Abstract
Arctic landscapes are changing rapidly in response to warming, but future predictions are hindered by difficulties in scaling ecological relationships from plots to biomes. Unmanned aerial systems (hereafter ‘drones’) are increasingly used to observe Arctic ecosystems over broader extents than can be measured using ground-based approaches and are facilitating the interpretation of coarse-grained remotely sensed data. However, more information is needed about how drone-acquired remote sensing observations correspond with ecosystem attributes such as aboveground biomass. Working across a willow shrub-dominated alluvial fan at a focal study site in the Canadian Arctic, we conducted peak growing season drone surveys with an RGB camera and a multispectral multi-camera array. We derived photogrammetric reconstructions of canopy height and normalised difference vegetation index (NDVI) maps along with in situ point-intercept measurements and aboveground vascular biomass harvests from 36, 0.25 m2 plots. We found high correspondence between canopy height measured using in situ point-intercept methods compared to drone-photogrammetry (concordance correlation coefficient = 0.808), although the photogrammetry heights were positively biased by 0.14 m relative to point-intercept heights. Canopy height was strongly and linearly related to aboveground biomass, with similar coefficients of determination for point-intercept (R2 = 0.92) and drone-based methods (R2 = 0.90). NDVI was positively related to aboveground biomass, phytomass and leaf biomass. However, NDVI only explained a small proportion of the variance in biomass (R2 between 0.14 and 0.23 for logged total biomass) and we found moss cover influenced the NDVI-phytomass relationship. Vascular plant biomass is challenging to infer from drone-derived NDVI, particularly in ecosystems where bryophytes cover a large proportion of the land surface. Our findings suggest caution with broadly attributing change in fine-grained NDVI to biomass differences across biologically and topographically complex tundra landscapes. By comparing structural, spectral and on-the-ground ecological measurements, we can improve understanding of tundra vegetation change as inferred from remote sensing.
Highlights
Arctic ecosystems are warming rapidly (IPCC, 2013) and plant communities are responding (Elmendorf et al, 2012b, 2015; Myers-Smith et al, 2011, 2019)
We found strong agreement between canopy heights as observed with point-intercept method and structure-from-motion photogrammetry (Figures 2 and S1)
We found a positive bias in canopy heights measured with point-intercept relative to photogrammetry, which we attribute to differences in the way the two approaches quantify canopy architecture
Summary
Arctic ecosystems are warming rapidly (IPCC, 2013) and plant communities are responding (Elmendorf et al, 2012b, 2015; Myers-Smith et al, 2011, 2019). There is limited understanding of the controls on vegetation change in tundra plant communities (Myers-Smith et al, 2020; Post et al, 2019). We do not yet have standardised methods of quantifying changes in tundra plant canopy structures and growth across the landscape and there are few allometric relationships relating observable plant dimensions to aboveground biomass in Arctic ecosystems (Berner et al, 2015). One of the key challenges in tundra ecological monitoring is acquiring scale-appropriate observations due to the small growth forms of many plants in this large extent and often less accessible biome (Fisher et al, 2018). Remote-sensing approaches have been widely employed to gather information about changing Arctic landscapes
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