Standard procedures to obtain high‐quality images of wood samples have become a bottleneck in the digitization of dendrochronology. Digitization is currently dominated by flatbed scanners, but the use of these devices is limited by sample length and surface flatness. Although several solutions based on digital photography have been published, they lack effective digitization processes or are too expensive to be widely adopted. Free open‐source software and hardware has emerged as an alternative to create research tools that combine reduced costs with high reliability. Here we present CaptuRING, an open‐source tool for wood sample digitization combining a do‐it‐yourself hardware based on Arduino® with a DSLR camera and a free open‐source software with an easy‐to‐use graphical user interface. We compared CaptuRing with image acquisition from a standard flatbed scanner Epson® V750PRO. CaptuRING outperforms scanner image resolution and sharpness, while it removes sample size limitations. Moreover, CaptuRing performs this task in less than half of the time needed by Epson® V750PRO flatbed scanner. CaptuRING emerges as a reliable and low‐cost tool to capture high‐resolution images of wood samples boosting current digitization processes. The combination of free open software and hardware empowers dendrochronology to advance in wood sample digitization.

Climate warming is driving an advance of leaf unfolding date in temperate deciduous forests, promoting longer growing seasons and higher carbon gains. However, an earlier leaf phenology also increases the risk of late frost defoliation (LFD) events. Compiling the spatiotemporal patterns of defoliations caused by spring frost events is critical to unveil whether the balance between the current advance in leaf unfolding dates and the frequency of LFD occurrence is changing and represents a threaten for the future viability and persistence of deciduous forests. We combined satellite imagery with machine learning techniques to reconstruct the spatiotemporal patterns of LFD events for the 2003–2018 period in the Iberian range of European beech (Fagus sylvatica), at the drier distribution edge of the species. We used MODIS Vegetation Index Products to generate a Normalized Difference Vegetation Index (NDVI) time series for each 250 × 250 m pixel in a total area of 1,013 km² (16,218 pixels). A semi‐supervised approach was used to train a machine learning model, in which a binary classifier called Support Vector Machine with Global Alignment Kernel was used to differentiate between late frost and non‐late frost pixels. We verified the obtained estimates with photointerpretation and existing beech tree‐ring chronologies to iteratively improve the model. Then, we used the model output to identify topographical and climatic factors that determined the spatial incidence of LFD. During the study period, LFD was a low recurrence phenomenon that occurred every 15.2 yr on average and showed high spatiotemporal heterogeneity. Most LFD events were condensed in 5 yr and clustered in western forests (86.5% in one‐fifth of the pixels) located at high elevation with lower than average precipitation. Elevation and longitude were the major LFD risk factors, followed by annual precipitation. The synergistic effects of increasing drought intensity and rising temperature combined with more frequent late frost events may determine the future performance and distribution of beech forests. This interaction might be critical at the beech drier range edge, where the concentration of LFD at high elevations could constrain beech altitudinal shifts and/or favor species with higher resistance to late frosts.

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