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• W2900417775 abstract "Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R2 0.21–0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R2 0.33–0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling." @default.
• W2900417775 created "2018-11-16" @default.
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• W2900417775 date "2018-11-08" @default.
• W2900417775 modified "2023-10-16" @default.
• W2900417775 title "Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling" @default.
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• W2900417775 doi "https://doi.org/10.1080/01431161.2018.1524176" @default.
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