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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
14/07/2023 |
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Actualizado : |
14/07/2023 |
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Autor : |
LESSEL, J.; CECCATO, P. |
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Afiliación : |
JERROD LESSEL, The International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, NY, United States; PIETRO CECCATO, The International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, NY, United States. |
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Título : |
Creating a basic customizable framework for crop detection using Landsat imagery. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
International Journal of Remote Sensing. 2016, Volume 37, Issue 24, Pages 6097-6107. https://doi.org/10.1080/2150704X.2016.1252471 |
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DOI : |
10.1080/2150704X.2016.1252471 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 06 May 2016, Accepted 15 Oct 2016, Published online: 14 Nov 2016. -- Correspondence author: Lessel, J.; The International Research Institute for Climate and Society, The Earth Institute, Columbia University, Lamont Campus, 61 Route 9W, Monell Building, Palisades, NY, United States; email:jlessel@iri.columbia.edu -- Acknowledgements: The authors thanks Walter Baethgen and Guadalupe Tiscornia for all their guidance and valuable conversations. We would also wish to give a special thanks to INIA for providing the 2013-2014 proposed crop plan and the verified partial crop location maps. -- |
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Contenido : |
Remotely sensed crop identification is essential for countries whose economic vitality is closely tied to agriculture, such as Uruguay. It has been shown that using Normalized Difference Vegetation Index (NDVI) can sometimes produce spurious results when classifying land cover in certain environments. Furthermore, many current crop identification tools use NDVI in order to study and identify crop land-cover for classification techniques. In this study, we present the basic framework for a semi-automated crop identification methodology, which uses a time series analysis to identify soil and vegetation patterns for various crop-cycle scenarios by using the pixel Hue values for land cover identification, at high (30 m) spatial resolution. This is accomplished by converting the Red-Green-Blue (RGB) colour space of a shortwave infrared (SWIR), near-infrared, and red channel composite images, into a Hue-Saturation-Value colour space, then extracting the Hue pixel values that correspond to soil and vegetation over a series of images. We then combine the soil and vegetation pixels in order to create a ?time series? to identify which pixels match different crop-cycle scenarios and isolate them. The shapes are then further isolated to only include those that fit a specific shape area (>20 ha), in order to eliminate spurious results. Our results show an 80% accuracy score between the crop identification methodology and a proposed crop plan over the years 2013-2014 and probabilities of detection of 0.76, 0.89, and 0.88 for the seasons of 2009-2010, 2010-2011, and 2011-2012 respectively, when compared to verified partial crop location maps. The proposed crop plan and the partial crop location maps were provided to us by the Instituto Nacional de Investigación Agropecuaria (INIA) in Uruguay. We also quantitatively investigated the shortcomings of the crop identification methodology, which mostly came from cloud cover and low temporal resolution of the images. © 2016 Informa UK Limited, trading as Taylor & Francis Group. MenosRemotely sensed crop identification is essential for countries whose economic vitality is closely tied to agriculture, such as Uruguay. It has been shown that using Normalized Difference Vegetation Index (NDVI) can sometimes produce spurious results when classifying land cover in certain environments. Furthermore, many current crop identification tools use NDVI in order to study and identify crop land-cover for classification techniques. In this study, we present the basic framework for a semi-automated crop identification methodology, which uses a time series analysis to identify soil and vegetation patterns for various crop-cycle scenarios by using the pixel Hue values for land cover identification, at high (30 m) spatial resolution. This is accomplished by converting the Red-Green-Blue (RGB) colour space of a shortwave infrared (SWIR), near-infrared, and red channel composite images, into a Hue-Saturation-Value colour space, then extracting the Hue pixel values that correspond to soil and vegetation over a series of images. We then combine the soil and vegetation pixels in order to create a ?time series? to identify which pixels match different crop-cycle scenarios and isolate them. The shapes are then further isolated to only include those that fit a specific shape area (>20 ha), in order to eliminate spurious results. Our results show an 80% accuracy score between the crop identification methodology and a proposed crop plan over the years 2013-2014 and probabilities of ... Presentar Todo |
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Palabras claves : |
Image processing; Land cover identifications; Landsat; Remote sensing; Satellite imagery; SOILS; Spatial resolution. |
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Thesagro : |
URUGUAY. |
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Asunto categoría : |
P01 Conservación de la naturaleza y recursos de La tierra |
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Marc : |
LEADER 03460naa a2200253 a 4500
001 1064254
005 2023-07-14
008 2016 bl uuuu u00u1 u #d
024 7 $a10.1080/2150704X.2016.1252471$2DOI
100 1 $aLESSEL, J.
245 $aCreating a basic customizable framework for crop detection using Landsat imagery.$h[electronic resource]
260 $c2016
500 $aArticle history: Received 06 May 2016, Accepted 15 Oct 2016, Published online: 14 Nov 2016. -- Correspondence author: Lessel, J.; The International Research Institute for Climate and Society, The Earth Institute, Columbia University, Lamont Campus, 61 Route 9W, Monell Building, Palisades, NY, United States; email:jlessel@iri.columbia.edu -- Acknowledgements: The authors thanks Walter Baethgen and Guadalupe Tiscornia for all their guidance and valuable conversations. We would also wish to give a special thanks to INIA for providing the 2013-2014 proposed crop plan and the verified partial crop location maps. --
520 $aRemotely sensed crop identification is essential for countries whose economic vitality is closely tied to agriculture, such as Uruguay. It has been shown that using Normalized Difference Vegetation Index (NDVI) can sometimes produce spurious results when classifying land cover in certain environments. Furthermore, many current crop identification tools use NDVI in order to study and identify crop land-cover for classification techniques. In this study, we present the basic framework for a semi-automated crop identification methodology, which uses a time series analysis to identify soil and vegetation patterns for various crop-cycle scenarios by using the pixel Hue values for land cover identification, at high (30 m) spatial resolution. This is accomplished by converting the Red-Green-Blue (RGB) colour space of a shortwave infrared (SWIR), near-infrared, and red channel composite images, into a Hue-Saturation-Value colour space, then extracting the Hue pixel values that correspond to soil and vegetation over a series of images. We then combine the soil and vegetation pixels in order to create a ?time series? to identify which pixels match different crop-cycle scenarios and isolate them. The shapes are then further isolated to only include those that fit a specific shape area (>20 ha), in order to eliminate spurious results. Our results show an 80% accuracy score between the crop identification methodology and a proposed crop plan over the years 2013-2014 and probabilities of detection of 0.76, 0.89, and 0.88 for the seasons of 2009-2010, 2010-2011, and 2011-2012 respectively, when compared to verified partial crop location maps. The proposed crop plan and the partial crop location maps were provided to us by the Instituto Nacional de Investigación Agropecuaria (INIA) in Uruguay. We also quantitatively investigated the shortcomings of the crop identification methodology, which mostly came from cloud cover and low temporal resolution of the images. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
650 $aURUGUAY
653 $aImage processing
653 $aLand cover identifications
653 $aLandsat
653 $aRemote sensing
653 $aSatellite imagery
653 $aSOILS
653 $aSpatial resolution
700 1 $aCECCATO, P.
773 $tInternational Journal of Remote Sensing. 2016, Volume 37, Issue 24, Pages 6097-6107. https://doi.org/10.1080/2150704X.2016.1252471
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
17/06/2015 |
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Actualizado : |
20/06/2015 |
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Tipo de producción científica : |
Informes Agroclimáticos |
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Autor : |
CASTAÑO, J.; GIMENEZ, A.; FUREST, J.; OLIVERA, L. |
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Afiliación : |
JOSE PEDRO CASTAÑO SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AGUSTIN EDUARDO GIMENEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE MARIA FUREST CROCCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LAURA OLIVERA MC ALISTER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Informe Agroclimático 2007 - Situación a Enero. |
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Fecha de publicación : |
2007 |
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Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2007. |
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Páginas : |
7 p. |
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Idioma : |
Español |
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Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICOS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SIMETRICO; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
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Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPORACION; EVAPOTRANSPIRACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SUELO; TEMPERATURA; TERMOMETROS. |
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Asunto categoría : |
P40 Meteorología y climatología |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/4628/1/Inf.Agr.-enero-2007.pdf
http://www.inia.uy/Publicaciones/Paginas/publicacion-1425.aspx
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Marc : |
LEADER 02037nam a2200781 a 4500
001 1052783
005 2015-06-20
008 2007 bl uuuu u0uu1 u #d
100 1 $aCASTAÑO, J.
245 $aInforme Agroclimático 2007 - Situación a Enero.$h[electronic resource]
260 $aMontevideo (Uruguay): INIA$c2007
300 $a7 p.
650 $aAGROCLIMATOLOGIA
650 $aCAMBIO CLIMATICO
650 $aCLIMA
650 $aCLIMATOLOGIA
650 $aESTACIONES METEOROLOGICAS
650 $aESTRES HIDRICO
650 $aEVAPORACION
650 $aEVAPOTRANSPIRACION
650 $aHUMEDAD
650 $aHUMEDAD RELATIVA
650 $aLLUVIA
650 $aMETEOROLOGIA
650 $aPERSPECTIVAS
650 $aPLUVIOMETROS
650 $aPRONOSTICO DEL TIEMPO
650 $aSENSORES
650 $aSISTEMAS
650 $aSISTEMAS DE INFORMACION
650 $aSUELO
650 $aTEMPERATURA
650 $aTERMOMETROS
653 $aAGROCLIMA
653 $aAGROCLIMATOLOGÍA
653 $aBOLETIN AGROCLIMÁTICO
653 $aCARACTERIZACIÓN AGROCLIMÁTICA
653 $aDIRECCION VIENTO
653 $aESTACIONES AGROMETEOROLOGICAS
653 $aESTACIONES AUTOMATICAS
653 $aESTACIONES INIA
653 $aESTADO DEL TIEMPO
653 $aESTRÉS HÍDRICO
653 $aGRAFICAS AGROCLIMATICOS
653 $aGRAS
653 $aHELIOFANOGRAFO
653 $aINFORMACION SATELITAL
653 $aINUNDACIONES
653 $aLLUVIAS DIARIAS
653 $aMAXIMA
653 $aMEDIA
653 $aMINIMA
653 $aPANEL SOLAR
653 $aPERSPECTIVAS CLIMATICAS
653 $aPLUVIOMETRO
653 $aPRECIPITACION NACIONAL
653 $aPREVENCION HELADAS
653 $aPRONOSTICO
653 $aSENSOR
653 $aSIMETRICO
653 $aTANQUE A
653 $aTERMOCUPLAS
653 $aTERMOHIDROGRAFO
653 $aVARIABLES AGROCLIMATICAS
653 $aVELETA
700 1 $aGIMENEZ, A.
700 1 $aFUREST, J.
700 1 $aOLIVERA, L.
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