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 (R2adj. 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.
Highlights
Vegetation monitoring is a tool for detecting the impacts of climate change on the composition and phenology of arctic ecosystems
The pixel size of the commonly used satellite imageries, e.g. those obtained from the Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, ranges from tens to hundreds of meters and cannot reveal fine-scale heterogeneity in vegetation and ecosystem properties (Laidler et al 2008, Virtanen and Ek 2014, Mora et al 2015, Bratsch et al 2016)
The seasonal amplitudes of the vascular and total leaf-area index (LAI) was largest in those communities with abundant graminoid vegetation, i.e. in the wet fen, graminoid tundra and flood meadow (figures 3(d)À(i)
Summary
Vegetation monitoring is a tool for detecting the impacts of climate change on the composition and phenology of arctic ecosystems. The pixel size of the commonly used satellite imageries, e.g. those obtained from the Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, ranges from tens to hundreds of meters and cannot reveal fine-scale heterogeneity in vegetation and ecosystem properties (Laidler et al 2008, Virtanen and Ek 2014, Mora et al 2015, Bratsch et al 2016) This complicates the examination of plant growth responses to warming, which may vary among neighboring communities (e.g. McManus et al 2012, Bratsch et al 2016). The bestquality image often does not temporally match the ground-truth data
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