Dynamics of the indices NDVI and GNDVI in a rice growing in its reproduction phase from multi-spectral aerial images taken by drones

dc.contributor.authorGarcía Cárdenas, Diego Alejandro
dc.contributor.authorRamón Valencia, Jacipt Alexander
dc.contributor.authorAlzate Velásquez, Diego Fernando
dc.contributor.authorPalacios Gonzalez, Jordi Rafael
dc.date.accessioned2019-07-03T13:39:26Z
dc.date.available2019-07-03T13:39:26Z
dc.date.issued2018-11-21
dc.description.abstractIn 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.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdf
dc.identifier.citationGarcía Cárdenas, D. A., Ramón Valencia, J. A., Alzate Velásquez, D. F., & Palacios Gonzalez, J. R. (2018). Dynamics of the indices NDVI and GNDVI in a rice growing in its reproduction phase from multi-spectral aerial images taken by drones. Bogotá: doi:10.1007/978-3-030-04447-3_7spa
dc.identifier.doihttps://doi.org/10.1007/978-3-030-04447-3_7spa
dc.identifier.urihttp://hdl.handle.net/11634/17217
dc.publisher.branchCRAI-USTA Bogotáspa
dc.relation.referencesSanint, 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)spa
dc.relation.referencesLau, 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)spa
dc.relation.referencesCuevas, A.: El clima y el cultivo del arroz en Norte de Santander. Revista arroz 60(497), 4–8 (2012)spa
dc.relation.referencesDíaz, 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.pdfspa
dc.relation.referencesFajardo, 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=1spa
dc.relation.referencesBerni, 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.2008.2010457spa
dc.relation.referencesZhou, 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.2017.05.003spa
dc.relation.referencesTeoh, 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.6.4.898spa
dc.relation.referencesBendig, 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/rs61110395spa
dc.relation.referencesMoquete, 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.pdfspa
dc.relation.referencesBerrio, 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.n1.2015.1647spa
dc.relation.referencesEsri: Función NDVI. https://pro.arcgis.com/es/pro-app/help/data/imagery/ndvi-function.htm. Accessed 07 June 2018spa
dc.relation.referencesAraque, 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)spa
dc.relation.referencesSanger, 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)spa
dc.relation.referencesAbdullah, 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)spa
dc.relation.referencesDegiovanni, 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.v9i1.620spa
dc.relation.referencesCandiago, 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/rs70404026spa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.subject.keywordPrecision farmingspa
dc.subject.keywordDronesspa
dc.subject.keywordGeoprocessingspa
dc.subject.keywordMulti-spectral imagesspa
dc.titleDynamics of the indices NDVI and GNDVI in a rice growing in its reproduction phase from multi-spectral aerial images taken by dronesspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Dynamics of the indices NDVI and GNDVI in a rice growing in its reproduction phase from multi-spectral aerial images taken by drones.pdf
Tamaño:
2.42 MB
Formato:
Adobe Portable Document Format
Descripción:
Artículo SCOPUS

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción: