Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data

David Atkinson,Paul Treitz

doi:10.3390/rs4123948

Abstract

As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%–79% and Kappa values of 0.54–0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These classification schemes are critical for modeling ecosystem processes.

Highlights

Arctic tundra vegetation covers approximately six million square kilometres of the Earth’s surface, is a major circumpolar ecosystem, and is an important indicator biome within the context of global climate change [1,2,3]
Supplementary environmental variables that were collected in the field, such as soil moisture; exposed rock and soil are not used during the derivation of the ordination but can be projected passively onto the biplot ordination
The results of this study illustrate that a combination of ecological and remote sensing techniques can produce image classifications that are ecologically meaningful and spectrally significant in a high

Summary

Introduction

Arctic tundra vegetation covers approximately six million square kilometres of the Earth’s surface, is a major circumpolar ecosystem, and is an important indicator biome within the context of global climate change [1,2,3]. Arctic ecosystems have the potential to shift from a sink for carbon-based greenhouse gases to a source, possibly creating a positive feedback mechanism, intensifying global climate change [5,6,7]. It has been illustrated that the NEE of Low Arctic ecosystems may be predicted, with acceptable accuracy, without necessarily identifying species or vegetation, by utilizing spectral vegetation indices; greater accuracy would require mapping of the landscape based on some minimum number of vegetation classes and the light response of each class [6]. ER is related to many factors including: vegetation type, soil organic matter, soil moisture,

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Conclusion