Multi‐temporal analysis of MODIS data to classify sugarcane crop

Xavier, Alexandre Cândido; Rudorff, Bernardo Friedrich Theodor; Shimabukuro, Yosio Edemir; Berka, Luciana Miura Sugawara; Moreira, Maurício Alves

doi:10.1080/01431160500296735

Cited by 81 publications

(51 citation statements)

References 24 publications

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“…This variability is illustrated by a boxplot representation of NDVI over the course of the growing season (Figure 3). Such inter-field variability has been reported by many authors, for example Xavier et al (2006), who had difficulty selecting homogeneous samples from the MODIS time series suitable for supervised classification of the cane cropped area in Brazil. However, at our study sites, climatic factors such as rain and temperature play the role of regulators, resulting in synchronization of the vegetative growth of the ratoon crops with a NDVI peak and minimal inter-field variability at the end of the rainy season (Figure 3) about two months before the harvest season (around May in Reunion, and December in Guadeloupe).…”

Section: Spatio-temporal Variability Of Sugarcane Ndvimentioning

confidence: 82%

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Bégué

Lebourgeois

Bappel

et al. 2010

International Journal of Remote Sensing

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Sugarcane is a semi-perennial grass whose cultivation is characterized by an extended harvest season lasting several months leading to very high spatio-temporal variability of the crop development and radiometry. The objective of this paper is to understand this variability in order to propose appropriate spectral indicators for yield forecast. To do this, we used ground observations and SPOT4 and SPOT5 time series acquired monthly over a 2-year period over Reunion Island and Guadeloupe (French West Indies). We showed that variations in the NDVI (Normalized Difference Vegetation Index) of sugarcane at the field scale are the result of the interaction between the sugarcane crop calendar and plant phenology in a given climatic environment. We linked these variations to crop variables measured in the field (LAI and leaf color), and derived simple, appropriate NDVI-based indicators of sugarcane yield components at the field scale (cane yield and sugar content). For biomass forecast, the best correlation (r² = 0.78) was obtained with images acquired about 2 months before the harvest season, when all the fields are fully developed but before the maturation stage. For sugar content, a polynomial relationship (r² = 0.75) was observed between the field NDVI acquired during the maturation stage and sugar content in the stalk.

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Section: Spatio-temporal Variability Of Sugarcane Ndvimentioning

confidence: 82%

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Bégué

Lebourgeois

Bappel

et al. 2010

International Journal of Remote Sensing

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“…Isso implicou em valores de índice de vegetação que se mantiveram abaixo de 0,2, no EVI, e de 0,3 no NDVI (Figura 2), significativamente diferente das demais classes avaliadas em ambos os índices de vegetação, nessas datas (Galford et al, 2008;Arvor et al, 2011). A cultura da cana-de-açúcar também tende a apresentar queda nos valores do EVI ao longo do período de seca (Xavier et al, 2006). No entanto, a estação seca afetou de maneira mais pronunciada as áreas de pastagem e de cerrado, em que o estresse hídrico e a ocorrência de fogo causaram queda na biomassa verde (Klink & Machado, 2005).…”

