Monitoring pigment‐driven vegetation changes in a low‐Arctic tundra ecosystem using digital cameras

Abstract

Abstract. Arctic vegetation phenology is a sensitive indicator of a changing climate, and rapid assessment of vegetation status is necessary to more comprehensively understand the impacts on foliar condition and photosynthetic activity. Airborne and space-borne optical remote sensing has been successfully used to monitor vegetation phenology in Arctic ecosystems by exploiting the biophysical and biochemical changes associated with vegetation growth and senescence. However, persistent cloud cover and low sun angles in the region make the acquisition of high-quality temporal optical data within one growing season challenging. In the following study, we examine the capability of "near-field" remote sensing technologies, in this case digital, true-color cameras to produce surrogate in situ spectral data to characterize changes in vegetation driven by seasonal pigment dynamics. Simple linear regression was used to investigate relationships between common pigment-driven spectral indices calculated from field-based spectrometry and red, green, and blue (RGB) indices from corresponding digital photographs in three dominant vegetation communities across three major seasons at Toolik Lake, North Slope, Alaska. We chose the strongest and most consistent RGB index across all communities to represent each spectral index. Next, linear regressions were used to relate RGB indices and extracted leaf-level pigment content with a simple additive error propagation of the root mean square error. Results indicate that the green-based RGB indices had the strongest relationship with chlorophyll a and total chlorophyll, while a red-based RGB index showed moderate relationships with the chlorophyll to carotenoid ratio. The results suggest that vegetation color contributes strongly to the response of pigment-driven spectral indices and RGB data can act as a surrogate to track seasonal vegetation change associated with pigment development and degradation. Overall, we find that low-cost, easy-to-use digital cameras can monitor vegetation status and changes related to seasonal foliar condition and photosynthetic activity in three dominant, low-Arctic vegetation communities.

Overview: Integrative and Comprehensive Understanding on Polar Environments (iCUPE) – concept and initial results

“…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 a more accurate estimation of vegetation composition, vigor, and biomass (Beamish et al, 2017). Figure 17.…”

“…As part of iCUPE, we are working beyond the recent retrievals of satellite remote sensing data to include advanced current and upcoming optical remote sensing missions with improved spatial, temporal, and spectral resolutions and their potential for the characterization of Arctic regions. In particular, hyperspectral remote sensing (or imaging spectroscopy) has been shown to provide superior derivation of key biophysical surface variables in snow-free permafrost areas during the summer months based on field and airborne remote sensing data (Buchhorn et al, 2013;Bratsch et al, 2016;Liu et al, 2017;Beamish et al, 2020). The datasets compiled as part of the iCUPE project (see Sect.…”

“…The main goal of this work was to provide an initial charac- Figure 16. Canopy-level spectral reflectance in percent as a function of wavelength in nanometers from a dwarf shrub community in three major phenological phases of leaf-out, maximum canopy, and senescence (Beamish et al, 2017).…”

“…MNT: moist nonacidic tundra; MAT: moist acidic tundra; MT: moss tundra. PSRI is used to track plant senescence related to the degradation of chlorophyll pigments (Beamish et al, 2018).…”

“…In a final effort to explore the applications of hyperspectral remote sensing to the characterization of Arctic vegetation, we explored the spectral sensitivity of airborne hyperspectral imagery to the fractional cover of plant functional types at Toolik Lake, Alaska ( Fig. 18; Beamish et al, 2020).…”