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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
11/04/2022 |
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Actualizado : |
11/04/2022 |
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Autor : |
ROVIRA, L.; CARDELLINO, G. |
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Afiliación : |
LUIS ROVIRA; GUILLERMO CARDELLINO. |
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Título : |
Elementos de riego para productores. |
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Fecha de publicación : |
1987 |
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Fuente / Imprenta : |
Montevideo (Uruguay): MGAP, 1987. |
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Páginas : |
83 p. |
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Idioma : |
Español |
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Palabras claves : |
MOTORES Y BOMBAS PARA RIEGO. |
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Thesagro : |
CONTENIDO DE AGUA EN EL SUELO; EQUIPO DE RIEGO; NECESIDADES DE AGUA; RIEGO; RIEGO POR ASPERSION; RIEGO POR GOTEO. |
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Asunto categoría : |
F06 Riego |
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Marc : |
LEADER 00552nam a2200205 a 4500
001 1062999
005 2022-04-11
008 1987 bl uuuu u00u1 u #d
100 1 $aROVIRA, L.
245 $aElementos de riego para productores.
260 $aMontevideo (Uruguay): MGAP$c1987
300 $a83 p.
650 $aCONTENIDO DE AGUA EN EL SUELO
650 $aEQUIPO DE RIEGO
650 $aNECESIDADES DE AGUA
650 $aRIEGO
650 $aRIEGO POR ASPERSION
650 $aRIEGO POR GOTEO
653 $aMOTORES Y BOMBAS PARA RIEGO
700 1 $aCARDELLINO, G.
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Registro original : |
INIA Treinta y Tres (TT) |
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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
005 2021-04-09
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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INIA Las Brujas (LB) |
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