Skip to main content

Dynamics of the Indices NDVI and GNDVI in a Rice Growing in Its Reproduction Phase from Multi-spectral Aerial Images Taken by Drones

  • Conference paper
  • First Online:
Book cover Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change II (AACC 2018)

Abstract

In this study, the dynamics of two vegetation indices, the normalized differential vegetative index (NDVI) and the variant of the NDVI that uses the green band (GNDVI) in a rice growing of the variety fedearroz 2000 in reproduction phase, are analyzed. These indices were calculated through the geoprocessing of multi-spectral aerial images taken by a drone or UAVs, with the aim of identifying which zones of the crops are under stress, healthy or dense. The rice growing had an area of approximately 4,1 hectares and its location corresponds to the farm El Faro in the footpath Campo Hermoso within the municipal district of San José de Cúcuta – Norte de Santander. For this research, two flights were carried out, one at the beginning of the reproduction phase dated September 4th 2016 and the second one at the end corresponding to October 8th 2016; these flights were performed with a Iris+ 3DR drone, a canon S100 camera was implemented as a catch images sensor converted into NDVI by using a NGB filter (Near infrared, Green and Blue). As a result, 4 mosaics are shown, one NDVI and one GNDVI on September 4th 2016 and one NDVI and one GNDVI on October 8th 2016, each one of them were classified according to the characteristics observed in field in zones under stress or with low development, healthy and dense zones. Finally, a NDVI dynamic analysis was completed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sanint, L.: Nuevos retos y grandes oportunidades tecnológicas para los sistemas arroceros: Producción, seguridad alimentaria y disminución de la pobreza en América Latina y el Caribe. In: Degiovani, V., Martínez, C., Motta, F. (eds.) Producción ecoeficiente del arroz en América Latina, vol. 370, pp. 3–12. Centro internacional de agricultura tropical (CIAT), Cali, Colombia (2010)

    Google Scholar 

  2. Lau, C., Jarvis, A., Ramírez, J.: Agricultura Colombiana: Adaptación al Cambio Climático. In: CIAT Políticas en Síntesis No. 1, pp. 1–4. Centro Internacional de Agricultura Tropical (CIAT), Cali, Colombia (2011)

    Google Scholar 

  3. Cuevas, A.: El clima y el cultivo del arroz en Norte de Santander. Revista arroz 60(497), 4–8 (2012)

    Google Scholar 

  4. Díaz, J.: Estudio de índices de vegetación a partir de imágenes aéreas tomadas desde UAS/RPAS y aplicaciones de estos a la agricultura de precisión. Universidad Complutense de Madrid, Madrid, España (2015). https://eprints.ucm.es/31423/1/TFM_Juan_Diaz_Cervignon.pdf

  5. Fajardo, J.C.: Apoyo a la agricultura de precisión en Colombia a partir de imágenes adquiridas desde vehículos aéreos no tripulados (UAV’s). Universidad Javeriana, Bogotá D.C., Colombia (2014). https://repository.javeriana.edu.co/bitstream/handle/10554/16484/FajardoJuncoJuanCamilo2014.pdf?sequence=1

  6. Berni, J., Zarco-Tejada, P., Suárez, L., Fereres, E.: Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47, 722–738 (2009). https://doi.org/10.1109/TGRS.2008.2010457

    Article  Google Scholar 

  7. Zhou, X., Zheng, H.B., Xu, X.Q., He, J.Y., Ge, X.K., Yao, X., Cheng, T., Zhu Y., Cao, W.X., Tian, Y.C.: Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. Photogramm. Remote. Sens. 130, 246–255 (2017). https://doi.org/10.1016/j.isprsjprs.2017.05.003

    Article  Google Scholar 

  8. Teoh, C., Mohd Nadzim, N., Mohd Shahmihaizan, M., Mohd Khairil Izani, I., Faizal, K., Mohd Shukry, H.: Rice yield estimation using below cloud remote sensing images acquired by unmanned airborne vehicle system. Int. J. Adv. Sci. Eng. Inf. Technol. 6(4), 516–519 (2016). http://dx.doi.org/10.18517/ijaseit.6.4.898

    Article  Google Scholar 

  9. Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., Bareth, G.: Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens. 6, 10395–10412 (2014). https://doi.org/10.3390/rs61110395

    Article  Google Scholar 

  10. Moquete, C.: Guía técnica el cultivo de arroz. In: Serie Cultivos, No. 37, pp. 1–166. CEDAF, Santo Domingo, República Dominicana (2010). http://www.cedaf.org.do/publicaciones/guias/download/arroz.pdf

  11. Berrio, V., Mosquera, J., Alzate, D.: Uso de drones para el análisis de imágenes multiespectrales en agricultura de precisión. Ciencia y Tecnología Alimentaria, 13(1), 28–40 (2015). https://doi.org/10.24054/16927125.v1.n1.2015.1647

  12. Esri: Función NDVI. https://pro.arcgis.com/es/pro-app/help/data/imagery/ndvi-function.htm. Accessed 07 June 2018

  13. Araque, L., Jiménez, A.: Caracterización de firma espectral a partir de sensores remotos para el manejo de sanidad vegetal en el cultivo de palma de aceite. Revista Palmas 30(3), 63–79 (2009)

    Google Scholar 

  14. Sanger, J.E.: Quantitative investigation of leaf pigments from their inception in buds through autumn coloration to decomposition in falling leaves. Ecology 52, 1075–1089 (1971)

    Article  Google Scholar 

  15. Abdullah, A., Umer, M.: Applications of remote sensing in pest scouting: evaluating options and exploring possibilities. In: Proceedings of 7th ICPA, Julio 25–28, Minneapolis, MN, USA (2004)

    Google Scholar 

  16. Degiovanni, V., Gómez, J., Sierra, J.: Análisis de crecimiento y etapas de desarrollo de tres variedades de arroz (Oryza sativa L.) en Montería, Córdoba. Temas Agrarios, 9(1), 21–29 (2004). https://doi.org/10.21897/rta.v9i1.620

    Article  Google Scholar 

  17. Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., Gattelli, M.: Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 7(4), 4026–4047 (2015). https://doi.org/10.3390/rs70404026

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Diego Alejandro García Cárdenas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

García Cárdenas, D.A., Ramón Valencia, J.A., Alzate Velásquez, D.F., Palacios Gonzalez, J.R. (2019). Dynamics of the Indices NDVI and GNDVI in a Rice Growing in Its Reproduction Phase from Multi-spectral Aerial Images Taken by Drones. In: Corrales, J., Angelov, P., Iglesias, J. (eds) Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change II. AACC 2018. Advances in Intelligent Systems and Computing, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-030-04447-3_7

Download citation

Publish with us

Policies and ethics