Study of post-fire severity in the Valencia region comparing the NBR, RdNBR and RBR indexes derived from Landsat 8 images

Authors

  • M. A. Botella-Martínez Vaersa (Generalitat Valenciana)
  • A. Fernández-Manso Universidad de León

DOI:

https://doi.org/10.4995/raet.2017.7095

Keywords:

post-fire severity, initial assessment, Mediterranean area, Landsat 8, dNBR, RdNBR, RBR, classification thresholds

Abstract

In Mediterranean territories, with their characteristic climate that implies long periods of drought and rains often concentrated in torrential episodes, forest managers are faced with a series of decisions that can be urgent after a wildfire, some of them strongly correlated with the degree of damage caused by fire. In this sense, the object of this study was to provide a fast and reliable tool for the initial assessment of post-fire severity in these kinds of territories, by means of remote sensing techniques. Using Landsat 8 imagery, we have calculated three fire severity indices (dNBR, RdNBR, and RBR) for nine fires occurred in the Valencia region, a typical Mediterranean area. For each index, post-fire severity classification thresholds have been obtained taking into account the following categories: unburned, low, moderate, and high. These thresholds have been validated using, as ground-reference, aerial photographs taken from a helicopter. Afterwards, the degree to which post-fire severity was influenced by factors associated with pre-fire vegetation was evaluated, using a variance analysis. This analysis served to compare the three indices in terms of their robustness against the influence of these factors. With the obtained data, and with the study of classification accuracies employing the Kappa statistic, we were able to propose the most suitable index for calculating post-fire severity in the Valencia region, along with its operating thresholds. The findings suggest that the results could be extrapolated to other areas of similar characteristics.

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References

Ariza, A. 2013. Descripción y Corrección de Productos Landsat 8 LDCM (Landsat Data Continuity Mission). Centro de Investigación y Desarrollo en Información Geográfica del IGAC -CIAF, 46.

Cansler, C. A., McKenzie, D. 2012. How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods. Remote Sensing, 4(12), 456–483. https://doi.org/10.3390/rs4020456

Congalton, R. G., Green, K. 2009. Assessing the accuracy of remotely sensed data. Principles and practices. 2nd ed. Boca Ratón: CRC Press. Taylor & Francis. https://doi.org/10.1017/CBO9781107415324.004

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. https:// doi.org/10.4996/fireecology.0301003

Generalitat Valenciana. 2013. Plan de Accion Territorial Forestal de la Comunitat Valenciana (PATFOR). Web consultada el 21 de septiembre de 2015, http:// www.habitatge.gva.es/web/medio-natural/patfor

Gibson, K., Negrón, J. F. 2009. Fire and bark beetle interactions. The Western Bark Beetle Research Group: A Unique Collaboration With Forest Health Protection: Proceedings of a Symposium at the 2007 Society of American Foresters Conference, 51–70.

Key, C. H. 2006. Ecological and sampling constraints on defining landscape fire severity. Fire Ecology, 2(2), 34–59. https://doi.org/10.4996/fireecology.0202034

Key, C. H., Benson, N. C. 2006. Landscape assessment (LA): Sampling and analysis methods. USDA Forest Service General Technical Report RMS-GTR-164- CD, 1–55.

Lentile, L. B., Smith, F. W., Shepperd, W. D. 2006. Influence of topography and forest structure on patterns of mixed severity fire in ponderosa pine forests of the South Dakota Black Hills, USA. International Journal of Wildland Fire, 15 (October 2015), 557–566. https://doi.org/10.1071/WF05096

Masek, J. G., Vermote, E. F., Saleous, N., Wolfe, R., Hall, F. G., Huemmrich, F., Gao, F., Kutler, J., Lim, T. K. 2013. LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2. Oak Ridge, Tennessee, USA: ORNL DAAC.

Miller, J. D., Knapp, E. E., Key, C. H., Skinner, C. N., Isbell, C. J., Creasy, R. M., Sherlock, J. W. 2009. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sensing of Environment, 113(3), 645–656. https://doi. org/10.1016/j.rse.2008.11.009

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. https://doi.org/10.1016/j.rse.2006.12.006

Neary, D. G., Ryan, K. C., DeBano, L. F. 2005. Wildland Fire in Ecosystems. Rocky Mountain Research Station General Technical Report, 4 (RMRSGTR-42).

Parks, S., Dillon, G., Miller, C. 2014. A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio. Remote Sensing, 6(3), 1827–1844. https://doi. org/10.3390/rs6031827

Parsons, A., Robichaud, P. R., Lewis, S. A, Napper, C., Clark, J., Jain, T. B. 2010. Field guide for mapping post-fire soil burn severity. Water, (October), 49. https://doi.org/10.2737/RMRS-GTR-243

Quintano, C., Fernández-Manso, A., Roberts, D. A. 2013. Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sensing of Environment, 136, 76–88. https:// doi.org/10.1016/j.rse.2013.04.017

Stehman, S. V, Czaplewski, R. L. 1998. Design and Analysis for Thematic Map Accuracy Assessment - an application of satellite imagery. Remote Sensing of Environment, 64(January), 331–344. https://doi. org/10.1016/S0034-4257(98)00010-8

Published

2017-12-05