Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño
ilustraciones, mapas, tablas
- Autores:
-
Lozano Arias, Laura
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86135
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Secuestro de carbono
Carbon sequestration
Estimación de las existencias de carbono
Carbon stock assessments
Mangles
Control remoto
Remote control
Biomasa
Manglar
Teledetección
Reservas de carbono
Aprendizaje automático
Biomass
Carbon stocks
Machine learning
Mangrove
Remote sensing
Worldview-2
Tumaco
Nariño
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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oai:repositorio.unal.edu.co:unal/86135 |
network_acronym_str |
UNACIONAL2 |
network_name_str |
Universidad Nacional de Colombia |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
dc.title.translated.eng.fl_str_mv |
A new approach for estimating living biomass and stored carbon in mangrove forests using remote sensing and machine learning: Tumaco-Nariño case study. |
title |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
spellingShingle |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Secuestro de carbono Carbon sequestration Estimación de las existencias de carbono Carbon stock assessments Mangles Control remoto Remote control Biomasa Manglar Teledetección Reservas de carbono Aprendizaje automático Biomass Carbon stocks Machine learning Mangrove Remote sensing Worldview-2 Tumaco Nariño |
title_short |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
title_full |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
title_fullStr |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
title_full_unstemmed |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
title_sort |
Un nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-Nariño |
dc.creator.fl_str_mv |
Lozano Arias, Laura |
dc.contributor.advisor.none.fl_str_mv |
Selvaraj, John Josephraj Gallego Pérez, Bryan Ernesto |
dc.contributor.author.none.fl_str_mv |
Lozano Arias, Laura |
dc.contributor.researchgroup.spa.fl_str_mv |
Recursos Hidrobiológicos |
dc.contributor.orcid.spa.fl_str_mv |
Lozano Arias, Laura [0009-0008-7538-0067] |
dc.contributor.cvlac.spa.fl_str_mv |
Lozano Arias, Laura [1144200812] |
dc.contributor.researchgate.spa.fl_str_mv |
Lozano Arias, Laura [https://www.researchgate.net/profile/Laura-Lozano-23] |
dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Secuestro de carbono Carbon sequestration Estimación de las existencias de carbono Carbon stock assessments Mangles Control remoto Remote control Biomasa Manglar Teledetección Reservas de carbono Aprendizaje automático Biomass Carbon stocks Machine learning Mangrove Remote sensing Worldview-2 Tumaco Nariño |
dc.subject.agrovoc.none.fl_str_mv |
Secuestro de carbono Carbon sequestration Estimación de las existencias de carbono Carbon stock assessments Mangles Control remoto Remote control |
dc.subject.proposal.spa.fl_str_mv |
Biomasa Manglar Teledetección Reservas de carbono Aprendizaje automático |
dc.subject.proposal.eng.fl_str_mv |
Biomass Carbon stocks Machine learning Mangrove Remote sensing Worldview-2 |
dc.subject.unesco.none.fl_str_mv |
Tumaco Nariño |
description |
ilustraciones, mapas, tablas |
publishDate |
2023 |
dc.date.issued.none.fl_str_mv |
2023-12-01 |
dc.date.accessioned.none.fl_str_mv |
2024-05-22T18:29:24Z |
dc.date.available.none.fl_str_mv |
2024-05-22T18:29:24Z |
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 |
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/86135 |
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/86135 https://repositorio.unal.edu.co/ |
identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.references.spa.fl_str_mv |
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M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42–57). Elsevier Inc. https://doi.org/10.1016/j.rse.2014.02.015 Palacios- penaranda, M. L. (2017). Evaluación del almacenamiento de carbono como servicio ecosistémico en bosques de manglar de la costa pacífica colombiana. [UNIVERSIDAD DEL VALLE]. https://bibliotecadigital.univalle.edu.co/handle/10893/14485 Rahman, M. M., Lagomasino, D., Lee, S. K., Fatoyinbo, T., Ahmed, I., & Kanzaki, M. (2019). Improved assessment of mangrove forests in Sundarbans East Wildlife Sanctuary using WorldView 2 and TanDEM-X high resolution imagery. Remote Sensing in Ecology and Conservation, 5(2), 136–149. https://doi.org/10.1002/rse2.105 Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. 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Estuarine, Coastal and Shelf Science, 227(April 2018), 106299. https://doi.org/10.1016/j.ecss.2019.106299 Saldarriaga, J. G., Duque, A. J., & Álvarez, E. (2011). Modelos para la estimación de la biomasa y el carbono en diferentes tipos de bosque del choco biogeográfico, Colombia: manglar, guandal y bosques de colina localizados en los Consejos Comunitarios de Bajo Mira y Concosta a partir de los datos colectados e. Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), Ministerio de Medio Ambiente, Vivienda y Desarrollo Territorial (MAVDT), Fundación Natura, Fundación Gordon y Betty Moore – USAID. Santos, T., & Freire, S. (2015). Testing the Contribution of WorldView-2 Improved Spectral Resolution for Extracting Vegetation Cover in Urban Environments. Canadian Journal of Remote Sensing, 41(6), 505–514. https://doi.org/10.1080/07038992.2015.1110011 SciPy Community. (2024). SciPy documentation. SciPy v1.12.0 Manual. https://docs.scipy.org/doc/scipy/index.html Selvaraj, J. 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A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: Application to Ciénaga Grande de Santa Marta, Colombia. Remote Sensing of Environment, 112(5), 2131–2144. https://doi.org/10.1016/j.rse.2007.10.012 Spalding, M. D., & Leal, M. (2021). The State of the World’s Mangroves 2021. Global Mangrove Alliance. |
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Universidad Nacional de Colombia |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Selvaraj, John Josephraj539c2f732e0e2883867d8ca4e8fd6141Gallego Pérez, Bryan Ernesto7ab1c2e2868563c0c1e812576c47092fLozano Arias, Laura545fd70b73e45864c53a3749702309fb600Recursos HidrobiológicosLozano Arias, Laura [0009-0008-7538-0067]Lozano Arias, Laura [1144200812]Lozano Arias, Laura [https://www.researchgate.net/profile/Laura-Lozano-23]http://vocab.getty.edu/page/tgn/1024046Tumaco, Nariño, Colombia2024-05-22T18:29:24Z2024-05-22T18:29:24Z2023-12-01https://repositorio.unal.edu.co/handle/unal/86135Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, mapas, tablasLos manglares desempeñan un papel crucial en la mitigación del cambio climático al absorber y retener hasta cinco veces más carbono que otros bosques. Es importante determinar la biomasa viva y el carbono almacenado en estos ecosistemas para proporcionar una base sólida en la planificación y gestión gubernamental. Este estudio presenta un enfoque innovador ya que utiliza herramientas de teledetección junto con datos recolectados en campo, imágenes Worldview 2 y evalúa dos algoritmos de aprendizaje automático, Random Forest y Support Vector Regression. El caso de estudio en el manglar de Tumaco, Nariño, incluyó el cálculo de la superficie del bosque por medio de una clasificación supervisada, la estimación de la biomasa viva (aboveground y belowground) y el carbono almacenado, y la evaluación de los modelos. Los resultados revelaron una precisión global del 87% en la clasificación de coberturas, con valores promedio de 192.50 ± 102.78 para la biomasa aérea, 79.95 ± 56.85 para la biomasa subterránea y 127.43 ± 73.49 para el carbono almacenado. El modelo basado en Random Forest destacó con un rendimiento sobresaliente, mostrando un RMSE de 140.68 ± 98.76 y un R2 de 0.78 ± 0.28, superando a modelos globales. Adicionalmente, se evidenció que los índices espectrales fortalecen la capacidad del modelo para explicar y predecir la biomasa aérea. Se sugiere explorar el uso de imágenes Lidar y datos SAR para mejorar la precisión en estudios locales con mayor resolución espacial. (Texto tomado de la fuente)Mangroves play a crucial role in climate change mitigation by absorbing and sequestering up to five times more carbon than other forests. It is important to determine the living biomass and carbon stored in these ecosystems to provide a sound basis for government planning and management. This study presents an innovative approach using remote sensing tools together with field collected data, using Worldview 2 imagery and evaluating two machine learning algorithms, Random Forest and Support Vector Regression. The case study in the mangrove forest of Tumaco, Nariño, included the calculation of forest area by supervised classification, estimation of live biomass (aboveground and belowground) and carbon stock, and evaluation of the models. The results revealed an overall accuracy of 87% in cover classification, with average values of 192.50 ± 102.78 for aboveground biomass, 79.95 ± 56.85 for belowground biomass and 127.43 ± 73.49 for carbon stock. The Random Forest based model stood out with an outstanding performance, showing an RMSE of 140.68 ± 98.76 and an R2 of 0.78 ± 0.28, outperforming global models. Additionally, it was evidenced that the spectral indices strengthen the model's ability to explain and predict aerial biomass. It is suggested to explore the use of Lidar images and SAR data to improve accuracy in local studies with higher spatial resolution.MaestríaMagíster en Ingeniería - Ingeniería AmbientalEs