Ensminger Lab

New Paper from Ensminger Lab

D’Odorico, P., Besik, A., Wong, C. Y. S., Isabel, N., & Ensminger, I. (2020). High-throughput drone based remote sensing reliably tracks phenology in thousands of conifer seedlings. New Phytologist https://doi.org/10.1111/nph.16488

In this study we used a novel high-throughput drone-based phenotyping approach to assess phenology in 6000 conifer seedlings in parallel in the field. This drone-based approach is a novel and efficient tool for studying climate change impacts and environmental variation on the physiological status of thousands of field-grown conifers at unprecedented speed and scale. Changes in the phenology, with an earlier onset of the growing season and delay in the cessation of growth, are considered as some of the strongest evidence that vegetation already responds to climate change. In conifers, monitoring phenology of photosynthesis at large-scale through remote sensing has been unreliable, because needle foliage varies little throughout the year. This is challenging for modelling ecosystem carbon uptake and monitoring phenology for genomics assisted breeding, for conversation programs and for forest health. The drone-based approach described in this paper is based on the detection of changes in leaf spectral characteristics which reflect photosynthesis and adjustments in photosynthetic pigments in response to growth conditions. This work was conducted by Postdoc Petra D’Odorico, PhD student Chris Wong and Master student Ariana Besik from the Ensminger lab.

 

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