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Arctic tundra is facing unprecedented warming, resulting in shifts in the vegetation, thaw regimes, and potentially in the ecosystem–atmosphere exchange of carbon (C). However, the estimates of regional carbon dioxide (CO 2) and methane (CH 4) budgets are highly uncertain. We measured CO 2 and CH 4 fluxes, vegetation composition and leaf area index (LAI), thaw depth, and soil wetness in Tiksi (71 ∘ N, 128 ∘ E), a heterogeneous site located within the prostrate dwarf-shrub tundra zone in northeastern Siberia. Using the closed chamber method, we determined the net ecosystem exchange (NEE) of CO 2 , ecosystem respiration in the dark (ER), ecosystem gross photosynthesis (Pg), and CH 4 flux during the growing season. We applied a previously developed high-spatial-resolution land cover map over an area of 35.8 km 2 for spatial extrapolation. Among the land cover types varying from barren to dwarf-shrub tundra and tundra wetlands, the NEE and Pg at the photosynthetically active photon flux density of 800 µ mol m -2 h -1 (NEE 800 and Pg 800) were greatest in the graminoid-dominated habitats, i.e., streamside meadow and fens, with NEE 800 and Pg 800 of up to - 21 (uptake) and 28 mmol m -2 h -1 , respectively. Vascular LAI was a robust predictor of both NEE 800 and Pg 800 and, on a landscape scale, the fens were disproportionately important for the summertime CO 2 sequestration. Dry tundra, including the dwarf-shrub and lichen tundra, had smaller CO 2 exchange rates. The fens were the largest source of CH 4 , while the dry mineral soil tundra consumed atmospheric CH 4 , which on a landscape scale amounted to - 9 % of the total CH 4 balance during the growing season. The largest seasonal mean CH 4 consumption rate of 0.02 mmol m -2 h -1 occurred in sand- and stone-covered barren areas. The high consumption rate agrees with the estimate based on the eddy covariance measurements at the same site. We acknowledge the uncertainty involved in spatial extrapolations due to a small number of replicates per land cover type. This study highlights the need to distinguish different land cover types including the dry tundra habitats to account for their different CO 2 and CH 4 flux patterns, especially the consumption of atmospheric CH 4 , when estimating tundra C exchange on a larger spatial scale. [ABSTRACT FROM AUTHOR]

Arctic tundra is facing unprecedented warming, resulting in shifts in the vegetation, thaw regimes, and potentially in the ecosystem-atmosphere exchange of carbon (C). The estimates of regional carbon dioxide (CO2) and methane (CH4) budgets, however, are highly uncertain. We measured CO2 and CH4 fluxes, vegetation composition and leaf area index (LAI), thaw depth, and soil wetness in Tiksi (71° N, 128° E), a heterogeneous site located within the prostrate dwarf-shrub tundra zone in northeastern Siberia. Using the closed chamber method, we determined net ecosystem exchange (NEE) of CO2, dark ecosystem respiration (ER), ecosystem gross photosynthesis (Pg), and CH4 fluxes during the growing season. We applied a previously developed high-spatial-resolution land-cover map over an m area of 35.8 km². Among the land-cover types varying from barrens to dwarf-shrub tundra and tundra wetlands, the light-saturated NEE and Pg scaled with the LAI of vascular plants. Thus, the graminoid-dominated tundra wetlands, with high LAI and the deepest thaw depth, had the highest light-saturated NEE and Pg (up to -21 (uptake) and 28 mmol m-2 h-1, respectively) and were disproportionately important for the summertime CO2 sequestration on a landscape scale. Dry tundra, including the dwarf-shrub-dominated vegetation and only sparsely vegetated lichen tundra, had only small CO2 exchange rates. While tundra wetlands were sources of CH4, lichen tundra, including bare ground habitats, consumed atmospheric CH4 at a substantial rate. On a landscape scale, the consumption by lichen tundra and barrens could offset ca. 10% of the CH4 emissions. We acknowledge the uncertainty involved in spatial extrapolations due to a small number of replicates per land-cover type. This study, however, highlights the need for distinguishing different land-cover types including the dry tundra habitats to account for their consumption of the atmospheric CH4 when estimating tundra C-exchange on a larger spatial scale. [ABSTRACT FROM AUTHOR]

