A novel computer-aided tree-ring analysis software (CATS): oak earlywood vessel size reveals a clear spring heat sum response
The use of cell-size parameters from oak trees as a powerful proxy for past climate reconstruction needs to be more intensively studied. The freeware tool CATS will help pushing these high-volume cell-size measurements.
In this study, we analyzed the response of vessel size chronologies to daily meteorological records. For this purpose, we developed a computer-aided tree-ring analysis software (CATS) for high-volume cell-size measurements. To stress test CATS, quantitative vessel parameters were measured in 22 living ring-porous oak trees from two riparian-like forests (Mainfranken, southern Germany). Climate response analysis was performed using daily meteorological records. To make the measurement of millions of such vessels within wooden samples feasible, semi-automated computer-aided software for high-volume analysis is crucial. To expedite the measurement of the cells within distinct tree rings, CATS was developed to enable the automated measurement of a high number of samples and tree rings. CATS uses common image segmentation techniques as well as novel software algorithms to detect earlywood vessel superclusters that are used to identify distinct tree rings. The software structure of CATS is described in detail. The climate–vessel response analysis shows a clear temperature signal from mid-March to mid-April (March 14–April 18). This effect is particularly pronounced for a 50-year span in the twentieth century (1915–1964).
KeywordsQuantitative wood anatomy Temperature Image analysis software Microscopy
Dendroclimatologists use classical tree ring proxies (ring width or maximum latewood density) to reconstruct climate variability from thousands of years ago with annual to seasonal resolution (Büntgen et al. 2011; Esper et al. 2013; Land et al. 2015; Schönbein et al. 2015; Seftigen et al. 2013; Wilson et al. 2013). These proxies are very common and an extensive network of tree ring series is currently available for the entire Northern Hemisphere (St. George 2014). For Central Europe, millennia-length tree-ring chronologies as well as many precisely dated wooden samples are available (Friedrich et al. 2004; Leuschner et al. 2002; Spurk et al. 1998) spanning the entire Holocene. These climate tree-ring archives are perfectly suited for investigations into past climate variability and environmental changes (Land et al. 2015; Land 2014; Schönbein et al. 2015; Spurk et al. 2002) on a spatio-temporal scale. Trees develop an immense number of earlywood vessels of varying size and shape every growing season, thus recording particular weather conditions. In the past decade, an increasing number of investigations have been performed to study the response of xylem cell anatomy and hydraulic architecture to environmental and climatic conditions (Fonti et al. 2010 and references therein; Land 2014) to generate climate proxies with a much higher resolution (down to monthly) compared to the classical proxies of ring width and maximum latewood density. Today, wood-anatomical variables (e.g., cross-sectional lumen area) are discussed as potential proxies for climate reconstructions (Fonti et al. 2010; Fonti and García-González 2008; Kniesel et al. 2015; Land 2014) to ensure monthly to seasonally resolved insights into past environmental changes. In contrast to the classical total-ring width proxy, the measurement of cell sizes within tree rings is strongly hindered by the time-consuming preparation process necessary to obtain a smooth cross-sectional surface of the wooden probes. Using wood-anatomical variables (e.g., mean annual cross-sectional vessel area) for climate reconstruction, a high number of correctly dated tree rings from ancient to living samples are needed to ensure a noise-free climate signal and to diminish the influence of tree-individual xylem hydraulic architecture of individual trees. High-volume analysis is therefore essential, necessitating measurements of millions of cross-sectional cells in a short time.
Here we present a computer-aided tree-ring analysis software (CATS) for the accurate, efficient and nearly fully automated measurement of earlywood vessel sizes in ring-porous tree species for mass data analysis. Reliable image segmentation is critical, which is a fundamental part of the methodology described here (Peng 2008) to distinguish vessels to be measured from uninteresting background structures, or also to recognize disturbances within objects of interest which should be eliminated. Deletion of falsely detected objects and the use of knowledge-based verification of identified shapes was implemented to expedite the detection of earlywood vessels within annual growth rings and to improve the measurement process. The algorithms used to achieve these goals as well as results of climate response analysis of vessel proxies using daily meteorological records are presented.
