Analysis of radar satellite images for terrains characterization in the Colombian countryside
Study of Colombian terrains, which are part of PDET municipalities and projects of Colombia Sostenible, through radar images from the Sentinel-1 satellite of the European Space Agency and fieldwork data from the 2014 National Agricultural Census, to identify vegetation levels and bodies of water in...
- Autores:
-
Ramírez Milano, Daniel Enrique
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/59488
- Acceso en línea:
- http://hdl.handle.net/1992/59488
- Palabra clave:
- Radar Vegetation Index
Sentinel-1
Radar Images
Ingeniería
- Rights
- openAccess
- License
- Attribution-NoDerivatives 4.0 Internacional
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Analysis of radar satellite images for terrains characterization in the Colombian countryside |
| title |
Analysis of radar satellite images for terrains characterization in the Colombian countryside |
| spellingShingle |
Analysis of radar satellite images for terrains characterization in the Colombian countryside Radar Vegetation Index Sentinel-1 Radar Images Ingeniería |
| title_short |
Analysis of radar satellite images for terrains characterization in the Colombian countryside |
| title_full |
Analysis of radar satellite images for terrains characterization in the Colombian countryside |
| title_fullStr |
Analysis of radar satellite images for terrains characterization in the Colombian countryside |
| title_full_unstemmed |
Analysis of radar satellite images for terrains characterization in the Colombian countryside |
| title_sort |
Analysis of radar satellite images for terrains characterization in the Colombian countryside |
| dc.creator.fl_str_mv |
Ramírez Milano, Daniel Enrique |
| dc.contributor.advisor.none.fl_str_mv |
Jiménez Vargas, José Fernando |
| dc.contributor.author.none.fl_str_mv |
Ramírez Milano, Daniel Enrique |
| dc.contributor.other.none.fl_str_mv |
Carroll Janer, Iván Enrique |
| dc.contributor.jury.none.fl_str_mv |
Pearse, Jillian |
| dc.subject.keyword.none.fl_str_mv |
Radar Vegetation Index Sentinel-1 Radar Images |
| topic |
Radar Vegetation Index Sentinel-1 Radar Images Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
Study of Colombian terrains, which are part of PDET municipalities and projects of Colombia Sostenible, through radar images from the Sentinel-1 satellite of the European Space Agency and fieldwork data from the 2014 National Agricultural Census, to identify vegetation levels and bodies of water in them. |
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2022 |
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2022-08-02T18:13:56Z |
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2022-08-02T18:13:56Z |
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2022-06-29 |
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Trabajo de grado - Pregrado |
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Agencia de Renovación para el Territorio. (n.d.). Conozca cómo avanza la implementación de los PDET.https://www.renovacionterritorio.gov.co/especiales/especial_PDET/index.html#Todo sSomosPDET Colombia Sostenible. (n.d.). Qué es Colombia Sostenible. https://www.colombiasostenible.gov.co/index.php/nosotros Chen, V., & Ling, H. (2001). Time-Frequency Transforms for Radar Imaging and Signal Analysis. Artech. Debusscher, B., & Van Coillie, F. (2019). Object-based flood analysis using a graph-based representation. Remote Sensing, 11(16), 2-25. Departamento Administrativo Nacional de Estadísticas (DANE). (2014). Censo Nacional Agropecuario 2014: Las Unidades de Producción Agropecuaria (UPA): Infraestructura, asistencia técnica y financiamiento. (Nro. 3). DANE Departamento Administrativo Nacional de Estadísticas (DANE). (2015). Informe de contexto del 3er Censo Nacional Agropecuario. (Nro. 3). DANE Earth Online. SNAP - Earth