Discriminação de áreas de soja por meio de imagens EVI/MODIS e análise baseada em geo-objeto

Silva, Carlos Antônio da; Frank, Thiago; Rodrigues, Taíssa Caroline Silva

doi:10.1590/s1415-43662014000100007

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…By using MODerate-resolution Imaging Spectrometer (MODIS) products that include the Enhanced Vegetation Index (EVI) [29], among others, we have access to a complete record of data from each of the Terra and Aqua MODIS sensors, at varying spatial (250 m, 0.05 degree) and temporal (8-day) resolutions validated with accuracies depicted by a pixel reliability flag and with globally averaged uncertainties of 0.015 units. Further, the MODIS/EVI combination is a robust set for exploring seasonal crops (soybeans) [22]. The spatial resolution adopted are consistent in expressing accurate cropland information in fields that are larger than 20 ha [13], and appropriate to this task [30].…”

Section: Remote Sensingmentioning

confidence: 99%

“…In common is that References [11][12][13]18,[21][22][23][24] share the best results in the crop classification with their methodologies within the studied area, and present the challenges related to the limited amount of training data to scale up the process. As each of the studies was performed within unique field information and conditions, including different crop varieties contrasted, the results cannot be compared properly.…”

Section: Introductionmentioning

confidence: 99%

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Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification

et al. 2020

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Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country’s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas.

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Section: Remote Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification

et al. 2020

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“…The Landsat-8 platform provides temporal continuity to the Landsat programme and includes new bands in spectral regions that respond to aerosols and cirrus high clouds, two thermal bands, higher signal-to-noise performance, and higher radiometric resolution. In addition, Landsat data are freely available and provide opportunities to analyse land-cover change detection at multiple time scales (Silva Junior, Frank, & Rodrigues, 2014;. Using a radiometric calibration process within the ENVI 5.1 software, all bands of both scenes were transformed from digital numbers to spectral radiance measurements.…”

Section: Remote Sensingmentioning

confidence: 99%

Achieving low‐carbon cattle ranching in the Amazon: ‘Pasture sudden death’ as a window of opportunity

et al. 2018

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Livestock farming is the key sector that can most contribute to low‐carbon agriculture in the Amazon, the region of Brazil that stands most to gain from land‐use intensification and most to lose from further primary habitat loss. Cattle pastures affected by the ‘sudden death’ syndrome, which has decimated exotic grass pastures across seven Amazonian states, have forced cattle ranchers to begin renewing their grazing lands. Vast areas of pasture die‐off create an opportunity to catalyse livestock intensification through public policies. More productive livestock husbandry results in avoided deforestation, mitigation of methane emissions from enteric digestion, and the release of vast previously deforested areas to either more intensive agriculture or forest restoration. This, however, comes with a cautionary note as the much debated promising paradigm of agricultural intensification remains largely untested as a land‐sparing strategy across the humid tropics. Well‐designed government subsidies will, therefore, be required to ensure that thousands of landowners can take advantage of this opportunity with minimal environmental side effects.

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“…Currently, the monitoring of agricultural production in Brazil is still largely performed by questioning farmers and/or cooperatives, known as sampling, carried out by the Brazilian official bodies. This technique requires a long period of time becoming costly (Pino, 2001;Mercante, 2007;Silva Junior et al, 2014). Prior knowledge of agricultural production is important for national economic control and for supplying the internal market.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Winter Crops and Second-Crop Corn in the Paraná State-Brazil, Using Multitemporal Images From MODIS Sensor

Santos¹,

Mercante²,

Johann³

et al. 2019

JAS

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The use of effective technologies for the monitoring of agricultural crops should seek methodologies that provide information regarding crop development, preferably before harvesting. The study of the monitoring and/or estimation of areas using vegetation indices derived from multitemporal data from MODIS sensors is being studied in the search for greater objectivity of the generated values. In this context, the objective of this study was to map areas with winter and second-crop corn using EVI/MODIS time series from the Terra and Aqua satellites, for the seasons from 2012 to 2014 in the Paraná state of Brazil. Accuracy analysis of the mappings was performed in spatial resolution images of 30 m (LISS-III and Landsat-8), to identify and validate the masks the crops of interest. The accuracy of the mapping obtained values of global precision 87.5%, 79.5%, and 82.0%, with Kappa index of 0.81, 0.69, and 0.73, in the 2012, 2013, and 2014 harvests, respectively. Comparing with data from the Brazilian Institute of Geography and Statistics (IBGE), the areas obtained by the mappings were underestimated for the second-crop corn in the 2012 and 2013 seasons and overestimated in 2014. The winter crops were overestimated for the three seasons investigated. The use of remote sensing data and techniques can contribute to a quick estimation of crop area information, and can assist in the surveys conducted by official institutions.

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Discriminação de áreas de soja por meio de imagens EVI/MODIS e análise baseada em geo-objeto

Cited by 14 publications

References 22 publications

Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification

Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification

Achieving low‐carbon cattle ranching in the Amazon: ‘Pasture sudden death’ as a window of opportunity

Mapping of Winter Crops and Second-Crop Corn in the Paraná State-Brazil, Using Multitemporal Images From MODIS Sensor

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