Use of Remote Sensing for Multitemporal Analysis of the Severity of the Fontibón Fire in Pamplona, Colombia

Authors

  • Carlos E. Oliveros-Valero

    Faculty of Engineering and Architecture, Universidad Católica de Manizales, Manizales 170001, Colombia; Secretaría Departamental, Gestión del Riesgo de Norte de Santander, Bucaramanga 680006, Colombia
  • Javier Yesid Villamizar Vera

    Faculty of Engineering and Architecture, Universidad Católica de Manizales, Manizales 170001, Colombia; Instituto Geográfico Agustín Codazzi-IGAC, Bogotá 111061, Colombia
  • William Vera Arias

    Secretaría Departamental, Gestión del Riesgo de Norte de Santander, Bucaramanga 680006, Colombia
  • Sergio Daniel Parada

    Secretaría Departamental, Gestión del Riesgo de Norte de Santander, Bucaramanga 680006, Colombia
  • Klaus Faber Mogollon

    Alcaldía de Pamplona, Pamplona 543050, Colombia
  • Antonio Manuel Collazos Gamez

    Secretaría Departamental, Gestión del Riesgo de Norte de Santander, Bucaramanga 680006, Colombia
  • Vladimir Henao-Céspedes

    Faculty of Engineering and Architecture, Universidad Católica de Manizales, Manizales 170001, Colombia
  • Yeison Alberto Garcés-Gómez

    Faculty of Engineering and Architecture, Universidad Católica de Manizales, Manizales 170001, Colombia

DOI:

https://doi.org/10.30564/re.v8i4.13568
Received: 27 May 2026 | Revised: 28 July 2026 | Accepted: 5 August 2026 | Published Online: 24 August 2026

Abstract

Wildfires in tropical mountain ecosystems are difficult to assess because steep terrain and persistent cloud cover constrain field surveys and satellite observation. This study developed an integrated workflow combining Unmanned Aerial Vehicle (UAV) photogrammetry, Sentinel-2 imagery, and Google Earth Engine to quantify burn severity and early vegetation recovery after the January 2024 Fontibón fire in Pamplona, Colombia. RGB imagery acquired with a DJI Mavic 3E was processed in Agisoft Metashape to delineate the affected area and produce 3-cm orthophotos. Sentinel-2 Normalized Burn Ratio (NBR) and differenced Normalized Burn Ratio (dNBR) were used to classify severity, while Normalized Difference Vegetation Index (NDVI) images from January 2024 and January 2026 characterized greenness trajectories. UAV orthophotos supported image-based validation through 500 purely random checkpoints. The fire affected 175 ha, of which 61.9% showed moderate-high or high severity. Classification agreement with UAV visual interpretation was 87.4% (Cohen's κ = 0.82). Mean NDVI declined from 0.62 ± 0.09 before the fire to 0.28 ± 0.13 immediately afterward and increased to 0.38 ± 0.06 by 2026 (paired t-test, p < 0.001). Although 98.7% of the area showed moderate greenness by 2026, no zone returned to the pre-fire high-NDVI class. These results indicate widespread spectral greening without evidence of complete structural or ecological recovery. The workflow provides operational inputs for restoration planning, but UAV image interpretation is not independent ecological ground truth, the validation sample was not stratified by severity, and the two-year observation window limits long-term ecological inference.

Keywords:

Sentinel-2; Burn Severity; Vegetation Recovery; dNBR; UAV; Colombian Andes

References

[1] Pausas, J.G., Keeley, J.E., 2019. Wildfires as an ecosystem service. Frontiers in Ecology and the Environment. 17(5), 289–295. DOI: https://doi.org/10.1002/fee.2044

[2] Certini, G., 2005. Effects of fire on properties of forest soils: A review. Oecologia. 143(1), 1–10. DOI: https://doi.org/10.1007/s00442-004-1788-8

[3] Mataix-Solera, J., Cerdà, A., Arcenegui, V., et al., 2011. Fire effects on soil aggregation: A review. Earth-Science Reviews. 109(1–2), 44–60. DOI: https://doi.org/10.1016/j.earscirev.2011.08.002

[4] van der Werf, G.R., Randerson, J.T., Giglio, L., et al., 2017. Global fire emissions estimates during 1997–2016. Earth System Science Data. 9(2), 697–720. DOI: https://doi.org/10.5194/essd-9-697-2017

[5] Doerr, S.H., Santín, C., 2016. Global trends in wildfire and its impacts: Perceptions versus realities in a changing world. Philosophical Transactions of the Royal Society B: Biological Sciences. 371(1696), 20150345. DOI: https://doi.org/10.1098/rstb.2015.0345

