Phenology is the study of the timing of periodic biological occurrences, from flowering and leaf emergence to animal migrations and breeding patterns, all of which vary between and within species and are influenced to a certain degree by the climate (Lieth, 1974; Richardson et al., 2013a). Phenological monitoring allows us to quantify and track seasonal cycles and irregular natural events, as well as how vegetation phenology responds to meteorological events and climate change. At the ecosystem level, the timing of leaf emergence and senescence in plants has a major influence on the length of the growing season (Dragoni et al., 2011; Keenan et al., 2014b). On larger scales, phenological responses feed back to the climate system by altering the land surface energy balance and the coupled cycles of water, carbon, and nutrients from an ecosystem (Peñuelas et al., 2009; Richardson et al., 2013a). Research suggests that contemporary warming has advanced spring onset in the Northern Hemisphere by ca. 2.8 days per decade since the 1970s (Parmesan, 2007), with even higher rates of change (4.8 days per decade) in more recent decades (Keenan et al., 2014b). Likewise, in the Southern Hemisphere, advancement in spring onset has been estimated at a rate of 2.2 days per decade (Chambers et al., 2013).

In response to increasing evidence of earlier spring onsets, the Intergovernmental Panel on Climate Change (IPCC) has placed a high importance on the gathering and interpretation of phenology observations to improve our understanding of the ecological impacts of climate change (Ciais et al., 2013). Understanding the processes that lead to modifications in phenology, as well as the complex links between phenological shifts and climatic variability and change on local, regional, and global scales, requires more detailed field-based observations that underpin larger-scale remote sensing and ecological modelling tools (de Jong et al., 2012; Migliavacca et al., 2012; Melaas et al., 2013).

Repeat photography with digital cameras (phenocams) has emerged as a powerful observational tool for ecological research and evaluation of remote sensing data products and model simulations (Richardson et al., 2007; Migliavacca et al., 2011; Hufkens et al., 2012; Wingate et al., 2015; Brown et al., 2016). Digital images collected from phenocams typically provide red–green–blue (RGB) colour channel information that can be partitioned and converted to quantitative indices (Woebbecke et al., 1995; Gillespie et al., 1987; Sonnentag et al., 2012). These indices have been shown to closely track vegetation colour changes, such as canopy greenness, and are indicative of different plant development stages in a variety of ecosystems (Sonnentag et al., 2011; Inoue et al., 2014; Keenan et al., 2014a; Peichl et al., 2014; Toomey et al., 2015). A loosely organised global phenocam network is emerging with regional networks including the PhenoCam Network (Richardson et al., 2009a) and the National Ecological Observatory Network (NEON) in the USA (Keller et al., 2008), the EUROPhen network in Europe (Wingate et al., 2015; Brown et al., 2016), the Phenological Eyes Network (PEN) in Japan (Nasahara and Nagai, 2015), and most recently, the Australian Phenocam Network (Brown et al., 2016). These networks provide support for phenocam data collection and sharing in the wider scientific community and are a platform for the development of open data and data sharing standards.

Phenology information can also be derived from satellite remote sensing imagery. Satellite data provides spatially explicit time series of vegetation phenology proxies such as the enhanced vegetation index (EVI; Huete et al., 2002) and the normalised difference vegetation index (NDVI; Tucker, 1979), as well as estimates of canopy cover fraction, the fraction of absorbed photosynthetically active radiation and leaf area index (LAI) derived from optical remote sensing (Gonsamo and Chen, 2014). In addition, vegetation optical depth and biomass can be derived from radar or passive microwave remote sensing (Andela et al., 2013; Liu et al., 2007, 2011, 2013). Such remotely sensed vegetation phenology proxies can be analysed to estimate temporal changes in the vegetation characteristics on the landscape scale and contribute toward the development of satellite phenology products, such as the MODerate resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics product (Ganguly et al., 2010) and the Australian satellite land surface phenology product (Broich et al., 2015).