Context: Spatial patterns of CH4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing. Objectives: How well can the CH4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables? Methods: We measured CH4 fluxes in 279 plots in a 12.4 km2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM). Results: The landscape acted as a net source of CH4 (253–502 µg m−2 h−1) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately. Conclusions: CH4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH4 fluxes and their spatial patterns. [ABSTRACT FROM AUTHOR]

The non-uniform spatial integration, an inherent feature of the eddy covariance (EC) method, creates a challenge for flux data interpretation in a heterogeneous environment, where the contribution of different land cover types varies with flow conditions, potentially resulting in biased estimates in comparison to the areally averaged fluxes and land cover attributes. We modelled flux footprints and characterized the spatial scale of our EC measurements in Tiksi, a tundra site in northern Siberia. We used leaf area index (LAI) and land cover class (LCC) data, derived from very-high-spatial-resolution satellite imagery and field surveys, and quantified the sensor location bias. We found that methane (CH4) fluxes varied strongly with wind direction (-0.09 to 0.59 µgCH4m-2s-1 on average) during summer 2014, reflecting the distribution of different LCCs. Other environmental factors had only a minor effect on short-term flux variations but influenced the seasonal trend. Using footprint weights of grouped LCCs as explanatory variables for the measured CH4 flux, we developed a multiple regression model to estimate LCC group-specific fluxes. This model showed that wet fen and graminoid tundra patches in locations with topography-enhanced wetness acted as strong sources (1.0 µgCH4m-2s-1 during the peak emission period), while mineral soils were significant sinks (-0.13 µgCH4m-2s-1). To assess the representativeness of measurements, we upscaled the LCC group-specific fluxes to different spatial scales. Despite the landscape heterogeneity and rather poor representativeness of EC data with respect to the areally averaged LAI and coverage of some LCCs, the mean flux was close to the CH4 balance upscaled to an area of 6.3 km 2 , with a location bias of 14 %. We recommend that EC site descriptions in a heterogeneous environment should be complemented with footprint-weighted high-resolution data on vegetation and other site characteristics. [ABSTRACT FROM AUTHOR]

The non-uniform spatial integration inherent in the eddy covariance (EC) method provides an additional challenge for data interpretation when fluxes are measured in a heterogeneous environment, as the contribution of different surface types varies with flow conditions, potentially resulting in a bias as compared to the true areally averaged fluxes and surface attributes. We modelled flux footprints and characterized the spatial scale of our EC measurements at Tiksi, a tundra site in northern Siberia, including a comparison of different source area definitions. We used leaf area index (LAI) and land cover class (LCC) data, derived from very high spatial resolution satellite imagery and field surveys, and quantified the sensor location bias. We found that methane (CH4) fluxes varied strongly with wind direction (from -0.09 to 0.59 μg m-2 s-1 on average), reflecting the distribution of different LCCs. Using footprint weights of grouped LCCs as explanatory variables for the measured CH4 flux, we then developed a multiple regression model to estimate LCC-specific fluxes. This model showed that wet fen and graminoid tundra patches in locations with a high topography-based wetness index acted as strong CH4 sources (0.95 μg m-2 s-1), while mineral soils were significant sinks (-0.13 μg m-2 s-1). Finally, to assess the representativeness of CH4 flux measurements, we upscaled the LCC-specific fluxes to different spatial scales. This assessment showed that, despite the surface heterogeneity and rather poor representativeness of EC data with respect to the areally averaged LAI and coverage of some LCCs, the mean CH4 flux measured during summer 2014 was close to the corresponding balance upscaled to an area of 6.3 km², with a location bias of 14 %. We recommend that EC site descriptions in a heterogeneous environment should be complemented with footprint-weighted high-resolution data on vegetation and other relevant site characteristics. [ABSTRACT FROM AUTHOR]

