{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:18:01Z","timestamp":1743049081725,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030588168"},{"type":"electronic","value":"9783030588175"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58817-5_41","type":"book-chapter","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T16:46:50Z","timestamp":1601398010000},"page":"560-575","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pasture Monitoring Applying Normalized Difference Vegetation Index (NDVI) Time Series with Sentinel-2 and Landsat 8 Images, to Improve Milk Production at Santa M\u00f3nica Farm, Imbabura, Ecuador"],"prefix":"10.1007","author":[{"given":"Garrido","family":"Fernando","sequence":"first","affiliation":[]},{"given":"Caranqui","family":"V\u00edctor","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,30]]},"reference":[{"key":"41_CR1","unstructured":"Altieri, M., Nicholls, C.: Agroecolog\u00eda Teor\u00eda y pr\u00e1ctica para una agricultura sostenible. Programa de las Naciones Unidas para el Medio Ambiente, M\u00e9xico D.F (M\u00e9xico). Primera edici\u00f3n (2000)"},{"key":"41_CR2","unstructured":"Abecia, J.: La \u201cganader\u00eda de precisi\u00f3n\u201d en el sector de los peque\u00f1os rumiantes. Ganader\u00eda, N\u00ba. 95, pp. 34\u201337 (2015). ISSN 1695-1123"},{"key":"41_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","volume":"120","author":"M Drusch","year":"2012","unstructured":"Drusch, M., et al.: Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 120, 25\u201336 (2012)","journal-title":"Remote Sens. Environ."},{"key":"41_CR4","unstructured":"Granados, F.: Uso de Veh\u00edculos A\u00e9reos no trip\u00falados (UAV) para la evaluaci\u00f3n de la producci\u00f3n agrar\u00eda. Instituto de Agricultura Sostenible-IAS\/CSIC (2011)"},{"key":"41_CR5","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.rse.2018.09.028","volume":"218","author":"SM Punalekar","year":"2018","unstructured":"Punalekar, S.M., Verhoef, A., Quaife, T.L., Bermingham, L., Reynolds, C.K.: Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model. Remote Sens. Environ. 218, 207\u2013220 (2018)","journal-title":"Remote Sens. Environ."},{"key":"41_CR6","doi-asserted-by":"publisher","first-page":"12619","DOI":"10.3390\/rs61212619","volume":"6","author":"N Mishra","year":"2014","unstructured":"Mishra, N., Md, O.H., Leigh, L., Aaron, D., Helder, D., Markham, B.: Radiometric cross calibration of Landsat 8 operational land imager (OLI) and Landsat 7 enhanced thematic mapper plus (ETM+). Remote Sens. 6, 12619\u201312638 (2014)","journal-title":"Remote Sens."},{"key":"41_CR7","first-page":"39","volume":"53","author":"H Aguilar","year":"2014","unstructured":"Aguilar, H., Mora, R., Vargas, Ch.: Centro Nacional de Alta Tecnolog\u00eda, Costa Rica: Metodolog\u00eda para la correcci\u00f3n atmosf\u00e9rica de im\u00e1genes aster, rapideye, spot 2 y landsat 8 con el m\u00f3dulo flaash del software ENVI. Revista Geogr\u00e1fica de Am\u00e9rica Central. 53, 39\u201359 (2014)","journal-title":"Revista Geogr\u00e1fica de Am\u00e9rica Central."},{"key":"41_CR8","unstructured":"Bravo, N.: Teledetecci\u00f3n espacial Landsat, Sentinel2, Aster l1t y Modis. 1ra. edici\u00f3n. Geom\u00e1tica Ambiental S.R.L., Hu\u00e1nuco, Per\u00fa (2017)"},{"key":"41_CR9","unstructured":"Benavides, M.F., Nieuwenhuyse, A., Villanueva, C., Ibrahim, M., Tobar, D., Robalino, J.: Capitulo 2 Evaluaci\u00f3n de la condici\u00f3n de pasturas de Brachiaria Brizantha y su impacto econ\u00f3mico en la producci\u00f3n ganadera en la cuenca media del r\u00edo Jes\u00fas Mar\u00eda, Costa Rica. Tesis Maestr\u00eda de socio econom\u00eda ambiental- CATIE (2017)"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Avogadro, D., Padr\u00f3, J.: Diferenciaci\u00f3n de plantaciones forestales en entre r\u00edos (Argentina): comparaci\u00f3n de m\u00e9todos de clasificaci\u00f3n aplicados a im\u00e1genes Sentinel-2 y Landsat-8. Revista Internacional de Ciencia y