Drones en la Gestión de Incendios Forestales: Estrategias Avanzadas para la Detección Temprana y Prevención de Incendios en Áreas Vulnerables de Colombia.
| dc.contributor.advisor | Hernández Mejía, Paola Andrea | |
| dc.contributor.author | Home Ballesteros, Gisela | |
| dc.contributor.author | Ramos Castillejo, Yamile Johanna | |
| dc.contributor.corporatename | Universidad Santo Tomás | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000117204 | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001838372 | |
| dc.contributor.orcid | https://orcid.org/0000-0002-4092-0499 | |
| dc.date.accessioned | 2025-07-12T15:44:45Z | |
| dc.date.available | 2025-07-12T15:44:45Z | |
| dc.date.issued | 2025-07-10 | |
| dc.description | El incremento en la frecuencia de incendios forestales plantea un desafío significativo tanto ambiental como social en Colombia. Factores como el cambio climático y la expansión de actividades humanas han intensificado la ocurrencia de estos eventos, afectando no solo la biodiversidad, sino también a las comunidades locales. En el presente artículo se examina el uso de drones como herramienta clave para la detección temprana y gestión de incendios en áreas vulnerables del país. A través de una revisión exhaustiva de la literatura científica disponible en bases de datos académicas, se identificaron avances en la integración de drones con sistemas de inteligencia artificial y con el Internet de las Cosas (IoT), tecnologías que permiten realizar monitoreos en tiempo real y emitir alertas automáticas. Estas alternativas contribuyen a optimizar la respuesta ante emergencias y a reducir los costos operativos. Los resultados destacan la eficacia de los drones en la identificación de focos de incendio, proponiéndolos como una solución accesible y sostenible para gestionar riesgos en zonas de difícil acceso. El artículo subraya la importancia de incorporar estas tecnologías en las políticas de gestión ambiental en Colombia y sugiere futuras investigaciones enfocadas en mejorar la precisión y autonomía de los drones para una respuesta efectiva frente a incendios forestales. | |
| dc.description.abstract | The increase in the frequency of forest fires poses a significant environmental and social challenge in Colombia. Factors such as climate change and the expansion of human activities have intensified the occurrence of these events, affecting not only biodiversity, but also local communities. This article examines the use of drones as a key tool for the early detection and management of fires in vulnerable areas of the country. Through an exhaustive review of the scientific literature available in academic databases, advances were identified in the integration of drones with artificial intelligence systems and the Internet of Things (IoT), technologies that allow real-time monitoring and issuing automatic alerts. These alternatives contribute to optimizing emergency response and reducing operating costs. The results highlight the effectiveness of drones in identifying fire outbreaks, proposing them as an accessible and sustainable solution to manage risks in hard-to-reach areas. The article highlights the importance of incorporating these technologies into environmental management policies in Colombia and suggests future research focused on improving the precision and autonomy of drones for an effective response to forest fires. | |
| dc.description.degreelevel | Especialización | spa |
| dc.description.degreename | Especialista en Derecho Procesal | spa |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Home Ballesteros, G. y Ramos Castillejo. Y. J. (2025). Drones en la Gestión de Incendios Forestales: Estrategias Avanzadas para la Detección Temprana y Prevención de Incendios en Áreas Vulnerables de Colombia.. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional. | |
| dc.identifier.instname | instname:Universidad Santo Tomás | spa |
| dc.identifier.reponame | reponame:Repositorio Institucional Universidad Santo Tomás | spa |
| dc.identifier.repourl | repourl:https://repository.usta.edu.co | spa |
| dc.identifier.uri | http://hdl.handle.net/11634/68315 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad Santo Tomás | spa |
| dc.publisher.branch | CRAI-USTA Bogotá | |
| dc.publisher.faculty | Facultad de Derecho | spa |
| dc.publisher.program | Especialización Derecho Procesal | spa |
