## 1 Introduction  
One aspect of vegetation dynamics that is receiving increasing attention is that of phenology, the cycle of events that drive the seasonal progression of vegetation through stages of dormancy, active growth and senescence [1, 2]. The timing of such phenological events is indicative of the impact of both short- and long-term climatic changes on the terrestrial biosphere [3]. The onset and eventual loss of leaves regularly alters land-surface boundary conditions through physical changes in structure which alters surface albedo, roughness, and surface water and energy fluxes [4, 5]. Change to the associated length of the growing season and thus primary productivity has an inherent impact on the exchange of carbon dioxide with the atmosphere [6, 7]. Any extension in the growing season leading to increased canopy longevity and carbon gain may provide an increased sink for atmospheric carbon [8], although this may contribute to warming due to a decrease in surface albedo [9]. In addition to the close links between global environmental change and phenology, any alteration in the timing of phenological events has implications for competition between plant species and interactions with heterotrophic organisms [10]. Furthermore, ecosystem services to humans of plants, such as the production of food, fibre and extractable chemical substances, as well as the seasonal suitability of landscapes for recreational activities are impacted by phenology [11]. Consequently, the continuous and automated monitoring of phenology is a key issue in science [12], and as such should be fully integrated in to a systematic and scientifically credible monitoring programme [13, 14]. Although plant phenology is one of the most easily observable traits in nature [15, 16], there remains a challenge in capturing its timing, location and magnitude at appropriate resolutions with certainty [17, 18]. Two principal approaches have dominated the measurement of phenological events; field-based observations and satellite-based observations. These two methods differ greatly in their scale of measurement and the technology used; also there are limitations in the integration of these measures [19]. Field observations by individuals afford a detailed perspective, but can be compromised by logistical challenges and a lack of consistency, continuity and objectivity, and high costs [20]. Though nationwide observation networks (for example USA National Phenology Network, www.usanpn.org [21]; and UK Phenological Network, www.naturescalendar.org.uk [22]), overcome some of these limitations in providing a geographical spread, they still rely on contributions by volunteers. The deployment of radiometric sensors at the stand level are proving useful [23, 24], but are expensive and currently have limited extents [25]. Satellite remote sensing affords a large-scale perspective and methods for extraction of phenological metrics are proving robust (e.g., from the medium resolution MODIS and MERIS sensors which have a high revisit rate [26]), however, current pixel resolutions mean that measurements are often too coarse and make it nearly impossible to detect critical organism, species, or community-level responses [27, 28]. Further challenges to using satellite data include its sensitivity to effects of clouds and atmospheric conditions [29, 30]. Moreover, there is the issue of a lack of parallel ground-based phenological observations and scalable field models [31]. Visible light digital cameras have been recognised as a measurement approach that could provide inexpensive, spatially representative and objective information with the required resolution. The potential of these cameras has been recognised in a number of ecological applications: studies on vegetation growth and biomass [15, 32], nitrogen status and plant stresses [24], and canopy green-up and senescence [27], as well as for automating a range of agricultural monitoring practices [33].