Online. https://earth.esa.int/eogateway/tools/snap#:~:text=The%20Sentinel%20Application%20Plat form%20(SNAP,%2C%20Skywatch%2C%20Sensar%20and%20C%2DS. European Comission. (2022). Open Access Hub. Scihub. https://scihub.copernicus.eu. Filipponi, F. (2019). Sentinel-1 GRD preprocessing workflow. Multidisciplinary digital publishing institute proceedings, 18, (1), 1-4 Glavan, M. (2018). Inundation Areas near Urban Areas in the MexicanTropical Wet Inundation Areas near Urban Areas in the Mexican Tropical Wet en López, A., Tapia, F., & Rivera, G (Eds.), Water Challenges of an Urbanizing World (pp.127-144). IntechOpen. Holtgrave, A. K., Röder, N., Ackermann, A., Erasmi, S., & Kleinschmit, B. (2020). Comparing Sentinel-1 and-2 data and indices for agricultural land use monitoring. Remote Sensing, 12(18), 2-27 Janer, I., & Jimenez, F. (2022). Land-use changes by Old Colonies Mennonites in Mexico with Sentinel 2 and Trends Earth. European Journal of Agriculture and Food Sciences, 4(3), 17- 23. Kumar, D., Rao, S., & Sharma, J. R. (2013). Radar Vegetation Index as an alternative to NDVI for monitoring of soyabean and cotton. Indian Cartographer,33, 91-96. Mancera-Flórez, J. & Lizarazo, I. (2020). Land cover classification at three different levels of detail from optical and radar Sentinel SAR data: a case study in Cundinamarca (Colombia). DYNA, 87(215), 136-145. Ministerio de Agricultura y Desarrollo Rural. (n.d.). Zonas de reserva campesina. https://www.minagricultura.gov.co/Normatividad/Paginas/Decreto-1071-2015/Zonas-dereserva-campesina.aspx Schowengerdt, R. (2007). Remote Sensing: Models and Methods for Image Processing. (3er ed). Academic Press. The Canada Centre for Mapping and Earth Observation. (2014). Temporal Resolution. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-airphotos/tutorial-fundamentals-remote-sensing/satellites-and-sensors/temporalresolution/9365 The Canada Centre for Mapping and Earth Observation. (2015a). Radar Basics. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-airphotos/tutorial-fundamentals-remote-sensing/microwave-remote-sensing/radar-basics/9355 The Canada Centre for Mapping and Earth Observation. (2015b). Radar Image Properties. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satelliteimagery-and-air-photos/tutorial-fundamentals-remote-sensing/microwave-remotesensing/radar-image-properties/9299 The Canada Centre for Mapping and Earth Observation. (2015c). Radar Polarimetry. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-airphotos/tutorial-fundamentals-remote-sensing/microwave-remote-sensing/radarpolarimetry/9275 The Canada Centre for Mapping and Earth Observation. (2016). Viewing Geometry and Spatial Resolution. Government of Canada. https://www.nrcan.gc.ca/maps-tools-andpublications/satellite-imagery-and-air-photos/tutorial-fundamentals-remotesensing/microwave-remote-sensing/viewing-geometry-and-spatial-resolution/9341 The European Space Agency. (n.d.-a). Europe's Copernicus programme. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Europe_s_Copernicus_p rogramme The European Space Agency. (n.d.-h). Extra Wide Swath https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/acquisitionmodes/extra-wide-swath The European Space Agency. (n.d.-g). Interferometric Wide Swath. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/acquisitionmodes/interferometric-wide-swath The European Space Agency. (n.d.-d). Instrument Payload. https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/instrument-payload The European Space Agency. (n.d.-e). Level-1. https://sentinels.copernicus.eu/web/sentinel/userguides/sentinel-1-sar/product-types-processing-levels/level-1#:~:text=Level- 1%20data%20can%20be,reduce%20the%20impact%20of%20speckle. The European Space Agency. (n.d.-b). SAR Instrument. https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/sar-instrument The European Space Agency. (n.d.