| dc.relation.references | Acero Calderón, J. G., Chamorro Quijano, S. A., Muñoz Suazo, W. C., & Rivera Paredes, H. H. (2023). Design of a Drone Applying Multisensor Information for Early Detection of Forest Fires. Universidad Continental. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10601205 | |
| dc.relation.references | Bezas, K., Tsoumanis, G., & Oikonomou, K. (2021). A Coverage Path Planning Algorithm for Self-Organizing Drone Swarms. 2021 International Balkan Conference on Communications and Networking, BalkanCom 2021, 122–126. https://doi.org/10.1109/BalkanCom53780.2021.9593145 | |
| dc.relation.references | Chen, X., Hopkins, B., Wang, H., O’Neill, L., Afghah, F., Razi, A., Fulé, P., Coen, J., Rowell, E., & Watts, A. (2022). Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset. IEEE Access, 10, 121301–121317. https://doi.org/10.1109/ACCESS.2022.3222805 | |
| dc.relation.references | Chowdary, V., Deogharia, D., Sowrabh, S., & Dubey, S. (2022). Forest fire detection system using barrier coverage in wireless sensor networks. Materials Today: Proceedings, 64, 1322–1327. https://doi.org/10.1016/j.matpr.2022.04.202 | |
| dc.relation.references | Cruz, H., Eckert, M., Meneses, J., & Martínez, J. F. (2016). Efficient forest fire detection index for application in Unmanned Aerial Systems (UASs). Sensors (Switzerland), 16(6). https://doi.org/10.3390/s16060893 | |
| dc.relation.references | Easwaran, D., & Palanisamy, S. (2023). Lightweight Deep Neural Network-Based Forest Fire Detection. Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Communications, and Computing, IEEE, 1-5. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10201047 | |
| dc.relation.references | Fouda, M. M., Sakib, S., Fadlullah, Z. M., Nasser, N., & Guizani, M. (2022). A lightweight hierarchical AI model for UAV-enabled edge computing with forest fire detection. IEEE Network, 36(6), 38-45. DOI: 10.1109/MNET.003.2100325. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/9839647 | |
| dc.relation.references | Galvez, M., Saroliya, A., & Gutiérrez, S. (2023). A New Approach for Early Fire Detection in a Semi-Arid Zone Using Drones and the Internet of Things. Proceedings of the 3rd International Conference on Technological Advances in Computational Sciences (ICTACS), IEEE, 1340-1345. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10390224 | |
| dc.relation.references | Gupta, V., Roy, S., Jaiswal, V., Bhardwaj, K., & Rana, P. S. (2022). Drone Assisted Deep Learning based Wildfire Detection System. PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, 162–166. https://doi.org/10.1109/PDGC56933.2022.10053123 | |
| dc.relation.references | Hristov, G., Raychev, J., Kinaneva, D., & Zahariev, P. (2018). Emerging methods for early detection of forest fires using unmanned aerial vehixles and LoRaWAN sensor networks. Institute of Electrical and Electronics Engineers. | |
| dc.relation.references | Engineering (ICACITE), Pune, India, pp. 20-24. IEEE. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10617076 | |
| dc.relation.references | Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM). (2023). Reporte anual de incendios forestales en Colombia. IDEAM. https://www.ideam.gov.co/sala-de-prensa/boletines | |
| dc.relation.references | Ibrahim, A. A., Radhi, A. A., Ali, A. M., & Mohammed, K. H. (2023). IoT-Based Forest Fire Detection Techniques: A Review. IEEE Xplore. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10461943 | |
| dc.relation.references | Jagruthi, H., Rao, V. G., & Nayak, V. Y. (2023). Drones en la mitigación de incendios forestales. Proceedings of the 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023), IEEE, 1-5. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10252566 | |
| dc.relation.references | Kinaneva, D., Hristov, G., Raychev, J., & Zahariev, P. (2019). Early Forest Fire Detection Using Drones and Artificial Intelligence. | |
| dc.relation.references | Li, S., Qiao, L., Zhang, Y., & Yan, J. (2022). An Early Forest Fire Detection System Based on DJI M300 Drone and H20T Camera. 2022 International Conference on Unmanned Aircraft Systems, ICUAS 2022, 932–937. https://doi.org/10.1109/ICUAS54217.2022.9836119 | |
| dc.relation.references | Majli Jaafar, A. H., Dharmesh Dhabi, D., & Al-Farouni, M. (2024). Utilizando la comunicación AI e IoT para SMART Sistema Ambiental en Incidentes de Intrusión e Incendio Escenarios. En Actas de la 4ª Conferencia Internacional sobre Computación Avanzada y Tecnologías Innovadoras en | |
