Forest fire severity estimation based on the LiDAR-PNOA data and the values of the Composite Burn Index

A. L. Montealegre, M. T. Lamelas, M. A. Tanase, J. de la Riva


Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities. An estimation of the fire severity as accurate as possible is required by forest managers to decide which strategy is most appropriate to mitigate the effect of fire. The aim of this research is to estimate the post-fire severity, relating a pool of independent variables derived from the LiDAR (Light Detection And Ranging) points clouds delivered by the National Plan for Aerial Orthophotography (PNOA) to field data based on Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables. In addition, the obtained results are compared to different spectral indices derived from Landsat Thematic Mapper.


Fire severity; CBI; LiDAR; Mediterranean forest; logistic regression

Full Text:



Angelo, J.J., Duncan, B.W., Weishampel, J.F. 2010. Using Lidar-derived vegetation profiles to predict time since fire in an oak scrub landscape in EastCentral Florida. Remote Sens., 2, 514–525. https://

Bergen, K.M., Goetz, S.J., Dubayah, R.O., Henebry, G.M., Hunsaker, C.T., Imhoff, M.L., Nelson, R.F., Parker, G.G., Radeloff, V.C. 2009. Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. J. Geophys. Res. Biogeosciences, 114, G00E06. https://doi. org/10.1029/2008JG000883

Chuvieco, E. 2010. Teledetección ambiental. La observación de la Tierra desde el espacio. Barcelona: Ariel Ciencia.

Chuvieco, E. 2009. Earth Observation of Wildland Fires in Mediterranean Ecosystems. Alcalá de Henares: Springer.

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 Sens. Environ., 113, 554–562.

De Santis, A., Chuvieco, E. 2007. Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models. Remote Sens. Environ., 108, 422–435. rse.2006.11.022

Evans, J., Hudak, A., Faux, R., Smith, A.M. 2009. Discrete Return Lidar in Natural Resources: Recommendations for Project Planning, Data Processing, and Deliverables. Remote Sens., 1, 776– 794.

Evans, J.S., Hudak, A.T. 2007. A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments. Geosci. Remote Sens. IEEE Trans. On, 45, 1029–1038. TGRS.2006.890412

Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C. 1999. Análisis multivariante, 5a. ed. Madrid: Prentice Hall Iberia.

Hanley, J.A., McNeil, B. 1982. The meaning and use of the área under a Receiver Operating Characteristic (ROC) curve. Radiology, 143, 29–36. https://doi. org/10.1148/radiology.143.1.7063747

Hutchinson, M.F., 1989. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. J. Hydrol., 106, 211–232.

Kane, V.R., Lutz, J.A., Roberts, S.L., Smith, D.F., McGaughey, R.J., Povak, N.A., Brooks, M.L. 2013a. Landscape-scale effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park. For. Ecol. Manag., 287, 17–31.

Kane, V.R., North, M.P., Lutz, J.A., Churchill, D.J., Roberts, S.L., Smith, D.F., McGaughey, R.J., Kane, J.T., Brooks, M.L. 2013b. Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park. Remote Sens. Environ., 151, 89-101. https://doi. org/10.1016/j.rse.2013.07.041

Key, C.H., Benson, N. 1999. The Composite Burn Index (CBI): Field rating of burn severity. Geological Survey, U.S.

Key, C.H., Benson, N.C. 2006. Landscape assessment (LA) sampling and analysis methods. USDA For. Serv. Rocky Mt. Res. Stn. Gen Tech Rep RMRSGTR-164.

McCarley, T.R., Kolden, C.A., Vaillant N.M., Hudak, A.T., Smith, A.M.S., Wing B.M., Kellogg B.S., Kreitler J. 2017. Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure. Remote Sens. Environ., 191, 419- 432,

McGaughey, R. 2009. FUSION/LDV: Software for LIDAR Data Analysis and Visualization. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Seattle, USA.

Menard, S. 2010. Logistic regression: From introductory to advanced concepts and applications. USA: SAGE.

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 Sens. Environ., 109, 66–80. https://doi. org/10.1016/j.rse.2006.12.006

Montealegre, A.L., Lamelas, M.T., de la Riva, J. 2015. A Comparison of Open-Source LiDAR Filtering Algorithms in a Mediterranean Forest Environment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 4072–4085. JSTARS.2015.2436974

Montorio, R., Pérez-Cabello, F., García-Martín, A., Vlassova, L., Fernández, J. 2014. La severidad del fuego: revisión de conceptos, métodos y efectos ambientales, en: Geoecología, cambio ambiental y paisaje: homenaje al profesor José María García Ruiz, Instituto Pirenaico de Ecología, Universidad de La Rioja, 427–440.

Pardo, A., Ruíz, M.A. 2005. Análisis de datos con SPSS 13 Base. Madrid: Mc Graw Hill, 600 pp

Tanase, M., de la Riva, J., Pérez-Cabello, F. 2011a. Estimating burn severity at the regional level using optically based indices. Can. J. For. Res., 41, 863– 872.

Tanase, M., de la Riva, J., Santoro, M., Pérez-Cabello, F., Kasischke, E., 2011b. Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests. Remote Sens. Environ., 115, 2075– 2085.

Tanase, M.A., Santoro, M., Wegmüller, U., de la Riva, J., Pérez-Cabello, F. 2010. Properties of X-, C- and L-band repeat-pass interferometric SAR coherence in Mediterranean pine forests affected by fires. Remote Sens. Environ., 114, 2182–2194. https://doi. org/10.1016/j.rse.2010.04.021

Vosselman, G., Maas, H.-G., 2010. Airborne and Terrestrial Laser Scanning. Dunbeath: Whittles Publishing.

Wang, C., Glenn, N.F., 2009. Estimation of fire severity using pre- and post-fire LiDAR data in sagebrush steppe rangelands. Int. J. Wildland Fire, 18, 848– 856.

Wulder, M.A., White, J.C., Alvarez, F., Han, T., Rogan, J., Hawkes, B. 2009. Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data. Remote Sens. Environ., 113, 1540–1555.

Abstract Views

Metrics Loading ...

Metrics powered by PLOS ALM

This journal is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Universitat Politècnica de València

Official Journal of the Spanish Association of Remote Sensing

e-ISSN: 1988-8740    ISSN: 1133-0953