Metodología para la evaluación de los incendios de la vegetación en la Sierra de la Macarena utilizando técnicas de análisis de imágenes basadas en objetos geográficos y árboles de decisión

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dc.contributor.advisorDaza Medina, Rubén Javier
dc.contributor.authorCabezas MartÍn, Gonzalo
dc.contributor.orcidDaza Medina, Rubén Javier [0000-0002-9851-9761]
dc.date.accessioned2025-11-06T04:00:07Z
dc.date.available2025-11-06T04:00:07Z
dc.date.created2025-10-16
dc.descriptionEste estudio evalúa la severidad y el impacto de los incendios forestales en la Sierra de La Macarena, Meta, durante el periodo 2018-2024, empleando imágenes Sentinel-2 MSI y técnicas avanzadas de análisis geoespacial. A través de un enfoque basado en el análisis de imágenes por objetos geográficos (GEOBIA) y la implementación de árboles de decisión y minería de patrones secuenciales, se clasificaron las áreas afectadas, permitiendo no solo cuantificar la severidad del fuego, sino también identificar patrones de regeneración vegetal a lo largo del tiempo. Los resultados ofrecen información crucial para comprender cómo los incendios han transformado el ecosistema y cómo éste responde a lo largo de los años, proporcionando una base sólida para la planificación y gestión post-incendio en este importante parque natural. Este enfoque integral permitió un monitoreo continuo y actualizado del impacto del fuego, apoyando la conservación de uno de los ecosistemas más biodiversos de Colombia.
dc.description.abstractThis study evaluates the severity and impact of forest fires in the Sierra de la Macarena, Meta, during the period 2018–2024, using Sentinel-2 MSI imagery and advanced geospatial analysis techniques. Through an object-based image analysis (GEOBIA) approach combined with the implementation of decision trees and sequential pattern mining, affected areas were classified, allowing not only the quantification of fire severity but also the identification of vegetation regeneration patterns over time. The results provide crucial insights into how fires have transformed the ecosystem and how it has responded over the years, offering a solid basis for post-fire planning and management in this important natural park. This comprehensive approach enabled continuous and updated monitoring of fire impacts, supporting the conservation of one of Colombia’s most biodiverse ecosystems.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/99745
dc.publisherUniversidad Distrital Francisco José de Caldas
dc.relation.referencesAlexis Gabadinho, Matthias Studer, Nicolas Müller, Reto Bürgin, Pierre-Alexandre Fonta, & Gilbert Ritschard. (2024). Trajectory Miner: a Sequence Analysis Toolkit Depends. http://traminer.unige.ch
dc.relation.referencesAngélica Franco-May, D., & Gabriela Mancilla-Montelongo, M. (2023). El espectro invisible: una fuente de luz en la investigación científica de productos naturales. Bioagrociencias, 16(2). https://doi.org/10.56369/BAC.5091
dc.relation.referencesArellano, S., Vega, J. A., Rodríguez Y Silva, F., Fernández, C., Vega-Nieva, D., Álvarez-González, J. G., & Ruiz-González, A. D. (2017). Validation of the remote sensing indices dNBR and RdNBR to assess fire severity in the Oia-O Rosal (Pontevedra) wildfire in 2013. Revista de Teledetección, 2017(49), 49–61. https://doi.org/10.4995/RAET.2017.7137
dc.relation.referencesArmenteras, D., Sebastian Barreto, J., Tabor, K., Molowny-Horas, R., & Retana, J. (2017). Changing patterns of fire occurrence in proximity to forest edges, roads and rivers between NW Amazonian countries. Biogeosciences, 14(11), 2755–2765. https://doi.org/10.5194/BG-14-2755-2017
dc.relation.referencesBelgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/J.ISPRSJPRS.2016.01.011
dc.relation.referencesBinet, R., Bergsma, E., Poulain, V., & Binet, R. (2022). Accurate Sentinel-2 inter-band time delays. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(1), 57–66. https://doi.org/10.5194/ISPRS-ANNALS-V-1-2022-57-2022
dc.relation.referencesBlaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. https://doi.org/10.1016/J.ISPRSJPRS.2009.06.004
dc.relation.referencesBlaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Queiroz Feitosa, R., van der Meer, F., van der Werff, H., van Coillie, F., & Tiede, D. (2014). Geographic Object-Based Image Analysis – Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180–191. https://doi.org/10.1016/J.ISPRSJPRS.2013.09.014
dc.relation.referencesBreiman, L., Friedman, J. H., Olsen, R. A., & Stone, C. J. (1984). Classification and Regression Trees by Leo Breiman. Chapter 7, January, 1–7. https://books.google.com/books/about/Classification_and_Regression_Trees.html?hl=es&id=JwQx-WOmSyQC
dc.relation.referencesCansler, C. A., Hood, S. M., Varner, J. M., van Mantgem, P. J., Agne, M. C., Andrus, R. A., Ayres, M. P., Ayres, B. D., Bakker, J. D., Battaglia, M. A., Bentz, B. J., Breece, C. R., Brown, J. K., Cluck, D. R., Coleman, T. W., Corace, R. G., Covington, W. W., Cram, D. S., Cronan, J. B., … Wright, M. C. (2020). The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire. Scientific Data 2020 7:1, 7(1), 1–14. https://doi.org/10.1038/s41597-020-0522-7
dc.relation.referencesChuvieco, E. (2008). Teledetección Ambiental: La Observación de la Tierra desde el Espacio.
