Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data

Abstract

Abstract. Arctic tundra ecosystems will play 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) and to characterize them, sampled 92 study plots for plant and soil 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. We found that soils ranged from mineral soils in bare soil and lichen tundra LCTs to soils of high percentage of organic matter (OM) in graminoid tundra, bog, dry fen and wet fen. OM content of the top soil 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, total vascular shoot mass from 7 to 112 g m−2 and vascular leaf area index (LAI) from 0.04 to 0.95 among LCTs. In late summer, soil temperatures at 15 cm depth were on average 14 ∘C in bare soil and lichen tundra, and varied from 5 to 9 ∘C in other LCTs. On average, depth of the biologically active, unfrozen soil layer doubled from early July to mid-August. When contrasted across study plots, moss biomass was positively associated with soil OM % and OM content and negatively associated with soil temperature, explaining 14–34 % of variation. Vascular shoot mass and LAI were also positively associated with soil OM content, and LAI with active layer depth, but only explained 6–15 % of variation. NDVI captured variation in vascular LAI better than in moss biomass, but while this difference was significant with late season NDVI, it was minimal with early season NDVI. For this reason, soil attributes associated with moss mass were better captured by early season NDVI. Topographic attributes were related to LAI and many soil attributes, but not to moss biomass and could not increase the amount of spatial variation explained in plant and soil attributes above that achieved by NDVI. The LCT map we produced had low to moderate uncertainty in predictions for plant and soil properties except for moss biomass and bare soil and lichen tundra LCTs. 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, topography or a LCT map. Despite the general accuracy of landscape level predictions in our LCT approach, this indicates challenges in the spatial extrapolation of some of those vegetation and soil attributes that are relevant for the regional ecosystem and global climate models.

Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation

“…This result is in line with earlier findings concerning peatland vegetation mapping that show how different moss and graminoid species locate differently on ordination axes (Harris et al., ; Middleton et al., ). However, in other types of environments with fragmented vegetation patterns but higher forb and shrub species richness, such as tundra, an extended PFT approach can be more valuable (Mikola et al., ; Virtanen & Ek, ). Overall, the value of the PFT generalization depends on the studied system and the choice of traits that are used to form PFTs.…”

Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS)

“…Cover classes have an advantage for scaling to new areas and linkage to coarser-resolution remote sensing [21]. Because functional cover types have been linked to CH 4 emissions in high latitudes, mapping vegetation using broad cover types that are related to CH 4 emissions is useful for understanding landscape change and provides context and evidence for changes in fluxes related to climate change as well as ties to field observations [3,42,66].…”

Vegetation Indices Do Not Capture Forest Cover Variation in Upland Siberian Larch Forests

“…Very high-resolution multispectral satellite data (i.e., <~4 m) from commercial platforms, such as IKONOS, QuickBird, and WorldView, provide even finer spatial detail and can be combined with historical aerial photographs to confirm hypothesized vegetation changes such as shrub and tree expansion into tundra [20], which in turn can be linked with NDVI trends [21]. Though very high-resolution satellite data has relatively poor temporal resolution, it has proven useful for mapping fine-scale variation in vegetation or ecosystem types and vegetation indices, primarily in tundra ecosystems [22][23][24][25]. Spatial relationships between vegetation properties and NDVI in Arctic tundra ecosystems are often used to support interpretations of temporal trends in NDVI [26].…”