Watching the trees at DendroNet
Oct 17, 2025
This week I joined the first day of the Nordic Dendrometer Network (DendroNet) Meeting, organized by my colleague, Danielle Creek, held on my campus the Norwegian University of Life Sciences. I had planned on attending the full conference, but alas the germs at my child’s kindergarten had other plans.
Dendrometers are high-precision sensors that directly measure tiny changes in a tree’s stem diameter. These data are collected continuously and often at micrometer resolution. Stem diameter fluctuations reflect both reversible changes in water status, and irreversible growth from new wood formation. By recording these signals, within a year and across years, dendrometers capture fine-scale physiological responses of trees to variation in temperature, precipitation, soil moisture, and seasonal dynamics.
What stood out to me the most was how much information can be extracted from these subtle signals. Coming from a remote sensing background, where trees are primarily observed through structural or spectral traits, dendrometer data provide the missing physiological context, showing the processes driving the patterns we detect remotely. In that sense, dendrometers can provide a link between what trees experience and what can be observed remotely.
Dendrometers and remote sensing offer complementary information of tree growth, water use, and stress. Integrating these data connects dynamics at the tree level and provides a pathway to scale from individual trees to broader forest responses. And this integration matters for forest monitoring and ecological research because it allows us to translate fine-scale physiological measurements into actionable insights at larger scales. By linking tree-level dendrometer and remote sensing responses, we may be able to detect early signs of stress, track productivity trends, and better understand how forests respond to climate change and extreme events like heatwave and droughts, and guide management. Ultimately, combining dendrometer and remote sensing data helps bridge the gap between detailed physiological knowledge and practical forest management, improving our ability to conserve, manage, and predict forest ecosystem function.