Five Years of Near-Surface Land Cover Reflectance in a Large Scale Hydrological Manipulation in an Arctic Tundra Landscape

Climate change appears to be most pronounced at high northern latitudes. Many of the observed and modeled climate change responses in arctic tundra ecosystems have profound effects on surface energy budgets, land-atmosphere carbon exchange, plant phenology, and geomorphic processes. Detecting biotic responses to a changing environment is essential for understanding the consequences of global change. Plants can work as very effective indicators of changing conditions and, depending on the nature of the change, respond by increasing or decreasing amounts of green-leaf biomass, chlorophyll, and water content. Shifts in the composition and abundance of plant species have important effects on ecosystem processes such as net primary production and nutrient cycling. Vegetation is expected to be responsive to arctic warming, although there is some uncertainty as to how the interplay between geomorphic, hydrologic, climatic and other biotic will manifest over a range of spatial scales. The NSF-supported Biocomplexity project in Barrow, Alaska, involves experimental manipulation of water table (drained, flooded, and control treatments) in a vegetated arctic thaw lake basin to investigate the effects of altered hydrology on land-atmosphere carbon balance. In each experimental treatment, hyperspectral reflectance data were collected in the visible and near IR range of the spectrum using a robotic tram system that operated along a 300m tramline during the snow free growing period between June and August 2005-09. Water table depths and soil volumetric water content was also collected along these transects. The years 2005-2007 were control or unmanipulated experimental years and 2008 and 2009 were experimental years where water table was raised (+10cm) and lowered (-10cm) in flooding and draining experiments respectively. This presentation will document the change in phenology (NDVI) between years, treatments, and land cover types. Findings from this research have implications for remote sensing, ecosystem modeling, terrestrial ecology and the Arctic Observing Network.