dc.relation.referencesChuvieco, E. (2019). Teledetección Ambiental : La Observación de la Tierra Desde el Espacio [Book]. Digital Reasons.
dc.relation.referencesChuvieco Salinero, E., & Martín, M. P. (2001). Propuesta de un nuevo índice para la cartografía de áreas quemadas: aplicación a imágenes NOAA-AVHRR y Landsat-TM. Revista de Teledetección: Revista de La Asociación Española de Teledetección, ISSN 1133-0953, No. 16, 2001, Págs. 57-64, 16, 57–64. https://dialnet.unirioja.es/servlet/articulo?codigo=942269
dc.relation.referencesCongalton, R. G., & Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data : Principles and Practices, Second Edition. Assessing the Accuracy of Remotely Sensed Data. https://doi.org/10.1201/9781420055139
dc.relation.referencesCortes, C., Vapnik, V., & Saitta, L. (1995). Support-vector networks. Machine Learning 1995 20:3, 20(3), 273–297. https://doi.org/10.1007/BF00994018
dc.relation.referencesDe Santis, A., & Chuvieco, E. (2007). Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models. Remote Sensing of Environment, 108(4), 422–435. https://doi.org/10.1016/J.RSE.2006.11.022
dc.relation.referencesElen, A., Baş, S., & Közkurt, C. (2022). An Adaptive Gaussian Kernel for Support Vector Machine. Arabian Journal for Science and Engineering, 47(8), 10579–10588. https://doi.org/10.1007/S13369-022-06654-3/METRICS
dc.relation.referencesFelipe, J., Samudio, G., Murcia, U., Arias, J., Higuera Díaz, D., Cantor Baéz, L., Daza, E. B., Apolinar, E., Montealegre, N., & Escobar Bejar, V. (2022). Parque Nacional Natural Sierra de la Macarena poblamiento y superposición de territorialidades.
