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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.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional |
dc.contributor.advisor | Selvaraj, John Josephraj |
dc.contributor.advisor | Gallego Pérez, Bryan Ernesto |
dc.contributor.author | Lozano Arias, Laura |
dc.coverage.temporal | http://vocab.getty.edu/page/tgn/1024046 |
dc.date.accessioned | 2024-05-22T18:29:24Z |
dc.date.available | 2024-05-22T18:29:24Z |
dc.date.issued | 2023-12-01 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/86135 |
dc.description | ilustraciones, mapas, tablas |
dc.description.abstract | Los 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) |
dc.description.abstract | 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. |
dc.format.extent | xiv, 82 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | spa |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject.ddc | 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
dc.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 |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Palmira - Ingeniería y Administración - Maestría en Ingeniería - Ingeniería Ambiental |
dc.contributor.researchgroup | Recursos Hidrobiológicos |
dc.coverage.region | Tumaco, Nariño, Colombia |
dc.description.degreelevel | Maestría |
dc.description.degreename | Magíster en Ingeniería - Ingeniería Ambiental |
dc.description.methods | 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. |
dc.description.researcharea | Monitoreo, modelación y gestión de recursos naturales |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.faculty | Facultad de Ingeniería y Administración |
dc.publisher.place | Palmira, Valle del Cauca, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Palmira |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.agrovoc | Secuestro de carbono |
dc.subject.agrovoc | Carbon sequestration |
dc.subject.agrovoc | Estimación de las existencias de carbono |
dc.subject.agrovoc | Carbon stock assessments |
dc.subject.agrovoc | Mangles |
dc.subject.agrovoc | Control remoto |
dc.subject.agrovoc | Remote control |
dc.subject.proposal | Biomasa |
dc.subject.proposal | Biomass |
dc.subject.proposal | Carbon stocks |
dc.subject.proposal | Machine learning |
dc.subject.proposal | Mangrove |
dc.subject.proposal | Remote sensing |
dc.subject.proposal | Manglar |
dc.subject.proposal | Teledetección |
dc.subject.proposal | Worldview-2 |
dc.subject.proposal | Reservas de carbono |
dc.subject.proposal | Aprendizaje automático |
dc.subject.unesco | Tumaco |
dc.subject.unesco | Nariño |
dc.title.translated | A new approach for estimating living biomass and stored carbon in mangrove forests using remote sensing and machine learning: Tumaco-Nariño case study. |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
oaire.awardtitle | Investigación de servicios ecosistémicos derivados de bosques de manglar en el Pacífico Colombiano: Valle del Cauca, Cauca, Nariño, Chocó |
oaire.fundername | Sistema General de Regalías |
dcterms.audience.professionaldevelopment | Estudiantes |
dcterms.audience.professionaldevelopment | Grupos comunitarios |
dcterms.audience.professionaldevelopment | Investigadores |
dcterms.audience.professionaldevelopment | Maestros |
dcterms.audience.professionaldevelopment | Medios de comunicación |
dcterms.audience.professionaldevelopment | Padres y familias |
dcterms.audience.professionaldevelopment | Personal de apoyo escolar |
dcterms.audience.professionaldevelopment | Público general |
dcterms.audience.professionaldevelopment | Responsables políticos |
dc.description.curriculararea | Ingeniería.Sede Palmira |
dc.contributor.orcid | Lozano Arias, Laura [0009-0008-7538-0067] |
dc.contributor.cvlac | Lozano Arias, Laura [1144200812] |
dc.contributor.researchgate | Lozano Arias, Laura [https://www.researchgate.net/profile/Laura-Lozano-23] |
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