Modelling Biophysical Variables and Carbon Dioxide Exchange in Arctic Tundra Landscapes using High Spatial Resolution Remote Sensing Data

Abstract

Vegetation community patterns and processes are indicators and integrators of climate. Recently, scientists have shown that climate change is most pronounced in circumpolar regions. Arctic ecosystems have traditionally been sequestering carbon and accumulating large carbon stores. However, given enhanced warming in the Arctic, the potential exists for intensified global climate change if these ecosystems transition from sinks to sources of atmospheric CO2. In the Mid and High Arctic, ecosystems exhibit extreme levels of spatial heterogeneity, particularly at landscape scales. High spatial-resolution (e.g., 4m) remote sensing data capture heterogeneous vegetation patterns of the Arctic landscape and have the potential to model ecosystem biophysical properties and CO2 fluxes. The following conditions are required to model arctic ecosystem processes: (i) unique spectral signatures that correspond to variations in the landscape pattern; (ii) models that transform remote sensing data into derivative values pertaining to the landscape; and (iii) field measures of the variables to calibrate and validate the models. First, this research creates an ecosystem classification scheme through ordination, clustering, and spectral-separability of ground cover data to generate ecologically meaningful and spectrally distinct image classifications. Classifications had overall accuracies between 69% - 79% and Kappa values of 0.54 - 0.69. Secondly, biophysical variable models of percent vegetation cover, aboveground biomass, and soil moisture are calibrated and validated using a k-fold cross-validation linear bivariate regression methodology. Percent vegetation cover and percent soil moisture produce the strongest and most consistent results (r2 ≥ 0.84 and 0.73) across both study sites. Finally, in situ CO2 exchange rate data, an NDVI model for each component flux, which explains between 42% and 95% of the variation at each site, is generated. Analysis of coincidence indicates that a single model for each component flux can be applied, independent of site. This research begins to fill a gap in the application of high spatial-resolution remote sensing data for modelling Arctic ecosystem biophysical variables and carbon dioxide exchange, particularly in the Canadian Arctic. The results of this research also indicate high levels of functional convergence in ecosystem-level structure and function within Arctic landscapes.