Abstract
Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture. These attributes result in similar spectral characteristics between distinct vegetation communities. In this study we examine spectral variability at three phenological phases of leaf-out, maximum canopy, and senescence of ground-based spectroscopy, as well as a simulated Environmental Mapping and Analysis Program (EnMAP) and simulated Sentinel-2 reflectance spectra, from five dominant low-Arctic tundra vegetation communities in the Toolik Lake Research Area, Alaska, in order to inform spectral differentiation and subsequent vegetation classification at both the ground and satellite scale. We used the InStability Index (ISI), a ratio of between endmember and within endmember variability, to determine the most discriminative phenophase and wavelength regions for identification of each vegetation community. Our results show that the senescent phase was the most discriminative phenophase for the identification of the majority of communities when using both ground-based and simulated EnMAP reflectance spectra. Maximum canopy was the most discriminative phenophase for the majority of simulated Sentinel-2 reflectance data. As with previous ground-based spectral characterization of Alaskan low-Arctic tundra, the blue, red, and red-edge parts of the spectrum were most discriminative for all three reflectance datasets. Differences in vegetation colour driven by pigment dynamics appear to be the optimal areas of the spectrum for differentiation using high spectral resolution field spectroscopy and simulated hyperspectral EnMAP and multispectral Sentinel-2 reflectance spectra. The phenological aspect of this study highlights the potential exploitation of more extreme colour differences in vegetation observed during senescence when hyperspectral data is available. The results provide insight into both the community and seasonal dynamics of spectral variability to better understand and interpret currently used broadband vegetation indices and also for improved spectral unmixing of hyperspectral aerial and satellite data which is useful for a wide range of applications from fine-scale monitoring of shifting vegetation composition to the identification of vegetation vigor.
Highlights
Arctic tundra ecosystems exhibit small-scale variations in micro-topography as well as significant spatial and temporal variations in soil moisture
The smoothed and averaged reflectance spectra indicate that the five tundra vegetation communities are relatively similar spectrally within each phenophase and between leaf-out and senescence
DT had the greatest Near Infrared (NIR) reflectance at leaf-out and senescence while MAT and ST had the highest reflectance in the NIR at maximum canopy
Summary
Arctic tundra ecosystems exhibit small-scale variations in micro-topography as well as significant spatial and temporal variations in soil moisture. Large homogenous patches of one or two species rarely exist outside water tracks, disturbed areas, or dry uplands where erect and prostrate dwarf shrub species dominate. This combination of small-scale heterogeneity in vegetation composition and soil moisture as well as the prostrate nature of tundra species leads to highly mixed, variable, and often similar spectral signatures between distinct vegetation communities. The sometimes dominant presence of non-vascular components (mosses and lichens) and barren areas contribute to the unique spectral signatures of tundra landscapes [4] This high spectral similarity can be observed with ground-based Visible-Near Infrared (VNIR) remote sensing data making spectral separation challenging [5,6,7]
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