Use of remote sensing tools for severity analysis and greenhouse gases estimation in large forest fires. Case study of La Rufina forest fire, VI Region of L. G. B. O´Higgins, Chile


  • P. Vidal Universidad Mayor
  • A. De Santis HOPE (Humanitarian Operations); Centro Regional Fundación CEQUA
  • W. Pérez Universidad Mayor
  • P. Honeyman Universidad Mayor



La Rufina, Landsat, dNBR, NDVI, burn severity, greenhouse gas


Wildfires destroy thousands of hectares of vegetation every year in Chile, a phenomenon that has steadily increased over time, both in terms of the number of fires and the area affected. Since 1985 until 2016 have occurred 1,476 wildfires severe in intensity (> 200 ha), that burned a total of about 1,243,407 ha of vegetation, and an average of 40,000 ha affected per year. Depending on the type and intensity of the fire, there are different levels of severity with which the fire affects the vegetation, a variation that is crucial for the estimation GEI in the event. The purpose of this research was to analyze the burn severity of Rufina wildfires occurred in 1999, in the VI Region of L. G. B. O’Higgins in Chile, south of the capital Santiago, using Landsat 5 TM and Landsat 7 ETM+ imagery, including in the analysis the estimated greenhouse gases emitted in relation to with the vegetation and burn severity. Burn severity was estimated through the Normalized Burn Ratio (dNBR) and GEI with the equation proposed by IPCC in 2006, which was adjusted with the combustion efficiency coefficients proposed by De Santis et al. (2010). The results show that around 16,783 ha were affected by fires of different severity and the native forest and tree plantations were affected by high severity. The ton of GEI for each level of burn severity and type of vegetation was estimated, being carbon dioxide (CO2 ) the main GEI emitted to the atmosphere in the fire. The highest emissions occurred in the areas of grasslands and scrublands, with high severity, with values ranging between 186 and 170 t/ha respectively


Download data is not yet available.

Author Biographies

P. Vidal, Universidad Mayor

Hémera Centro de Observación de la Tierra. Facultad de Ciencias.

Magíster en Teledetección.

A. De Santis, HOPE (Humanitarian Operations); Centro Regional Fundación CEQUA

HOPE (Humanitarian Operations), Belgium

W. Pérez, Universidad Mayor

Hémera Centro de Observación de la Tierra. Facultad de Ciencias.

Magíster en Teledetección.

P. Honeyman, Universidad Mayor

Oterra Centro de Estudios de Recursos Naturales. Facultad de Ciencias


Bastarrika, A., Chuvieco, E., Martín, M. P. 2011. Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sensing of Environment, 115(4), 1003-1012. rse.2010.12.005

Boer, M. M., Macfarlane, C., Norris, J., Sadler, R. J., Wallace, J., Grierson, P. F. 2008. Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index. Remote Sensing of Environment, 112(12), 4358-4369. rse.2008.08.005

Cano, J., Sartori, A., Quintanilla, O., Oyarzún, V., Sidman, G., Casarim, F., MacMurray, A., Pearson, T., Gayoso, J., Sandoval, V., Almonacid, N., Bahamondez, C., Rojas, Y., Sagardia, Y., Honeyman, P. 2016. Nivel de Referencia de Emisiones Forestales/Nivel de Referencia Forestal del Bosque Nativo de Chile. Documento Preliminar. Santiago de Chile: CONAF.

Chaves, J. 2014. Incendios Forestales y Cambio Climático. Estimación de Emisiones en los Incendios de Andilla y Cortes de Pallás. Trabajo de fin de máster. Universidad Politécnica de Valencia, España.

Chuvieco, E., Martín M. P., Palacios, A. 2002. Assessment of diferent spectral indices in the red–near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23(23), 5103-5110. https://doi. org/10.1080/01431160210153129

Chuvieco, E., Ventura, G., Martín, M. P., Gómez, I. 2005. Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping. Remote Sensing of Environment, 94(4), 450-462. https://doi. org/10.1016/j.rse.2004.11.006

Chuvieco, E., Opazo, S., Sione, W., Valle, H.d, Anaya, J., Bella, C.D, Cruz, I., Manzo, L., López, G., Mari, N., González-Alonso, F., Morelli, F., Setzer, A., Csiszar, I., Kanpandegi, J.A., Bastarrika, A. and Libonati, R., 2008. Global burned-land estimation in Latin America using MODIS Composite Data. Ecological Applications, 18(1), 64-79. https://doi. org/10.1890/06-2148.1

Chuvieco, E. 2009. Earth Observation of Wildland Fires in Mediterranean Ecosystems. Berlin, Heidelberg: Springer.