importante determinar la biomasa viva y el carbono almacenado en estos ecosistemas para proporcionar una base sólida en la planificación y gestión gubernamental. Este estudio presenta un enfoque innovador ya que utiliza herramientas de teledetección junto con datos recolectados en campo, imágenes Worldview 2 y evalúa dos algoritmos de aprendizaje automático, Random Forest y Support Vector Regression. El caso de estudio en el manglar de Tumaco, Nariño, incluyó el cálculo de la superficie del bosque por medio de una clasificación supervisada, la estimación de la biomasa viva (aboveground y belowground) y el carbono almacenado, y la evaluación de los modelos.Monitoreo, modelación y gestión de recursos naturalesIngeniería.Sede Palmiraxiv, 82 páginasapplication/pdfspaUniversidad Nacional de ColombiaPalmira - Ingeniería y Administración - Maestría en Ingeniería - Ingeniería AmbientalFacultad de Ingeniería y AdministraciónPalmira, Valle del Cauca, ColombiaUniversidad Nacional de Colombia - Sede Palmira620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaSecuestro de carbonoCarbon sequestrationEstimación de las existencias de carbonoCarbon stock assessmentsManglesControl remotoRemote controlBiomasaManglarTeledetecciónReservas de carbonoAprendizaje automáticoBiomassCarbon stocksMachine learningMangroveRemote sensingWorldview-2TumacoNariñoUn nuevo modelo para la estimación de la biomasa viva y el carbono almacenado en los bosques de manglar usando sensoramiento remoto y aprendizaje de máquina: caso de estudio Tumaco-NariñoA new approach for estimating living biomass and stored carbon in mangrove forests using remote sensing and machine learning: Tumaco-Nariño case study.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAhmad, A., Gilani, H., & Ahmad, S. R. (2021). Forest aboveground biomass estimation and mapping through high-resolution optical satellite imagery—a literature review. Forests, 12(7), 914. https://doi.org/10.3390/F12070914/S1Alboukadel, K. (2023). RDocumentation. Factoextra. fviz_contrib (1.0.7). https://www.rdocumentation.org/packages/factoextra/versions/1.0.7/topics/fviz_contribAldrich, R. C., Bailey, W. F., & Heller, R. C. (1959). Large Scale 70 mm. Color Photography Techniques and Equipment and Their Application to a Forest Sampling Problem. Photogrammetric Engineering, 25, 747–754.FAO. (2007). The world’s mangroves 1980-2005. In FAO Forestry Paper (Vol. 153).FAO, Yigini, Y., Olmedo, G. F., Reiter, S., Baritz, R., Viatkin, K., & Vargas, R. R. (2018). Soil Organic Carbon Mapping Cookbook. 2nd Edition, Rome. https://fao-gsp.github.io/SOC-Mapping-Cookbook/mappingMethods.html#rfCongalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: Principles and practices, second edition. In Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition.Anand, A., Pandey, P. C., Petropoulos, G. P., Pavlides, A., Srivastava, P. K., Sharma, J. K., & Malhi, R. K. M. (2020). Use of hyperion for mangrove forest carbon stock assessment in bhitarkanika forest reserve: A contribution towards blue carbon initiative. Remote Sensing, 12(4). https://doi.org/10.3390/rs12040597Escobar, H. A. T. (2010). Documento síntesis: Caracterización, Diagnóstico y Zonificación de los Manglares en el Departamento de Nariño.Forkuor, G., Benewinde Zoungrana, J. B., Dimobe, K., Ouattara, B., Vadrevu, K. P., & Tondoh, J. E. (2020). Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study. Remote Sensing of Environment, 236(November 2019), 111496. https://doi.org/10.1016/j.rse.2019.111496Gao, B.-C. (1996). NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. REMOTE SENS. 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Remote Sensing of Environment, 112(5), 2131–2144. https://doi.org/10.1016/j.rse.2007.10.012Spalding, M. D., & Leal, M. (2021). The State of the World’s Mangroves 2021. Global Mangrove Alliance.Investigación de servicios ecosistémicos derivados de bosques de manglar en el Pacífico Colombiano: Valle del Cauca, Cauca, Nariño, ChocóSistema General de RegalíasEstudiantesGrupos comunitariosInvestigadoresMaestrosMedios de comunicaciónPadres y familiasPersonal de apoyo escolarPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86135/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1144200812.2024.pdf1144200812.2024.pdfapplication/pdf2952513https://repositorio.unal.edu.co/bitstream/unal/86135/2/1144200812.2024.pdf07639ee190f34706854fff07e9a3c39fMD52THUMBNAIL1144200812.2024.pdf.jpg1144200812.2024.pdf.jpgGenerated Thumbnailimage/jpeg5678https://repositorio.unal.edu.co/bitstream/unal/86135/3/1144200812.2024.pdf.jpg354dd9e8f8933982f7330e40600fc4a1MD53unal/86135oai:repositorio.unal.edu.co:unal/861352024-08-24 23:14:20.012Repositorio Institucional Universidad Nacional 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