Tecnolog\u00eda de la Informaci\u00f3n Geogr\u00e1fica. Departamento de Geograf\u00eda, Universitat Aut\u00f2noma de Barcelona Campus de Bellaterra, Catalu\u00f1a, Espa\u00f1a (2019). http:\/\/dx.doi.org\/10.21138\/GF.652","DOI":"10.21138\/GF.652"},{"key":"41_CR11","unstructured":"Agnusdei, M.: Ecofisiolog\u00eda aplicada a pasturas. Unidad 1, Crecimiento de forraje. Grupo Producci\u00f3n y Utilizaci\u00f3n de Pasturas. Argentina: UI EEA INTA Balcarce, FCA UNM (2009)"},{"key":"41_CR12","unstructured":"Le\u00f3n, R., Bonifaz, N., Guti\u00e9rrez, F.: Pastos y forrajes del Ecuador. Siembra y producci\u00f3n de pasturas. Editorial Universitaria Abya-Yala. Universidad Polit\u00e9cnica Salesiana. Quito - Ecuador (2018)"},{"key":"41_CR13","unstructured":"Godoy, P.: Desarrollo de un modelo espacial de riesgo de infecci\u00f3n de fasciola hepatica en vacunos lecheros de la sierra central. Tesis para optar el grado de maestro magister Scientiae en Producci\u00f3n Animal, Lima, Per\u00fa (2018)"},{"key":"41_CR14","unstructured":"FAO. AGP - Praderas, pastizales y cultivos forrajeros. https:\/\/goo.gl\/CsVaKw. Accessed 13 Aug 2019"},{"key":"41_CR15","unstructured":"Rinc\u00f3n, J.J.: Cuantas unidades animales por hect\u00e1rea podemos manejar -PARTE 1 Conceptos b\u00e1sicos necesarios (2017) https:\/\/www.engormix.com\/ganaderia-leche\/articulos\/cuantas-unidades-animales-hectarea-t41122.htm. 14 June 2019"},{"key":"41_CR16","doi-asserted-by":"publisher","unstructured":"Gebremedhin, A., Badenhorst, P., Wang, J., Spangenberg, G., Smith, K.: Prospects for Measurement of Dry Matter Yield in Forage Breeding Programs Using Sensor Technologies. Agronomy MDPI. (2019). https:\/\/doi.org\/10.3390\/agronomy9020065","DOI":"10.3390\/agronomy9020065"},{"key":"41_CR17","unstructured":"ESA. Sentinel. S2 MPC Sen2Cor Software Release Note. Reference: S2-PDGS-MPC-L2A-SRN-V2.8.0 Issue: 02 (2019)"},{"key":"41_CR18","unstructured":"D\u00edaz, J.: Estudio de \u00cdndices de vegetaci\u00f3n a partir de im\u00e1genes a\u00e9reas tomadas desde UAS\/RPAS y aplicaciones de estos a la agricultura de precisi\u00f3n. Trabajo fin de m\u00e1ster curso 2014\u20132015. Universidad Complutense de Madrid, Madrid, Espa\u00f1a (2015)"},{"key":"41_CR19","unstructured":"Tello, J., G\u00f3mez-B\u00e1guena, R., Casterad, M.A.: Comparaci\u00f3n y ajuste en zonas agr\u00edcolas de \u00edndices de vegetaci\u00f3n derivados de Landsat-8 y Sentinel-2. In: Ruiz, L.A., Estornell, J., Erena, M. (eds.) Nuevas plataformas y sensores de teledetecci\u00f3n. XVII Congreso de la Asociaci\u00f3n Espa\u00f1ola de Teledetecci\u00f3n, pp. 81\u201384, Murcia, Espa\u00f1a (2017)"},{"key":"41_CR20","unstructured":"ESA (European Space Agency). http:\/\/www.esa.int\/. Accessed 16 Feb 2020"},{"key":"41_CR21","unstructured":"USGS (U.S. Geological Survey). https:\/\/www.usgs.gov\/. Accessed 10 Mar 2020"},{"key":"41_CR22","doi-asserted-by":"crossref","unstructured":"Zaraza, M.A., Manrique, L.M.: Generaci\u00f3n de datos de cambio de coberturas vegetales en la sabana de Bogot\u00e1 mediante el uso de series temporales con im\u00e1genes Landsat e im\u00e1genes sint\u00e9ticas MODIS-Landsat entre los a\u00f1os 2007 y 2013. Revista de Teledetecci\u00f3n Asociaci\u00f3n Espa\u00f1ola de Teledetecci\u00f3n (2019). https:\/\/doi.org\/10.4995\/raet.2019.12280","DOI":"10.4995\/raet.2019.12280"},{"key":"41_CR23","unstructured":"Hern\u00e1ndez, H.: Procesamiento digital de im\u00e1genes. Universidad de Chile. (2011). ISBN 978-956-353-324-8"},{"key":"41_CR24","unstructured":"Chuvieco, E.: Teledetecci\u00f3n Ambiental: La observaci\u00f3n de la Tierra desde el Espacio, 3ra edici\u00f3n. Ariel Ciencia, Barcelona, Espa\u00f1a (2008)"},{"key":"41_CR25","unstructured":"Rouse, J., Haas, R., Schell, J., Deering, D.: Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. In: Fraden, S.C. (ed.) Third ERTS-1 Symposium, 10\u201314 December 1973, NASA SP-351, pp. 309-317. Goddard Space Flight Center Texas A&M University College Station, Texas (1974)"},{"key":"41_CR26","first-page":"417","volume":"13","author":"Z Maskova","year":"2008","unstructured":"Maskova, Z., Zemek, F., Kvet, J.: Normalized difference vegetation index (NDVI) management of mountain meadows. Boreal Environ. Res. 13, 417\u2013432 (2008)","journal-title":"Boreal Environ. Res."