Multi-scale correlations between topography and vegetation in a hillside catchment of Honduras

  • Authors:

  • A. Nelson;T. Oberthür;S. Cook

  • Affiliations:

  • School of Geography, University of Leeds, Leeds LS2 9JT, UK;Global Environment Monitoring Unit (GEM), Institute for Environment and Sustainability (IES), Joint Research Centre of the European Commission, I-21020 Ispra (VA), Italy;Global Environment Monitoring Unit (GEM), Institute for Environment and Sustainability (IES), Joint Research Centre of the European Commission, I-21020 Ispra (VA), Italy

  • Venue:
  • International Journal of Geographical Information Science
  • Year:
  • 2007

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Abstract

All systems have causes and effects that can be appreciated at different spatial scales. Understanding and representing the complexity of multi-scale patterns in maps and spatial models are key research objectives. We describe the use of three types of correlation analyses: (1) a standard Pearson correlation coefficient, (2) a 'global' multi-scale correlation, and (3) local geographically weighted correlation. These methods were applied to topographic and vegetation indices in a small catchment in Honduras that is representative of the country's hillsides agro-ecosystem which suffers from severe environmental degradation due to land-use decisions that lead to deforestation, overgrazing, and unsustainable agricultural. If the geographical scale at which topography matters for land-use allocation can be determined, then integration of knowledge systems can be focused. Our preliminary results show that: (1) single-scale correlations do not adequately represent the relationship between NDVI and topographic indices; (2) peaks in the global multi-scale correlations in agricultural areas coincided with the median farm size, but there was no evidence of any community or larger-scale land-use planning or optimization; and (3) local multi-scale correlations varied considerably from the global results at all scales, and these variations have a strong spatial structure which may indicate local optimization of land use.