Arctic tundra ecosystems will have a key role in future climate change due to intensifying permafrost thawing, plant growth and ecosystem carbon exchange, but monitoring these changes may be challenging due to the heterogeneity of Arctic landscapes. We examined spatial variation and linkages of soil and plant attributes in a site of Siberian Arctic tundra in Tiksi, northeast Russia, and evaluated possibilities to capture this variation by remote sensing for the benefit of carbon exchange measurements and landscape extrapolation. We distinguished nine land cover types (LCTs) - bare soil, lichen tundra, shrub tundra, flood meadow, graminoid tundra, bog, dry fen, wet den and water - to classify the variation in our site. To characterize the LCTs, we sampled 92 study plots for plant (biomass and leaf area index, LAI) and soil (organic matter OM%, bulk density, moisture, pH, litter layer depth, litter mass loss, temperature and active layer depth) attributes in 2014. Moreover, to test if variation in plant and soil attributes can be detected using remote sensing, we produced a normalized difference vegetation index (NDVI) and topographical parameters for each study plot using three very high spatial resolution multispectral satellite images (QuickBird and WorldView-2, portraying vegetation at 180, 220 and 750 growing degree days, DD with 0 °C threshold) and a digital elevation model (derived from a WV-2 stereo-pair image). We found that soils in our site ranged from mineral soils in bare soil and lichen tundra (on average 3.9 % OM) to soils of high OM% in graminoid tundra, bog, dry fen and wet fen (38 %), with shrub tundra and flood meadow being intermediate (21 %). Soil OM content was on average 14 g dm-3 in bare soil and lichen tundra and 89 g dm-3 in other LCTs. Total moss biomass varied from 0 to 820 g m-2 among LCTs and high moss mass was associated with high soil OM%, except that wet fens with high OM% sustained low moss mass. Total vascular shoot mass was 7 g m-2 in bare soil, on average 53 g m-2 in lichen tundra and dry fen and 91 g m-2 in other LCTs. Vascular LAI was on average 0.12 in bare soil and lichen tundra, 0.50 in bog, dry fen, shrub tundra and graminoid tundra, and 0.90 in flood meadow and wet fen. In late summer, soil temperatures at 15 cm depth were on average 14 °C in bare soil and lichen tundra, 9 °C in flood meadow and wet fen and 6 °C in other LCTs. Depth of the active soil layer doubled from early July to middle August, when it reached on average 42 cm in flood meadow and wet fen, 35 cm in bog, dry fen and graminoid tundra and 26 cm in bare soil and lichen and shrub tundra. When contrasted across study plots, total moss biomass was positively associated with soil OM% and OM content and negatively with soil temperature, explaining 14-34 % of variation in soil attributes. Vascular shoot mass and LAI were also positively associated with soil OM content, and LAI with active layer depth, but the amount of variation explained was significantly lower (6-15 %). NDVI captured variation in peak season vascular LAI better than variation in moss biomass, but the difference depended on the phase of the growing season in the image: 180-DD, 220-DD and 750-DD NDVI captured 23, 17 and 7 % of moss mass variation and 25, 34 and 50 % of vascular LAI variation, respectively. For this reason, soil attributes associated with moss mass were better captured by early season NDVI and those associated with LAI by late season NDVI. Topographic attributes were related to LAI and many soil attributes, but not to moss biomass and they could not increase the amount of spatial variation explained in plant and soil attributes above that achieved by NDVI. Our results illustrate a typical tundra ecosystem with great fine-scale spatial variation in both plant and soil attributes. Mosses dominate plant biomass and control many soil attributes, including OM% and temperature, but variation in moss biomass is difficult to capture by remote sensing reflectance or topography. This suggests that using simple reflectance indices and DEM for spatial extrapolation of those vegetation and soil attributes that are relevant for regional ecosystem and global climate models warrants further inspection. Meanwhile, land cover maps of LCTs and their attributes, derived from remote sensing data and effective field sampling in different LCTs, seem to provide the most reliable way to extrapolate vegetation and soil properties within Arctic tundra landscapes. [ABSTRACT FROM AUTHOR]

