Global change is shifting the seasonality of vegetation in ecosystems around the globe. Highfrequency digital camera imagery, and vegetation indices derived from that imagery, is facilitating better ...tracking of phenological responses to environmental variation. This method, commonly referred to as the ‘phenocam’ approach, is well suited to several specific applications, including: close-up observation of individual organisms; long-term canopy-level monitoring at individual sites; automated phenological monitoring in regional-to-continental scale observatory networks; and tracking responses to experimental treatments. Several camera networks are already well established, andsome camera records are a more than a decade long. These data can be used to identify the environmental controls on phenology in different ecosystems, which will contribute to the development of improved prognostic phenology models.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Autumn senescence regulates multiple aspects of ecosystem function, along with associated feedbacks to the climate system. Despite its importance, current understanding of the drivers of senescence ...is limited, leading to a large spread in predictions of how the timing of senescence, and thus the length of the growing season, will change under future climate conditions. The most commonly held paradigm is that temperature and photoperiod are the primary controls, which suggests a future extension of the autumnal growing season as global temperatures rise. Here, using two decades of ground‐ and satellite‐based observations of temperate deciduous forest phenology, we show that the timing of autumn senescence is correlated with the timing of spring budburst across the entire eastern United States. On a year‐to‐year basis, an earlier/later spring was associated with an earlier/later autumn senescence, both for individual species and at a regional scale. We use the observed relationship to develop a novel model of autumn phenology. In contrast to current phenology models, this model predicts that the potential response of autumn phenology to future climate change is strongly limited by the impact of climate change on spring phenology. Current models of autumn phenology therefore may overpredict future increases in the length of the growing season, with subsequent impacts for modeling future CO₂ uptake and evapotranspiration.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Plant phenology is a sensitive indicator of the effects of global change on terrestrial ecosystems and controls the timing of key ecosystem functions including photosynthesis and transpiration. ...Aerial drone imagery and photogrammetric techniques promise to advance the study of phenology by enabling the creation of distortion-free orthomosaics of plant canopies at the landscape scale, but with branch-level image resolution. The main goal of this study is to determine the leaf life cycle events corresponding to phenological metrics derived from automated analyses based on color indices calculated from drone imagery. For an oak-dominated, temperate deciduous forest in the northeastern USA, we find that plant area index (PAI) correlates with a canopy greenness index during spring green-up, and a canopy redness index during autumn senescence. Additionally, greenness and redness metrics are significantly correlated with the timing of budburst and leaf expansion on individual trees in spring. However, we note that the specific color index for individual trees must be carefully chosen if new foliage in spring appears red, rather than green-which we observed for some oak trees. In autumn, both decreasing greenness and increasing redness correlate with leaf senescence. Maximum redness indicates the beginning of leaf fall, and the progression of leaf fall correlates with decreasing redness. We also find that cooler air temperature microclimates near a forest edge bordering a wetland advance the onset of senescence. These results demonstrate the use of drones for characterizing the organismic-level variability of phenology in a forested landscape and advance our understanding of which phenophase transitions correspond to color-based metrics derived from digital image analysis.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Climate change affects the phenology of many species. As temperature and precipitation are thought to control autumn color change in temperate deciduous trees, it is possible that climate change ...might also affect the phenology of autumn colors. Using long-term data for eight tree species in a New England hardwood forest, we show that the timing and cumulative amount of autumn color are correlated with variation in temperature and precipitation at specific times of the year. A phenological model driven by accumulated cold degree-days and photoperiod reproduces most of the interspecific and interannual variability in the timing of autumn colors. We use this process-oriented model to predict changes in the phenology of autumn colors to 2099, showing that, while responses vary among species, climate change under standard IPCC projections will lead to an overall increase in the amount of autumn colors for most species.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
► The sensitivity of vegetation phenology to climate change varies among biomes. ► Key weaknesses in our current understanding of phenology drivers are identified. ► Phenology controls many feedbacks ...of vegetation to the climate system. ► The size and seasonality of these feedbacks will shift as phenology shifts. ► Models that couple the land surface to the climate system need better phenology.