Section: Resultsunclassified

Índices de vegetação Modis aplicados na discriminação de áreas de soja

Risso¹,

Rizzi

Rudorff³

et al. 2012

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar o desempenho do índice de vegetação realçado (EVI) e do índice de vegetação da diferença normalizada (NDVI) -ambos do sensor "moderate resolution imaging spectroradiometer" (Modis) -, para discriminar áreas de soja das áreas de cana-de-açúcar, pastagem, cerrado e floresta, no Estado do Mato Grosso. Foram utilizadas imagens adquiridas em dois períodos: durante a entressafra e por ocasião do pleno desenvolvimento da cultura da soja. Para cada classe analisada, foram selecionadas 31 amostras de mapas de referência e avaliadas as diferenças nos valores de cada índice de vegetação, para a classe soja, foram avaliadas frente às demais classes, por meio do teste de Tukey-Kramer. Em seguida, foram avaliadas as diferenças entre os índices de vegetação, por meio do teste de Wilcoxon pareado. O NDVI apresentou melhor desempenho na discriminação das áreas de soja na entressafra, particularmente com uso das imagens do dia do ano (DA) 161 a 273, enquanto o EVI apresentou melhor desempenho no período de pleno desenvolvimento da cultura, especificamente com uso das imagens de DA 353 a 33. Portanto, o melhor resultado para classificação da soja, no Estado do Mato Grosso, via séries temporais do sensor Modis, pode ser obtida por meio do uso combinado do NDVI na entresssafra e do EVI no pleno desenvolvimento da soja.Termos para indexação: classificação multitemporal de imagens, dados Modis, estimativas de área agrícola, imagens de satélite, sensoriamento remoto. Modis vegetation indices applied to soybean area discriminationAbstract -The objective of this work was to evaluate the performance of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI) -both from the moderate resolution imaging spectroradiometer (Modis) sensor -to discriminate soybean cultivated areas from sugarcane, pasture, cerrado, and forest ones in the state of Mato Grosso, Brazil. Images acquired during two periods were used: off-season and maximum soybean crop development. For each analyzed class, 31 samples were selected from reference maps, and the differences in the values of each soybean vegetation index were evaluated against the other classes using the Tukey-Kramer test. Afterwards, the differences between the vegetation indices were assessed using the Wilcoxon paired test. NDVI performed best in discriminating soybean areas during the off-season period, particularly when using images acquired from day of year (DOY) 161 to 273, whereas EVI performed best during maximum crop development, particularly when using images from DOY 353 to 33. Therefore, best classification results for soybean in the state of Mato Grosso can be achieved by coupling Modis NDVI images acquired during off-season period and EVI images acquired during the maximum crop development period.Index terms: multi-temporal image classification, Modis data, crop area estimates, satellite images, remote sensing. IntroduçãoMonitorar sistemas dinâmicos, como a agricultura de ciclo anual, é um desafio que demanda recu...

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“…In short, the Canasat sugarcane map overestimated in about 6% the sugarcane area in stratum A. Sugarcane overestimation was also observed for stratum B where the mean errors of inclusion and omission were 1%. Inclusion error in stratum B might be associated with cattle raising activity in the vicinity of sugarcane cultivated area which can cause interpretation errors, especially with well-cultivated pasture land [10,43]. In stratum C, the mean inclusion error of 2% (UAs = 98%) was compensated by the mean omission error of 2% (Pas = 98%) providing accurate area estimation.…”

Section: Resultsmentioning

confidence: 99%

A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil

Adami¹,

Mello²,

Aguiar³

et al. 2012

Remote Sensing

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Abstract:The ability to monitor sugarcane expansion in Brazil, the world's largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of flex fuel cars in 2003 the sugarcane area almost doubled over the last decade in the South-Central region of Brazil. Using remote sensing images, the sugarcane cultivation area was annually monitored and mapped between 2003 and 2012, a period of major sugarcane expansion. The objective of this work was to assess the thematic mapping accuracy of sugarcane, in the crop year 2010/2011, with the novel approach of developing a web platform that integrates different spatial and temporal image resolutions to assist interpreters in classifying a large number of points selected by stratified random sampling. A field campaign confirmed the suitability of the web platform to generate the reference data set. An overall accuracy of 98% with an area estimation error of −0.5% was achieved for the sugarcane map of 2010/11. The accuracy assessment indicated that the map is of excellent quality, offering very accurate sugarcane area estimation for the purpose of agricultural statistics. Moreover, the web platform showed to be very effective in the construction of the reference dataset.

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Multi‐temporal analysis of MODIS data to classify sugarcane crop

Cited by 81 publications

References 24 publications

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Índices de vegetação Modis aplicados na discriminação de áreas de soja

A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil

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