Imagens hiperespectrais no monitoramento da nutrição em citros

• Autor(a) principal: Barbosa, Ana Paula [UNESP]
• Data de Publicação: 2014
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/110954
• topic: Citrus; Plantas - Nutrição; Nitrogenio na agricultura; Imagens de sensoriamento remoto; Agricultura de precisão; Sugar-cane

Many studies in remote sensing aim to identify varietal differences, predict yield, identify crop nutrition deficiencies, crop area estimates, and other spectral information of crops. Airborne hyperspectral imagery is a potential tool to monitor the nutrient status of citrus trees, since this sensing technology has been shown to efficiently detect nitrogen deficiency in other crops. Therefore, it contributes to save cost, time and labor when used over large areas. The objective of this study was: (i) to determine the spectral behavior of a citrus orchard in high resolution hyperspectral imagery collected with aircraft; (ii) to analyze the correlation between plant parameters and radiometric parameters, especially of the nitrogen nutritional status of citrus orchards. The experiment was conducted in orchards located near Lake Alfred and Auburndale, Polk County, Florida, USA. The variety studied is Valencia (Citrus sinensis). Hyperspectral images consist of 128 visible and near-infrared wavelengths (457.2 – 921.7 nm). Detailed ground truth data were collected at the same time as the imagery for assessing foliar nutrition of citrus groves. The spectral features of the individual trees were identified using pixel-based average spectral reflectance values at several wavelengths from the image. Hyperspectral images and ground truth data were assessed with statistical, GIS and modeling tools, i.e. Vegetation Index (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Red Edge Simple Ratio, Vogelmann Red Edge Index 1, and others.). The methods Backward Multiple Linear Regression and Principal Component Analysis (PCA) were used to develop prediction models for leaf nitrogen. These results will contribute to the development of rapid detection techniques of nutritional status in citrus groves, in order to reduce the sampling procedures for foliar analysis, which ...