Satellite-derived phenology can be compared to changes observed on smaller scales with phenocams. For example, Hufkens et al. (2012) and Klosterman et al. (2014) used phenocams to evaluate the MODIS MCD12Q2 product for deciduous forests in the north-eastern USA. Despite discrepancies related to spatial scale and site representativeness, the results showed good agreement between the phenocam- and satellite-derived timing of phenological events (i.e. phenophases). For various temperate and boreal forest, grassland, and peatland ecosystems, time series data of canopy greenness derived from phenocam imagery have been used to analyse seasonal changes in net ecosystem exchange (NEE) and gross primary productivity (GPP) (Ahrends et al., 2009; Richardson et al., 2007, 2009b; Zhu et al., 2013; Toomey et al., 2015). Characterising changes in plant physiology (Keenan et al., 2014a), modelling of canopy development (Wingate et al., 2015), and quantifying inter-annual variation in phenology (Inoue et al., 2014) have also been achieved using phenocams. Despite the promising applications of phenocam data in North America, Europe, and Asia, there has been a slow uptake of phenocam research in Australia, a continent characterized by diverse ecosystem types often with distinctly different phenological characteristics compared to temperate and boreal deciduous ecosystems (Chambers et al., 2013).

The diversity of Australian ecosystems, and their marked differences to Northern Hemisphere temperate deciduous and boreal biomes, poses the challenge of how to best define and quantify vegetation phenology signals, especially due to their responses to irregular natural events such as rainfall, flooding, fire, and extreme temperatures. While the traditional definition of the term phenology as the “timing of recurrent biological events” (Lieth, 1974) is generally assumed to apply to seasonally cyclical events, such as canopy bud burst and senescence in winter-deciduous forests, a closer look at life patterns in many Australian biomes yields a more complex picture of non-seasonal, yet still periodic, events as the major drivers of phenology (Specht and Brouwer, 1975; Pook et al., 1997; Ma et al., 2015).

In this synthesis, we examine the drivers of plant phenological cycles across Australia, illustrate the potential measurement tools available, and suggest future avenues of required research. On the national scale, we demonstrate how satellite observations can be used to characterise broad-scale phenological variability across the continent and discuss the major drivers underlying these patterns. On the ecosystem scale, we highlight the value of using digital image archives obtained from phenocams installed at a selection of contrasting OzFlux sites (Australian and New Zealand flux tower network; see Beringer et al., 2016), for examining the phenology of canopies and individual plants in Australian ecosystems. We discuss how greenness information obtained from phenocam imagery can be compared with flux tower estimates of GPP and why such relationships are stronger from some Australian ecosystems than for others. Finally, we assess the feasibility and effectiveness of phenocams for continuously observing Australia's diverse ecosystems.

Australian ecosystems include a diverse range of biomes spanning tropical savannas and rainforests in the north to arid and semi-arid shrublands and grasslands in the centre and finishing with temperate evergreen forests and woodlands in the south (Fig. 1; see also Beringer et al., 2016, in this issue). Stern and Dehani (2013) recently identified the major climate zones in Australia as ranging from equatorial and tropical in the north through to subtropical, desert, grassland, and temperate in the south (Fig. 1). This wide-ranging climatic variability results in long-term mean annual rainfall ranging from 134 to 2804 mm and mean annual air temperature ranging from 3.8 to 29.0 ∘C (from 1961–1990 data gridded at 0.1∘ (Australian Bureau of Meteorology, 2013; Fig. 1)). Therefore, Australian vegetation has adapted a number of unique phenologies to respond to these climatic drivers.

Temperature is an important phenological driver in Australian ecosystems (Chambers et al., 2013), just as it is in temperate deciduous and boreal ecosystems in the Northern Hemisphere. However, Australian ecosystem dynamics are arguably influenced more strongly by the availability of water, because 70 % of its land mass is classified as arid or semi-arid deserts and grasslands (Fig. 1, Chambers et al., 2013). The response of LAI and vegetation cover to rainfall events, mean annual rainfall, and the corresponding soil water availability is well documented (Pook, 1985; Specht and Specht, 1989; Ellis and Hatton, 2008; Donohue et al., 2009, 2013; Andela et al., 2013; Duursma et al., 2016). Similarly, vegetation density closely follows climate gradients in average rainfall (Ellis and Hatton, 2008; Donohue et al., 2009; Hutley et al., 2011; Ma et al., 2013). Large-scale variability in climatic conditions governs vegetation distribution across the continent and most Australian vegetation has adapted in some form around the need to maintain a balance between water access and water loss.