Materials and methods
24-bit color information was transformed down to 8-bit grayscale through simple (R + G + B)/3 reduction of the color channels. Following auto-contrast processing, we automatically adjusted the histogram in regard to image brightness and contrast to optimize conditions for subsequent image processing as it is difficult to achieve binary images which display the earlywood vessels in their original geometry with minimal interference. As this is one of the most critical steps for the end result, we added an iterative fine-tuning of the global brightness applied to the image in response to object detection and compared to our model of vessels at each iteration: in case the iterative step results in objects which fit better to our model for vessels, the previously determined brightness was then regarded as being sub-optimal. A fine-scaling was then performed within a certain window around the newly detected brightness value. The image was iteratively subjected to a threshold with the given values and the contours that resulted from the step measured for five positions within the newly defined brightness window. For each step, object detection was applied to measure the vessels geometry and size. These values were again compared to our model of vessels. The best result of each iteration process was chosen (geometry of “potential vessels” is one criterion in addition to the area), but was once again regarded as sub-optimal for the next iteration with a much tighter window spanned around that value. It was demonstrated that no more than three such iterations were necessary to achieve the best and final value for brightness, which did not overly affect earlywood vessel geometry when Otsu’s (1979) thresholding was applied. In the next step, the diversity of gray scale information was reduced to produce the final binary image in order to detect vessels within. To automatically compute a threshold, we used histogram accumulation. We used an empirically determined window of 30% for the bright and 0.5% for the dark section of the accumulated histogram for binning in two pixel classes.
Another challenge arises regarding structures within the vessels which could lead to incorrectly measured cross-sectional areas (Fig. 2).
To remove smaller disturbances and achieve an overall smoothing of vessel borders, we used a combination of opening- and watershed-based dilation (Cousty et al. 2006) prior to the application of the last step in image segmentation: the statistical investigation of all detected contours. The structuring element of this opening procedure is a simple circle with a diameter of 19 pixels.
Identification of earlywood vessels
One result of the image segmentation was an array of objects identified as most likely being earlywood vessels, as they fit to our model for an ideal vessel through pattern recognition at least at 90% identity. These are stored as an array of objects (earlywood vessels within the growth ring) including their shape and location.
Clusters through near neighbor analysis
For some wood probes, large gaps between earlywood vessels of the same tree ring appear. One explanation for this could be vascular rays in oaks. After conducting the cluster analysis, our software performed an analysis of the clusters detected. “Unsafe” clusters were marked at this point. One criterion for identification of an unsafe cluster is, for example, the number of vessels within said cluster. The mean number of vessels within a cluster was established in the previous step. As a knowledge-based analysis system, we implemented a confidence interval of 0.90 at both ends of extreme values to be regarded as being safe. Unsafe clusters were omitted from further investigation, but were still included in the results (marked as “unsafe”).
Supercluster as tree-ring model
The final step in image analysis was to generate tree-ring-based results and store these in a result file. As our software runs in batch mode, it is possible to automatically store all results as individual files (with the same name as the image) or in a single result file. Probe label (image name), number of detected tree rings and associated standard earlywood vessel parameters suited for climate reconstruction (e.g., arithmetic mean of cross-sectional area, standard deviation) are listed. The file format is compatible to Excel, so that further numerical evaluation could be conducted there.
