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Abstract:
The increasingly important effect of climate
change and extremes on alpine phenology highlights the
need to establish accurate monitoring methods to track interannual
variation (IAV) and long-term trends in plant phenology.
We evaluated four different indices of phenological
development (two for plant productivity, i.e., green biomass
and leaf area index; two for plant greenness, i.e., greenness
from visual inspection and from digital images) from a 5-
year monitoring of ecosystem phenology, here defined as
the seasonal development of the grassland canopy, in a subalpine
grassland site (NW Alps). Our aim was to establish an
effective observation strategy that enables the detection of
shifts in grassland phenology in response to climate trends
and meteorological extremes. The seasonal development of
the vegetation at this site appears strongly controlled by
snowmelt mostly in its first stages and to a lesser extent
in the overall development trajectory. All indices were able
to detect an anomalous beginning of the growing season
in 2011 due to an exceptionally early snowmelt, whereas only some of them revealed a later beginning of the growing season in 2013 due to a late snowmelt. A method is developed
to derive the number of samples that maximise the
trade-off between sampling effort and accuracy in IAV
detection in the context of long-term phenology monitoring
programmes. Results show that spring phenology requires a
smaller number of samples than autumn phenology to track
a given target of IAV. Additionally, productivity indices
(leaf area index and green biomass) have a higher sampling
requirement than greenness derived from visual estimation
and from the analysis of digital images. Of the latter two,
the analysis of digital images stands out as the more effective, rapid and objective method to detect IAV in vegetation development.
