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Skip to Main Content. Skip to Search Box. Skip to Top Navigation Bar. Skip to Left Navigation Bar. Skip to Organizational Offices. Skip to Bottom Navigation. The nature of spatial transitions in the Arctic. Journal of Biogeography J. Description Aim Describe the spatial and temporal properties of transitions in the Arctic and develop a conceptual understanding of the nature of these spatial transitions in the face of directional environmental change. Methods We synthesize information from numerous studies on tundra and treeline ecosystems in an effort to document the spatial changes that occur across four arctic transitions.

The relationship between EOF and the autumnal NEE shift from sink to source was not correlated, which we interpret as a result of litter fall and a resulting increase in respiration rates from microbial decomposition of the litter Mikan et al. Hence, the shift to a net release of CO 2 during senescence would appear earlier than the camera-based EOF, in agreement with the findings in our study. Despite being both site- and camera-specific, the significant fit of the visualized model Fig. The better model fit obtained by adding a second-order polynomial suggests a variation in the intersect of the linear correlation between GPP and GCC over time.

This is likely related to a hysteresis caused by a higher photosynthetic efficiency per leaf area in spring than in late season Westergaard-Nielsen et al. Consequently, the establishment of robust 3D-models can improve the potential use of GCC as a site-specific GPP proxy in the studied area. The link between growing season duration and ecosystem productivity is ambiguous Euskirchen et al. Growing season length can thus not solely be used as an indicator of productivity Parmentier et al.

However, Xia et al. Following this rationale, camera-derived GCC data has a unique applicability in temporary setups of eddy covariance campaigns with overlapping time series of images. When a robust site-specific model has been established Fig. Nevertheless, further research on applied methods to allow direct comparison of GCC from different sites and cameras is urgently needed to expand and improve the scientific value of camera-derived indices in ecosystem monitoring.

Using JPG images introduces artifacts related to the image compression and conversion between color spaces. The available data did not offer lossless images, and we chose JPG as this involved the fewest processing steps and thus fewer risks of losing image information. The use of a dynamic threshold in the binary classification of snow-covered pixels allows for detection of snow, despite changes in scene illumination.

However, misclassified pixels are unavoidable.

The active layer in the soil can still be frozen immediately after end of snowmelt, resulting in poor drainage of the melt water and thus standing water at the ground surface. From certain illumination angles causing high reflectance in the camera direction, such water-covered pixels could erroneously be classified as snow.

Following snowmelt, there was a local minimum in the computed GCC before it increased as a result of greenup Fig. We interpret the GCC dip as being caused by the water-saturated conditions following snowmelt. The moist conditions in a majority of illumination angles appear darker than dry conditions, which can result in lower GCC.

A similar phenomenon is known from satellite-derived time series of NDVI, which decrease over very moist surfaces that cause NIR absorption Farrar et al. The influence of scene illumination is apparent in GCC Sonnentag et al. The available frequency in this study does not allow for daily averaging so we addressed the issue by fitting a smoothing model. Nevertheless, missing data or a number of successive days with poor illumination e. We addressed the problem by visually sorting the images and dismissing outlier images.

Preferably, this subjective selection should be avoided, yet we did not find automated selection criteria e. The fitted sigmoid models and the associated computed transition dates are numerically solved, and thus include an uncertainty. We have quantified the uncertainty to be larger at the end of season than seen for the start of season, and with a bias toward an underestimation of the growing season length.

Based on the end of season uncertainty, we see no marked differences in the timing of EOF between the four regions. However, SOS seen in region 1 is still earlier than the corresponding dates for regions 2, 3, and 4, when taking the uncertainty into account. In general, the region-specific variations are less pronounced when including the uncertainty, while the annual variation is considerably larger.

Arguably, bi-directional effects are influencing the time series of GCC and must be taken into consideration when comparing the different regions. At the initial installation of the cameras, the coverage and continuity of the field of view were prioritized instead of camera angle. Ultimately, the fixed position of the cameras allows us to compare the time series inter-annually, but possible biases in absolute GCC values must be considered when comparing data from the regions.

