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Updated: Jun 29, 2026

Tree Core Analysis with X-ray Computed Tomography

Tree Core Analysis with X-ray Computed Tomography

Published on: September 22, 2023

Support, shape and number of replicate samples for tree foliage analysis.

Sebastiaan Luyssaert¹, Jan Mertens, Hannu Raitio

¹Parkano Research Station, Finnish Forest Research Institute, Kaironiementie 54, FIN-39700 Parkano, Finland. Sebastiaan.Luyssaert@metla.fi

Journal of Environmental Monitoring : JEM

|July 2, 2003

Summary

Optimizing sample support size and shape is crucial for accurate environmental monitoring. This study found that a 0.405 m3 sample support is optimal for assessing cadmium in tree crowns, requiring 11 replicates for reliable results.

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Area of Science:

Environmental Science
Geochemistry
Statistical Modeling

Background:

Sampling program design is influenced by object heterogeneity and statistical parameters like error (alpha), power (beta), and effect size (ES).
Sample support, often overlooked, significantly impacts sampling efficiency and requires careful consideration alongside other factors.

Purpose of the Study:

To investigate the effect of sample support dimensions and shape on the number of replicate samples needed.
To determine optimal sampling strategies for element concentration analysis in tree crowns.

Main Methods:

A resampling method was employed to analyze the impact of sample support on variance.
Simulated cadmium distribution in a Salix fragilis L. tree crown was used for analysis.

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Main Results:

Increasing sample support dimensions reduces the variance of element concentration.
Optimal sample support orientation aligns with the direction of greatest heterogeneity (tree crown height).
An optimal support of 0.405 m3 (23 g dry mass) with 11 replicates is identified for testing average cadmium concentration thresholds (alpha=0.05, beta=0.20, ES=1.0 mg kg(-1) dry mass).

Conclusions:

Fixed sample support is essential for valid statistical testing, though challenging for long-term monitoring.
Considering sample support-variance relationships enhances sampling precision and cost-efficiency.
The identified optimal support is specific to the simulated conditions and may vary for different tree species, elements, or hypotheses.