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Pasture Monitoring Applying Normalized Difference Vegetation Index (NDVI) Time Series with Sentinel-2 and Landsat 8 Images, to Improve Milk Production at Santa Mónica Farm, Imbabura, Ecuador

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

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Abstract

The soils have had an increasing pressure due to the intensification of their use for agriculture, forestry, grazing and urbanization. In this way, the implementation of good practices for sustainable soil management are essential to reverse their tendency to degradation as preventive measures and so, guarantee food security and protect the provision of different ecosystem services associated with the soil. The advent of the Sentinel and Landsat satellite programs provide free data sets with good spatial and temporal resolution that can be a valuable source of information for monitoring pasture resources. In order to evaluate this type of techniques, a time series (TS) was generated with images of the Landsat 8 (L8) OLI (Operational Land Imager) sensor and a time series with images of the Sentinel-2 (S2), MSI (Multispectral Imager) sensor to determine the best results in the quantification of changes in the coverage of pastures at the Santa Mónica farm. In this study, pastures were analyzed using the normalized difference vegetation index (NDVI) time series obtained from median quarterly mosaics obtained in 2019. Different samples were drawn that represent the change trend throughout the time series and were classified according to their degree of change and persistence in the series. The results indicate that the densification of the time series allows to provide better results in the quantification of the changes and dynamics of the coverage. The established methodology represents a great advance on the generation of images and the monitoring and detection of changes in coverage through time series [22]. Hence, it is one the first studies carried out in the country that incorporate this type of process. It was concluded that the determination of spectral signatures with the index used together with the near infrared (NIR) and short wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by pastures is identified with an acceptable level of accuracy.

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Author information

Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, Faculty of Engineering in Applied Sciences, Technical University of the North, Avenue 17 de Julio 5-21, Ibarra, Ecuador

    Garrido Fernando & Caranqui Víctor

Authors
  1. Garrido Fernando
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  2. Caranqui Víctor
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Correspondence to Garrido Fernando .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Fernando, G., Víctor, C. (2020). Pasture Monitoring Applying Normalized Difference Vegetation Index (NDVI) Time Series with Sentinel-2 and Landsat 8 Images, to Improve Milk Production at Santa Mónica Farm, Imbabura, Ecuador. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_41

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  • DOI: https://doi.org/10.1007/978-3-030-58817-5_41

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  • Online ISBN: 978-3-030-58817-5

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