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.

No abstract

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.

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.

Abstract:To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, GRVI showed the most significant correlations with NDVI among all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (G DN − R DN )/(G DN + R DN ) where G DN is Green digital number and R DN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic.

Some previous studies have detected the timing of leaf expansion and defoliation using the normalized-difference vegetation index (NDVI), but to examine tree phenology using satellite data, NDVI results should be confirmed using ground-truthing. We examined the 45 relationship between NDVI and tree phenology during leaf expansion and defoliation by simultaneously observing the spectral reflectance of the canopy surface and canopy surface images in a cool-temperate deciduous broadleaved forest. To define the timing of leaf expansion and defoliation using NDVI, the index should meet three criteria: (1) NDVI should exhibit a monotonous increase or decrease (monotonicity). (2) The 50 relationship between NDVI and the forest canopy's status should be unique (uniqueness).

No abstract

Revealing the seasonal and interannual variations in forest canopy photosynthesis is a critical issue in understanding the ecological mechanisms underlying the dynamics of carbon dioxide exchange between the atmosphere and deciduous forests. This study examined the effects of temporal variations of canopy leaf area index (LAI) and leaf photosynthetic capacity [the maximum velocity of carboxylation (V (cmax))] on gross primary production (GPP) of a cool-temperate deciduous broadleaf forest for 5 years in Takayama AsiaFlux site, central Japan. We made two estimations to examine the effects of canopy properties on GPP; one is to incorporate the in situ observation of V (cmax) and LAI throughout the growing season, and another considers seasonality of LAI but constantly high V (cmax). The simulations indicated that variation in V (cmax) and LAI, especially in the leaf expansion period, had remarkable effects on GPP, and if V (cmax) was assumed constant GPP will be overestimated by 15%. Monthly examination of air temperature, radiation, LAI and GPP suggested that spring temperature could affect canopy phenology, and also that GPP in summer was determined mainly by incoming radiation. However, the consequences among these factors responsible for interannual changes of GPP are not straightforward since leaf expansion and senescence patterns and summer meteorological conditions influence GPP independently. This simulation based on in situ ecophysiological research suggests the importance of intensive consideration and understanding of the phenology of leaf photosynthetic capacity and LAI to analyze and predict carbon fixation in forest ecosystems.