The environmental "risky" region: identifying land degradation processes through integration of socio-economic and ecological indicators in a multivariate regionalization model

Environ Manage. 2009 Nov;44(5):888-98. doi: 10.1007/s00267-009-9378-5. Epub 2009 Sep 29.


Although several studies have assessed Land Degradation (LD) states in the Mediterranean basin through the use of composite indices, relatively few have evaluated the impact of specific LD drivers at the local scale. In this work, a computational strategy is introduced to define homogeneous areas at risk and the main factors acting as determinants of LD. The procedure consists of three steps and is applied to a set of ten environmental indicators available at the municipality scale in Latium, central Italy. A principal component analysis extracting latent patterns and simplifying data complexity was carried out on the original data matrix. Subsequently, a k-means cluster analysis was applied on a restricted number of meaningful, latent factors extracted by PCA in order to produce a classification of the study area into homogeneous regions. Finally, a stepwise discriminant analysis was performed to determine which indicators contributed the most to the definition of homogeneous regions. Three classes of "risky" regions were identified according to the main drivers of LD acting at the local scale. These include: (i) soil sealing (coupled with landscape fragmentation, fire risk, and related processes), (ii) soil salinization due to agricultural intensification, and (iii) soil erosion due to farmland depopulation and land abandonment in sloping areas. Areas at risk for LD covered 56 and 63% of the investigated areas in 1970 and 2000, respectively.

MeSH terms

  • Cluster Analysis
  • Discriminant Analysis
  • Environment*
  • Geography / statistics & numerical data*
  • Italy
  • Principal Component Analysis
  • Risk Assessment
  • Socioeconomic Factors