We show that the green chromatic coordinate vegetation index can successfully be used to determine phenological transitions in vegetation growth in high-Arctic ecosystems. Moreover, we show that timing of snowmelt and end-of-winter snow water equivalents are closely linked to not only spring greenup, but also peak timing and peak green chromatic coordinate value as well as the growing season duration. We find that air temperatures are not the primary explanatory variable for growing season length when based on transition dates from the green chromatic coordinate.

Rather, snow water equivalents and soil moisture were the most limiting factors for plant growth in the studied area, of the analyzed parameters, suggesting that elevated temperatures alone will not prolong the growing season. Finally, we conclude that the green chromatic coordinate is a robust proxy for gross primary productivity across years with considerable climatic variations, and that it, in combination with temporal modeling of seasonal plant phenology, has great potential in estimating plant productivity and ecosystem carbon cycling in low productive high-Arctic ecosystems.

However, further research on the derivation of absolute or comparable data across camera models are needed to improve and expand the use of camera-derived vegetation indices in ecosystem monitoring. Data were provided by the Greenland Ecosystem Monitoring Program.

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Sincere thanks to the employees at the Richardson Lab, OEB Harvard for valuable discussions and input, and to the field assistants at Bio- and GeoBasis for putting such an effort into collecting the field data. His research interests include satellite and near-field based remote sensing of Arctic ecosystems at both a technical and applied level. His research interests include energy fluxes in terrestrial ecosystems and related links to biogeochemistry and climate.

Rapid Arctic Transitions

Magnus Lund is managing the GeoBasis programme at Zackenberg. Her research interests include snow modeling in complex Arctic ecosystems and related links to both biotic and abiotic parameters. His research interests include analytical population ecology and related interaction webs. His research interests include modeling of terrestrial ecosystems and plant phenology and related links to biometeorology. His research interests include atmospheric climatology and related glaciological processes. His research interests include climatology and remote sensing of Arctic terrestrial ecosystems.

National Center for Biotechnology Information , U. Journal List Ambio v. Published online Jan This article has been cited by other articles in PMC. Abstract Climate-induced changes in vegetation phenology at northern latitudes are still poorly understood. Electronic supplementary material The online version of this article doi: Introduction Vegetation growth and phenology are important indicators of climate change on both plant level Cleland et al. Open in a separate window.

Spread Salix arctica individuals. Processing To detect snow and vegetation greenness, the annual time series of images were manually looked through and filtered for low-quality data winter darkness, fog, precipitation, and heavy shadows , resulting in an average of 71 images per growing season covering a period from late May to mid-September. Vegetation greenness was computed for pixels not covered by snow as the green chromatic coordinate GCC: Estimating transition dates Various methods have been proposed to derive dates for significant shifts in data time series describing vegetation phenology, including absolute thresholds, derivatives, model fit, and transformations de Beurs and Henebry Each sigmoid model can be expressed as follows: Climate and monitoring data Climate data Robust year-round precipitation data in the studied period are scarce in Zackenberg.

Ecosystem productivity Measurements of the net ecosystem exchange of CO 2 NEE using the eddy covariance technique have been conducted since at the Cassiope -dominated heath site in Zackenberg, i. Statistics Statistical analyses of correlations and trends between the growing season as inferred from the camera imagery and the ambient biotic and abiotic conditions were computed with the Statistical Analysis System SAS Institute , using ordinary least squares linear regressions and generalized linear models.

Results The terminology in this section is based on Fig. Snow-cover There was no significant advancement of end of snowmelt during the studied period. Phenological transitions The biotic transition dates are defined as start of spring SOS , middle of spring, end of spring, peak of season timing, start of fall, middle of fall, end of fall EOF ; see Fig.

Temperature and transition dates Below-zero temperatures in the autumn were expected to accelerate senescence, and thus affect the duration of the greendown period. Ecosystem productivity The shift from positive to negative NEE flux i. Discussion Region-specific variations The highly significant correlation between end of snowmelt and SOS is consistent with earlier studies on Arctic ecosystems Mastepanov et al. Inter-annual variation The inter-annual variability in snow-cover is marked, which is in agreement with the reported observations by Pedersen et al.