-c). Sentinel-1 Overview. https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/overview The European Space Agency. (n.d.-f). Stripmap. https://sentinels.copernicus.eu/web/sentinel/userguides/sentinel-1-sar/acquisition-modes/stripmap The European Space Agency. (n.d.-j). Topsar Processing. https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/productsalgorithms/level-1-algorithms/topsar-processing The European Space Agency. (n.d.-i). Wave. https://sentinels.copernicus.eu/web/sentinel/userguides/sentinel-1-sar/acquisition-modes/wave Ulaby, F., Long, D., Blackwell, W., Elachi, C., Fung, A., Ruf, C., Sarabandi, K., Zyl, J., & Zebker, H. (2014). Microwave Radar and Radiometric Remote Sensing. The University of Michigan Press. Wagner, W., Bauer-Marschallinger, B., Navacchi, C., Reuß, F., Cao, S., Reimer, C., Schramm, M., & Briese, C. (2021). A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications. Remote Sensing, 13(22), 2-18. |
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Attribution-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Jiménez Vargas, José Fernandovirtual::15700-1Ramírez Milano, Daniel Enrique0ea19ddc-1c53-431d-a452-e8a1984e3211600Carroll Janer, Iván EnriquePearse, Jillian2022-08-02T18:13:56Z2022-08-02T18:13:56Z2022-06-29http://hdl.handle.net/1992/59488instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Study of Colombian terrains, which are part of PDET municipalities and projects of Colombia Sostenible, through radar images from the Sentinel-1 satellite of the European Space Agency and fieldwork data from the 2014 National Agricultural Census, to identify vegetation levels and bodies of water in them.Ingeniero ElectrónicoPregrado50 páginasapplication/pdfengUniversidad de los AndesIngeniería ElectrónicaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaAnalysis of radar satellite images for terrains characterization in the Colombian countrysideTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPRadar Vegetation IndexSentinel-1Radar ImagesIngenieríaAgencia de Renovación para el Territorio. (n.d.). Conozca cómo avanza la implementación de los PDET.https://www.renovacionterritorio.gov.co/especiales/especial_PDET/index.html#Todo sSomosPDETColombia Sostenible. (n.d.). Qué es Colombia Sostenible. https://www.colombiasostenible.gov.co/index.php/nosotrosChen, V., & Ling, H. (2001). Time-Frequency Transforms for Radar Imaging and Signal Analysis. Artech.Debusscher, B., & Van Coillie, F. (2019). Object-based flood analysis using a graph-based representation. Remote Sensing, 11(16), 2-25.Departamento Administrativo Nacional de Estadísticas (DANE). (2014). Censo Nacional Agropecuario 2014: Las Unidades de Producción Agropecuaria (UPA): Infraestructura, asistencia técnica y financiamiento. (Nro. 3). DANEDepartamento Administrativo Nacional de Estadísticas (DANE). (2015). Informe de contexto del 3er Censo Nacional Agropecuario. (Nro. 3). DANEEarth Online. SNAP - Earth Online. https://earth.esa.int/eogateway/tools/snap#:~:text=The%20Sentinel%20Application%20Plat form%20(SNAP,%2C%20Skywatch%2C%20Sensar%20and%20C%2DS.European Comission. (2022). Open Access Hub. Scihub. https://scihub.copernicus.eu.Filipponi, F. (2019). Sentinel-1 GRD preprocessing workflow. Multidisciplinary digital publishing institute proceedings, 18, (1), 1-4Glavan, M. (2018). Inundation Areas near Urban Areas in the MexicanTropical Wet Inundation Areas near Urban Areas in the Mexican Tropical Wet en López, A., Tapia, F., & Rivera, G (Eds.), Water Challenges of an Urbanizing World (pp.127-144). IntechOpen.Holtgrave, A. K., Röder, N., Ackermann, A., Erasmi, S., & Kleinschmit, B. (2020). Comparing Sentinel-1 and-2 data and indices for agricultural land use monitoring. Remote Sensing, 12(18), 2-27Janer, I., & Jimenez, F. (2022). Land-use changes by Old Colonies Mennonites in Mexico with Sentinel 2 and Trends Earth. European Journal of Agriculture and Food