Species‐specific spring and autumn leaf phenology captured by time‐lapse digital cameras

Abstract

Plant leaf phenology is typically observed either via ground‐based visual observations on individuals or via remote sensing of land surface vegetation. To integrate phenological information from both data sources, collected at different spatial scales using different observational protocols, digital cameras were deployed spanning canopy areas with enough spatial resolution to identify temporal changes in individual deciduous tree species with continuous observations. Comparisons of phenology between camera photography and in situ observations have been reported in prior studies; however, it is still unclear that how these camera images relate to field observations at individual and species levels, and how the metrics from those images provide comparable species‐specific phenological responses to environmental variation. We set a suite of digital time‐lapse cameras to acquire continuous photographs of deciduous tree canopies and conducted ground‐based visual observations in Connecticut, USA, from 2012 to 2014. Comparisons between image‐derived dates and observed phenological dates showed that both green and red color indices could be matched to ground observations, and red color indices showed good performance in matching autumn phenology across our group of eight tree species that dominate the southern New England forests. Linear mixed‐effects models were applied to investigate the relationships between climatic/weather conditions and the timing of peak and of intensity of red color in fall foliage for each species. Model results suggested that temperature, precipitation, drought stress in autumn, and heat stress in summer are all important factors to the timing of peak fall foliage color and that higher minimum temperatures (or lower cold degree‐day accumulation) in the autumn are linked to higher intensity of red coloration at least in sugar maples. This study improves our understanding of temporal and spatial variation in the phenology of deciduous trees captured by digital cameras. As well, this provides insights into relating species‐specific information on phenology from visual observations in the field to near‐surface remote sensing and points to the need for further research on autumn phenology using the change in redness of tree canopies.

“Green pointillism”: detecting the within-population variability of budburst in temperate deciduous trees with phenological cameras

“…The comparison of budburst dates obtained from ground observations and from phenocams are usually good (e.g. Keenan et al , 2014; Xie et al , 2018). To date, the potential of phenocams has mostly been assessed at the canopy scale, corresponding to the camera field of view (Keenan et al , 2014; Klosterman et al , 2014).…”

“…To date, the potential of phenocams has mostly been assessed at the canopy scale, corresponding to the camera field of view (Keenan et al , 2014; Klosterman et al , 2014). Some studies have also considered the scale of individual trees (Ahrends et al , 2008; Berra et al , 2016; Kosmala et al , 2016; Xie et al , 2018), but those studies pointed to tree inter-specific differences, not pointing particularly to the within-population (i.e. intra-specific) variability of phenology and the characterization of its inter-annual variability.…”

Does an Early Spring Indicate an Early Summer? Relationships Between Intraseasonal Growing Degree Day Thresholds

“…Future work could entail building a tool to calculate relationships between any selected thresholds on any base temperature for a location. However, as the growing season progresses into summer and autumn, variables such as photoperiod or accumulated chill can play a larger role in driving phenology (Delpierre et al, ; Richardson et al, ; Xie et al, ) and this approach may not represent phenological activity as well as in the spring season.…”

Urban plant phenology monitoring: Expanding the functions of widespread surveillance cameras to nature rhythm understanding

“…foliation, blossom, blossom and then withering away, defoliation and meadow greening (background change), possibly occurring within the SC foreground. For such complex scenarios, the traditional solution based on the bi-logistic models (Richardson et al, 2007;Ahrends et al, 2008) or many other kinds of species-specific phenological models (Chuine et al, 1998;Henneken et al, 2013;Zhang and Zhang, 2015;Xie et al, 2018) cannot work well. That is, the bi-logistic models appropriate for monitoring of leaf dynamics cannot be used for simultaneously monitoring of the flowering processes, because detection of the flowering phases is more complicated than only observing leaf color changes, e.g.…”