dc.relation.referencesFilipponi, F. (2018). BAIS2: Burned Area Index for Sentinel-2. 364. https://doi.org/10.3390/ECRS-2-05177
dc.relation.referencesFlores, B. M., Montoya, E., Sakschewski, B., Nascimento, N., Staal, A., Betts, R. A., Levis, C., Lapola, D. M., Esquível-Muelbert, A., Jakovac, C., Nobre, C. A., Oliveira, R. S., Borma, L. S., Nian, D., Boers, N., Hecht, S. B., ter Steege, H., Arieira, J., Lucas, I. L., … Hirota, M. (2024). Critical transitions in the Amazon forest system. Nature 2024 626:7999, 626(7999), 555–564. https://doi.org/10.1038/s41586-023-06970-0
dc.relation.referencesFoody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4
dc.relation.referencesGarcía, M. J. L., & Caselles, V. (1991). Mapping burns and natural reforestation using thematic mapper data. Geocarto International, 6(1), 31–37. https://doi.org/10.1080/10106049109354290
dc.relation.referencesGorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/J.RSE.2017.06.031
dc.relation.referencesHan, A., Qing, S., Bao, Y., Na, L., Bao, Y., Liu, X., Zhang, J., & Wang, C. (2021). Short‐term effects of fire severity on vegetation based on sentinel‐2 satellite data. Sustainability (Switzerland), 13(1), 1–22. https://doi.org/10.3390/SU13010432
dc.relation.referencesHay, G. J., & Castilla, G. (2008). Geographic object-based image analysis (GEOBIA): A new name for a new discipline. Lecture Notes in Geoinformation and Cartography, 0(9783540770572), 75–89. https://doi.org/10.1007/978-3-540-77058-9_4
dc.relation.referencesHeydari, S. S., & Mountrakis, G. (2019). Meta-analysis of deep neural networks in remote sensing: A comparative study of mono-temporal classification to support vector machines. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 192–210. https://doi.org/10.1016/J.ISPRSJPRS.2019.04.016
dc.relation.referencesHijmans, R. J. (2024). Spatial Data Analysis [R package terra version 1.7-83]. CRAN: Contributed Packages. https://doi.org/10.32614/CRAN.PACKAGE.TERRA
dc.relation.referencesHuynh, B., Trinh, C., Huynh, H., Van, T. T., Vo, B., & Snasel, V. (2018). An efficient approach for mining sequential patterns using multiple threads on very large databases. Engineering Applications of Artificial Intelligence, 74, 242–251. https://doi.org/10.1016/J.ENGAPPAI.2018.06.009
dc.relation.referencesIbrahim, E., Lema, L., Barnabé, P., Lacroix, P., & Pirard, E. (2020). Small-scale surface mining of gold placers: Detection, mapping, and temporal analysis through the use of free satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 93, 102194. https://doi.org/10.1016/J.JAG.2020.102194
dc.relation.referencesInternational Union of Forest Research Organizations. (2018). Global Fire Challenges In A Warming World Summary Note of a Global Expert Workshop on Fire and Climate Change.
dc.relation.referencesJain, T. (2004). Tongue-tied. www.wildfiremag.com
dc.relation.referencesJu, Y., Dronova, I., & Delclòs-Alió, X. (2022). A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap. Scientific Data 2022 9:1, 9(1), 1–9. https://doi.org/10.1038/s41597-022-01701-y
dc.relation.referencesKasischke, E. S., Hoy, E. E., French, N. H., & Turetsky, M. R. (2007). Post-fire evaluation of the effects of fire on the environment using remotely-sensed data. TOWARDS AN OPERATIONAL USE OF REMOTE SENSING IN FOREST FIRE MANAGEMENT, 38.
dc.relation.referencesKeeley, J. E. (2009). Fire intensity, fire severity and burn severity: A brief review and suggested usage. International Journal of Wildland Fire, 18(1), 116–126. https://doi.org/10.1071/WF07049
dc.relation.referencesKucharczyk, M., Hay, G. J., Ghaffarian, S., & Hugenholtz, C. H. (2020). Geographic Object-Based Image Analysis: A Primer and Future Directions. Remote Sensing 2020, Vol. 12, Page 2012, 12(12), 2012. https://doi.org/10.3390/RS12122012
dc.relation.referencesLee, K., van Leeuwen, W. J. D., Gillan, J. K., & Falk, D. A. (2024). Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms. Remote Sensing, 16(10). https://doi.org/10.3390/RS16101803
dc.relation.referencesLentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Morgan, P., Lewis, S. A., Gessler, P. E., & Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15(3), 319–345. https://doi.org/10.1071/WF05097
dc.relation.referencesLiang, J., Sawut, M., Cui, J., Hu, X., Xue, Z., Zhao, M., Zhang, X., Rouzi, A., Ye, X., & Xilike, A. (2024). Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images. Scientific Reports 2024 14:1, 14(1), 1–22. https://doi.org/10.1038/s41598-024-68991-7
dc.relation.referencesLiao, T. F., Bolano, D., Brzinsky-Fay, C., Cornwell, B., Fasang, A. E., Helske, S., Piccarreta, R., Raab, M., Ritschard, G., Struffolino, E., & Studer, M. (2022). Sequence analysis: Its past, present, and future. Social Science Research, 107, 102772. https://doi.org/10.1016/J.SSRESEARCH.2022.102772
dc.relation.referencesLillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote sensing and Image interpretation. Remote Sensing and Image Interpretation, 360–363. https://www.wiley.com/en-sg/Remote+Sensing+and+Image+Interpretation%2C+7th+Edition-p-9781118343289
dc.relation.referencesLosso, A., Challis, A., Gauthey, A., Nolan, R. H., Hislop, S., Roff, A., Boer, M. M., Jiang, M., Medlyn, B. E., & Choat, B. (2022). Canopy dieback and recovery in Australian native forests following extreme drought. Scientific Reports 2022 12:1, 12(1), 1–14. https://doi.org/10.1038/s41598-022-24833-y
dc.relation.referencesLutes, D. C., Keane, R. E., Caratti, J. F., Key, C. H., Benson, N. C., Sutherland, S., & Gangi, L. J. (2006). FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 1 CD., 164. https://doi.org/10.2737/RMRS-GTR-164
dc.relation.referencesMarques, E. Q., Silvério, D. V., Galvão, L. S., Aragão, L. E. O. C., Uribe, M. R., Macedo, M. N., Rattis, L., Alencar, A. A. C., & Brando, P. M. (2024). Assessing the effectiveness of vegetation indices in detecting forest disturbances in the southeast Amazon. Scientific Reports 2024 14:1, 14(1), 1–15. https://doi.org/10.1038/s41598-024-77924-3
dc.relation.referencesMatricardi, E. A. T., Skole, D. L., Costa, O. B., Pedlowski, M. A., Samek, J. H., & Miguel, E. P. (2020). Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science, 369(6509), 1378–1382. https://doi.org/10.1126/SCIENCE.ABB3021/SUPPL_FILE/ABB3021_MATRICARDI_SM.PDF
dc.relation.referencesMontorio LLovería, R., Pérez-Cabello, F., vLassova, L., & de La Riva FeRnández, J. (2015). La severidad del fuego: revisión de conceptos, métodos y efectos ambientales.
dc.relation.referencesMoody, J. A., Shakesby, R. A., Robichaud, P. R., Cannon, S. H., & Martin, D. A. (2013). Current research issues related to post-wildfire runoff and erosion processes. Earth-Science Reviews, 122, 10–37. https://doi.org/10.1016/J.EARSCIREV.2013.03.004
dc.relation.referencesMorgan, P., Keane, R. E., Dillon, G. K., Jain, T. B., Hudak, A. T., Karau, E. C., Sikkink, P. G., Holden, Z. A., & Strand, E. K. (2014). Challenges of assessing fire and burn severity using field measures, remote sensing and modelling. International Journal of Wildland Fire, 23(8), 1045–1060. https://doi.org/10.1071/WF13058
dc.relation.referencesParuelo, J., Di Bella, C., & Milkovic, M. (2014). Percepción remota y sistemas de información geográfica. Sus aplicaciones en Agronomía y Ciencias Ambientales. https://www.researchgate.net/publication/281744956_Percepcion_remota_y_sistemas_de_informacion_geografica_Sus_aplicaciones_en_Agronomia_y_Ciencias_Ambientales
dc.relation.referencesPerea Ardila M, Muñoz Vásquez S, & Sopchaki C. (2023). Análise de áreas queimadas utilizando imagens Sentinel-2 no Parque Estadual do Cocó, Região Metropolitana de Fortaleza (Ceará). https://doi.org/10.5281/ZENODO.10440566
dc.relation.referencesRifai, S. W., De Kauwe, M. G., Gallagher, R. V., Cernusak, L. A., Meir, P., & Pitman, A. J. (2024). Burn Severity and Post-Fire Weather Are Key to Predicting Time-To-Recover From Australian Forest Fires. Earth’s Future, 12(4). https://doi.org/10.1029/2023EF003780
dc.relation.referencesRodríguez-Trejo, D., Martínez-Muñoz, P., Pulido-Luna, J., Martínez-Lara, P., & Cruz-López, J. (2019). Instructivo de Quemas Prescritas para el Manejo Integral del Fuego en el municipio de Villaflores y la Reserva de la Biosfera La Sepultura, Chiapas, México. 183–183.
