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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
23/10/2020 |
Actualizado : |
09/04/2021 |
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
Autor : |
HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A. |
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. |
Título : |
Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper) |
Fecha de publicación : |
2020 |
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 |
ISBN : |
e-ISBN: 978-3-030-58811-3 |
DOI : |
10.1007/978-3-030-58811-3_52 |
Idioma : |
Inglés |
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 |
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. |
Palabras claves : |
Agricultural region; Google earth engine; Land-cover map; Supervised classification. |
Asunto categoría : |
A50 Investigación agraria |
Marc : |
LEADER 02413nam a2200229 a 4500 001 1061424 005 2021-04-09 008 2020 bl uuuu u0uu1 u #d 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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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
29/03/2021 |
Actualizado : |
29/03/2021 |
Tipo de producción científica : |
Documentos |
Autor : |
CONSORCIO CITRÍCOLA DEL URUGUAY |
Título : |
Catálogo de variedades cítricas. |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
Salto (UY): INIA-MGAP-UPEFRUY, 2020. |
Páginas : |
19 p. |
Idioma : |
Español |
Notas : |
El denominado Consorcio Citrícola del Uruguay está integrado por INIA Salto Grande (con sede en la zona de Salto Grande), conjuntamente con el Ministerio de Ganadería Agricultura y Pesca (MGAP) y la Unión de Productores de Frutas del Uruguay (UPEFRUY). |
Contenido : |
CONTENIDO:
Nuevas variedades de mandarinas -- Calendario de cosecha nuevas mandarinas -- Ventanas de mercado -- Características generales (F7P3, F4P7, F3P8, F2P3) -- Nuevas variedades de Valencia -- Calendario de cosecha nuevas naranjas -- Características generales (NVA 033, SELP 100, NVA 036). |
Palabras claves : |
NARANJA VALENCIA. |
Thesagro : |
CITRUS; COSECHA; MANDARINAS; MERCADO; NARANJA; VARIEDADES. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15396/1/Consorcio-Citricola-Uruguay-Catalogo-variedades-citricas-2020.pdf
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Marc : |
LEADER 01090nam a2200217 a 4500 001 1061874 005 2021-03-29 008 2020 bl uuuu u0uu1 u #d 100 1 $aCONSORCIO CITRÍCOLA DEL URUGUAY 245 $aCatálogo de variedades cítricas.$h[electronic resource] 260 $aSalto (UY): INIA-MGAP-UPEFRUY$c2020 300 $a19 p. 500 $aEl denominado Consorcio Citrícola del Uruguay está integrado por INIA Salto Grande (con sede en la zona de Salto Grande), conjuntamente con el Ministerio de Ganadería Agricultura y Pesca (MGAP) y la Unión de Productores de Frutas del Uruguay (UPEFRUY). 520 $aCONTENIDO: Nuevas variedades de mandarinas -- Calendario de cosecha nuevas mandarinas -- Ventanas de mercado -- Características generales (F7P3, F4P7, F3P8, F2P3) -- Nuevas variedades de Valencia -- Calendario de cosecha nuevas naranjas -- Características generales (NVA 033, SELP 100, NVA 036). 650 $aCITRUS 650 $aCOSECHA 650 $aMANDARINAS 650 $aMERCADO 650 $aNARANJA 650 $aVARIEDADES 653 $aNARANJA VALENCIA
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