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
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86135
https://repositorio.unal.edu.co/
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
id UNACIONAL2_53ea791b4a7b30940d64502882727e68
oai_identifier_str 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
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spelling 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. 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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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