It has been argued that even centimeter-level resolution is needed for mapping vegetation patterns in spatially heterogeneous landscapes such as northern peatlands. However, there are few systematic tests for determining what kind of spatial resolution and data combinations are needed and what the differences in mapping accuracy are when different datasets are omitted or included. We conducted 78 different object-based supervised random forest classifications on a patterned fen and its surroundings in Kaamanen, northern Finland, using remotely sensed optical imagery, topography, and vegetation height datasets from different platforms (unmanned aerial vehicle (UAV), aerial, satellite) with spatial resolution ranging from 5 cm to 3 m. We compared differences in classification performance when we altered (1) classification and segmentation input data and features calculated from the data, or (2) the segmentation scale. We constructed training data with the help of transect-based field sampling and UAV imagery and tested classification accuracy using 412 field-surveyed vegetation plots. The most accurate classifications (75.7% overall accuracy) were obtained when we segmented a 5 cm resolution UAV image with a small segmentation scale and calculated features from all datasets. Classification accuracy was 2.2 percentage points (pp) lower with the most accurate aerial image (50 cm resolution) based classification, and 7.6 pp and 11.9 pp lower with the most accurate WorldView-2 (2 m resolution) and PlanetScope (3 m resolution) satellite image based classifications respectively. Classification accuracies were low (46.7–56.0%) when we used only spectral data from one dataset. The inclusion of gray-level co-occurrence matrix textural features increased classification accuracy by 0.4–12.1 pp and inclusion of multiple datasets by 8.2–25.0 pp. Segmentation scale had a minor effect on classification accuracy (2.5–7.3 pp difference between the finest and coarsest segmentation scale); however, both too small and large segmentation scale might lead to suboptimal classification. The differences in land cover type areal coverage were relatively small between classifications with multiple datasets, but if classifications included features from only one dataset, the differences were larger. We conclude that multiple different optical, topographical, and vegetation height datasets should be used when mapping vegetation in spatially heterogeneous landscapes, and that sub-meter resolution data (e.g. UAV or aerial) are necessary for the most accurate maps. Although UAV data is not essentially needed for classification, it is useful for training dataset construction and especially helpful in areas lacking other sub-meter resolution data. • How input data, its resolution and segmentation scale affect mapping accuracy? • Sub-meter resolution data is needed to map patchy fen vegetation patterns. • Combination of optical, topographic and vegetation height data is important. • Segmentation scale has a minor effect on classification accuracy. [ABSTRACT FROM AUTHOR]

Peatland landscapes such as northern fens are fragmented and patchy in terms of their vegetation, land cover and hydrology. Such heterogeneity leads to variation in biogeochemical cycles of e.g. carbon, nitrogen and water among different land cover types and atmosphere. Thus, there is an urgent need to classify vegetation and land cover accurately. There have been contrasting claims on the spatial resolution requirements for mapping the mosaic-like patterns in treeless northern landscapes such as tundra and peatlands. Some have argued that Landsat-scale resolution (ca. 30 m) is sufficient (Bartsch et al. 2016, Treat et al. 2018). However, according to others, a very-high spatial resolution (of the order of <5 m) is needed for constructing realistic maps of tundra and peatland vegetation (Virtanen and Ek 2014, Siewert et al. 2015, Räsänen et al. 2018). Moreover, recent studies suggest that a centimeter-level spatial resolution, obtained only with unmanned aerial vehicles, should be used for an accurate characterization of patchy landscapes (Palace et al. 2018).We present here a case study highlighting the impact of the choice of the spatial and spectral resolution, dataset combination used as source data in classification, and classification technique on the accuracy of land cover classification in a patchy subarctic landscape. This study will also shed light on the importance of accurate land cover characterization in interpreting chamber and eddy covariance carbon dioxide and methane fluxes measurements with footprint modelling, upscaling the carbon balance to the catchment scale and in process‐based biogeochemical modeling.The study area is located in Kaamanen, northern Finland (69.14°N, 27.27°E; 155 m a.s.l.) in a northern boreal vegetation zone and subarctic climate zone. The main research area is dominated by a treeless mesotrophic patterned fen, characterized by a strong pattern of strings with dwarf shrub vegetation, and flarks with sedge and brown moss vegetation. The catchment area includes also upland pine forests, shrub-dominated pine peatland, and some streams and lakes. Measurements of CO2 and CH4 exchange conducted in 2017 and 2018 using eddy covariance and manual chamber methods at representative land cover types will be used in this study.ReferencesBartsch, A.A. et al. 2016. Land Cover Mapping in Northern High Latitude Permafrost Regions with Satellite Data: Achievements and Remaining Challenges. Remote Sensing 8(12)Palace, M. et al. 2018. Determining subarctic peatland vegetation using an unmanned aerial system (UAS). Remote Sensing 10(9)Räsänen, A. et al. 2018. Predicting biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling. International Journal of Remote SensingSiewert, M. B. et al. 2015. Comparing carbon storage of Siberian tundra and taiga permafrost ecosystems at very high spatial resolution. Journal of Geophysical Research-Biogeosciences 120(10): 1973-1994Treat, C. C. et al. 2018. Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic. Global Change Biology 24(11): 5188-5204Virtanen, T., and M. Ek. 2014. The fragmented nature of tundra landscape. International Journal of Applied Earth Observation and Geoinformation 27: 4-12 [ABSTRACT FROM AUTHOR]