A classificatory approach integrating fuzzy set theory and permutation techniques for land cover analysis: a case study on a degrading area of the Rift Valley (Ethiopia)
We suggest a classificatory approach for land cover analysis that integrates fuzzy set theory with
permutation techniques. It represents a non parametric alternative and/or a complement of
traditional multivariate statistics when data are scarce, missing, burdened with high degree of
uncertainty and originated from different sources and/or times. According to this approach, the
Operational Geographic Units (OGUs) in which landscape is subdivided and sampled are classified
with hierarchical clustering methods. The clusters of a classification which are significantly sharp
are used to define fuzzy sets. In this way, the original data scores are transformed by degrees of
belonging. We introduce the concepts of endogenous and exogenous fuzzy sets and we suggest to
apply the Mantel test between the similarity matrices of these fuzzy sets to test the predictivity of
internal variables with respect to external variables. The approach is applied to OGUs
corresponding to the smallest administrative units (kebeles) of the Ethiopian Rift Valley, a
degrading area with high risk of further degradation. We found that: 1) there is a high correlation
between geophysical features of the landscape (geology, rainfall and elevation) and some indicators
of the human pressure such as land use/cover, land management for livestock breeding and human,
household and livestock densities, 2) there is a high correlation between land degradation, measured
with relative loss of Normalized Difference Vegetation Index (NDVI) and the human pressure.
However, the correlation is higher when the human pressure is considered in the geophysical
context of the landscape. The approach can be easily applied to produce maps useful for planning
purposes thanks to geographical information system (GIS) technology that is becoming available at
low cost even to small administrative units of developing countries.
