Measurement and prediction of bark thickness in <i>Picea abies</i>: assessment of accuracy, precision, and sample size requirements

Stängle, Stefan M.; Weiskittel, Aaron R.; Dormann, Carsten F.; Brüchert, Franka

doi:10.1139/cjfr-2015-0263

Cited by 30 publications

(38 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, field-measured DBH was based on the peripheral surface of the bark, thus leading to an underestimated result compared to our algorithm. We calculated the bark thicknesses in our study site according to Equation (4) in [54]. The mean bark thickness is ∼18 mm, which is similar to the underestimation of 15 mm in our study.…”

Section: Error Analysissupporting

confidence: 76%

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

et al. 2016

View full text Add to dashboard Cite

Abstract:Terrestrial laser scanning (TLS) is a promising technique for plot-wise acquisition of geometric attributes of forests. However, there still exists a need for TLS applications in mountain forests where tree stems' growing directions are not vertical. This paper presents a novel method to model tree stems precisely in an alpine landslide-affected forest using TLS. Tree stems are automatically detected by a two-layer projection method. Stems are modeled by fitting a series of cylinders based on a 2D-3D random sample consensus (RANSAC)-based approach. Diameter at breast height (DBH) was manually measured in the field, and stem curves were measured from the point cloud as reference data. The results showed that all trees in the test area can be detected. The root mean square error (RMSE) of estimated DBH was 1.80 cm (5.5%). Stem curves were automatically generated and compared with reference data, as well as stem volumes. The results imply that the proposed method is able to map and model the stem curve precisely in complex forest conditions. The resulting stem parameters can be employed in single tree biomass estimation, tree growth quantification and other forest-related studies.

show abstract

Section: Error Analysissupporting

confidence: 76%

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Measurement locations were at breast height (1.3 m above ground) and along the tree bole in 2-m increments up to a top diameter of approximately 10-15 cm. This distribution of locations within trees was shown to be adequate to describe within-tree variability within the merchantable bole length above 1.3 m (Stängle et al 2016). Variability of bark thickness below 1.3 m could, however, significantly increase due to changes in stem form and bark profiles.…”

Section: Measurementsmentioning

confidence: 93%

“…Mixed-species stands and pure spruce stands were chosen, and trees from thinning operations as well as from final harvest in selection-cutting systems were chosen. Sample size for plots and trees per plot was chosen after the variation in the Altherr et al (1978) data had been assessed by Stängle et al (2016).…”

Section: Data Originmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of models for estimating bark thickness of Picea abies in southwest Germany: the role of tree, stand, and environmental factors

Stängle

Sauter

Dormann

2017

Annals of Forest Science

Self Cite

View full text Add to dashboard Cite

& Key message Bark thickness was shown to vary between regions, stands, and trees. Bark thickness prediction equations of different model complexity can be suggested depending on the purpose of application. Site and stand conditions, which influence variation of growth rate to a large extent, seem to have a strong influence on bark thickness, with better site quality leading to smaller relative bark thickness. & Context For many applications in forestry and forest science, local or regional species-specific bark thickness equations are used to estimate inside-bark diameters with outsidebark diameter measurements. & Aims The objectives of this study were (1) to assess variation in bark thickness due to tree and stand factors in two Norway spruce (Picea abies (L.) Karst) datasets from different time periods, (2) to compare and evaluate alternative established model forms for estimating bark thickness, and (3) to assess spatial variation in bark thickness to estimate the effects of environmental factors on bark thickness. & Methods Different bark thickness models were chosen from the literature and compared for their predictive quality for new measurements and a dataset from the 1970s. Mixed-effect modelling was applied to account for the hierarchical data structure, and generalized additive mixed models were used to analyse spatial effects and the influence of climatic factors, such as precipitation and temperature. & Results A strong positional autocorrelation of bark thickness within trees and within plots could be shown. Bark thickness was smaller in the new data compared to the measurements from the 1970s. The variation between stands could not be explained by the tested environmental factors, but tree age had a strong positive effect on bark thickness. & Conclusion In the study region, the variation of site productivity and individual growth rate seem to have a strong influence on bark thickness, whereas no significant effect of large scale climatic factors could be found.

show abstract

“…Thus, the variable di is widely employed as an independent model for the relationship between the diameter with and without bark at any height of the stem. In this way, the bark thickness can be easily calculated by subtraction once the estimates are obtained (CORDERO;KANNINEN, 2003;LI;WEISKITTEL, 2011;STÄNGLE et al, 2015STÄNGLE et al, , 2017.…”

Section: Discussionmentioning

confidence: 99%

Thickness Accuracy of Teak Bark by Artificial Intelligence

Vendruscolo

Cerqueira

Medeiros

et al. 2019

View full text Add to dashboard Cite

Estimates of tree bark thickness are fundamental for forest management, however, the degree of precision is conditioned to the adoption of efficient modeling techniques. The objective of this study was to evaluate and propose a model of artificial neural networks to estimate the thickness of the tree bark of Tectona grandis (Teak). The data originated from the measurement of 68 dominant trees, ranging in age from 6 to 33 years. The thickness of the bark was correlated with variables inherent to the tree, being: diameter in the different positions of the stem (di); diameter at 1.3 m height (dbh); total height (ht); relative height (hi_rel); and age (id). The trained networks were of the multilayer perceptron type, and a linear regression model was adjusted as a comparative support. The accuracy of the estimates was evaluated through statistical indicators and graphical analysis. The results showed a strong correlation between bark thickness and tree diameter, as well as relative height, with values above 0.70. Age also exerted a strong influence on the thickness of the bark of the trees. The artificial intelligence technique has demonstrated the potential for such application and the model proposed with the input variables: diameter, relative height and age was the one that presented the best statistical performance, and thus was the most suitable for predicting the bark in Teak trees.

show abstract

Measurement and prediction of bark thickness in Picea abies: assessment of accuracy, precision, and sample size requirements

Cited by 30 publications

References 25 publications

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

Comparison of models for estimating bark thickness of Picea abies in southwest Germany: the role of tree, stand, and environmental factors

Thickness Accuracy of Teak Bark by Artificial Intelligence

Contact Info

Product

Resources

About