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Leaf phenology of sessile oak (Quercus petraea) and Seasonal variations in the fraction of light intercepted by the tree crowns

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DataCite Commons2025-05-16 更新2025-04-16 收录
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https://entrepot.recherche.data.gouv.fr/citation?persistentId=doi:10.57745/RZOBCY
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The dataset results from a 10-year experiment conducted on sessile oak (Quercus petraea) both in a provenance plantation (Sillegny, NE France) and in a network of mature stands along a wide altitudinal gradient (Pyrénées, SW France). The aim was to assess the use of seasonal variations in the fraction of light intercepted by the tree canopy to (1) date the successive events of leafing in spring and leaf senescence in autumn, and (2) monitor environmental gradients in leaf phenology, such as those induced by altitude. The fraction of radiation intercepted (FiR) by the canopy of each studied tree population was estimated from continuous measurements of the radiation transmitted through the tree crowns and the radiation incident above the canopy. This was done in the wavelengths of photosynthetically active radiation (PAR) and in the blue and green spectral bands using two types of sensors, quantum sensors and photodiodes, respectively. In a given spectral band, FiR =1- Transmittance, where Transmittance= transmitted radiation / incident radiation. The time series of FiR have been produced with a daily frequency year by year. The dataset includes all the time series produced, from the raw measurements to the annual series of daily FiR. Temporal variations in FiR were smoothed to different degrees to best capture seasonal transitions using several models: non-parametric smoothers (Spline, Whittaker) and a parametric model (Gompertz). Metrics were extracted that systematically date variations in the modelled annual trajectory during leafing and senescence. The dataset includes the metrics produced for each model for each population, year by year. The metrics were evaluated by comparing them with visual phenological observations in the field, pooled by population. These data are also included.
提供机构:
Recherche Data Gouv
创建时间:
2023-12-07
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