The BGS has used remotely sensed imagery to detect the presence of peat and histosols.
Histosols are organic rich soils that contain at least 20–30% organic matter by weight and are more than 40 cm thick. They typically have a low bulk density.
The aim of the research was to gain a better understanding of the spatial extent of peat and histosols in Great Britain.
Peat is not always exposed at surface and the methodology employed in this work relies on measuring spectral properties that occur on the surface of the earth.
It was therefore necessary to look for the surface expression of peat such as: heather; tussock grass and dark organic soils, while being aware that the presence of these features does not necessarily guarantee the presence of peat beneath.
Wetness and moisture content is an important factor in modelling the presence of peat and organic soils and so remotely sensed imagery of a similar date were used to prevent any differences in the abundance of peat due to moisture content.
A combination of Landsat and ASTER satellite imagery was used in the research.
For the majority of Britain, Landsat imagery was used, however, where available ASTER data was used to fill in any gaps where cloud cover and cloud shadow obscured the ground.
As peat does not form in any great volumes on steep slopes, terrain data from the NEXTMap Britain model was used to limit the research to areas of relatively gentle slopes.
The results of the histosol mapping using remote sensing data are very encouraging. Strong correlations exist between the existing peat mapping and the modelled organic soil mapping. Some differences are present and field and air photo checking has been carried out.
The North York Moors area has been field checked and observed in Geovisionary to check the accuracy of the model and the Hexam area has been revised following discussions with experienced geologists.
Mapping of upland areas such as North and South Wales, Scotland and in particular western Scotland and Cornwall has been completed. All areas have been classified using Landsat and ASTER data and have been checked using aerial photography in Geovisionary for accuracy.
For further information please contact Marieta Garcia-Bajo