WikiPEATia - a web based platform for assembling peatland data through ‘crowd sourcing’

The Earth System Science community is realizing that peatlands are an important and unique terrestrial ecosystem that has not yet been well-integrated into large-scale earth system analyses. A major hurdle is the lack of accessible, geospatial data of peatland distribution, coupled with data on peatland properties (e.g., vegetation composition, peat depth, basal dates, soil chemistry, peatland class) at the global scale. This data, however, is available at the local scale. Although a comprehensive global database on peatlands probably lags similar data on more economically important ecosystems such as forests, grasslands, croplands, a large amount of field data have been collected over the past several decades. A few efforts have been made to map peatlands at large scales but existing data have not been assembled into a single geospatial database that is publicly accessible or do not depict data with a level of detail that is needed in the Earth System Science Community. A global peatland database would contribute to advances in a number of research fields such as hydrology, vegetation and ecosystem modeling, permafrost modeling, and earth system modeling. We present a Web 2.0 approach that uses state-of-the-art webserver and innovative online mapping technologies and is designed to create such a global database through ‘crowd-sourcing’. Primary functions of the online system include form-driven textual user input of peatland research metadata, spatial data input of peatland areas via a mapping interface, database editing and querying editing capabilities, as well as advanced visualization and data analysis tools. WikiPEATia provides an integrated information technology platform for assembling, integrating, and posting peatland-related geospatial datasets facilitates and encourages research community involvement. A successful effort will make existing peatland data much more useful to the research community, and will help to identify significant data gaps.