There has been increasing concern over the effects of climate change on the Arctic. The most fundamental issue of this concern is how natural systems will respond to these changes. Vegetation is both an integrator and indicator of climate and ecosystem properties. Arctic warming is expected to: (i) promote plant growth and sequestration of carbon from the atmosphere; and (ii) increase soil microbial respiration rates, releasing more carbon to the atmosphere (Stieglitz et al. 2000). If ecosystem respiration (ER) exceeds gross ecosystem production (GEP) a positive feedback loop could occur, thus intensifying global climate change.

Remote Sensing can provide spatially-continuous data on arctic vegetation and terrain patterns, in a range of spatial, spectral, and temporal resolutions. These data can be used for observing, investigating, and analyzing biophysical properties of vegetation at various landscape scales (Stow et al., 1998, Stow et al., 2000). In the Mid and High Arctic, ecosystem processes and vegetation community types have a high degree of spatial variability (figure 1). Detailed community level knowledge along with high resolution remote sensing can provide us with the ability to understand fine-grain spatial variation and improve our ability to scale to synoptic predictions. Knowledge obtained through detailed studies at local sites can be used to develop inputs to models of arctic ecosystem processes from community to landscape scales. Remote sensing has the potential to provide valuable information for the assessment and monitoring of vegetation patterns which can then be used to predict patterns of carbon dioxide flux.

Three requirements are needed for carbon storage and flux patterns to be predicted from remote sensing data: (i) unique electromagnetic signatures need to exist and correspond to variations in vegetation patterns and structure; (ii) one or more models are needed to transform remotely sensed data into derivative values pertaining to the type or condition of the land cover and then to estimate carbon flux from this derivative variable; and (iii) measurements of carbon flux rates and storage amounts to calibrate and validate the models used to estimate the carbon flux distribution from the remotely sensed data (Stow et al., 1998). The distribution of vegetation and the amount of above-ground biomass within a community are important factors for estimating carbon dioxide fluxes.

This study explores the relationship between the normalized difference vegetation index (NDVI) and above-ground biomass in two latitudinal disparate tundra environments where variations in soil moisture, exposed soil, and gravel till have significant influence on spectral response, and hence, the characterization of vegetation communities. IKONOS multispectral (4m spatial resolution) data were collected for Cape Bounty, Melville Island (74º55’N, 109º35’W), and the Lord Lindsay River watershed west of Sanagak Lake (70º11’N, 93º44’W) on the Boothia Peninsula, Nunavut. Vegetation plots for each site were initially identified through a visual inspection, selecting areas of distinct homogeneous vegetation cover. Though this study uses high spatial resolution imagery, field data was collected to allow for scaling up to mid resolution imagery at a later time, for this reason each plot is 100m x 100m (1 ha). Each plot was sampled to confidently characterize the community in terms of species composition, percent cover, above-ground biomass, and soil moisture at peak growing season. Over 880 biomass samples were collected between the Cape Bounty site (488) and the Boothia site (398).

Biomass values were transformed using a natural logarithm transformation and linear regression analysis was performed. Preliminary results from plot level mean values show strong, significant linear relationships between transformed wet and dry biomass values with NDVI. At Cape Bounty r² values of 0.91 and 0.60 (figure 2) were obtained, while the Boothia Peninsula site had r² values of 0.78 and 0.86 (figure 3), each for wet and dry biomass respectively.

The results suggest that above-ground biomass is highly correlated with high spatial resolution NDVI values, thereby indicating strong potential for modeling biomass variations. Understanding the structure of arctic vegetation communities at a high resolution can also help in the process of scaling up to coarser spatial resolution imagery. These results are important given the need for improved mapping of arctic vegetation, associated biophysical variables, and to predict patterns of carbon dioxide flux.