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| 2. |  | GUTIERREZ, F.; CALISTRO, E.; ROSSI, C.; NOLLA, F.; CARRERE, M. Producción de forraje de mezcla de festuca mediterránea, línea experimental IGP5 en mezcla con Alfalfa, en diferentes sistemas de siembra. (GSF 21). [Forage production of mediterranean fescue, experimental line IGP5, in mixtures with Alfalfa, in different planting systems]. [abstract]. Sección temática: Mejoramiento Genético y Producción de Semillas de Forrajeras. In: Congreso Argentino de Producción Animal, 47., "Ciencia y tecnología para escenarios desafiantes"; 25-27 setiembre 2024, Reconquista, Santa Fé (Argentina). p.57 (Revista Argentina de Producción Animal; vol.44, Suplemento 1)| Biblioteca(s): INIA Las Brujas. |
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| 4. | | VASEN, F.; SIERRA, M.; PARUELO, J.; NEGRO, C.; NOLLA, F.; LAPETINA, J.; SALVAGNO, M. Evaluation of technical production in agricultural sciences a new certification scheme in Uruguay. [La evaluación de la producción tecnológica en ciencias agrariasun nuevo esquema de certificación en Uruguay.]. [Avaliação da produção técnica nas ciências agrícolas um novo esquema de certificação no Uruguai.] Section: Social Sciences, Rural Sociology and Agrarian Economy. Agrociencia Uruguay, 2021, vol. 25, n.2, article e491. Doi: https://doi.org/10.31285/AGRO.25.491 Article history: Received 30 October 2020; Accepted 01 June 2021; Published 12 July 2021.
Editor: Marta Chiappe Hernández, Universidad de la República Montevideo, Uruguay.
Correspondence: Federico Vasen, Universidad de Buenos Aires,...| Biblioteca(s): INIA Las Brujas. |
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| 7. | | FABER, A.C.; ROCHÓN, D.; NOLLA, F.; ROSSI, C.; LATTANZI, F. Yield variation, yield components and yield losses in rainfed tall fescue and annual ryegrass seed production in subtropical Uruguay. [abstract]. In: International Herbage Seed Group Conference (IHSG), 11 th., Angers, Maine-et-Loire, France, June 11-18, 2023. Proceedings and abstracts, p.199.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 7 | |
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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 : |
12/10/2020 |
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Actualizado : |
09/04/2021 |
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Tipo de producción científica : |
Artículos Indexados |
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Autor : |
CAL, A.; TISCORNIA, G. |
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Afiliación : |
ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Unsupervised Methodology to In-Season Mapping of Summer Crops in Uruguay with Modis EVI's Temporal Series and Machine Learning. (Conference-paper) |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, March 2020, Article number 9165614, Pages 183-188. Doi: https://doi.org/10.1109/LAGIRS48042.2020.9165614 |
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ISBN : |
e-ISBN: 978-1-7281-4350-7 |
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Idioma : |
Inglés |
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Notas : |
Artilce history: Date of Conference: 22-26 March 2020. Date Added to IEEE Xplore: 12 August 2020. Published in: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). INSPEC Accession Number: 19872572. Publisher: IEEE. Conference Location: Santiago, Chile, Chile. |
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Contenido : |
ABSTRACT. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of l,667,400 hectares for the 2014-15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops. |
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Palabras claves : |
CROP MAPPING; ELBOW METHOD; EVI; K-MEANS; SMOOTHING SPLINE; TIME-SERIES; UNSUPERVISED. |
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Asunto categoría : |
P40 Meteorología y climatología |
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Marc : |
LEADER 02424nam a2200217 a 4500
001 1061411
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008 2020 bl uuuu u01u1 u #d
100 1 $aCAL, A.
245 $aUnsupervised Methodology to In-Season Mapping of Summer Crops in Uruguay with Modis EVI's Temporal Series and Machine Learning. (Conference-paper)$h[electronic resource]
260 $aIEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, March 2020, Article number 9165614, Pages 183-188. Doi: https://doi.org/10.1109/LAGIRS48042.2020.9165614$c2020
500 $aArtilce history: Date of Conference: 22-26 March 2020. Date Added to IEEE Xplore: 12 August 2020. Published in: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). INSPEC Accession Number: 19872572. Publisher: IEEE. Conference Location: Santiago, Chile, Chile.
520 $aABSTRACT. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of l,667,400 hectares for the 2014-15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops.
653 $aCROP MAPPING
653 $aELBOW METHOD
653 $aEVI
653 $aK-MEANS
653 $aSMOOTHING SPLINE
653 $aTIME-SERIES
653 $aUNSUPERVISED
700 1 $aTISCORNIA, G.
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