Vegetation phenology is highly sensitive to climate change. Phenology also controls many feedbacks of vegetation to the climate system by influencing the seasonality of albedo, surface roughness length, canopy conductance, and fluxes of water, energy, CO2 and biogenic volatile organic compounds. In this review, we first discuss the environmental drivers of phenology, and the impacts of climate change on phenology, in different biomes. We then examine the vegetation-climate feedbacks that are mediated by phenology, and assess the potential impact on these feedbacks of shifts in phenology driven by climate change. We finish with an overview of phenological modeling and we suggest ways in which models might be improved using existing data sets. Several key weaknesses in our current understanding emerge from this analysis. First, we need a better understanding of the drivers of phenology, particularly in under-studied biomes (e.g. tropical forests). We do not have a mechanistic understanding of the role of photoperiod, even in well-studied biomes. In all biomes, the factors controlling senescence and dormancy are not well-documented. Second, for the most part (i.e. with the exception of phenology impacts on CO2 exchange) we have only a qualitative understanding of the feedbacks between vegetation and climate that are mediated by phenology. We need to quantify the magnitude of these feedbacks, and ensure that they are accurately reproduced by models. Third, we need to work towards a new understanding of phenological processes that enables progress beyond the modeling paradigms currently in use. Accurate representation of phenological processes in models that couple the land surface to the climate system is particularly important, especially when such models are being used to predict future climate.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
As the spatial and temporal resolution of remotely sensed imagery has improved over the last four decades, algorithms for monitoring and mapping seasonal changes in surface properties have evolved ...rapidly. Most recently, the availability of daily PlanetScope imagery has created new opportunities for monitoring the land surface phenology (LSP) of terrestrial ecosystems at high spatial resolution. However, the quality and value of LSP information from PlanetScope imagery have not been systematically examined. In this paper, we evaluate the character and quality of LSP information derived from PlanetScope by comparing time series of vegetation indices and LSP metrics from PlanetScope to corresponding time series and LSP metrics derived from Harmonized Landsat 8 and Sentinel-2 (HLS) imagery and PhenoCams at six sites that span a diverse range of land cover types and climate. Results show that vegetation index time series from all three data sources show high temporal correlation, and LSP metrics derived from HLS, PlanetScope, and PhenoCam show high agreement with negligible bias. Semi-variograms for phenometrics estimated from PlanetScope imagery indicate that the majority of spatial variance captured in PlanetScope phenometrics occurs well below the spatial resolution HLS imagery. At the same time, LSP metrics from HLS are most strongly correlated with the 50–75% quantiles of 3 m LSP metrics from PlanetScope. This indicates that HLS captures the average phenology at sub-pixel scale captured in PlanetScope imagery. Our results represent the first comprehensive comparison of LSP metrics estimated from PlanetScope and publicly available moderate spatial resolution imagery, and provide insights regarding: (1) the quality and character of LSP metrics derived from HLS and PlanetScope; and (2) the relative merits and trade-offs associated with the use of each data source for LSP studies.
•Vegetation index time series from all three data sources show strong agreement.•EVI2 from PlanetScope is lower than EVI2 from HLS due to bias in the red band.•Phenometrics from each source show strong agreement at seasonal time scales.•Phenometrics from each source weaker agreement at sub-seasonal time scales.•Fine-scale variation in LSP is captured by PlanetScope but not by HLS.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition ...dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both "greenness rising" and "greenness falling" transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
• Despite the importance of nonstructural carbohydrates (NSC) for growth and survival in woody plants, we know little about whole-tree NSC storage. The conventional theory suggests that NSC reserves ...will increase over the growing season and decrease over the dormant season. Here, we compare storage in five temperate tree species to determine the size and seasonal fluctuation of whole-tree total NSC pools as well as the contribution of individual organs.
• NSC concentrations in the branches, stemwood, and roots of 24 trees were measured across 12 months. We then scaled up concentrations to the whole-tree and ecosystem levels using allometric equations and forest stand inventory data.
• While whole-tree total NSC pools followed the conventional theory, sugar pools peaked in the dormant season and starch pools in the growing season. Seasonal depletion of total NSCs was minimal at the whole-tree level, but substantial at the organ level, particularly in branches. Surprisingly, roots were not the major storage organ as branches stored comparable amounts of starch throughout the year, and root reserves were not used to support springtime growth.
• Scaling up NSC concentrations to the ecosystem level, we find that commonly used, process-based ecosystem and land surface models all overpredict NSC storage.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Terrestrial plants remove CO2 from the atmosphere through photosynthesis, a process that is accompanied by the loss of water vapour from leaves. The ratio of water loss to carbon gain, or water-use ...efficiency, is a key characteristic of ecosystem function that is central to the global cycles of water, energy and carbon. Here we analyse direct, long-term measurements of whole-ecosystem carbon and water exchange. We find a substantial increase in water-use efficiency in temperate and boreal forests of the Northern Hemisphere over the past two decades. We systematically assess various competing hypotheses to explain this trend, and find that the observed increase is most consistent with a strong CO2 fertilization effect. The results suggest a partial closure of stomata-small pores on the leaf surface that regulate gas exchange-to maintain a near-constant concentration of CO2 inside the leaf even under continually increasing atmospheric CO2 levels. The observed increase in forest water-use efficiency is larger than that predicted by existing theory and 13 terrestrial biosphere models. The increase is associated with trends of increasing ecosystem-level photosynthesis and net carbon uptake, and decreasing evapotranspiration. Our findings suggest a shift in the carbon- and water-based economics of terrestrial vegetation, which may require a reassessment of the role of stomatal control in regulating interactions between forests and climate change, and a re-evaluation of coupled vegetation-climate models.
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DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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