Metodología para la estimación de biomasa aérea en un bosque tropical amazónico a partir de imágenes ópticas y de radar. Caso de estudio: Calamar – Guaviare
| dc.contributor.advisor | Lizarazo Salcedo, Ivan Albert | |
| dc.contributor.author | Osorio Romero, Juan Ricardo Jannereth | |
| dc.contributor.orcid | Osorio Romero, Juan Ricardo Jannereth [0009-0006-7491-750X] | |
| dc.date.accessioned | 2025-09-03T13:18:28Z | |
| dc.date.available | 2025-09-03T13:18:28Z | |
| dc.date.issued | 2025 | |
| dc.description | ilustraciones, diagramas, mapas | |
| dc.description.abstract | Esta investigación propone una metodología para estimar biomasa aérea en bosque tropical amazónico usando imágenes de radar (Sentinel-1), ópticas (Sentinel-2) y algoritmos de aprendizaje de máquina. Se compararon dos enfoques: Random Forest clásico (RF) y su versión geográfica (GRF), que incorpora autocorrelación espacial. Los resultados muestran que RF alcanzó valores de R² entre 0.53 y 0.57, mientras que GRF logró hasta 0.66. Esto indica una mejora sustancial en la precisión del modelo. La metodología demuestra el potencial de integrar sensores remotos y técnicas espaciales para estimar biomasa en regiones de difícil acceso. Sus resultados pueden apoyar iniciativas de monitoreo forestal y conservación, especialmente en contextos de deforestación y cambio climático. (Texto tomado de la fuente) | spa |
| dc.description.abstract | This research proposes a methodology to estimate aboveground biomass in tropical Amazonian forest using radar (Sentinel-1), optical imagery (Sentinel-2), and machine learning. Two approaches were compared: classic Random Forest (RF) and its geographical version (GRF), which includes spatial autocorrelation. RF achieved R² values between 0.53 and 0.57, while GRF reached up to 0.66, showing a significant improvement in accuracy. This confirms the advantage of including spatial structure in biomass modeling. The results highlight the potential of integrating remote sensing and spatial methods in forest biomass estimation, especially in inaccessible tropical areas. The methodology supports forest monitoring and conservation efforts in deforestation and climate mitigation contexts. | eng |
| dc.description.curriculararea | Ciencias Agronómicas.Sede Bogotá | |
| dc.description.degreelevel | Maestría | |
| dc.description.degreename | Magister en Geomática | |
| dc.description.methods | La metodología del estudio se estructuró en cuatro fases principales para estimar la biomasa aérea en bosques tropicales amazónicos mediante imágenes satelitales y algoritmos de aprendizaje de máquina. Primero, se prepararon los datos de entrada a partir de estimaciones de biomasa de la ESA y se definieron dos escenarios de muestreo (con y sin valores de biomasa igual a cero). Luego, en Google Earth Engine se preprocesaron imágenes ópticas Sentinel-2 y de radar Sentinel-1, aplicando correcciones radiométricas y filtros para estandarizar los productos. En la tercera fase, se generaron variables predictoras como índices de vegetación (EVI y SAVI), bandas de radar (VV, VH y derivados), métricas texturales GLCM y variables topográficas. Finalmente, se implementaron y compararon los modelos Random Forest (RF) y Geographically Weighted Random Forest (GRF) en RStudio, optimizando hiperparámetros y evaluando su desempeño mediante métricas de regresión (R², MAE y MSE), así como mapas de error para analizar la distribución espacial de las predicciones | |
| dc.description.researcharea | Tecnologías Geoespaciales | |
| dc.description.technicalinfo | El flujo de trabajo desarrollado para la implementación de los modelos Random Forest (RF) y Geographical Random Forest (GRF), incluyendo el script empleado (con comentarios), los insumos necesarios y el archivo README que orienta su uso, se encuentra disponible en el repositorio de GitHub: https://github.com/juanrosorior/GEOGRAPHICAL-RANDOM-FOREST.git | spa |
| dc.description.technicalinfo | The workflow developed for the implementation of the Random Forest (RF) and Geographical Random Forest (GRF) models, including the script used (with comments), the required inputs, and the README file with usage instructions, is available in the following GitHub repository: https://github.com/juanrosorior/GEOGRAPHICAL-RANDOM-FOREST.git | eng |
| dc.format.extent | xiv, 136 páginas | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.instname | Universidad Nacional de Colombia | spa |
| dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
| dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/88567 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad Nacional de Colombia | |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | |
| dc.publisher.faculty | Facultad de Ciencias Agrarias | |
| dc.publisher.place | Bogotá, Colombia | |
| dc.publisher.program | Bogotá - Ciencias Agrarias - Maestría en Geomática | |
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| dc.rights.license | Atribución-NoComercial 4.0 Internacional | |
| dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales | |
| dc.subject.ddc | 600 - Tecnología (Ciencias aplicadas) | |
| dc.subject.ddc | 520 - Astronomía y ciencias afines::526 - Geografía matemática | |
| dc.subject.ddc | 550 - Ciencias de la tierra::558 - Ciencias de la tierra de América del Sur | |
| dc.subject.lemb | Bosques tropicales | spa |
| dc.subject.lemb | Tropical forests | eng |
| dc.subject.lemb | Reconocimiento de bosques | spa |
| dc.subject.lemb | Forest surveys | eng |
| dc.subject.lemb | Deforestación | spa |
| dc.subject.lemb | Deforestation | eng |
| dc.subject.lemb | Biomasa forestal | spa |
| dc.subject.lemb | Forest biomass | eng |
| dc.subject.other | Teledetección | spa |
| dc.subject.other | Remote sensing | eng |
| dc.subject.proposal | Biomasa aérea | spa |
| dc.subject.proposal | Aprendizaje de máquina | spa |
| dc.subject.proposal | Random Forest | eng |
| dc.subject.proposal | Sensores remotos | spa |
| dc.subject.proposal | Amazonía | spa |
| dc.subject.proposal | Autocorrelación espacial | spa |
| dc.subject.proposal | Random forest geográfico | eng |
| dc.title | Metodología para la estimación de biomasa aérea en un bosque tropical amazónico a partir de imágenes ópticas y de radar. Caso de estudio: Calamar – Guaviare | spa |
| dc.title.translated | Methodology for estimating aboveground biomass in an Amazonian tropical forest using optical and radar imagery. Case study: Calama r– Guaviare | eng |
| dc.type | Trabajo de grado - Maestría | |
| 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.driver | info:eu-repo/semantics/masterThesis | |
| dc.type.redcol | http://purl.org/redcol/resource_type/TM | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dcterms.audience.professionaldevelopment | Estudiantes | |
| dcterms.audience.professionaldevelopment | Investigadores | |
| dcterms.audience.professionaldevelopment | Maestros | |
| dcterms.audience.professionaldevelopment | Bibliotecarios | |
| dcterms.audience.professionaldevelopment | Público general |