dc.relation.referencesShuchao, L., Xiaozhong, C., Xianlin, Q., Guifen, S., & Xiaotong, L. (2018). Remote Sensing Assessment of Forest Fire Damage Degree in Bilahe Forest Farm,Inner Mongolia. Forest and Grassland Resources Research, 0(1), 90. https://doi.org/10.13466/J.CNKI.LYZYGL.2018.01.013
dc.relation.referencesSimafranca, N., Willoughby, B., O’Neil, E., Farr, S., Reich, B. J., Giertych, N., Johnson, M. C., & Pascolini-Campbell, M. A. (2024). Modeling wildland fire burn severity in California using a spatial Super Learner approach. Environmental and Ecological Statistics, 31(2), 387–408. https://doi.org/10.1007/S10651-024-00601-1
dc.relation.referencesSingh, P., Singh, N., Singh, K. K., & Singh, A. (2021). Diagnosing of disease using machine learning. Machine Learning and the Internet of Medical Things in Healthcare, 89–111. https://doi.org/10.1016/B978-0-12-821229-5.00003-3
dc.relation.referencesSrikant, R., & Agrawal, R. (1996). Mining Sequential Patterns: Generalizations and Performance Improvements.
dc.relation.referencesSuwanprasit, C., & Shahnawaz. (2024). Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques. Scientific Reports 2024 14:1, 14(1), 1–13. https://doi.org/10.1038/s41598-024-60512-w
dc.relation.referencesThomas, N., Simard, M., Castañeda-Moya, E., Byrd, K., Windham-Myers, L., Bevington, A., & Twilley, R. R. (2019). High-resolution mapping of biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana. International Journal of Applied Earth Observation and Geoinformation, 80, 257–267. https://doi.org/10.1016/J.JAG.2019.03.013
dc.relation.referencesVan Der Werf, G. R., Randerson, J. T., Giglio, L., Van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., Van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., & Kasibhatla, P. S. (2017). Global fire emissions estimates during 1997-2016. Earth System Science Data, 9(2), 697–720. https://doi.org/10.5194/ESSD-9-697-2017,
dc.relation.referencesVapnik, V. N. . (1998). Statistical learning theory. 736. https://www.wiley.com/en-us/Statistical+Learning+Theory-p-9780471030034
dc.relation.referencesVeraverbeke, S., Verstraeten, W. W., Lhermitte, S., & Goossens, R. (2010). Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece. International Journal of Wildland Fire, 19(5), 558–569. https://doi.org/10.1071/WF09069
dc.relation.referencesWhite, R. A., Piraino, K., Shortridge, A., & Arbogast, A. F. (2019). Measurement of vegetation change in critical dune sites along the eastern shores of Lake Michigan from 1938 to 2014 with object-based image analysis. Journal of Coastal Research, 35(4), 842–851. https://doi.org/10.2112/JCOASTRES-D-17-00141.1
dc.relation.referencesZha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
dc.relation.referencesZhou, X. X., Li, Y. Y., Luo, Y. K., Sun, Y. W., Su, Y. J., Tan, C. W., & Liu, Y. J. (2022). Research on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal imageries. Scientific Reports 2022 12:1, 12(1), 1–14. https://doi.org/10.1038/s41598-022-15414-0
dc.rights.accesoAbierto (Texto Completo)
dc.rights.accessrightsOpenAccess
dc.subjectSensores remotos
dc.subjectIncendios forestales
dc.subjectSierra de la Macarena
dc.subjectGEOBIA
dc.subjectSVM
dc.subjectÁrboles de decisión
dc.subject.keywordRemote sensing
dc.subject.keywordForest fires
dc.subject.keywordSierra de la Macarena
dc.subject.keywordGEOBIA
dc.subject.keywordSVM,
dc.subject.keywordDecision trees
dc.subject.lembMaestría en Ciencias de la Información y las Comunicaciones -- Tesis y disertaciones académicas
dc.subject.lembIncendios forestales
dc.subject.lembTeledetección
dc.subject.lembÁrboles de decisión
dc.subject.lembRegeneración (Botánica)
dc.titleMetodología para la evaluación de los incendios de la vegetación en la Sierra de la Macarena utilizando técnicas de análisis de imágenes basadas en objetos geográficos y árboles de decisión
dc.title.titleenglishMethodology for the evaluation of vegetation fires in the sierra de la macarena using object-based image analysis techniques and decision trees
dc.typemasterThesis
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.degreeMonografía

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