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e-ISSN 1828-1427

  

Rivista trimestrale di Sanità Pubblica Veterinaria edita dall'Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise ‘G. Caporale'

A quarterly journal devoted to veterinary public health, veterinary science and medicine published by the Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise ‘G. Caporale’ in Teramo, Italy

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2007 - Volume 43 (3) July-September
   
 
Hélène Guis, Annelise Tran, Frédéric Mauny, Thierry Baldet, Bruno Barragué, Guillaume Gerbier, Jean-Francois Viel, Francois Roger & Stéphane de La Rocque
A multiple fine-scale satellite-derived landscape approach: example of bluetongue modelling in Corsica 699-707
       
Summary
Landscape ecology is seldom used in epidemiology. The aim of this study is to assess the possible improvements that can be derived from the use of landscape approaches on several scales when exploring local differences in disease distribution, using bluetongue (BT) in Corsica as an example. The environment of BT-free and BT-infected sheep farms is described on a fine scale, using high resolution satellite images and a digital elevation model. Land-coverage is characterised by classifying the satellite image. Landscape metrics are calculated to quantify the number, diversity, length of edge and connectance of vegetation patches. The environment is described for three sizes of buffers around the farms. The models are tested with and without landscape metrics to see if such metrics improve the models. Internal and external validation of the models is performed and the relative impact of scale versus variables on the discriminatory ability of the models is explored. Results show that for all scales and irrespective of the number of parameters included, models with landscape metrics perform better than those without. The 1-km buffer model combines both the best scale of application and the best set of variables. It has a good discriminating ability and good sensitivity and specificity.

Keywords
Bluetongue, Corsica, Epidemiology, Geographic information system, Landscape, Remote sensing.


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