Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno

• Ano de defesa: 2017
• Autor(a) principal: Nicolau, Rafaela Fernandes
• Orientador(a): Mercante, Erivelto
• Banca de defesa: Correa, Marcus Metri , Prior, Maritane , Maggi, Marcio Furlan , Souza, Carlos Henrique Wachholz de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Paraná; Cascavel
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Agrícola
• Departamento: Centro de Ciências Exatas e Tecnológicas
• País: Brasil
• Palavras-chave em Português: Sensor MODIS; Sensoriamento remoto; Série temporal
• Palavras-chave em Inglês: MODIS sensor; Temporal series; Remote sensing
• Área do conhecimento CNPq: CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://tede.unioeste.br/handle/tede/3057

The acquisition of effective technologies for prediction and monitoring of agricultural crops highlights the search for methodologies that make this information available before harvesting. Currently, the monitoring of agricultural production is still partially carried out through subjective and onerous techniques by Brazilian official bodies. The study of the agricultural monitoring and/or estimation of winter crops yield, using vegetation indexes extracted from multitemporal images of the MODIS sensor, is a reality that has been tested by several authors, in the search for greater objectivity to the generated figures. In this context, this research aims to map and estimate areas with winter and maize crops, using temporal series of the EVI vegetation index from the MODIS sensor of the Terra and Aqua satellites, for the 2012, 2013, and 2014 harvests for the state of Paraná. As a way of adjusting the mapping through the MODIS sensor (250 meters), the visual analysis was performed in which images of medium spatial resolution (30 meters) were used to identify the chosen cultures. In article 1, color compositions were generated using images from the pre-planting period until the initial development and images representing the vegetative peak of the crops. Subsequently, the extraction of cultivated areas with the crops of interest was performed, so that these could be compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 2, colored compositions were generated using only the vegetative peak images of the cultures to be classified using the Spectral Angle Mapper (SAM) algorithm. Subsequently, the masks were compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 1, an underestimation of the safflower and winter crops areas was found for the 2012 and 2013 crops, and an overestimation for the 2014 safflower, and for the winter crops, overestimation. By the accuracy analyzes, the masks were classified with excellence. In Article 2, it was verified that the data of areas were overestimated for the safflower corn and underestimated for the winter crops. The accuracy analyzes were classified as excellent, in relation to the medium resolution image.