Aboveground biomass patterns across treeless northern landscapes

• Published in: International Journal of Remote Sensing
• Main Authors: Räsänen, Aleksi, Wagner, Julia, Hugelius, Gustaf, Virtanen, Tarmo
• Other Authors: Ecosystems and Environment Research Programme, Tarmo Virtanen / Principal Investigator, Environmental Change Research Unit (ECRU)
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis 2021
• Subjects: 1181 Ecology; evolutionary biology; Arctic; Canada; Tundra; Siberia
• Online Access: http://hdl.handle.net/10138/328004

Aboveground vegetation biomass in northern treeless landscapes - peatlands and Arctic tundra - has been modelled with spectral information derived from optical remote sensing in several studies. However, synthesized overviews of biomass patterns across circumpolar sites have been limited. Based on data from eight study sites in Europe, Siberia and Canada, we ask (1) how biomass is divided between plant functional types (PFTs) and (2) how well biomass patterns can be detected with widely available, moderate spatial resolution (3-10 m) satellite imagery and topographic data. We explain biomass patterns using random forest regressions with the predictors being spectral bands and indices calculated from multi-temporal Sentinel-2 and PlanetScope imagery and topographic information calculated from ArcticDEM data. Our results indicate that there are notable differences in vegetation composition between northern landscapes with mosses, graminoids and deciduous shrubs being the most dominant PFTs. Remote sensing data detects biomass patterns, but regression performance varies between sites (explained variance 36-70%, normalized root mean square error 9-19%). There is also variability between sites whether Sentinel-2 or PlanetScope data is more suitable to detect biomass patterns and which the most important predictors are. Topographic information has a minor or negligible importance in most of the sites. Our results suggest that there is no easily generalizable relationship between satellite-derived vegetation greenness and biomass. Peer reviewed