Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/15623
Título: Análise espaçotemporal da sigatoka amarela da bananeira utilizando sensoriamento remoto e geoestatística
Título(s) alternativo(s): Space-temporal analysis of yellow sigatoka in banana using remote sensing and geostatistics
Autores: Alves, Marcelo de Carvalho
Pozza, Edson Ampélio
Alves, Marcelo de Carvalho
Oliveira, Marcelo Silva de
Freitas, Aurivan Soares de
Palavras-chave: Banana - Doenças e pragas - Distribuição espacial
Banana - Doenças e pragas - Análise de séries temporais
Sigatoka amarela
Geologia - Métodos estatísticos
Sensoriamento remoto
Bananas - Diseases and pests - Spatial distribution
Bananas - Diseases and pests - Time-series analysis
Yellow sigatoka
Geostatistics
Remote sensing
Data do documento: 6-Nov-2017
Editor: Universidade Federal de Lavras
Citação: RODRIGUES, J. D. P. Análise espaçotemporal da sigatoka amarela da bananeira utilizando sensoriamento remoto e geoestatística. 2017. 87 p. Dissertação (Mestrado em Engenharia Agrícola)-Universidade Federal de Lavras, Lavras, 2017.
Resumo: Yellow Sigatoka leaf spot, caused by Pseudocercospora musae (Mycosphaerella musicola), is among the diseases that most affect banana crop. The disease progress occurs in both time and space, and the Remote Sensing sciences and the space-time Geostatistics provide support for the analysis of this dynamic process. Therefore, the objective of this study was to perform the spatiotemporal prediction of yellow Sigatoka at different times, to calculate and evaluate vegetation indices derived from the Enhanced Thematic Mapper Plus (ETM +) sensor, from the Landsat 7 satellite, to infer about the disease occurrence, to evaluate the existence of spatial and temporal correlation between data obtained by the sensor and data in situ, and finally to analyze the spectral signature of the plant affected by the disease. The first study used images from the Landsat 7 satellite, ETM + sensor, with atmospheric correction method Dark Object Substraction 1 (DOS1) and Second Simulation of Sattelite Signal in the Solar Spectrum (6S). Normalized Difference Vegetation Index (NDVI), Green Standard Difference Vegetation Index (GNDVI) and Normalized Difference Water Index (NDWI) were calculated. In the spectral signature related to the months of September and October, the mid-infrared region allowed the characterization of the disease in the plant. The NDVI and GNDVI indices showed differences in the atmospheric correction and in relation to the disease the NDWI presented better result. In the second study, Gneiting's separable spatiotemporal, Double Exponential, and non-separable covariance models were tested with the Weight Least Squares (WLS), Restricted Maximum Likelihood (REML) and Likelihood Pairwise methods. The Gneiting‟s non-separable model, WLS adjustment method, in which the trend was modeled, allowed to reduce as uncertainties of spatial and temporal prediction of the disease, as well as to characterize the pattern of monocycle temporality of the disease.
URI: http://repositorio.ufla.br/jspui/handle/1/15623
Aparece nas coleções:Engenharia Agrícola - Mestrado (Dissertações)



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