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

• Published in: Environmental Research Letters
• Main Authors: Sari Juutinen, Tarmo Virtanen, Vladimir Kondratyev, Tuomas Laurila, Maiju Linkosalmi, Juha Mikola, Johanna Nyman, Aleksi Räsänen, Juha-Pekka Tuovinen, Mika Aurela
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2017
• Subjects: arctic; multispectral; LAI; VHSR; vegetation; Environmental technology. Sanitary engineering; TD1-1066; Environmental sciences; GE1-350; Science; Q; Physics; QC1-999; Arctic; Tiksi; Tundra; Siberia
• Online Access: https://doi.org/10.1088/1748-9326/aa7f85; https://doaj.org/article/214c06fd14f24be3b81e57adbd3f3911

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.