[6] Poveda, G., Salas, H.D., Mesa, Ó.J., et al., 2020. Generalized synchronization between ENSO and hydrological variables in Colombia: A recurrence quantification approach. Frontiers in Applied Mathematics and Statistics. 6, 3. DOI: https://doi.org/10.3389/fams.2020.00003

[7] Armenteras, D., González, T.M., Vargas, J.O., et al., 2020. Fire in the ecosystems of northern South America: Advances in the ecology of tropical fires in Colombia, Ecuador and Peru. Caldasia. 42(1), 1–16. DOI: https://doi.org/10.15446/caldasia.v42n1.77353 (in Spanish)

[8] Lentile, L.B., Holden, Z.A., Smith, A.M.S., et al., 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire. 15(3), 319–345. DOI: https://doi.org/10.1071/WF05097

[9] Parsons, A., Robichaud, P.R., Lewis, S.A., et al., 2010. Field Guide for Mapping Post-Fire Soil Burn Severity. Department of Agriculture: Fort Collins, CO, USA.

[10] Quintano, C., Fernández-Manso, A., Roberts, D.A., 2017. Burn severity mapping from Landsat MESMA fraction images and land surface temperature. Remote Sensing of Environment. 190, 83–95. DOI: https://doi.org/10.1016/j.rse.2016.12.009

[11] Chuvieco, E., Aguado, I., Salas, J., et al., 2020. Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports. 6(2), 81–96. DOI: https://doi.org/10.1007/s40725-020-00116-5

[12] Zhu, Z., Wulder, M.A., Roy, D.P., et al., 2019. Benefits of the free and open Landsat data policy. Remote Sensing of Environment. 224, 382–385. DOI: https://doi.org/10.1016/j.rse.2019.02.016

[13] Pérez-Cabello, F., Montorio, R., Alves, D.B., 2021. Remote sensing techniques to assess post-fire vegetation recovery. Current Opinion in Environmental Science & Health. 21, 100251. DOI: https://doi.org/10.1016/j.coesh.2021.100251

[14] Eidenshink, J., Schwind, B., Brewer, K., et al., 2007. A project for monitoring trends in burn severity. Fire Ecology. 3(1), 3–21. DOI: https://doi.org/10.4996/fireecology.0301003

[15] Key, C.H., Benson, N.C., 2006. Landscape assessment: Sampling and analysis methods. In: Lutes, D.C., Keane, R.E., Caratti, J.F., et al. (Eds.). FIREMON: Fire Effects Monitoring and Inventory System. Department of Agriculture: Fort Collins, CO, USA.

[16] Parks, S.A., Dillon, G.K., Miller, C., 2014. A new metric for quantifying burn severity: the relativized burn ratio. Remote Sensing. 6(3), 1827–1844. DOI: https://doi.org/10.3390/rs6031827

[17] Simes, T., Pádua, L., Moutinho, A., 2024. Wildfire burnt area severity classification from UAV-based RGB and multispectral imagery. Remote Sensing. 16(1), 30. DOI: https://doi.org/10.3390/rs16010030

[18] Robichaud, P.R., Lewis, S.A., Laes, D.Y.M., et al., 2007. Postfire soil burn severity mapping with hyperspectral image unmixing. Remote Sensing of Environment. 108(4), 467–480. DOI: https://doi.org/10.1016/j.rse.2006.11.027

[19] Guiop-Servan, R.E., Cotrina-Sanchez, A., Puerta-Culqui, J., et al., 2025. Remote sensing for wildfire mapping: A comprehensive review of advances, platforms, and algorithms. Fire. 8(8), 316. DOI: https://doi.org/10.3390/fire8080316

[20] Hoyos, J.D., Suárez, G., Velásquez, J.D., et al., 2025. Complex remote sensing analysis of fire precursors in the tropical region: Case study. Forests. 16(5), 779. DOI: https://doi.org/10.3390/f16050779

[21] Morante-Carballo, F., Bravo-Montero, L., Carrión-Mero, P., et al., 2022. Forest fire assessment using remote sensing to support the development of an action plan proposal in Ecuador. Remote Sensing. 14(8), 1783. DOI: https://doi.org/10.3390/rs14081783

[22] Gao, B., Jia, W., Wang, Q., et al., 2025. All-weather forest fire automatic monitoring and early warning application based on multi-source remote sensing data: Case study of Yunnan. Fire. 8(9), 344. DOI: https://doi.org/10.3390/fire8090344

[23] Chen, X., Zhang, Y., Wang, S., et al., 2024. Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery. Frontiers in Remote Sensing. 5, 1446641. DOI: https://doi.org/10.3389/frsen.2024.1446641

[24] Suwanprasit, C., Shahnawaz, 2024. Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques. Scientific Reports. 14, 9609. DOI: https://doi.org/10.1038/s41598-024-60512-w

