A hierarchic approach to examining panArctic vegetation with a focus on the linkages between remote sensing and plot-based studies.
Friday, 18 December 2015
Poster Hall (Moscone South)
A circumpolar view of Arctic vegetation developed with the advent of satellite-derived remote-sensing products. Interpretations of what the revealed patterns mean are dependent on a foundation of in-situ plot-based observations. Despite the importance of ground-based observations, only a few areas have been intensively sampled and mapped, mainly in the vicinity of permanent Arctic observatories. Much of the information is project specific and is based on sampling protocols that are difficult to compare across sites. Here, we demonstrate a more consistent multi-level hierarchic approach for vegetation description and analysis at the Toolik Lake Field Station, Alaska. We advocate for a well-coordinated, interdisciplinary network of vegetation observation stations. Key recommendations include: (1) Complete local floras for many more areas in than presently exist. Species names should be standardized using the Pan-Arctic Flora. (2) Permanently marked and replicated vegetation monitoring plots in the full range of habitats at each station. Methods of establishing and monitoring the plots should include consistent internationally accepted standards for vegetation data collection, vegetation classification, plot markings, and standardized approaches to describe the local environment, including photo points showing the vegetation and soils up close and in landscape view. (3) Standardized approaches for collecting in-situ time-series of spectral data. Standardized methods for collecting and analyzing phytomass data are especially needed. (4) Interdisciplinary studies. Vegetation observations should be conducted in concert with observations of soils, permafrost, animals and ecosystem processes at the same plots. (5) Periodic (perhaps every 5-10 years) ground-based surveys. These should include surveys of species composition, canopy structure, biomass, leaf-area index, and NDVI, along with high-resolution satellite-based remote-sensing products at the same time.