Image-based high-volume analysis using samples from living oak trees
To stress test the CATS software living oak (Quercus robur L.), samples from the Hohenheim tree-ring archive were used. The trees originated from riparian forests which are economically managed within the Mainfranken region (southern Germany) and are located in the vicinity of the Main River. The trees are well water-supplied and flooding occurs regularly in winter, but rarely during the summer. The samples were taken from 22 adult individuals with an increment borer (2 cores per tree at breast height). The cores were fixed on wooden holders and the cross-sectional surface was smoothed with a core-microtome. Tyloses were removed from earlywood vessels using compressed air. To enhance the optical contrast, chalk was filled into the vessels before the cross section of each sample was scanned with a resolution of 4800 dpi (Epson Expression 10000 XL, Seiko Epson Corporation, Japan).
The samples used in this study to stress test CATS are part of the Holocene oak chronology (HOC) Hohenheim (located at University of Hohenheim) containing a large number of precisely dated ancient to modern trees spanning the entire Holocene (Friedrich et al. 2004). This collection has been used to address different questions regarding climate dynamics and environmental changes over the past 12 millennia (Friedrich et al. 2001, 2006; Land et al. 2015; Land 2014; Schönbein et al. 2015; Spurk et al. 2002).
Vessel chronologies and daily meteorological records for climate response analysis
The mean earlywood vessel area and median earlywood vessel area close to the tree-ring border were tested for their potential response to daily meteorological conditions. Similar to total-ring width series, vessel dimensions also show an age-related growth trend. This trend was removed by applying a cubic smoothing spline with 50% frequency–response cutoff at 10, 30 and 70% for each series (Cook and Peters 1981). Autocorrelation was removed and variance was stabilized before the corresponding chronologies were developed by using a biweight robust mean (hereafter referred to as Mean Spl10, Mean Spl30, Mean Spl70, Median Spl10, Median Spl30 and Median Spl70).
Expressed population signal (EPS) and mean inter-series correlation (RBAR) were calculated in a 50-year window with 25-year overlap. The standardization procedure was carried out with the software ARSTAN (Cook and Krusic 2006).
We used daily records from 1880 until present of the weather station Bamberg (49.88N, 10.92E) and the run-off gauge of Schweinfurt (50.04N, 10.24E), both located very near to the study site. Daily sum of precipitation and mean/minimum/maximum temperature (weather station Bamberg) were obtained from KNMI Climate Explorer (https://climexp.knmi.nl) and the daily run-off data from the Bavarian Ministry for the Environment. Heat sum was calculated from the daily mean temperature data set. Daily mean temperature below +5 and +7.5 °C was not considered. All records were checked for missing data and the only gap was found in the temperature records (mean, minimum, maximum) for the year 1882 from May 9 to May 12. Thus the year 1882 was omitted from further analysis.
Climate response analysis
For the identification of sensitive periods, the vessel chronologies were compared to daily meteorological records. We developed a MATLAB® (The MathWorks®2013) script that aggregates the meteorological data for each year altering (1) the length of the data interval used for correlation (from 11 to 111 days in steps of five days) and (2) the starting date of the data derived from (1) between January 1 and December 15 (Schönbein 2011). A Pearson correlation between each newly generated meteorological series and each vessel chronology was calculated. This step was done for the entire time period from 1880 to 2004 (number of trees ≥11). Statistical significance was attained when the level of significance was below p < 0.01. To evaluate changes in temporal sensitivity and consistency, a response analysis was performed for eleven 20-year (e.g., 1905–1924, 1935–1954) and three 62-year-long overlapping sub-periods (e.g., 1913–1974) to evaluate changes in temporal sensitivity and consistency.
Vessel measurements and chronologies
The corresponding chronologies of mean versus median vessel area are highly correlated for the period between 1880 and 2004 (Spl10: r = 0.79, Spl30: r = 0.86, Spl70: r = 0.85). All chronologies show a low variability from 1950 onwards.
Based on the daily meteorological data, the performed climate–response analysis revealed a large quantity of results. Here the highest relationships for the entire period (1880–2004) and for several sub-periods (e.g., 1913–1974, 1885–1904) are shown.
Results for minimum and maximum temperature show highly positive relationships, whereas precipitation and run-off data revealed less significant negative correlations.