[25] Lee, S.H., Lee, M.H., Kang, T.H., et al., 2025. Comparative analysis of dNBR, dNDVI, SVM kernels, and ISODATA for wildfire-burned area mapping using Sentinel-2 imagery. Remote Sensing. 17(13), 2196. DOI: https://doi.org/10.3390/rs17132196

[26] El Khatib, D., Kallas, G., Bechara, J., et al., 2026. Digital landscapes: assessing fire severity and its drivers using remote sensing and Google Earth Engine based on dNBR and NPP indicators. Remote Sensing. 18(10), 1654. DOI: https://doi.org/10.3390/rs18101654

[27] Quintero, N., Viedma, O., Urbieta, I.R., et al., 2019. Assessing landscape fire hazard by multitemporal automatic classification of Landsat time series using Google Earth Engine in west-central Spain. Forests. 10(6), 518. DOI: https://doi.org/10.3390/f10060518

[28] Llorens, R., Sobrino, J.A., Fernández, C., et al., 2024. Soil burn severity assessment using Sentinel-2 and radiometric measurements. Fire. 7(12), 487. DOI: https://doi.org/10.3390/fire7120487

[29] Epting, J., Verbyla, D., Sorbel, B., 2005. Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment. 96(3–4), 328–339. DOI: https://doi.org/10.1016/j.rse.2005.03.002

[30] Drusch, M., Del Bello, U., Carlier, S., et al., 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment. 120, 25–36. DOI: https://doi.org/10.1016/j.rse.2011.11.026

[31] Castro-Melgar, I., Tsagkou, A., Zacharopoulou, M., et al., 2025. Wildfires during early summer in Greece (2024): Burn severity and land use dynamics through Sentinel-2 data. Forests. 16(2), 268. DOI: https://doi.org/10.3390/f16020268

[32] Moreira, F., Ascoli, D., Safford, H., et al., 2020. Wildfire management in Mediterranean-type regions: Paradigm change needed. Environmental Research Letters. 15(1), 011001. DOI: https://doi.org/10.1088/1748-9326/ab541e

[33] Baur, M.J., Friend, A.D., Pellegrini, A.F.A., 2024. Widespread and systematic effects of fire on plant-soil water relations. Nature Geoscience. 17(11), 1115–1120. DOI: https://doi.org/10.1038/s41561-024-01563-6

[34] Bodí, M.B., Mataix-Solera, J., Doerr, S.H., et al., 2011. The wettability of ash from burned vegetation and its relationship to Mediterranean plant species type, burn severity and total organic carbon content. Geoderma. 160(3–4), 599–607.

[35] Lv, Q., Chen, Z., Wu, C., et al., 2025. Increasing severity of large-scale fires prolongs recovery time of forests globally since 2001. Nature Ecology & Evolution. 9, 980–992. DOI: https://doi.org/10.1038/s41559-025-02683-x

[36] Bousquet, E., Mialon, A., Rodriguez-Fernandez, N., et al., 2022. Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing. Biogeosciences. 19, 3317–3336. DOI: https://doi.org/10.5194/bg-19-3317-2022

[37] Priya, R.S., Vani, K., 2024. Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning. Scientific Reports. 14, 12611. DOI: https://doi.org/10.1038/s41598-024-63047-2

[38] Oseghae, I., Bhaganagar, K., Mestas-Nuñez, A.M., 2024. The Dolan Fire of central coastal California: burn severity and progression using satellite observations. Remote Sensing. 16(10), 1693. DOI: https://doi.org/10.3390/rs16101693

[39] San-Miguel-Ayanz, J., Durrant, T., Boca, R., et al., 2020. Forest Fires in Europe, Middle East and North Africa 2019. Publications Office of the European Union: Luxembourg City, Luxembourg.

Downloads

How to Cite

Oliveros-Valero, C. E., Villamizar Vera, J. Y., Vera Arias, W., Parada, S. D., Mogollon, K. F., Collazos Gamez, A. M., Henao-Céspedes, V., & Garcés-Gómez, Y. A. (2026). Use of Remote Sensing for Multitemporal Analysis of the Severity of the Fontibón Fire in Pamplona, Colombia. Research in Ecology, 8(4), 327–338. https://doi.org/10.30564/re.v8i4.13568

Issue

Vol.8, Iss.4 (August 2026)

Article Type

Article

License

Copyright © 2026 Carlos E. Oliveros-Valero, Javier Yesid Villamizar Vera, William Vera Arias, Sergio Daniel Parada, Klaus Faber Mogollon, Antonio Manuel Collazos Gamez, Vladimir Henao-Céspedes, Yeison Alberto Garcés-Gómez

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC