Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery

Abstract

Abstract:Coffee is the second most valuable traded commodity worldwide. Brazil is the world's largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on OPEN ACCESSRemote Sens. 2012, 4 2493 yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield.

Relationship Between Coffee Crop Productivity and Vegetation Indexes Derived From Oli / Landsat-8 Sensor Data With and Without Topographic Correction

“…The determination coefficient values decreased from phase I to III. The results corroborate with values found by Bernardes et al (2012) who found higher correlation between coffee productivity and NDVI in the period of August / September. This fact may be associated to an increase in leaf area index that cause an increase in internal shading and, consequently, a change in crop reflectance.…”

Developing detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor

“…An object-based Bayesian classifier was used to develop a coffee production map for Rwanda that can be useful in agricultural planning and yield prediction (Mukashema et al 2014). It was also demonstrated that it is possible to estimate coffee yield from phenological behaviour of coffee extracted from remotely sensed data (Moreira et al 2004;Brunsell et al 2009;Bernardes et al 2012). A comparative study showed that the high-resolution IKONOS imagery performed better than Landsat ETM+ in land cover classification in a coffee-dominated landscape (Ramirez et al 2006).…”

Sensoriamento remoto multiespectral na identificação e mapeamento das variáveis bióticas e abióticas do cafeeiro

“…Já Santos et al (2014), com o auxílio do índice de vegetação EVI-2, derivado do sensor MODIS, encontraram correlação de (R 2 = 0,81) entre a umidade no perfil do solo, até 100 cm de profundidade, e o vigor do cafeeiro (Coffea arabica). De forma semelhante, Bernardes et al (2012) evidenciaram as variações provocadas pela bienalidade na produção de café, em lavoura no sul do Estado de Minas Gerais, entre 2002 e 2009, utilizando os índices de vegetação EVI e NDVI, derivados de imagens MODIS. No entanto, esses estudos não avaliaram o potencial dos índices de vegetação obtidos a partir de imagens Landsat-5 Thematic Mapper (TM), para avaliar as condições do cafeeiro.…”

“…No entanto, vários fatores podem reduzir essa produtividade e, por isso, a cafeicultura necessita de uma metodologia robusta, confiável e de baixo custo, de monitoramento de doenças, pragas, estresse hídrico, fertilidade do solo, condições nutricionais e de outros fatores de estresses na cultura(Chemura et al, 2017).Nos últimos anos, as metodologias de sensoriamento remoto têm sido amplamente utilizadas no monitoramento das culturas agrícolas e na tomada de decisão, para melhores práticas de manejo. Na cultura do café, o uso de dados de sensoriamento remoto provou ser muito promissor, face à dificuldade de se obter dados de campo em escala regional, especialmente para os mapeamentos de campo, que necessitam de mão de obra qualificada, vistorias nas plantas, em lavouras cultivadas em grandes áreas, tempo excessivo de atividade e, consequentemente, aumento do custo de produção(Bernardes et al, 2012).O sensoriamento remoto espectral fornece a possibilidade de avaliação precoce, eficiente, objetiva e não destrutiva, das respostas das plantas a diferentes fatores de estresse do meio ambiente(Li et al, 2010). As plantas reagem aos estresses biótico e abiótico por meio de mudanças biofísicas e bioquímicas, como reduções de biomassa, do teor de clorofila e alterações nas estruturas internas das folhas, que podem ser facilmente detectadas por meio das diferenças de energia refletida nas regiões espectrais do visível e do infravermelho próximo(Barton, 2012;Mahajan et al, 2014).…”