A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska

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.

Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

“…Its data is especially important for the specific characterization of spatial and temporal vegetation changes over several growing seasons, combined with in-situ field validation. Examples include: urban ecosystem service mapping, using a combination of Sentinel-1A SAR and Sentinel-2A [34], estimation of the above-ground biomass of mangrove forests [35], vegetation salinity assessment [36], monitoring of Low-Arctic Tundra vegetation [37], vegetation states indices in grassland and savanna [38], and estimation of biophysical variables in vegetation [39].…”

Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling

“…In Arctic environments, satellite based estimates of biomass distribution have mostly been carried out using rather coarse spatial resolution images (Walker et al 2003;Heiskanen 2006;Raynolds, Walker, and Maier 2006;Epstein et al 2012;Raynolds et al 2012;Buchhorn et al 2013;Doiron et al 2013;Johansen and Tommervik 2014;Berner et al 2018), such as Landsat (30 m pixel size) (Heiskanen 2006;Johansen and Tommervik 2014;Berner et al 2018), MODIS (250 m pixel size) (Westergaard-Nielsen et al 2015), and AVHRR (>1 km pixel size) (Walker et al 2003;Raynolds, Walker, and Maier 2006;Epstein et al 2012;Raynolds et al 2012;Buchhorn et al 2013;Doiron et al 2013). Although the images with coarse spatial resolution have high temporal resolution and they have proved to be suitable for circumpolar studies and detecting coarse-scale biomass patterns (coefficient of determination (R 2 ) up to 0.89) (Walker et al 2003), they are incapable of representing the fragmented nature of tundra environment and fine-scale changes in vegetation and carbon dynamics (Laidler and Treitz 2003;Virtanen and Ek 2014;Siewert et al 2015;Beamish et al 2017).…”

Integrative and comprehensive Understanding on Polar Environments (iCUPE): the concept and initial results

“…Figure 16. Canopy-level spectral reflectance from a dwarf shrub community at three major phenological phases of leaf-out, maximum canopy, and senescence (Beamish et al, 2017). modelling is feeding its own components, which adds more depth to the tree.…”

“…The difference between the narrow and broadband data is likely due to the extreme color differences observed during senescence that are well captured by imaging spectroscopy but not by broadband data. These results provide important information for better interpreting current broadband and future narrowband spectral reflectance data for more accurate estimation of vegetation composition, vigor and biomass (Beamish et al, 2017). 555…”