Rapid changes to the biosphere are altering ecological processes worldwide. Developing informed policies for mitigating the impacts of environmental change requires an exponential increase in the quantity, diversity, and resolution of field‐collected data, which, in turn, necessitates greater reliance on innovative technologies to monitor ecological processes across local to global scales. Automated digital time‐lapse cameras – “phenocams” – can monitor vegetation status and environmental changes over long periods of time. Phenocams are ideal for documenting changes in phenology, snow cover, fire frequency, and other disturbance events. However, effective monitoring of global environmental change with phenocams requires adoption of data standards. New continental‐scale ecological research networks, such as the US National Ecological Observatory Network (NEON) and the European Union's Integrated Carbon Observation System (ICOS), can serve as templates for developing rigorous data standards and extending the utility of phenocam data through standardized ground‐truthing. Open‐source tools for analysis, visualization, and collaboration will make phenocam data more widely usable.

The Phenological Eyes Network (PEN), which was established in 2003, is a network of long‐term ground observation sites. The aim of the PEN is to validate terrestrial ecological remote sensing, with a particular focus on seasonal changes (phenology) in vegetation. There are three types of core sensors at PEN sites: an Automatic Digital Fish‐eye Camera, a HemiSpherical SpectroRadiometer, and a Sun Photometer. As of 2014, there are approximately 30 PEN sites, among which many are also FluxNet and/or International Long Term Ecological Research sites. The PEN is now part of a biodiversity observation framework. Collaborations between remote sensing scientists and ecologists working on PEN data have produced various outcomes about remote sensing and long‐term in situ monitoring of ecosystem features, such as phenology, gross primary production, and leaf area index. This article reviews the design concept and the outcomes of the PEN, and discusses its future strategy.

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Leaf area index (LAI) is an important quantity in the study of forest ecosystems, but field measurements of LAI often contain errors because of the vertical complexity of the forest canopy. In this study, we established a practical method for field measurement of LAI in the canopy of a deciduous broadleaved forest by accounting for its vertical complexity. First, we produced a semiempirical model for the vertical integration of leaf dry mass per unit leaf area. We also quantified the litterfall for each tree species. These data enabled us to estimate the LAI of each species in autumn. By periodic in situ monitoring of some fixed sample shoots throughout the growing season, we were able to estimate the seasonality of leaf area (as a proportion of the annual maximum value at each point in time) of each species. By using this seasonality to extrapolate LAI values as a proportion of the LAI data in the leaffall season, we were able to estimate LAI throughout the year. We applied this method in a cooltemperate deciduous forest in central Japan (Takayama) in 2006 and validated our results using two conventional methods of LAI measurement: the plant canopy analyzer (LAI2000) and the Tracing Radiation and Architecture of Canopies (TRAC) approach. LAI estimated by TRAC was in good agreement with our results, but LAI estimated using the LAI2000 was only half the value estimated using our method. The use of basal area data as a proxy for species specific leaf areas may save labor and time. Our method will be useful for studying the dynamics and interactions of multiple species because it can estimate LAI and its seasonal changes for each species.