Technical considerations Using JPG images introduces artifacts related to the image compression and conversion between color spaces. Electronic supplementary material Below is the link to the electronic supplementary material. Stephen Klosterman is a Ph. Footnotes 1 Available from www.

Vegetation Mapping of Zackenberg valley, Northeast Greenland. Danish Polar Center and Botanical Museums. Snow-vegetation relations in a High Arctic ecosystem: Inter-annual variability inferred from new monitoring and modeling concepts. Remote Sensing of Environment.

Electronic supplementary material

Phenological tracking enables positive species responses to climate change. Shifting plant phenology in response to global change. Circular Utah snow sampler and scales for measuring water content of snow. Spatio-temporal statistical methods for modelling land surface phenology. Soil and plant community-characteristics and dynamics at Zackenberg.

Meltofte H, Morten R, et al.

The longest greenup duration, i. Public Internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system. The lack of a correlation between phenological transition dates in the fall and temperature variables likewise suggests that a general atmospheric warming may not prolong the growing season markedly because it is rather limited by incoming solar radiation and water availability. Events occurring relatively later in time are referred to as a postponement. Continuous measurements of SWE and soil moisture conditions were only available for region 4. It is therefore not surprising that of the four transitions studied this is the one that appears to be responding most rapidly to climatic warming.

Advances in ecological research. Inter-annual variability and controls of plant phenology and productivity at Zackenberg. Meltofte H, Morten R, editors. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystems. The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana.

NDVI response to soil oisture. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Public Internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system. Present-Day Climate at Zackenberg. In Advances in Ecological Research , ed. Evidence and implications of recent climate change in Northern Alaska and other Arctic regions. Longer growing seasons lead to less carbon sequestration by a subalpine forest.

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Shorter flowering seasons and declining abundance of flower visitors in a warmer Arctic. A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems. Phenology shifts at start vs. Rapid responses of permafrost and vegetation to experimentally increased snow cover in sub-arctic Sweden.

Cambridge University Press; Kerby JT, Post E. Advancing plant phenology and reduced herbivore production in a terrestrial system associated with sea ice decline. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Phenology and its role in carbon dioxide exchange processes in northern peatlands. Journal of Geophysical Research: Soil physical conditions and plant growth. Trends in CO 2 exchange in a high Arctic tundra heath, — Revisiting factors controlling methane emissions from high-arctic tundra.

Sensitivity of the carbon cycle in the Arctic to climate change. Its importance to the global change community. European phenological response to climate change matches the warming pattern. Two decades of experimental manipulations of heaths and forest understory in the subarctic. Temperature controls of microbial respiration in arctic tundra soils above and below freezing. Phenological response of tundra plants to background climate variation tested using the International Tundra Experiment.

Philosophical Transactions of the Royal Society B: Longer growing seasons do not increase net carbon uptake in the northeastern Siberian tundra. Spatiotemporal characteristics of seasonal snow cover in Northeast Greenland from in situ observations.

Arctic, Antarctic, and Alpine Research. Ecological dynamics across the arctic associated with recent climate change. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system.

Agricultural and Forest Meteorology. Snow cover monitoring with images from digital camera systems. Italian Journal of Remote Sensing. An ecological function crisis? Digital repeat photography for phenological research in forest ecosystems. High-resolution satellite data reveal an increase in peak growing season gross primary production in a high-Arctic wet tundra ecosystem — Spectral measures and mixed models as valuable tools for investigating controls on land surface phenology in high arctic Greenland.

Plant community responses to experimental warming across the tundra biome. Community and ecosystem responses to recent climate change. Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Temperature and vegetation seasonality diminishment over northern lands.

Recent changes in phenology over the northern high latitudes detected from multi-satellite data. Shifts in Arctic phenology in response to climate and anthropogenic factors as detected from multiple satellite time series. International Journal of Environment. Articles from Ambio are provided here courtesy of Springer. Support Center Support Center.