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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 : |
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 : |
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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Registro original : |
INIA Las Brujas (LB) |
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| Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
03/04/2017 |
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Actualizado : |
29/07/2021 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
AZZIMONTI, G.; GONZALEZ, N.; STANTON, J.; SAINT PIERRE, C.; SINGH, PK.; GERMAN, S.; PEREYRA, S. |
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Afiliación : |
GUSTAVO AZZIMONTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NESTOR RICARDO GONZALEZ PEREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Plant Pathology, University of Minnesota, Saint Paul, MN, USA.; CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo).; CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo).; SILVIA ELISA GERMAN FAEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA ANTONIA PEREYRA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Precision field-based phenotyping for fusarium head blight, within a new multiple diseases platform in uruguay: 2015 results. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
In: International Symposium on Fusarium Head Blight, 5o., International Workshop on Wheat Blast, 2o.,Florianópolis, SC, Brazil,2016. |
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Páginas : |
p.41. |
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Idioma : |
Español |
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Notas : |
P18: Session 1-Germplasm Development and Breeding for Scab Resistance. |
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Palabras claves : |
ENFERMEDADES MULTIPLES; FENOTIPICACIÓN. |
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Thesagro : |
ENFERMEDADES DE LAS PLANTAS; RESISTENCIA. |
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Asunto categoría : |
H20 Enfermedades de las plantas |
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Marc : |
LEADER 00891nam a2200241 a 4500
001 1056952
005 2021-07-29
008 2016 bl uuuu u01u1 u #d
100 1 $aAZZIMONTI, G.
245 $aPrecision field-based phenotyping for fusarium head blight, within a new multiple diseases platform in uruguay$b2015 results.$h[electronic resource]
260 $aIn: International Symposium on Fusarium Head Blight, 5o., International Workshop on Wheat Blast, 2o.,Florianópolis, SC, Brazil$c2016
300 $ap.41.
500 $aP18: Session 1-Germplasm Development and Breeding for Scab Resistance.
650 $aENFERMEDADES DE LAS PLANTAS
650 $aRESISTENCIA
653 $aENFERMEDADES MULTIPLES
653 $aFENOTIPICACIÓN
700 1 $aGONZALEZ, N.
700 1 $aSTANTON, J.
700 1 $aSAINT PIERRE, C.
700 1 $aSINGH, PK.
700 1 $aGERMAN, S.
700 1 $aPEREYRA, S.
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INIA La Estanzuela (LE) |
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