited by 5 publications

(14 citation statements)

References 19 publications

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“…(2) plots on a slope with the dry and smooth ground surface, and (3) plots on a slope with the moist and soft ground surface. In prior studies, the DBH estimation error for PLS-based data was usually from 1-4 cm for RMSE [4,[6][7][8][9][10][11][12][13][14]. However, this accuracy level was not always achieved in this study.…”

Section: The Adaptivity Of Pls For Forest Stands On the Slopementioning

confidence: 62%

“…In prior research, circle fitting-based DBH estimation has been developed into a mature method [7][8][9]11]. However, a number of PLS-based DBH estimation experiments have been conducted using a cylinder fitting approach [4][5][6]10], which has the advantage of taking the spatial structure of the point clouds into consideration. For PLS-based DBH estimation using cylinder fitting algorithms, iterative outermost point removal and polar angle distribution analysis are also suitable in theory.…”

Section: Discussionmentioning

confidence: 99%

“…All the horizontal point cloud slices were classified according to the value p. A smaller p means the distribution of the projected point cloud is more similar to a circle, which usually means better data quality. To assess whether ANPDA was reliable for stem-level inventory in each class, the effective rate was also calculated with Equation 10: E f f ective rate = n e n × 100% (10) where n denotes the number of the stems, and n e denotes the number of stems that achieved a smaller estimation error after applying ANPDA. The average error reduction after applying was also calculated to evaluate the efficacy, with Equation 11:…”

Section: Evaluation Of Anpdamentioning

confidence: 99%

“…To explore the full potential of PLS, automatic data processing was a primary concern in this study. In prior research, a mostly applied processing framework was "ground point identification-stem positioning-breast height point cloud slicing-DBH extraction" [4,5,10,[12][13][14]. Although not all the researchers have declared clearly whether their processing was automatic or not, their attempts have already confirmed this framework to have great potential for automatic DBH estimation.…”

Section: Anpda For Automatic and Hierarchical Semi-automatic Dbh Estimentioning

confidence: 99%

“…For the past few years, an emerging technology known as personal laser scanning (PLS) has shown good potential for in-situ forestry measurements, especially for DBH estimation [4][5][6][7][8][9][10][11][12][13][14]. By the time the concept of PLS was developed, laser scanning technology has been gradually replacing manual DBH measurement due to its high efficiency in collecting accurate data.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

Duanmu

Xing

2020

Remote Sensing

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Personal laser scanning (PLS) has significant potential for estimating the in-situ diameter of breast height (DBH) with high efficiency and precision, which improves the understanding of forest structure and aids in building carbon cycle models in the big data era. PLS collects more complete stem point cloud data compared with the present laser scanning technology. However, there is still no significant advantage of DBH estimation accuracy. Because the error caused by merging different point cloud fragments has not yet been eliminated, overlapping and inaccurate co-registered point cloud fragments are often inevitable, which are usually the leading error sources of PLS-based DBH estimation. In this study, a novel pre-processing algorithm named annular neighboring points distribution analysis (ANPDA) was developed to improve PLS-based DBH estimation accuracy. To reduce the impact of inaccurately co-registered point cloud fragments, ANPDA identified outliers through iterative removal of outermost points and analyzing the distribution of annular neighboring points. Six plots containing 247 trees under different forest conditions were selected to evaluate the ANPDA. Results showed that in the six plots, error reductions of 53.80–87.13% for bias, 38.82–57.30% for mean absolute error (MAE), and 27.17–56.02% for root mean squared error (RMSE) were achieved after applying ANPDA. These results confirmed that ANPDA was generally effective for improving PLS-based DBH estimation accuracy. It appeared that ANPDA could be conveniently fused with an automatic PLS-based DBH estimation process as a preprocessing algorithm. Furthermore, it has the potential to predict and warn operators of potential large errors during hierarchical semi-automatic DBH estimation.

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Section: The Adaptivity Of Pls For Forest Stands On the Slopementioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Evaluation Of Anpdamentioning

confidence: 99%

Section: Anpda For Automatic and Hierarchical Semi-automatic Dbh Estimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

Duanmu

Xing

2020

Remote Sensing

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Automatic three‐dimensional mapping for tree diameter measurements in inventory operations

Tremblay

Béland

Gagnon

et al. 2020

Journal of Field Robotics

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Forestry is a major industry in many parts of the world, yet this potential domain of application area has been overlooked by the robotics community. For instance, forest inventory, a cornerstone of efficient and sustainable forestry, is still traditionally performed manually by qualified professionals. The lack of automation in this particular task, consisting chiefly of measuring tree attributes, limits its speed, and, therefore, the area that can be economically covered. To this effect, we propose to use recent advancements in three-dimensional mapping approaches in forests to automatically measure tree diameters from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging and large-scale forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new publicly-available dataset which includes four different forest sites, 11 trajectories, totaling 1458 tree observations, and 14,000 m 2. From our extensive validation, we concluded that our mapping method is usable in the context of automated forest inventory, with our best diameter estimation method yielding a root mean square error of 3.45 cm for our whole dataset and 2.04 cm in ideal conditions consisting of mature forest with well-spaced trees. Furthermore, we release this dataset to the public (https://norlab.ulaval.ca/research/montmorencydataset), to spur further research in robotic forest inventories. Finally, stemming from this large-scale experiment, we provide recommendations for future deployments of mobile robots in a forestry context.

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Estimating diameter at breast height using personal laser scanning data based on stem surface nodes in polar coordinates

Duanmu

Xing

2020

Remote Sensing Letters

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Forest mapping and trunk parameter measurement on slope using a 3D-LIDAR

Cited by 5 publications

References 19 publications

Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

Automatic three‐dimensional mapping for tree diameter measurements in inventory operations

Estimating diameter at breast height using personal laser scanning data based on stem surface nodes in polar coordinates

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