Among the exceptions to this concept are temperate and tropical rainforest ecosystems found along the east coast of the continent, where rainfall is almost continually in excess of water requirement (Fig. 1). In contrast, C4 grass phenology is driven by wet-season rainfall across Australia's tropical savanna ecosystems, whilst the C3 eucalypt overstorey remains relatively static throughout the year, supported by access to deeper soil water reserves (Walker and Langridge, 1997; Eamus et al., 2002; Hutley et al., 2011). Recurrent dry spells and droughts are often associated with high-mortality events in south-western Australia (Evans and Lyons, 2013; Matusick et al., 2013) and in both riparian (Davies et al., 2008; Gehrke et al., 2006) and non-riparian trees (Semple et al., 2010) in south-eastern Australia. Over longer timescales, non-seasonal shifts in temperature and rainfall, as well as second-order effects on soil water availability, have been attributed to forest declines (Evans et al., 2013) and mortality (Evans and Lyons, 2013) that can be linked to changes in phenology.

Fire events commonly occur in Australia and are often triggered by drought and dry conditions, which are not traditionally regarded as phenological drivers but nonetheless have a cyclical character in many Australian ecosystems (Maier and Russell-Smith, 2012). Most eucalypt forests in Australia suffer periodic burning and have adapted strategies to allow prompt regrowth after fire (epicormic growth) to obtain a rapid photosynthetic advantage over other vegetation competing for light (Hodgkinson, 1998; Burrows, 2008). Drought can also trigger insect attacks, which have been observed to result in rapid canopy defoliation and ecosystem carbon loss in a eucalypt forest in south-eastern Australia (Keith et al., 2012; van Gorsel et al., 2013). Seasonal grazing due to insects and other arboreal browsers is another periodic disturbance in Australian ecosystems that can inhibit new leaf production and greatly modify the canopy, thus thwarting phenology responses observable in these ecosystems (Lowman, 1985; Specht, 1985; Melzer et al., 2000).

Temporal variations in leaf colour, although not as pronounced as in temperate deciduous forests, do occur in evergreen species that dominate Australian ecosystems. For example, eucalypt leaves can contain large amounts of anthocyanin, causing a reddening of the leaf surface (Sharma and Crowden, 1974). While there is no general consensus on why anthocyanins are present in leaves (Gould et al., 2000), their accumulation is associated with the expansion and subsequent predation of young foliage (Close and Beadle, 2003), nutrient deficits that affect photosynthesis (Terashima and Evans, 1988; Sugiharto et al., 1990), or as a protection against solar radiation (Gould, 2004). The accumulation of foliar anthocyanin has been observed in leaves of many Australian plant species (Nittler and Kenny, 1976; Hodges and Nozzolillo, 1996; Kumar and Sharma, 1999; Close et al., 2000, 2001a, b), as has reddening of adult leaves due to a changing anthocyanin-to-chlorophyll ratio when exposed to stressors such as cold or drought (Close et al., 2001a; Stone et al., 2001; Barry et al., 2009). In other cases, exposure to stressful processes can result in a decrease in leaf chlorophyll content, accompanied by or as the precursor to, visible chlorosis (Coops et al., 2004). Such leaf reddening events are visible and often occur in a cyclical manner; therefore, they can be considered as phenological in nature and may be identified by phenocams. The examples listed in this section illustrate the wide ranging drivers of vegetation phenology in Australian ecosystems and highlight the need for continued research to better understand how phenology may respond to climate change in the future.

To characterise the phenological diversity of Australian ecosystems on the continental scale, we used satellite remote sensing observations to map land surface seasonal variability in measures of vegetation greenness. For this, we used EVI as a phenology indicator to construct an EVI seasonality map for Australia using 8-day composites of spectral reflectance observations from MODIS (MCD43C4.005 product) at 0.05∘ (ca. 5 km) resolution for the period 2000–2012. We calculated EVI (Huete et al., 2002) as Eq. (1): EVI=GρNIR-ρredρNIR+L+C1×ρred-C2×ρblue, where ρNIR, ρred, and ρblue are atmospherically corrected spectral reflectance in near-infrared, red, and blue wavelength ranges, respectively; G is a gain factor (G=2.5); C1 and C2 are aerosol resistance coefficients (C1=6, C2=7.5); and L is a soil-adjustment factor (L=1). Values of EVI below 0 and above 1 were removed, as these pixels were either contaminated by cloud or open water.

