An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data

Wagner, Bernd; Ginzler, Christian; Bürgi, Anton; Santini, Silvia; Gärtner, Holger

doi:10.1007/s00468-017-1618-3

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…We tested a selection of hypotheses based on theoretical assumptions of the intrinsic relationships between the morphology and physiology of the trees, and their functions in shaping the wood anatomy. Corollary to previous findings (Kankare, et al 2022, Lenz, et al 2012, Wagner, et al 2018, our results showed that the radial growth rates and therefore the age of wood can be quite reliably estimated based on the morphology of the tree stems and crowns, but LWP and RD were more challenging to connect with the morphological properties at an annual resolution. However, despite the weak explanatory powers in predicting LWP and RD, our analysis gave clues of significant relationships between tree morphology and wood anatomy and enabled logical interpretations of the background mechanisms.…”

Section: Analysis Of the Resultssupporting

confidence: 70%

How tree morphology reflects ring properties in Norway spruce?

Pehkonen¹,

Holopainen²,

Kukko³

et al. 2022

Preprint

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We studied the relationships of wood anatomy (ring size and composition) with select morphological features (stem geometry and branching) in Norway spruce (Picea Abies H. Karst). We acquired the morphological modelling data by tracking the 1179 sawlogs of 479 trees from 14 even-aged, spruce-dominated stands in South-Eastern Finland to a sawmill, where X-ray log tomographic measurements were taken. We smoothed three measures of knottiness with continuous functions of log height and diameter, namely knot index (ratio of knot volume to log volume), knot size index (ratio of knot volume to number of whorls) and shoot lengths (whorl-to-whorl distances). The ring properties data came from a subset of 172 ring sample discs from variable heights in 51 of these trees. X-ray densitometric measurements were conducted to define the cambial age (CA), ring area (RA), latewood percentage (LWP), and ring density (RD) of each ring. We established reference mixed models for the ring properties like those frequently used in the literature. The reference models explained up to 80% of CA and RA, and around 44% of LWP and RD, with additional 6–20% explained by stand-level random variance. Next, we tested the relationships of the ring properties with the stem and knottiness features at individual ring-level. The tree morphology showed clear connection with the CA and RA: stem size, relative height, knottiness, within-crown positions, and apical shoot lengths explained 69% of CA, and 53% of RA, in addition to 12–16% stand-level variance. However, the tree morphology accounted for only 7% and 4% of the variation in LWP and RD, respectively, while the stand-level random variance accounted for another 22%–23% in these models. Based on previous literature, LWP and RD are largely affected by site fertility and annual weather conditions that control especially the timing and rate of early- and latewood production, and cell-wall thickness in latewood. Therefore, the statistical predictions of such subtle, intra-annual changes with the coarser morphological developments of crown and stem remain limited in accuracy. The analysis of our results revealed that increased crown vigour and longevity was reflected in increased earlywood production in juvenile wood. Therefore, the higher the growth rates in young trees, the higher the percentage of low-density juvenile wood in mature stems, especially on fertile and moist sites where trees favoured water uptake in the expense of structural support. We found limited possibilities to increase RD on the more fertile sites by increasing stocking density. The stem and knottiness features used here resemble those re-constructible from laser-scanners and photogrammetric sensors increasingly often used in both forested and industrial environments. The results here highlighted some of the possibilities and remaining knowledge gaps regarding the relationships of observable tree morphologies and the wood anatomy that require solving before feasible methods to, e.g., remote-sensing-aided wood property surveys are possible.

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Section: Analysis Of the Resultssupporting

confidence: 70%

How tree morphology reflects ring properties in Norway spruce?

Pehkonen¹,

Holopainen²,

Kukko³

et al. 2022

Preprint

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“…Luterbacher et al, 2004), and expectation maximization approaches (Mann et al, 2007; Rutherford et al, 2005; Schneider, 2001). Advances in random forest reconstructions (Athey et al, 2019; Wagner et al, 2018), neural networks (Gholami et al, 2019; Woodhouse, 1999), and paleoclimate data assimilation (DA; Acevedo et al, 2017), to name a few, are also expanding the potential applications of tree-ring data. Over nearly a century of statistical and numerical pioneering, many early dendrochronological limitations have been overcome by a combination of increased computing power and decreased computational costs and the development of advanced programming languages for analyses of large data sets.…”

Section: Statistical and Numerical Frontiersmentioning

confidence: 99%

“…Thus, LiDAR has the potential to help find trees that are particularly sensitive to many specific environmental variables or to expand geomorphic investigations to deepen our understanding of how surficial processes are recorded in tree-ring records. For example, Lopez Saez et al (2011) used the high resolution of LiDAR to recognize and precisely delineate abandoned debris flow channels and Wagner et al (2018) combined ground-based LiDAR (or Terrestrial Laser Scanning (TLS)) to produce an annual volumetric analysis of tree growth, capturing tree volume and carbon storage in a given tree at a point in time. The same method has been used to preserve precise surface details of wood excavated from archaeological sites, including individual tool marks (Tegel et al, 2012).…”

Section: Tree Rings and Remote Sensingmentioning

confidence: 99%

New frontiers in tree-ring research

et al. 2020

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From its inception as a scientific discipline, tree-ring research has been used as a trans-disciplinary tool for dating and environmental reconstruction. Tree-ring chronologies in some regions extend back many thousands of years, opening up new potential for the study of climate, people, and ecology at annual and sub-annual resolution. As such, they are a frequently used resource for a diverse range of studies spanning the Holocene. They are also the focus of a constantly evolving array of analytical techniques and multidisciplinary approaches to research questions. This literature review discusses case studies at the cutting-edge of interdisciplinary tree-ring research, notes recent breakthroughs and limitations, and identifies key frontiers for the future of tree-ring research.

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“…Furthermore, in addition to the regular tree attributes measured in practical field inventories, more detailed tree attributes, such as the stem curve or taper curve (stem diameter as a function of height), which reveal the wood productivity and quality, can be derived from TLS with high degrees of accuracy [9]. Studies have explored the use of TLS at the plot level for assessing stem volume and biomass components [10][11][12][13] for individual tree reconstruction, including branches, twigs, and leaves [3,14,15], in order to improve the physiological understanding of tree growth [16]. Moreover, TLS has been used for determining the stem curve [17,18], which is not easily measurable using conventional tools.…”

Section: Introductionmentioning

confidence: 99%

Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

et al. 2019

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The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

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An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data

Cited by 7 publications

References 37 publications

How tree morphology reflects ring properties in Norway spruce?

How tree morphology reflects ring properties in Norway spruce?

New frontiers in tree-ring research

Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

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