A method for trend-based change analysis in Arctic tundra using the 25-year Landsat archive

Abstract

Remote sensing has provided evidence of vegetation changes in Arctic tundra that may be attributable to recent climate warming. These changes are evident from local scales as expanding shrub cover observed in aerial photos, to continental scales as greening trends based on satellite vegetation indices. One challenge in applying conventional two date, satellite change detection in tundra environments is the short growing season observation window, combined with high inter-annual variability in vegetation conditions. We present an alternative approach for investigating tundra vegetation and surface cover changes based on trend analysis of long-term (1985-present) Landsat TM/ETM+ image stacks. The Tasseled Cap brightness, greenness, and wetness indices, representing linear transformations of the optical channels, are analysed for per-pixel trends using robust linear regression. The index trends are then related to changes in fractional shrub and other vegetation covers using a regression tree classifier trained with high resolution land cover. Fractional trends can be summarised by vegetation or ecosystem type to reveal any consistent patterns. Example results are shown for a 3 000 km2 study area in northern Yukon, Canada where index and fractional changes are related to growth of vascular plants and coastal erosion.