Temporal changes of vessel response
Sensitivity of the two vessel chronologies (Mean Spl30 and Median Spl10) to seasonal heat sum in the 62-year sub-periods (p < 0.05)
Mean Spl30 interval (r)
Median Spl10 interval (r)
March 17–April 16 (0.37)
March 17–April 16 (0.35)
March 17–April 16 (0.48)
March 17–April 16 (0.50)
February 27–May 5 (0.36)
March 14–April 18 (0.32)
From 1925 to 1954, a significant negative relationship (r = −0.76, p < 0.01, N = 30) between the chronology Mean Spl30 and the heat sum in winter (November 7–February 15) was found.
We present the application of a new computer-aided tree-ring analysis software (CATS) for high-volume analysis of vessel sizes. In this study, the freeware automated tool was applied to analyze two common earlywood vessel parameters in 22 oak trees (Q. robur L.) using long-term climate data sets. The vessel chronologies were correlated with daily meteorological records from nearby climate and run-off station. The vessel size chronologies demonstrated a very similar behavior in the climate–growth analysis. The earlywood vessel chronologies (Mean Spl30 and Median Spl10) yielded a positive response to the heat sum (>7.5 °C) in spring (March 14–April 18 and March 17–April 16) during the entire investigated period from 1880 to 2004. Vessel parameters also showed a slightly less significant response to maximum daily temperature in spring. Several studies about earlywood vessel lumen area on Q. robur report positive temperature signals in spring (e.g., Matison and Dauškane 2009), in winter (e.g., Pritzkow et al. 2016), in winter–spring (Tumajer and Treml 2016) as well as a negative precipitation signal during the autumn–winter period (González-González et al. 2015). Studies of other Quercus species often reveal a clear response to precipitation (e.g., Fonti et al. 2009; González-González et al. 2014). Our findings agree with the studies showing a direct positive correlation of temperature and earlywood vessel size during the time of formation in spring. Several physiological explanations of vessel–temperature interactions have been discussed in other publications (e.g., Aloni 2015; González-González et al. 2014; Pritzkow et al. 2016). An influence of stem temperature reaching a threshold of 5 °C with a delay of 1–2 weeks before cambial activity begins has been reported by Güney et al. (2016) in Lebanon cedar and Kudo et al. (2014) found that cambial reactivation in the ring-porous hardwood Quercus serrata is triggered mainly by temperature. The detected close relationship of air heat sum seems to initialize cambial activity and earlywood vessel development directly in a positive manner. Rising heat sum in spring may lead to larger vessels by changing the plant hormonal status for cambial tissue (Aloni 2015).
The climate–response analysis for sub-periods (e.g., 20-year), showed that the relationship between vessel sizes and mid-March to mid-April heat sum is strongly evident during the years from 1915 until 1964. From 1925 to 1954, a strong negative relationship between the heat sum during winter (November 7–February 15) and mean vessel area is evident. During these years, winter conditions in Central Europe had been very harsh (e.g., below −20 °C in 1925, 1929, 1939, 1940, 1942). This supports the hypothesis that severely cold winter temperatures stress cambial cell initials (Aloni 2015) and could lead to smaller vessels in the following growing season. The noticeably reduced variability of vessel sizes during the past five decades (from 1955 onwards) cannot be explained by forest management activities or changes in the run-off regime. The documented massive insect attacks of the green oak leaf roller (Tortrix viridana L.) in this region (in the years 1948 and 1954–1958) also provide, to our best knowledge, no explanation for this reduction and thus the reason remains unclear. Pritzkow et al. (2016) detected a very strong positive winter temperature response from 1951 to 2010 for pedunculate oak trees from northern Poland. For the same period, the vessel sizes of our study show a weak but significant (p < 0.05) response.