| dc.relation.references | Sabino, A., Lima, L. N., Brito, C., et al. (2024). Forest Fire Monitoring System Supported by Unmanned Aerial Vehicles and Edge Computing: A Performance Evaluation Using Petri Nets. Cluster Computing, 27, 9735–9755. https://link-springer-com.crai-ustadigital.usantotomas.edu.co/article/10.1007/s10586-024-04504-5 | |
| dc.relation.references | Srinath, K., Sri Sathya, K. B., Raguvaran, S., Girish, V. G., & Sriram, V. (2024). Integrated Forest Fire Detection System for Identifying Living Beings Using Drones by Employing a Custom Trained YOLOv5 Model. In Proceedings of the International Conference on Science and Technology Engineering (ICSTEM), Coimbatore, India, pp. 2081–1021. IEEE Xplore. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10560880 | |
| dc.relation.references | Pasolli, E., Melgani, F., Tuia, D., Pacifici, F., & Emery, W. J. (2014). SVM active learning approach for image classification using spatial information. IEEE Transactions on Geoscience and Remote Sensing, 52(4), 2217–2223. https://doi.org/10.1109/TGRS.2013.2258676 | |
| dc.relation.references | Tehseen, A., Zafar, N. A., & Ali, T. (2021). Graph Theory-Based Formal Modeling of Forest Fire Management System using IoT and Drone. 3rd International Conference on Communication Technologies, ComTech 2021, 132–137. https://doi.org/10.1109/ComTech52583.2021.9616876 | |
| dc.relation.references | Ul Ain Tahir, H., Waqar, A., Khalid, S., & Usman, S. M. (2022). Wildfire detection in aerial images using deep learning. 2022 2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022. https://doi.org/10.1109/ICoDT255437.2022.9787417 | |
| dc.relation.references | Wang, Y., Wang, Y., Xu, C., Wang, X., & Zhang, Y. (2024). Forest Fire and Smoke Recognition Driven by Computer Vision Using IoT Drone Cameras. Springer Nature. https://link-springer-com.crai-ustadigital.usantotomas.edu.co/article/10.1007/s11276-024-03718-0 | |
| dc.relation.references | Sairi, A., Labed, S., & Miles, B. (2023). A Review on Early Detection of Forest Fires Using IoT-Enabled WSN. International Conference on Advances in Electronics, Control, and Communication Systems (ICAECCS). IEEE. https://ieeexplore-ieee-org.crai-ustadigital.usantotomas.edu.co/document/10104887 | |
| dc.relation.references | Yandouzi, M., Moussaoui, O., Grari, M., Berrahal, M., Idrissi, I., Azizi, M., Ghoumid, K., & Elmiad, A. K. (2023). Investigation on the Combination of Deep Learning Object Recognition with Drones for Forest Fire Detection and Tracking. International Journal of Advanced Computer Science and Applications (IJACSA), 14(3), 377-380. https://thesai.org/Publications/ViewPaper?Volume=14&Issue=3&Code=IJACSA&SerialNo=42 | |
| dc.relation.references | Yandouzi, M., Mounir GRARI, M., Boukabous, M., Matsi, L., Moussaoui, O., Ghoumid, K., & Lari, L. (2022). Forest Fires Detection using Deep Transfer Learning Aissa KERKOUR ELMIAD 8. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 13, Issue 8). www.ijacsa.thesai.org | |
| dc.rights | Attribution-NonCommercial-NoDerivs 2.5 Colombia | en |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | |
| dc.subject.keyword | Climate Change | |
| dc.subject.keyword | Deforestation | |
| dc.subject.keyword | Forest Fires | |
| dc.subject.keyword | Drones | |
| dc.subject.lemb | Ingeniería ambiental | |
| dc.subject.lemb | Vehículos aéreos no tripulados | |
| dc.subject.lemb | Inteligencia artificial | |
| dc.subject.lemb | Gestión de riesgos -- Aspectos ambientales | |
| dc.subject.proposal | Cambio climático | |
| dc.subject.proposal | Deforestación | |
| dc.subject.proposal | Incendios Forestales | |
| dc.subject.proposal | Drones | |
| dc.title | Drones en la Gestión de Incendios Forestales: Estrategias Avanzadas para la Detección Temprana y Prevención de Incendios en Áreas Vulnerables de Colombia. | |
| dc.type | bachelor thesis | |
| dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | |
| dc.type.drive | info:eu-repo/semantics/bachelorThesis | |
| dc.type.local | Trabajo de grado | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion |
Archivos
Bloque original
1 - 3 de 3
Cargando...
- Nombre:
- 2025giselahome.pdf
- Tamaño:
- 480.11 KB
- Formato:
- Adobe Portable Document Format
Cargando...
- Nombre:
- 2025cartadefacultad.pdf
- Tamaño:
- 1.06 MB
- Formato:
- Adobe Portable Document Format
Cargando...
- Nombre:
- 2025cartadederechosdeautor.pdf
- Tamaño:
- 1.01 MB
- Formato:
- Adobe Portable Document Format
Bloque de licencias
1 - 1 de 1
Cargando...
- Nombre:
- license.txt
- Tamaño:
- 807 B
- Formato:
- Item-specific license agreed upon to submission
- Descripción:

