Accurate derivation of stem curve and volume using backpack mobile laser scanning

Hyyppä, Eric; Kukko, Antero; Kaijaluoto, Risto; White, Joanne C.; Wulder, Michael A.; Pyörälä, Jiri; Liang, Xinlian; Yu, Xiaowei; Wang, Yunsheng; Kaartinen, Harri; Virtanen, Juho‐Pekka; Hyyppä, Juha

doi:10.1016/j.isprsjprs.2020.01.018

Cited by 98 publications

(92 citation statements)

References 41 publications

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“…However, increasing the number of individual scans used in a multi-scan approach decreases the cost-efficiency of TLS data acquisition. Use of mobile laser scanning (MLS) instead, where a point cloud is collected with a laser scanner mounted on an all-terrain vehicle [49,50] or a backpack [50,51], or with a hand-held laser scanner [52,53], would presumably be a more cost-efficient option to cover entire forest stands. When combined with SLAM (Simultaneous Localization and Mapping)-technology [54], the accuracy of MLS-derived tree attribute estimates is expected to be close to the TLS-derived estimates [51,53].…”

Section: Discussionmentioning

confidence: 99%

“…Use of mobile laser scanning (MLS) instead, where a point cloud is collected with a laser scanner mounted on an all-terrain vehicle [49,50] or a backpack [50,51], or with a hand-held laser scanner [52,53], would presumably be a more cost-efficient option to cover entire forest stands. When combined with SLAM (Simultaneous Localization and Mapping)-technology [54], the accuracy of MLS-derived tree attribute estimates is expected to be close to the TLS-derived estimates [51,53]. Another mobile platform for a laser scanner to suit close-range forest monitoring is an unmanned aerial vehicle (UAV) which can be used to collect detailed point clouds from above a forest canopy [55][56][57].…”

Section: Discussionmentioning

confidence: 99%

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Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

et al. 2020

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Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99–1.22 cm for diameter at breast height (Δdbh), 44.14–55.49 cm2 for basal area (Δg), and 1.91–4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60–1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔDg), 0.81–2.26 m for changes in basal area-weighted mean height (ΔHg), 1.40–2.34 m2/ha for changes in mean basal area (ΔG), and 74–193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

et al. 2020

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show abstract

“…However, increasing the number of individual scans used in a multi-scan approach decreases the cost-efficiency of TLS data acquisition. Use of mobile laser scanning (MLS) instead, where a point cloud is collected with a laser scanner mounted on an allterrain vehicle [50,51] or a backpack [51,52], or with a hand-held laser scanner [53,54], would presumably be a more cost-efficient option to cover entire forest stands. When combined with SLAM (Simultaneous Localization and Mapping) -technology [55], the accuracy of MLS-derived tree attribute estimates is expected to be close to the TLS-derived estimates [52,54].…”

Section: Discussionmentioning

confidence: 99%

“…Use of mobile laser scanning (MLS) instead, where a point cloud is collected with a laser scanner mounted on an allterrain vehicle [50,51] or a backpack [51,52], or with a hand-held laser scanner [53,54], would presumably be a more cost-efficient option to cover entire forest stands. When combined with SLAM (Simultaneous Localization and Mapping) -technology [55], the accuracy of MLS-derived tree attribute estimates is expected to be close to the TLS-derived estimates [52,54]. Another mobile platform for a laser scanner to suit close-range forest monitoring is an unmanned aerial vehicle (UAV) which can be used to collect detailed point clouds from above a forest canopy [56][57][58].…”

Section: Discussionmentioning

confidence: 99%

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

Yrttimaa¹,

Luoma²,

Saarinen³

et al. 2020

Preprint

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Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99-1.22 cm for diameter at breast height (Δdbh), 44.14-55.49 cm2 for basal area (Δg), and 1.91-4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60-1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔDg), 0.81-2.26 m for changes in basal area-weighted mean height (ΔHg), 1.40-2.34 m2/ha for changes in mean basal area (ΔG), and 74-193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.

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“…However, other types of laser scanning technologies are capable of capturing similar point cloud data as TLS and simultaneously bring a benefit of movability and higher data collection rate. The Mobile Laser Scanning (MLS) techniques can according to Hyyppä et al (2020b) be divided into phone-based scanning, vehicle based scanning, Unmanned Aircraft Vehicle (UAV) -based, handheld and other personal laser scanning techniques. The evaluation of the product acquired by these data collection approaches was described (Čerňava et al, 2019;Hyyppä et al, 2017;Liang et al, 2016;Oveland et al, 2018Oveland et al, , 2017Tomaštík et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Handheld Mobile Laser Scanners as a Tool for Accurate Positioning Under Forest Canopy

Chudá

Hunčaga

Tuček

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Nowadays it is important to shift positional accuracy of object measurements under the forest canopy closer to the accuracy standards for land surveys due to the requirements in the field of ecosystem protection, sustainable forest management, property relations, and land register. Simultaneously, it is desirable to use the technology of environmental data acquisition which is not time consuming and cost demanding. Global Navigation Satellite Systems (GNSS) are the most used for positioning today. However, the usefulness and also the accuracy of the measurements with this technology depend on various factors (the strength of the GNSS signal, the geometric position of satellites, the multipath effect etc.). Based on the above mentioned facts, the usability of technology independent of GNSS indicates an ideal solution for positioning under the forest canopy. Several studies have studied the usability of Handheld Mobile Laser Scanners (HMLS) in complex environment. The goal of this paper was to verify a new data collection approach (HMLS with Simultaneous Localization and Mapping (SLAM) technology) for the forest environment practice. The main objective of our study was to reach a precision which complies with the accuracy standards for land surveys. The RMSE of derived positions from point cloud, produced by SLAM devices were 25.3 cm and 28.4 cm, for ZEB REVO and ZEB HORIZON, the handheld mobile laser SLAM scanners used in this study. ZEB HORIZON achieved twice as big accuracy of diameter of breast height (DBH) estimation as ZEB REVO.

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Accurate derivation of stem curve and volume using backpack mobile laser scanning

Cited by 98 publications

References 41 publications

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

The Handheld Mobile Laser Scanners as a Tool for Accurate Positioning Under Forest Canopy

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