},{"issue":"2","key":"41_CR27","first-page":"167","volume":"12","author":"J Soria","year":"2005","unstructured":"Soria, J., Granados, R.: Relaci\u00f3n entre los \u00edndices de vegetaci\u00f3n obtenidos de los sensores AVHRR del sat\u00e9lite NOAA y TM del Landsat. Ciencia Ergo Sum 12(2), 167\u2013174 (2005). Universidad Aut\u00f3noma del Estado de M\u00e9xico, M\u00e9xico","journal-title":"Ciencia Ergo Sum"},{"issue":"5","key":"41_CR28","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/S1002-0160(10)60053-7","volume":"20","author":"CH Ju","year":"2010","unstructured":"Ju, C.H., Tian, Y.C., Yao, X., Cao, W.X., Zhu, Y., Hannaway, D.: Estimating leaf chlorophyll content using red edge parameters. Pedosphere 20(5), 633\u2013644 (2010)","journal-title":"Pedosphere"},{"key":"41_CR29","unstructured":"LANDVIEWER. https:\/\/eos.com\/lv\/es\/, https:\/\/eos.com\/landviewer\/?id=LE07_L1GT_010060_20200328_20200330_01_RT&b=Red,Green,Blue&anti&pansharpening&lat=-0.00195&lng=-77.26364&z=8. Accessed 03 Apr 2020"},{"key":"41_CR30","unstructured":"Congedo, L.: (SCP) Semi-Automatic Classification Plugin. Documentation. Versi\u00f3n 6.4.0.2 (2020). https:\/\/plugins.qgis.org\/plugins\/SemiAutomaticClassificationPlugin\/"},{"key":"41_CR31","unstructured":"Carmelo, A., Moreno, A., Rodr\u00edguez, E.: Determinaci\u00f3n experimental de la firma espectral de la vegetaci\u00f3n. una sencilla pr\u00e1ctica de introducci\u00f3n a la Teledetecci\u00f3n. In: Avances y Aplicaciones. VIII Congreso Nacional de Teledetecci\u00f3n, Albacete, Espa\u00f1a, pp. 429\u2013432 (1999)"},{"key":"41_CR32","unstructured":"AccuWeather. https:\/\/www.accuweather.com\/es\/ec\/national\/satellite-wv. Accessed 24 Feb 2020"},{"key":"41_CR33","unstructured":"INAMHI. https:\/\/www.serviciometeorologico.gob.ec"},{"key":"41_CR34","unstructured":"Brizuela, A., Aguirre, C., Velasco, I.: Aplicaci\u00f3n de m\u00e9todos de correcci\u00f3n atmosf\u00e9rica de datos Landsat 5 para an\u00e1lisis multitemporal. Teledetecci\u00f3n, Ed. Martin, UBA, Buenos Aires, Argentina (2007)"},{"key":"41_CR35","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","volume":"24","author":"J Chavez","year":"1988","unstructured":"Chavez, J.: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24, 459\u2013479 (1988)","journal-title":"Remote Sens. Environ."},{"key":"41_CR36","unstructured":"Tagestad, J.: Radiometric standardization of adjacent Landsat Thematic Mapper Image for multi-scene mosaics. Master of Science, Utah State University, Logan, Utah (2000)"},{"key":"41_CR37","doi-asserted-by":"publisher","first-page":"353366","DOI":"10.1080\/014311600210876","volume":"21","author":"JA Sobrino","year":"2000","unstructured":"Sobrino, J.A., Raissouni, N.: Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. Int. J. Remote Sens. 21, 353366 (2000)","journal-title":"Int. J. Remote Sens."},{"key":"41_CR38","unstructured":"GRASS GIS. https:\/\/grass.osgeo.org\/, https:\/\/grass.osgeo.org\/grass78\/manuals\/i.landsat.toar.html. Accessed 17 Sept 2019"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58817-5_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T14:46:59Z","timestamp":1619189219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58817-5_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030588168","9783030588175"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58817-5_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cyber chair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1450","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"466","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held virtually due to COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}