Sciences, 4(3), 17- 23.Kumar, D., Rao, S., & Sharma, J. R. (2013). Radar Vegetation Index as an alternative to NDVI for monitoring of soyabean and cotton. Indian Cartographer,33, 91-96.Mancera-Flórez, J. & Lizarazo, I. (2020). Land cover classification at three different levels of detail from optical and radar Sentinel SAR data: a case study in Cundinamarca (Colombia). DYNA, 87(215), 136-145.Ministerio de Agricultura y Desarrollo Rural. (n.d.). Zonas de reserva campesina. https://www.minagricultura.gov.co/Normatividad/Paginas/Decreto-1071-2015/Zonas-dereserva-campesina.aspxSchowengerdt, R. (2007). Remote Sensing: Models and Methods for Image Processing. (3er ed). Academic Press.The Canada Centre for Mapping and Earth Observation. (2014). Temporal Resolution. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-airphotos/tutorial-fundamentals-remote-sensing/satellites-and-sensors/temporalresolution/9365The Canada Centre for Mapping and Earth Observation. (2015a). Radar Basics. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-airphotos/tutorial-fundamentals-remote-sensing/microwave-remote-sensing/radar-basics/9355The Canada Centre for Mapping and Earth Observation. (2015b). Radar Image Properties. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satelliteimagery-and-air-photos/tutorial-fundamentals-remote-sensing/microwave-remotesensing/radar-image-properties/9299The Canada Centre for Mapping and Earth Observation. (2015c). Radar Polarimetry. Government of Canada. https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-airphotos/tutorial-fundamentals-remote-sensing/microwave-remote-sensing/radarpolarimetry/9275The Canada Centre for Mapping and Earth Observation. (2016). Viewing Geometry and Spatial Resolution. Government of Canada. https://www.nrcan.gc.ca/maps-tools-andpublications/satellite-imagery-and-air-photos/tutorial-fundamentals-remotesensing/microwave-remote-sensing/viewing-geometry-and-spatial-resolution/9341The European Space Agency. (n.d.-a). Europe's Copernicus programme. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Europe_s_Copernicus_p rogrammeThe European Space Agency. (n.d.-h). Extra Wide Swath https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/acquisitionmodes/extra-wide-swathThe European Space Agency. (n.d.-g). Interferometric Wide Swath. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/acquisitionmodes/interferometric-wide-swathThe European Space Agency. (n.d.-d). Instrument Payload. https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/instrument-payloadThe European Space Agency. (n.d.-e). Level-1. https://sentinels.copernicus.eu/web/sentinel/userguides/sentinel-1-sar/product-types-processing-levels/level-1#:~:text=Level- 1%20data%20can%20be,reduce%20the%20impact%20of%20speckle.The European Space Agency. (n.d.-b). SAR Instrument. https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/sar-instrumentThe European Space Agency. (n.d.-c). Sentinel-1 Overview. https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/overviewThe European Space Agency. (n.d.-f). Stripmap. https://sentinels.copernicus.eu/web/sentinel/userguides/sentinel-1-sar/acquisition-modes/stripmapThe European Space Agency. (n.d.-j). Topsar Processing. https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/productsalgorithms/level-1-algorithms/topsar-processingThe European Space Agency. (n.d.-i). Wave. https://sentinels.copernicus.eu/web/sentinel/userguides/sentinel-1-sar/acquisition-modes/waveUlaby, F., Long, D., Blackwell, W., Elachi, C., Fung, A., Ruf, C., Sarabandi, K., Zyl, J., & Zebker, H. (2014). Microwave Radar and Radiometric Remote Sensing. The University of Michigan Press.Wagner, W., Bauer-Marschallinger, B., Navacchi, C., Reuß, F., Cao, S., Reimer, C., Schramm, M., & Briese, C. (2021). A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications. 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