Our results show that a temporal aspect is crucial when investigating climate–vessel relationships, meaning that more quantity of climate–vessel studies should be performed in the near future in general. To this end, our novel computer-aided tree-ring image analysis software CATS (freeware) will aid in performing such analysis in a reasonable time frame.
Future enhancements of CATS and outlook
Technical evolution clearly shows that images of significantly higher resolution can be captured from innovative microscopic techniques in the future, such as two-photon laser scanning microscopy which offers large-scale screening of images. Thus, CATS can also be applied for latewood-cell analysis which is currently not feasible due to insufficient image quality and resolution, thereby enabling analysis of these features in large-scale projects. The necessary basis for quantitative latewood-cell analysis is already included in CATS, which would allow efficient analysis of whole tree rings. Time-consuming manual detection of tree-ring boundaries, identification and elimination of preparation-induced artifacts by the user will be fully automated.
From a dendroclimatological point of view, CATS will be improved in terms of species-specific analysis by adding species-specific wood-anatomical characters from a database at the beginning of the measurement process to make analysis much more efficient. One further software project (ELìA∞) will soon be completed and will be available for common and royalty-free download at http://www.elia8.org when launched. This ExpertSystem for Light Microscopy integrates a highly complex relational database system for both data annotation and biosystematics. ELìA∞ includes ELì∫A (which is an offline but updateable biosystematics database and interface for annotation of more than 680,000 taxa), which incorporates automated internet-based data mining support. As ELì∫A and ELìA∞ are connected by crosstalk, we will work hard to implement an automated database search of currently investigated wood probes: CATS will annotate its results through the relational database system of ELìA∞. We plan to use ELìA∞’s data crosstalk to ELì∫A for automated, internet-based data mining to verify and improve climate data reconstruction. This approach and its progress will be described in further publications.
The next evolution-step will push CATS into complete tree-ring analysis using millennial-aged subfossil and historical oak samples. CATS is, along with some sample images, available for common download at http://www.hs-emden-leer.de/softlinks/catrassupplementalkauerlandzip.html.
Computer-aided tree-ring analysis software is our first automated software which analyzes wood probes using image analysis to obtain high volumes of dendroclimatologically relevant data. The image analysis workflow has proved to be suitable for automated tree-ring detection. A stress test using images of distinct tree rings from 22 living oak samples has shown that this software approach is suitable for quantitative cell-size measurements. In the case of very narrow tree rings, the fully automated reconstruction may generate some mismatched superclusters which should be corrected manually. Further improvements for such issues are a focal point in current software development.
The climate growth analysis over the entire period (1880–2004) using two vessel chronologies in combination with a long-term daily meteorological record reveal that the occurrence of larger cross-sectional surface areas of the vessels are positively correlated with the heat sum between mid-March to mid-April. This effect is particularly pronounced for five decades in the twentieth century (1915–1964).
Using daily meteorological records to conduct climate–vessel analysis is highly recommended as trees respond with cambial activity to climatic and hydrologic effects and are decoupled from monthly records and thus a more precise analysis can be obtained.
Availability and requirements
Project name: CATS.
Operating system: Windows 7, Windows 10, 64Bit.
Programming language: C#, .NET.
Other requirements: computer should be able to run OpenGl/OpenCv.
License: experimental academic software. Royalty-free, non-exclusive.
Author contribution statement
AL and GK wrote the manuscript and all authors reviewed and revised the manuscript. GK, MW, KUR and SE developed the main part of CATS. AL and DR performed the high-volume cell-size measurements and conducted the response analysis. The authors read and approved the final manuscript.
We are grateful to Margaret Janke and Reiner Zimmermann for their assistance in reviewing this article. The authors thank the anonymous reviewers for valuable comments on an earlier draft of the manuscript.
Compliance with ethical standards
This work was supported by the University of Hohenheim and University of Applied Sciences Emden-Leer by travelling support.
Conflict of interest
The authors declare that they have no conflict of interest.
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