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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 Treinta y Tres. |
Fecha actual : |
14/09/2017 |
Actualizado : |
06/03/2020 |
Tipo de producción científica : |
Abstracts/Resúmenes |
Autor : |
MACEDO, I.; TERRA, J.A. |
Afiliación : |
IGNACIO MACEDO YAPOR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Rice-pastures rotations conversion to more intensive soil use systems: soil organic carbon dynamics impacts.[Abstract]. |
Fecha de publicación : |
2017 |
Fuente / Imprenta : |
In: INTERNATIONAL SYMPOSIUM ON SOIL ORGANIC MATTER (6., 3-7 Sep. 2017, HARPENDER, UK9. Proceedings. Harpender, UK: BSSS, 2017. |
Páginas : |
p. 410. |
Idioma : |
Inglés |
Notas : |
Sessin 6c: SOM in rice paddy systems |
Palabras claves : |
CARBONO ORGÁNICO DEL SUELO; EXPERIMENTOS DE LARGO PLAZO; INTENSIFICACIÓN SOSTENIBLE; LONG TERM EXPERIMENT; RICE ROTATIONS; ROTACIONES ARROCERAS; SISTEMA ARROZ-PASTURAS; SISTEMA ARROZ-SOJA; SOIL ORGANIC CARBON; SUSTAINABLE INTENSIFICATION. |
Thesagro : |
Arroz; SISTEMAS DE PRODUCCIÓN. |
Asunto categoría : |
P36 Erosión conservación y recuperación del suelo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/7263/1/Congreso-2017-Macedo-1.pdf
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Marc : |
LEADER 01008nam a2200277 a 4500 001 1057570 005 2020-03-06 008 2017 bl uuuu u01u1 u #d 100 1 $aMACEDO, I. 245 $aRice-pastures rotations conversion to more intensive soil use systems$bsoil organic carbon dynamics impacts.[Abstract].$h[electronic resource] 260 $aIn: INTERNATIONAL SYMPOSIUM ON SOIL ORGANIC MATTER (6., 3-7 Sep. 2017, HARPENDER, UK9. Proceedings. Harpender, UK: BSSS$c2017 300 $ap. 410. 500 $aSessin 6c: SOM in rice paddy systems 650 $aArroz 650 $aSISTEMAS DE PRODUCCIÓN 653 $aCARBONO ORGÁNICO DEL SUELO 653 $aEXPERIMENTOS DE LARGO PLAZO 653 $aINTENSIFICACIÓN SOSTENIBLE 653 $aLONG TERM EXPERIMENT 653 $aRICE ROTATIONS 653 $aROTACIONES ARROCERAS 653 $aSISTEMA ARROZ-PASTURAS 653 $aSISTEMA ARROZ-SOJA 653 $aSOIL ORGANIC CARBON 653 $aSUSTAINABLE INTENSIFICATION 700 1 $aTERRA, J.A.
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