Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

Liang, Xinlian; Wang, Yunsheng; Pyörälä, Jiri; Lehtomäki, Matti; Yu, Xiaowei; Kaartinen, Harri; Kukko, Antero; Honkavaara, Eija; Issaoui, Aimad El; Nevalainen, Olli; Vaaja, Matti; Virtanen, Juho‐Pekka; Katoh, Masato; Deng, Songqiu

doi:10.1186/s40663-019-0173-3

Cited by 104 publications

(101 citation statements)

References 31 publications

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“…Early studies by Jaakkola et al [11] and Wallace et al [12] first introduced the use of UAV-LS for forest inventory. Since then, an increasing number of studies adopted single tree methods to assess biophysical properties such as tree height [13][14][15], tree crown properties [15][16][17], tree density [15], or diameter at breast height (DBH) measurements [14,18,19] and AGB [20,21]. The latter two variables are the most challenging and yet relevant, which potentially can justify the high costs of acquiring UAV-LS data.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the reported omission errors from UAV-LS data (15%-80%) and difficulties of measuring DBH for all trees [20,21], it is not foreseeable in the near future to obtain UAV-LS based AGB or growing stock predictions for all trees in a population. A key scientific challenge is, therefore, to develop methods for using the available UAV-LS single tree measurements into plot-, stand-, or forest-level estimates while accounting for the non-probability nature of the sampled trees.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data?

2020

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Laser scanning data from unmanned aerial vehicles (UAV-LS) offer new opportunities to estimate forest growing stock volume ( V ) exclusively based on the UAV-LS data. We propose a method to measure tree attributes and using these measurements to estimate V without the use of field data for calibration. The method consists of five steps: i) Using UAV-LS data, tree crowns are automatically identified and segmented wall-to-wall. ii) From all detected tree crowns, a sample is taken where diameter at breast height (DBH) can be recorded reliably as determined by visual assessment in the UAV-LS data. iii) Another sample of crowns is taken where tree species were identifiable from UAV image data. iv) DBH and tree species models are fit using the samples and applied to all detected tree crowns. v) Single tree volumes are predicted with existing allometric models using predicted species and DBH, and height directly obtained from UAV-LS. The method was applied to a Riegl-VUX data set with an average density of 1130 points m−2 and 3 cm orthomosaic acquired over an 8.8 ha managed boreal forest. The volumes of the identified trees were aggregated to estimate plot-, stand-, and forest-level volumes which were validated using 58 independently measured field plots. The root-mean-square deviance ( R M S D % ) decreased when increasing the spatial scale from the plot (32.2%) to stand (27.1%) and forest level (3.5%). The accuracy of the UAV-LS estimates varied given forest structure and was highest in open pine stands and lowest in dense birch or spruce stands. On the forest level, the estimates based on UAV-LS data were well within the 95% confidence interval of the intense field survey estimate, and both estimates had a similar precision. While the results are encouraging for further use of UAV-LS in the context of fully airborne forest inventories, future studies should confirm our findings in a variety of forest types and conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data?

2020

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“…The improvements for the estimates of TPH, G, Dg, Hg, and Vmean between [49] and our study is also noticeable indicating the effect of forest structure but also scan design in reliable characterization of forest structure. In [55] automatic and manual measurements of tree height from 3D point clouds acquired with a laser sensor mounted on an UAV were carried out and the results showed RMSE of ≥10% and bias of ~3%. Although, the inclusion of UAV point clouds provided only slightly lower RMSE (2.8%) and bias (-2.2%) for Hg compared to TLS data only (4.3% and -3.6%, respectively), RMSE especially was notably lower than reported by [55].…”

Section: Discussionmentioning

confidence: 99%

“…In [55] automatic and manual measurements of tree height from 3D point clouds acquired with a laser sensor mounted on an UAV were carried out and the results showed RMSE of ≥10% and bias of ~3%. Although, the inclusion of UAV point clouds provided only slightly lower RMSE (2.8%) and bias (-2.2%) for Hg compared to TLS data only (4.3% and -3.6%, respectively), RMSE especially was notably lower than reported by [55]. Although there was a difference between UAV sensors the difference in heterogeneity in forest structure is assumed to be the main reason for the differences between these two studies.…”

Section: Discussionmentioning

confidence: 99%

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (<em>Pinus sylvestris </em>L.) Stands

Yrttimaa¹,

Saarinen²,

Kankare³

et al. 2020

Preprint

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Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably the root mean square error (RMSE) in Hg improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

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“…Terrestrial laser scanning (TLS) is a promising technology for accurately retrieving stem curves because of its capability to document the 3D information of individual trees at the millimeter level (Dassot et al 2011;Liang et al 2019). Some studies have revealed high accuracies of the stem curve estimation using multi-scan TLS data (Liang et al 2013), in which the reported root mean square error (RMSE) of the estimated stem curves were about 1.2 cm.…”

Section: Introductionmentioning

confidence: 99%

Quantification of occlusions influencing the tree stem curve retrieving from single-scan terrestrial laser scanning data

et al. 2019

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Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume. This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning (TLS) data, evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves. Methods: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data. Results: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong (r = 0.60-0.83), so was the correlations between the occlusion rate and its influencing factors (r = 0.84-0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves. Conclusions: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size (32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot (< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.

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Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

Cited by 104 publications

References 31 publications

Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data?

Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data?

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (<em>Pinus sylvestris </em>L.) Stands

Quantification of occlusions influencing the tree stem curve retrieving from single-scan terrestrial laser scanning data

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