Using environmental radioactivity to create a national scale peatlands map: a case study from Ireland

Historically, peatlands have acted as globally important carbon sequestration habitats via the storage of organic material. Modern degraded/drained peatlands emit this carbon as CO2 via decomposition of the stored organic material. Through restoration projects, in which the water table is raised, peatlands may become carbon neutral or possibly carbon negative. National restoration plans require a knowledge of peatland extent and spatial distribution across large geographic areas.

Globally, current peatland maps are created in a variety of ways including the use of optical satellite remote sensing or combinations of legacy soil/quaternary maps. However, optical remote sensing cannot detect peatlands under landcover such as forest or grassland. Legacy maps are often created from sparse in-situ augur, borehole, or trial pit data. These types of measurements do not allow for accurate measurement of peatland boundaries.

Radiometrics, a geophysical method that measures radiation emitted from geological materials, is particularly suited to peatland studies. Modelling of radiometric attenuation shows that a statistical difference is present in recorded potassium, equivalent uranium and equivalent thorium counts acquired over peat, compared to those acquired over a non-peat/mineral soil. Mineral soils contain geological material which acts as a source of gamma radiation. Peat, being a mostly organic material, is generally not considered a source of radiation. Peat also tends to be saturated and water acts to attenuate the recorded gamma signature. These effects combined means that peatlands are represented as a “low” radiometric signal in the landscape.

In Ireland, the Tellus survey, acquired by the Geological Survey, Ireland (GSI) aims to acquire airborne data including electromagnetic, magnetic, and radiometric data, consistently across the country (flight line spacing of 200m). This study uses Tellus airborne radiometric data in combination with machine learning classification techniques, to identify peatlands under modified landcover, such as forestry and grasslands and to increase the spatial resolution of existing peatland map to 50 x 50 m. The methodology is robust and can be applied in all areas where these data exist. The results may update national and international carbon inventories of peatlands area and geographic distribution and inform European policy.