The mean seasonal pattern for each pixel, based on variability in EVI from 2000–2012, was calculated as the mean annual pattern in 8-day values over the 13 years. Subsequently, this seasonal pattern was subtracted from the time series for each pixel to yield seasonally adjusted anomalies. We then calculated (i) the standard deviation in these seasonally adjusted anomalies (n=585), as well as (ii) the overall mean EVI and (iii) the standard deviation of the mean seasonal pattern (n=45). Combining these three measures allowed us to distinguish areas in Australia where the phenological signal was primarily “non-seasonally dynamic” (EVI varied but not in accordance with seasons), “constantly high” (EVI remained high year-round), or “seasonally dynamic” (EVI showed regular seasonal variability).

To explore phenological variability in more detail around Australia, EVI is displayed by time series plots for selected sites (Fig. 2a–f). Our continental phenological response map shows that the tropical region of northern Australia (Fig. 2a, b) experiences a predictable seasonal phenology, as it receives reliable summer monsoon rainfall (Cook and Heerdegen, 2001). In contrast, large areas of Australia are characterised by non-seasonal variability in vegetation cover (Fig. 2c). These areas primarily fall within the arid interior, as well as in the south-western and south-eastern subhumid regions, where rainfall variability was particularly strong during the observation period, including the worst multi-year drought on record (Van Dijk et al., 2013; Broich et al., 2014).

Tropical savanna ecosystems in northern Australia depict both seasonal (monsoon-driven) and non-seasonal changes (fire-driven), represented by EVI for Howard Springs (location A), which has a high level of variability as shown by the highest amplitude in EVI for all sites (i.e. amplitude of 0.31; Fig. 2a). In contrast, the tropical rainforest at Cape Tribulation (location B) shows a regular but low level of seasonal variability, where the amplitude varies much less than that for site A (i.e. amplitude of 0.15; Fig. 2b). An exception to this regular cycle is the impact of cyclone Larry in early 2006. Cape Tribulation is located nearby and within the same tropical rainforest as the Cow Bay OzFlux site (Fig. 2b). Both sites show the same seasonal trend, and these changes in EVI can be related to changes in NEE that are observed at the OzFlux towers at both sites (Beringer et al., 2016).

Further inland, floodplain vegetation in the arid Channel Country (location C) responds primarily to infrequent and irregular flood events produced in the large upstream catchment. For a flood event in 2010, the time series shows an initial reduction in EVI (open water produces negative EVI; Huete et al., 2002) with a subsequent green flush (i.e. amplitude increases from 0.1 to 0.48; Fig. 2c). The Western Australian wheat belt (location D) shows a winter peak in vegetation (i.e. June–September in the Southern Hemisphere), corresponding with maximum leaf area before the cropping of winter wheat (i.e. EVI amplitude of 0.41; Fig. 2d). In contrast to location C, the year 2010 was the driest on record with poor harvests, recognisable as a lower EVI signal for that year at location D. Subtropical evergreen forests on the central New South Wales (NSW) coast (location E) show a seasonal cycle (i.e. amplitude of 0.20; Fig. 2e) more like that of tropical rainforests further north (location B), which is most likely a phenological adaptation to rainfall during both summer and winter (Bowman, 2000). Highland vegetation in Tasmania (location F) shows a summer green phenology that is more reminiscent of temperate forest ecosystems in the Northern Hemisphere. These selected examples suggest that for most, but not all, of Australia's ecosystems, it is not temperature or radiation but water availability and extreme events that drive vegetation phenology. Furthermore, in addition to the more or less predictable seasonal variations in precipitation, there is very strong variability in rainfall between years due to the strong influence of ocean circulation modes such as the El Niño–Southern Oscillation, Indian Ocean Dipole, Southern Annular Mode, and Pacific Decadal Oscillation (Van Dijk et al., 2013; Broich et al., 2014; Cleverly et al., 2016).

Digital repeat photography has already been shown to provide high temporal-resolution phenology information throughout many vegetation biomes in the Northern Hemisphere (i.e. Richardson et al., 2007; Migliavacca et al., 2011; Sonnentag et al., 2012; Keenan et al., 2014a; Klosterman et al., 2014; Toomey et al., 2015). Images collected by phenocams are typically stored in JPEG format with red, green, and blue (RGB) digital numbers (DNs) for each pixel in the image. Raw image format can also be used, which is discussed in detail by Richardson et al. (2013b) and Brown et al. (2016). Variability in scene illumination can affect the RGB brightness levels (Woebbecke et al., 1995; Richardson et al., 2007), which can be minimised by using a transformation of the RGB numeric values to chromatic coordinates (Gillespie et al., 1987; Woebbecke et al., 1995; Sonnentag et al., 2012). Of these indices, the green chromatic coordinate (GCC) has been identified as the most relevant to green vegetation phenology, given by Eq. (2): GCC=G/(R+G+B), where R, G, and B are the per-pixel DNs stored in the image, recorded as arbitrary units of intensity by the camera's charge-coupled device. GCC is calculated for each pixel of the image and then averaged over a user-defined region of interest (ROI). The red (RCC) and blue (BCC) chromatic coordinates were also calculated, in the same way as GCC in Eq. (2). We calculated chromatic coordinates using only images collected between the hours of 10:00 and 16:00, based on local site time, to reduce the bias of seasonal solar radiation change on the “daily” values, which is a common phenocam analysis technique (Sonnentag et al., 2012; Filippa et al., 2016).

