Generación de un mapa dinámico de zonas propensas a blanqueamiento en el arrecife de Varadero’s Hope Spot, Cartagena de Indias, Colombia, aplicando algoritmos de machine learning en imágenes de PlanetScope y datos de temperatura de la superficie del mar de NOAA en el periodo 2023-2024.
| dc.contributor.advisor | Medina Daza, Rubén Javier | |
| dc.contributor.author | Peña González, Viviana Andrea | |
| dc.contributor.author | Almanza Padilla, José Gabriel | |
| dc.contributor.orcid | Medina Daza, Rubén Javier [0000-0002-9851-9761] | |
| dc.date.accessioned | 2025-03-04T22:06:39Z | |
| dc.date.available | 2025-03-04T22:06:39Z | |
| dc.date.created | 2025-02-13 | |
| dc.description | El proyecto se centra en la creación de un mapa dinámico de las zonas propensas al blanqueamiento coralino en el Arrecife de Varadero's Hope Spot, ubicado en la Bahía de Cartagena, Colombia, durante el período 2023-2024. Se aplicaron técnicas avanzadas de teledetección y algoritmos de machine learning (Random Forest, ANN y U-Net) a imágenes satelitales de PlanetScope y datos de temperatura marina de NOAA para identificar y clasificar las áreas de alto riesgo. El modelo U-Net demostró el mejor desempeño en la segmentación, con una precisión global del 93.5% y un índice Kappa de 0.90. Las validaciones en campo se realizaron mediante inmersiones en el arrecife para confirmar los resultados. El mapa final es una herramienta clave para la gestión ambiental y apoya el desarrollo de estrategias de conservación destinadas a proteger este ecosistema único y resiliente. | |
| dc.description.abstract | The project focuses on creating a dynamic map of bleaching-prone areas in Varadero's Hope Spot Reef, located in the Bay of Cartagena, Colombia, during the 2023-2024 period. Advanced remote sensing techniques and machine learning algorithms (Random Forest, ANN, and U-Net) were applied to PlanetScope satellite images and NOAA sea temperature data to identify and classify high-risk areas. The U-Net model demonstrated the best performance in segmentation, achieving an overall accuracy of 93.5% and a Kappa index of 0.90. Field validations were conducted through reef dives to confirm the results. The final map serves as a key tool for environmental management and supports the development of conservation strategies aimed at protecting this unique and resilient ecosystem. | |
| dc.format.mimetype | ||
| dc.identifier.uri | http://hdl.handle.net/11349/93231 | |
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| dc.rights.acceso | Abierto (Texto Completo) | |
| dc.subject | Blanqueamiento coralino | |
| dc.subject | Arrecife de Varadero's Hope Spot, Cartagena de Indias | |
| dc.subject | Aprendizaje automático | |
| dc.subject | PlanetScope | |
| dc.subject | Red Neuronal UNET | |
| dc.subject | Monitoreo de áreas marinas | |
| dc.subject.keyword | Coral bleaching | |
| dc.subject.keyword | Varadero’s Hope Spot Reef, Cartagena de Indias | |
| dc.subject.keyword | Machine learning | |
| dc.subject.keyword | PlanetScope | |
| dc.subject.keyword | UNET Neural Network | |
| dc.subject.keyword | Marine area monitoring | |
| dc.subject.lemb | Ingeniería Catastral y Geodesia -- Tesis y disertaciones académicas | |
| dc.subject.lemb | Detección a distancia | |
| dc.subject.lemb | Aprendizaje automático (Inteligenia artificial) | |
| dc.subject.lemb | Arrecifes madreporicos | |
| dc.subject.lemb | Gestión ambiental | |
| dc.title | Generación de un mapa dinámico de zonas propensas a blanqueamiento en el arrecife de Varadero’s Hope Spot, Cartagena de Indias, Colombia, aplicando algoritmos de machine learning en imágenes de PlanetScope y datos de temperatura de la superficie del mar de NOAA en el periodo 2023-2024. | |
| dc.title.alternative | Uso de machine learning para la detección de zonas de blanqueamiento en corales de Varadero. | |
| dc.title.titleenglish | Generation of a dynamic map of coral bleaching risk zones in Varadero’s Hope Spot reef, Cartagena de Indias, Colombia, using machine learning with PlanetScope images and NOAA sea surface temperature data (2023-2024). | |
| dc.type | bachelorThesis | |
| dc.type.degree | Monografía |
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