Burn severity and regeneration in large forest firesan analysis from Landsat time series

  1. Martínez, S.
  2. Chuvieco, E.
  3. Aguado, I.
  4. Salas, J.
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Year of publication: 2017

Issue Title: Special issue: Avances en el análisis de la severidad y la dinámica ambiental post-fuego mediante teledetección

Issue: 49

Pages: 17-32

Type: Article

DOI: 10.4995/RAET.2017.7182 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de teledetección: Revista de la Asociación Española de Teledetección


The main objective of this study is to take a close look at post-fire recovery patterns in forestry areas under different burn severity conditions. We also investigate the time that forestry ecosystems take to recover their pre-fire condition. In this context, this study analyses both the level of severity in Uncastillo forest wildfire (7.664ha), one of the greatest occurred in Spain in 1994, and the pattern of natural recovery in the following decades (until 2014) using annual Landsat time series (sensors TM&ETM+). Burn severity has been estimated by means of PROSPECT and GeoSAIL radiative transfer models following methodologies described in De Santis and Chuvieco (2009). On the other hand, recovery processes have been assessed from spectral profiles using the LandTrendr model (Landsat-based Detection of Trends in Disturbance and Recovery) (Kennedy et al., 2010). Results contribute to a further understanding of the post-fire evolution in forestry areas and to develop effective strategies for sustainable forest management.

Funding information

Este trabajo se ha desarrollado con la financiación procedente del proyecto Severidad y regeneración en grandes incendios forestales mediante teledetección y S.I.G (SERGISAT) (Ref. CGL2014-57013-C2-1-R–SERGISAT; CGL2014-57013-C2-2-R–SERGISAT, Ministerio de Economía y Competitividad). Nuestro agradecimiento también al Dr. Justin Braaten del Laboratory for Applications of Remote Sensing in Ecology de la Oregon State University, por su apoyo en el uso de LandTrendr.


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