An advantage of being able to transform the phenocam images into these quantitative indices is that it allows for automated quality control of the data. This is important when a large network of automated measurements is being considered on a continental and global scale (Brown et al., 2016). Automated quality assurance and quality control (QA/QC) routines are regularly used for large-scale processing of other network data, including ecosystem flux data such as OzFlux (Beringer et al., 2016; Isaac et al., 2016) and continental-scale observatories such as NEON (Taylor and Loescher, 2013) and the Terrestrial Ecosystem Research Network (TERN; Karan et al., 2016). Furthermore, these automated routines can allow for the fast calculation of online summary metrics that can be used for diagnosing instrument problems and highlighting potential flaws in data collection (Smith et al., 2014). As a result, instrument downtime can be minimized and data collection can be optimized across a large network of sensors.

We have shown the value of phenocams for ecosystem monitoring in Australia and how they can be used to inform on species-level changes, supplement large-scale satellite remote sensing data, and aid in interpreting ecosystem GPP. In Australia, phenocams have primarily been installed at several pre-existing TERN supported OzFlux sites (http://www.ozflux.org.au/), at which eddy covariance flux towers are used to study carbon, water, and energy exchanges between ecosystems and the atmosphere. Phenocams are currently being deployed across TERN and the Australian SuperSite Network (http://www.tern-supersites.net.au), where a comprehensive set of co-located measurements of vegetation, faunal biodiversity, soil or water, and remote sensing is being made. Despite these recent deployments, the spatial and temporal coverage of phenocams in Australia remains limited (Fig. 2).

Several of the phenocams in Australian ecosystems are either in the early stages of operation or have experienced issues with long-term stability in data collection (i.e. FOV shifts, lack of timestamp standards, loss of data), and some ecosystems in Australia are missing altogether (i.e. alpine ecosystems, managed agricultural land, mangroves; Fig. 2). Phenocams are one of few observing methods that can bridge across spatial and temporal scales, from individual plants to the ecosystem and continental scales and between biophysical function and ecological condition and composition. They can capture seasonal trends (Figs. 5, 6, and 7) and short-lived events (Figs. 3, 4) that are often missed by coarser-scale techniques (i.e. satellite sensors; Fig. 2). As such, the establishment of a national phenocam network in Australia will be an extremely valuable contribution to help integrate data products between TERN facilities addressing different ecosystem questions on different scales (i.e. the SuperSites, OzFlux, LTERN, AusCover, and eMAST).

We see considerable potential for the developing Australian Phenocam Network. Existing infrastructure, supported by TERN, will assist in the establishment of this network, the implementation of more phenocams around the country, and the standardisation of data collection, analysis, and sharing. The Australian Phenocam Network would also provide valuable data for monitoring networks such as OzFlux, SuperSites, AusPlots, and eMAST. Currently, there are only a small number of continuously operating phenocams in Australia, with even fewer uploading data to the Australian phenocam website. Expansion of the currently sparse distribution of Australian phenocams will help improve our understanding of the diversity of phenological strategies employed by Australian ecosystems. Combined with satellite remote sensing techniques, phenocams can improve our ability to quantify and predict the functioning of Australia's ecosystem within the Earth system.

The Australian phenology map (Fig. 2) was constructed using freely available MODIS EVI data (2000–2012) obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (https://daac.ornl.gov/MODIS/). The eddy covariance data used in this manuscript are also freely available from the OzFlux data portal (http://ozflux.org.au). Howard Springs flux data are provided to this database by Beringer (2013a) and Whroo data by Beringer (2013b). Likewise, the phenocam data are freely available through the recently formed Australian Phenocam Network (https://phenocam.org.au).