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Relationship between soil burn severity in forest fires measured in situ and through spectral indices of remote detection

Author(s): Jose Antonio Sobrino, Rafael Llorens, Cristina Fernández, José M. Fernández-Alonso, José A. Vega
Year Published: 2019
Description:

Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after a forest fire (changes in soil cover are noticeably appreciated with spectral information). To calculate these variations, the spectral indices NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) are used, both before and after the fire and their differences (dNBR and dNDVI, respectively). In addition, as a reference for a correct discrimination between severity degrees, severity data measured in situ after the fire are used to classified at 5 levels of severity and 6 levels of severity. Therefore, this study aims to establish a methodology, which relates remote-sensing data (spectral indices) and severity degrees measured in situ. The R2 statistic and the pixel classification accuracy results show the existing synergy of the Sentinel-2 dNBR index with the 5 severity degrees classification (R2 = 0.74 and 81% of global accuracy) and, for this case, the good applicability of remote sensing in the forest fire field.

Citation: Sobrino, Jose Antonio; Llorens, Rafael; Fernández, Cristina; Fernández-Alonso, José M.; Vega, José Antonio. 2019. Relationship between soil burn severity in forest fires measured in situ and through spectral indices of remote detection. Forests 10(5):457. https://doi.org/10.3390/f10050457
Topic(s): Fire Effects, Recovery after fire, Restoration
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 19657
FRAMES RCS number: 57934
Record updated: Jun 10, 2019