Phenology of woody species: a review

An attempt has been made to synthesize a brief account on research advances on various phases of phenology. Climate has positive impact on the timing of phenology; cold-air drainage may influence the start of leaf growth, however leaf phenology in tropical evergreen forests is not determined by the seasonality of precipitation. Climate warming in late winter and spring may enhance sensitivity of the growing season’s spatial response due to the relationship of beginning date to temperature. Elevated temperature strongly influences greater in C3 plants than in C4 plants but the disadvantages of warming may be considerably attenuated by elevated CO2, especially for C3 grasses. Species with high wood densities can able to store only limited quantities of water in their trunks; leaf fall in these species occurred during the dry season. Flowering phenology may be changing faster and precipitation may play a more important role in semi-arid grasslands than in humid-temperate systems. Peak flowering and fruiting are dependent on seasons for both endemic and non-endemic species. Population sensitivity to global warming might be stable for a given species, in spite of its possible local adaptation. It might be possible for ecologists to establish comprehensive networks for long-term monitoring of potential photosynthetic capacity from regional to global scales by linking satellite-based programme. Use of satellite-derived metrics,such as VARI, may be used for evaluating the spatial patterns and temporal dynamics of species composition across broad geographic regions

Seasonal patterns of forest canopy and their relevance for the global carbon cycle

In the terrestrial biosphere forests have a significant role as a carbon sink. Under recent climate change, it is increasingly important to detect seasonal change or ‘phenology’ that can influence the global carbon cycle. Monitoring canopies using camera systems has offered an inexpensive means to quantify the phenological changes. However, the reliability is not well known. In order to examine the usefulness of cameras to observe forest phenology, we analysed canopy images taken in two deciduous forests in Japan and England and investigate which colour index is best for tracking forest phenology and predict carbon uptake by trees. A camera test using model leaves under controlled conditions has also carried out to examine sensitivity of colour indices for discriminating leaf colours. The main findings of the present study are: 1) Time courses of colour indices derived from images taken in deciduous forests showed typical patterns throughout the growing season. Although cameras are not calibrated instrument, analysis of images allowed detecting the timings of phenological events such as leaf onset and leaf fall; 2) The strength of the green channel (or chromatic coordinate of green) was useful to observe leaf expansion as well as damage by spring late frost. However, the results of the camera test using model leaves suggested that this index was not sufficiently sensitive to detect leaf senescence. Amongst colour indices, Hue was the most robust metric for different cameras, different atmospheric conditions and different distances. The test also revealed Hue was useful to track nitrogen status of leaves; 3) Modelling results using a light use efficiency model for GPP showed a strong relationship between GPP and Hue, which was stronger than the relationships using alternative traditional indices

Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem

AbstractWe investigated the usefulness of a ground-based digital photography to evaluate seasonal changes in the aboveground green biomass and foliage phenology in a short-grass grassland in Japan. For ground-truthing purposes, the ecological variables of aboveground green biomass and spectral reflectance of aboveground plant parts were also measured monthly. Seasonal change in a camera-based index (rG: ratio of green channel) reflected the characteristic events of the foliage phenology such as the leaf-flush and leaf senescence. In addition, the seasonal pattern of the rG was similar to that of the aboveground green biomass throughout the year. Moreover, there was a positive linear relationship between rG and aboveground green biomass (R2=0.81, p<0.05), as was the case with spectra-based vegetation indices. On the basis of these results, we conclude that continuous observation using digital cameras is a useful tool that is less labor intensive than conventional methods for estimating aboveground green biomass and monitoring foliage phenology in short-grass grasslands in Japan

The photosynthesis-foliar nitrogen relationship in deciduous and evergreen forests in New Hampshire

Biomass production in forests is a key process in the global carbon (C) cycle that is strongly linked to photosynthesis and related leaf traits. Spatially, relationships among leaf traits can vary as a function of climate, soils and species composition. As modeling approaches to estimate C gain improve, the need to understand variability in leaf traits becomes increasingly important. Here, we characterized the relationship between photosynthetic capacity (Amax), foliar nitrogen and leaf mass per area (LMA) within and across species in northern hardwood and evergreen stands of the White Mountain National Forest in New Hampshire, a region that has been underrepresented in past leaf trait studies. Results were used to parameterize a forest ecosystem model (PnET) that has been widely used in the Northeast region to predict ecosystem C fluxes. Within all species, Amax was strongly and positively related to mass-based foliar percent nitrogen (%N). The observed relationship between foliar %N and Amax differed significantly from the previously used model parameterization that was based on leaf trait data from forest stands in Wisconsin, and was largely a function of differences in leaf mass per area. Using site-specific foliar %N and LMA to estimate Amax in PnET improved the estimation of GPP by 5.5% in comparison with GPP estimates derived from an eddy covariance tower

Networked web-cameras monitor congruent seasonal development of birches with phenological field observations

Ecosystems' potential to provide services, e.g. to sequester carbon, is largely driven by the phonological cycle of vegetation. Timing of phenological events is required for understanding and predicting the influence of climate change on ecosystems and to support analyses of ecosystem functioning. Analyses of conventional camera time series mounted near vegetation has been suggested as a means of monitoring phenological events and supporting wider monitoring of phenological cycle of biomes that is frequently done with satellite earth observation (EO). Especially in the boreal biome, sparsely scattered deciduous trees amongst conifer-dominant forests pose a problem for EO techniques as species phenological signal mix, and render EO data difficult to interpret. Therefore, deriving phonological information from on the ground measurements would provide valuable reference data for earth observed phonology products in a larger scale. Keeping this in mind, we established a network of digital cameras for automated monitoring of phenological activity of vegetation in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. In this study, we used data from 12 sites to investigate how well networked cameras can detect the phenological development of birches (Betula spp.) along a latitudinal gradient. Birches typically appear in small quantities within the dominant species. We tested whether the small, scattered birch image elements allow a reliable extraction of colour indices and the temporal changes therein. We compared automatically derived phenological dates from these birch image elements both to visually determined dates from the same image time series and to independent observations recorded in the phenological monitoring network covering the same region, Automatically extracted season start dates, which were based on the change of green colour fraction in spring, corresponded well with the visually interpreted start of the season, and also to the budburst dates observed in the field. Red colour fraction turned out to be superior to the green colour-based indices in predicting leaf yellowing and fall. The latitudinal gradients derived using automated phenological date extraction corresponded well with the gradients estimated from the phenological field observations. We conclude that small and scattered birch image elements allow reliable extraction of key phonological dates for the season start and end of deciduous species studied here, thus providing important species-specific data for model validation and for explaining the temporal variation in EO phenology products.Peer reviewe

Interpreting canopy development and physiology using the EUROPhen camera network at flux sites

Peer reviewe

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems