Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar
ilustraciones, diagramas, fotografías, mapas a color, planos
- Autores:
-
Luque Sanabria, Nadia Yurany
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84734
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Industria de la papa
Agricultura
Tecnología agricola
Potato industry
Agriculture
Agricultural technology
Fusión
Imagen óptica
Imagen radar
bosques aleatorios
Máquina de vector de soporte
Fusion
Optical image
Radar image
Random forests
Support vector machine
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
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UNACIONAL2 |
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| dc.title.none.fl_str_mv |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar |
| dc.title.translated.none.fl_str_mv |
Methodology to identify and characterize areas planted in potato in the department of Cundinamarca from the integration of optical and radar images. |
| title |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar |
| spellingShingle |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Industria de la papa Agricultura Tecnología agricola Potato industry Agriculture Agricultural technology Fusión Imagen óptica Imagen radar bosques aleatorios Máquina de vector de soporte Fusion Optical image Radar image Random forests Support vector machine |
| title_short |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar |
| title_full |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar |
| title_fullStr |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar |
| title_full_unstemmed |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar |
| title_sort |
Metodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radar |
| dc.creator.fl_str_mv |
Luque Sanabria, Nadia Yurany |
| dc.contributor.advisor.none.fl_str_mv |
Martinez Martinez, Luis Joel |
| dc.contributor.author.none.fl_str_mv |
Luque Sanabria, Nadia Yurany |
| dc.contributor.orcid.spa.fl_str_mv |
Luque Sanabria, Nadia [https://orcid.org/0000-0002-4108-3231] |
| dc.contributor.cvlac.spa.fl_str_mv |
Luque Sanabria, Nadia [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000840661] |
| dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Industria de la papa Agricultura Tecnología agricola Potato industry Agriculture Agricultural technology Fusión Imagen óptica Imagen radar bosques aleatorios Máquina de vector de soporte Fusion Optical image Radar image Random forests Support vector machine |
| dc.subject.lemb.spa.fl_str_mv |
Industria de la papa Agricultura Tecnología agricola |
| dc.subject.lemb.eng.fl_str_mv |
Potato industry Agriculture Agricultural technology |
| dc.subject.proposal.spa.fl_str_mv |
Fusión Imagen óptica Imagen radar bosques aleatorios Máquina de vector de soporte |
| dc.subject.proposal.eng.fl_str_mv |
Fusion Optical image Radar image Random forests Support vector machine |
| description |
ilustraciones, diagramas, fotografías, mapas a color, planos |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-09-26T15:43:08Z |
| dc.date.available.none.fl_str_mv |
2023-09-26T15:43:08Z |
| dc.date.issued.none.fl_str_mv |
2023-09 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Dataset Image Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/84734 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/84734 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.relation.references.spa.fl_str_mv |
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Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Martinez Martinez, Luis Joel877b052e1ab0d38aa1ac3657f41c949eLuque Sanabria, Nadia Yurany243051a4cce7b039c8ea49b357d2e1c7Luque Sanabria, Nadia [https://orcid.org/0000-0002-4108-3231]Luque Sanabria, Nadia [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000840661]2023-09-26T15:43:08Z2023-09-26T15:43:08Z2023-09https://repositorio.unal.edu.co/handle/unal/84734Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografías, mapas a color, planosIdentificar áreas cultivadas en papa en Colombia permitiría programar mejor los periodos de siembra, disminuyendo la sobreoferta que reduce el precio a los productores. Las imágenes ópticas permiten identificar áreas de cultivo, pero la presencia de nubes es un factor limitante que se puede contrarrestar a través del método de fusión con imágenes radar, que complementa la información faltante. Con base en lo anterior, se desarrolló una metodología para identificar y caracterizar áreas sembradas en papa en Cundinamarca, integrando imágenes Sentinel 1 y 2. Como datos de referencia, se georreferenciaron lotes de papa del primer semestre de 2022 en Villapinzón y Lenguazaque. Se procesaron imágenes de Sentinel 1, Sentinel 2 y Sentinel 2 más índices espectrales de vegetación. Se realizó la fusión por análisis de componentes principales (ACP) y se construyó una imagen multitemporal con los seis primeros componentes de las imágenes seleccionadas. Se aplicaron algoritmos de bosques aleatorios (BA) y máquinas de vector de soporte (MVS). La evaluación de exactitud mejoró en la fusión con ACP, pues el algoritmo MVS aumento 4.7% y BA 7.3% con respecto a la exactitud global hallada con la imagen multitemporal de radar. Con respecto a lo encontrado con las imagenes multitemporales de imágenes ópticas sin incluir e incluyendo índices espectrales, no se encuentra diferencia en la exactitud global de BA, que fue del 96% para las tres imágenes. Para la clase papa, la métrica de exactitud F1 estuvo entre 89 y 92%. El área obtenida para el cultivo de papa en el primer semestre de 2022 con la imagen multitemporal por fusión de ACP y algoritmo BA, fue de 13.202 ha. Este valor es coherente y cercano a la realidad si se compara con lo obtenido en 2021 para el área de estudio (27.593 ha/año; aproximadamente 13.797 ha/semestre). (Texto tomado de la fuente)Identifying cultivated areas with potato crops in Colombia would allow better programming of planting periods, reducing the oversupply that reduces the price to producers. Optical images make it possible to identify croping areas, but clouds are a barrier. This can be counteracted through the fusion method with radar images, which complements the missing information. Based on this, a methodology is presented to identify and characterize areas planted with potatoes in Cundinamarca, integrating Sentinel 1 and 2 images. As reference data, potato lots from the first semester of 2022 in Villapinzon and Lenguazaque were georeferenced. Sentinel 1 and 2 images were processed, including vegetation indices. Fusion was performed by principal component analysis (PCA) and a multitemporal image was constructed with the first six components of the selected images. Random forest (RF) and support vector machine (SVM) algorithms were applied. The accuracy evaluation improved in the fusion with PCA, since the SVM algorithm increased 4.7% and RF 7.3% with respect to the global accuracy found with the multitemporal radar image. With respect to what was found with the multitemporal images of optical images without including and including spectral indices, no difference was found in the global accuracy of RF, which was 96% for the three images. For the potato class, the F1 accuracy metric was between 89 and 92%. Area obtained for potato cultivation in the first semester of 2022, with the multitemporal image by PCA fusion and RF algorithm was 13,202 ha. This value is coherent and close to reality when compared to what was obtained in 2021 for the study area (27,593 ha/year; approximately 13,797 ha/semester).MaestríaGeoinformación para el uso sostenible de los recursos naturalesxv, 83 páginasapplication/pdfUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresIndustria de la papaAgriculturaTecnología agricolaPotato industryAgricultureAgricultural technologyFusiónImagen ópticaImagen radarbosques aleatoriosMáquina de vector de soporteFusionOptical imageRadar imageRandom forestsSupport vector machineMetodología para identificar y caracterizar áreas sembradas en papa en el departamento de Cundinamarca a partir de la integración de imágenes ópticas y de radarMethodology to identify and characterize areas planted in potato in the department of Cundinamarca from the integration of optical and radar images.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionDatasetImageTexthttp://purl.org/redcol/resource_type/TMColombiaCundinamarcaAgronet (s. f.). 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Image and Vision Computing, 21(11), 977–1000. https://doi.org/10.1016/S0262-8856(03)00137-9EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84734/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL53115304.2023.pdf53115304.2023.pdfTesis de Maestría en Geomáticaapplication/pdf5403690https://repositorio.unal.edu.co/bitstream/unal/84734/2/53115304.2023.pdf206833526d2be797b742aa59e59c8cddMD52THUMBNAIL53115304.2023.pdf.jpg53115304.2023.pdf.jpgGenerated Thumbnailimage/jpeg5267https://repositorio.unal.edu.co/bitstream/unal/84734/3/53115304.2023.pdf.jpgdaf8b1cfd0ef63bfc760a22e3192ef1dMD53unal/84734oai:repositorio.unal.edu.co:unal/847342024-08-18 23:13:23.121Repositorio Institucional Universidad Nacional de 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