Spatial variation and seasonal dynamics of leaf-area index in the arctic tundra-implications for linking ground observations and satellite images

Abstract

Vegetation in the arctic tundra typically consists of a small-scale mosaic of plant communities, with species differing in growth forms, seasonality, and biogeochemical properties. Characterization of this variation is essential for understanding and modeling the functioning of the arctic tundra in global carbon cycling, as well as for evaluating the resolution requirements for remote sensing. Our objective was to quantify the seasonal development of the leaf-area index (LAI) and its variation among plant communities in the arctic tundra near Tiksi, coastal Siberia, consisting of graminoid, dwarf shrub, moss, and lichen vegetation. We measured the LAI in the field and used two very-high-spatial resolution multispectral satellite images (QuickBird and WorldView-2), acquired at different phenological stages, to predict landscape-scale patterns. We used the empirical relationships between the plant community-specific LAI and degree-day accumulation (0°C threshold) and quantified the relationship between the LAI and satellite NDVI (normalized difference vegetation index). Due to the temporal difference between the field data and satellite images, the LAI was approximated for the imagery dates, using the empirical model. LAI explained variation in the NDVI values well (R 2 adj. 0.42-0.92). Of the plant functional types, the graminoid LAI showed the largest seasonal amplitudes and was the main cause of the varying spatial patterns of the NDVI and the related LAI between the two images. Our results illustrate how the short growing season, rapid development of the LAI, yearly climatic variation, and timing of the satellite data should be accounted for in matching imagery and field verification data in the Arctic region.

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].…”

Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness

“…Such heterogeneity concerns both the composition and configuration of land cover properties. This is clearly manifested by the leaf area index (LAI), which shows a higher relative variation among sites in tundra than in any other biome (Asner et al, 2003), and there are pronounced spatial and temporal LAI patterns at the landscape scale (Marushchak et al, 2013;Juutinen et al, 2017). Surface heterogeneity also generates high variability in the ecosystem-atmosphere fluxes of GHGs, including methane (CH 4 ) (Olefeldt et al, 2013).…”

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

“…As suggested by earlier investigations (Langford et al, 2016;Juutinen et al, 2017), the ability of NDVI to capture variation in vegetation depended a lot on the timing of the satellite image. Variation in moss biomass was better captured by early-season images, most likely due to the low cover of vascular plant leaf area at that time, whereas late-season images were needed to capture variation in peak LAI.…”

“…As 30 plant communities differ in phenology (Juutinen et al, 2017), both WV-2 images were employed. The images were orthocorrected with the help of the constructed DEM and co-registered using field measured GPS data, and in addition to the optical data, DEM-derived features were used for classification.…”

“…The ability of satellite images to capture spatial variation in plant abundances and LAI depends on the phase of growing season at the time of image acquisition (Langford et al, 2016;Juutinen et al, 2017). It is therefore likely that 15 timing of imaging can also affect its ability to capture spatial variation of those soil attributes that are associated with vegetation attributes, but to our knowledge this has not been tested before.…”