Evaluation of a linear spectral mixture model and vegetation indices (NDVI and EVI) in a study of schistosomiasis mansoni and Biomphalaria glabrata distribution in the state of Minas Gerais, Brazil

• Autor(a) principal: Guimarães,Ricardo JPS
• Data de Publicação: 2010
• Outros Autores: Freitas,Corina C, Dutra,Luciano V, Scholte,Ronaldo GC, Amaral,Ronaldo S, Drummond,Sandra C, Shimabukuro,Yosio E, Oliveira,Guilherme C, Carvalho,Omar S
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Memórias do Instituto Oswaldo Cruz
• Texto Completo: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762010000400028
• topic: schistosomiasis; geographical information system; linear spectral mixture model; Biomphalaria glabrata; epidemiology

This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.