Digital soil mapping enables informed decisions to conserve soils within
protected areas
Abstract
Protected areas are often regarded as pristine land, but in reality,
they require rehabilitation and effective management to prevent
increased land degradation. Soil management requires soil maps to make
informed decisions, which is difficult to create in protected areas due
to the large size of land, limited accessibility, little available soil
data and limited budgets of such projects. In this paper a hybrid expert
knowledge and machine learning digital soil mapping (DSM) method is used
to create such maps for Benfontein, a 9900 ha protected area in the
semi-arid region of South Africa. The hybrid method uses soil landscape
rules to determine virtual soil observations which is added to the
training observations used in a machine learning algorithm to create a
soil associations map. Soil properties were assigned to each soil class
at the 0.1, 0.5 and 0.9 percentile level, to indicate the range of
properties at an 80% certainty. The soil maps were interpreted to
indicate soil carbon sequestration potential, soil erodibility and
off-road driving potential. The soil association map was acceptable as
it achieved a kappa value of 0.69. Additionally, it was determined that
the site has a large potential for carbon sequestration, the soils are
relatively stable against water erosion, and off-road driving should be
prohibited on approximately half of the area. The results indicate that
the hybrid DSM method is viable to create useful soil maps to inform
management decisions in the unique settings of protected areas.