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| Registros recuperados : 6 | |
| 1. |  | NAVAS, R.; GAMAZO, P.; VERVOORT, R.W. Bayesian inference of synthetic daily rating curves by coupling Chebyshev Polynomials and the GR4J model. [Conference paper]. IAHS Scientific Assembly 2022 - Hydrological Sciences in the Anthropocene, IAHS 2022, Montpellier (France), 29 May - 3 June 2022. In: Proceedings of the International Association of Hydrological Sciences (IAHS), 2024, Volume 385, Pages 399-406. https://doi.org/10.5194/piahs-385-399-2024 -- OPEN ACCESS. 2199-8981 Article history: Received 13 May 2022, Revised 30 May 2023, Accepted 28 August 2023, Published 19 April 2024. -- Correspondence: : Rafael Navas (rafaelnavas23@gmail.com) -- Source type: Conference Proceedings. -- Document type: Conference...| Biblioteca(s): INIA Las Brujas. |
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| 2. | | NAVAS, R.; TISCORNIA, G.; BERGER, A.; OTERO, A. Assessing MODIS16A2 actual evapotranspiration across three spatial resolutions in Uruguay. [Evaluación de la evapotranspiración de MODIS16A2 en tres resoluciones espaciales en Uruguay.]. [Avaliação do producto da evapotranspiração MODIS16A2 em três resoluções espaciais no Uruguai.] Section: Natural and environmental resources. Agrociencia Uruguay, 2021, vol. 25, n.2, article e429. Doi: https://doi.org/10.31285/AGRO.25.429 Article history: Received 22 Oct 2020; Accepted 04 May 2021; Published 26 Jun 2021.
Editor: Mónica M. Barbazán, Universidad de la República, Montevideo, Uruguay.
Correspondence: Rafael Navas, mail: rafaelnavas23@gmail.com| Biblioteca(s): INIA Las Brujas. |
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| 4. | | NAVAS, R.; MONETTA, A.; ROEL, A.; BLANCO, N.; GIL, A.; GAMAZO, P. Flume calibration on irrigated systems by video image processing and bayesian inference. [Calibración de canales aforadores en sistemas irrigados mediante el procesamiento de imágenes de video y la inferencia bayesiana.]. [Calibração de calhas da vazão em sistemas irrigados por processamento de imagens de vídeo e inferência bayesiana.]. Advances in Water in Agroscience. Integrated catchment management. Agrociencia Uruguay, 2023, Vol.27(NE1), e1182. https://doi.org/10.31285/AGRO.27.1182 -- OPEN ACCESS. Article history: Received 22 April 2023; Accepted 17 August 2023; Published 06 February 2024. -- Editor: Ángela Gorgoglione, Universidad de la República, Montevideo, Uruguay. -- Correspondence: Rafael Navas, rafaelnavas23@gmail.com --...| Biblioteca(s): INIA Las Brujas. |
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| 5. | | HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A. Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper) In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52 Article history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media...| Biblioteca(s): INIA Las Brujas. |
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| 6. | | GELÓS, M.; NEIGHBUR, N.; KOK, P.; BADANO, L.; HASTINGS, F.; NERVI, E.; ALONSO, J.; NAVAS, R.; VERVOORT, W.; BAETHGEN, W. On the prediction of phosphorus fluxes in the santa lucía basin under different land use and management practices using swat model. [abstract] Theme 5 - Impact of phosphorus on environmental quality and on biodiversity. Oral presentation. In: Michelini, D.; Garaycochea, S. (Eds.). 7th Phosphorus in Soils and Plants Symposium (PSP7). "Towards a sustainable phosphorus utilization in agroecosystems." Book of abstracts. PSP7, 3-7 October 2022, Montevideo, Uruguay. p.81. Published By: The organizing committee of the 7th Symposium on Phosphorus in Soils and Plants (PSP7)- National Agricultural Research Institute and School of Agronomy, Universidad de la República, Uruguay.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 6 | |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/10/2020 |
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Actualizado : |
09/04/2021 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A. |
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Afiliación : |
FLORENCIA HASTINGS, School of Agronomy Universidad de la República, Montevideo, Uruguay; Directorate of Natural Resources, Ministry of Agriculture, Livestock and Fisheries, Montevideo, Uruguay; IGNACIO FUENTES, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia; MARIO PÉREZ-BIDEGAIN, School of Agronomy, Universidad de la República, Montevideo, Uruguay; RAFAEL NAVAS NÚÑEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ÁNGELA GORGOGLIONE, School of Engineering, Universidad de la República, Montevideo, Uruguay. |
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Título : |
Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper) |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52 |
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ISBN : |
e-ISBN: 978-3-030-58811-3 |
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DOI : |
10.1007/978-3-030-58811-3_52 |
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Idioma : |
Inglés |
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Notas : |
Article history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH.
20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529.
Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy |
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Contenido : |
Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG. |
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Palabras claves : |
Agricultural region; Google earth engine; Land-cover map; Supervised classification. |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
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024 7 $a10.1007/978-3-030-58811-3_52$2DOI
100 1 $aHASTINGS, F.
245 $aLand-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper)$h[electronic resource]
260 $aIn: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52$c1007
500 $aArticle history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH. 20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529. Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy
520 $aLand-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG.
653 $aAgricultural region
653 $aGoogle earth engine
653 $aLand-cover map
653 $aSupervised classification
700 1 $aFUENTES, I.
700 1 $aPÉREZ-BIDEGAIN, M.
700 1 $aNAVAS, R.
700 1 $aGORGOGLIONE, A.
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