Related Experiment Video
Updated: Apr 8, 2026

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
Published on: September 22, 2023
A simple program to measure and analyse tree rings using Excel, R and SigmaScan
1Institute of Botany, Universität für Bodenkultur Wien, Gregor Mendel-Str. 33, 1180 Vienna, Austria.
Abstract:
I present a new software that links a program for image analysis (SigmaScan), one for spreadsheets (Excel) and one for statistical analysis (R) for applications of tree-ring analysis. The first macro measures ring width marked by the user on scanned images, stores raw and detrended data in Excel and calculates the distance to the pith and inter-series correlations. A second macro measures darkness along a defined path to identify latewood-earlywood transition in conifers, and a third shows the potential for automatic detection of boundaries. Written in Visual Basic for Applications, the code makes use of the advantages of existing programs and is consequently very economic and relatively simple to adjust to the requirements of specific projects or to expand making use of already available code.
More Related Videos
Related Concept Videos
Interpreting R Charts
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
The R Chart
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
Microsoft Excel: Plotting Mean, SD, and SE
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...

