Defining potential peatland management zones using self-organising map
clustering on airborne radiometric data
Abstract
Peatlands are becoming recognized as important carbon sequestration
centers where, through restoration projects, peatlands may stop being
considered as carbon sources and become carbon neutral or possibly
carbon negative. Restoration projects require a knowledge of intra-peat
variation across these, sometimes, vast areas. The integration of
multidimensional geophysical tools, digital elevation models and
satellite remote sensing products combined with modern data analytic
techniques may provide a rapid means of accessing variations over broad
spatial scale. In this study an airborne radiometric survey, being flown
nationally over the Republic of Ireland, is shown to be able to
delineate potential management zones within an industrial raised peat
bog. Radiometric data is particularly suited to peat studies as they are
sensitive to water content. Peat, as a mostly organic material, acts as
a low signal environment where variations in the signal are linked to
intra-peat variation of either depth or water content. This study uses
an unsupervised, self-organizing map clustering methodology to group the
radiometric signal into three zones interpreted as 1) the edge of the
bog where peat layer is thinning and there is influence on the
radiometric signal from non-peat soils outside of the bog, 2) the normal
peat conditions where depth and saturation appear as a relative constant
in the radiometric response and 3) areas where the peat is either
thinner, drier, or potentially somewhat mineralised. The addition of
other data layers such as elevation, slope and satellite imagery may
help better define these zones and a ground geophysical survey is
planned to test the results of this study. The definition of such
potential management zones could aid any restoration project in the
initial stages or act as a baseline study to monitor changes to the
peatland during and after a restoration project is complete.