Under-canopy UAV laser scanning for accurate forest field measurements

Hyyppä, Eric; Hyyppä, Juha; Hakala, Teemu; Kukko, Antero; Wulder, Michael A.; White, Joanne C.; Pyörälä, Jiri; Yu, Xiaowei; Wang, Yunsheng; Virtanen, Juho‐Pekka; Pohjavirta, Onni; Liang, Xinlian; Holopainen, Markus; Kaartinen, Harri

doi:10.1016/j.isprsjprs.2020.03.021

Cited by 123 publications

(110 citation statements)

References 46 publications

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“…We assume that lower accuracy of ZEB REVO (RMSE of 3.28 cm and rRMSE 10.66 %) is caused by the parameters of the scanner but also by the scanning trajectory, which might create noise points on the surface of the trunks, the inconsistency of scanned data and therefore, affect the process of diameter estimation. Hyyppä et al (2020a) obtained better RMSE of DBH estimation 0.6 cm and rRMSE 2.2 %; however, estimations based on TLS point cloud were used as reference.…”

Section: Discussionmentioning

confidence: 91%

“…As on one of the most important variables for forestry, different authors focus on DBH estimation, with different approaches. For example Liang et al (2016) created a great overview of TLS techniques related to forest inventories and reported that accuracy of DBH estimation varied from 0.7 cm to 7.0 cm (1.8 -3.3 cm, 3.4 -7.0 cm, 0.7 -2.4 cm, and 2.0 -4.2 cm), Čerňava et al (2019) estimated DBH based on MLS system adjusted for forest environment which is shown to be the fastest data collection ground based approach with RMSE of DBH 3.06 cm, more portable backpack method using SLAM achieved high accuracy in steam curve estimation (RMSE of the extracted stem curves was 1.2 cm and 1.7 cm) comparing to TLS references (Hyyppä et al, 2020b), Under-canopy UAV laser scanning method with SLAM corrected point cloud collection was presented as accurate and efficient in comparison with multi-scan TLS approach (Hyyppä et al, 2020a) where RMSE values for the DBH were 0.69 cm at the sparse plot, and 0.92 cm in the obstructed plot. We presents results compared against reference data collected in forest environment measured with the highest accuracy requirements.…”

Section: Discussionmentioning

confidence: 95%

“…One of the most promising technology able to provide efficiency and productivity within forest are those that use laser scanners as a main sensor to create the 3D model of environment with positioning. It is necessary to say that the high potential of various types of laser scanning technologies was described by several authors (Chen et al, 2019;Hyyppä et al, 2020a;James and Quinton, 2014;Liang et al, 2019;Tomaštík et al, 2017 and others). The most commonly used type within forestry research is Terrestrial Laser Scanning (TLS).…”

Section: Introductionmentioning

confidence: 97%

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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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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

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.

View full text Add to dashboard Cite

show abstract

“…Due to different data acquisition geometries between terrestrial and UAV-borne point clouds, UAV-borne laser scanning is more suitable for characterization of the vertical forest structure whereas TLS or MLS can better capture the horizontal forest structure. An alternative option to combine the benefits of both terrestrial and aerial point cloud-based approaches is to collect the UAV-borne point clouds from inside the canopy [58] or to use a multisensorial approach [59] where photogrammetric UAV point clouds were integrated with TLS data. Use of a combination of bi-temporal terrestrial and aerial point clouds is expected to improve the accuracy of vertical forest characterization in space and time.…”

Section: Discussionmentioning

confidence: 99%

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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“…Due to different data acquisition geometries between terrestrial and UAV-borne point clouds, UAV-borne laser scanning is more suitable for characterization of the vertical forest structure whereas TLS or MLS can better capture the horizontal forest structure. An alternative option to combine the benefits of both terrestrial and aerial point cloud-based approaches is to collect the UAV-borne point clouds from inside the canopy [59] or to use a multisensorial approach [60] where photogrammetric UAV point clouds were integrated with TLS data. Use of a combination of bi-temporal terrestrial and aerial point clouds is expected to improve the accuracy of vertical forest characterization in space and time.…”

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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Under-canopy UAV laser scanning for accurate forest field measurements

Cited by 123 publications

References 46 publications

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

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

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

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

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