Civco, D. L. 1989. Topographic Normalization of Landsat Thematic Mapper Digital Imagery. Photogrammetric Engineering and Remote Sensing, 55(9), 1303-1309.

CONAF, 1996. Catastro y Evaluación de Recursos Vegetacionales Nativos de Chile. Catastro de Bosque Nativo de la Región de O´Higgins. Disponible en [Último acceso el 11 de noviembre de 2016].

Conard, S. G., Sukhinin, A. I., Stocks, B. J., Cahoon, D. R., Davidenko, E. P., Ivanova, G. A. 2002. Determining effects of area burned and fire severity on carbon cycling and emissions in Siberia. Climatic Change, 55(1-2), 197-211. https://doi. org/10.1023/A:1020207710195

De 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://

De Santis, A. y Vaughan, P. 2009. Revisión de las técnicas de identificación cartográfica de áreas quemadas. Recursos Rurais, 5, 93-100.

De Santis, A., Chuvieco, E. 2009. GeoCBI: a modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sensing of Environment, 113(3), 554-562. https://doi. org/10.1016/j.rse.2008.10.011

De Santis, A., Asner, G. P., Vaughan, P. J., Knapp, D. E. 2010. Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery. Remote Sensing of Environment, 114(7), 1535-1545. rse.2010.02.008

Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z., Quayle, B., Howard, S. 2007. A Project for Monitoring Trends in Burn Severity. Fire Ecology, 3(1), 3-21.

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.

González, D. C., Rios, R. C. 2009. Fire Danger, Fire Detection, Quantification of Burned Areas and Description of Post-Fire Vegetation in the Central Area of Chile. In: Chuvieco E. (eds) Earth Observation of Wildland Fires in Mediterranean Ecosystems. Springer, Berlin, Heidelberg. https://

Gitas, I. Z, De Santis, A., Mitri, G. H. 2009. Remote Sensing of Burn Severity. In: Chuvieco E. (eds) Earth Observation of Wildland Fires in Mediterranean Ecosystems. Springer, Berlin, Heidelberg. https://

IPCC, 2006. Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, forestry and other land use. Harald Aalde (Norway), Patrick Gonzalez (USA), Michael Gytarsky (Russian Federation), Thelma Krug (Brazil), Werner A. Kurz (Canada), Rodel D. Lasco (Philippines), Daniel L. Martino (Uruguay), Brian G. McConkey (Canada), Stephen Ogle (USA), Keith Paustian (USA), John Raison (Australia), N.H. Ravindranath (India), Dieter Schoene (FAO).

Ireland, G., Petropoulos, G. P. 2015. Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada. Applied Geography, 56, 232-248. apgeog.2014.11.016

Key, C. H., Benson, N. 2005. Landscape assessment: Ground measure of severity, the Composite Burn Index; and remote sensing of severity, the Normalized Burn Ratio. In D.C. Lutes, R.E. Keane, J.F. Caratti, C.H. Key, N.C. Benson & L.J. Gangi (Eds.), FIREMON: Fire Effects Monitoring and Inventory System (pp. CD:LA1-LA51). Ogden, UT: USDA Forest Service, Rocky Mountain Research Station, Gen. Tech. Rep. RMRS-GTR-164.

Lentile, 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. WF05097

Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C. 2012. Estimación del área quemada en el Parque Nacional Torres del Paine utilizando datos de teledetección. Revista de Teledetección, 38, 36-50.

Miller, J. D., Thode, A. E. 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta normalized burn ratio (dNBR). Remote Sensing of Environment, 109(1), 66-80.

MMA, 2014. Sistema Nacional de Inventarios de Gases de Efecto Invernadero de Chile. Serie temporal 1990-2010. Chile: Oficina de Cambio Climático, Ministerio del Medio Ambiente.

Mouillot, F., Schultz M. G., Yue, C., Cadule, P., Tansey, K., Ciais, P., Chuvieco, E. 2014. Ten years of global burned area products from spaceborne remote sensing-A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26, 64-79. https://doi. org/10.1016/j.jag.2013.05.014

United States Geological Survey (USGS). Último acceso: 11 noviembre de 2016, de http://glovis.usgs. gov/

White, J. D., Ryan, K. C., Key, C. C., Running, S. W. 1996. Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6(3), 125-136. https://doi. org/10.1071/WF9960125

Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X., O’Neill, S., Wynne, K.K., 2006. Estimating emissions from fires in North America for air quality modeling. Atmospheric Environment, 40(19), 3419-3432. https://doi. org/10.1016/j.atmosenv.2006.02.010





Practical cases