, Volume 10, Issue 4, pp 562–578

Regression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape

  • James D. A. Millington
  • George L. W. Perry
  • Raúl Romero-Calcerrada

DOI: 10.1007/s10021-007-9020-4

Cite this article as:
Millington, J.D.A., Perry, G.L.W. & Romero-Calcerrada, R. Ecosystems (2007) 10: 562. doi:10.1007/s10021-007-9020-4


In many areas of the northern Mediterranean Basin the abundance of forest and scrubland vegetation is increasing, commensurate with decreases in agricultural land use(s). Much of the land use/cover change (LUCC) in this region is associated with the marginalization of traditional agricultural practices due to ongoing socioeconomic shifts and subsequent ecological change. Regression-based models of LUCC have two purposes: (i) to aid explanation of the processes driving change and/or (ii) spatial projection of the changes themselves. The independent variables contained in the single ‘best’ regression model (that is, that which minimizes variation in the dependent variable) cannot be inferred as providing the strongest causal relationship with the dependent variable. Here, we examine the utility of hierarchical partitioning and multinomial regression models for, respectively, explanation and prediction of LUCC in EU Special Protection Area 56, ‘Encinares del río Alberche y Cofio’ (SPA 56) near Madrid, Spain. Hierarchical partitioning estimates the contribution of regression model variables, both independently and in conjunction with other variables in a model, to the total variance explained by that model and is a tool to isolate important causal variables. By using hierarchical partitioning we find that the combined effects of factors driving land cover transitions varies with land cover classification, with a coarser classification reducing explained variance in LUCC. We use multinomial logistic regression models solely for projecting change, finding that accuracies of maps produced vary by land cover classification and are influenced by differing spatial resolutions of socioeconomic and biophysical data. When examining LUCC in human-dominated landscapes such as those of the Mediterranean Basin, the availability and analysis of spatial data at scales that match causal processes is vital to the performance of the statistical modelling techniques used here.


land use/cover changeregression modelling; hierarchical partitioningland cover classificationSpain

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • James D. A. Millington
    • 1
  • George L. W. Perry
    • 2
  • Raúl Romero-Calcerrada
    • 3
  1. 1.Environmental Monitoring and Modelling Research Group, Department of GeographyKing’s College LondonLondonUK
  2. 2.School of Geography and Environmental ScienceUniversity of AucklandAucklandNew Zealand
  3. 3.School of Engineering Science and TechnologyRey Juan Carlos UniversityMóstolesSpain